INTERACT results for Saskatoon 2020 - Wave 2

The INTErventions, Research, and Action in Cities Team (INTERACT) is a national research collaboration of scientists, urban planners, and engaged citizens uncovering how the design of our cities is shaping the health and wellbeing of Canadians (www.teaminteract.ca). INTERACT is conducting longitudinal, mixed-methods natural experiment studies in four Canadian cities, with the aim of providing evidence on the impacts of urban transformations on people’s physical activity, social connectedness, and wellbeing, and inequalities in these outcomes.

The Saskatoon study evaluates the impacts of a Bus Rapid Transit system (BRT) along three major roadways. Participants who rode the bus at least once in a typical month were eligible to participate. Exclusion criteria across all sites were being younger than 18 years old, not being able to read or write English (or English or French in Montreal) well enough to answer an online survey and any intention to move out of the region in the next two years.

Participants were recruited through social media, news media and partner newsletters. In Saskatoon, 110 returning participants, and 155 new participants completed the Health Questionnaire, for a total of 265 responses. Responses were collected from September 17th, 2020 to February 7th, 2021.

Section 1: Transportation

Which Saskatoon Transit Go Pass do you own?

var_name <- w2$sask_bus_pass
w2$var_name_f <- recode_factor(var_name, "1" = "Monthly adult pass", 
                                                "2" = "Eco Pass", 
                                                "3"= "UPass", 
                                                "4" = "Student Pass", 
                                                "5" = "Discounted Pass", 
                                                "6" = "Low Income Pass", 
                                                "7" = "I do not use a Go pass, I use a multi-use pass", 
                                                "8" ="I do not use a Go pass, I use cash", 
                                                "9" = "I do not currently ride the bus")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Monthly adult pass	21	7.92
	Eco Pass	13	4.91
	UPass	32	12.08
	Student Pass	25	9.43
	Discounted Pass	6	2.26
	Low Income Pass	11	4.15
	I do not use a Go pass, I use a multi-use pass	55	20.75
	I do not use a Go pass, I use cash	24	9.06
	I do not currently ride the bus	78	29.43

On a scale of 1 to 4, with 1 being ‘very safe and 4 being ’very unsafe’, overall, how safe do you think traveling by bus is in your city?

var_name <- w2$bus_safe
w2$var_name_f <- recode_factor(var_name, "1" = "Very Safe", 
                                                "2" = "Somewhat Safe", 
                                                "3" = "Somewhat Unsafe", 
                                                "4" = "Very Unsafe", 
                                                "77" = "I don't know")


var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very Safe	86	32.45
	Somewhat Safe	135	50.94
	Somewhat Unsafe	34	12.83
	Very Unsafe	7	2.64
	I don’t know	3	1.13

On a scale of 1 to 4, with 1 being ‘very reliable’ and 4 being ‘very unreliable’, overall, how reliable do you think traveling by bus is in your city?

var_name <- w2$bus_reliable
w2$var_name_f <- recode_factor(var_name, "1" = "Very Reliable", 
                                                "2" = "Somewhat Reliable", 
                                                "3" = "Somewhat Unreliable", 
                                                "4" = "Very Unreliable", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very Reliable	42	15.85
	Somewhat Reliable	134	50.57
	Somewhat Unreliable	65	24.53
	Very Unreliable	19	7.17
	I don’t know	5	1.89

On a scale of 1 to 4, with 1 being ‘very convenient’ and 4 being very inconvenient’, overall, how convenient do you think traveling by bus is in your city?

var_name <- w2$bus_convenient
w2$var_name_f <- recode_factor(var_name, "1" = "Very convenient", 
                                                "2" = "Somewhat convenient", 
                                                "3" = "Somewhat inconvenient", 
                                                "4" = "Very inconvenient", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very convenient	21	7.92
	Somewhat convenient	113	42.64
	Somewhat inconvenient	89	33.58
	Very inconvenient	38	14.34
	I don’t know	4	1.51

How often do you typically travel by bus during each season?

Fall

ggplot(w2, aes(x =  bus_freq_a
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#E5364D") + xlab("Days in Fall") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(w2$bus_freq_a)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    3.00   26.00   31.11   65.00   91.00

Winter

ggplot(w2, aes(x =  bus_freq_b
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#1596FF") + xlab("Days in Winter") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(w2$bus_freq_b)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    3.00   26.00   33.35   65.00   91.00

Spring

ggplot(w2, aes(x =  bus_freq_c
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#76D24A") + xlab("Days in Spring") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(w2$bus_freq_c)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    0.00   15.00   27.44   52.00   91.00

Summer

ggplot(w2, aes(x =  bus_freq_d
)) + geom_histogram(na.rm = TRUE,bins = 10, fill = "#FFD21F") + xlab("Days in Summer") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(w2$bus_freq_d)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    0.00    6.00   22.66   45.00   91.00

Do you currently travel on any of the following streets when you travel by bus in Saskatoon?

a. 8th street

var_name <- w2$saskroads_a
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	137	51.70
	No	119	44.91
	I don’t know	9	3.40

b. 22nd street

var_name <- w2$saskroads_b
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	78	29.43
	No	171	64.53
	I don’t know	16	6.04

c. College Drive

var_name <- w2$saskroads_c
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	152	57.36
	No	99	37.36
	I don’t know	14	5.28

d. Preston Avenue

var_name <- w2$saskroads_d
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	125	47.17
	No	127	47.92
	I don’t know	13	4.91

e. Attridge Drive

var_name <- w2$saskroads_e
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	40	15.09
	No	205	77.36
	I don’t know	20	7.55

f. Warman Rd

var_name <- w2$saskroads_f
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	37	13.96
	No	208	78.49
	I don’t know	20	7.55

g. Idylwyld Dr N

var_name <- w2$saskroads_g
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	58	21.89
	No	193	72.83
	I don’t know	14	5.28

h. 3rd Avenue N

var_name <- w2$saskroads_h
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	90	33.96
	No	157	59.25
	I don’t know	18	6.79

i. Broadway Avenue

var_name <- w2$saskroads_i
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	122	46.04
	No	130	49.06
	I don’t know	13	4.91

How much would you agree with the following statement: “I would like to travel by bus in Saskatoon more than I do now”?

var_name <- w2$sask_bus_more
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly agree", 
                                                "2" = "Somewhat agree", 
                                                "3" = "Somewhat disagree", 
                                                "4" = "Strongly disagree", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly agree	79	29.81
	Somewhat agree	107	40.38
	Somewhat disagree	39	14.72
	Strongly disagree	29	10.94
	I don’t know	11	4.15

How much more likely would you be to travel by bus more if?

a. the bus on the main part of your route ran every 10 minutes or less?

var_name <- w2$bus_moti_a
w2$var_name_f <- recode_factor(var_name, "1" = "Much more likely", 
                                                "2" = "Somewhat more likely", 
                                                "3" = "Not at all more likely", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p<- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshorterfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

#### b. the bus route took you closer to your destination?

var_name <- w2$bus_moti_b
w2$var_name_f <- recode_factor(var_name, "1" = "Much more likely", 
                                                "2" = "Somewhat more likely", 
                                                "3" = "Not at all more likely", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshorterfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Much more likely	142	53.58
	Somewhat more likely	80	30.19
	Not at all more likely	36	13.58
	I don’t know	7	2.64

c. the bus and shelters were cleaner and in better condition?

var_name <- w2$bus_moti_c
w2$var_name_f <- recode_factor(var_name, "1" = "Much more likely", 
                                                "2" = "Somewhat more likely", 
                                                "3" = "Not at all more likely", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshorterfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Much more likely	108	40.75
	Somewhat more likely	89	33.58
	Not at all more likely	58	21.89
	I don’t know	10	3.77

d. the buses were on time and transfers were more reliable?

var_name <- w2$bus_moti_d
w2$var_name_f <- recode_factor(var_name, "1" = "Much more likely", 
                                                "2" = "Somewhat more likely", 
                                                "3" = "Not at all more likely", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshorterfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Much more likely	179	67.55
	Somewhat more likely	58	21.89
	Not at all more likely	21	7.92
	I don’t know	7	2.64

e. the cost of bus passes or fare was lower?

var_name <- w2$bus_moti_e
w2$var_name_f <- recode_factor(var_name, "1" = "Much more likely", 
                                                "2" = "Somewhat more likely", 
                                                "3" = "Not at all more likely", 
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshorterfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Much more likely	97	36.60
	Somewhat more likely	84	31.70
	Not at all more likely	73	27.55
	I don’t know	11	4.15

Other

print(w2$bus_moti_f_txt[w2$bus_moti_f_txt!= ""])

##  [1] "more and more frequent buses available on weekends"                                                                                                                                                         
##  [2] "If there was no COVID"                                                                                                                                                                                      
##  [3] "If there wasn't Covid"                                                                                                                                                                                      
##  [4] "More professional Drivers"                                                                                                                                                                                  
##  [5] "solar powered heated bus shelters would make the difference"                                                                                                                                                
##  [6] "If they were lit , would be great if they had a security guard..."                                                                                                                                          
##  [7] "Covid has thrown a wrench in my travel plans."                                                                                                                                                              
##  [8] "It took less time to take the bus than to walk"                                                                                                                                                             
##  [9] "More direct routes with less transfers"                                                                                                                                                                     
## [10] "Covid19 Pandemic is over"                                                                                                                                                                                   
## [11] "If COVID-19 and close contact on buses was not a concern."                                                                                                                                                  
## [12] "Travelling by bus is not economic for me as I have my own vehicle I use to get around the city."                                                                                                            
## [13] "If I lived along a decent transit line"                                                                                                                                                                     
## [14] "Bus were safe from risk of COVID-19 infection"                                                                                                                                                              
## [15] "If grad.students had bus pass in Summer"                                                                                                                                                                    
## [16] "Covid ends"                                                                                                                                                                                                 
## [17] "evening service to my area after 6 PM was more frequent"                                                                                                                                                    
## [18] "Different bus routes that have the same start and end points run at staggered intervals"                                                                                                                    
## [19] "Mandatory masks were enforced"                                                                                                                                                                              
## [20] "There wasn't the threat of COVID"                                                                                                                                                                           
## [21] "Running a bit later"                                                                                                                                                                                        
## [22] "The routes were faster and more efficientcien"                                                                                                                                                              
## [23] "If it were safer"                                                                                                                                                                                           
## [24] "With a growing city time on transfers need to be increased"                                                                                                                                                 
## [25] "If there wasn't a Covid-19 outbreak in our city"                                                                                                                                                            
## [26] "Travel times were shorter"                                                                                                                                                                                  
## [27] "the pandemic was over, and I was physically back in my office"                                                                                                                                              
## [28] "Once COVID is done"                                                                                                                                                                                         
## [29] "I would ride the bus more if it didn't have to go to the central bus terminal before going to my destination (Pacific Heights to Exhibition)."                                                              
## [30] "Bus system took into account busier times of day. There have been many times when the bus was too full and it was easier to walk the 30-45 minutes than take the chance that the next bus would have space."
## [31] "During COVID if I were confident that everyone would be wearing a mask."                                                                                                                                    
## [32] "If there were more buses going from my area to the university in a timely or express manner"                                                                                                                
## [33] "If it didn’t take so long to get across city"                                                                                                                                                               
## [34] "Safer"                                                                                                                                                                                                      
## [35] "Better route in Brighton area"                                                                                                                                                                              
## [36] "there are few bus route options in the city"                                                                                                                                                                
## [37] "There was better security (there are a lot of creepy people)"                                                                                                                                               
## [38] "Heated central terminals, it's 2020 ffs"                                                                                                                                                                    
## [39] "transfers were minimal"                                                                                                                                                                                     
## [40] "Bus had free wifi and winter shelters"

print(w2$bus_moti_g_txt[w2$bus_moti_g_txt!= ""])

##  [1] "each stop having heated seats or protection from wind"                 
##  [2] "If I was vaccinated for COVID"                                         
##  [3] "If everyone consistently wore masks properly"                          
##  [4] "right now I will not travel more / at all by bus due to Covid concerns"
##  [5] "If they were heated and were more of a shelter from the weather"       
##  [6] "The bus routes included circle drive routes"                           
##  [7] "Operating earlier and later"                                           
##  [8] "Route out to the new Costco area"                                      
##  [9] "the bus shelters are not adequate for the winter"                      
## [10] "Bus hubs or large stops were close to amenities"

Rank the following in order of how much they would influence your decision to use the bus.

1 as least important, 7 as most important #### a. the bus on the main part of your route ran every 10 minutes or less?

var_name <- w2$bus_moti_rank_a
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	85	32.08
	2	35	13.21
	3	28	10.57
	4	29	10.94
	5	16	6.04
	6	26	9.81
	7	46	17.36

b. the bus route took you closer to your destination?

var_name <- w2$bus_moti_rank_b
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	38	14.34
	2	53	20.00
	3	43	16.23
	4	37	13.96
	5	32	12.08
	6	27	10.19
	7	35	13.21

c. the bus and shelters were cleaner and in better condition?

var_name <- w2$bus_moti_rank_c
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	24	9.06
	2	17	6.42
	3	39	14.72
	4	68	25.66
	5	66	24.91
	6	23	8.68
	7	28	10.57

d. the buses were on time and transfers were more reliable?

var_name <- w2$bus_moti_rank_d
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	54	20.38
	2	54	20.38
	3	41	15.47
	4	29	10.94
	5	20	7.55
	6	23	8.68
	7	44	16.60

e. the cost of bus passes or fare was lower?

var_name <- w2$bus_moti_rank_e
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	41	15.47
	2	27	10.19
	3	37	13.96
	4	57	21.51
	5	53	20.00
	6	19	7.17
	7	31	11.70

Place the slider between the following features of a future bus system, based on how important they are to you, with the slider closer to the more important feature.

Greater frequency of buses [=0] / Bus stops closer to destination [=100]

ggplot(w2,
       aes(x = bus_moti_slider 
       )) + geom_histogram(na.rm = TRUE, bins = 15, fill= "#76D24A")  + xlab("Rank") + facet_grid( ~ compare)

summary(w2$bus_moti_slider)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   18.00   35.00   43.09   70.00  100.00

Do you think Saskatoon Transit Service today is:

a. Reliable

var_name <- w2$sask_bus_now_a
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	28	10.57
	Moderately	141	53.21
	Slightly	60	22.64
	Not at all	26	9.81
	I don’t know	10	3.77

b. Clean

var_name <- w2$sask_bus_now_b
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	43	16.23
	Moderately	125	47.17
	Slightly	69	26.04
	Not at all	15	5.66
	I don’t know	13	4.91

c. Safe

var_name <- w2$sask_bus_now_c
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	75	28.30
	Moderately	118	44.53
	Slightly	47	17.74
	Not at all	14	5.28
	I don’t know	11	4.15

d. Convenient

var_name <- w2$sask_bus_now_d
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	18	6.79
	Moderately	86	32.45
	Slightly	102	38.49
	Not at all	50	18.87
	I don’t know	9	3.40

e. Too expensive

var_name <- w2$sask_bus_now_e
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	30	11.32
	Moderately	62	23.40
	Slightly	88	33.21
	Not at all	60	22.64
	I don’t know	25	9.43

f. Too cheap

var_name <- w2$sask_bus_now_f
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	3	1.13
	Moderately	11	4.15
	Slightly	21	7.92
	Not at all	199	75.09
	I don’t know	31	11.70

g. Professional

var_name <- w2$sask_bus_now_g
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	52	19.62
	Moderately	103	38.87
	Slightly	72	27.17
	Not at all	19	7.17
	I don’t know	19	7.17

h. Environmentally friendly

var_name <- w2$sask_bus_now_h
w2$var_name_f <- recode_factor(var_name, "1" = "Very", 
                                                "2" = "Moderately", 
                                                "3" = "Slightly", 
                                                "4" = "Not at all",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very	48	18.11
	Moderately	90	33.96
	Slightly	77	29.06
	Not at all	19	7.17
	I don’t know	31	11.70

Have you ever heard of the Bus Rapid Transit (BRT) Corridors in Saskatoon?

var_name <- w2$brt_familiarity
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	180	67.92
	No	85	32.08

Do you think that the Bus Rapid Transit (BRT) corridors are a good or bad idea for Saskatoon? It is

var_name <- w2$brt_idea
w2$var_name_f <- recode_factor(var_name, "1" = "Very good idea", 
                                                "2" = "Somewhat good idea", 
                                                "3" = "Somewhat bad idea", 
                                                "4" = "Very bad idea",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good idea	135	50.94
	Somewhat good idea	108	40.75
	Somewhat bad idea	8	3.02
	Very bad idea	6	2.26
	I don’t know	8	3.02

Do you think the BRT will be good for:

a. The Environment

var_name <- w2$brt_good_a
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	84	31.70
	Somewhat good	110	41.51
	Somewhat bad	25	9.43
	Very bad	7	2.64
	I don’t know	39	14.72

b. Increasing access to transportation

var_name <- w2$brt_good_b
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	163	61.51
	Somewhat good	75	28.30
	Somewhat bad	7	2.64
	Very bad	4	1.51
	I don’t know	16	6.04

c. Making Saskatoon a vibrant city

var_name <- w2$brt_good_c
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	104	39.25
	Somewhat good	110	41.51
	Somewhat bad	10	3.77
	Very bad	6	2.26
	I don’t know	35	13.21

d. Tourism

var_name <- w2$brt_good_d
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	87	32.83
	Somewhat good	107	40.38
	Somewhat bad	12	4.53
	Very bad	6	2.26
	I don’t know	53	20.00

e. Reducing traffic congestion

var_name <- w2$brt_good_e
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	113	42.64
	Somewhat good	95	35.85
	Somewhat bad	21	7.92
	Very bad	10	3.77
	I don’t know	26	9.81

f. Health

var_name <- w2$brt_good_f
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	70	26.42
	Somewhat good	115	43.40
	Somewhat bad	12	4.53
	Very bad	5	1.89
	I don’t know	63	23.77

g. Local business

var_name <- w2$brt_good_g
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", 
                                                "2" = "Somewhat good", 
                                                "3" = "Somewhat bad", 
                                                "4" = "Very bad",
                                                "77" = "I don't know")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

                   
p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very good	104	39.25
	Somewhat good	99	37.36
	Somewhat bad	13	4.91
	Very bad	8	3.02
	I don’t know	41	15.47

Will you likely travel by bus more than you currently do once the Bus Rapid Transit (BRT) corridors are in place?

var_name <- w2$brt_bus_more
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	206	77.74
	No	59	22.26

Do you currently have a valid driver’s license?

var_name <- w2$license
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	213	80.38
	No	52	19.62

Do you have access to a car?

var_name <- w2$car_access
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	203	76.6
	No	62	23.4

How do you most often access a vehicle?

My household owns a vehicle

w2$cars_access_where_1[w2$cars_access_where_1==0] <- 2
w2$cars_access_where_1[w2$cars_access_where_1==-7] <- NA

var_name <- w2$cars_access_where_1
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	187	70.57
	No	16	6.04
	NA	62	23.40

I borrow a friend’s or relative’s vehicle

w2$cars_access_where_2[w2$cars_access_where_2==0] <- 2
w2$cars_access_where_2[w2$cars_access_where_2==-7] <- NA

var_name <- w2$cars_access_where_2
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	13	4.91
	No	190	71.70
	NA	62	23.40

I am a member of a car-sharing program (Saskatoon CarShare Co-op, etc.)

w2$cars_access_where_3[w2$cars_access_where_3==0] <- 2
w2$cars_access_where_3[w2$cars_access_where_3==-7] <- NA

var_name <- w2$cars_access_where_3
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	5	1.89
	No	198	74.72
	NA	62	23.40

How many cars, trucks, or vans are kept in your household?

w2$cars_household[w2$cars_household==-7] <- NA

var_name <- w2$cars_household
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "#1596FF") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	93	35.09
	2	67	25.28
	3	16	6.04
	4	7	2.64
	5	3	1.13
	6	1	0.38
	NA	78	29.43

How much do you enjoy using each transportation mode?

a. Walking

var_name <- w2$preferred_mode_a2
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot", "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	127	47.92
	2	79	29.81
	3	32	12.08
	4 Not at all	12	4.53
	Not applicable	15	5.66

b. Biking

var_name <- w2$preferred_mode_b
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot", "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	79	29.81
	2	54	20.38
	3	39	14.72
	4 Not at all	47	17.74
	Not applicable	46	17.36

c. Public Transit

var_name <- w2$preferred_mode_c
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot","2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	34	12.83
	2	73	27.55
	3	86	32.45
	4 Not at all	54	20.38
	Not applicable	18	6.79

d. Car

var_name <- w2$preferred_mode_d
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot","2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	104	39.25
	2	80	30.19
	3	44	16.60
	4 Not at all	15	5.66
	Not applicable	22	8.30

e. Motorcycle or scooter

var_name <- w2$preferred_mode_e
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot", "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	3	1.13
	2	5	1.89
	3	9	3.40
	4 Not at all	51	19.25
	Not applicable	197	74.34

f. Other (Please specify)

print(w2$preferred_mode_f_txt[w2$preferred_mode_f_txt!=""])

## [1] "It's a pandemic I'm almost entirely at home and don't enjoy it"
## [2] "Cab or Uber"                                                   
## [3] "Taxi"

Thinking of how you felt prior to the COVID-19 pandemic, how much did you enjoy using each transportation mode?

walking

#preferred_mode_a walking

var_name <- w2$preferred_mode_prior_a2
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot", "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	135	50.94
	2	70	26.42
	3	38	14.34
	4 Not at all	8	3.02
	Not applicable	14	5.28

biking

#preferred_mode_b biking

var_name <- w2$preferred_mode_prior_b
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot", "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	79	29.81
	2	51	19.25
	3	46	17.36
	4 Not at all	42	15.85
	Not applicable	47	17.74

public transit

#preferred_mode_c public transit
var_name <- w2$preferred_mode_prior_c
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot", "2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))


p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") 
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

var_name_f	n	pct
1 A lot	61	23.02
2	83	31.32
3	60	22.64
4 Not at all	46	17.36
Not applicable	15	5.66

car

#preferred_mode_d car
var_name <- w2$preferred_mode_prior_d
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot","2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))


p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	108	40.75
	2	69	26.04
	3	46	17.36
	4 Not at all	22	8.30
	Not applicable	20	7.55

motorcycle or scooter

#preferred_mode_e motorcycle or scooter
var_name <- w2$preferred_mode_prior_e
w2$var_name_f <- recode_factor(var_name, "1" = "1 A lot","2" = "2", "3" = "3", "4" = "4 Not at all", "5" = "Not applicable")
var_name_f <- w2$var_name_f

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))


p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") +  
  facet_wrap(~ compare)
plot(p) 

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1 A lot	2	0.75
	2	8	3.02
	3	7	2.64
	4 Not at all	41	15.47
	Not applicable	207	78.11

Section 2: Physical Activity

During the last 7 days, on how many days did you do vigorous physical activities like heavy lifting, digging, heavy construction, or climbing up stairs as part of your work? Think about only those physical activities that you did for at least 10 minutes at a time.

#work_vigpa

ggplot(w2, aes(x = w2$work_vigpa)) + geom_histogram(na.rm = TRUE, fill = "#1596FF") + xlab("N days vigorous job-related physical activity") + facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$work_vigpa)), Percentage = round(as.numeric(prop.table(table(w2$work_vigpa)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	177	66.79
1	12	4.53
2	15	5.66
3	20	7.55
4	11	4.15
5	20	7.55
6	6	2.26
7	4	1.51

How much time did you usually spend on one of those days doing vigorous physical activities as part of your work?

#work_vigpa_freq

w2$work_vigpa_freq[w2$work_vigpa_freq==-7] <- NA

ggplot(w2, aes(x = work_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes vigorous job-related physical activity") + facet_wrap(~ compare)

summary(w2$work_vigpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##     0.0    30.0    60.0   110.9   180.0   480.0     177

During the last 7 days, on how many days did you travel in a motor vehicle like a train, bus, car, or metro?

#travel_motor

ggplot(w2, aes(x = w2$travel_motor)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days") + facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$travel_motor)), Percentage = round(as.numeric(prop.table(table(w2$travel_motor)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	16	6.04
1	21	7.92
2	26	9.81
3	36	13.58
4	38	14.34
5	37	13.96
6	34	12.83
7	57	21.51

How much time did you usually spend on one of those days travelling in a train, bus, car, metro, or other kind of motor vehicle?

#travel_motor_freq

w2$travel_motor_freq[w2$travel_motor_freq==-7] <- NA
ggplot(w2, aes(x = w2$travel_motor_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") + facet_wrap(~ compare)

summary(w2$travel_motor_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1.00   30.00   40.00   60.35   60.00  720.00      16

During the last 7 days, on how many days did you bicycle for at least 10 minutes at a time to go from place to place?

#travel_bike

ggplot(w2, aes(x = w2$travel_bike)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$travel_bike)), Percentage = round(as.numeric(prop.table(table(w2$travel_bike)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	214	80.75
1	15	5.66
2	7	2.64
3	13	4.91
4	4	1.51
5	6	2.26
6	3	1.13
7	3	1.13

How much time did you usually spend on one of those days to bicycle from place to place?

#travel_bike_freq

w2$travel_bike_freq[w2$travel_bike_freq==-7] <- NA
ggplot(w2, aes(x = w2$travel_bike_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") + facet_wrap(~ compare)

summary(w2$travel_bike_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   13.50   30.00   45.88   60.00  180.00     214

During the last 7 days, on how many days did you walk for at least 10 minutes at a time to go from place to place?

#travel_walk

ggplot(w2, aes(x = w2$travel_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("# of days in the last 7 days")+ facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$travel_walk)), Percentage = round(as.numeric(prop.table(table(w2$travel_walk)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	58	21.89
1	33	12.45
2	37	13.96
3	36	13.58
4	27	10.19
5	25	9.43
6	15	5.66
7	34	12.83

How much time did you usually spend on one of those days walking from place to place?

#travel_walk_freq

w2$travel_walk_freq[w2$travel_walk_freq==-7] <- NA

ggplot(w2, aes(x = w2$travel_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") + facet_wrap(~ compare)

summary(w2$travel_walk_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   20.00   30.00   47.31   60.00  900.00      58

Not counting any walking for transportation that you have already mentioned, during the last 7 days, on how many days did you walk for at least 10 minutes at a time in your leisure time?

#leisure_walk

ggplot(w2, aes(x = w2$leisure_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$leisure_walk)), Percentage = round(as.numeric(prop.table(table(w2$leisure_walk))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	66	25
1	31	12
2	40	15
3	29	11
4	28	11
5	31	12
6	8	3
7	32	12

How much time did you usually spend on one of those days walking in your leisure time?

#leisure_walk_freq

w2$leisure_walk_freq[w2$leisure_walk_freq==-7] <- NA

ggplot(w2, aes(x = w2$leisure_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") + facet_wrap(~ compare)

summary(w2$leisure_walk_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   25.00   30.00   46.05   60.00  900.00      66

Think about only those physical activities that you did for at least 10 minutes at a time, not counting any activity for transportation or work that you have already mentioned. During the last 7 days, on how many days did you do vigorous physical activities like aerobics, running, fast bicycling, or fast swimming in your leisure time?

#leisure_vigpa

ggplot(w2, aes(x = leisure_vigpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$leisure_vigpa)), Percentage = round(as.numeric(prop.table(table(w2$leisure_vigpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	145	55
1	19	7
2	22	8
3	31	12
4	22	8
5	12	5
6	9	3
7	5	2

How much time did you usually spend on one of those days doing vigorous physical activities in your leisure time?

#leisure_vigpa_freq

w2$leisure_vigpa_freq[w2$leisure_vigpa_freq==-7] <- NA

ggplot(w2, aes(x = leisure_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 

summary(w2$leisure_vigpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00   30.00   45.00   49.81   60.00  120.00     145

During the last 7 days, on how many days did you do moderate physical activities like bicycling at a regular pace, swimming at a regular pace, or doubles tennis in your leisure time?

#leisure_modpa

ggplot(w2, aes(x = leisure_modpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ compare)

kable(data.frame(Days = 0:7, N = as.numeric(table(w2$leisure_modpa)), Percentage = round(as.numeric(prop.table(table(w2$leisure_modpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Days	N	Percentage
0	187	71
1	26	10
2	14	5
3	16	6
4	10	4
5	6	2
6	2	1
7	4	2

How much time did you usually spend on one of those days doing moderate physical activities in your leisure time?

#leisure_modpa_freq

w2$leisure_modpa_freq[w2$leisure_modpa_freq==-7] <- NA

ggplot(w2, aes(x = w2$leisure_modpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") + facet_wrap(~ compare)

summary(w2$leisure_modpa_freq)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    3.00   30.00   30.00   49.32   60.00  180.00     187

During the last 7 days, how much time did you usually spend sitting on a weekday?

#sit_weekday

ggplot(w2, aes(x = w2$sit_weekday/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekday") + facet_wrap(~ compare)

summary(w2$sit_weekday)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    45.0   240.0   420.0   427.7   600.0   960.0

During the last 7 days, how much time did you usually spend sitting on a weekend day?

#sit_weekend

ggplot(w2, aes(x = w2$sit_weekend/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekend") + facet_wrap(~ compare)

summary(w2$sit_weekend)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    60.0   240.0   360.0   382.1   480.0   960.0

Section 3: General Health

How tall are you?

#height

#exclude outliers?

ggplot(w2, aes(x = w2$height)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#76D24A") + xlab("Height (cm)") + facet_wrap(~ compare)

summary(w2$height)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     147     163     168     169     173     206

How much do you weigh?

#weight

ggplot(w2, aes(x = w2$weight)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#76D24A") + xlab("Weight (kg)") + facet_wrap(~ compare)

summary(w2$weight)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   44.00   63.00   73.00   78.34   91.00  169.00

In general, would you say your health is:

#sf1

var_name <- w2$sf1
w2$var_name_f <- recode_factor(var_name, "1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Excellent	35	13.21
	Very good	101	38.11
	Good	94	35.47
	Fair	29	10.94
	Poor	6	2.26

The following questions are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much?

a. Moderate activities such as moving a table, pushing a vacuum cleaner, bowling, or playing golf

var_name <- w2$sf2
w2$var_name_f <- recode_factor(var_name, "1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes, limited a lot	9	3.40
	Yes, limited a little	31	11.70
	No, not at all	225	84.91

b. Climbing several flights of stairs

var_name <- w2$sf3
w2$var_name_f <- recode_factor(var_name, "1" = "Yes, limited a lot",  "2" = "Yes, limited a little", "3" = "No, not at all")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes, limited a lot	18	6.79
	Yes, limited a little	54	20.38
	No, not at all	193	72.83

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health?

a. Accomplished less than you would like

#sf4 
var_name <- w2$sf4
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	62	23.4
	No	203	76.6

b. Were limited in the kind of work or other activities

var_name <- w2$sf5
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	57	21.51
	No	208	78.49

During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?

a. Accomplished less than you would like

var_name <- w2$sf6
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	112	42.26
	No	153	57.74

b. Did work or activities less carefully than usual

var_name <- w2$sf7
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	70	26.42
	No	195	73.58

During the past 4 weeks, how much did pain interfere with your normal work (including work outside the home and housework)?

var_name <- w2$sf8
w2$var_name_f <- recode_factor(var_name, "1" = "Not at all",  "2" = "Slightly", "3" = "Moderately", "4" =  "Quite a bit", "5" = "Extremely")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Not at all	127	47.92
	Slightly	81	30.57
	Moderately	35	13.21
	Quite a bit	19	7.17
	Extremely	3	1.13

How much of the time during the past 4 weeks.

a. Have you felt calm and peaceful?

var_name <- w2$sf9
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	All of the time	15	5.66
	Most of the time	67	25.28
	A good bit of the time	92	34.72
	Some of the time	53	20.00
	A little of the time	33	12.45
	None of the time	5	1.89

b. Did you have a lot of energy?

var_name <- w2$sf10
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	All of the time	14	5.28
	Most of the time	62	23.40
	A good bit of the time	80	30.19
	Some of the time	69	26.04
	A little of the time	33	12.45
	None of the time	7	2.64

c. Have you felt downhearted and blue?

var_name <- w2$sf11
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	All of the time	8	3.02
	Most of the time	21	7.92
	A good bit of the time	44	16.60
	Some of the time	72	27.17
	A little of the time	93	35.09
	None of the time	27	10.19

During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting friends, relatives, etc.)?

var_name <- w2$sf12
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time",  "2" = "Most of the time", "3" = "A good bit of the time", "4" =  "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	All of the time	9	3.40
	Most of the time	10	3.77
	A good bit of the time	32	12.08
	Some of the time	40	15.09
	A little of the time	68	25.66
	None of the time	106	40.00

Section 4: Well-being

Thinking about your own life and personal circumstances, how satisfied are you

t_1 <- select(w2, compare, pwb_a, pwb_b, pwb_c, pwb_d, pwb_e, pwb_f, pwb_g, pwb_h, pwb_i)
t_1 <- pivot_longer(t_1,
   cols = starts_with("pwb_"),
   names_to = "perception",
   names_prefix = "pwb_",
   values_to = "values",
   values_drop_na = TRUE)
## rename 

t_1$perception[t_1$perception== "a"] <- "a. With your life as a whole?"
t_1$perception[t_1$perception== "b"] <- "b. With your standard of living?"
t_1$perception[t_1$perception== "c"] <- "c. With your health?"
t_1$perception[t_1$perception== "d"] <- "d. With what you are achieving in life?"
t_1$perception[t_1$perception== "e"] <- "e. With your personal relationships?"
t_1$perception[t_1$perception== "f"] <- "f. With how safe you feel?"
t_1$perception[t_1$perception== "g"] <- "g. With feeling part of your community?"
t_1$perception[t_1$perception== "h"] <- "h. With your future security?"
t_1$perception[t_1$perception== "i"] <- "i. With your spirituality or religion?"

## recode

t_1$values <- recode_factor(t_1$values, "10" = "10- Completely satisfied", "9" = "9","8" = "8","7" = "7", "6" = "6", "5" = "5", "4" = "4", "3" = "3", "2" = "2", "1" = "1","0" = "0-Completely dissatisfied")

##### Table
t_1<- t_1 %>% 
  group_by(compare, perception, values) %>% 
  dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = INTERACTPalettecont11) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare)
  plot(p)

In general, I consider myself:

#gwb_a

var_name <- w2$gwb_a
w2$var_name_f <- recode_factor(var_name, "1" = "1- Not a very happy person", "2" = "2", "3" = "", "4" = "4", "5" = "5", "6" = "6", "7" = "7- A very happy person")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1- Not a very happy person	5	1.89
	2	6	2.26
		12	4.53
	4	25	9.43
	5	86	32.45
	6	80	30.19
	7- A very happy person	51	19.25

Compared with most of my peers, I consider myself:

#gwb_b

var_name <- w2$gwb_b
w2$var_name_f <- recode_factor(var_name, "1" = "1- Less happy","2" = "2", "3" = "", "4" = "4", "5" = "5", "6" = "6",  "7" = "7- More happy")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1- Less happy	9	3.40
	2	9	3.40
		21	7.92
	4	47	17.74
	5	80	30.19
	6	48	18.11
	7- More happy	51	19.25

Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?

#gwb_c

var_name <- w2$gwb_c
w2$var_name_f <- recode_factor(var_name, "1" = "1- Not at all", "2" = "2", "3" = "", "4" = "4", "5" = "5", "6" = "6", "7" = "7- A great deal")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1- Not at all	8	3.02
	2	24	9.06
		23	8.68
	4	44	16.60
	5	76	28.68
	6	56	21.13
	7- A great deal	34	12.83

Some people are generally not very happy. Although they are not depressed, they never seem as happy as they might be. To what extent does this characterization describe you?

#gwb_d
var_name <- w2$gwb_d
w2$var_name_f <- recode_factor(var_name, "1" = "1- Not at all", "2" = "2", "3" = "", "4" = "4", "5" = "5", "6" = "6",  "7" = "7- A great deal")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1- Not at all	62	23.40
	2	64	24.15
		39	14.72
	4	30	11.32
	5	39	14.72
	6	19	7.17
	7- A great deal	12	4.53

Tell us how often you feel this way.

t_1 <- select(w2, compare, loneliness_a, loneliness_b, loneliness_c)
t_1 <- pivot_longer(t_1,
   cols = starts_with("loneliness_"),
   names_to = "perception",
   names_prefix = "loneliness_",
   values_to = "values",
   values_drop_na = TRUE)
## rename 

t_1$perception[t_1$perception== "a"] <- "a. How often do you feel that you lack companionship?"
t_1$perception[t_1$perception== "b"] <- "b. How often do you feel left out?"
t_1$perception[t_1$perception== "c"] <- "c. How often do you feel isolated from others?"


## recode

t_1$values <- recode_factor(t_1$values, "1" = "Hardly ever", "2" = "Some of the time", "3" = "Often")

##### Table
t_1<- t_1 %>% 
  group_by(compare, perception, values) %>% 
  dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = rev(INTERACTshorterfade3)) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare)
  plot(p)

Section 5: Social Participation

How would you describe your sense of belonging to your local community? Would you say it is:

var_name <- w2$belonging
w2$var_name_f <- recode_factor(var_name, "1" = "Very strong", "2" = "Somewhat strong", "3" = "Somewhat weak", "4" = "Very weak", "77" ="I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very strong	21	7.92
	Somewhat strong	83	31.32
	Somewhat weak	101	38.11
	Very weak	47	17.74
	I don’t know	13	4.91

How often do you.

a. Say hello to a neighbour?

#spat_a

#per week
ggplot(w2, aes(x = w2$spat_a/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week") + facet_wrap( ~ compare)

summary(w2$spat_a/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.6904  1.9945  2.5057  3.9890  6.9808

b. Stop and have a chat with a neighbour?

#spat_b

#per week
ggplot(w2, aes(x = w2$spat_b/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ compare)

summary(w2$spat_b/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.05753 0.69041 1.20626 1.99452 6.98082

c. Visit a neighbour, or receive a visit from a neighbour?

#spat_c

#per week
ggplot(w2, aes(x = w2$spat_c/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ compare)

summary(w2$spat_c/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.4167  0.2301  6.9808

d. Go somewhere (e.g., to a shop; to a restaurant), together with a neighbour?

#spat_d

#per week
ggplot(w2, aes(x = w2$spat_d/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ compare)

summary(w2$spat_d/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.1708  0.0000  4.9863

e. Ask help/advice from or do you help/give advice to a neighbour yourself?

#spat_e

#per week
ggplot(w2, aes(x = w2$spat_e/52.1429)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ compare)

summary(w2$spat_e/52.1429)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
## 0.00000 0.00000 0.01918 0.27001 0.23014 4.98630

Thinking about your neighbourhood, how would you rate the following statements?

a. This is a close-knit neighbourhood

# plot spat2_a

var_name <- w2$spat2_a
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly disagree	42	15.85
	Somewhat disagree	40	15.09
	Neither agree or disagree	115	43.40
	Somewhat agree	50	18.87
	Strongly agree	18	6.79

b. People generally do not get along

# plot spat2_b
var_name <- w2$spat2_b
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly disagree	79	29.81
	Somewhat disagree	91	34.34
	Neither agree or disagree	80	30.19
	Somewhat agree	11	4.15
	Strongly agree	4	1.51

c. People are willing to help neighbours

# plot spat2_c
var_name <- w2$spat2_c
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly disagree	6	2.26
	Somewhat disagree	20	7.55
	Neither agree or disagree	63	23.77
	Somewhat agree	128	48.30
	Strongly agree	48	18.11

d. People do not share same values

var_name <- w2$spat2_d
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly disagree	25	9.43
	Somewhat disagree	79	29.81
	Neither agree or disagree	126	47.55
	Somewhat agree	27	10.19
	Strongly agree	8	3.02

e. People can be trusted

# plot spat2_e

var_name <- w2$spat2_e
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly disagree	4	1.51
	Somewhat disagree	22	8.30
	Neither agree or disagree	75	28.30
	Somewhat agree	117	44.15
	Strongly agree	47	17.74

If you lost a wallet or purse that contained two hundred dollars, how likely is it to be returned with the money in it, if it was found:

a. By someone who lives close by? Would it be:

#spat3_a
var_name <- w2$spat3_a
w2$var_name_f <- recode_factor(var_name, "1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very likely	72	27.17
	Somewhat likely	123	46.42
	Not at all likely	30	11.32
	I don’t know	40	15.09

b. By a complete stranger? Would it be:

#spat3_b
var_name <- w2$spat3_b
w2$var_name_f <- recode_factor(var_name, "1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very likely	8	3.02
	Somewhat likely	81	30.57
	Not at all likely	122	46.04
	I don’t know	54	20.38

How many close friends do you have (that is, people who are not your relatives, but who you feel at ease with, can talk to about what is on your mind, or call on for help)?

#confide

ggplot(w2, aes(x = w2$confide)) + 
  geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ compare)

summary(w2$confide)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   3.000   5.000   5.449   6.000  40.000

Here are some questions about your satisfaction with the neighbourhood in which you live. Please indicate your satisfaction with each item.How satisfied are you with…

a. your neighbourhood as a good place to live?

#neighb_a

var_name <- w2$neighb_a
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly satisfied	95	35.85
	Satisfied	135	50.94
	Neither satisfied nor dissatisfied	21	7.92
	Dissatisfied	10	3.77
	Strongly dissatisfied	4	1.51

b. the number of people you know in your neighbourhood?

#neighb_b
var_name <- w2$neighb_b
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly satisfied	32	12.08
	Satisfied	92	34.72
	Neither satisfied nor dissatisfied	97	36.60
	Dissatisfied	38	14.34
	Strongly dissatisfied	6	2.26

c. the ethnic diversity of your neighbourhood?

#neighb_c
var_name <- w2$neighb_c
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly satisfied	37	13.96
	Satisfied	96	36.23
	Neither satisfied nor dissatisfied	99	37.36
	Dissatisfied	30	11.32
	Strongly dissatisfied	3	1.13

d. your neighbourhood as a good place to raise children

#neighb_d

var_name <- w2$neighb_d
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Strongly satisfied	77	29.06
	Satisfied	107	40.38
	Neither satisfied nor dissatisfied	59	22.26
	Dissatisfied	17	6.42
	Strongly dissatisfied	5	1.89

Section 7: Neighbourhood Selection

Before moving into your current dwelling, when you were looking for a neighbourhood to live in, to what extent were the following characteristics important? Please report your perspectives, even if the neighbourhood where you currently live does not have these characteristics

a. Good access to public transportation

#neigh_pref_a

var_name <- w2$neigh_pref_a
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	129	48.68
	Somewhat important	75	28.30
	Not very important	28	10.57
	Not important at all	24	9.06
	I don’t know	9	3.40

b. Sufficient parks and green spaces

var_name <- w2$neigh_pref_b
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	136	51.32
	Somewhat important	95	35.85
	Not very important	23	8.68
	Not important at all	5	1.89
	I don’t know	6	2.26

c. Sufficient shops and services

#neigh_pref_c
var_name <- w2$neigh_pref_c
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	114	43.02
	Somewhat important	110	41.51
	Not very important	30	11.32
	Not important at all	5	1.89
	I don’t know	6	2.26

d. Proximity to doctors, a pharmacy or other health services

var_name <- w2$neigh_pref_d
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	70	26.42
	Somewhat important	90	33.96
	Not very important	80	30.19
	Not important at all	18	6.79
	I don’t know	7	2.64

e. A good knowledge of the neighbourhood

var_name <- w2$neigh_pref_e
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	58	21.89
	Somewhat important	116	43.77
	Not very important	64	24.15
	Not important at all	15	5.66
	I don’t know	12	4.53

f. Presence of relatives, friends or acquaintances

#neigh_pref_f

var_name <- w2$neigh_pref_f
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	43	16.23
	Somewhat important	67	25.28
	Not very important	80	30.19
	Not important at all	66	24.91
	I don’t know	9	3.40

g. A neighbourhood where it is pleasant to walk

#neigh_pref_g
var_name <- w2$neigh_pref_g
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	139	52.45
	Somewhat important	93	35.09
	Not very important	21	7.92
	Not important at all	7	2.64
	I don’t know	5	1.89

h. A neighbourhood where it is practical to move around by car (ease of parking, low traffic, good access by car)

#neigh_pref_h
var_name <- w2$neigh_pref_h
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	68	25.66
	Somewhat important	84	31.70
	Not very important	50	18.87
	Not important at all	52	19.62
	I don’t know	11	4.15

i. Presence of good schools

#neigh_pref_i
var_name <- w2$neigh_pref_i
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very important	72	27.17
	Somewhat important	51	19.25
	Not very important	47	17.74
	Not important at all	78	29.43
	I don’t know	17	6.42

Section 8: COVID-19 - most closed phase

The following questions are about your activities and how you felt during the most closed phase of the COVID-19 lockdown.

During the most closed phase of the COVID-19 lockdown, compared to your typical habits prior to the COVID-19 pandemic, how many trips per week did you make?

typical habits prior to COVID-19

excluded outliers where more than 100 trips a week

#cov_con_trips_pre
w2$cov_con_trips_pre[w2$cov_con_trips_pre>=100] <- NA

ggplot(w2, aes(x= cov_con_trips_pre)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of trips per week") + facet_wrap( ~ compare)

summary(w2$cov_con_trips_pre)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    5.50   10.00   11.76   15.00   60.00       2

most closed phase of COVID-19

excluded outliers where more than 100 trips a week

#cov_con_trips
w2$cov_con_trips[w2$cov_con_trips>=100] <- NA

ggplot(w2, aes(x= cov_con_trips)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of trips per week") + facet_wrap( ~ compare)

summary(w2$cov_con_trips)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    0.00    0.00    2.00    3.28    4.00   30.00       1

During the most closed phase of the COVID-19 lockdown, did you make trips for the following activities?

t_1 <- select(w2, compare, cov_con_triptype_a, cov_con_triptype_b, cov_con_triptype_c, cov_con_triptype_d, cov_con_triptype_e, cov_con_triptype_f)
t_1 <- pivot_longer(t_1,
   cols = starts_with("cov_con_triptype_"),
   names_to = "perception",
   names_prefix = "cov_con_triptype_",
   values_to = "values",
   values_drop_na = TRUE)
## rename 

t_1$perception[t_1$perception== "a"] <- "Work"
t_1$perception[t_1$perception== "b"] <- "School"
t_1$perception[t_1$perception== "c"] <- "Groceries"
t_1$perception[t_1$perception== "d"] <- "Medical trips"
t_1$perception[t_1$perception== "e"] <- "Care-taking"
t_1$perception[t_1$perception== "f"] <- "Social, entertainment, eating out"
t_1$perception[t_1$perception== "g"] <- "Recreation / exercise"


## recode

t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No")
##### Table
t_1<- t_1 %>% 
  group_by(compare, perception, values) %>% 
  dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare)
  plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	perception	values	n	pct
	Care-taking	Yes	21	7.92
	Care-taking	No	244	92.08
	Groceries	Yes	226	85.28
	Groceries	No	39	14.72
	Medical trips	Yes	89	33.58
	Medical trips	No	176	66.42
	School	Yes	17	6.42
	School	No	248	93.58
	Social, entertainment, eating out	Yes	50	18.87
	Social, entertainment, eating out	No	215	81.13
	Work	Yes	89	33.58
	Work	No	176	66.42

During the most closed phase of the COVID-19 lockdown, did you use the following modes of transportation in a typical week more than, less than,or the same as you did prior to the COVID-19 pandemic?

t_1 <- select(w2, compare, cov_con_mode_a, cov_con_mode_b, cov_con_mode_c, cov_con_mode_d)
t_1 <- pivot_longer(t_1,
   cols = starts_with("cov_con_mode_"),
   names_to = "perception",
   names_prefix = "cov_con_mode_",
   values_to = "values",
   values_drop_na = TRUE)
## rename 

t_1$perception[t_1$perception== "a"] <- "Driving"
t_1$perception[t_1$perception== "b"] <- "Cycling"
t_1$perception[t_1$perception== "c"] <- "Walking"
t_1$perception[t_1$perception== "d"] <- "Public Transit"


## recode

t_1$values <- recode_factor(t_1$values, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
##### Table
t_1<- t_1 %>% 
  group_by(compare, perception, values) %>% 
  dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = rev(INTERACTshorterfade3)) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare)
  plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	perception	values	n	pct
	Cycling	Less	101	38.11
	Cycling	Same as before COVID-19	113	42.64
	Cycling	More	51	19.25
	Driving	Less	132	49.81
	Driving	Same as before COVID-19	69	26.04
	Driving	More	64	24.15
	Public Transit	Less	189	71.32
	Public Transit	Same as before COVID-19	71	26.79
	Public Transit	More	5	1.89
	Walking	Less	61	23.02
	Walking	Same as before COVID-19	92	34.72
	Walking	More	112	42.26

Please tell us why you used public transit more by ranking the following items based on how importantly they factored into your decision.

#cov_con_brt_more_a

w2$cov_con_brt_more_a[w2$cov_con_brt_more_a==-7] <- NA
w2$cov_con_brt_more_b[w2$cov_con_brt_more_b==-7] <- NA
w2$cov_con_brt_more_c[w2$cov_con_brt_more_c==-7] <- NA
w2$cov_con_brt_more_d[w2$cov_con_brt_more_d==-7] <- NA
w2$cov_con_brt_more_e[w2$cov_con_brt_more_e==-7] <- NA



var_name <- w2$cov_con_brt_more_a
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Needed a place to spend time") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	2	0.75
	Least important	3	1.13
	NA	260	98.11

#cov_con_brt_more_b

var_name <- w2$cov_con_brt_more_b
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Public spaces were closed") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	1	0.38
	Least important	4	1.51
	NA	260	98.11

#cov_con_brt_more_c

var_name <- w2$cov_con_brt_more_c
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Free transit meant I could access more places") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	2	0.75
	2	1	0.38
	3	1	0.38
	Least important	1	0.38
	NA	260	98.11

#cov_con_brt_more_d

var_name <- w2$cov_con_brt_more_d
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Preferred using transit over another mode of transportation") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	2	0.75
	2	1	0.38
	Least important	2	0.75
	NA	260	98.11

#cov_con_brt_more_e

var_name <- w2$cov_con_brt_more_e
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Less risk of COVID transmission compared to other modes") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	1	0.38
	3	1	0.38
	4	1	0.38
	Least important	2	0.75
	NA	260	98.11

Please tell us why you used public transit less by ranking the following items based on how importantly they factored into your decision.

w2$cov_con_brt_less_a[w2$cov_con_brt_less_a==-7] <- NA
w2$cov_con_brt_less_b[w2$cov_con_brt_less_b==-7] <- NA
w2$cov_con_brt_less_c[w2$cov_con_brt_less_c==-7] <- NA
w2$cov_con_brt_less_d[w2$cov_con_brt_less_d==-7] <- NA
w2$cov_con_brt_less_e[w2$cov_con_brt_less_e==-7] <- NA
w2$cov_con_brt_less_f[w2$cov_con_brt_less_f==-7] <- NA
w2$cov_con_brt_less_f[w2$cov_con_brt_less_g==-7] <- NA

#cov_con_brt_less_a
var_name <- w2$cov_con_brt_less_a
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Didn’t feel safe from contracting COVID-19")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	105	39.62
	2	20	7.55
	3	22	8.30
	4	12	4.53
	5	7	2.64
	6	5	1.89
	Least important	18	6.79
	NA	76	28.68

#cov_con_brt_less_b
var_name <- w2$cov_con_brt_less_b
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Change in schedule made it not feasible")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	33	12.45
	2	21	7.92
	3	16	6.04
	4	23	8.68
	5	11	4.15
	6	24	9.06
	Least important	61	23.02
	NA	76	28.68

#cov_con_brt_less_c
var_name <- w2$cov_con_brt_less_c
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Change in route made it not feasible")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	18	6.79
	2	11	4.15
	3	16	6.04
	4	25	9.43
	5	15	5.66
	6	26	9.81
	Least important	78	29.43
	NA	76	28.68

#cov_con_brt_less_d
var_name <- w2$cov_con_brt_less_d
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Used active transportation instead (walking, cycling, etc.)" )+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	37	13.96
	2	35	13.21
	3	21	7.92
	4	23	8.68
	5	12	4.53
	6	15	5.66
	Least important	46	17.36
	NA	76	28.68

#cov_con_brt_less_e
var_name <- w2$cov_con_brt_less_e
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Used a car instead   ")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	73	27.55
	2	26	9.81
	3	25	9.43
	4	14	5.28
	5	10	3.77
	6	4	1.51
	Least important	37	13.96
	NA	76	28.68

#cov_con_brt_less_f
var_name <- w2$cov_con_brt_less_f
w2$var_name_f <- recode_factor(var_name,"1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("My destinations were out of reach of transit ")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	18	6.79
	2	14	5.28
	3	13	4.91
	4	16	6.04
	5	22	8.30
	6	27	10.19
	Least important	79	29.81
	NA	76	28.68

#cov_con_brt_less_g
var_name <- w2$cov_con_brt_less_g
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6",  " 7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("I did not need transit to access my destinations")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	61	23.02
	2	31	11.70
	3	13	4.91
	4	20	7.55
	5	9	3.40
	6	9	3.40
	Least important	46	17.36
	NA	76	28.68

During the most closed phase of the COVID-19 lockdown, how satisfied were you with your level of physical activity?

var_name <- w2$cov_con_pa
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied", 
                                                "2" = "Somewhat satisfied", 
                                                "3" = "Neutral", 
                                                "4" = "Somewhat dissatisfied", 
                                                "5" = "Very dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very satisfied	32	12.08
	Somewhat satisfied	79	29.81
	Neutral	37	13.96
	Somewhat dissatisfied	75	28.30
	Very dissatisfied	42	15.85

During the most closed phase of the COVID-19 lockdown, how satisfied were you with your ability to connect with others?

var_name <- w2$cov_con_social
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied", 
                                                "2" = "Somewhat satisfied", 
                                                "3" = "Neutral", 
                                                "4" = "Somewhat dissatisfied", 
                                                "5" = "Very dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very satisfied	18	6.79
	Somewhat satisfied	57	21.51
	Neutral	55	20.75
	Somewhat dissatisfied	77	29.06
	Very dissatisfied	58	21.89

During the most closed phase of the COVID-19 lockdown, how would you have rated your overall well-being?

var_name <- w2$cov_con_wb

w2$var_name_f <- recode_factor(var_name, "0" = "0. As bad as it could be", 
                                                "1" = "1",
                                                "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", 
                                                "5" = "5", 
                                                "6" = "6", 
                                                "7" = "7",
                                                "8" = "8",
                                                "9" = "9",
                                                "10" = "10. As good as it could be")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTfade)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	As bad as it could be	7	2.64
	1	2	0.75
	2	16	6.04
	3	29	10.94
	4	32	12.08
	5	33	12.45
	6	38	14.34
	7	44	16.60
	8	24	9.06
	9	15	5.66
	As good as it could be	25	9.43

During the height of the COVID-19 lockdown, how satisfied were you with Saskatoon Transit’s response to the lockdown?

var_name <- w2$cov_sati_resp
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied", 
                                                "2" = "Somewhat satisfied", 
                                                "3" = "Neutral", 
                                                "4" = "Somewhat dissatisfied", 
                                                "5" = "Very dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very satisfied	51	19.25
	Somewhat satisfied	76	28.68
	Neutral	105	39.62
	Somewhat dissatisfied	27	10.19
	Very dissatisfied	6	2.26

Please rank the following temporary interventions from 1 being most effective to 5 as least effective to encourage mobility in Saskatoon during the height of the COVID-19 lockdown.

Reduced capacity on buses to accommodate for physical distancing

var_name <- w2$cov_mob_rank_a
w2$var_name_f <- recode_factor(var_name, "1" = "Most effective", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", "5" = "Least effective")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most effective	134	50.57
	2	65	24.53
	3	44	16.60
	4	15	5.66
	Least effective	7	2.64

Back door loading/ no fares collected

var_name <- w2$cov_mob_rank_b
w2$var_name_f <- recode_factor(var_name, "1" = "Most effective", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4",  "5" = "Least effective")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most effective	120	45.28
	2	71	26.79
	3	54	20.38
	4	8	3.02
	Least effective	12	4.53

Changes in routes/schedule

var_name <- w2$cov_mob_rank_c
w2$var_name_f <- recode_factor(var_name, "1" = "Most effective", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4",  "5" = "Least effective")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most effective	20	7.55
	2	28	10.57
	3	85	32.08
	4	59	22.26
	Least effective	73	27.55

Extra buses on busy routes

var_name <- w2$cov_mob_rank_d
w2$var_name_f <- recode_factor(var_name, "1" = "Most effective", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", "5" = "Least effective")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most effective	91	34.34
	2	62	23.40
	3	69	26.04
	4	29	10.94
	Least effective	14	5.28

Public Paid Parking not enforced

var_name <- w2$cov_mob_rank_e
w2$var_name_f <- recode_factor(var_name, "1" = "Most effective","2" = "2", 
                                                "3" = "3", 
                                                "4" = "4",  "5" = "Least effective")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most effective	98	36.98
	2	39	14.72
	3	38	14.34
	4	31	11.70
	Least effective	59	22.26

When thinking of the term “safety” and the use of public transit, please rank the following from safest to least safe.

Risk of contracting COVID-19

var_name <- w2$cov_safety_a
w2$var_name_f <- recode_factor(var_name, "1" = "Safest", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4",  "5" = "Least safe")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Safest	24	9.06
	2	34	12.83
	3	73	27.55
	4	65	24.53
	Least safe	69	26.04

Using the bus late at night

var_name <- w2$cov_safety_b
w2$var_name_f <- recode_factor(var_name, "1" = "Safest", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", "5" = "Least safe")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Safest	20	7.55
	2	36	13.58
	3	61	23.02
	4	69	26.04
	Least safe	79	29.81

Waiting for the bus at bus shelters

var_name <- w2$cov_safety_c
w2$var_name_f <- recode_factor(var_name, "1" = "Safest", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", "5" = "Least safe")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Safest	22	8.30
	2	59	22.26
	3	83	31.32
	4	61	23.02
	Least safe	40	15.09

Physical safety from other riders on the bus

var_name <- w2$cov_safety_d
w2$var_name_f <- recode_factor(var_name, "1" = "Safest", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", "5" = "Least safe")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Safest	39	14.72
	2	77	29.06
	3	76	28.68
	4	43	16.23
	Least safe	30	11.32

Being involved in a collision while riding the bus

var_name <- w2$cov_safety_e
w2$var_name_f <- recode_factor(var_name, "1" = "Safest", "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", "5" = "Least safe")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Safest	108	40.75
	2	51	19.25
	3	47	17.74
	4	26	9.81
	Least safe	33	12.45

During the height of the COVID-19 lockdown, which of the following statement best represents the reason you took public transit?

var_name <- w2$cov_reasonuse
w2$var_name_f <- recode_factor(var_name, "1" = "I used the bus for essential trips only", "2" = "I used the bus for mainly essential trips but also some trips for leisure", "3" = "I used the bus for leisure trips only", "4" = "I used the bus for mainly leisure trips but also some trips for essential", "5" = "I did not use the bus during the height of the COVID-19 lockdown")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	I used the bus for essential trips only	66	24.91
	I used the bus for mainly essential trips but also some trips for leisure	16	6.04
	I used the bus for leisure trips only	3	1.13
	I used the bus for mainly leisure trips but also some trips for essential	3	1.13
	I did not use the bus during the height of the COVID-19 lockdown	177	66.79

Section 9: COVID-19 Current context

The following questions are about your activities and how you feel now in the current context.

In the current context, how many trips do you make per week?

#cov_decon_trips

ggplot(w2, aes(x= cov_decon_trips)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of trips per week") + facet_wrap( ~ compare)

summary(w2$cov_decon_trips)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00    2.00    6.00    7.66   12.00   40.00

In the current context, do you make trips for the following activities?

t_1 <- select(w2, compare, cov_decon_triptype_a, cov_decon_triptype_b, cov_decon_triptype_c, cov_decon_triptype_d, cov_decon_triptype_e, cov_decon_triptype_f)
t_1 <- pivot_longer(t_1,
   cols = starts_with("cov_decon_triptype_"),
   names_to = "perception",
   names_prefix = "cov_decon_triptype_",
   values_to = "values",
   values_drop_na = TRUE)
## rename 

t_1$perception[t_1$perception== "a"] <- "Work"
t_1$perception[t_1$perception== "b"] <- "School"
t_1$perception[t_1$perception== "c"] <- "Groceries"
t_1$perception[t_1$perception== "d"] <- "Medical trips"
t_1$perception[t_1$perception== "e"] <- "Care-taking"
t_1$perception[t_1$perception== "f"] <- "Social, entertainment, eating out"
t_1$perception[t_1$perception== "g"] <- "Recreation / exercise"


## recode

t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No")
##### Table
t_1<- t_1 %>% 
  group_by(compare, perception, values) %>% 
  dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare)
  plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	perception	values	n	pct
	Care-taking	Yes	25	9.43
	Care-taking	No	240	90.57
	Groceries	Yes	227	85.66
	Groceries	No	38	14.34
	Medical trips	Yes	146	55.09
	Medical trips	No	119	44.91
	School	Yes	38	14.34
	School	No	227	85.66
	Social, entertainment, eating out	Yes	103	38.87
	Social, entertainment, eating out	No	162	61.13
	Work	Yes	130	49.06
	Work	No	135	50.94

In the current context, do you use the following modes of transportation less than, more than, or the same as you did prior to the COVID-19 pandemic?

t_1 <- select(w2, compare, cov_decon_mode_a, cov_decon_mode_b, cov_decon_mode_c, cov_decon_mode_d)
t_1 <- pivot_longer(t_1,
   cols = starts_with("cov_decon_mode_"),
   names_to = "perception",
   names_prefix = "cov_decon_mode_",
   values_to = "values",
   values_drop_na = TRUE)
## rename 

t_1$perception[t_1$perception== "a"] <- "Driving"
t_1$perception[t_1$perception== "b"] <- "Cycling"
t_1$perception[t_1$perception== "c"] <- "Walking"
t_1$perception[t_1$perception== "d"] <- "Public Transit"


## recode

t_1$values <- recode_factor(t_1$values, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
##### Table
t_1<- t_1 %>% 
  group_by(compare, perception, values) %>% 
  dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = rev(INTERACTshorterfade3)) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare)
  plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	perception	values	n	pct
	Cycling	Less	78	29.43
	Cycling	Same as before COVID-19	152	57.36
	Cycling	More	35	13.21
	Driving	Less	84	31.70
	Driving	Same as before COVID-19	104	39.25
	Driving	More	77	29.06
	Public Transit	Less	152	57.36
	Public Transit	Same as before COVID-19	90	33.96
	Public Transit	More	23	8.68
	Walking	Less	52	19.62
	Walking	Same as before COVID-19	116	43.77
	Walking	More	97	36.60

Please tell us why you use public transit more by ranking the following items based on how importantly they factor into your decision.

#cov_decon_brt_more

w2$cov_decon_brt_more_a[w2$cov_decon_brt_more_a==-7] <- NA
w2$cov_decon_brt_more_b[w2$cov_decon_brt_more_b==-7] <- NA
w2$cov_decon_brt_more_d[w2$cov_decon_brt_more_d==-7] <- NA
w2$cov_decon_brt_more_e[w2$cov_decon_brt_more_e==-7] <- NA



var_name <- w2$cov_decon_brt_more_a
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", 
                                                "3" = "3", "4" = "4. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Needed a place to spend time") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	2	0.75
	2	2	0.75
	3	3	1.13
	Least important	16	6.04
	NA	242	91.32

#cov_decon_brt_more_b

var_name <- w2$cov_decon_brt_more_b
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", 
                                                "3" = "3", "4" = "4. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Public spaces were closed") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	4	1.51
	2	6	2.26
	3	4	1.51
	Least important	9	3.40
	NA	242	91.32

#cov_decon_brt_more_d

var_name <- w2$cov_decon_brt_more_d
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", 
                                                "3" = "3", "4" = "4. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Preferred using transit over another mode of transportation") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	15	5.66
	2	1	0.38
	3	3	1.13
	Least important	4	1.51
	NA	242	91.32

#cov_decon_brt_more_e

var_name <- w2$cov_decon_brt_more_e
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", 
                                                "3" = "3", "4" = "4. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill= "76D24A") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Less risk of COVID transmission compared to other modes") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	2	0.75
	2	7	2.64
	3	5	1.89
	Least important	9	3.40
	NA	242	91.32

Please tell us why you use public transit less by ranking the following items based on how importantly they factor into your decision.

w2$cov_decon_brt_less_a[w2$cov_decon_brt_less_a==-7] <- NA
w2$cov_decon_brt_less_b[w2$cov_decon_brt_less_b==-7] <- NA
w2$cov_decon_brt_less_c[w2$cov_decon_brt_less_c==-7] <- NA
w2$cov_decon_brt_less_d[w2$cov_decon_brt_less_d==-7] <- NA
w2$cov_decon_brt_less_e[w2$cov_decon_brt_less_e==-7] <- NA
w2$cov_decon_brt_less_f[w2$cov_decon_brt_less_f==-7] <- NA
w2$cov_decon_brt_less_g[w2$cov_decon_brt_less_g==-7] <- NA

#cov_decon_brt_less_a
var_name <- w2$cov_decon_brt_less_a
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Didn’t feel safe from contracting COVID-19")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	84	31.70
	2	13	4.91
	3	25	9.43
	4	14	5.28
	5	4	1.51
	6	3	1.13
	Least important	9	3.40
	NA	113	42.64

#cov_decon_brt_less_b
var_name <- w2$cov_decon_brt_less_b
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6","7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Change in schedule made it not feasible")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	21	7.92
	2	13	4.91
	3	12	4.53
	4	23	8.68
	5	19	7.17
	6	22	8.30
	Least important	42	15.85
	NA	113	42.64

#cov_decon_brt_less_c
var_name <- w2$cov_decon_brt_less_c
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Change in route made it not feasible")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	8	3.02
	2	13	4.91
	3	13	4.91
	4	26	9.81
	5	17	6.42
	6	25	9.43
	Least important	50	18.87
	NA	113	42.64

#cov_decon_brt_less_d
var_name <- w2$cov_decon_brt_less_d
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Used active transportation instead (walking, cycling, etc.)" )+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	26	9.81
	2	25	9.43
	3	19	7.17
	4	32	12.08
	5	11	4.15
	6	12	4.53
	Least important	27	10.19
	NA	113	42.64

#cov_decon_brt_less_e
var_name <- w2$cov_decon_brt_less_e
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("Used a car instead   ")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	61	23.02
	2	28	10.57
	3	23	8.68
	4	9	3.40
	5	8	3.02
	6	2	0.75
	Least important	21	7.92
	NA	113	42.64

#cov_decon_brt_less_f
var_name <- w2$cov_decon_brt_less_f
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("My destinations were out of reach of transit ")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	11	4.15
	2	4	1.51
	3	15	5.66
	4	28	10.57
	5	18	6.79
	6	23	8.68
	Least important	53	20.00
	NA	113	42.64

#cov_decon_brt_less_g
var_name <- w2$cov_decon_brt_less_g
w2$var_name_f <- recode_factor(var_name, "1" = "1. Most important", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6","7" = "7. Least important ")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  ggtitle("I did not need transit to access my destinations")+
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Most important	55	20.75
	2	18	6.79
	3	13	4.91
	4	18	6.79
	5	10	3.77
	6	9	3.40
	Least important	29	10.94
	NA	113	42.64

In the current context, how satisfied are you with your level of physical activity?

var_name <- w2$cov_decon_pa
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied", 
                                                "2" = "Somewhat satisfied", 
                                                "3" = "Neutral", 
                                                "4" = "Somewhat dissatisfied", 
                                                "5" = "Very dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very satisfied	33	12.45
	Somewhat satisfied	91	34.34
	Neutral	49	18.49
	Somewhat dissatisfied	65	24.53
	Very dissatisfied	27	10.19

In the current context, how satisfied are you with your ability to connect with others?

var_name <- w2$cov_decon_social
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied", 
                                                "2" = "Somewhat satisfied", 
                                                "3" = "Neutral", 
                                                "4" = "Somewhat dissatisfied", 
                                                "5" = "Very dissatisfied")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very satisfied	19	7.17
	Somewhat satisfied	112	42.26
	Neutral	45	16.98
	Somewhat dissatisfied	59	22.26
	Very dissatisfied	30	11.32

In the current context, how would you rate your overall well-being?

var_name <- w2$cov_decon_wb

w2$var_name_f <- recode_factor(var_name, "0" = "0. As bad as it could be", 
                                                "1" = "1",
                                                "2" = "2", 
                                                "3" = "3", 
                                                "4" = "4", 
                                                "5" = "5", 
                                                "6" = "6", 
                                                "7" = "7",
                                                "8" = "8",
                                                "9" = "9",
                                                "10" = "10. As good as it could be")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	1	1	0.38
	2	6	2.26
	3	18	6.79
	4	21	7.92
	5	35	13.21
	6	31	11.70
	7	64	24.15
	8	43	16.23
	9	26	9.81
	As good as it could be	20	7.55

Section 10: Demographics

What is your current gender identity?

#gender 
var_name <- w2$gender

w2$var_name_f <- recode_factor(var_name, "1"="Man",
                                                "2"="Woman",
                                                "3"="Trans man", 
                                                "4"="Trans woman", 
                                                "5"="Genderqueer/Gender non-conforming", 
                                                "6"="Different identity")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Man	76	28.68
	Woman	182	68.68
	Trans man	2	0.75
	Genderqueer/Gender non-conforming	5	1.89

What sex were you assigned at birth?

*Asked only to new participants

# Sex

var_name <- w2$sex
w2$var_name_f <- recode_factor(var_name, "1"="Male",
                                                "2"="Female",
                                                "3"="Other")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Male	75	28.30
	Female	182	68.68
	Other	1	0.38
	NA	7	2.64

What is your marital status? Are you…

#marital_status

var_name <- w2$marital_status
w2$var_name_f <- recode_factor(var_name, "1" = "Single", "2" = "Married/commonlaw", "3" = "Separated/divorced", "4" = "Widowed")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Single	120	45.28
	Married/commonlaw	130	49.06
	Separated/divorced	13	4.91
	Widowed	2	0.75

Do you have children?

#children

var_name <- w2$children
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	88	33.21
	No	177	66.79

How many children do you have?

#living_children

w2$living_children[w2$living_children==-7] <- NA
ggplot(w2, aes(x= living_children)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of children") + facet_grid(~ compare)

summary(w2$living_children)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   1.000   1.000   2.000   1.932   2.000   4.000     177

What is your current living arrangement? Do you live

Participants could choose multiple answers

#living_arrange  

w2$living_arrange_1[w2$living_arrange_1==-7] <- NA

var_name <- w2$living_arrange_1
w2$var_name_f <- recode_factor(var_name, "1" = "Alone", "0" = "With other people")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Alone	54	20.38
	With other people	211	79.62

# Create a vector with variable names
response = paste0("living_arrange_", 2:7)

# Empty vector to stor output
living_arrange_prop <- c()

# Calculate univariate proportions
for(i in response){
  living_arrange_prop[i] <- sum(w2[,i]) / nrow(w2)
}

# Transform
living_arrange_prop <- as.data.frame(living_arrange_prop)
living_arrange_prop$Response <- c("With a spouse (or partner)","With children","With grandchildren","With relatives or siblings?", "With friends", "With other people")

living_arrange_prop$plot<- factor(living_arrange_prop$Response, living_arrange_prop$Response)

ggplot(living_arrange_prop, aes(x = plot, y = living_arrange_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percentage of participants who selected this answer") + theme(axis.text.x  = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 

living_arrange_prop$living_arrange_prop <- round(living_arrange_prop$living_arrange_prop*100,2)

colnames(living_arrange_prop) <- c("Response", "Percentage of participants who selected this answer")

living_arrange_prop <- living_arrange_prop[-c(3)]
kable(living_arrange_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")  

	Response	Percentage of participants who selected this answer
living_arrange_2	50.94	With a spouse (or partner)
living_arrange_3	22.26	With children
living_arrange_4	0.38	With grandchildren
living_arrange_5	13.21	With relatives or siblings?
living_arrange_6	6.79	With friends
living_arrange_7	6.42	With other people

How many children under the age of 16 live in your household?

#children_household

p <- ggplot(w2, aes(x = children_household)) + 
  geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) +
  xlab("Number of children under 16 in household") + 
  facet_wrap(~ compare)

plot(p)

summary(w2$children_household)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0000  0.0000  0.3509  0.0000  4.0000

How many adults aged 16 or older live in your household including yourself?

ggplot(w2, aes(x= adults_household)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of adults in household") + facet_wrap(~ compare)

summary(w2$adults_household)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   1.000   2.000   2.000   2.155   2.000  11.000

Thinking about where you live now, are you

#house_tenure
w2$house_tenure[w2$house_tenure==-7] <- NA

var_name <- w2$house_tenure

w2$var_name_f <- recode_factor(var_name, "1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "77" = "I don't know")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_grid(~compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	An owner	118	44.53
	A tenant	105	39.62
	Resident in a relative or friend’s home	30	11.32
	Resident other than in a relative or friend’s home	2	0.75
	Other	2	0.75
	I don’t know	8	3.02

In what type of dwelling do you currently live? Is it:

#dwelling_type
w2$dwelling_type[w2$dwelling_type==-7] <- NA

var_name <- w2$dwelling_type

w2$var_name_f <- recode_factor(var_name, "1" = "Single detached house", "2" = "Semi-detached house", "3" = "Row house",  "4" = "An apartment (or condo) in a duplex or triplex", "5" = "Apartment (or condo) in building with fewer than 5 storeys", "6" = "Apartment (or condo) in building with more than 5 storeys", "7" = "Mobile home/movable dwelling", "8" = "Senior's home", "9" = "Other", "77" = "Don't know")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response")+ 
  facet_grid(~compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Single detached house	126	47.55
	Semi-detached house	9	3.40
	Row house	18	6.79
	An apartment (or condo) in a duplex or triplex	18	6.79
	Apartment (or condo) in building with fewer than 5 storeys	60	22.64
	Apartment (or condo) in building with more than 5 storeys	14	5.28
	Mobile home/movable dwelling	1	0.38
	Other	7	2.64
	Don’t know	7	2.64
	NA	5	1.89

When did you move to your current residence?

*Note: Question only asked to new (n=106) and moved participants (n=17) at w2, reporting w1 data for returning participants

#residence
w2$residence[w2$residence==""] <- NA

residence <- as.integer(format(as.Date(w2$residence),"%Y"))
time <- 2021 - residence

ggplot(w2, aes(x = time)) + geom_histogram(na.rm=TRUE, binwidth = 1, fill="#76D24A") + xlab("Years since moving to current residence") + facet_grid(~compare)

summary(time)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##   0.000   2.000   4.000   7.454   9.250  44.000       5

Were you born in Canada?

*Note: Question only asked to new participants at w2 (n=155), reporting w1 data for returning participants

#born_can
var_name <- w2$born_can
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") +
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Yes	202	76.23
	No	56	21.13
	NA	7	2.64

When did you move to Canada?

*Note: Question only asked to new participants at w2 or(n=155), reporting w1 data for returning participants Question asked only to new participants not born in Canada (n=38)

#move_can 

w2$move_can[w2$move_can==-7] <- NA
ggplot(w2, aes(x = w2$move_can)) + geom_histogram (na.rm=TRUE, binwidth = 1, fill="#76D24A") + xlab("Year of move to Canada")

summary(w2$move_can)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##    1961    2011    2017    2012    2019    2021     209

To which ethnic or cultural group(s) do you belong? (Check all that apply)

var_name <- w2$group
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f


##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, hjust = 1, vjust = .6)) + 
  geom_bar(stat="identity", fill = "76d24a") +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Indigenous or Aboriginal	7	2.64
	White	173	65.28
	South Asian	18	6.79
	Chinese	7	2.64
	Black	2	0.75
	Filipino	9	3.40
	Latin American	4	1.51
	Arab	2	0.75
	Southeast Asian	4	1.51
	West Asian	3	1.13
	Other	2	0.75
	Mixed identity	6	2.26
	I don’t know/ prefer not to answer	5	1.89
	NA	23	8.68

Which category best describes your annual household income, taking into account all sources of income?

#w2$income[w2$income==-7] <- NA

var_name <- w2$income 

w2$var_name_f <- recode_factor(var_name, "1" = "No income", "2" = "$1 to $9,999", "3" = "$10,000 to $14,999", "4" = "$15,000 to $19,999", "5" = "$20,000 to $29,999", "6" = "$30,000 to $39,999", "7" = "$40,000 to $49,999", "8" = "$50,000 to $99,999", "9" = "$100,000 to $149,999", "10" = " $150,000 to $199,999", "11" = "$200,000 or more", "77" = "Don't know/prefer no answer", "-7" = "NA")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, hjust = 1, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = rev(INTERACTfade)) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	No income	2	0.75
	$1 to $9,999	8	3.02
	$10,000 to $14,999	17	6.42
	$15,000 to $19,999	15	5.66
	$20,000 to $29,999	23	8.68
	$30,000 to $39,999	24	9.06
	$40,000 to $49,999	20	7.55
	$50,000 to $99,999	60	22.64
	$100,000 to $149,999	36	13.58
	$150,000 to $199,999	14	5.28
	$200,000 or more	13	4.91
	Don’t know/prefer no answer	33	12.45

To what extent does this annual household income allow you to satisfy your household’s needs?

#income_needs

var_name <- w2$income_needs

w2$var_name_f <- recode_factor(var_name, "1" = "Very well", "2" = "Well", "3" = "Not so well", "4" = "Not at all", "77" = "Don't know/prefer no answer")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Very well	75	28.30
	Well	112	42.26
	Not so well	55	20.75
	Not at all	12	4.53
	Don’t know/prefer no answer	11	4.15

What are your houshold’s total monthly housing costs?

This includes rent payments, mortgage payments, property taxes, condominium fees, and utility payments, like heating, water and electricity. If you live with roommates, please only include your share of the housing costs.

#housing costs 

w2$housing_cost[w2$housing_cost==-7] <- NA

# ggplot(w2, aes(x = housing_cost)) + geom_histogram (na.rm =TRUE, binwidth = 1, fill="#76D24A") + xlab ("Monthly housing costs") + facet_wrap(~ compare)

summary(w2$housing_cost)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
##       0     750    1300    1420    2000    4000      83

What is your highest education level?

#education

var_name <- w2$education

w2$var_name_f <- recode_factor(var_name, "1" = "Primary/Elementary school", "2" = "Secondary school", "3" = "Trade/Technical school or college diploma", "4" = "University degree", "5" = "Graduate degree", "77" ="I don't know/Prefer not to answer")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, hjust =1, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Primary/Elementary school	2	0.75
	Secondary school	38	14.34
	Trade/Technical school or college diploma	39	14.72
	University degree	109	41.13
	Graduate degree	71	26.79
	I don’t know/Prefer not to answer	6	2.26

What is your current employment status?

#employment
var_name <- w2$employment

w2$var_name_f <- recode_factor(var_name, "1" = "Retired and not working", "2" = "Employed full-time", "3" = "Employed part-time", "4" = "Unemployed and looking for work", "5" = "Unemployed and not looking for work", "6" ="Other" )

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, hjust=1, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	Retired and not working	16	6.04
	Employed full-time	125	47.17
	Employed part-time	47	17.74
	Unemployed and looking for work	26	9.81
	Unemployed and not looking for work	17	6.42
	Other	34	12.83

How has the COVID-19 pandemic impacted your job?

w2$employment_covid_1[w2$employment_covid_1==0] <- 2
w2$employment_covid_2[w2$employment_covid_2==0] <- 2
w2$employment_covid_3[w2$employment_covid_3==0] <- 2
w2$employment_covid_4[w2$employment_covid_4==0] <- 2
w2$employment_covid_5[w2$employment_covid_5==0] <- 2
w2$employment_covid_6[w2$employment_covid_6==0] <- 2
w2$employment_covid_7[w2$employment_covid_7==0] <- 2
w2$employment_covid_8[w2$employment_covid_8==0] <- 2
w2$employment_covid_9[w2$employment_covid_9==0] <- 2
w2$employment_covid_10[w2$employment_covid_10==0] <- 2
w2$employment_covid_99[w2$employment_covid_99==0] <- 2

t_1 <- select(w2, compare, employment_covid_1,employment_covid_2, employment_covid_3, employment_covid_4, employment_covid_5, employment_covid_6, employment_covid_7, employment_covid_8, employment_covid_9, employment_covid_10)
t_1 <- pivot_longer(t_1,
   cols = starts_with("employment_covid_"),
   names_to = "feature",
   names_prefix = "employment_covid_",
   values_to = "values",
   values_drop_na = TRUE)

t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No", "77" = "I don't know")


## rename 
t_1$feature[t_1$feature== "1"] <- "I work from home."
t_1$feature[t_1$feature== "2"] <- "I work partly from home, partly at my normal workplace"
t_1$feature[t_1$feature== "3"] <- "I continue to work at my normal place of work."
t_1$feature[t_1$feature== "4"] <- "My paid work #hours have been reduced."
t_1$feature[t_1$feature== "5"] <- "My hourly rate has been reduced"
t_1$feature[t_1$feature== "6"] <- "My paid work hours have increased" 
t_1$feature[t_1$feature== "7"] <- "My hourly rate has increased."
t_1$feature[t_1$feature== "8"] <- "My job has been deemed essential by the government."
t_1$feature[t_1$feature== "9"] <- "I lost my job"
t_1$feature[t_1$feature== "10"] <-"I have started a new job"


t_1 <- t_1 %>% 
  group_by(compare, feature, values) %>% 
  dplyr::summarise(n = n()) %>%
  dplyr:: mutate(pct = round(100*n/sum(n),2))

p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat= "identity") +
     coord_flip() +
  scale_fill_manual(values = INTERACTPaletteYN) +
      ylab("Percent") +
      xlab("Response") + 
    facet_wrap(~ compare) + 
  scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30)) 

plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	feature	values	n	pct
	I continue to work at my normal place of work.	Yes	75	28.30
	I continue to work at my normal place of work.	No	190	71.70
	I have started a new job	Yes	14	5.28
	I have started a new job	No	251	94.72
	I lost my job	Yes	24	9.06
	I lost my job	No	241	90.94
	I work from home.	Yes	72	27.17
	I work from home.	No	193	72.83
	I work partly from home, partly at my normal workplace	Yes	33	12.45
	I work partly from home, partly at my normal workplace	No	232	87.55
	My hourly rate has been reduced	Yes	9	3.40
	My hourly rate has been reduced	No	256	96.60
	My hourly rate has increased.	Yes	6	2.26
	My hourly rate has increased.	No	259	97.74
	My job has been deemed essential by the government.	Yes	22	8.30
	My job has been deemed essential by the government.	No	243	91.70
	My paid work #hours have been reduced.	Yes	16	6.04
	My paid work #hours have been reduced.	No	249	93.96
	My paid work hours have increased	Yes	8	3.02
	My paid work hours have increased	No	257	96.98

Which of the following best describes your usual work schedule at your current job?

#shift 
w2$shift[w2$shift==-7] <- NA

var_name <- w2$shift

w2$var_name_f <- recode_factor(var_name, "1" = "A regular daytime schedule or shift.", "2" = "A regular evening shift   ", "3" = "A regular night shift", "4" = "A rotating shift, a split shift, or an irregular schedule", "5" = "On call or casual", "6" ="Other") 

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            summarise(n = n()) %>%
            mutate(pct = round(100*n/sum(n),2))

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, hjust=1, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	var_name_f	n	pct
	A regular daytime schedule or shift.	126	47.55
	A regular evening shift	10	3.77
	A regular night shift	1	0.38
	A rotating shift, a split shift, or an irregular schedule	22	8.30
	On call or casual	9	3.40
	Other	4	1.51
	NA	93	35.09

Age

# Categorize age variable
## reviens-y

w2$age_cat <- NA

w2$age_cat[w2$age  %in% c(18:24)] <- "18-24"
w2$age_cat[w2$age  %in% c(25:34)] <- "25-34"
w2$age_cat[w2$age  %in% c(35:44)] <- "35-44"
w2$age_cat[w2$age  %in% c(45:54)] <- "45-54"
w2$age_cat[w2$age  %in% c(55:64)] <- "55-64"
w2$age_cat[w2$age  %in% c(65:74)] <- "65-74"
w2$age_cat[w2$age  %in% c(75:100)] <- "75+"


##### Table
t_1 <- w2 %>%
          group_by(compare, age_cat) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: mutate(pct = round(100*n/sum(n),2))


p <- ggplot(t_1, aes(age_cat,  y = pct, fill = age_cat)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) + 
  geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response") + 
  facet_wrap(~ compare)
plot(p)

kable(t_1)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

compare	age_cat	n	pct
	18-24	55	20.75
	25-34	85	32.08
	35-44	57	21.51
	45-54	33	12.45
	55-64	24	9.06
	65-74	10	3.77
	NA	1	0.38