INTERACT results for Vancouver 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 Arbutus Greenway in Vancouver is a 9-km former rail corridor, which is being developed into a continuous walking and cycling corridor connecting South Vancouver to False Creek. Participants who lived in one of the 12 Forward Sortation Area (FSA) within 3 km of the Arbutus Greenway 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, street and community events outreach, and partner newsletters. Responses were collected between August 26th, 2020 and February 7th, 2021. In Vancouver, 194 returning participants, and 106 new participants completed the Health Questionnaire, for a total of 300 responses.

Section 1: Transportation

What is your main mode of transportation?

#transp_main_mode

var_name <- w2$transp_main_mode2

w2$var_name_f <- recode_factor(var_name, "1" = "Walking / Wheeling (with a mobility assistive device)",  "2" = "Biking", "3"= "Public Transit", "4" = "Car", "5"= "Motorcycle or scooter", "6"= "Other")

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 = 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
	Walking / Wheeling (with a mobility assistive device)	110	36.67
	Biking	49	16.33
	Public Transit	26	8.67
	Car	111	37.00
	Motorcycle or scooter	2	0.67
	Other	2	0.67

How much do you enjoy using each transportation mode?

Walking

#preferred_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("Count") +
      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	208	69.33
	2	59	19.67
	3	19	6.33
	4 Not at all	3	1.00
	Not applicable	11	3.67

Biking

#preferred_mode_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	115	38.33
	2	50	16.67
	3	47	15.67
	4 Not at all	27	9.00
	Not applicable	61	20.33

Public transit

#preferred_mode_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	14	4.67
	2	42	14.00
	3	116	38.67
	4 Not at all	96	32.00
	Not applicable	32	10.67

Car

#preferred_mode_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	68	22.67
	2	100	33.33
	3	95	31.67
	4 Not at all	12	4.00
	Not applicable	25	8.33

Motorcycle or scooter

#preferred_mode_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	7	2.33
	2	3	1.00
	3	3	1.00
	4 Not at all	57	19.00
	Not applicable	230	76.67

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("Count") +
      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	210	70.00
	2	59	19.67
	3	15	5.00
	4 Not at all	3	1.00
	Not applicable	13	4.33

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	118	39.33
	2	67	22.33
	3	34	11.33
	4 Not at all	18	6.00
	Not applicable	63	21.00

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(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	58	19.33
	2	108	36.00
	3	72	24.00
	4 Not at all	43	14.33
	Not applicable	19	6.33

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	74	24.67
	2	104	34.67
	3	82	27.33
	4 Not at all	16	5.33
	Not applicable	24	8.00

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	6	2
	2	3	1
	3	3	1
	4 Not at all	39	13
	Not applicable	249	83

Do you have access to a car?

var_name <- w2$car_access
w2$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()) %>%
            dplyr:: 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("Count") +
      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	265	88.33
	No	35	11.67

Do you have access to a bicycle?

#bike_access 

var_name <- w2$bike_access
w2$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()) %>%
            dplyr:: 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("Count") +
      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	235	78.33
	No	65	21.67

On a scale of 1 to 5, with 1 being ‘very safe’ and 5 being ‘very dangerous’, overall, how safe do you think cycling is in your city?

#bike_safety
var_name <- w2$bike_safety
w2$var_name_f <- recode_factor(var_name, "1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous", "77" = "I don't know/prefer not to say")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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 safe	50	16.67
	Somewhat safe	156	52.00
	Neither safe nor unsafe	31	10.33
	Somewhat dangerous	58	19.33
	Very dangerous	3	1.00
	I don’t know/prefer not to say	2	0.67

Have you ever heard of the Arbutus Greenway?

#ag_familiarty 

var_name <- w2$ag_familiarity
w2$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()) %>%
            dplyr:: 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("Count") +
      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	298	99.33
	No	2	0.67

Do you think that the Arbutus Greenway is a good or bad idea for Vancouver? It is a…

var_name <- w2$ag_idea
w2$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

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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 good idea	278	92.67
	Somewhat good idea	17	5.67
	Somewhat bad idea	3	1.00
	I don’t know	2	0.67

Have you ever used the Arbutus Greenway?

#ag_used_ever
var_name <- w2$ag_used_ever
w2$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()) %>%
            dplyr:: 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("Count") +
      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	284	94.67
	No	16	5.33

How often do you typically travel by foot along the Arbutus Greenway during each season?

# ag_walk_freq_a
w2$ag_walk_freq2_a[w2$ag_walk_freq2_a==-7] <- NA
w2$ag_walk_freq2_b[w2$ag_walk_freq2_b==-7] <- NA
w2$ag_walk_freq2_c[w2$ag_walk_freq2_c==-7] <- NA
w2$ag_walk_freq2_d[w2$ag_walk_freq2_d==-7] <- NA

fall <- ggplot(w2, aes(x = w2$ag_walk_freq2_a)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#BF5B04") + xlab("Times in the fall") + facet_wrap(~ compare)
winter <- ggplot(w2, aes(x = w2$ag_walk_freq2_b)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#35AAF2") + xlab("Times in the winter") + facet_wrap(~ compare)
spring <- ggplot(w2, aes(x = w2$ag_walk_freq2_c)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#76D24A") + xlab("Times in the spring")+ facet_wrap(~ compare)
summer <- ggplot(w2, aes(x = w2$ag_walk_freq2_d)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#F2B705") + xlab("Times in the summer")+ facet_wrap(~ compare)

grid.arrange(fall,winter,spring,summer)

How often do you typically travel by bicycle along the Arbutus Greenway during each season?

# ag_bike_freq_a

w2$ag_bike_freq_a[w2$ag_bike_freq_a==-7] <- NA
w2$ag_bike_freq_b[w2$ag_bike_freq_b==-7] <- NA
w2$ag_bike_freq_c[w2$ag_bike_freq_c==-7] <- NA
w2$ag_bike_freq_d[w2$ag_bike_freq_d==-7] <- NA

fall <- ggplot(w2, aes(x = w2$ag_bike_freq_a)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#BF5B04") + xlab("Times in the fall")     + facet_wrap(~ compare)
winter <- ggplot(w2, aes(x = w2$ag_bike_freq_b)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#35AAF2") + xlab("Times in the winter")+ facet_wrap(~ compare)
spring <- ggplot(w2, aes(x = w2$ag_bike_freq_c)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#76D24A") + xlab("Times in the spring")+ facet_wrap(~ compare)
summer <- ggplot(w2, aes(x = w2$ag_bike_freq_d)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#F2B705") + xlab("Times in the summer")+ facet_wrap(~ compare)


grid.arrange(fall,winter,spring,summer)

How do you usually get to the Arbutus Greenway?

#intercept_ag_mode
w2$intercept_ag_mode2[w2$intercept_ag_mode2==-7] <- NA

var_name <- w2$intercept_ag_mode2
w2$var_name_f <- recode_factor(var_name, "1" = "Walking",  "2" = "Running/Jogging", "3"= "Biking", "4" = "Public Transit", "5"= "Car", "6"= "Motorcycle or scooter", "7" ="Other")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Walking	148	49.33
	Running/Jogging	14	4.67
	Biking	105	35.00
	Public Transit	3	1.00
	Car	9	3.00
	Other	5	1.67
	NA	16	5.33

What is your usual reason for using the Arbutus Greenway?

# intercept_ag_reason
w2$intercept_ag_reason[w2$intercept_ag_reason==-7] <- NA
var_name <- w2$intercept_ag_reason
w2$var_name_f <- recode_factor(var_name, "1" = "For recreation", "2" = "For transportation", "3" = "Both for recreation and transportation")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPaletteYN) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	For recreation	106	35.33
	For transportation	35	11.67
	Both for recreation and transportation	143	47.67
	NA	16	5.33

In your opinion, the maintenance of the Arbutus Greenway is excellent, good, fair, or poor?

## intercept_ag_maintenance
w2$intercept_ag_maintenance[w2$intercept_ag_maintenance==-7] <- NA
var_name <- w2$intercept_ag_maintenance
w2$var_name_f <- recode_factor(var_name, "1" = "Excellent", "2" = "Good", "3" = "Fair", "4" ="Poor", "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()) %>%
            dplyr:: 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 = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	134	44.67
	Good	126	42.00
	Fair	20	6.67
	Poor	1	0.33
	I don’t know	3	1.00
	NA	16	5.33

How safe do you feel travelling along the Arbutus Greenway in terms of

safety from traffic?

#intercept_ag_safety_traffic 

w2$intercept_ag_safety_traffic[w2$intercept_ag_safety_traffic==-7] <- NA
var_name <- w2$intercept_ag_safety_traffic
w2$var_name_f <- recode_factor(var_name, "1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" ="Somewhat unsafe", "5" =  "Very unsafe" , "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()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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 safe	186	62.00
	Somewhat safe	82	27.33
	Neither safe nor unsafe	9	3.00
	Somewhat unsafe	6	2.00
	Very unsafe	1	0.33
	NA	16	5.33

personal safety?

#intercept_ag_safety_personal 
w2$intercept_ag_safety_personal[w2$intercept_ag_safety_personal==-7] <- NA
w2$intercept_ag_safety_traffic[w2$intercept_ag_safety_personal==-7] <- NA
var_name <- w2$intercept_ag_safety_personal
w2$var_name_f <- recode_factor(var_name, "1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" ="Somewhat unsafe", "5" =  "Very unsafe" , "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()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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 safe	191	63.67
	Somewhat safe	81	27.00
	Neither safe nor unsafe	7	2.33
	Somewhat unsafe	4	1.33
	I don’t know	1	0.33
	NA	16	5.33

Are you using the Arbutus Greenway (walking, biking, etc.) more, less, or the same since spring 2017?

#intercept_ag_spring

w2$intercept_ag_spring2[w2$intercept_ag_spring2==-7] <- NA

var_name <- w2$intercept_ag_spring2
w2$var_name_f <- recode_factor(var_name, "1" = "More", "2" = "Same", "3" = "Less", "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()) %>%
            dplyr:: 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 = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	More	137	45.67
	Same	93	31.00
	Less	50	16.67
	I don’t know	4	1.33
	NA	16	5.33

Do you plan to use the Arbutus Greenway in the future?

# intercept_ag_future
var_name <- w2$intercept_ag_future
w2$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()) %>%
            dplyr:: 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("Count") +
      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	295	98.33
	No	5	1.67

Would any of the following amenities encourage you to use the Arbutus Greenway more? Check ALL that apply.

New participants

*Participants could choose many options. Count is number of times option was selected.

# 1 Recycling or waste receptacles/garbage cans
# 2 Benches
# 3 Bathrooms
# 4 Lighting
# 5 Water fountains
# 6 Picnic areas
# 7 Playgrounds
# 8 Mobi stations
# 9 Public art
# 10    Shady zones
# 99    Other (Please specify)
# -7    Not applicable

#new
intercept_ag_amenities_1 <-   as.numeric(table(new$intercept_ag_amenities_1[new$intercept_ag_amenities_1==1]))
intercept_ag_amenities_2 <-   as.numeric(table(new$intercept_ag_amenities_2[new$intercept_ag_amenities_2==1]))
intercept_ag_amenities_3 <-   as.numeric(table(new$intercept_ag_amenities_3[new$intercept_ag_amenities_3==1]))
intercept_ag_amenities_4 <-   as.numeric(table(new$intercept_ag_amenities_4[new$intercept_ag_amenities_4==1]))
intercept_ag_amenities_5 <-   as.numeric(table(new$intercept_ag_amenities_5[new$intercept_ag_amenities_5==1]))
intercept_ag_amenities_6 <-   as.numeric(table(new$intercept_ag_amenities_6[new$intercept_ag_amenities_6==1]))
intercept_ag_amenities_7 <-   as.numeric(table(new$intercept_ag_amenities_7[new$intercept_ag_amenities_7==1]))
intercept_ag_amenities_8 <-   as.numeric(table(new$intercept_ag_amenities_8[new$intercept_ag_amenities_8==1]))
intercept_ag_amenities_9 <-   as.numeric(table(new$intercept_ag_amenities_9[new$intercept_ag_amenities_9==1]))
intercept_ag_amenities_10 <- as.numeric(table(new$intercept_ag_amenities_10[new$intercept_ag_amenities_10==1]))
intercept_ag_amenities_99 <- as.numeric(table(new$intercept_ag_amenities_99[new$intercept_ag_amenities_99==1]))

amenities_new <- data.frame(Response = c("Recycling or waste receptacles/garbage cans", 
                            "Benches", 
                            "Bathrooms", 
                            "Lighting", 
                            "Water fountains", 
                            "Picnic areas", 
                            "Playgrounds", 
                            "Mobi Stations", 
                            "Public art", 
                            "Shady zones", 
                            "Other"), 
                            Frequency= c(intercept_ag_amenities_1 ,intercept_ag_amenities_2 ,intercept_ag_amenities_3 ,intercept_ag_amenities_4 ,intercept_ag_amenities_5 ,intercept_ag_amenities_6 ,intercept_ag_amenities_7 ,intercept_ag_amenities_8 ,intercept_ag_amenities_9 ,intercept_ag_amenities_10,intercept_ag_amenities_99))
 
kable(amenities_new) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	Frequency
Recycling or waste receptacles/garbage cans	46
Benches	50
Bathrooms	51
Lighting	66
Water fountains	37
Picnic areas	42
Playgrounds	18
Mobi Stations	3
Public art	45
Shady zones	37
Other	15

Returning participants

*Participants could choose many options. Count is number of times option was selected.

#returning

intercept_ag_amenities_1 <-  as.numeric(table(ret$intercept_ag_amenities_1[ret$intercept_ag_amenities_1==1]))
intercept_ag_amenities_2 <-  as.numeric(table(ret$intercept_ag_amenities_2[ret$intercept_ag_amenities_2==1]))
intercept_ag_amenities_3 <-  as.numeric(table(ret$intercept_ag_amenities_3[ret$intercept_ag_amenities_3==1]))
intercept_ag_amenities_4 <-  as.numeric(table(ret$intercept_ag_amenities_4[ret$intercept_ag_amenities_4==1]))
intercept_ag_amenities_5 <-  as.numeric(table(ret$intercept_ag_amenities_5[ret$intercept_ag_amenities_5==1]))
intercept_ag_amenities_6 <-  as.numeric(table(ret$intercept_ag_amenities_6[ret$intercept_ag_amenities_6==1]))
intercept_ag_amenities_7 <-  as.numeric(table(ret$intercept_ag_amenities_7[ret$intercept_ag_amenities_7==1]))
intercept_ag_amenities_8 <-  as.numeric(table(ret$intercept_ag_amenities_8[ret$intercept_ag_amenities_8==1]))
intercept_ag_amenities_9 <-  as.numeric(table(ret$intercept_ag_amenities_9[ret$intercept_ag_amenities_9==1]))
intercept_ag_amenities_10 <- as.numeric(table(ret$intercept_ag_amenities_10[ret$intercept_ag_amenities_10==1]))
intercept_ag_amenities_99 <- as.numeric(table(ret$intercept_ag_amenities_99[ret$intercept_ag_amenities_99==1]))

amenities_ret <- data.frame(Response = c("Recycling or waste receptacles/garbage cans", 
                            "Benches", 
                            "Bathrooms", 
                            "Lighting", 
                            "Water fountains", 
                            "Picnic areas", 
                            "Playgrounds", 
                            "Mobi Stations", 
                            "Public art", 
                            "Shady zones", 
                            "Other"), 
                            Frequency= c(intercept_ag_amenities_1 ,intercept_ag_amenities_2 ,intercept_ag_amenities_3 ,intercept_ag_amenities_4 ,intercept_ag_amenities_5 ,intercept_ag_amenities_6 ,intercept_ag_amenities_7 ,intercept_ag_amenities_8 ,intercept_ag_amenities_9 ,intercept_ag_amenities_10,intercept_ag_amenities_99))
 
kable(amenities_ret) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	Frequency
Recycling or waste receptacles/garbage cans	78
Benches	90
Bathrooms	87
Lighting	93
Water fountains	64
Picnic areas	52
Playgrounds	22
Mobi Stations	12
Public art	61
Shady zones	73
Other	38

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	241	80.33
1	17	5.67
2	7	2.33
3	6	2.00
4	9	3.00
5	7	2.33
6	2	0.67
7	11	3.67

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 
##    0.00   15.00   60.00   71.69   85.00  480.00     241

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	38	12.67
1	44	14.67
2	52	17.33
3	43	14.33
4	36	12.00
5	30	10.00
6	22	7.33
7	35	11.67

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 
##    0.00   30.00   40.00   52.62   60.00  600.00      38

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	185	61.67
1	33	11.00
2	22	7.33
3	21	7.00
4	16	5.33
5	12	4.00
6	7	2.33
7	4	1.33

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   30.00   60.00   55.96   60.00  240.00     185

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	23	7.67
1	17	5.67
2	39	13.00
3	43	14.33
4	34	11.33
5	35	11.67
6	23	7.67
7	86	28.67

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 
##   10.00   30.00   40.00   48.62   60.00  300.00      23

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	48	16
1	25	8
2	41	14
3	32	11
4	26	9
5	20	7
6	20	7
7	88	29

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 
##    3.00   30.00   60.00   57.87   60.00  300.00      48

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	139	46
1	30	10
2	35	12
3	37	12
4	20	7
5	22	7
6	10	3
7	7	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   60.00   74.38   60.00 1800.00     139

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	174	58
1	34	11
2	31	10
3	22	7
4	14	5
5	12	4
6	6	2
7	7	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 
##   10.00   30.00   60.00   66.75   60.00  900.00     174

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. 
##    20.0   240.0   360.0   387.2   495.0   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. 
##       0     180     300     317     420     960

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. 
##   150.0   163.0   168.0   169.5   175.0   193.0

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. 
##   45.00   60.00   68.00   71.11   80.00  141.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	61	20.33
	Very good	129	43.00
	Good	87	29.00
	Fair	17	5.67
	Poor	6	2.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	8	2.67
	Yes, limited a little	40	13.33
	No, not at all	252	84.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	17	5.67
	Yes, limited a little	49	16.33
	No, not at all	234	78.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	72	24
	No	228	76

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	56	18.67
	No	244	81.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	111	37
	No	189	63

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	19
	No	243	81

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	172	57.33
	Slightly	86	28.67
	Moderately	28	9.33
	Quite a bit	10	3.33
	Extremely	4	1.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	20	6.67
	Most of the time	98	32.67
	A good bit of the time	77	25.67
	Some of the time	71	23.67
	A little of the time	25	8.33
	None of the time	9	3.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	20	6.67
	Most of the time	83	27.67
	A good bit of the time	74	24.67
	Some of the time	76	25.33
	A little of the time	32	10.67
	None of the time	15	5.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	5	1.67
	Most of the time	17	5.67
	A good bit of the time	29	9.67
	Some of the time	58	19.33
	A little of the time	127	42.33
	None of the time	64	21.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	5	1.67
	Most of the time	12	4.00
	A good bit of the time	18	6.00
	Some of the time	37	12.33
	A little of the time	49	16.33
	None of the time	179	59.67

Section 4: Well-being

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

t_1 <- select(w2, compare, pwb_vic_a, pwb_vic_b, pwb_vic_c, pwb_vic_d, pwb_vic_e, pwb_vic_f, pwb_vic_g, pwb_vic_h, pwb_vic_i)
t_1 <- pivot_longer(t_1,
   cols = starts_with("pwb_vic_"),
   names_to = "perception",
   names_prefix = "pwb_vic_",
   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 dissatisfied", "9" = "9","8" = "8","7" = "7", "6" = "6", "5" = "5", "4" = "4", "3" = "3", "2" = "2", "1" = "1- Completely satisfied")

##### 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(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" = "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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	4	1.33
	2	6	2.00
	3	7	2.33
	4	24	8.00
	5	64	21.33
	6	126	42.00
	7- A very happy person	69	23.00

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" = "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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	7	2.33
	2	6	2.00
	3	17	5.67
	4	39	13.00
	5	88	29.33
	6	100	33.33
	7- More happy	43	14.33

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" = "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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	11	3.67
	2	14	4.67
	3	19	6.33
	4	46	15.33
	5	68	22.67
	6	92	30.67
	7- A great deal	50	16.67

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" = "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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	103	34.33
	2	65	21.67
	3	41	13.67
	4	32	10.67
	5	30	10.00
	6	18	6.00
	7- A great deal	11	3.67

The next questions are about how you feel about different aspects of your life. For each one, tell us how often you feel that way.

a. How often do you feel that you lack companionship?

#loneliness_a
var_name <- w2$loneliness_a
w2$var_name_f <- recode_factor(var_name, "1" = "Hardly ever", "2" = "Some of the time", "3" = "Often")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTshorterfade3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Hardly ever	163	54.33
	Some of the time	98	32.67
	Often	39	13.00

b. How often do you feel left out?

var_name <- w2$loneliness_b
w2$var_name_f <- recode_factor(var_name, "1" = "Hardly ever", "2" = "Some of the time", "3" = "Often")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTshorterfade3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Hardly ever	164	54.67
	Some of the time	109	36.33
	Often	27	9.00

c. How often do you feel isolated from others?

#loneliness_c
var_name <- w2$loneliness_c
w2$var_name_f <- recode_factor(var_name, "1" = "Hardly ever", "2" = "Some of the time", "3" = "Often")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTshorterfade3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Hardly ever	148	49.33
	Some of the time	112	37.33
	Often	40	13.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	39	13.00
	Somewhat strong	130	43.33
	Somewhat weak	86	28.67
	Very weak	42	14.00
	I don’t know	3	1.00

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. 
##   0.000   1.995   3.989   3.920   6.981   7.000

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. 
##  0.0000  0.4603  0.9973  2.2027  3.9890  6.9808

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. 
## 0.00000 0.00000 0.01918 0.62808 0.92055 6.98082

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. 
## 0.00000 0.00000 0.00000 0.32015 0.07671 6.98082

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. 
##  0.0000  0.0000  0.2301  0.6105  0.9205  6.9808

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	41	13.67
	Somewhat disagree	42	14.00
	Neither agree or disagree	117	39.00
	Somewhat agree	78	26.00
	Strongly agree	22	7.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	108	36.00
	Somewhat disagree	96	32.00
	Neither agree or disagree	72	24.00
	Somewhat agree	20	6.67
	Strongly agree	4	1.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	9	3.00
	Somewhat disagree	22	7.33
	Neither agree or disagree	71	23.67
	Somewhat agree	143	47.67
	Strongly agree	55	18.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	47	15.67
	Somewhat disagree	81	27.00
	Neither agree or disagree	129	43.00
	Somewhat agree	35	11.67
	Strongly agree	8	2.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = rev(INTERACTPalette3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	5	1.67
	Somewhat disagree	20	6.67
	Neither agree or disagree	63	21.00
	Somewhat agree	143	47.67
	Strongly agree	69	23.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	127	42.33
	Somewhat likely	123	41.00
	Not at all likely	16	5.33
	I don’t know	34	11.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	14	4.67
	Somewhat likely	128	42.67
	Not at all likely	97	32.33
	I don’t know	61	20.33

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   7.363  10.000  50.000

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	171	57.00
	Satisfied	108	36.00
	Neither satisfied nor dissatisfied	10	3.33
	Dissatisfied	10	3.33
	Strongly dissatisfied	1	0.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	45	15.00
	Satisfied	107	35.67
	Neither satisfied nor dissatisfied	88	29.33
	Dissatisfied	48	16.00
	Strongly dissatisfied	12	4.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	39	13.00
	Satisfied	105	35.00
	Neither satisfied nor dissatisfied	105	35.00
	Dissatisfied	38	12.67
	Strongly dissatisfied	13	4.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	98	32.67
	Satisfied	116	38.67
	Neither satisfied nor dissatisfied	61	20.33
	Dissatisfied	16	5.33
	Strongly dissatisfied	9	3.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	155	51.67
	Somewhat important	100	33.33
	Not very important	28	9.33
	Not important at all	13	4.33
	I don’t know	4	1.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	179	59.67
	Somewhat important	99	33.00
	Not very important	15	5.00
	Not important at all	3	1.00
	I don’t know	4	1.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	172	57.33
	Somewhat important	111	37.00
	Not very important	13	4.33
	Not important at all	2	0.67
	I don’t know	2	0.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	71	23.67
	Somewhat important	124	41.33
	Not very important	76	25.33
	Not important at all	27	9.00
	I don’t know	2	0.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	62	20.67
	Somewhat important	142	47.33
	Not very important	70	23.33
	Not important at all	17	5.67
	I don’t know	9	3.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	34	11.33
	Somewhat important	87	29.00
	Not very important	102	34.00
	Not important at all	70	23.33
	I don’t know	7	2.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	190	63.33
	Somewhat important	91	30.33
	Not very important	12	4.00
	Not important at all	4	1.33
	I don’t know	3	1.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	61	20.33
	Somewhat important	102	34.00
	Not very important	73	24.33
	Not important at all	57	19.00
	I don’t know	7	2.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	69	23.00
	Somewhat important	64	21.33
	Not very important	45	15.00
	Not important at all	102	34.00
	I don’t know	20	6.67

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?

#cov_con_trips

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. 
##   0.000   1.000   3.000   4.513   6.000  40.000

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	32	10.67
	Care-taking	No	268	89.33
	Groceries	Yes	274	91.33
	Groceries	No	26	8.67
	Medical trips	Yes	108	36.00
	Medical trips	No	192	64.00
	School	Yes	9	3.00
	School	No	291	97.00
	Social, entertainment, eating out	Yes	43	14.33
	Social, entertainment, eating out	No	257	85.67
	Work	Yes	73	24.33
	Work	No	227	75.67

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	110	36.67
	Cycling	Same as before COVID-19	130	43.33
	Cycling	More	60	20.00
	Driving	Less	179	59.67
	Driving	Same as before COVID-19	71	23.67
	Driving	More	50	16.67
	Public Transit	Less	250	83.33
	Public Transit	Same as before COVID-19	47	15.67
	Public Transit	More	3	1.00
	Walking	Less	59	19.67
	Walking	Same as before COVID-19	94	31.33
	Walking	More	147	49.00

During the most closed phase of the COVID-19 lockdown, were you using the Arbutus Greenway less than, more than, or the same as you did prior to the COVID-19 pandemic?

#cov_con_aag

var_name <- w2$cov_con_aag
w2$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTshorterfade3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Less	106	35.33
	Same as before COVID-19	124	41.33
	More	70	23.33

Please select all the reasons explaining why you used the Arbutus Greenway more during the most closed phase of the COVID-19 lockdown.

*Participants could choose many options. Count is number of times option was selected.

#cov_con_aag_more
# 1 Staying active
# 2 To meet up with people at a distance
# 3 Felt safe despite COVID-19
# 4 Nearby walking destination
# 5 Felt safe from traffic
# 99    Other (please specify)

cov_con_aag_more_1 <- as.numeric(table(w2$cov_con_aag_more_1[w2$cov_con_aag_more_1==1]))
cov_con_aag_more_2 <- as.numeric(table(w2$cov_con_aag_more_2[w2$cov_con_aag_more_2==1]))
cov_con_aag_more_3 <- as.numeric(table(w2$cov_con_aag_more_3[w2$cov_con_aag_more_3==1]))
cov_con_aag_more_4 <- as.numeric(table(w2$cov_con_aag_more_4[w2$cov_con_aag_more_4==1]))
cov_con_aag_more_5 <- as.numeric(table(w2$cov_con_aag_more_5[w2$cov_con_aag_more_5==1]))


cov_con_aag_more <- data.frame(Response = c("Staying active", "To meet up with people at a distance", "Felt safe despite COVID-19", "Nearby walking destination", "Felt safe from traffic"), 
                               Frequence = c(cov_con_aag_more_1, cov_con_aag_more_2, cov_con_aag_more_3, cov_con_aag_more_4, cov_con_aag_more_5))

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

Response	Frequence
Staying active	66
To meet up with people at a distance	17
Felt safe despite COVID-19	42
Nearby walking destination	49
Felt safe from traffic	37

Please select all the reasons explaining why you used the Arbutus Greenway less during the most closed phase of the COVID-19 lockdown.

*Participants could choose many options. Count is number of times option was selected.

#cov_con_aag_more
# 1 Used other outdoor spaces instead
# 2 Too crowded
# 3 Didn’t feel safe because of COVID-19
# 4 Too far away
# 5 Didn’t feel safe from traffic
# 6 Other

cov_con_aag_less_1 <- as.numeric(table(w2$cov_con_aag_less_1[w2$cov_con_aag_less_1==1]))
cov_con_aag_less_2 <- as.numeric(table(w2$cov_con_aag_less_2[w2$cov_con_aag_less_2==1]))
cov_con_aag_less_3 <- as.numeric(table(w2$cov_con_aag_less_3[w2$cov_con_aag_less_3==1]))
cov_con_aag_less_4 <- as.numeric(table(w2$cov_con_aag_less_4[w2$cov_con_aag_less_4==1]))
cov_con_aag_less_5 <- as.numeric(table(w2$cov_con_aag_less_5[w2$cov_con_aag_less_5==1]))



cov_con_aag_less <- data.frame(Response = c("Used other outdoor spaces instead", 
                                            "Too crowded", 
                                            "Didn’t feel safe because of COVID-19", 
                                            "Too far away", 
                                            "Didn’t feel safe from traffic"), 
                               Frequence = c(cov_con_aag_less_1, 
                                             cov_con_aag_less_2, 
                                             cov_con_aag_less_3, 
                                             cov_con_aag_less_4, 
                                             cov_con_aag_less_5))
 
kable(cov_con_aag_less) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	Frequence
Used other outdoor spaces instead	42
Too crowded	51
Didn’t feel safe because of COVID-19	41
Too far away	12
Didn’t feel safe from traffic	1

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	64	21.33
	Somewhat satisfied	92	30.67
	Neutral	15	5.00
	Somewhat dissatisfied	83	27.67
	Very dissatisfied	46	15.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	6.00
	Somewhat satisfied	71	23.67
	Neutral	34	11.33
	Somewhat dissatisfied	121	40.33
	Very dissatisfied	56	18.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTfade)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	4	1.33
	1	1	0.33
	2	12	4.00
	3	25	8.33
	4	23	7.67
	5	42	14.00
	6	30	10.00
	7	47	15.67
	8	46	15.33
	9	35	11.67
	As good as it could be	35	11.67

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    4.00    7.00    8.73   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	40	13.33
	Care-taking	No	260	86.67
	Groceries	Yes	287	95.67
	Groceries	No	13	4.33
	Medical trips	Yes	173	57.67
	Medical trips	No	127	42.33
	School	Yes	28	9.33
	School	No	272	90.67
	Social, entertainment, eating out	Yes	151	50.33
	Social, entertainment, eating out	No	149	49.67
	Work	Yes	104	34.67
	Work	No	196	65.33

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	75	25.00
	Cycling	Same as before COVID-19	173	57.67
	Cycling	More	52	17.33
	Driving	Less	130	43.33
	Driving	Same as before COVID-19	109	36.33
	Driving	More	61	20.33
	Public Transit	Less	229	76.33
	Public Transit	Same as before COVID-19	66	22.00
	Public Transit	More	5	1.67
	Walking	Less	42	14.00
	Walking	Same as before COVID-19	119	39.67
	Walking	More	139	46.33

In the current context, have you been using the Arbutus Greenway less than, more than, or the same as you did prior to the COVID-19 pandemic?

#cov_decon_aag

var_name <- w2$cov_decon_aag
w2$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTshorterfade3)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Less	88	29.33
	Same as before COVID-19	139	46.33
	More	73	24.33

Please select all the reasons explaining why you use the Arbutus Greenway more now than prior to the COVID-19 pandemic.

*Participants could choose many options. Count is number of times option was selected.

# 1 Staying active
# 2 To meet up with people at a distance
# 3 Felt safe despite COVID-19
# 4 Nearby walking destination
# 5 Felt safe from traffic
# 99    Other (please specify)

cov_decon_aag_more_1 <- as.numeric(table(w2$cov_decon_aag_more_1[w2$cov_decon_aag_more_1==1]))
cov_decon_aag_more_2 <- as.numeric(table(w2$cov_decon_aag_more_2[w2$cov_decon_aag_more_2==1]))
cov_decon_aag_more_3 <- as.numeric(table(w2$cov_decon_aag_more_3[w2$cov_decon_aag_more_3==1]))
cov_decon_aag_more_4 <- as.numeric(table(w2$cov_decon_aag_more_4[w2$cov_decon_aag_more_4==1]))
cov_decon_aag_more_5 <- as.numeric(table(w2$cov_decon_aag_more_5[w2$cov_decon_aag_more_5==1]))


cov_decon_aag_more <- data.frame(Response = c("Staying active", "To meet up with people at a distance", "Felt safe despite COVID-19", "Nearby walking destination", "Felt safe from traffic"), 
                               Frequence = c(cov_decon_aag_more_1, cov_decon_aag_more_2, cov_decon_aag_more_3, cov_decon_aag_more_4, cov_decon_aag_more_5))

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

Response	Frequence
Staying active	67
To meet up with people at a distance	22
Felt safe despite COVID-19	34
Nearby walking destination	51
Felt safe from traffic	35

Please select all the reasons explaining why you use the Arbutus Greenway less now than prior to the COVID-19 pandemic.

*Participants could choose many options. Count is number of times option was selected.

cov_decon_aag_less_1 <- as.numeric(table(w2$cov_decon_aag_less_1[w2$cov_decon_aag_less_1==1]))
cov_decon_aag_less_2 <- as.numeric(table(w2$cov_decon_aag_less_2[w2$cov_decon_aag_less_2==1]))
cov_decon_aag_less_3 <- as.numeric(table(w2$cov_decon_aag_less_3[w2$cov_decon_aag_less_3==1]))
cov_decon_aag_less_4 <- as.numeric(table(w2$cov_decon_aag_less_4[w2$cov_decon_aag_less_4==1]))
cov_decon_aag_less_5 <- as.numeric(table(w2$cov_decon_aag_less_5[w2$cov_decon_aag_less_5==1]))



cov_decon_aag_less <- data.frame(Response = c("Used other outdoor spaces instead", 
                                            "Too crowded", 
                                            "Didn’t feel safe because of COVID-19", 
                                            "Too far away", 
                                            "Didn’t feel safe from traffic"), 
                               Frequence = c(cov_decon_aag_less_1, 
                                             cov_decon_aag_less_2, 
                                             cov_decon_aag_less_3, 
                                             cov_decon_aag_less_4, 
                                             cov_decon_aag_less_5))
 
kable(cov_decon_aag_less) %>%  kable_styling(bootstrap_options = "striped", full_width = T, position = "left")

Response	Frequence
Used other outdoor spaces instead	37
Too crowded	39
Didn’t feel safe because of COVID-19	27
Too far away	12
Didn’t feel safe from traffic	3

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	63	21.00
	Somewhat satisfied	125	41.67
	Neutral	18	6.00
	Somewhat dissatisfied	66	22.00
	Very dissatisfied	28	9.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalette3) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	27	9.00
	Somewhat satisfied	92	30.67
	Neutral	45	15.00
	Somewhat dissatisfied	109	36.33
	Very dissatisfied	27	9.00

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

t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = rev(INTERACTfade)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	2	0.67
	1	2	0.67
	2	5	1.67
	3	14	4.67
	4	19	6.33
	5	29	9.67
	6	37	12.33
	7	54	18.00
	8	65	21.67
	9	35	11.67
	As good as it could be	38	12.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	97	32.33
	Woman	199	66.33
	Genderqueer/Gender non-conforming	3	1.00
	Different identity	1	0.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	94	31.33
	Female	201	67.00
	Other	1	0.33
	NA	4	1.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	64	21.33
	Married/commonlaw	189	63.00
	Separated/divorced	36	12.00
	Widowed	11	3.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	166	55.33
	No	134	44.67

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   2.000   2.000   2.157   3.000   6.000     134

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	84	28
	With other people	216	72

# 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)

living_arrange_prop <- setcolorder(living_arrange_prop, c("Response", "living_arrange_prop"))

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	With a spouse (or partner)	63.67
living_arrange_3	With children	27.33
living_arrange_4	With grandchildren	0.67
living_arrange_5	With relatives or siblings?	3.00
living_arrange_6	With friends	2.67
living_arrange_7	With other people	2.67

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  0.2133  0.0000  3.0000

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   1.000   2.000   2.057   2.000  20.000

Thinking about where you live now, are you

*Note: Question only asked to new (n=106) and moved participants (n=17) at w2, reporting w1 data for returning participants

#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", "6" = "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()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	190	63.33
	A tenant	90	30.00
	Resident in a relative or friend’s home	6	2.00
	Resident other than in a relative or friend’s home	1	0.33
	Other	10	3.33
	NA	3	1.00

In what type of dwelling do you currently live? Is it:

*Note: Question only asked to new (n=106) and moved participants (n=17) at w2, reporting w1 data for returning participants

#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" = "NA")

var_name_f <- w2$var_name_f

##### Table
t_1 <- w2 %>%
          group_by(compare, var_name_f) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	101	33.67
	Semi-detached house	12	4.00
	Row house	16	5.33
	An apartment (or condo) in a duplex or triplex	12	4.00
	Apartment (or condo) in building with fewer than 5 storeys	101	33.67
	Apartment (or condo) in building with more than 5 storeys	44	14.67
	Senior’s home	1	0.33
	Other	8	2.67
	NA	2	0.67
	NA	3	1.00

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 
##    0.00    5.00   14.00   15.96   23.00   71.00       3

Were you born in Canada?

*Note: Question only asked to new participants at w2 (n=106), 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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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("Count") +
      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	71.00
	No	83	27.67
	NA	4	1.33

When did you move to Canada?

*Note: Question only asked to new participants at w2 or(n=106), reporting w1 data for returning participants Question asked only to those not born in Canada (n=83)

#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 
##    1948    1966    1980    1981    1998    2019     217

To which ethnic or cultural group(s) do you belong? (Check all that apply)

*Note: Question only asked to new participants at w2 or(n=106), reporting w1 data for returning participants

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Asian	24	8.00
	Black	1	0.33
	Caucasian	236	78.67
	Latin American	5	1.67
	Other	10	3.33
	Mixed identity	13	4.33
	NA	11	3.67

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = rev(INTERACTfade)) +
  guides(fill=FALSE) +
      ylab("Count") +
      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 to $9,999	1	0.33
	$10,000 to $14,999	2	0.67
	$15,000 to $19,999	5	1.67
	$20,000 to $29,999	11	3.67
	$30,000 to $39,999	10	3.33
	$40,000 to $49,999	17	5.67
	$50,000 to $99,999	68	22.67
	$100,000 to $149,999	48	16.00
	$150,000 to $199,999	36	12.00
	$200,000 or more	50	16.67
	Don’t know/prefer no answer	52	17.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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 = INTERACTshortfade) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	154	51.33
	Well	111	37.00
	Not so well	29	9.67
	Don’t know/prefer no answer	6	2.00

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(t_1, 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 
##      75    1015    1805    3570    3000  200000      97

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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
	Secondary school	21	7.00
	Trade/Technical school or college diploma	34	11.33
	University degree	107	35.67
	Graduate degree	137	45.67
	I don’t know/Prefer not to answer	1	0.33

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPaletteSet) +
  guides(fill=FALSE) +
      ylab("Count") +
      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	91	30.33
	Employed full-time	114	38.00
	Employed part-time	46	15.33
	Unemployed and looking for work	12	4.00
	Unemployed and not looking for work	9	3.00
	Other	28	9.33

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	38	12.67
	I continue to work at my normal place of work.	No	262	87.33
	I have started a new job	Yes	5	1.67
	I have started a new job	No	295	98.33
	I lost my job	Yes	13	4.33
	I lost my job	No	287	95.67
	I work from home.	Yes	75	25.00
	I work from home.	No	225	75.00
	I work partly from home, partly at my normal workplace	Yes	46	15.33
	I work partly from home, partly at my normal workplace	No	254	84.67
	My hourly rate has been reduced	Yes	3	1.00
	My hourly rate has been reduced	No	297	99.00
	My hourly rate has increased.	Yes	6	2.00
	My hourly rate has increased.	No	294	98.00
	My job has been deemed essential by the government.	Yes	25	8.33
	My job has been deemed essential by the government.	No	275	91.67
	My paid work #hours have been reduced.	Yes	26	8.67
	My paid work #hours have been reduced.	No	274	91.33
	My paid work hours have increased	Yes	6	2.00
	My paid work hours have increased	No	294	98.00

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) %>%
            dplyr::summarise(n = n()) %>%
            dplyr:: 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=90, vjust = .6)) + 
    geom_bar(stat= "identity") +
  scale_fill_manual(values = INTERACTPalettecont) +
  guides(fill=FALSE) +
      ylab("Count") +
      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.	127	42.33
	A regular evening shift	2	0.67
	A rotating shift, a split shift, or an irregular schedule	18	6.00
	On call or casual	8	2.67
	Other	5	1.67
	NA	140	46.67

Do you use a mobility aid when you walk?

#aid

var_name <- w2$aid
w2$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()) %>%
            dplyr:: 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("Count") +
      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	4	1.33
	No	296	98.67

# 1 Cane
# 2 Walker
# 3 Scooter
# 4 Wheelchair
# 5 Other (Please specify)
# -7    Not applicable
# table(w2$aid_type_txt)


Type <- c("Cane", "Walker", "Guide dog", "Poles")
Count <- c("1", "1", "1", "2")

summaryaid_type <- data.frame(Type, Count)
kable(summaryaid_type)  %>%   kable_styling(bootstrap_options = "striped", full_width = T, position = "left") 

Type	Count
Cane	1
Walker	1
Guide dog	1
Poles	2