INTERACT results for Saskatoon 2022 - Wave 3

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

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

Participants were recruited through social media, news media and partner newsletters. In Saskatoon, 150 returning participants, and 170 new participants completed the Health Questionnaire, for a total of 320 responses. Responses were collected from September 27th, 2022 to January 18th, 2023.

Cohort demographics

Age distribution

summary(skt_data$age) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
0	26	33	35.4	40.2	83

fillCount = length(unique(skt_data$age))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(skt_data) +
  geom_histogram(aes(age, y=after_stat(count / sum(count)), fill=factor(age)), binwidth = 5, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

# Add a age category variable
age_classes <- c("18 - 30", "31 - 50", "51 - 65", "65+")
skt_data <- skt_data %>%
  mutate(age_class = case_when(age < 31 ~ age_classes[1],
                               age < 51 ~ age_classes[2],
                               age < 66 ~ age_classes[3],
                               TRUE ~ age_classes[4]),
         age_class = factor(age_class, levels = age_classes))

table(skt_data$age_class, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Age categories', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Age categories	N
18 - 30	137
31 - 50	136
51 - 65	34
65+	13

ggplot(skt_data) +
  geom_bar(aes(age_class, y=after_stat(count / sum(count)), fill=age_class), show.legend = FALSE) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

What is your current gender identity?

gender <- fromJSON('[
        {
          "label": "Man",
          "value": 1
        },
        {
          "label": "Woman",
          "value": 2
        },
        {
          "label": "Trans man",
          "value": 3
        },
        {
          "label": "Trans woman",
          "value": 4
        },
        {
          "label": "Genderqueer/Gender non-conforming",
          "value": 5
        },
        {
          "label": "Different identity",
          "value": 99
        }]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(gender, by=c("gender"="value")) %>%
  transmute(interact_id = interact_id,
            gender = label)

# Add gender as factor to dataset (for crosstab below)
skt_data <- skt_data %>%
  left_join(gender, by=c("gender"="value")) %>%
  mutate(gender_label = label) %>%
  select(!label)

table(.ggpdf$gender, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Gender', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Gender	N
Man	118
Woman	194
Trans man	2
Trans woman	0
Genderqueer/Gender non-conforming	6
Different identity	0

ggplot(.ggpdf) +
  geom_bar(aes(gender, y=after_stat(count / sum(count)), fill=gender), show.legend = FALSE) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

# grouping gender non-conforming under LGBTQ2S+
gender_groups <- c("Man", "Woman", "Gender minorities")
skt_data <- skt_data %>%
  mutate(gender_group = case_when(gender == 1 ~ gender_groups[1],
                                  gender == 2 ~ gender_groups[2],
                                  TRUE ~ gender_groups[3]),
         gender_group = factor(gender_group, levels = gender_groups))

What is your current living arrangement?

NB More than one possible answer

living_arrange <- fromJSON('[
        {
          "label": "Alone?",
          "value": 1
        },
        { 
          "label": "With a spouse (or partner)?",
          "value": 2
        },
        {
          "label": "With children?",
          "value": 3
        },
        {
          "label": "With grandchildren?",
          "value": 4
        },
        {
          "label": "With relatives or siblings?",
          "value": 5
        },
        {
          "label": "With friends?",
          "value": 6
        },
        {
          "label": "With other people?",
          "value": 7
        }
      ]')

.ggpdf <- skt_data %>%
  transmute(interact_id = interact_id,
            living_arrange = strsplit(str_sub(living_arrange, 2, -2), ', ')) %>%
  unnest(living_arrange) %>%
  mutate(living_arrange = as.integer(living_arrange)) %>%
  left_join(living_arrange, by=c("living_arrange"="value")) %>%
  transmute(interact_id = interact_id,
            living_arrange = case_when(is.na(living_arrange) ~ 'Living alone',
                                       TRUE ~ str_sub(label, 1, -2)))

table(.ggpdf$living_arrange, useNA = "ifany") %>%
  knitr::kable(col.names=c('Living arrangement', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Living arrangement	N
Alone	61
With a spouse (or partner)	181
With children	110
With friends	19
With grandchildren	2
With other people	11
With relatives or siblings	43

ggplot(.ggpdf) +
  geom_bar(aes(x=living_arrange, y=after_stat(count / sum(count)), fill=living_arrange), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels=percent) + ylab("")

Which category best describes your annual household income, taking into account all sources of income?

income <- fromJSON('[
        {
          "label": "No income",
          "value": 1
        },
        {
          "label": "$1 to $9,999",
          "value": 2
        },
        {
          "label": "$10,000 to $14,999",
          "value": 3
        },
        {
          "label": "$15,000 to $19,999",
          "value": 4
        },
        {
          "label": "$20,000 to $29,999",
          "value": 5
        },
        {
          "label": "$30,000 to $39,999",
          "value": 6
        },
        {
          "label": "$40,000 to $49,999",
          "value": 7
        },
        {
          "label": "$50,000 to $99,999",
          "value": 8
        },
        {
          "label": "$100,000 to $149,999",
          "value": 9
        },
        {
          "label": "$150,000 to $199,999",
          "value": 10
        },
        {
          "label": "$200,000 or more",
          "value": 11
        },
        {
          "label": "I don’t know/Prefer not to answer",
          "value": 77
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(income, by=c("income"="value")) %>%
  transmute(interact_id = interact_id,
            income = label)

table(.ggpdf$income, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Income', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Income	N
No income	4
$1 to $9,999	11
$10,000 to $14,999	14
$15,000 to $19,999	11
$20,000 to $29,999	17
$30,000 to $39,999	21
$40,000 to $49,999	27
$50,000 to $99,999	98
$100,000 to $149,999	39
$150,000 to $199,999	25
$200,000 or more	17
I don’t know/Prefer not to answer	36

ggplot(.ggpdf) +
  geom_bar(aes(y=income, x=after_stat(count / sum(count)), fill=income), show.legend = FALSE) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_x_continuous(labels=percent) + xlab("")

What is your highest education level?

education <- fromJSON('[
        {
          "label": "Primary/Elementary school",
          "value": 1
        },
        {
          "label": "Secondary school",
          "value": 2
        },
        {
          "label": "Trade/Technical school or college diploma",
          "value": 3
        },
        {
          "label": "University degree",
          "value": 4
        },
        {
          "label": "Graduate degree",
          "value": 5
        },
        {
          "label": "I don’t know/Prefer not to answer",
          "value": 77
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(education, by=c("education"="value")) %>%
  transmute(interact_id = interact_id,
            education = label)

table(.ggpdf$education, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Education', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Education	N
Primary/Elementary school	1
Secondary school	41
Trade/Technical school or college diploma	61
University degree	134
Graduate degree	80
I don’t know/Prefer not to answer	3

ggplot(.ggpdf) +
  geom_bar(aes(education, y=after_stat(count / sum(count)), fill=education), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_x_discrete(labels = scales::label_wrap(25)) +
  scale_y_continuous(labels=percent) + ylab("")

Are you currently a student?

student <- fromJSON('[
        {
          "label": "Yes",
          "value": 1
        },
        {
          "label": "No",
          "value": 2
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(student, by=c("student"="value")) %>%
  transmute(interact_id = interact_id,
            student = label)

table(.ggpdf$student, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Student', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Student	N
Yes	97
No	223

ggplot(.ggpdf) +
  geom_bar(aes(student, y=after_stat(count / sum(count)), fill=student), show.legend = FALSE) +
  theme_light() +
  scale_fill_manual(values = INTERACTPaletteYN) +
  scale_x_discrete(labels = scales::label_wrap(25)) +
  scale_y_continuous(labels=percent) + ylab("")

What is your current employment status?

employment <- fromJSON('[
        {
          "label": "Retired and not working",
          "value": 1
        },
        {
          "label": "Employed full-time",
          "value": 2
        },
        {
          "label": "Employed part-time",
          "value": 3
        },
        {
          "label": "Unemployed and looking for work",
          "value": 4
        },
        {
          "label": "Unemployed and not looking for work",
          "value": 5
        },
        {
          "label": "Other",
          "value": 99
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(employment, by=c("employment"="value")) %>%
  transmute(interact_id = interact_id,
            employment = label)

table(.ggpdf$employment, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Employment', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Employment	N
Retired and not working	15
Employed full-time	190
Employed part-time	56
Unemployed and looking for work	17
Unemployed and not looking for work	18
Other	24

ggplot(.ggpdf) +
  geom_bar(aes(employment, y=after_stat(count / sum(count)), fill=employment), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_x_discrete(labels = scales::label_wrap(25)) +
  scale_y_continuous(labels=percent) + ylab("")

To which ethnic or cultural groups did your ancestors belong?

NB More than one ethnic group possible

ethnicity <- fromJSON('[
        {
          "label": "Aboriginal / Indigenous",
          "value": 1
        },
        {
          "label": "Asian",
          "value": 2
        },
        {
          "label": "Black",
          "value": 3
        },
        {
          "label": "Caucasian",
          "value": 4
        },
        {
          "label": "Latin American",
          "value": 5
        },
        {
          "label": "Middle Eastern",
          "value": 6
        },
        {
          "label": "Other",
          "value": 99
        },
        {
          "label": "I don’t know/Prefer not to answer",
          "value": 77
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(group_id_w1_w2, by="interact_id") %>%
  transmute(interact_id = interact_id,
            group_id = coalesce(group_id, group_id_w1_w2)) %>%
  transmute(interact_id = interact_id,
           group_id = strsplit(str_sub(group_id, 2, -2), ', ')) %>%
  unnest(group_id) %>%
  mutate(group_id = as.integer(group_id)) %>%
  left_join(ethnicity, by=c("group_id"="value")) %>%
  transmute(interact_id = interact_id,
            ethnicity = label) %>%
  filter(!is.na(ethnicity))


table(.ggpdf$ethnicity, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Ethnicity', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Ethnicity	N
Aboriginal / Indigenous	28
Asian	44
Black	21
Caucasian	182
Latin American	16
Middle Eastern	10
Other	0
I don’t know/Prefer not to answer	4

ggplot(.ggpdf) +
  geom_bar(aes(ethnicity, y=after_stat(count / sum(count)), fill=ethnicity), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_x_discrete(labels = scales::label_wrap(20))+
  scale_y_continuous(labels=percent) + ylab("")

Housing tenure

house_tenure <- fromJSON('[
        {
          "label": "An owner",
          "value": 1
        },
        {
          "label": "A tenant",
          "value": 2
        },
        {
          "label": "A resident in a relative or friend’s home",
          "value": 3
        },
        {
          "label": "A resident other than in a relative or friend’s home",
          "value": 4
        },
        {
          "label": "Other",
          "value": 99
        },
        {
          "label": "I don’t know/Prefer not to answer",
          "value": 77
        }
      ]') %>%
  mutate(label = factor(label, levels = label))


## rename -7 to NA
skt_data$house_tenure <- replace(skt_data$house_tenure, skt_data$house_tenure < 0, NA)



.ggpdf <- skt_data %>%
  left_join(skt_data_w2w1, by="interact_id") %>%
  transmute(interact_id = interact_id,
            house_tenure = coalesce(house_tenure, house_tenure_w2, house_tenure_w1)) %>%
  left_join(house_tenure, by=c("house_tenure"="value")) %>%
  transmute(interact_id = interact_id,
            house_tenure = label) %>%
  filter(!is.na(house_tenure))

table(.ggpdf$house_tenure, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Housing tenure', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Housing tenure	N
An owner	124
A tenant	128
A resident in a relative or friend’s home	43
A resident other than in a relative or friend’s home	1
Other	4
I don’t know/Prefer not to answer	4

ggplot(.ggpdf) +
  geom_bar(aes(house_tenure, y=after_stat(count / sum(count)), fill=house_tenure), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_x_discrete(labels = scales::label_wrap(25))+
  scale_y_continuous(labels=percent) + ylab("")

Dwelling

dwelling_type <- fromJSON('[
        {
          "label": "A single-detached house",
          "value": 1
        },
        {
          "label": "A semi-detached house",
          "value": 2
        },
        {
          "label": "A row house",
          "value": 3
        },
        {
          "label": "An apartment (or condo) in a duplex or triplex (two or three dwellings located one above the other)",
          "value": 4
        },
        {
          "label": "An apartment (or condo) in a building that has fewer than five storeys",
          "value": 5
        },
        {
          "label": "An apartment (or condo) in a building that has five or more storeys",
          "value": 6
        },
        {
          "label": "A mobile home or other movable dwelling",
          "value": 7
        },
        {
          "label": "A seniors’ home",
          "value": 8
        },
        {
          "label": "Other",
          "value": 99
        },
        {
          "label": "I don’t know/Prefer not to answer",
          "value": 77
        }
      ]') %>%
  mutate(label = factor(label, levels = label))


## rename -7 to NA
skt_data$dwelling_type <- replace(skt_data$dwelling_type, skt_data$dwelling_type < 0, NA)

.ggpdf <- skt_data %>%
  left_join(skt_data_w2w1, by="interact_id") %>%
  transmute(interact_id = interact_id,
            dwelling_type = coalesce(dwelling_type, dwelling_type_w2, dwelling_type_w1)) %>%
  left_join(dwelling_type, by=c("dwelling_type"="value")) %>%
  transmute(interact_id = interact_id,
            dwelling_type = label)%>%
 filter(!is.na(dwelling_type))

table(.ggpdf$dwelling_type, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Dwelling', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Dwelling	N
A single-detached house	144
A semi-detached house	20
A row house	16
An apartment (or condo) in a duplex or triplex (two or three dwellings located one above the other)	30
An apartment (or condo) in a building that has fewer than five storeys	55
An apartment (or condo) in a building that has five or more storeys	21
A mobile home or other movable dwelling	6
A seniors’ home	0
Other	7
I don’t know/Prefer not to answer	4

ggplot(.ggpdf) +
  geom_bar(aes(y=dwelling_type, x=after_stat(count / sum(count)), fill=dwelling_type), show.legend = FALSE) +
  theme_light() +
  #theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_discrete(label=label_wrap(50))+
  scale_x_continuous(labels=percent) + xlab("")

Returning or new participant

skt_data %>%
  count(questionnaire) %>%
  kableExtra::kable() %>%
  kableExtra::kable_styling("hover", full_width = F)

questionnaire	n
New	170
Returning	150

ggplot(skt_data) +
  geom_bar(aes(questionnaire, y=after_stat(count / sum(count)), fill=questionnaire), show.legend = FALSE) +
  theme_light() +
  #theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey")+
  scale_y_continuous(labels=percent) + ylab("")

Transit and Transportation

Which Saskatoon Transit Go Pass do you own?

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

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

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

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

var_name_f	n	pct
Monthly adult pass	99	30.94
Eco Pass	10	3.12
UPass	43	13.44
Student Pass	41	12.81
Discounted Pass	8	2.50
Low Income Pass	4	1.25
I do not use a Go pass, I use a multi-use pass	35	10.94
I do not use a Go pass, I use cash	30	9.38
I do not currently ride the bus	50	15.62

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

Fall

df<- skt_data %>% filter(bus_freq_a < 91) %>%
  transmute(interact_id = interact_id, 
            bus_freq_a = bus_freq_a/13)

ggplot(df, aes(x =  (bus_freq_a)
)) + geom_histogram(na.rm = TRUE, fill = "#E5364D") + xlab("Days per week in the fall") +
  theme(axis.text.x = element_text(angle = 30, hjust = 1))

summary(df$bus_freq_a)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.4615  2.3077  2.5038  4.0000  6.9231

Winter

df<- skt_data %>% filter(bus_freq_b < 91) %>%
  transmute(interact_id = interact_id, 
            bus_freq_b = bus_freq_b/13)

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

summary(df$bus_freq_b)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.6923  2.3077  2.5190  4.0000  6.9231

Spring

df<- skt_data %>% filter(bus_freq_c < 91) %>%
  transmute(interact_id = interact_id, 
            bus_freq_c = bus_freq_c/13)

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

summary(df$bus_freq_c)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.4615  2.0000  2.3259  4.0000  6.9231

Summer

df<- skt_data %>% filter(bus_freq_d < 91) %>%
  transmute(interact_id = interact_id, 
            bus_freq_d = bus_freq_d/13)

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

summary(df$bus_freq_d)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   0.000   0.000   1.154   1.892   3.115   6.923

Average number of days of bus use per week (based on an annual average)

## add all seasons 

annual <- select(skt_data, interact_id, bus_freq_a, bus_freq_b, bus_freq_c, bus_freq_d) 

annual <- annual %>%
  mutate(across(starts_with("bus_freq_"), ~ pmin(., 91)))

annual$annual_bus_freq <- annual$bus_freq_a + annual$bus_freq_b + annual$bus_freq_c + annual$bus_freq_d

annual$mean_bus_freq <- annual$annual_bus_freq/52


# Add a freq of use category variable
bus_freq <- c("less than once a week", "1-3 times a week", "4 or more times a week")
annual <- annual %>%
  mutate(intervention_freq_use_class = case_when(mean_bus_freq < 1 ~ bus_freq[1],
                               mean_bus_freq < 3.5 ~ bus_freq[2],
                               TRUE ~ bus_freq[3]),
         intervention_freq_use_class = factor(intervention_freq_use_class, level = bus_freq))

annual<- select(annual, interact_id, intervention_freq_use_class)


# Join newly created var to skt_data
skt_data <- skt_data %>%
  left_join(annual, by = c("interact_id" = "interact_id"))

# 
# table(skt_data$intervention_freq_use_class, useNA = "ifany") %>%
#   kableExtra::kable(col.names=c('Taking the bus frequency', 'N')) %>%
#   kableExtra::kable_styling("hover", full_width = F)


ggplot(skt_data) +
  geom_bar(aes(intervention_freq_use_class, y=after_stat(count/sum(count)), fill=intervention_freq_use_class), show.legend = FALSE) +
  theme_light() +
  #theme(axis.text.x=element_text(angle=30, hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

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

a. 8th street

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

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

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

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

var_name_f	n	pct
Yes	170	53.12
No	146	45.62
I don’t know	4	1.25

b. 22nd street

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

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

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

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

var_name_f	n	pct
Yes	134	41.88
No	177	55.31
I don’t know	9	2.81

c. College Drive

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

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

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

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

var_name_f	n	pct
Yes	206	64.38
No	107	33.44
I don’t know	7	2.19

d. Preston Avenue

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

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

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

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

var_name_f	n	pct
Yes	136	42.5
No	176	55.0
I don’t know	8	2.5

e. Attridge Drive

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

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

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

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

var_name_f	n	pct
Yes	80	25.00
No	225	70.31
I don’t know	15	4.69

f. Warman Rd

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

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

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

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

var_name_f	n	pct
Yes	75	23.44
No	231	72.19
I don’t know	14	4.38

g. Idylwyld Dr N

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

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

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

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

var_name_f	n	pct
Yes	113	35.31
No	194	60.62
I don’t know	13	4.06

h. 3rd Avenue N

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

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

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

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

var_name_f	n	pct
Yes	136	42.50
No	165	51.56
I don’t know	19	5.94

i. Broadway Avenue

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

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

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

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

var_name_f	n	pct
Yes	158	49.38
No	155	48.44
I don’t know	7	2.19

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

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

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

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

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

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

var_name_f	n	pct
Much more likely	166	51.88
Somewhat more likely	118	36.88
Not at all more likely	30	9.38
I don’t know	6	1.88

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

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

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

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

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

var_name_f	n	pct
Much more likely	173	54.06
Somewhat more likely	109	34.06
Not at all more likely	31	9.69
I don’t know	7	2.19

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

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

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

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

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

var_name_f	n	pct
Much more likely	132	41.25
Somewhat more likely	125	39.06
Not at all more likely	47	14.69
I don’t know	16	5.00

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

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

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

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

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

var_name_f	n	pct
Much more likely	198	61.88
Somewhat more likely	96	30.00
Not at all more likely	22	6.88
I don’t know	4	1.25

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

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

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

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

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

var_name_f	n	pct
Much more likely	134	41.88
Somewhat more likely	102	31.88
Not at all more likely	61	19.06
I don’t know	23	7.19

Other

skt_data %>%
  select(bus_moti_f_txt) %>%
  filter(!is.na(bus_moti_f_txt)) %>%
  kableExtra::kable(col.names = "Motivations")

Motivations
Buses ran with more frequency later into evenings and on Sundays!
I would ride the bus more often if I had a longer commute
I wish the connections were better. Often the connections to #7 are bad. I often see it leaving as the bus I am on is about to pull up behind it. This means a half hour wait for the next #7
50% of The drivers are miserable and terrible drivers. They smash over curbs all the time and stop violently like jerks. Bad morale=bad employer. My partner and I have both arrived at our stops to yellow signs saying they’re cancelled.
Ran more frequently in the evening
the route existed and was efficient
Reliability.
The buses have been running late especially No.6 that I take to the University from 7th St E
Busses were cleaner and on time/not full
To catch the bus and make sure the buses are meeting up at the same time, in order to make a transfer.
People keep their feet with shoes or boots on the seat sometimes and makes it dirty
More routes available
shelters were upgraded on the west side
If there was a way to stay warm while waiting for the bus (like with subway, metro, or train stations)
The bus route itself was much more direct/faster
More accurate bus tracking in the app
It was made easie for seniors with some mobility issues ttravel
If buses (and stations) were as accessible as train stations in other cities then you would see a dramatic increase inn usership
No homeless people using the bus as a shelter
the bus route is more direct
If bus shelters existed at most stops
Heated bus shelters, bus every 5 minutes, bud ride no longer than 15-20 from downtown to my neighborhood.
there were less transfers/more direct routes
my bus stop had a shelter
Warmer place to wait for bus
the bus routes used to be much better, stopping on main streets.
Bus shelters were heated (or at the very least enclosed) during the winter
the bus stop to get to university was closer to my house
Buses transfers were better.
There were more shelters/warm places to wait along my routes
Reliable and efficient
Need to have better bus sheltors
There were faster bus routes to my workplace
my main reason for not taking the bus more is too busy when school is in session
If the bus routes were more direct and faster
That it doesn’t take 1.5 hours to get from point a to point b
For me the most important factor is service levels, frequency from the Broadway area to the University is the main factor.
There are many stops on which we dont have a shelter to stand or a seat. There are times in winters when bus get delayed coz of obvious reasons and it becomes very difficult to wait. If we can do something about it keeping in mind the harsh weather we get, it will make bus travel more easy. Thanks
Right now i have a back injury so i probably would have trouble stepping into the bus
Maybe when the pandemic is over I would take the bus again.
Transit officers
I bought a car in 2020. Ecopass was no longer offered through the university because of the pandemic.
I still have concerns about COVID, as someone who is immunecompromised, so if COVID numbers were much lower or inexistent
Ideally, the better a transit system is, the more likely people are too use it. However, SK culture is so vehicle-centric that you would have to change the way that people think to have any large scale impact.
The service returned to Feb2020 scheduling
Closer stop to my home, fewer transfers required
Route took less long than walking
There was more social distancing
I could have my small dog with me.
Primary issue is long commute. For the small city like Saskatoon, spending 1h one way to travel from centre to the north industrial area is not okay. Reduction of timing needed for commute.
Bus reliability has become a major problem over the last year. The city needs to address bus maintenance so that we stop having cancelled routes due to broken down buses. The lack of reliability is the biggest reason why my wife and I chose to purchase a vehicle for her commute to work.
My bus, #27 Silverspring, runs EVERY 40 MINUTES. Outrageous. It also DOES NOT GO DOWNTOWN - I loathe transferring in general. Transit is unreliable; and as you know winter in Saskatoon is hella cold……it should go downtown if it only comes every 40 minutes - I’d make that work.
real-time updates on every bus/route
They have a mask mandate
Extended hours especially on sundays
Bus drivers were friendlier
The routes aligned better between my starting point and destination (ie: fewer transfers; not single hub-and-spoke
More frequency and earlier start would allow me to get to work at the hospital on time
Having Timers or Real Time Tracking on busses available at bus hubs or through the app with free wifi at bus hubs
If masks were mandatory.
if the price of gas goes up further

skt_data %>%
  select(bus_moti_g_txt) %>%
  filter(!is.na(bus_moti_g_txt)) %>%
  kableExtra::kable(col.names = "Motivations")

Motivations
Even the driver watch me running and waving and I am just 10 ft. away from the door, they will close the door and I need to wait for next bus in this winter for 30 mins.
Sometimes the steps to get in and out of the bus are high.
There was a bit more time allowed in transfer tickets
If it was simpler to navigate (like your proposed idea of having a loop like most large city metro maps) with easy transfer points and routes listed on the buses
the bus does not wait for long periods at transfer points
There were fewer busses that skipped stops because they were full.
safety. I work at St. Paul’s Hospital. It’s extremely unnerving catching the bus there. And I wont take another bus that requires me to walk to or from my destination in that neighborhood
Safer buses
I can’t walk to far or stand to long
Friendly transit drivers, Clearer bus, mask laws enforcerment
The routes that entered Place Riel in Feb2020 resumed service into campus
If it started earlier we hospital workers could get to the hospital on time for our shifts
Having clean busses with dedicated lanes, not getting stuck in traffic. Having smart traffic lights which switch when busses are coming. Making bus a better option then driving alone in cars

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

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

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

summary(skt_data$bus_moti_slider)

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    0.00   10.00   30.00   37.19   62.50  100.00

Do you think Saskatoon Transit Service today is:

a. Reliable

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

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

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

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

var_name_f	n	pct
Very	44	13.75
Moderately	158	49.38
Slightly	82	25.62
Not at all	30	9.38
I don’t know	6	1.88

b. Clean

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

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

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

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

var_name_f	n	pct
Very	50	15.62
Moderately	181	56.56
Slightly	60	18.75
Not at all	17	5.31
I don’t know	12	3.75

c. Safe

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

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

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

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

var_name_f	n	pct
Very	92	28.75
Moderately	152	47.50
Slightly	56	17.50
Not at all	8	2.50
I don’t know	12	3.75

d. Convenient

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

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

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

plot(p)

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

var_name_f	n	pct
Very	43	13.44
Moderately	115	35.94
Slightly	95	29.69
Not at all	62	19.38
I don’t know	5	1.56

e. Too expensive

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

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

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

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

var_name_f	n	pct
Very	32	10.00
Moderately	75	23.44
Slightly	122	38.12
Not at all	71	22.19
I don’t know	20	6.25

f. Too cheap

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

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

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

  plot(p)

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

var_name_f	n	pct
Very	13	4.06
Moderately	35	10.94
Slightly	47	14.69
Not at all	199	62.19
I don’t know	26	8.12

g. Professional

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

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

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

plot(p)

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

var_name_f	n	pct
Very	69	21.56
Moderately	141	44.06
Slightly	80	25.00
Not at all	12	3.75
I don’t know	18	5.62

h. Environmentally friendly

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

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

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

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

var_name_f	n	pct
Very	69	21.56
Moderately	137	42.81
Slightly	60	18.75
Not at all	15	4.69
I don’t know	39	12.19

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

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

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

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

plot(p)

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

var_name_f	n	pct
Yes	220	68.75
No	100	31.25

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

var_name <- skt_data$brt_idea
skt_data$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 <- skt_data$var_name_f

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

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

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

var_name_f	n	pct
Very good idea	182	56.88
Somewhat good idea	109	34.06
Somewhat bad idea	9	2.81
Very bad idea	10	3.12
I don’t know	10	3.12

Do you think the BRT will be good for:

a. The Environment

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

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

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

plot(p)

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

var_name_f	n	pct
Very good	105	32.81
Somewhat good	150	46.88
Somewhat bad	21	6.56
Very bad	2	0.62
I don’t know	42	13.12

b. Increasing access to transportation

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

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

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

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

var_name_f	n	pct
Very good	185	57.81
Somewhat good	113	35.31
Somewhat bad	5	1.56
Very bad	3	0.94
I don’t know	14	4.38

c. Making Saskatoon a vibrant city

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

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

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

plot(p)

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

var_name_f	n	pct
Very good	141	44.06
Somewhat good	120	37.50
Somewhat bad	15	4.69
Very bad	4	1.25
I don’t know	40	12.50

d. Tourism

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

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

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

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

var_name_f	n	pct
Very good	104	32.50
Somewhat good	158	49.38
Somewhat bad	9	2.81
Very bad	6	1.88
I don’t know	43	13.44

e. Reducing traffic congestion

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

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

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

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

var_name_f	n	pct
Very good	137	42.81
Somewhat good	126	39.38
Somewhat bad	20	6.25
Very bad	9	2.81
I don’t know	28	8.75

f. Health

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

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

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

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

var_name_f	n	pct
Very good	95	29.69
Somewhat good	135	42.19
Somewhat bad	12	3.75
Very bad	3	0.94
I don’t know	75	23.44

g. Local business

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

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

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

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

var_name_f	n	pct
Very good	136	42.50
Somewhat good	123	38.44
Somewhat bad	11	3.44
Very bad	5	1.56
I don’t know	45	14.06

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

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

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

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

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

var_name_f	n	pct
Yes	256	80
No	64	20

In the current context, do you use transit less than, more than, or the same as you did prior to the COVID-19 pandemic?

var_name <- skt_data$cov_decon_mode_d
skt_data$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before", "3" = "More")
var_name_f <- skt_data$var_name_f

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

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

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

var_name_f	n	pct
Less	150	46.88
Same as before	129	40.31
More	41	12.81

Compared with before the COVID-19 pandemic, how would you rate your mental health (such as feeling anxious, depressed, or irritable) in general now?

var_name <- skt_data$sf_covid_mental
skt_data$var_name_f <- recode_factor(var_name, "1" = "Much better", "2" = "Slightly better", "3" = "About the same", "4" = "Slightly worse", "5" = "Much worse")
var_name_f <- skt_data$var_name_f

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

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT5likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response")
plot(p)

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

var_name_f	n	pct
Much better	54	16.88
Slightly better	73	22.81
About the same	95	29.69
Slightly worse	77	24.06
Much worse	21	6.56

Compared with before the COVID-19 pandemic, how would you rate your physical health in general now?

var_name <- skt_data$sf_covid_physical
skt_data$var_name_f <- recode_factor(var_name, "1" = "Much better", "2" = "Slightly better", "3" = "About the same", "4" = "Slightly worse", "5" = "Much worse")
var_name_f <- skt_data$var_name_f

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

##### Figure
p <- ggplot(t_1, aes(var_name_f,  y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT5likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("Response")
plot(p)

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

var_name_f	n	pct
Much better	59	18.44
Slightly better	86	26.88
About the same	90	28.12
Slightly worse	70	21.88
Much worse	15	4.69

What is your main mode of transportation?

transp_main_mode <- fromJSON('[
        {
          "label": "Walking",
          "value": 1
        },
        {
          "label": "Cycling",
          "value": 2
        },
        {
          "label": "Public Transit",
          "value": 3
        },
        {
          "label": "Car",
          "value": 4
        },
        {
          "label": "Motorcycle or scooter",
          "value": 5
        },
        {
          "label": "Other",
          "value": 99
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(transp_main_mode, by=c("transp_main_mode"="value")) %>%
  transmute(interact_id = interact_id,
            transp_main_mode = label)

table(.ggpdf$transp_main_mode, useNA = "ifany") %>%
  as.data.frame() %>%
  arrange(desc(Freq)) %>%
  kableExtra::kable(col.names=c('Main mode of transportation', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Main mode of transportation	N
Car	129
Public Transit	124
Cycling	34
Walking	30
Other	2
Motorcycle or scooter	1

skt_data %>%
  select(transp_main_mode_txt) %>%
  filter(!is.na(transp_main_mode_txt)) %>%
  unique() %>%
  kableExtra::kable(col.names = "Other listed main transportation modes") %>%
  kableExtra::kable_styling("hover", full_width = F)

Other listed main transportation modes
Bus and car
Rides with other people as I do not drive

ggplot(count(.ggpdf, transp_main_mode)) +
  geom_bar(aes(y=reorder(transp_main_mode, -n), x=n/nrow(skt_data), fill=reorder(transp_main_mode, -n)), stat="identity", show.legend = FALSE) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey", direction=-1) +
  scale_x_continuous(labels = percent) +
  ylab("transp_main_mode") + xlab("")

Do you have access to a car?

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

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

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

plot(p)

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

var_name_f	n	pct
Yes	252	78.75
No	68	21.25

Do you have access to a bike?

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

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

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

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

var_name_f	n	pct
Yes	220	68.75
No	100	31.25

How much do you enjoy using each transportation mode?

a. Walking

var_name <- skt_data$preferred_mode_a2
skt_data$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 <- skt_data$var_name_f

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

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT4likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") 
plot(p)

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

var_name_f	n	pct
1 A lot	135	42.19
2	92	28.75
3	80	25.00
4 Not at all	11	3.44
Not applicable	2	0.62

b. Biking

var_name <- skt_data$preferred_mode_b
skt_data$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 <- skt_data$var_name_f

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

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT4likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") 

plot(p)

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

var_name_f	n	pct
1 A lot	80	25.00
2	82	25.62
3	66	20.62
4 Not at all	36	11.25
Not applicable	56	17.50

c. Public Transit

var_name <- skt_data$preferred_mode_c
skt_data$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 <- skt_data$var_name_f

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

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT4likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") 

plot(p)

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

var_name_f	n	pct
1 A lot	76	23.75
2	109	34.06
3	88	27.50
4 Not at all	40	12.50
Not applicable	7	2.19

d. Car

var_name <- skt_data$preferred_mode_d
skt_data$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 <- skt_data$var_name_f

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

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT4likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("") 
plot(p)

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

var_name_f	n	pct
1 A lot	112	35.00
2	104	32.50
3	66	20.62
4 Not at all	19	5.94
Not applicable	19	5.94

f. Other (Please specify)

skt_data %>%
  select(preferred_mode_f_txt) %>%
  filter(!is.na(preferred_mode_f_txt)) %>%
  kableExtra::kable(col.names = "Enjoyment of other modes")

Enjoyment of other modes
I love public transit, but Saskatoon transit makes it awful. I want to love it, but it’s garbage. Make downtown inaccessible by car. Cars are stupid, people should all take public transit but here it’s only for losers who can’t afford cars. Nice job, mo.
Anything but garbage busses in this city
metro/subway/trains (when I lived in Asia and Europe)
Physical difficulties make some methods of travel difficult
While there are clear benefits to the BRT, it still suffers from the same problems as regular buses: vehicle congestion, train crossings, CO2 emissions
Taxi
I don’t bike in the city because I don’t feel safe. I would like to use the bus but the service does not meet my needs.
Uber/taxi
I do love to walk but can’t to far. The park by my place has benches every couple hundred feet so i walk walk there quite often
I find biking in Saskatoon to be awful compared to Ottawa where I used to live

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?

var_name <- skt_data$bike_safety
skt_data$var_name_f <- recode_factor(var_name, "1" = "Very Safe","2" = "Somewhat safe", "3" = "Neither safe, nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous")
var_name_f <- skt_data$var_name_f

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

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT5likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("")

plot(p)

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

var_name_f	n	pct
Very Safe	38	11.88
Somewhat safe	112	35.00
Neither safe, nor unsafe	42	13.12
Somewhat dangerous	88	27.50
Very dangerous	24	7.50
NA	16	5.00

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

var_name <- skt_data$walk_safety
skt_data$var_name_f <- recode_factor(var_name, "1" = "Very Safe","2" = "Somewhat safe", "3" = "Neither safe, nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous")
var_name_f <- skt_data$var_name_f

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

p <- ggplot(t_1, aes(x = var_name_f, y = pct,  fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) + 
  geom_bar(stat="identity") +
  scale_fill_manual(values = INTERACT5likert) +
  guides(fill=FALSE) +
      ylab("Percent") +
      xlab("") +
      ggtitle("")

plot(p)

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

var_name_f	n	pct
Very Safe	90	28.12
Somewhat safe	162	50.62
Neither safe, nor unsafe	26	8.12
Somewhat dangerous	33	10.31
Very dangerous	9	2.81

How satisfied are you with your current levels of physical activity?

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

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

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

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

var_name_f	n	pct
Very satisfied	70	21.88
Somewhat satisfied	140	43.75
Neutral	35	10.94
Somewhat dissatisfied	60	18.75
Very dissatisfied	15	4.69

Impacts

Minutes of physical activity

Minutes of total medium-vigorous physical activity per day, including work, travel, leisure. Computed from modpa and vigpa variables from health survey:

Global

.ggpdf <- skt_data %>%
  transmute(interact_id = interact_id,
            mvpa = ((coalesce(vigpa_days, 0) * coalesce(vigpa_freq, 0) + coalesce(modpa_days, 0) * coalesce(modpa_freq, 0))  / 7)) %>%   
  filter(mvpa >= 0 & mvpa < 900) 


# Link back MVPA to main dataset
skt_data <- skt_data %>%
  left_join(.ggpdf, by="interact_id") 

summary(skt_data$mvpa) %>% 
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
0	17.1	34.3	63.5	68.6	822.9

fillCount = length(unique(.ggpdf$mvpa))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(skt_data) +
  geom_histogram(aes(mvpa, y=after_stat(count/sum(count)), fill=factor(mvpa)), binwidth=20, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  xlab("average minutes of MVPA per day, in the last 7 days") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

fillCount = length(unique(skt_data$mvpa))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(filter(skt_data, mvpa >=0)) +
  geom_histogram(aes(mvpa, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(mvpa)), binwidth=30, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  facet_wrap(vars(intervention_freq_use_class), ncol=3) + 
  xlab("average minutes of MVPA per day, in the last 7 days") +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Health

Using sf1 variable: In general, would you say your health is…

Global

sf1 <- fromJSON('[
        {
          "label": "Excellent",
          "value": 1
        },
        {
          "label": "Very good",
          "value": 2
        },
        {
          "label": "Good",
          "value": 3
        },
        {
          "label": "Fair",
          "value": 4
        },
        {
          "label": "Poor",
          "value": 5
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(sf1, by=c("sf1"="value")) %>%
  transmute(interact_id = interact_id,
            sf1 = label)

table(.ggpdf$sf1, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Health status', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Health status	N
Excellent	39
Very good	113
Good	124
Fair	36
Poor	8

ggplot(.ggpdf) +
  geom_bar(aes(sf1, fill=sf1, y=after_stat(count/sum(count))), show.legend = FALSE) +
  theme_light() +
  #theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

.ggpdf <- skt_data %>%
  left_join(sf1, by=c("sf1"="value")) %>%
  transmute(interact_id = interact_id,
            intervention_freq_use_class = intervention_freq_use_class,
            sf1 = label)

rbind(table(pull(filter(.ggpdf, intervention_freq_use_class == bus_freq[1]), sf1)),
      table(pull(filter(.ggpdf, intervention_freq_use_class == bus_freq[2]), sf1)),
      table(pull(filter(.ggpdf, intervention_freq_use_class == bus_freq[3]), sf1))) %>%
  t() %>%
  kableExtra::kable(col.names = bus_freq) %>%
  kableExtra::kable_styling("hover", full_width = F)

	less than once a week	1-3 times a week	4 or more times a week
Excellent	6	15	18
Very good	33	50	30
Good	33	50	41
Fair	17	8	11
Poor	2	4	2

ggplot(.ggpdf) +
  geom_bar(aes(sf1, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=sf1), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  facet_wrap(vars(intervention_freq_use_class), ncol=3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Personal wellbeing

Overall life satisfaction, from 0 (completely dissatisfied) to 10 (completely satisfied). Computed from Personal Wellbeing Index (PWI) Scale, excluding the general life satisfaction (pwb_a) and spirituality items (pwb_i).

Global

skt_data <- skt_data %>%
  mutate(pwb = rowMeans(select(., pwb_b:pwb_h), na.rm = T)) # excluding pwb_a

.ggpdf <- skt_data %>%
  transmute(interact_id = interact_id,
            pwb = pwb,
            pwb_int = as.integer(round(pwb))) 

summary(.ggpdf$pwb) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
2.4	6.3	7.3	7	8	10

fillCount = length(unique(.ggpdf$pwb_int))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(.ggpdf) +
  geom_histogram(aes(pwb, y=after_stat(count/sum(count)), fill=factor(pwb_int)), binwidth=1, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

.ggpdf <- skt_data %>%
  transmute(interact_id = interact_id,
            intervention_freq_use_class = intervention_freq_use_class,
            pwb = pwb,
            pwb_int = as.integer(round(pwb))) 

fillCount = length(unique(.ggpdf$pwb_int))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(.ggpdf) +
  geom_histogram(aes(pwb, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(pwb_int)), binwidth=1, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  facet_wrap(vars(intervention_freq_use_class), ncol=3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Number of close friends

Global

summary(skt_data$confide) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
0	2	3	4.4	5	50

fillCount = length(unique(skt_data$confide))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(skt_data) +
  geom_histogram(aes(confide, y=after_stat(count/sum(count)), fill=factor(confide)), binwidth=2, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

fillCount = length(unique(skt_data$confide))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(skt_data) +
  geom_histogram(aes(confide, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(confide)), binwidth=5, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  facet_wrap(vars(intervention_freq_use_class), ncol=3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Sense of belonging

Global

belonging <- fromJSON('[
        {
          "label": "Very strong",
          "value": 1
        },
        {
          "label": "Somewhat strong",
          "value": 2
        },
        {
          "label": "Somewhat weak",
          "value": 3
        },
        {
          "label": "Very weak",
          "value": 4
        },
        {
          "label": "I don’t know",
          "value": 77
        }
      ]') %>%
  mutate(label = factor(label, levels = label))

.ggpdf <- skt_data %>%
  left_join(belonging, by=c("belonging"="value")) %>%
  transmute(interact_id = interact_id,
            belonging = label)

table(.ggpdf$belonging, useNA = "ifany") %>%
  kableExtra::kable(col.names=c('Sense of belonging', 'N')) %>%
  kableExtra::kable_styling("hover", full_width = F)

Sense of belonging	N
Very strong	54
Somewhat strong	129
Somewhat weak	94
Very weak	27
I don’t know	16

ggplot(.ggpdf) +
  geom_bar(aes(belonging, y=after_stat(count/sum(count)), fill=belonging), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

.ggpdf <- skt_data %>%
  left_join(belonging, by=c("belonging"="value")) %>%
  transmute(interact_id = interact_id,
            intervention_freq_use_class = intervention_freq_use_class,
            belonging = label)

rbind(table(pull(filter(.ggpdf, intervention_freq_use_class == bus_freq[1]), belonging)),
      table(pull(filter(.ggpdf, intervention_freq_use_class == bus_freq[2]), belonging)),
      table(pull(filter(.ggpdf, intervention_freq_use_class == bus_freq[3]), belonging))) %>%
  t() %>%
  kableExtra::kable(col.names = bus_freq) %>%
  kableExtra::kable_styling("hover", full_width = F)

	less than once a week	1-3 times a week	4 or more times a week
Very strong	8	23	23
Somewhat strong	36	53	40
Somewhat weak	28	37	29
Very weak	14	6	7
I don’t know	5	8	3

ggplot(.ggpdf) +
  geom_bar(aes(belonging, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=belonging), show.legend = FALSE) +
  theme_light() +
  theme(axis.text.x=element_text(angle=30,hjust=1)) +
  scale_fill_brewer(palette="GnBu", na.value = "grey") + 
  facet_wrap(vars(intervention_freq_use_class), ncol = 3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Loneliness

Derived from 3 items:

1. How often do you feel left out;
2. lack companionship;
3. feel isolated.

Responses are rated as either 1 (hardly ever), 2 (some of the time), or 3 (often), and summed to a total score from 3 to 9. Higher scores indicate greater loneliness.

Global

skt_data <- skt_data %>%
  mutate(loneliness = loneliness_a + loneliness_b + loneliness_c)

summary(skt_data$loneliness) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
3	3	5	4.8	6	9

ggplot(skt_data) +
  geom_bar(aes(factor(loneliness), y=after_stat(count/sum(count)), fill=factor(loneliness)), show.legend = F) +
  theme_light() +
  labs(x = "loneliness") +
  scale_fill_brewer(palette="GnBu", na.value = "grey", ) +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

ggplot(skt_data) +
  geom_bar(aes(factor(loneliness), y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(loneliness)), show.legend = F) +
  theme_light() +
  labs(x = "loneliness") +
  scale_fill_brewer(palette="GnBu", na.value = "grey", ) + 
  facet_wrap(vars(intervention_freq_use_class), ncol = 3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Social cohesion

Derived from 5 items:

1. people around here are willing to help their neighbors;
2. this is a close-knit neighborhood;
3. people in this neighborhood can be trusted;
4. people in this neighborhood generally do not get along with one another; and
5. people in this neighborhood do not share the same values.

Average of 5 items scored on a scale from strongly agree to strongly disagree. Higher score indicates higher social cohesion.

Global

skt_data <- skt_data %>%
  mutate(spat_soc_cohesion = (spat2_a + 6 - spat2_b + spat2_c + 6 - spat2_d + spat2_e) / 5)

summary(skt_data$spat_soc_cohesion) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
1.4	3.2	3.6	3.5	3.8	5

ggplot(skt_data) +
  geom_bar(aes(factor(as.integer(round(spat_soc_cohesion))), y=after_stat(count/sum(count)), fill=factor(as.integer(round(spat_soc_cohesion)))), show.legend = F) +
  theme_light() +
  labs(x = "spat_soc_cohesion") +
  scale_fill_brewer(palette="GnBu", na.value = "grey", )  +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

ggplot(skt_data) +
  geom_bar(aes(factor(as.integer(round(spat_soc_cohesion))), y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(as.integer(round(spat_soc_cohesion)))), show.legend = F) +
  theme_light() +
  labs(x = "spat_soc_cohesion") +
  scale_fill_brewer(palette="GnBu", na.value = "grey", ) +
  facet_wrap(vars(intervention_freq_use_class), ncol = 3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Neighbouring (informal)

Frequency of informal social interactions among neighbours (saying hello or having chat) per week, on a scale of 0 (never) to 7 (almost daily).

Global

# spa_freq <- fromJSON('[
#         {
#           "label": "per week",
#           "coeff": 52,
#           "value": 1
#         },
#         {
#           "label": "per month",
#           "coeff": 12,
#           "value": 2
#         },
#         {
#           "label": "per year",
#           "coeff": 1,
#           "value": 3
#         }
#       ]') %>%
#   mutate(label = factor(label, levels = label))
# 
.existing_cols <- colnames(skt_data)

skt_data <- skt_data %>%
  mutate(spat_talking_nghb = (coalesce(spat_a, 0) + coalesce(spat_b, 0)) /2 / 52) %>%
  select(.existing_cols, spat_talking_nghb)

summary(skt_data$spat_talking_nghb) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
0	0.5	1.5	2	3	7

ggplot(skt_data) +
  geom_histogram(aes(spat_talking_nghb, y=after_stat(count/sum(count)), fill=factor(as.integer(round(spat_talking_nghb)))), binwidth=1, show.legend = F) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

ggplot(skt_data) +
  geom_histogram(aes(spat_talking_nghb, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(as.integer(round(spat_talking_nghb)))), binwidth=1, show.legend = F) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  facet_wrap(vars(intervention_freq_use_class), ncol = 3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")

Neighbouring (formal)

Frequency of formal social interactions among neighbours (visiting or going somewhere with neighbour, giving or receiving help from neighbour) per week, on a scale of 0 (never) to 7 (almost daily).

Global

.existing_cols <- colnames(skt_data)

skt_data <- skt_data %>%
  mutate(spat_interact_nghb = (coalesce(spat_c, 0) + coalesce(spat_2e, 0)) / 2 / 52) %>%
  select(.existing_cols, spat_interact_nghb)

summary(skt_data$spat_interact_nghb) %>%
  as.list() %>%
  data.frame() %>%
  knitr::kable(digits = 1, col.names = c("Min", "1st Qu.", "Median", "Mean", "3rd Qu.", "Max")) %>%
  kableExtra::kable_styling("hover", full_width = F)

Min	1st Qu.	Median	Mean	3rd Qu.	Max
0	0	0	0.3	0.3	6.5

fillCount = length(unique(as.integer(round(skt_data$spat_interact_nghb))))
getPalette = colorRampPalette(RColorBrewer::brewer.pal(9, "GnBu"))

ggplot(skt_data) +
  geom_histogram(aes(spat_interact_nghb, y=after_stat(count/sum(count)), fill=factor(as.integer(round(spat_interact_nghb)))), binwidth = 1, show.legend = F) +
  theme_light() +
  scale_fill_manual(values = getPalette(fillCount), na.value = "grey") +
  scale_y_continuous(labels = percent) + ylab("")

By frequency of bus use

ggplot(skt_data) +
  geom_histogram(aes(spat_interact_nghb, y = after_stat(count/tapply(count, PANEL, sum)[PANEL]), fill=factor(as.integer(round(spat_interact_nghb)))), binwidth=1, show.legend = F) +
  theme_light() +
  scale_fill_brewer(palette="GnBu", na.value = "grey") +
  facet_wrap(vars(intervention_freq_use_class), ncol = 3) +
  scale_y_continuous(labels = percent) + ylab("percent within each frequency group")