INTERACT Results from Montreal 2020 - Wave 2
The INTErventions, Research, and Action in Cities Team (INTERACT) is a national research collaboration of scientists, urban planners, and engaged citizens uncovering how the design of our cities is shaping the health and wellbeing of Canadians (www.teaminteract.ca). INTERACT is conducting longitudinal, mixed-methods natural experiment studies in four Canadian cities, with the aim of providing evidence on the impacts of urban transformations on people’s physical activity, social connectedness, and wellbeing, and inequalities in these outcomes.
As part of Plan Montreal durable 2016-2020 and Montreal’s Climate Plan for 2050, the City of Montreal, its boroughs, neighbouring cities and partnering organizations are implementing urban interventions, such as new cycling infrastructure, changes in public transit infrastructure, changes in public space, and greening programs. Sampled from Montreal and it’s surroundings (including Longueuil, Brossard, Saint-Lambert, and Laval) participants were recruited from September 2020 to February 2021.
Participants were entered into a prize draw to incentivize their participation. Inclusion criteria were being above 18 years old, having a basic comprehension of English or French and not having the intention of moving out of the region in the next two years. Participants were recruited through social media, media articles and partner networks within community organisations. Responses were collected from September 7th, 2020 to February 2nd, 2021.
Across Montreal, Laval, Longueuil, Brossard and Saint-Lambert, 383 returning participants, and 218 new participants completed the Health Questionnaire, for a total of 601 responses.
Section 1: Transportation
What modes of transportation do you use? Please check all that apply.
# w2$mode_used_1[w2$mode_used_1==0] <- 2
# w2$mode_used_2[w2$mode_used_2==0] <- 2
# w2$mode_used_3[w2$mode_used_3==0] <- 2
# w2$mode_used_4[w2$mode_used_4==0] <- 2
# w2$mode_used_5[w2$mode_used_5==0] <- 2
# w2$mode_used_6[w2$mode_used_6==0] <- 2
# w2$mode_used_7[w2$mode_used_7==0] <- 2
# w2$mode_used_8[w2$mode_used_8==0] <- 2
# mode used
t_1 <- select(w2, croise, mode_used_1, mode_used_2, mode_used_3, mode_used_4, mode_used_5, mode_used_6, mode_used_7, mode_used_8)
t_1 <- pivot_longer(t_1,
cols = starts_with("mode_used_"),
names_to = "mode",
names_prefix = "mode_used_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$mode[t_1$mode== 1] <- "1. Walking"
t_1$mode[t_1$mode== 2] <- "2. Biking "
t_1$mode[t_1$mode== 3] <- "3. Public Transitl"
t_1$mode[t_1$mode== 4] <- "4. Car"
t_1$mode[t_1$mode== 5] <- "5. Motorcycle/scooter"
t_1$mode[t_1$mode== 6] <- "6. Taxi/Uber"
t_1$mode[t_1$mode== 7] <- "7. Car-sharing service"
t_1$mode[t_1$mode== 8] <- "8. Other"
## recode
t_1$values <- recode_factor(t_1$values, "1" = "Yes", "0" = "No")
##### Table
t_1<- t_1 %>%
group_by(croise, mode, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= mode, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACTPaletteYN) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
Other modes- write-in options
## [1] "BIXI"
## [2] "Course"
## [3] "Trottinette"
## [4] "Location occasionnelle de courte durée d'une voiture"
## [5] "my personal car"
## [6] "Bixi"
## [7] "Covoiturage"
## [8] "Trotinette électrique"
## [9] "CAR"
## [10] "Co-voiturage."
## [11] "amigo"Even if you do not personally use this mode of transportation, do you find
cycling in your city to be.
#perception_cycling_a
w2$perception_cycling_a <- recode_factor(w2$perception_cycling_a, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_cycling_b <- recode_factor(w2$perception_cycling_b, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_cycling_c <- recode_factor(w2$perception_cycling_c, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_cycling_d <- recode_factor(w2$perception_cycling_d, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_cycling_e <- recode_factor(w2$perception_cycling_e, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
t_1 <- select(w2, croise, perception_cycling_a, perception_cycling_b, perception_cycling_c, perception_cycling_d, perception_cycling_e)
t_1 <- pivot_longer(t_1,
cols = starts_with("perception_cycling_"),
names_to = "perception",
names_prefix = "perception_cycling_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "a. Safe"
t_1$perception[t_1$perception== "b"] <- "b. Reliable"
t_1$perception[t_1$perception== "c"] <- "c. Practical"
t_1$perception[t_1$perception== "d"] <- "d. Enjoyable"
t_1$perception[t_1$perception== "e"] <- "e. Affordable"
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
driving in your city to be.
w2$perception_driving_a <- recode_factor(w2$perception_driving_a, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_driving_b <- recode_factor(w2$perception_driving_b, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_driving_c <- recode_factor(w2$perception_driving_c, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_driving_d <- recode_factor(w2$perception_driving_d, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_driving_e <- recode_factor(w2$perception_driving_e, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
t_1 <- select(w2, croise, perception_driving_a, perception_driving_b, perception_driving_c, perception_driving_d, perception_driving_e)
t_1 <- pivot_longer(t_1,
cols = starts_with("perception_driving_"),
names_to = "perception",
names_prefix = "perception_driving_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "a. Safe"
t_1$perception[t_1$perception== "b"] <- "b. Reliable"
t_1$perception[t_1$perception== "c"] <- "c. Practical"
t_1$perception[t_1$perception== "d"] <- "d. Enjoyable"
t_1$perception[t_1$perception== "e"] <- "e. Affordable"
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
walking in your city to be.
w2$perception_walking_a <- recode_factor(w2$perception_walking_a, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_walking_b <- recode_factor(w2$perception_walking_b, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_walking_c <- recode_factor(w2$perception_walking_c, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_walking_d <- recode_factor(w2$perception_walking_d, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_walking_e <- recode_factor(w2$perception_walking_e, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
t_1 <- select(w2, croise, perception_walking_a, perception_walking_b, perception_walking_c, perception_walking_d, perception_walking_e)
t_1 <- pivot_longer(t_1,
cols = starts_with("perception_walking_"),
names_to = "perception",
names_prefix = "perception_walking_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "a. Safe"
t_1$perception[t_1$perception== "b"] <- "b. Reliable"
t_1$perception[t_1$perception== "c"] <- "c. Practical"
t_1$perception[t_1$perception== "d"] <- "d. Enjoyable"
t_1$perception[t_1$perception== "e"] <- "e. Affordable"
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
transit in your city to be.
w2$perception_transit_a <- recode_factor(w2$perception_transit_a, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_transit_b <- recode_factor(w2$perception_transit_b, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_transit_c <- recode_factor(w2$perception_transit_c, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_transit_d <- recode_factor(w2$perception_transit_d, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
w2$perception_transit_e <- recode_factor(w2$perception_transit_e, "1" = "1. Very", "2" = "2", "3" ="3", "4" = "4. Not at all", "77" = "I don't know")
t_1 <- select(w2, croise, perception_transit_a, perception_transit_b, perception_transit_c, perception_transit_d, perception_transit_e)
t_1 <- pivot_longer(t_1,
cols = starts_with("perception_transit_"),
names_to = "perception",
names_prefix = "perception_transit_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "a. Safe"
t_1$perception[t_1$perception== "b"] <- "b. Reliable"
t_1$perception[t_1$perception== "c"] <- "c. Practical"
t_1$perception[t_1$perception== "d"] <- "d. Enjoyable"
t_1$perception[t_1$perception== "e"] <- "e. Affordable"
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
How often do you typically travel during each season by
bicycle
w2$bike_freq_a[w2$bike_freq_a==-7] <- NA
p1 <- ggplot(w2, aes(x = bike_freq_a)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#FFA314") + xlab("Times in the fall") + facet_grid(~croise)
w2$bike_freq_b[w2$bike_freq_b==-7] <- NA
p2 <-ggplot(w2, aes(x = bike_freq_b)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#1596FF") + xlab("Times in the winter") + facet_grid(~croise)
w2$bike_freq_c[w2$bike_freq_c==-7] <- NA
p3 <- ggplot(w2, aes(x = bike_freq_c)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#76D24A") + xlab("Times in the spring") + facet_grid(~croise)
w2$bike_freq_d[w2$bike_freq_d==-7] <- NA
p4 <- ggplot(w2, aes(x = bike_freq_d)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#F2B705") + xlab("Times in the summer") + facet_grid(~croise)
grid.arrange(p1,p2, p3, p4 )
car
w2$car_freq_a[w2$car_freq_a==-7] <- NA
p1 <- ggplot(w2, aes(x = w2$car_freq_a)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#BF5B04") + xlab("Times in the fall") + facet_grid(~croise)
w2$car_freq_b[w2$car_freq_b==-7] <- NA
p2 <- ggplot(w2, aes(x = w2$car_freq_b)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#35AAF2") + xlab("Times in the winter") + facet_grid(~croise)
w2$car_freq_c[w2$car_freq_c==-7] <- NA
p3 <- ggplot(w2, aes(x = w2$car_freq_c)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#7C8C03") + xlab("Times in the spring") + facet_grid(~croise)
w2$car_freq_d[w2$car_freq_d==-7] <- NA
p4 <- ggplot(w2, aes(x = w2$car_freq_d)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#F2B705") + xlab("Times in the summer") + facet_grid(~croise)
grid.arrange(p1,p2, p3, p4)
foot
w2$walk_freq_a[w2$walk_freq_a==-7] <- NA
p1 <- ggplot(w2, aes(x = w2$walk_freq_a)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#BF5B04") + xlab("Times in the fall") + facet_grid(~croise)
w2$walk_freq_b[w2$walk_freq_b==-7] <- NA
p2 <- ggplot(w2, aes(x = w2$walk_freq_b)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#35AAF2") + xlab("Times in the winter") + facet_grid(~croise)
w2$walk_freq_c[w2$walk_freq_c==-7] <- NA
p3 <- ggplot(w2, aes(x = w2$walk_freq_c)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#7C8C03") + xlab("Times in the spring") + facet_grid(~croise)
w2$walk_freq_d[w2$walk_freq_d==-7] <- NA
p4<- ggplot(w2, aes(x = w2$walk_freq_d)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#F2B705") + xlab("Times in the summer") + facet_grid(~croise)
grid.arrange(p1,p2, p3, p4 )
transit
w2$transit_freq_a[w2$transit_freq_a==-7] <- NA
p1 <- ggplot(w2, aes(x = w2$transit_freq_a)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#BF5B04") + xlab("Times in the fall") + facet_grid(~croise)
w2$transit_freq_b[w2$transit_freq_b==-7] <- NA
p2 <- ggplot(w2, aes(x = w2$transit_freq_b)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#35AAF2") + xlab("Times in the winter") + facet_grid(~croise)
w2$transit_freq_c[w2$transit_freq_c==-7] <- NA
p3 <- ggplot(w2, aes(x = w2$transit_freq_c)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#7C8C03") + xlab("Times in the spring")+ facet_grid(~croise)
w2$transit_freq_d[w2$transit_freq_d==-7] <- NA
p4 <- ggplot(w2, aes(x = w2$transit_freq_d)) + geom_histogram (na.rm = TRUE, binwidth = 5, fill="#F2B705") + xlab("Times in the summer") + facet_grid(~croise)
grid.arrange(p1,p2, p3, p4 )
How many cars, trucks, or vans are kept in your household?
#cars_household
ggplot(w2, aes(x = cars_household)) + geom_histogram(na.rm = TRUE, fill="#76d24a") + xlab("Number of cars, trucks or vans in household") +facet_grid(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 1.0000 0.8752 1.0000 6.0000Do you have access to a car kept outside of your household?
note: Participants could select multiple answers. This table shows the percentage of participants who selected each answer.
#cars_access_outside
# Create a vector with variable names
response = paste0("cars_access_outside_", 1:4)
# Empty vector to stor output
car_access_outside_prop <- c()
# Calculate univariate proportions
for(i in response){
car_access_outside_prop[i] <- sum(w2[,i]) / nrow(w2)
}
# Transform
car_access_outside_prop <- as.data.frame(car_access_outside_prop)
car_access_outside_prop$Response <- c("Yes, I borrow a friend's or relative's car","Yes, I am a member of a car-sharing program (Communauto, Car2go, etc.) ","Yes, for another reason (Please specify)","No, I do not have access to a car kept outside of my household")
car_access_outside_prop$plot<- factor(car_access_outside_prop$Response, car_access_outside_prop$Response)
ggplot(car_access_outside_prop, aes(x = plot, y = car_access_outside_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percentage of participants who selected this answer") + theme(axis.text.x = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))
car_access_outside_prop$car_access_outside_prop <- round(car_access_outside_prop$car_access_outside_prop*100,2)
#car_access_outside_prop <- setcolorder(car_access_outside_prop, c("Response", "car_access_outside_prop"))
colnames(car_access_outside_prop) <- c("Response", "Percentage of participants who selected this answer")
car_access_outside_prop <- car_access_outside_prop[-c(3)]
kable(car_access_outside_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left") | Response | Percentage of participants who selected this answer | |
|---|---|---|
| cars_access_outside_1 | 12.65 | Yes, I borrow a friend’s or relative’s car |
| cars_access_outside_2 | 22.30 | Yes, I am a member of a car-sharing program (Communauto, Car2go, etc.) |
| cars_access_outside_3 | 6.49 | Yes, for another reason (Please specify) |
| cars_access_outside_4 | 65.56 | No, I do not have access to a car kept outside of my household |
Do you currently have a valid driver’s license?
#license
var_name <- w2$license
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 511 | 85.02 | |
| No | 90 | 14.98 |
Do you have access to a bicycle? Check all that apply.
note: Participants could select multiple answers. This table shows how frequently each answer was selected.
#bike_access_options
# Create a vector with variable names
response_bike = paste0("bike_access_options_", 1:5)
# Empty vector to stor output
bike_access_options_prop <- c()
# Calculate univariate proportions
for(i in response_bike){
bike_access_options_prop[i] <- sum(w2[,i]) / nrow(w2)
}
# Transform
bike_access_options_prop <- as.data.frame(bike_access_options_prop)
bike_access_options_prop$Response <- c("No, I do not have access to a bicycle","Yes, I own a bicycle","Yes, I borrow a friend's or relative's bicycle","Yes, I use a bike share service (BIXI, Dropbike, etc.)", "Yes, I have access to a bicycle through another way (please specify)")
bike_access_options_prop$plot<- factor(bike_access_options_prop$Response, bike_access_options_prop$Response)
ggplot(bike_access_options_prop, aes(x = plot, y = bike_access_options_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percentage of participants who selected this answer") + theme(axis.text.x = element_text(size=10, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))
bike_access_options_prop$bike_access_options_prop <- round(bike_access_options_prop$bike_access_options_prop*100,2)
#bike_access_options_prop <- setcolorder(bike_access_options_prop, c("Response", "bike_access_options_prop"))
colnames(bike_access_options_prop) <- c("Response", "Percentage of participants who selected this answer")
bike_access_options_prop <- bike_access_options_prop[-c(3)]
kable(bike_access_options_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left") | Response | Percentage of participants who selected this answer | |
|---|---|---|
| bike_access_options_1 | 19.80 | No, I do not have access to a bicycle |
| bike_access_options_2 | 73.21 | Yes, I own a bicycle |
| bike_access_options_3 | 1.66 | Yes, I borrow a friend’s or relative’s bicycle |
| bike_access_options_4 | 14.98 | Yes, I use a bike share service (BIXI, Dropbike, etc.) |
| bike_access_options_5 | 0.50 | Yes, I have access to a bicycle through another way (please specify) |
Section 2: Physical Activity
During the last 7 days, on how many days did you do vigorous physical activities like heavy lifting, digging, heavy construction, or climbing up stairs as part of your work? Think about only those physical activities that you did for at least 10 minutes at a time.
#work_vigpa
ggplot(w2, aes(x = work_vigpa)) + geom_histogram(na.rm = TRUE, fill = "#1596FF") + xlab("N days vigorous job-related physical activity") + facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$work_vigpa)), Percentage = round(as.numeric(prop.table(table(w2$work_vigpa)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 489 | 81.36 |
| 1 | 24 | 3.99 |
| 2 | 22 | 3.66 |
| 3 | 24 | 3.99 |
| 4 | 10 | 1.66 |
| 5 | 21 | 3.49 |
| 6 | 2 | 0.33 |
| 7 | 9 | 1.50 |
How much time did you usually spend on one of those days doing vigorous physical activities as part of your work?
#work_vigpa_freq
w2$work_vigpa_freq[w2$work_vigpa_freq==-7] <- NA
ggplot(w2, aes(x = work_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes vigorous job-related physical activity") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 20.00 60.00 96.21 120.00 720.00 489During the last 7 days, on how many days did you travel in a motor vehicle like a train, bus, car, or metro?
#travel_motor
ggplot(w2, aes(x = w2$travel_motor)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days") + facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$travel_motor)), Percentage = round(as.numeric(prop.table(table(w2$travel_motor)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 107 | 17.80 |
| 1 | 94 | 15.64 |
| 2 | 113 | 18.80 |
| 3 | 73 | 12.15 |
| 4 | 61 | 10.15 |
| 5 | 70 | 11.65 |
| 6 | 28 | 4.66 |
| 7 | 55 | 9.15 |
How much time did you usually spend on one of those days travelling in a train, bus, car, metro, or other kind of motor vehicle?
#travel_motor_freq
w2$travel_motor_freq[w2$travel_motor_freq==-7] <- NA
ggplot(w2, aes(x = w2$travel_motor_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 30.0 60.0 64.7 90.0 900.0 107During the last 7 days, on how many days did you bicycle for at least 10 minutes at a time to go from place to place?
#travel_bike
ggplot(w2, aes(x = w2$travel_bike)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$travel_bike)), Percentage = round(as.numeric(prop.table(table(w2$travel_bike)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 418 | 69.55 |
| 1 | 43 | 7.15 |
| 2 | 41 | 6.82 |
| 3 | 25 | 4.16 |
| 4 | 21 | 3.49 |
| 5 | 26 | 4.33 |
| 6 | 9 | 1.50 |
| 7 | 18 | 3.00 |
How much time did you usually spend on one of those days to bicycle from place to place?
#travel_bike_freq
w2$travel_bike_freq[w2$travel_bike_freq==-7] <- NA
ggplot(w2, aes(x = w2$travel_bike_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 30.00 45.00 58.14 60.00 360.00 418During the last 7 days, on how many days did you walk for at least 10 minutes at a time to go from place to place?
#travel_walk
ggplot(w2, aes(x = w2$travel_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("# of days in the last 7 days")+ facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$travel_walk)), Percentage = round(as.numeric(prop.table(table(w2$travel_walk)))*100,2))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 91 | 15.14 |
| 1 | 57 | 9.48 |
| 2 | 87 | 14.48 |
| 3 | 65 | 10.82 |
| 4 | 58 | 9.65 |
| 5 | 71 | 11.81 |
| 6 | 34 | 5.66 |
| 7 | 138 | 22.96 |
How much time did you usually spend on one of those days walking from place to place?
#travel_walk_freq
w2$travel_walk_freq[w2$travel_walk_freq==-7] <- NA
ggplot(w2, aes(x = w2$travel_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes travel time") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 20.00 30.00 44.32 60.00 420.00 91Not counting any walking for transportation that you have already mentioned, during the last 7 days, on how many days did you walk for at least 10 minutes at a time in your leisure time?
#leisure_walk
ggplot(w2, aes(x = w2$leisure_walk)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$leisure_walk)), Percentage = round(as.numeric(prop.table(table(w2$leisure_walk))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 178 | 30 |
| 1 | 77 | 13 |
| 2 | 95 | 16 |
| 3 | 50 | 8 |
| 4 | 40 | 7 |
| 5 | 49 | 8 |
| 6 | 16 | 3 |
| 7 | 96 | 16 |
How much time did you usually spend on one of those days walking in your leisure time?
#leisure_walk_freq
w2$leisure_walk_freq[w2$leisure_walk_freq==-7] <- NA
ggplot(w2, aes(x = w2$leisure_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 30.00 45.00 53.88 60.00 480.00 178Think about only those physical activities that you did for at least 10 minutes at a time, not counting any activity for transportation or work that you have already mentioned. During the last 7 days, on how many days did you do vigorous physical activities like aerobics, running, fast bicycling, or fast swimming in your leisure time?
#leisure_vigpa
ggplot(w2, aes(x = leisure_vigpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$leisure_vigpa)), Percentage = round(as.numeric(prop.table(table(w2$leisure_vigpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 357 | 59 |
| 1 | 47 | 8 |
| 2 | 48 | 8 |
| 3 | 62 | 10 |
| 4 | 39 | 6 |
| 5 | 26 | 4 |
| 6 | 16 | 3 |
| 7 | 6 | 1 |
How much time did you usually spend on one of those days doing vigorous physical activities in your leisure time?
#leisure_vigpa_freq
w2$leisure_vigpa_freq[w2$leisure_vigpa_freq==-7] <- NA
ggplot(w2, aes(x = leisure_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") 
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 30.00 47.50 65.29 60.00 1800.00 357During the last 7 days, on how many days did you do moderate physical activities like bicycling at a regular pace, swimming at a regular pace, or doubles tennis in your leisure time?
#leisure_modpa
ggplot(w2, aes(x = leisure_modpa)) + geom_histogram(na.rm = TRUE, fill="#1596FF") + xlab("N days")+ facet_wrap(~ croise)
kable(data.frame(Days = 0:7, N = as.numeric(table(w2$leisure_modpa)), Percentage = round(as.numeric(prop.table(table(w2$leisure_modpa))*100,2)))) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left")| Days | N | Percentage |
|---|---|---|
| 0 | 406 | 68 |
| 1 | 70 | 12 |
| 2 | 45 | 7 |
| 3 | 35 | 6 |
| 4 | 14 | 2 |
| 5 | 17 | 3 |
| 6 | 6 | 1 |
| 7 | 8 | 1 |
How much time did you usually spend on one of those days doing moderate physical activities in your leisure time?
#leisure_modpa_freq
w2$leisure_modpa_freq[w2$leisure_modpa_freq==-7] <- NA
ggplot(w2, aes(x = w2$leisure_modpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20, fill= "#35AAC2") + xlab("Minutes leisure time") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 5.0 30.0 45.0 66.3 60.0 900.0 406During the last 7 days, how much time did you usually spend sitting on a weekday?
#sit_weekday
ggplot(w2, aes(x = w2$sit_weekday/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekday") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 4.0 300.0 420.0 434.3 600.0 960.0During the last 7 days, how much time did you usually spend sitting on a weekend day?
#sit_weekend
ggplot(w2, aes(x = w2$sit_weekend/60)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill= "#35AAC2") + xlab("Hours sitting, weekend") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0 180.0 300.0 326.2 420.0 960.0Section 3: General Health
How tall are you?
#height
#exclude outliers?
ggplot(w2, aes(x = w2$height)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#76D24A") + xlab("Height (cm)") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 28.0 163.0 168.0 168.2 175.0 193.0 4How much do you weigh?
#weight
ggplot(w2, aes(x = w2$weight)) + geom_histogram(na.rm = TRUE, binwidth = 2, fill="#76D24A") + xlab("Weight (kg)") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 61.00 70.00 73.57 83.00 150.00In general, would you say your health is:
#sf1
var_name <- w2$sf1
w2$var_name_f <- recode_factor(var_name, "1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Excellent | 94 | 15.64 | |
| Very good | 244 | 40.60 | |
| Good | 215 | 35.77 | |
| Fair | 39 | 6.49 | |
| Poor | 9 | 1.50 |
The following questions are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much?
a. Moderate activities such as moving a table, pushing a vacuum cleaner, bowling, or playing golf
var_name <- w2$sf2
w2$var_name_f <- recode_factor(var_name, "1" = "Yes, limited a lot", "2" = "Yes, limited a little", "3" = "No, not at all")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes, limited a lot | 16 | 2.66 | |
| Yes, limited a little | 53 | 8.82 | |
| No, not at all | 532 | 88.52 |
b. Climbing several flights of stairs
var_name <- w2$sf3
w2$var_name_f <- recode_factor(var_name, "1" = "Yes, limited a lot", "2" = "Yes, limited a little", "3" = "No, not at all")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes, limited a lot | 31 | 5.16 | |
| Yes, limited a little | 100 | 16.64 | |
| No, not at all | 470 | 78.20 |
During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health?
a. Accomplished less than you would like
#sf4
var_name <- w2$sf4
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 130 | 21.63 | |
| No | 471 | 78.37 |
b. Were limited in the kind of work or other activities
var_name <- w2$sf5
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 105 | 17.47 | |
| No | 496 | 82.53 |
During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?
a. Accomplished less than you would like
var_name <- w2$sf6
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 243 | 40.43 | |
| No | 358 | 59.57 |
b. Did work or activities less carefully than usual
var_name <- w2$sf7
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 186 | 30.95 | |
| No | 415 | 69.05 |
During the past 4 weeks, how much did pain interfere with your normal work (including work outside the home and housework)?
var_name <- w2$sf8
w2$var_name_f <- recode_factor(var_name, "1" = "Not at all", "2" = "Slightly", "3" = "Moderately", "4" = "Quite a bit", "5" = "Extremely")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Not at all | 332 | 55.24 | |
| Slightly | 182 | 30.28 | |
| Moderately | 51 | 8.49 | |
| Quite a bit | 31 | 5.16 | |
| Extremely | 5 | 0.83 |
How much of the time during the past 4 weeks.
a. Have you felt calm and peaceful?
var_name <- w2$sf9
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| All of the time | 47 | 7.82 | |
| Most of the time | 185 | 30.78 | |
| A good bit of the time | 217 | 36.11 | |
| Some of the time | 105 | 17.47 | |
| A little of the time | 39 | 6.49 | |
| None of the time | 8 | 1.33 |
b. Did you have a lot of energy?
var_name <- w2$sf10
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| All of the time | 34 | 5.66 | |
| Most of the time | 137 | 22.80 | |
| A good bit of the time | 208 | 34.61 | |
| Some of the time | 143 | 23.79 | |
| A little of the time | 64 | 10.65 | |
| None of the time | 15 | 2.50 |
c. Have you felt downhearted and blue?
var_name <- w2$sf11
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| All of the time | 6 | 1.00 | |
| Most of the time | 46 | 7.65 | |
| A good bit of the time | 66 | 10.98 | |
| Some of the time | 188 | 31.28 | |
| A little of the time | 196 | 32.61 | |
| None of the time | 99 | 16.47 |
During the past 4 weeks, how much of the time has your physical health or emotional problems interfered with your social activities (like visiting friends, relatives, etc.)?
var_name <- w2$sf12
w2$var_name_f <- recode_factor(var_name, "1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| All of the time | 5 | 0.83 | |
| Most of the time | 22 | 3.66 | |
| A good bit of the time | 50 | 8.32 | |
| Some of the time | 94 | 15.64 | |
| A little of the time | 150 | 24.96 | |
| None of the time | 280 | 46.59 |
Section 4: Sleep
In the past month, when have you usually gone to bed?
#sleep_tq1
w2$sleep_tq1 <- as.POSIXct(w2$sleep_tq1,format="%H:%M:%S")
w2$sleep_tq1 <- hour(w2$sleep_tq1)
ggplot(w2, aes(x = sleep_tq1)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill="#76D24A") + xlab("Bed Time") + facet_grid(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 12.00 22.00 17.35 23.00 23.00In the past month, how long (in minutes) has it taken you to fall asleep each night?
#sleep_tq2 in mins
ggplot(w2, aes(x = sleep_tq2)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill="#76D24A") + xlab("Minutes to fall asleep") +facet_grid(~croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 10.00 15.00 21.44 30.00 300.00In the past month, when have you usually gotten up in the morning?
#sleep_tq3
w2$sleep_tq3 <- as.POSIXct(w2$sleep_tq3,format="%H:%M:%S")
w2$sleep_tq3 <- hour(w2$sleep_tq3)
ggplot(w2, aes(x = sleep_tq3)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill="#76D24A") + xlab("Wake Time") 
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 6.00 7.00 7.03 8.00 23.00During the past month, how would you rate your sleep quality overall?
#sleep_tq4
var_name <- w2$sleep_tq4
w2$var_name_f <- recode_factor(var_name, "1" = "Very good", "2" = "Fairly good", "3" = "Fairly bad", "4" = "Bad")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response")
plot(p)
| var_name_f | n | pct |
|---|---|---|
| Very good | 128 | 21.30 |
| Fairly good | 323 | 53.74 |
| Fairly bad | 132 | 21.96 |
| Bad | 18 | 3.00 |
How much do you typically sleep on
If I get less than _____ hours of sleep, I notice an impairment in my ability to function at work.
#sleep_c3
ggplot(w2, aes(x = sleep_c3)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill="#76D24A") + xlab("Hours")
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.500 6.000 6.304 7.000 11.000During the past month, how often have you had trouble staying awake while driving, eating meals, or engaging in social activity?
#sleepiness
var_name <- w2$sleepiness
w2$var_name_f <- recode_factor(var_name, "0" = "Not during the past month", "1" = "Less than once a week", "2" = "Once or twice a week", "3" = "Three or more times a week")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response")
plot(p)
| var_name_f | n | pct |
|---|---|---|
| Not during the past month | 413 | 68.72 |
| Less than once a week | 132 | 21.96 |
| Once or twice a week | 40 | 6.66 |
| Three or more times a week | 16 | 2.66 |
Section 4: Well-being
Thinking about your own life and personal circumstances, how satisfied are you.
t_1 <- select(w2, croise, pwb_a, pwb_b, pwb_c, pwb_d, pwb_e, pwb_f, pwb_g, pwb_h, pwb_i)
t_1 <- pivot_longer(t_1,
cols = starts_with("pwb_"),
names_to = "perception",
names_prefix = "pwb_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "a. With your life as a whole?"
t_1$perception[t_1$perception== "b"] <- "b. With your standard of living?"
t_1$perception[t_1$perception== "c"] <- "c. With your health?"
t_1$perception[t_1$perception== "d"] <- "d. With what you are achieving in life?"
t_1$perception[t_1$perception== "e"] <- "e. With your personal relationships?"
t_1$perception[t_1$perception== "f"] <- "f. With how safe you feel?"
t_1$perception[t_1$perception== "g"] <- "g. With feeling part of your community?"
t_1$perception[t_1$perception== "h"] <- "h. With your future security?"
t_1$perception[t_1$perception== "i"] <- "i. With your spirituality or religion?"
## recode
t_1$values <- recode_factor(t_1$values, "10" = "10- Completely satisfied", "9" = "9","8" = "8","7" = "7", "6" = "6", "5" = "5", "4" = "4", "3" = "3", "2" = "2", "1" = "1","0" = "0-Completely dissatisfied")
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACTPalettecont11) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
In general, I consider myself:
#gwb_a
var_name <- w2$gwb_a
w2$var_name_f <- recode_factor(var_name, "1" = "1- Not a very happy person", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7- A very happy person")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1- Not a very happy person | 3 | 0.50 | |
| 2 | 7 | 1.16 | |
| 3 | 21 | 3.49 | |
| 4 | 47 | 7.82 | |
| 5 | 143 | 23.79 | |
| 6 | 255 | 42.43 | |
| 7- A very happy person | 125 | 20.80 |
Compared with most of my peers, I consider myself:
#gwb_b
var_name <- w2$gwb_b
w2$var_name_f <- recode_factor(var_name, "1" = "1- Less happy", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7- More happy")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1- Less happy | 7 | 1.16 | |
| 2 | 16 | 2.66 | |
| 3 | 27 | 4.49 | |
| 4 | 79 | 13.14 | |
| 5 | 161 | 26.79 | |
| 6 | 195 | 32.45 | |
| 7- More happy | 116 | 19.30 |
Some people are generally very happy. They enjoy life regardless of what is going on, getting the most out of everything. To what extent does this characterization describe you?
#gwb_c
var_name <- w2$gwb_c
w2$var_name_f <- recode_factor(var_name, "1" = "1- Not at all", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7- A great deal")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1- Not at all | 19 | 3.16 | |
| 2 | 27 | 4.49 | |
| 3 | 52 | 8.65 | |
| 4 | 75 | 12.48 | |
| 5 | 133 | 22.13 | |
| 6 | 189 | 31.45 | |
| 7- A great deal | 106 | 17.64 |
Some people are generally not very happy. Although they are not depressed, they never seem as happy as they might be. To what extent does this characterization describe you?
#gwb_d
var_name <- w2$gwb_d
w2$var_name_f <- recode_factor(var_name, "1" = "1- Not at all","2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7- A great deal")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1- Not at all | 201 | 33.44 | |
| 2 | 142 | 23.63 | |
| 3 | 71 | 11.81 | |
| 4 | 49 | 8.15 | |
| 5 | 70 | 11.65 | |
| 6 | 52 | 8.65 | |
| 7- A great deal | 16 | 2.66 |
Tell us how often you feel this way.
t_1 <- select(w2, croise, loneliness_a, loneliness_b, loneliness_c)
t_1 <- pivot_longer(t_1,
cols = starts_with("loneliness_"),
names_to = "perception",
names_prefix = "loneliness_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "a. How often do you feel that you lack companionship?"
t_1$perception[t_1$perception== "b"] <- "b. How often do you feel left out?"
t_1$perception[t_1$perception== "c"] <- "c. How often do you feel isolated from others?"
## recode
t_1$values <- recode_factor(t_1$values, "1" = "Hardly ever", "2" = "Some of the time", "3" = "Often")
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
Section 5: Social Participation
How would you describe your sense of belonging to your local community? Would you say it is:
var_name <- w2$belonging
w2$var_name_f <- recode_factor(var_name, "1" = "Very strong", "2" = "Somewhat strong", "3" = "Somewhat weak", "4" = "Very weak", "77" ="I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very strong | 43 | 7.15 | |
| Somewhat strong | 267 | 44.43 | |
| Somewhat weak | 182 | 30.28 | |
| Very weak | 79 | 13.14 | |
| I don’t know | 30 | 4.99 |
How often do you.
a. Say hello to a neighbour?
#spat_a
#per week
ggplot(w2, aes(x = w2$spat_a/52.1429)) +
geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week") + facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.9973 2.9918 3.2625 4.9863 7.0000b. Stop and have a chat with a neighbour?
#spat_b
#per week
ggplot(w2, aes(x = w2$spat_b/52.1429)) +
geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.2301 0.9973 1.6929 2.9918 7.0000c. Visit a neighbour, or receive a visit from a neighbour?
#spat_c
#per week
ggplot(w2, aes(x = w2$spat_c/52.1429)) +
geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4581 0.2301 6.9808d. Go somewhere (e.g., to a shop; to a restaurant), together with a neighbour?
#spat_d
#per week
ggplot(w2, aes(x = w2$spat_d/52.1429)) +
geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1785 0.0000 6.9808e. Ask help/advice from or do you help/give advice to a neighbour yourself?
#spat_e
#per week
ggplot(w2, aes(x = w2$spat_e/52.1429)) +
geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Times per week")+ facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.03836 0.36553 0.23014 6.98082Thinking about your neighbourhood, how would you rate the following statements?
a. This is a close-knit neighbourhood
# plot spat2_a
var_name <- w2$spat2_a
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 51 | 8.49 | |
| Somewhat disagree | 99 | 16.47 | |
| Neither agree or disagree | 252 | 41.93 | |
| Somewhat agree | 152 | 25.29 | |
| Strongly agree | 47 | 7.82 |
b. People generally do not get along
# plot spat2_b
var_name <- w2$spat2_b
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 173 | 28.79 | |
| Somewhat disagree | 199 | 33.11 | |
| Neither agree or disagree | 176 | 29.28 | |
| Somewhat agree | 41 | 6.82 | |
| Strongly agree | 12 | 2.00 |
c. People are willing to help neighbours
# plot spat2_c
var_name <- w2$spat2_c
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 11 | 1.83 | |
| Somewhat disagree | 52 | 8.65 | |
| Neither agree or disagree | 179 | 29.78 | |
| Somewhat agree | 268 | 44.59 | |
| Strongly agree | 91 | 15.14 |
d. People do not share same values
var_name <- w2$spat2_d
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 83 | 13.81 | |
| Somewhat disagree | 186 | 30.95 | |
| Neither agree or disagree | 249 | 41.43 | |
| Somewhat agree | 58 | 9.65 | |
| Strongly agree | 25 | 4.16 |
e. People can be trusted
# plot spat2_e
var_name <- w2$spat2_e
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 17 | 2.83 | |
| Somewhat disagree | 45 | 7.49 | |
| Neither agree or disagree | 164 | 27.29 | |
| Somewhat agree | 281 | 46.76 | |
| Strongly agree | 94 | 15.64 |
If you lost a wallet or purse that contained two hundred dollars, how likely is it to be returned with the money in it, if it was found:
a. By someone who lives close by? Would it be:
#spat3_a
var_name <- w2$spat3_a
w2$var_name_f <- recode_factor(var_name, "1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very likely | 231 | 38.44 | |
| Somewhat likely | 259 | 43.09 | |
| Not at all likely | 59 | 9.82 | |
| I don’t know | 52 | 8.65 |
b. By a complete stranger? Would it be:
#spat3_b
var_name <- w2$spat3_b
w2$var_name_f <- recode_factor(var_name, "1"="Very likely", "2"="Somewhat likely", "3"="Not at all likely", "77"="I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very likely | 12 | 2.00 | |
| Somewhat likely | 128 | 21.30 | |
| Not at all likely | 340 | 56.57 | |
| I don’t know | 121 | 20.13 |
How many close friends do you have (that is, people who are not your relatives, but who you feel at ease with, can talk to about what is on your mind, or call on for help)?
#confide
ggplot(w2, aes(x = w2$confide)) +
geom_histogram(binwidth = 1, na.rm = TRUE, fill="#1596FF") + xlab("Friends")+ facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.000 5.000 6.015 8.000 50.000Here are some questions about your satisfaction with the neighbourhood in which you live. Please indicate your satisfaction with each item.How satisfied are you with…
a. your neighbourhood as a good place to live?
#neighb_a
var_name <- w2$neighb_a
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly satisfied | 272 | 45.26 | |
| Satisfied | 255 | 42.43 | |
| Neither satisfied nor dissatisfied | 44 | 7.32 | |
| Dissatisfied | 24 | 3.99 | |
| Strongly dissatisfied | 6 | 1.00 |
b. the number of people you know in your neighbourhood?
#neighb_b
var_name <- w2$neighb_b
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly satisfied | 106 | 17.64 | |
| Satisfied | 229 | 38.10 | |
| Neither satisfied nor dissatisfied | 181 | 30.12 | |
| Dissatisfied | 78 | 12.98 | |
| Strongly dissatisfied | 7 | 1.16 |
c. the ethnic diversity of your neighbourhood?
#neighb_c
var_name <- w2$neighb_c
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly satisfied | 112 | 18.64 | |
| Satisfied | 226 | 37.60 | |
| Neither satisfied nor dissatisfied | 212 | 35.27 | |
| Dissatisfied | 46 | 7.65 | |
| Strongly dissatisfied | 5 | 0.83 |
d. your neighbourhood as a good place to raise children
#neighb_d
var_name <- w2$neighb_d
w2$var_name_f <- recode_factor(var_name, "1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly satisfied | 213 | 35.44 | |
| Satisfied | 226 | 37.60 | |
| Neither satisfied nor dissatisfied | 120 | 19.97 | |
| Dissatisfied | 26 | 4.33 | |
| Strongly dissatisfied | 16 | 2.66 |
To what extent do you agree with the following statements?
a. People feel that neighbourhood efforts to improve this area are a waste of time.
#rds_a
var_name <- w2$rds_a
w2$var_name_f <- recode_factor(var_name, "1" = "1 - Strongly disagree", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7", "8" = "8", "9" = "9","10" = "10 - Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTfade)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1 - Strongly disagree | 110 | 18.30 | |
| 2 | 74 | 12.31 | |
| 3 | 132 | 21.96 | |
| 4 | 74 | 12.31 | |
| 5 | 98 | 16.31 | |
| 6 | 43 | 7.15 | |
| 7 | 29 | 4.83 | |
| 8 | 21 | 3.49 | |
| 9 | 9 | 1.50 | |
| 10 - Strongly agree | 11 | 1.83 |
b. When something needs to be improved in the neighbourhood, people from outside the neighbourhood are more likely to do something about it than people from inside the neighbourhood.
var_name <- w2$rds_b
w2$var_name_f <- recode_factor(var_name, "1" = "1 - Strongly disagree", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7", "8" = "8", "9" = "9", "10" = "10 - Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTfade)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1 - Strongly disagree | 114 | 18.97 | |
| 2 | 93 | 15.47 | |
| 3 | 121 | 20.13 | |
| 4 | 51 | 8.49 | |
| 5 | 119 | 19.80 | |
| 6 | 36 | 5.99 | |
| 7 | 22 | 3.66 | |
| 8 | 19 | 3.16 | |
| 9 | 13 | 2.16 | |
| 10 - Strongly agree | 13 | 2.16 |
c. The people in this neighbourhood have almost no influence over what happens here.
var_name <- w2$rds_c
w2$var_name_f <- recode_factor(var_name, "1" = "1 - Strongly disagree", "2" = "2", "3" = "3", "4" = "4", "5" = "5", "6" = "6", "7" = "7", "8" = "8", "9" = "9", "10" = "10 - Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTfade)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1 - Strongly disagree | 80 | 13.31 | |
| 2 | 70 | 11.65 | |
| 3 | 119 | 19.80 | |
| 4 | 84 | 13.98 | |
| 5 | 104 | 17.30 | |
| 6 | 43 | 7.15 | |
| 7 | 29 | 4.83 | |
| 8 | 34 | 5.66 | |
| 9 | 13 | 2.16 | |
| 10 - Strongly agree | 25 | 4.16 |
To what extent do you agree with the following statements?
I can get what I need in this neighbourhood
#sci_a
var_name <- w2$sci_a
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 14 | 2.33 | |
| Somewhat disagree | 58 | 9.65 | |
| Neither agree or disagree | 79 | 13.14 | |
| Somewhat agree | 247 | 41.10 | |
| Strongly agree | 203 | 33.78 |
This neighbourhood helps me fulfill my needs
var_name <- w2$sci_b
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 14 | 2.33 | |
| Somewhat disagree | 37 | 6.16 | |
| Neither agree or disagree | 97 | 16.14 | |
| Somewhat agree | 257 | 42.76 | |
| Strongly agree | 196 | 32.61 |
I feel like a member of this neighbourhood
var_name <- w2$sci_c
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 31 | 5.16 | |
| Somewhat disagree | 47 | 7.82 | |
| Neither agree or disagree | 165 | 27.45 | |
| Somewhat agree | 207 | 34.44 | |
| Strongly agree | 151 | 25.12 |
I belong in this neighbourhood
var_name <- w2$sci_d
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 30 | 4.99 | |
| Somewhat disagree | 41 | 6.82 | |
| Neither agree or disagree | 144 | 23.96 | |
| Somewhat agree | 217 | 36.11 | |
| Strongly agree | 169 | 28.12 |
I have a say about what goes on in my neighbourhood
var_name <- w2$sci_e
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 52 | 8.65 | |
| Somewhat disagree | 110 | 18.30 | |
| Neither agree or disagree | 217 | 36.11 | |
| Somewhat agree | 159 | 26.46 | |
| Strongly agree | 63 | 10.48 |
People in this neighbourhood are good at influencing each other
var_name <- w2$sci_f
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 25 | 4.16 | |
| Somewhat disagree | 81 | 13.48 | |
| Neither agree or disagree | 306 | 50.92 | |
| Somewhat agree | 148 | 24.63 | |
| Strongly agree | 41 | 6.82 |
I feel connected to this neighbourhood
var_name <- w2$sci_g
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 28 | 4.66 | |
| Somewhat disagree | 57 | 9.48 | |
| Neither agree or disagree | 108 | 17.97 | |
| Somewhat agree | 212 | 35.27 | |
| Strongly agree | 196 | 32.61 |
I have a good bond with others in this neighbourhood
var_name <- w2$sci_h
w2$var_name_f <- recode_factor(var_name, "1"="Strongly disagree", "2"="Somewhat disagree", "3"="Neither agree or disagree", "4"="Somewhat agree", "5"= "Strongly agree")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalette3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Strongly disagree | 13 | 2.16 | |
| Somewhat disagree | 39 | 6.49 | |
| Neither agree or disagree | 162 | 26.96 | |
| Somewhat agree | 250 | 41.60 | |
| Strongly agree | 137 | 22.80 |
Section 6: Neighbourhood Perception
In recent years, the urban environment of my neighbourhood has
#change_urbenv
var_name <- w2$change_urbenv
w2$var_name_f <- recode_factor(var_name, "1"="Gotten much better", "2"="Gotten a bit better", "3"="Stayed the same", "4"="Gotten a bit worse", "5"= "Gotten much worse")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Gotten much better | 134 | 22.30 | |
| Gotten a bit better | 290 | 48.25 | |
| Stayed the same | 113 | 18.80 | |
| Gotten a bit worse | 45 | 7.49 | |
| Gotten much worse | 19 | 3.16 |
To what extent do you agree or disagree with these statements. In my neighbourhood.
positive items
## POSITIVE ITEMS
pos <- select(w2, croise, feat_urbenv_b,feat_urbenv_d, feat_urbenv_f, feat_urbenv_g, feat_urbenv_h, feat_urbenv_j, feat_urbenv_l, feat_urbenv_m, feat_urbenv_o)
pos <- pivot_longer(pos,
cols = starts_with("feat_urbenv_"),
names_to = "feature",
names_prefix = "feat_urbenv_",
values_to = "values",
values_drop_na = TRUE)
pos$values <- recode_factor(pos$values, "1" = "1. Completely agree", "2" = "2", "3" = "3" , "4" = "4. Completely disagree", "77" = "I don't know")
## rename
pos$feature[pos$feature== "a"] <- "a. Parking is difficult in local shopping areas"
pos$feature[pos$feature== "b"] <- "b. Car traffic moves fluidly and efficiently"
pos$feature[pos$feature== "c"] <- "c. Streets can be excessively noisy"
pos$feature[pos$feature== "d"] <- "d. The sidewalks are in good condition"
pos$feature[pos$feature== "e"] <- "e. The sidewalks are not wide enough"
pos$feature[pos$feature== "f"] <- "f. There are enough trees along the street"
pos$feature[pos$feature== "g"] <- "g. There are many public spaces where people can relax and socialize (placottoirs, plazas, street seating, etc.)"
pos$feature[pos$feature== "h"] <- "h. Shops and services are easily accessible"
pos$feature[pos$feature== "i"] <- "i. There are no urban furnishings like benches or bike parking"
pos$feature[pos$feature== "j"] <- "j. There are sufficient public transit options nearby"
pos$feature[pos$feature== "k"] <- "k. The air is polluted"
pos$feature[pos$feature== "l"] <- "l. There are many parks nearby"
pos$feature[pos$feature== "m"] <- "m. There are lots of greened spaces, with features like trees, planters, green buffers between the road and the sidewalk."
pos$feature[pos$feature== "n"] <- "n. It is especially dangerous to bike on the street"
pos$feature[pos$feature== "o"] <- "o. There is a connected network of bike paths"
pos <- pos %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(pos, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
|
|
115 | 19.13 | |
|
2 | 223 | 37.10 | |
|
3 | 186 | 30.95 | |
|
|
77 | 12.81 | |
|
|
140 | 23.29 | |
|
2 | 250 | 41.60 | |
|
3 | 148 | 24.63 | |
|
|
63 | 10.48 | |
|
|
146 | 24.29 | |
|
2 | 192 | 31.95 | |
|
3 | 175 | 29.12 | |
|
|
88 | 14.64 | |
|
|
177 | 29.45 | |
|
2 | 233 | 38.77 | |
|
3 | 139 | 23.13 | |
|
|
52 | 8.65 | |
|
|
296 | 49.25 | |
|
2 | 207 | 34.44 | |
|
3 | 69 | 11.48 | |
|
|
29 | 4.83 | |
|
|
298 | 49.58 | |
|
2 | 175 | 29.12 | |
|
3 | 79 | 13.14 | |
|
|
49 | 8.15 | |
|
|
338 | 56.24 | |
|
2 | 208 | 34.61 | |
|
3 | 44 | 7.32 | |
|
|
11 | 1.83 | |
|
|
209 | 34.78 | |
|
2 | 226 | 37.60 | |
|
3 | 121 | 20.13 | |
|
|
45 | 7.49 | |
|
|
239 | 39.77 | |
|
2 | 222 | 36.94 | |
|
3 | 93 | 15.47 | |
|
|
47 | 7.82 |
negative items
neg <- select(w2, croise, feat_urbenv_a,feat_urbenv_c, feat_urbenv_e, feat_urbenv_i, feat_urbenv_k, feat_urbenv_n)
neg <- pivot_longer(neg,
cols = starts_with("feat_urbenv_"),
names_to = "feature",
names_prefix = "feat_urbenv_",
values_to = "values",
values_drop_na = TRUE)
neg$values <- recode_factor(neg$values, "1" = "1. Completely agree", "2" = "2", "3" = "3" , "4" = "4. Completely disagree", "77" = "I don't know")
## rename
neg$feature[neg$feature== "a"] <- "a. Parking is difficult in local shopping areas"
neg$feature[neg$feature== "c"] <- "c. Streets can be excessively noisy"
neg$feature[neg$feature== "e"] <- "e. The sidewalks are not wide enough"
neg$feature[neg$feature== "i"] <- "i. There are no urban furnishings like benches or bike parking"
neg$feature[neg$feature== "k"] <- "k. The air is polluted"
neg$feature[neg$feature== "n"] <- "n. It is especially dangerous to bike on the street"
neg <- neg %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(neg, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
|
|
98 | 16.31 | |
|
2 | 164 | 27.29 | |
|
3 | 170 | 28.29 | |
|
|
169 | 28.12 | |
|
|
99 | 16.47 | |
|
2 | 226 | 37.60 | |
|
3 | 189 | 31.45 | |
|
|
87 | 14.48 | |
|
|
107 | 17.80 | |
|
2 | 190 | 31.61 | |
|
3 | 198 | 32.95 | |
|
|
106 | 17.64 | |
|
|
41 | 6.82 | |
|
2 | 159 | 26.46 | |
|
3 | 234 | 38.94 | |
|
|
167 | 27.79 | |
|
|
71 | 11.81 | |
|
2 | 215 | 35.77 | |
|
3 | 215 | 35.77 | |
|
|
100 | 16.64 | |
|
|
59 | 9.82 | |
|
2 | 187 | 31.11 | |
|
3 | 240 | 39.93 | |
|
|
115 | 19.13 |
To what extent do you agree or disagree with the following statements? In my neighbourhood, there are more and more.
#increase_urbenv_a
t_1 <- select(w2, croise, increase_urbenv_a,increase_urbenv_b, increase_urbenv_c, increase_urbenv_d, increase_urbenv_e, increase_urbenv_f, increase_urbenv_g)
t_1 <- pivot_longer(t_1,
cols = starts_with("increase_urbenv_"),
names_to = "feature",
names_prefix = "increase_urbenv_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1" = "1. Completely agree", "2" = "2", "3" = "3" , "4" = "4. Completely disagree", "77" = "I don't know")
## rename
t_1$feature[t_1$feature== "a"] <- "a. Bike paths"
t_1$feature[t_1$feature== "b"] <- "b. Pedestrian-friendly designs, like wider sidewalks, speed-bumps, and stop signs"
t_1$feature[t_1$feature== "c"] <- "c. Potholes"
t_1$feature[t_1$feature== "d"] <- "d. Green alleys (ruelles vertes)"
t_1$feature[t_1$feature== "e"] <- "e. Greened spaces featuring trees, gardens, and planters"
t_1$feature[t_1$feature== "f"] <- "f. Graffiti"
t_1$feature[t_1$feature== "g"] <- "g. Pedestrianized streets"
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
|
|
288 | 47.92 | |
|
2 | 191 | 31.78 | |
|
3 | 76 | 12.65 | |
|
|
46 | 7.65 | |
|
|
202 | 33.61 | |
|
2 | 238 | 39.60 | |
|
3 | 116 | 19.30 | |
|
|
45 | 7.49 | |
|
|
152 | 25.29 | |
|
2 | 241 | 40.10 | |
|
3 | 188 | 31.28 | |
|
|
20 | 3.33 | |
|
|
109 | 18.14 | |
|
2 | 196 | 32.61 | |
|
3 | 172 | 28.62 | |
|
|
124 | 20.63 | |
|
|
137 | 22.80 | |
|
2 | 276 | 45.92 | |
|
3 | 134 | 22.30 | |
|
|
54 | 8.99 | |
|
|
79 | 13.14 | |
|
2 | 166 | 27.62 | |
|
3 | 253 | 42.10 | |
|
|
103 | 17.14 | |
|
|
69 | 11.48 | |
|
2 | 159 | 26.46 | |
|
3 | 153 | 25.46 | |
|
|
220 | 36.61 |
To what extent do you agree or disagree with the following statements?
#changeeffects_urbenv
t_1 <- select(w2, croise, changeeffects_urbenv_a,changeeffects_urbenv_b, changeeffects_urbenv_c, changeeffects_urbenv_d, changeeffects_urbenv_e)
t_1 <- pivot_longer(t_1,
cols = starts_with("changeeffects_urbenv_"),
names_to = "feature",
names_prefix = "changeeffects_urbenv_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1" = "1. Completely agree", "2" = "2", "3" = "3" , "4" = "4. Completely disagree", "77" = "I don't know")
## rename
t_1$feature[t_1$feature== "a"] <- "a. The City is investing in my neighbourhood"
t_1$feature[t_1$feature== "b"] <- "b. The changes in my neighbourhood are improving my quality of life"
t_1$feature[t_1$feature== "c"] <- "c. My neighbourhood is more and more dynamic"
t_1$feature[t_1$feature== "d"] <- "d. Low-income people can't afford to stay in this neighbourhood"
t_1$feature[t_1$feature== "e"] <- "e. I feel more and more anonymous in my neighbourhood"
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
|
|
140 | 23.29 | |
|
2 | 327 | 54.41 | |
|
3 | 78 | 12.98 | |
|
|
40 | 6.66 | |
|
I don’t know | 16 | 2.66 | |
|
|
196 | 32.61 | |
|
2 | 263 | 43.76 | |
|
3 | 61 | 10.15 | |
|
|
49 | 8.15 | |
|
I don’t know | 32 | 5.32 | |
|
|
147 | 24.46 | |
|
2 | 263 | 43.76 | |
|
3 | 117 | 19.47 | |
|
|
48 | 7.99 | |
|
I don’t know | 26 | 4.33 | |
|
|
233 | 38.77 | |
|
2 | 198 | 32.95 | |
|
3 | 103 | 17.14 | |
|
|
46 | 7.65 | |
|
I don’t know | 21 | 3.49 | |
|
|
38 | 6.32 | |
|
2 | 134 | 22.30 | |
|
3 | 247 | 41.10 | |
|
|
120 | 19.97 | |
|
I don’t know | 62 | 10.32 |
To what extent do you agree or disagree with the following statements? The changes in my neighbourhood are making it easier for me to get around:
- By foot
- By bike
- By car
- By transit
#changeeffects_urbenv_trans
w2$changeeffects_urbenv_trans_a[w2$changeeffects_urbenv_trans_a==-7] <- NA
w2$changeeffects_urbenv_trans_b[w2$changeeffects_urbenv_trans_b==-7] <- NA
w2$changeeffects_urbenv_trans_c[w2$changeeffects_urbenv_trans_c==-7] <- NA
w2$changeeffects_urbenv_trans_d[w2$changeeffects_urbenv_trans_d==-7] <- NA
t_1 <- select(w2, croise, changeeffects_urbenv_trans_a,changeeffects_urbenv_trans_b, changeeffects_urbenv_trans_c, changeeffects_urbenv_trans_d)
t_1 <- pivot_longer(t_1,
cols = starts_with("changeeffects_urbenv_trans_"),
names_to = "feature",
names_prefix = "changeeffects_urbenv_trans_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1" = "1. Completely agree", "2" = "2", "3" = "3" , "4" = "4. Completely disagree", "77" = "I don't know")
## rename
t_1$feature[t_1$feature== "a"] <- "By foot"
t_1$feature[t_1$feature== "b"] <- "By bike"
t_1$feature[t_1$feature== "c"] <- "By car"
t_1$feature[t_1$feature== "d"] <- "By transit"
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT4likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
| By bike |
|
228 | 46.72 | |
| By bike | 2 | 176 | 36.07 | |
| By bike | 3 | 61 | 12.50 | |
| By bike |
|
23 | 4.71 | |
| By car |
|
44 | 9.02 | |
| By car | 2 | 117 | 23.98 | |
| By car | 3 | 249 | 51.02 | |
| By car |
|
78 | 15.98 | |
| By foot |
|
197 | 40.37 | |
| By foot | 2 | 199 | 40.78 | |
| By foot | 3 | 67 | 13.73 | |
| By foot |
|
25 | 5.12 | |
| By transit |
|
91 | 18.65 | |
| By transit | 2 | 222 | 45.49 | |
| By transit | 3 | 140 | 28.69 | |
| By transit |
|
35 | 7.17 |
Section 7: PACER questions
Thinking about changes in your neighbourhood, please identify the degree to which the following changes have happened.
# pacer_typechange_a
INTERACT3likert <- c("#1596ff", "#EBF0F8", "#76D24A" , "#666666")
t_1 <- select(w2, croise, pacer_typechange_a, pacer_typechange_b, pacer_typechange_c, pacer_typechange_d, pacer_typechange_e, pacer_typechange_f, pacer_typechange_g, pacer_typechange_h, pacer_typechange_i)
t_1 <- pivot_longer(t_1,
cols = starts_with("pacer_typechange_"),
names_to = "feature",
names_prefix = "pacer_typechange_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1"="Not happening", "2"="Happening a little", "3"="Happening a lot", "77"="I don't know")
## rename
t_1$feature[t_1$feature== "a"] <- "New businesses are opening"
t_1$feature[t_1$feature== "b"] <- "Long-standing businesses are being replaced by different businesses."
t_1$feature[t_1$feature== "c"] <- "More expensive or fancier grocery stores are opening"
t_1$feature[t_1$feature== "d"] <- "The cost of housing have increased (i.e. renting or buying) "
t_1$feature[t_1$feature== "e"] <- "The costs of necessary expenses other than housing have increased (e.g., childcare; groceries; transit)"
t_1$feature[t_1$feature== "f"] <- "Construction of new buildings on vacant lots or to replace old buildings"
t_1$feature[t_1$feature== "g"] <- "Construction of new or improved resources, such as parks, bike lanes, transit, or sidewalks"
t_1$feature[t_1$feature== "h"] <- "People are “flipping” properties, buying and fixing them up to rent or sell."
t_1$feature[t_1$feature== "i"] <- "Changes are leading to tension or conflict between me and my neighbours"
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT3likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
| Changes are leading to tension or conflict between me and my neighbours | Not happening | 323 | 53.74 | |
| Changes are leading to tension or conflict between me and my neighbours | Happening a little | 119 | 19.80 | |
| Changes are leading to tension or conflict between me and my neighbours | Happening a lot | 32 | 5.32 | |
| Changes are leading to tension or conflict between me and my neighbours | I don’t know | 127 | 21.13 | |
| Construction of new buildings on vacant lots or to replace old buildings | Not happening | 85 | 14.14 | |
| Construction of new buildings on vacant lots or to replace old buildings | Happening a little | 218 | 36.27 | |
| Construction of new buildings on vacant lots or to replace old buildings | Happening a lot | 243 | 40.43 | |
| Construction of new buildings on vacant lots or to replace old buildings | I don’t know | 55 | 9.15 | |
| Construction of new or improved resources, such as parks, bike lanes, transit, or sidewalks | Not happening | 107 | 17.80 | |
| Construction of new or improved resources, such as parks, bike lanes, transit, or sidewalks | Happening a little | 304 | 50.58 | |
| Construction of new or improved resources, such as parks, bike lanes, transit, or sidewalks | Happening a lot | 164 | 27.29 | |
| Construction of new or improved resources, such as parks, bike lanes, transit, or sidewalks | I don’t know | 26 | 4.33 | |
| Long-standing businesses are being replaced by different businesses. | Not happening | 126 | 20.97 | |
| Long-standing businesses are being replaced by different businesses. | Happening a little | 317 | 52.75 | |
| Long-standing businesses are being replaced by different businesses. | Happening a lot | 84 | 13.98 | |
| Long-standing businesses are being replaced by different businesses. | I don’t know | 74 | 12.31 | |
| More expensive or fancier grocery stores are opening | Not happening | 239 | 39.77 | |
| More expensive or fancier grocery stores are opening | Happening a little | 231 | 38.44 | |
| More expensive or fancier grocery stores are opening | Happening a lot | 87 | 14.48 | |
| More expensive or fancier grocery stores are opening | I don’t know | 44 | 7.32 | |
| New businesses are opening | Not happening | 156 | 25.96 | |
| New businesses are opening | Happening a little | 310 | 51.58 | |
| New businesses are opening | Happening a lot | 101 | 16.81 | |
| New businesses are opening | I don’t know | 34 | 5.66 | |
| People are “flipping” properties, buying and fixing them up to rent or sell. | Not happening | 84 | 13.98 | |
| People are “flipping” properties, buying and fixing them up to rent or sell. | Happening a little | 171 | 28.45 | |
| People are “flipping” properties, buying and fixing them up to rent or sell. | Happening a lot | 198 | 32.95 | |
| People are “flipping” properties, buying and fixing them up to rent or sell. | I don’t know | 148 | 24.63 | |
| The cost of housing have increased (i.e. renting or buying) | Not happening | 15 | 2.50 | |
| The cost of housing have increased (i.e. renting or buying) | Happening a little | 147 | 24.46 | |
| The cost of housing have increased (i.e. renting or buying) | Happening a lot | 390 | 64.89 | |
| The cost of housing have increased (i.e. renting or buying) | I don’t know | 49 | 8.15 | |
| The costs of necessary expenses other than housing have increased (e.g., childcare; groceries; transit) | Not happening | 57 | 9.48 | |
| The costs of necessary expenses other than housing have increased (e.g., childcare; groceries; transit) | Happening a little | 267 | 44.43 | |
| The costs of necessary expenses other than housing have increased (e.g., childcare; groceries; transit) | Happening a lot | 184 | 30.62 | |
| The costs of necessary expenses other than housing have increased (e.g., childcare; groceries; transit) | I don’t know | 93 | 15.47 |
In what ways are people who are moving into your neighbourhood different than you? Check all that apply.
- asked only to people who said yes to previous question
- participants could select multiple answers. Figures and tables show how many times each answer was selected.
# 1 Racial or ethnic background Origine raciale ou ethnique
# 2 Income or wealth Revenu ou richesse
# 3 Job or employment Emploi
# 4 Education Éducation
# 5 They are students Ce sont des étudiants
# 6 Family structure Structure familiale
# 7 Age Âge
# 8 Religion Religion
# 9 Culture and values Culture et valeurs
# 10 The activities they enjoy Les activités qu’elles aiment
# 99 Other Autre, veuillez spécifier
# -7 Not applicable Non applicable
w2$pacer_differentpeople_1[w2$pacer_differentpeople_1==-7] <- NA
w2$pacer_differentpeople_2[w2$pacer_differentpeople_2==-7] <- NA
w2$pacer_differentpeople_3[w2$pacer_differentpeople_3==-7] <- NA
w2$pacer_differentpeople_4[w2$pacer_differentpeople_4==-7] <- NA
w2$pacer_differentpeople_5[w2$pacer_differentpeople_5==-7] <- NA
w2$pacer_differentpeople_6[w2$pacer_differentpeople_6==-7] <- NA
w2$pacer_differentpeople_7[w2$pacer_differentpeople_7==-7] <- NA
w2$pacer_differentpeople_8[w2$pacer_differentpeople_8==-7] <- NA
w2$pacer_differentpeople_9[w2$pacer_differentpeople_9==-7] <- NA
w2$pacer_differentpeople_10[w2$pacer_differentpeople_10==-7] <- NA
w2$pacer_differentpeople_1[w2$pacer_differentpeople_1==0] <- 2
w2$pacer_differentpeople_2[w2$pacer_differentpeople_2==0] <- 2
w2$pacer_differentpeople_3[w2$pacer_differentpeople_3==0] <- 2
w2$pacer_differentpeople_4[w2$pacer_differentpeople_4==0] <- 2
w2$pacer_differentpeople_5[w2$pacer_differentpeople_5==0] <- 2
w2$pacer_differentpeople_6[w2$pacer_differentpeople_6==0] <- 2
w2$pacer_differentpeople_7[w2$pacer_differentpeople_7==0] <- 2
w2$pacer_differentpeople_8[w2$pacer_differentpeople_8==0] <- 2
w2$pacer_differentpeople_9[w2$pacer_differentpeople_9==0] <- 2
w2$pacer_differentpeople_10[w2$pacer_differentpeople_10==0] <- 2
w2$pacer_differentpeople_99[w2$pacer_differentpeople_99==0] <- 2
t_1 <- select(w2, croise, pacer_differentpeople_1,pacer_differentpeople_2, pacer_differentpeople_3, pacer_differentpeople_4, pacer_differentpeople_5, pacer_differentpeople_6, pacer_differentpeople_7, pacer_differentpeople_8, pacer_differentpeople_9, pacer_differentpeople_10)
t_1 <- pivot_longer(t_1,
cols = starts_with("pacer_differentpeople_"),
names_to = "feature",
names_prefix = "pacer_differentpeople_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No", "77" = "I don't know")
## rename
t_1$feature[t_1$feature== "1"] <- "Racial or ethnic background"
t_1$feature[t_1$feature== "2"] <- "Income or wealth"
t_1$feature[t_1$feature== "3"] <- "Job or employment"
t_1$feature[t_1$feature== "4"] <- "Education"
t_1$feature[t_1$feature== "5"] <- "They are students"
t_1$feature[t_1$feature== "6"] <- "Family structure"
t_1$feature[t_1$feature== "7"] <- "Age"
t_1$feature[t_1$feature== "8"] <- "Religion"
t_1$feature[t_1$feature== "9"] <- "Culture and values"
t_1$feature[t_1$feature== "10"] <- "The activities they enjoy"
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACTPaletteYN) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
| Age | Yes | 123 | 41.55 | |
| Age | No | 173 | 58.45 | |
| Culture and values | Yes | 80 | 27.03 | |
| Culture and values | No | 216 | 72.97 | |
| Education | Yes | 61 | 20.61 | |
| Education | No | 235 | 79.39 | |
| Family structure | Yes | 89 | 30.07 | |
| Family structure | No | 207 | 69.93 | |
| Income or wealth | Yes | 127 | 42.91 | |
| Income or wealth | No | 169 | 57.09 | |
| Job or employment | Yes | 68 | 22.97 | |
| Job or employment | No | 228 | 77.03 | |
| Racial or ethnic background | Yes | 136 | 45.95 | |
| Racial or ethnic background | No | 160 | 54.05 | |
| Religion | Yes | 49 | 16.55 | |
| Religion | No | 247 | 83.45 | |
| The activities they enjoy | Yes | 42 | 14.19 | |
| The activities they enjoy | No | 254 | 85.81 | |
| They are students | Yes | 43 | 14.53 | |
| They are students | No | 253 | 85.47 |
Do people moving into your neighbourhood call it by a different name than long-term residents?
#pacer_neighname
var_name <- w2$pacer_neighname
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr:: summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 47 | 7.82 | |
| No | 324 | 53.91 | |
| I don’t know | 230 | 38.27 |
How much interest do people moving into your neighbourhood take in existing businesses or organizations (e.g. community organizations, schools)?
#pacer_interestplaces
var_name <- w2$pacer_interestplaces
w2$var_name_f <- recode_factor(var_name, "1" = "A lot of interest", "2" = "Some interest", "3" = "No interest", "4" = "NA", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr:: summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| A lot of interest | 59 | 9.82 | |
| Some interest | 171 | 28.45 | |
| No interest | 38 | 6.32 | |
| NA | 5 | 0.83 | |
| I don’t know | 328 | 54.58 |
On a scale from 1 to 10 with 1 being the least and 10 being the most, overall how much change has happened in your neighbourhood during the last three to five years?
#pacer_change
var_name <- w2$pacer_change
w2$var_name_f <- recode_factor(var_name, "1" = "1. Least change",
"2" = "2",
"3" = "3",
"4" = "4",
"5" = "5",
"6" = "6",
"7" = "7",
"8" = "8",
"9" = "9",
"10" = "10. Most change")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr:: summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalettecont)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
|
11 | 1.83 | |
| 2 | 22 | 3.66 | |
| 3 | 48 | 7.99 | |
| 4 | 53 | 8.82 | |
| 5 | 90 | 14.98 | |
| 6 | 102 | 16.97 | |
| 7 | 137 | 22.80 | |
| 8 | 76 | 12.65 | |
| 9 | 22 | 3.66 | |
|
40 | 6.66 |
On a scale from 1 to 10 with 1 being the slowest and 10 being the fastest, how quickly have changes been happening in your neighbourhood during the last three to five years?
#pacer_speed
var_name <- w2$pacer_speed
w2$var_name_f <- recode_factor(var_name, "1" = "1. Slowest", "1" = "1",
"2" = "2",
"3" = "3",
"4" = "4",
"5" = "5",
"6" = "6",
"7" = "7",
"8" = "8",
"9" = "9",
"10" = "10. Fastest")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr:: summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalettecont)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| 1 | 27 | 4.49 | |
| 2 | 19 | 3.16 | |
| 3 | 46 | 7.65 | |
| 4 | 62 | 10.32 | |
| 5 | 90 | 14.98 | |
| 6 | 105 | 17.47 | |
| 7 | 115 | 19.13 | |
| 8 | 88 | 14.64 | |
| 9 | 26 | 4.33 | |
|
23 | 3.83 |
These questions will ask for your feelings about any changes within your neighbourhood. Rate your agreement with each statement from “strongly agree” to “strongly disagree.”
#### pacer_feelings_a
INTERACT5likert <- c("#60C472", "#76D24A" , "#EBF0F8", "#1596ff" ,"#0c77cf", "#666666")
t_1 <- select(w2, croise, pacer_feelings_a, pacer_feelings_b, pacer_feelings_c, pacer_feelings_d, pacer_feelings_e, pacer_feelings_f, pacer_feelings_g, pacer_feelings_h, pacer_feelings_i)
t_1 <- pivot_longer(t_1,
cols = starts_with("pacer_feelings_"),
names_to = "feature",
names_prefix = "pacer_feelings_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1" = "Strongly agree", "2" = "Agree", "3" = "Neither", "4"= "Disagree", "5" ="Strongly disagree", "77" = "I don't know")
## rename
t_1$feature[t_1$feature== "a"] <- "If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood"
t_1$feature[t_1$feature== "b"] <- "I feel welcome in most new businesses in my neighbourhood"
t_1$feature[t_1$feature== "c"] <- "I feel the personality of my neighbourhood has changed"
t_1$feature[t_1$feature== "d"] <- "I trust people moving into my neighbourhood"
t_1$feature[t_1$feature== "e"] <- "I feel good about the changes happening in my neighbourhood"
t_1$feature[t_1$feature== "f"] <- "I am afraid of being pushed or forced out of my neighbourhood"
t_1$feature[t_1$feature== "g"] <- "I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here"
t_1$feature[t_1$feature== "h"] <- "Changes in my neighbourhood are meant for people like me"
t_1$feature[t_1$feature== "i"] <- "My neighbourhood is experiencing development that has caused concerns about higher cost of living."
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACT5likert) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
| Changes in my neighbourhood are meant for people like me | Strongly agree | 72 | 11.98 | |
| Changes in my neighbourhood are meant for people like me | Agree | 220 | 36.61 | |
| Changes in my neighbourhood are meant for people like me | Neither | 186 | 30.95 | |
| Changes in my neighbourhood are meant for people like me | Disagree | 61 | 10.15 | |
| Changes in my neighbourhood are meant for people like me | Strongly disagree | 40 | 6.66 | |
| Changes in my neighbourhood are meant for people like me | I don’t know | 22 | 3.66 | |
| I am afraid of being pushed or forced out of my neighbourhood | Strongly agree | 27 | 4.49 | |
| I am afraid of being pushed or forced out of my neighbourhood | Agree | 72 | 11.98 | |
| I am afraid of being pushed or forced out of my neighbourhood | Neither | 78 | 12.98 | |
| I am afraid of being pushed or forced out of my neighbourhood | Disagree | 136 | 22.63 | |
| I am afraid of being pushed or forced out of my neighbourhood | Strongly disagree | 240 | 39.93 | |
| I am afraid of being pushed or forced out of my neighbourhood | I don’t know | 48 | 7.99 | |
| I feel good about the changes happening in my neighbourhood | Strongly agree | 108 | 17.97 | |
| I feel good about the changes happening in my neighbourhood | Agree | 258 | 42.93 | |
| I feel good about the changes happening in my neighbourhood | Neither | 148 | 24.63 | |
| I feel good about the changes happening in my neighbourhood | Disagree | 50 | 8.32 | |
| I feel good about the changes happening in my neighbourhood | Strongly disagree | 22 | 3.66 | |
| I feel good about the changes happening in my neighbourhood | I don’t know | 15 | 2.50 | |
| I feel the personality of my neighbourhood has changed | Strongly agree | 30 | 4.99 | |
| I feel the personality of my neighbourhood has changed | Agree | 161 | 26.79 | |
| I feel the personality of my neighbourhood has changed | Neither | 212 | 35.27 | |
| I feel the personality of my neighbourhood has changed | Disagree | 121 | 20.13 | |
| I feel the personality of my neighbourhood has changed | Strongly disagree | 52 | 8.65 | |
| I feel the personality of my neighbourhood has changed | I don’t know | 25 | 4.16 | |
| I feel welcome in most new businesses in my neighbourhood | Strongly agree | 291 | 48.42 | |
| I feel welcome in most new businesses in my neighbourhood | Agree | 226 | 37.60 | |
| I feel welcome in most new businesses in my neighbourhood | Neither | 50 | 8.32 | |
| I feel welcome in most new businesses in my neighbourhood | Disagree | 10 | 1.66 | |
| I feel welcome in most new businesses in my neighbourhood | Strongly disagree | 6 | 1.00 | |
| I feel welcome in most new businesses in my neighbourhood | I don’t know | 18 | 3.00 | |
| I trust people moving into my neighbourhood | Strongly agree | 98 | 16.31 | |
| I trust people moving into my neighbourhood | Agree | 293 | 48.75 | |
| I trust people moving into my neighbourhood | Neither | 160 | 26.62 | |
| I trust people moving into my neighbourhood | Disagree | 27 | 4.49 | |
| I trust people moving into my neighbourhood | Strongly disagree | 12 | 2.00 | |
| I trust people moving into my neighbourhood | I don’t know | 11 | 1.83 | |
| I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here | Strongly agree | 82 | 13.64 | |
| I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here | Agree | 206 | 34.28 | |
| I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here | Neither | 164 | 27.29 | |
| I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here | Disagree | 93 | 15.47 | |
| I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here | Strongly disagree | 40 | 6.66 | |
| I would support changes to my neighbourhood (e.g. new stores, parks) even if the changes make it more expensive for me to live here | I don’t know | 16 | 2.66 | |
| If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood | Strongly agree | 78 | 12.98 | |
| If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood | Agree | 157 | 26.12 | |
| If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood | Neither | 81 | 13.48 | |
| If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood | Disagree | 136 | 22.63 | |
| If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood | Strongly disagree | 138 | 22.96 | |
| If I had to move right now, I could afford to move to a similar house or apartment within my neighbourhood | I don’t know | 11 | 1.83 | |
| My neighbourhood is experiencing development that has caused concerns about higher cost of living. | Strongly agree | 125 | 20.80 | |
| My neighbourhood is experiencing development that has caused concerns about higher cost of living. | Agree | 192 | 31.95 | |
| My neighbourhood is experiencing development that has caused concerns about higher cost of living. | Neither | 145 | 24.13 | |
| My neighbourhood is experiencing development that has caused concerns about higher cost of living. | Disagree | 74 | 12.31 | |
| My neighbourhood is experiencing development that has caused concerns about higher cost of living. | Strongly disagree | 40 | 6.66 | |
| My neighbourhood is experiencing development that has caused concerns about higher cost of living. | I don’t know | 25 | 4.16 |
On a scale from 1 to 10 with 1 being the not at all and 10 being very much so, do you think your neighbourhood is going through gentrification?
#gentri_percep
var_name <- w2$gentri_percep
w2$var_name_f <- recode_factor(var_name, "1" = "1. Not at all", "2" = "2",
"3" = "3",
"4" = "4",
"5" = "5",
"6" = "6",
"7" = "7",
"8" = "8",
"9" = "9",
"10" = "10. Very much so")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr:: summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(x = var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTPalettecont)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
|
59 | 9.82 | |
| 2 | 33 | 5.49 | |
| 3 | 30 | 4.99 | |
| 4 | 34 | 5.66 | |
| 5 | 45 | 7.49 | |
| 6 | 79 | 13.14 | |
| 7 | 92 | 15.31 | |
| 8 | 92 | 15.31 | |
| 9 | 52 | 8.65 | |
|
85 | 14.14 |
Section 8: Neighbourhood Selection
Before moving into your current dwelling, when you were looking for a neighbourhood to live in, to what extent were the following characteristics important? Please report your perspectives, even if the neighbourhood where you currently live does not have these characteristics
a. Good access to public transportation
#neigh_pref_a
var_name <- w2$neigh_pref_a
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 427 | 71.05 | |
| Somewhat important | 105 | 17.47 | |
| Not very important | 41 | 6.82 | |
| Not important at all | 21 | 3.49 | |
| I don’t know | 7 | 1.16 |
b. Sufficient parks and green spaces
var_name <- w2$neigh_pref_b
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 284 | 47.25 | |
| Somewhat important | 247 | 41.10 | |
| Not very important | 49 | 8.15 | |
| Not important at all | 12 | 2.00 | |
| I don’t know | 9 | 1.50 |
c. Sufficient shops and services
#neigh_pref_c
var_name <- w2$neigh_pref_c
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 310 | 51.58 | |
| Somewhat important | 237 | 39.43 | |
| Not very important | 38 | 6.32 | |
| Not important at all | 10 | 1.66 | |
| I don’t know | 6 | 1.00 |
d. Proximity to doctors, a pharmacy or other health services
var_name <- w2$neigh_pref_d
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 166 | 27.62 | |
| Somewhat important | 208 | 34.61 | |
| Not very important | 174 | 28.95 | |
| Not important at all | 43 | 7.15 | |
| I don’t know | 10 | 1.66 |
e. A good knowledge of the neighbourhood
var_name <- w2$neigh_pref_e
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 111 | 18.47 | |
| Somewhat important | 196 | 32.61 | |
| Not very important | 207 | 34.44 | |
| Not important at all | 75 | 12.48 | |
| I don’t know | 12 | 2.00 |
f. Presence of relatives, friends or acquaintances
#neigh_pref_f
var_name <- w2$neigh_pref_f
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 71 | 11.81 | |
| Somewhat important | 131 | 21.80 | |
| Not very important | 192 | 31.95 | |
| Not important at all | 196 | 32.61 | |
| I don’t know | 11 | 1.83 |
g. A neighbourhood where it is pleasant to walk
#neigh_pref_g
var_name <- w2$neigh_pref_g
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 308 | 51.25 | |
| Somewhat important | 233 | 38.77 | |
| Not very important | 43 | 7.15 | |
| Not important at all | 11 | 1.83 | |
| I don’t know | 6 | 1.00 |
h. A neighbourhood where it is practical to move around by car (ease of parking, low traffic, good access by car)
#neigh_pref_h
var_name <- w2$neigh_pref_h
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 96 | 15.97 | |
| Somewhat important | 148 | 24.63 | |
| Not very important | 122 | 20.30 | |
| Not important at all | 210 | 34.94 | |
| I don’t know | 25 | 4.16 |
i. Presence of good schools
#neigh_pref_i
var_name <- w2$neigh_pref_i
w2$var_name_f <- recode_factor(var_name, "1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very important | 98 | 16.31 | |
| Somewhat important | 131 | 21.80 | |
| Not very important | 74 | 12.31 | |
| Not important at all | 256 | 42.60 | |
| I don’t know | 42 | 6.99 |
If you had the choice, how long would you remain in your current home?
#neigh_stay
var_name <- w2$neigh_stay
w2$var_name_f <- recode_factor(var_name, "1" = "I would move now", "2" = "Less than 3 years", "3" = "3 to 5 years", "4" = "More than 5 years but less than 10 years", "5"= "10 or more years")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| I would move now | 71 | 11.81 | |
| Less than 3 years | 67 | 11.15 | |
| 3 to 5 years | 87 | 14.48 | |
| More than 5 years but less than 10 years | 90 | 14.98 | |
| 10 or more years | 286 | 47.59 |
Section 9: Activity Tracking
Do you currently own or use any of the following devices or smartphone apps to monitor your physical activity?
No
#tracking1 and tracking1_use
tracking1_no <- round((sum(w2$tracking1_no)/ length(w2$tracking1_no))*100,2)
tracking1_no_n <- sum(w2$tracking1_no)
tracking1_no_interest <- round((sum(w2$tracking1_no_interest)/ length(w2$tracking1_no_interest))*100,2)
tracking1_no_interest_n <- sum(w2$tracking1_no_interest)
no_tracking <- data.frame(Response=c("I do not have one but might be interested in trying one", "I do not have one and I am not interested in trying one"),
Count = c(tracking1_no_n, tracking1_no_interest_n),
Percentage= c(tracking1_no, tracking1_no_interest))
kable(no_tracking) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left") | Response | Count | Percentage |
|---|---|---|
| I do not have one but might be interested in trying one | 155 | 25.79 |
| I do not have one and I am not interested in trying one | 134 | 22.30 |
Own
#tracking own
# Create a vector with variable names
response = paste0("tracking1_own_", 1:4)
# Empty vector to stor output
tracking1_own_prop <- c()
# Calculate univariate proportions
for(i in response){
tracking1_own_prop[i] <- sum(w2[,i]) / nrow(w2)
}
# Transform
tracking1_own_prop <- as.data.frame(tracking1_own_prop)
tracking1_own_prop$Own <- c("Wearable devices (Fitbits, Garmins, and Jawbone, etc.)","Smart watches (Apple Watch, Galaxy Gear, Samsung Gear, etc.)","Smartphone app (Apple Health, Samsung Health, Google Fit, Strava, etc.)","Other")
tracking1_own_prop$plot<- factor(tracking1_own_prop$Own, tracking1_own_prop$Own)
ggplot(tracking1_own_prop, aes(x = plot, y = tracking1_own_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percent of participants who selected this answer") + theme(axis.text.x = element_text(size= 12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))
tracking1_own_prop <- tracking1_own_prop[-c(3)]
tracking1_own_prop$tracking1_own_prop <- round(tracking1_own_prop$tracking1_own_prop*100,2)
#tracking1_own_prop <- setcolorder(tracking1_own_prop, c("Own", "tracking1_own_prop"))
colnames(tracking1_own_prop) <- c("Response", "Percent of participants who selected this answer")
tracking1_own_prop$Count <- c(sum(w2$tracking1_own_1), sum(w2$tracking1_own_2),sum(w2$tracking1_own_3),sum(w2$tracking1_own_4))
kable(tracking1_own_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left") | Response | Percent of participants who selected this answer | Count | |
|---|---|---|---|
| tracking1_own_1 | 18.30 | Wearable devices (Fitbits, Garmins, and Jawbone, etc.) | 110 |
| tracking1_own_2 | 11.48 | Smart watches (Apple Watch, Galaxy Gear, Samsung Gear, etc.) | 69 |
| tracking1_own_3 | 31.28 | Smartphone app (Apple Health, Samsung Health, Google Fit, Strava, etc.) | 188 |
| tracking1_own_4 | 3.33 | Other | 20 |
Use
#tracking use
# Create a vector with variable names
response = paste0("tracking1_use_", 1:4)
# Empty vector to stor output
tracking1_use_prop <- c()
# Calculate univariate proportions
for(i in response){
tracking1_use_prop[i] <- sum(w2[,i]) / nrow(w2)
}
# Transform
tracking1_use_prop <- as.data.frame(tracking1_use_prop)
tracking1_use_prop$use <- c("Wearable devices (Fitbits, Garmins, and Jawbone, etc.)","Smart watches (Apple Watch, Galaxy Gear, Samsung Gear, etc.)","Smartphone app (Apple Health, Samsung Health, Google Fit, Strava, etc.)","Other")
tracking1_use_prop$plot<- factor(tracking1_use_prop$use, tracking1_use_prop$use)
ggplot(tracking1_use_prop, aes(x = plot, y = tracking1_use_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percent of participants who selected this answer") + theme(axis.text.x = element_text(angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10))
tracking1_use_prop <- tracking1_use_prop[-c(3)]
tracking1_use_prop$tracking1_use_prop <- round(tracking1_use_prop$tracking1_use_prop*100,2)
#tracking1_use_prop <- setcolorder(tracking1_use_prop, c("use", "tracking1_use_prop"))
colnames(tracking1_use_prop) <- c("Response", "Percent of participants who selected this answer")
tracking1_use_prop$Count <- c(sum(w2$tracking1_use_1), sum(w2$tracking1_use_2),sum(w2$tracking1_use_3),sum(w2$tracking1_use_4))
kable(tracking1_use_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left") | Response | Percent of participants who selected this answer | Count | |
|---|---|---|---|
| tracking1_use_1 | 11.98 | Wearable devices (Fitbits, Garmins, and Jawbone, etc.) | 72 |
| tracking1_use_2 | 9.15 | Smart watches (Apple Watch, Galaxy Gear, Samsung Gear, etc.) | 55 |
| tracking1_use_3 | 23.79 | Smartphone app (Apple Health, Samsung Health, Google Fit, Strava, etc.) | 143 |
| tracking1_use_4 | 3.00 | Other | 18 |
Thinking about a typical month, how many days on average do you use your device or smartphone app to monitor your physical activity? If you own several activity trackers, choose the one that you use most often.
#tracking2
w2$tracking2[w2$tracking2==-7] <- NA
ggplot(w2, aes(tracking2)) + geom_histogram(na.rm = TRUE, binwidth = 1, fill="#76D24A") + xlab("Days per month") 
When using a device or app to monitor your physical activity, how concerned are you about the possibility of your location being known by the company which developed the device or app?
#tracking3a
w2$tracking3a[w2$tracking3a==-7] <- NA
var_name <- w2$tracking3a
w2$var_name_f <- recode_factor(var_name, "1" = "Not at all", "2" = "Slightly", "3" = "Moderately", "4" = "Very much", "5" = "Extremely", "6"= "I have no opinion on the subject")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Not at all | 89 | 14.81 | |
| Slightly | 51 | 8.49 | |
| Moderately | 36 | 5.99 | |
| Very much | 19 | 3.16 | |
| Extremely | 7 | 1.16 | |
| I have no opinion on the subject | 10 | 1.66 | |
| NA | 389 | 64.73 |
If you had to use a device or app, how concerned would you be about the possibility of your location being known by the company which developed the device or app?
#tracking3b
w2$tracking3b[w2$tracking3b==-7] <- NA
var_name <- w2$tracking3b
w2$var_name_f <- recode_factor(var_name, "1" = "Not at all", "2" = "Slightly", "3" = "Moderately", "4" = "Very much", "5" = "Extremely", "6"= "I have no opinion on the subject")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalettecont) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Not at all | 73 | 12.15 | |
| Slightly | 71 | 11.81 | |
| Moderately | 60 | 9.98 | |
| Very much | 68 | 11.31 | |
| Extremely | 51 | 8.49 | |
| I have no opinion on the subject | 36 | 5.99 | |
| NA | 242 | 40.27 |
How concerned are you about the possibility of your location being known by your network mobile provider when using a smartphone?
#tracking4
w2$tracking4[w2$tracking4==-7] <- NA
var_name <- w2$tracking4
w2$var_name_f <- recode_factor(var_name, "1" = "Not at all", "2" = "Slightly", "3" = "Moderately", "4" = "Very much", "5" = "Extremely", "6"= "No opinion", "7" = "I do not use a smartphone")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalettecont) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Not at all | 166 | 27.62 | |
| Slightly | 126 | 20.97 | |
| Moderately | 135 | 22.46 | |
| Very much | 66 | 10.98 | |
| Extremely | 52 | 8.65 | |
| No opinion | 22 | 3.66 | |
| I do not use a smartphone | 34 | 5.66 |
Compared with friends of my age, my concern regarding protecting my privacy is.
#tracking5
w2$tracking5[w2$tracking5==-7] <- NA
var_name <- w2$tracking5
w2$var_name_f <- recode_factor(var_name, "1" = "Much lower", "2" = "Lower", "3" = "About the same", "4" = "Higher", "5" = "Much higher")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Much lower | 25 | 4.16 | |
| Lower | 91 | 15.14 | |
| About the same | 337 | 56.07 | |
| Higher | 123 | 20.47 | |
| Much higher | 25 | 4.16 |
Section 10: COVID-19 - most closed phase
The following questions are about your activities and how you felt during the most closed phase of the COVID-19 lockdown.
During the most closed phase of the COVID-19 lockdown, compared to your typical habits prior to the COVID-19 pandemic, how many trips per week did you make?
This time last year, I made…
#cov_con_trips_pre
#removing over 100 outlier:
w2$cov_con_trips_pre[w2$cov_con_trips_pre>=100] <- NA
ggplot(w2, aes(x= cov_con_trips_pre)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of trips per week") + facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 6.00 10.00 10.72 14.00 40.00 2During the most closed phase of the COVID-19 lockdown, I made…
#cov_con_trips
ggplot(w2, aes(x= cov_con_trips)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of trips per week") + facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 2.000 3.175 4.000 40.000During the most closed phase of the COVID-19 lockdown, did you make trips for the following activities?
t_1 <- select(w2, croise, cov_con_triptype_a, cov_con_triptype_b, cov_con_triptype_c, cov_con_triptype_d, cov_con_triptype_e, cov_con_triptype_f)
t_1 <- pivot_longer(t_1,
cols = starts_with("cov_con_triptype_"),
names_to = "perception",
names_prefix = "cov_con_triptype_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "Work"
t_1$perception[t_1$perception== "b"] <- "School"
t_1$perception[t_1$perception== "c"] <- "Groceries"
t_1$perception[t_1$perception== "d"] <- "Medical trips"
t_1$perception[t_1$perception== "e"] <- "Care-taking"
t_1$perception[t_1$perception== "f"] <- "Social, entertainment, eating out"
t_1$perception[t_1$perception== "g"] <- "Recreation / exercise"
## recode
t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No")
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACTPaletteYN) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | perception | values | n | pct |
|---|---|---|---|---|
| Care-taking | Yes | 116 | 19.30 | |
| Care-taking | No | 485 | 80.70 | |
| Groceries | Yes | 515 | 85.69 | |
| Groceries | No | 86 | 14.31 | |
| Medical trips | Yes | 188 | 31.28 | |
| Medical trips | No | 413 | 68.72 | |
| School | Yes | 26 | 4.33 | |
| School | No | 575 | 95.67 | |
| Social, entertainment, eating out | Yes | 97 | 16.14 | |
| Social, entertainment, eating out | No | 504 | 83.86 | |
| Work | Yes | 135 | 22.46 | |
| Work | No | 466 | 77.54 |
During the most closed phase of the COVID-19 lockdown, did you use the following modes of transportation in a typical week more than, less than,or the same as you did prior to the COVID-19 pandemic?
t_1 <- select(w2, croise, cov_con_mode_a, cov_con_mode_b, cov_con_mode_c, cov_con_mode_d)
t_1 <- pivot_longer(t_1,
cols = starts_with("cov_con_mode_"),
names_to = "perception",
names_prefix = "cov_con_mode_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "Driving"
t_1$perception[t_1$perception== "b"] <- "Cycling"
t_1$perception[t_1$perception== "c"] <- "Walking"
t_1$perception[t_1$perception== "d"] <- "Public Transit"
## recode
t_1$values <- recode_factor(t_1$values, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | perception | values | n | pct |
|---|---|---|---|---|
| Cycling | Less | 245 | 40.77 | |
| Cycling | Same as before COVID-19 | 266 | 44.26 | |
| Cycling | More | 90 | 14.98 | |
| Driving | Less | 291 | 48.42 | |
| Driving | Same as before COVID-19 | 188 | 31.28 | |
| Driving | More | 122 | 20.30 | |
| Public Transit | Less | 521 | 86.69 | |
| Public Transit | Same as before COVID-19 | 75 | 12.48 | |
| Public Transit | More | 5 | 0.83 | |
| Walking | Less | 126 | 20.97 | |
| Walking | Same as before COVID-19 | 172 | 28.62 | |
| Walking | More | 303 | 50.42 |
During the most closed phase of the COVID-19 lockdown, how satisfied were you with your level of physical activity?
var_name <- w2$cov_con_pa
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied",
"2" = "Somewhat satisfied",
"3" = "Neutral",
"4" = "Somewhat dissatisfied",
"5" = "Very dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very satisfied | 62 | 10.32 | |
| Somewhat satisfied | 163 | 27.12 | |
| Neutral | 84 | 13.98 | |
| Somewhat dissatisfied | 193 | 32.11 | |
| Very dissatisfied | 99 | 16.47 |
During the most closed phase of the COVID-19 lockdown, how satisfied were you with your ability to connect with others?
var_name <- w2$cov_con_social
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied",
"2" = "Somewhat satisfied",
"3" = "Neutral",
"4" = "Somewhat dissatisfied",
"5" = "Very dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very satisfied | 19 | 3.16 | |
| Somewhat satisfied | 107 | 17.80 | |
| Neutral | 104 | 17.30 | |
| Somewhat dissatisfied | 255 | 42.43 | |
| Very dissatisfied | 116 | 19.30 |
During the most closed phase of the COVID-19 lockdown, how would you have rated your overall well-being?
var_name <- w2$cov_con_wb
w2$var_name_f <- recode_factor(var_name, "0" = "0. As bad as it could be",
"1" = "1",
"2" = "2",
"3" = "3",
"4" = "4",
"5" = "5",
"6" = "6",
"7" = "7",
"8" = "8",
"9" = "9",
"10" = "10. As good as it could be")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTfade)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
|
7 | 1.16 | |
| 1 | 10 | 1.66 | |
| 2 | 13 | 2.16 | |
| 3 | 54 | 8.99 | |
| 4 | 62 | 10.32 | |
| 5 | 77 | 12.81 | |
| 6 | 79 | 13.14 | |
| 7 | 122 | 20.30 | |
| 8 | 90 | 14.98 | |
| 9 | 25 | 4.16 | |
|
62 | 10.32 |
Section 11: COVID-19 Current context
The following questions are about your activities and how you feel now in the current context.
In the current context, how many trips do you make per week?
#cov_decon_trips
ggplot(w2, aes(x= cov_decon_trips)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of trips per week") + facet_wrap( ~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 6.000 6.913 10.000 45.000In the current context, do you make trips for the following activities?
t_1 <- select(w2, croise, cov_decon_triptype_a, cov_decon_triptype_b, cov_decon_triptype_c, cov_decon_triptype_d, cov_decon_triptype_e, cov_decon_triptype_f)
t_1 <- pivot_longer(t_1,
cols = starts_with("cov_decon_triptype_"),
names_to = "perception",
names_prefix = "cov_decon_triptype_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "Work"
t_1$perception[t_1$perception== "b"] <- "School"
t_1$perception[t_1$perception== "c"] <- "Groceries"
t_1$perception[t_1$perception== "d"] <- "Medical trips"
t_1$perception[t_1$perception== "e"] <- "Care-taking"
t_1$perception[t_1$perception== "f"] <- "Social, entertainment, eating out"
t_1$perception[t_1$perception== "g"] <- "Recreation / exercise"
## recode
t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No")
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACTPaletteYN) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | perception | values | n | pct |
|---|---|---|---|---|
| Care-taking | Yes | 138 | 22.96 | |
| Care-taking | No | 463 | 77.04 | |
| Groceries | Yes | 560 | 93.18 | |
| Groceries | No | 41 | 6.82 | |
| Medical trips | Yes | 301 | 50.08 | |
| Medical trips | No | 300 | 49.92 | |
| School | Yes | 67 | 11.15 | |
| School | No | 534 | 88.85 | |
| Social, entertainment, eating out | Yes | 260 | 43.26 | |
| Social, entertainment, eating out | No | 341 | 56.74 | |
| Work | Yes | 239 | 39.77 | |
| Work | No | 362 | 60.23 |
In the current context, do you use the following modes of transportation less than, more than, or the same as you did prior to the COVID-19 pandemic?
t_1 <- select(w2, croise, cov_decon_mode_a, cov_decon_mode_b, cov_decon_mode_c, cov_decon_mode_d)
t_1 <- pivot_longer(t_1,
cols = starts_with("cov_decon_mode_"),
names_to = "perception",
names_prefix = "cov_decon_mode_",
values_to = "values",
values_drop_na = TRUE)
## rename
t_1$perception[t_1$perception== "a"] <- "Driving"
t_1$perception[t_1$perception== "b"] <- "Cycling"
t_1$perception[t_1$perception== "c"] <- "Walking"
t_1$perception[t_1$perception== "d"] <- "Public Transit"
## recode
t_1$values <- recode_factor(t_1$values, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
##### Table
t_1<- t_1 %>%
group_by(croise, perception, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= perception, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | perception | values | n | pct |
|---|---|---|---|---|
| Cycling | Less | 208 | 34.61 | |
| Cycling | Same as before COVID-19 | 309 | 51.41 | |
| Cycling | More | 84 | 13.98 | |
| Driving | Less | 226 | 37.60 | |
| Driving | Same as before COVID-19 | 270 | 44.93 | |
| Driving | More | 105 | 17.47 | |
| Public Transit | Less | 459 | 76.37 | |
| Public Transit | Same as before COVID-19 | 126 | 20.97 | |
| Public Transit | More | 16 | 2.66 | |
| Walking | Less | 99 | 16.47 | |
| Walking | Same as before COVID-19 | 252 | 41.93 | |
| Walking | More | 250 | 41.60 |
In the current context, have you been using the following less than, more than, or the same as you did prior to the COVID-19 pandemic?
Protected bike paths
#cov_decon_vas_a
var_name <- w2$cov_decon_vas_a
w2$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Less | 142 | 23.63 | |
| Same as before COVID-19 | 308 | 51.25 | |
| More | 151 | 25.12 |
Pedestrianized streets
#cov_decon_vas_b
var_name <- w2$cov_decon_vas_b
w2$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Less | 98 | 16.31 | |
| Same as before COVID-19 | 301 | 50.08 | |
| More | 202 | 33.61 |
City Parks
#cov_decon_vas_c
var_name <- w2$cov_decon_vas_c
w2$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Less | 104 | 17.30 | |
| Same as before COVID-19 | 245 | 40.77 | |
| More | 252 | 41.93 |
Public spaces (e.g. parklets, public plazas, squares, etc.)
#cov_decon_vas_d
var_name <- w2$cov_decon_vas_d
w2$var_name_f <- recode_factor(var_name, "1" = "Less", "2" = "Same as before COVID-19", "3" = "More")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTshorterfade3)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Less | 171 | 28.45 | |
| Same as before COVID-19 | 306 | 50.92 | |
| More | 124 | 20.63 |
In the current context, how satisfied are you with your level of physical activity?
var_name <- w2$cov_decon_pa
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied",
"2" = "Somewhat satisfied",
"3" = "Neutral",
"4" = "Somewhat dissatisfied",
"5" = "Very dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very satisfied | 63 | 10.48 | |
| Somewhat satisfied | 177 | 29.45 | |
| Neutral | 102 | 16.97 | |
| Somewhat dissatisfied | 190 | 31.61 | |
| Very dissatisfied | 69 | 11.48 |
In the current context, how satisfied are you with your ability to connect with others?
var_name <- w2$cov_decon_social
w2$var_name_f <- recode_factor(var_name, "1" = "Very satisfied",
"2" = "Somewhat satisfied",
"3" = "Neutral",
"4" = "Somewhat dissatisfied",
"5" = "Very dissatisfied")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalette3) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very satisfied | 28 | 4.66 | |
| Somewhat satisfied | 171 | 28.45 | |
| Neutral | 131 | 21.80 | |
| Somewhat dissatisfied | 213 | 35.44 | |
| Very dissatisfied | 58 | 9.65 |
In the current context, how would you rate your overall well-being?
var_name <- w2$cov_decon_wb
w2$var_name_f <- recode_factor(var_name, "0" = "0. As bad as it could be",
"1" = "1",
"2" = "2",
"3" = "3",
"4" = "4",
"5" = "5",
"6" = "6",
"7" = "7",
"8" = "8",
"9" = "9",
"10" = "10. As good as it could be")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTfade)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
|
9 | 1.50 | |
| 1 | 3 | 0.50 | |
| 2 | 5 | 0.83 | |
| 3 | 27 | 4.49 | |
| 4 | 44 | 7.32 | |
| 5 | 61 | 10.15 | |
| 6 | 82 | 13.64 | |
| 7 | 136 | 22.63 | |
| 8 | 130 | 21.63 | |
| 9 | 44 | 7.32 | |
|
60 | 9.98 |
Section 12: Demographics
Thinking about where you live now, are you
#house_tenure
## zoe check
w2$house_tenure[w2$house_tenure==-7] <- NA
var_name <- w2$house_tenure
w2$var_name_f <- recode_factor(var_name, "1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "77" = "I don't know")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_grid(~croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| An owner | 316 | 52.58 | |
| A tenant | 177 | 29.45 | |
| Resident in a relative or friend’s home | 27 | 4.49 | |
| Resident other than in a relative or friend’s home | 2 | 0.33 | |
| Other | 6 | 1.00 | |
| I don’t know | 1 | 0.17 | |
| NA | 72 | 11.98 |
In what type of dwelling do you currently live? Is it:
#dwelling_type
w2$dwelling_type[w2$dwelling_type==-7] <- NA
var_name <- w2$dwelling_type
w2$var_name_f <- recode_factor(var_name, "1" = "Single detached house", "2" = "Semi-detached house", "3" = "Row house", "4" = "An apartment (or condo) in a duplex or triplex", "5" = "Apartment (or condo) in building with fewer than 5 storeys", "6" = "Apartment (or condo) in building with more than 5 storeys", "7" = "Mobile home/movable dwelling", "8" = "Senior's home", "9" = "Other", "77" = "NA")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response")+
facet_grid(~croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Single detached house | 99 | 16.47 | |
| Semi-detached house | 46 | 7.65 | |
| Row house | 29 | 4.83 | |
| An apartment (or condo) in a duplex or triplex | 187 | 31.11 | |
| Apartment (or condo) in building with fewer than 5 storeys | 108 | 17.97 | |
| Apartment (or condo) in building with more than 5 storeys | 43 | 7.15 | |
| Mobile home/movable dwelling | 2 | 0.33 | |
| Senior’s home | 1 | 0.17 | |
| Other | 13 | 2.16 | |
| NA | 1 | 0.17 | |
| NA | 72 | 11.98 |
What is your current gender identity?
#gender
var_name <- w2$gender
w2$var_name_f <- recode_factor(var_name, "1"="Man",
"2"="Woman",
"3"="Trans man",
"4"="Trans woman",
"5"="Genderqueer/Gender non-conforming",
"6"="Different identity")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=45, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Man | 198 | 32.95 | |
| Woman | 389 | 64.73 | |
| Trans woman | 1 | 0.17 | |
| Genderqueer/Gender non-conforming | 11 | 1.83 | |
| Different identity | 2 | 0.33 |
What sex were you assigned at birth?
*Asked only to new participants - w1 data reported for returning participants
# Sex
var_name <- w2$sex
w2$var_name_f <- recode_factor(var_name, "1"="Male",
"2"="Female",
"3"="Other")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Male | 197 | 32.78 | |
| Female | 392 | 65.22 | |
| Other | 1 | 0.17 | |
| NA | 11 | 1.83 |
What is your marital status? Are you…
#marital_status
var_name <- w2$marital_status
w2$var_name_f <- recode_factor(var_name, "1" = "Single", "2" = "Married/commonlaw", "3" = "Separated/divorced", "4" = "Widowed")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Single | 189 | 31.45 | |
| Married/commonlaw | 330 | 54.91 | |
| Separated/divorced | 68 | 11.31 | |
| Widowed | 14 | 2.33 |
Do you have children?
#children
var_name <- w2$children
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 308 | 51.25 | |
| No | 293 | 48.75 |
How many children do you have?
#living_children
w2$living_children[w2$living_children==-7] <- NA
ggplot(w2, aes(x= living_children)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of children") + facet_grid(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 1.000 2.000 1.951 2.000 5.000 293What is your current living arrangement? Do you live
Participants could choose multiple answers
#living_arrange
w2$living_arrange_1[w2$living_arrange_1==-7] <- NA
var_name <- w2$living_arrange_1
w2$var_name_f <- recode_factor(var_name, "1" = "Alone", "0" = "With other people")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Alone | 168 | 27.95 | |
| With other people | 433 | 72.05 |
# Create a vector with variable names
response = paste0("living_arrange_", 2:7)
# Empty vector to stor output
living_arrange_prop <- c()
# Calculate univariate proportions
for(i in response){
living_arrange_prop[i] <- sum(w2[,i]) / nrow(w2)
}
# Transform
living_arrange_prop <- as.data.frame(living_arrange_prop)
living_arrange_prop$Response <- c("With a spouse (or partner)","With children","With grandchildren","With relatives or siblings?", "With friends", "With other people")
living_arrange_prop$plot<- factor(living_arrange_prop$Response, living_arrange_prop$Response)
ggplot(living_arrange_prop, aes(x = plot, y = living_arrange_prop)) + geom_bar(stat = "identity", fill = "#76D24A") + xlab("") + ylab("Percentage of participants who selected this answer") + theme(axis.text.x = element_text(size=12, angle=0, vjust=.6)) + scale_x_discrete(labels = function(plot) str_wrap(plot, width = 10)) 
living_arrange_prop$living_arrange_prop <- round(living_arrange_prop$living_arrange_prop*100,2)
#living_arrange_prop <- setcolorder(living_arrange_prop, c("Response", "living_arrange_prop")
colnames(living_arrange_prop) <- c("Response", "Percentage of participants who selected this answer")
living_arrange_prop <- living_arrange_prop[-c(3)]
kable(living_arrange_prop) %>% kable_styling(bootstrap_options = "striped", full_width = T, position = "left") | Response | Percentage of participants who selected this answer | |
|---|---|---|
| living_arrange_2 | 57.40 | With a spouse (or partner) |
| living_arrange_3 | 28.29 | With children |
| living_arrange_4 | 0.50 | With grandchildren |
| living_arrange_5 | 5.66 | With relatives or siblings? |
| living_arrange_6 | 2.66 | With friends |
| living_arrange_7 | 2.83 | With other people |
How many children under the age of 16 live in your household?
#children_household
p <- ggplot(w2, aes(x = children_household)) +
geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) +
xlab("Number of children under 16 in household") +
facet_wrap(~ croise)
plot(p)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.4276 0.0000 22.0000How many adults aged 16 or older live in your household including yourself?
ggplot(w2, aes(x= adults_household)) + geom_bar(na.rm = TRUE,fill="#76D24A", binwidth = 1) + xlab("Number of adults in household") + facet_wrap(~ croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 1.00 2.00 1.94 2.00 11.00When did you move to your current residence?
#residence
w2$residence[w2$residence==""] <- NA
residence <- as.integer(format(as.Date(w2$residence),"%Y"))
time <- 2021 - residence
ggplot(w2, aes(x = time)) + geom_histogram(na.rm=TRUE, binwidth = 1, fill="#76D24A") + xlab("Years since moving to current residence") + facet_grid(~croise)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 4.00 8.00 12.03 17.00 62.00 11Were you born in Canada?
#born_can
var_name <- w2$born_can
w2$var_name_f <- recode_factor(var_name, "1" = "Yes", "2" = "No")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteYN) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Yes | 489 | 81.36 | |
| No | 101 | 16.81 | |
| NA | 11 | 1.83 |
When did you move to Canada?
#move_can
w2$move_can[w2$move_can==-7] <- NA
ggplot(w2, aes(x = w2$move_can)) + geom_histogram (na.rm=TRUE, binwidth = 1, fill="#76D24A") + xlab("Year of move to Canada")
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1957 1992 2004 2000 2012 2020 500To which ethnic or cultural group(s) do you belong? (Check all that apply)
var_name <- w2$group
w2$var_name_f <- as.factor(var_name)
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") + guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| White | 506 | 84.19 | |
| South Asian | 4 | 0.67 | |
| Chinese | 10 | 1.66 | |
| Black | 7 | 1.16 | |
| Latin American | 9 | 1.50 | |
| Arab | 7 | 1.16 | |
| Southeast Asian | 5 | 0.83 | |
| Japanese | 1 | 0.17 | |
| Other | 1 | 0.17 | |
| Mixed identity | 29 | 4.83 | |
| I don’t know/ prefer not to answer | 10 | 1.66 | |
| NA | 12 | 2.00 |
Which category best describes your annual household income, taking into account all sources of income?
#w2$income[w2$income==-7] <- NA
var_name <- w2$income
w2$var_name_f <- recode_factor(var_name, "1" = "No income", "2" = "$1 to $9,999", "3" = "$10,000 to $14,999", "4" = "$15,000 to $19,999", "5" = "$20,000 to $29,999", "6" = "$30,000 to $39,999", "7" = "$40,000 to $49,999", "8" = "$50,000 to $99,999", "9" = "$100,000 to $149,999", "10" = " $150,000 to $199,999", "11" = "$200,000 or more", "77" = "Don't know/prefer no answer", "-7" = "NA")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = rev(INTERACTfade)) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| No income | 1 | 0.17 | |
| $1 to $9,999 | 9 | 1.50 | |
| $10,000 to $14,999 | 14 | 2.33 | |
| $15,000 to $19,999 | 14 | 2.33 | |
| $20,000 to $29,999 | 30 | 4.99 | |
| $30,000 to $39,999 | 33 | 5.49 | |
| $40,000 to $49,999 | 46 | 7.65 | |
| $50,000 to $99,999 | 177 | 29.45 | |
| $100,000 to $149,999 | 103 | 17.14 | |
| $150,000 to $199,999 | 80 | 13.31 | |
| $200,000 or more | 33 | 5.49 | |
| Don’t know/prefer no answer | 61 | 10.15 |
To what extent does this annual household income allow you to satisfy your household’s needs?
#income_needs
var_name <- w2$income_needs
w2$var_name_f <- recode_factor(var_name, "1" = "Very well", "2" = "Well", "3" = "Not so well", "4" = "Not at all", "77" = "Don't know/prefer no answer")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTshortfade) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Very well | 248 | 41.26 | |
| Well | 268 | 44.59 | |
| Not so well | 66 | 10.98 | |
| Not at all | 9 | 1.50 | |
| Don’t know/prefer no answer | 10 | 1.66 |
What are your houshold’s total monthly housing costs?
This includes rent payments, mortgage payments, property taxes, condominium fees, and utility payments, like heating, water and electricity. If you live with roommates, please only include your share of the housing costs.
#housing costs
w2$housing_cost[w2$housing_cost==-7] <- NA
#ggplot(w_2, aes(x = housing_cost)) + geom_histogram (na.rm =TRUE, binwidth = 1, fill="#76D24A") + xlab ("Monthly housing costs") + facet_wrap(~ croise)
summary(w2$housing_cost)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0 775 1100 1298 1700 10000 136What is your highest education level?
#education
var_name <- w2$education
w2$var_name_f <- recode_factor(var_name, "1" = "Primary/Elementary school", "2" = "Secondary school", "3" = "Trade/Technical school or college diploma", "4" = "University degree", "5" = "Graduate degree", "77" ="I don't know/Prefer not to answer")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Secondary school | 32 | 5.32 | |
| Trade/Technical school or college diploma | 92 | 15.31 | |
| University degree | 218 | 36.27 | |
| Graduate degree | 256 | 42.60 | |
| I don’t know/Prefer not to answer | 3 | 0.50 |
What is your current employment status?
#employment
var_name <- w2$employment
w2$var_name_f <- recode_factor(var_name, "1" = "Retired and not working", "2" = "Employed full-time", "3" = "Employed part-time", "4" = "Unemployed and looking for work", "5" = "Unemployed and not looking for work", "6" ="Other" )
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPaletteSet) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| Retired and not working | 136 | 22.63 | |
| Employed full-time | 290 | 48.25 | |
| Employed part-time | 59 | 9.82 | |
| Unemployed and looking for work | 22 | 3.66 | |
| Unemployed and not looking for work | 22 | 3.66 | |
| Other | 72 | 11.98 |
Which of the following best describes your usual work schedule at your current job?
#shift
w2$shift[w2$shift==-7] <- NA
var_name <- w2$shift
w2$var_name_f <- recode_factor(var_name, "1" = "A regular daytime schedule or shift.", "2" = "A regular evening shift ", "3" = "A regular night shift", "4" = "A rotating shift, a split shift, or an irregular schedule", "5" = "On call or casual", "6" ="Other")
var_name_f <- w2$var_name_f
##### Table
t_1 <- w2 %>%
group_by(croise, var_name_f) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
##### Figure
p <- ggplot(t_1, aes(var_name_f, y = pct, fill = var_name_f)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalettecont) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | var_name_f | n | pct |
|---|---|---|---|
| A regular daytime schedule or shift. | 282 | 46.92 | |
| A regular evening shift | 6 | 1.00 | |
| A regular night shift | 3 | 0.50 | |
| A rotating shift, a split shift, or an irregular schedule | 31 | 5.16 | |
| On call or casual | 13 | 2.16 | |
| Other | 14 | 2.33 | |
| NA | 252 | 41.93 |
How has the COVID-19 pandemic impacted your job?
w2$employment_covid_1[w2$employment_covid_1==0] <- 2
w2$employment_covid_2[w2$employment_covid_2==0] <- 2
w2$employment_covid_3[w2$employment_covid_3==0] <- 2
w2$employment_covid_4[w2$employment_covid_4==0] <- 2
w2$employment_covid_5[w2$employment_covid_5==0] <- 2
w2$employment_covid_6[w2$employment_covid_6==0] <- 2
w2$employment_covid_7[w2$employment_covid_7==0] <- 2
w2$employment_covid_8[w2$employment_covid_8==0] <- 2
w2$employment_covid_9[w2$employment_covid_9==0] <- 2
w2$employment_covid_10[w2$employment_covid_10==0] <- 2
w2$employment_covid_99[w2$employment_covid_99==0] <- 2
t_1 <- select(w2, croise, employment_covid_1,employment_covid_2, employment_covid_3, employment_covid_4, employment_covid_5, employment_covid_6, employment_covid_7, employment_covid_8, employment_covid_9, employment_covid_10)
t_1 <- pivot_longer(t_1,
cols = starts_with("employment_covid_"),
names_to = "feature",
names_prefix = "employment_covid_",
values_to = "values",
values_drop_na = TRUE)
t_1$values <- recode_factor(t_1$values, "1" = "Yes", "2" = "No", "77" = "I don't know")
## rename
t_1$feature[t_1$feature== "1"] <- "I work from home."
t_1$feature[t_1$feature== "2"] <- "I work partly from home, partly at my normal workplace"
t_1$feature[t_1$feature== "3"] <- "I continue to work at my normal place of work."
t_1$feature[t_1$feature== "4"] <- "My paid work #hours have been reduced."
t_1$feature[t_1$feature== "5"] <- "My hourly rate has been reduced"
t_1$feature[t_1$feature== "6"] <- "My paid work hours have increased"
t_1$feature[t_1$feature== "7"] <- "My hourly rate has increased."
t_1$feature[t_1$feature== "8"] <- "My job has been deemed essential by the government."
t_1$feature[t_1$feature== "9"] <- "I lost my job"
t_1$feature[t_1$feature== "10"] <-"I have started a new job"
t_1 <- t_1 %>%
group_by(croise, feature, values) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(x= feature, y= pct, fill= values)) + theme(axis.text.x = element_text(angle=0, vjust = .6)) +
geom_bar(stat= "identity") +
coord_flip() +
scale_fill_manual(values = INTERACTPaletteYN) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise) +
scale_x_discrete(labels = function(feature) str_wrap(feature, width = 30))
plot(p)
| croise | feature | values | n | pct |
|---|---|---|---|---|
| I continue to work at my normal place of work. | Yes | 82 | 13.64 | |
| I continue to work at my normal place of work. | No | 519 | 86.36 | |
| I have started a new job | Yes | 29 | 4.83 | |
| I have started a new job | No | 572 | 95.17 | |
| I lost my job | Yes | 45 | 7.49 | |
| I lost my job | No | 556 | 92.51 | |
| I work from home. | Yes | 205 | 34.11 | |
| I work from home. | No | 396 | 65.89 | |
| I work partly from home, partly at my normal workplace | Yes | 89 | 14.81 | |
| I work partly from home, partly at my normal workplace | No | 512 | 85.19 | |
| My hourly rate has been reduced | Yes | 7 | 1.16 | |
| My hourly rate has been reduced | No | 594 | 98.84 | |
| My hourly rate has increased. | Yes | 14 | 2.33 | |
| My hourly rate has increased. | No | 587 | 97.67 | |
| My job has been deemed essential by the government. | Yes | 73 | 12.15 | |
| My job has been deemed essential by the government. | No | 528 | 87.85 | |
| My paid work #hours have been reduced. | Yes | 42 | 6.99 | |
| My paid work #hours have been reduced. | No | 559 | 93.01 | |
| My paid work hours have increased | Yes | 14 | 2.33 | |
| My paid work hours have increased | No | 587 | 97.67 |
Age
# Categorize age variable
## reviens-y
w2$age_cat <- NA
w2$age_cat[w2$age %in% c(18:24)] <- "18-24"
w2$age_cat[w2$age %in% c(25:34)] <- "25-34"
w2$age_cat[w2$age %in% c(35:44)] <- "35-44"
w2$age_cat[w2$age %in% c(45:54)] <- "45-54"
w2$age_cat[w2$age %in% c(55:64)] <- "55-64"
w2$age_cat[w2$age %in% c(65:74)] <- "65-74"
w2$age_cat[w2$age %in% c(75:100)] <- "75+"
##### Table
t_1 <- w2 %>%
group_by(croise, age_cat) %>%
dplyr::summarise(n = n()) %>%
dplyr:: mutate(pct = round(100*n/sum(n),2))
p <- ggplot(t_1, aes(age_cat, y = pct, fill = age_cat)) + theme(axis.text.x = element_text(angle=90, vjust = .6)) +
geom_bar(stat= "identity") +
scale_fill_manual(values = INTERACTPalettecont) +
guides(fill=FALSE) +
ylab("Percent") +
xlab("Response") +
facet_wrap(~ croise)
plot(p)
| croise | age_cat | n | pct |
|---|---|---|---|
| 18-24 | 29 | 4.83 | |
| 25-34 | 114 | 18.97 | |
| 35-44 | 116 | 19.30 | |
| 45-54 | 109 | 18.14 | |
| 55-64 | 126 | 20.97 | |
| 65-74 | 82 | 13.64 | |
| 75+ | 24 | 3.99 | |
| NA | 1 | 0.17 |