Interact 2017 Health Survey Descriptives
Jaimy Fischer
January 28, 2018
d <- read.csv("D:/sfuvault/Interact Tasks/Victoria Survey Descriptives/Data/Victoria_health_q_data_COPY.csv")- There are a total of 286 Participants in this survey.
- Some participants have NA values in their question responses. These are denoted as “NA” in summaries.
Section 1: Transportation
Q1:How many bicycles are kept in your household?
Adult bikes
ggplot(d, aes(x = d$Q1_transp_bikes_adults)) + geom_bar(na.rm = TRUE) + xlab("Number of bicycles for adults")
summary(d$Q1_transp_bikes_adults)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 3.000 3.294 4.000 20.000Children bikes
ggplot(d, aes(x = d$Q1_transp_bikes_children)) + geom_bar(na.rm = TRUE) + xlab("Number of bicycles for children") + scale_x_continuous()
summary(d$Q1_transp_bikes_children)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.6538 1.0000 10.0000Q2:On a scale of 1 to 5, with 1 being ‘very safe’ and 5 being ‘very dangerous’, overall, how safe do you think cycling is in your city?
t1 <- prop.table(table(factor(d$Q2_bike_safety, levels = 1:5)))*100
t1 <- as.data.frame(t1)
t1$group <- substring(row.names(t1), 1)
t1$group <- revalue(as.character(t1$group), c("1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous"))
t1$plot <- factor(t1$group, t1$group)
p <- ggplot(t1, aes(x=group, y=Freq, fill=group)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t1
p + geom_bar(aes(x = plot), data = t1, stat = "identity", colour="black") +
scale_fill_brewer(palette = "YlGn", direction=1) +
guides(fill=FALSE)+
ylab("Percent of total") +
xlab("Perception of bicycle risk") +
ggtitle("Overall, how safe do you think cycling is in your city?")
t1.tb <- as.factor(d$Q2_bike_safety)
t1.tb <- summary(t1.tb)
t1.tb <- as.data.frame(t1.tb)
t1.tb$Var1 <- substring(row.names(t1.tb), 1)
t1.tb$group <- revalue(as.character(t1.tb$Var1), c("1" = "Very safe", "2" = "Somewhat safe", "3" = "Neither safe nor unsafe", "4" = "Somewhat dangerous", "5" = "Very dangerous"))
plot.t1 <- merge(t1, t1.tb, by = "group")
plot.t1 <- plot.t1[-c(2, 4, 6)]
plot.t1 <- setcolorder(plot.t1, c("group", "t1.tb", "Freq"))
plot.t1$order <- c(3, 4, 2, 5, 1)
plot.t1 <- plot.t1 %>% arrange(order)
plot.t1 <- plot.t1[-c(4)]
colnames(plot.t1) <- c("Response", "N", "Proportion")
kable(plot.t1)| Response | N | Proportion |
|---|---|---|
| Very safe | 23 | 8.041958 |
| Somewhat safe | 168 | 58.741259 |
| Neither safe nor unsafe | 34 | 11.888112 |
| Somewhat dangerous | 56 | 19.580420 |
| Very dangerous | 5 | 1.748252 |
Q3: How often do you typically travel by bicycle during each season?
fall
t2 <- prop.table(table(factor(d$Q3_freq_fall, levels = 0:7)))*100
t2 <- as.data.frame(t2)
t2$days <- substring(row.names(t2), 1)
t2$days <- revalue(as.character(t2$days), c("1" = "0", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7"))
t2$plot <- factor(t2$days, t2$days)
p2 <- ggplot(t2, aes(x = days, y = Freq, fill = days)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t2
p2 + geom_bar(aes(x = plot), data = t2, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn") +
ylab("Percent of total") +
xlab("Bicycling number of days per week") +
ggtitle("How often do you typically travel by bicycle during the fall?")
summary(d$Q3_freq_fall)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 3.250 5.000 4.629 6.000 7.000winter
t3 <- prop.table(table(factor(d$Q3_freq_winter, levels = 0:7)))*100
t3 <- as.data.frame(t3)
t3$days <- substring(row.names(t3), 1)
t3$days <- revalue(as.character(t3$days), c("1" = "0", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7"))
t3$plot <- factor(t3$days, t3$days)
p3 <- ggplot(t3, aes(x = days, y = Freq, fill = days)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t3
p3 + geom_bar(aes(x = plot), data = t3, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("Bicycling number of days per week") +
ggtitle("How often do you typically travel by bicycle during the winter?")
summary(d$Q3_freq_winter)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 2.000 4.000 3.675 5.000 7.000spring
t4 <- prop.table(table(factor(d$Q3_freq_spring, levels = 0:7)))*100
t4 <- as.data.frame(t4)
t4$days <- substring(row.names(t4), 1)
t4$days <- revalue(as.character(t4$days), c("1" = "0", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7"))
t4$plot <- factor(t4$days, t4$days)
p4 <- ggplot(t4, aes(x = days, y = Freq, fill = days)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t4
p4 + geom_bar(aes(x = plot), data = t4, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("Bicycling number of days per week") +
ggtitle("How often do you typically travel by bicycle during the winter?")
summary(d$Q3_freq_spring)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 4.000 5.000 4.825 6.000 7.000summer
t5 <- prop.table(table(factor(d$Q3_freq_summer, levels = 0:7)))*100
t5 <- as.data.frame(t5)
t5$days <- substring(row.names(t5), 1)
t5$days <- revalue(as.character(t5$days), c("1" = "0", "2" = "1", "3" = "2", "4" = "3", "5" = "4", "6" = "5", "7" = "6", "8" = "7"))
t5$plot <- factor(t5$days, t5$days)
p4 <- ggplot(t5, aes(x = days, y = Freq, fill = days)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
p4 + geom_bar(aes(x = plot), data = t5, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("Bicycling number of days per week") +
ggtitle("How often do you typically travel by bicycle during the winter?")
summary(d$Q3_freq_summer)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 5.000 5.000 5.217 6.000 7.000Q4: How often are your cycling trips made with children?
bike.ch <- prop.table(table(factor(d$Q4_bike_children, levels = 1:4)))*100
bike.ch <- as.data.frame(bike.ch)
bike.ch$group <- substring(row.names(bike.ch), 1)
bike.ch$group <- revalue(as.character(bike.ch$group), c("1" = "Never", "2" = "Sometimes", "3" = "Often", "4" = "Always"))
bike.ch$plot <- factor(bike.ch$group, bike.ch$group)
ch <- ggplot(bike.ch, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey")
ch + geom_bar(aes(x = plot), data = bike.ch, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Bicyle trips with children") +
ggtitle("How often are your cycling trips made with children?")
trips.ch <- as.factor(d$Q4_bike_children)
trips.ch <- summary(trips.ch)
trips.ch <- as.data.frame(trips.ch)
trips.ch$Var1 <- substring(row.names(trips.ch), 1)
trips.ch$group <- revalue(as.character(trips.ch$Var1), c("1" = "Never", "2" = "Sometimes", "3" = "Often", "4" = "Always"))
plot.ch <- merge(bike.ch, trips.ch, by = "group")
plot.ch <- plot.ch[-c(1:2, 6)]
plot.ch <- setcolorder(plot.ch, c("plot", "trips.ch", "Freq"))
plot.ch$order <- c(4, 1, 3, 2)
plot.ch <- plot.ch %>% arrange(order)
plot.ch <- plot.ch[-c(4)]
colnames(plot.ch) <- c("Response", "N", "Proportion")
kable(plot.ch)| Response | N | Proportion |
|---|---|---|
| Never | 199 | 69.580420 |
| Sometimes | 53 | 18.531468 |
| Often | 29 | 10.139860 |
| Always | 5 | 1.748252 |
Q5: Do you currently use any of the following roads when you bike in Victoria?
Cook St or Fifth St
cook <- prop.table(table(factor(d$Q5_vicroads_a, levels = c("1", "2", "77"))))*100
cook <- as.data.frame(cook)
cook$answer <- substring(row.names(cook), 1)
cook$answer <- revalue(as.character(cook$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
cook$plot <- factor(cook$answer, cook$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
cook.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
cook.plot + geom_bar(aes(x = plot), data = cook, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
cook.tb <- as.factor(d$Q5_vicroads_a)
cook.tb <- summary(cook.tb)
cook.tb <- as.data.frame(cook.tb)
cook.tb$Var1 <- substring(row.names(cook.tb), 1)
cook.tb$answer <- revalue(as.character(cook.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.cook <- merge(cook, cook.tb, by = "answer")
plot.cook <- plot.cook[-c(2, 4, 6)]
plot.cook <- setcolorder(plot.cook, c("answer", "cook.tb", "Freq"))
plot.cook$order <- c(3, 2, 1)
plot.cook <- plot.cook %>% arrange(order)
plot.cook <- plot.cook[-c(4)]
colnames(plot.cook) <- c("Response", "N", "Proportion")
kable(plot.cook)| Response | N | Proportion |
|---|---|---|
| Yes | 182 | 63.636364 |
| No | 101 | 35.314685 |
| Don’t know | 3 | 1.048951 |
Fairfield Rd or Humboldt St
ffield <- prop.table(table(factor(d$Q5_vicroads_b, levels = c("1", "2", "77"))))*100
ffield <- as.data.frame(ffield)
ffield$answer <- substring(row.names(ffield), 1)
ffield$answer <- revalue(as.character(ffield$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
ffield$plot <- factor(ffield$answer, ffield$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
ffield.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
ffield.plot + geom_bar(aes(x = plot), data = ffield, stat = "identity", colour="black") 
ffield.tb <- as.factor(d$Q5_vicroads_b)
ffield.tb <- summary(ffield.tb)
ffield.tb <- as.data.frame(ffield.tb)
ffield.tb$Var1 <- substring(row.names(ffield.tb), 1)
ffield.tb$answer <- revalue(as.character(ffield.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.ffield <- merge(ffield, ffield.tb, by = "answer")
plot.ffield <- plot.ffield[-c(2, 4, 6)]
plot.ffield <- setcolorder(plot.ffield, c("answer", "ffield.tb", "Freq"))
plot.ffield$order <- c(3, 2, 1)
plot.ffield <- plot.ffield %>% arrange(order)
plot.ffield <- plot.ffield[-c(4)]
colnames(plot.ffield) <- c("Response", "N", "Proportion")
kable(plot.ffield)| Response | N | Proportion |
|---|---|---|
| Yes | 143 | 50.000000 |
| No | 140 | 48.951049 |
| Don’t know | 3 | 1.048951 |
Fort St
fort <- prop.table(table(factor(d$Q5_vicroads_c, levels = c("1", "2", "77"))))*100
fort <- as.data.frame(fort)
fort$answer <- substring(row.names(fort), 1)
fort$answer <- revalue(as.character(fort$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
fort$plot <- factor(fort$answer, fort$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
fort.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
fort.plot + geom_bar(aes(x = plot), data = fort, stat = "identity", colour="black") 
fort.tb <- as.factor(d$Q5_vicroads_c)
fort.tb <- summary(fort.tb)
fort.tb <- as.data.frame(fort.tb)
fort.tb$Var1 <- substring(row.names(fort.tb), 1)
fort.tb$answer <- revalue(as.character(fort.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.fort <- merge(fort, fort.tb, by = "answer")
plot.fort <- plot.fort[-c(2, 4, 6)]
plot.fort <- setcolorder(plot.fort, c("answer", "fort.tb", "Freq"))
plot.fort$order <- c(3, 2, 1)
plot.fort <- plot.fort %>% arrange(order)
plot.fort <- plot.fort[-c(4)]
colnames(plot.fort) <- c("Response", "N", "Proportion")
kable(plot.fort)| Response | N | Proportion |
|---|---|---|
| Yes | 221 | 77.2727273 |
| No | 64 | 22.3776224 |
| Don’t know | 1 | 0.3496503 |
| ###Government | St |
gvt <- prop.table(table(factor(d$Q5_vicroads_d, levels = c("1", "2", "77"))))*100
gvt <- as.data.frame(gvt)
gvt$answer <- substring(row.names(gvt), 1)
gvt$answer <- revalue(as.character(gvt$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
gvt$plot <- factor(gvt$answer, gvt$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
gvt.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
gvt.plot + geom_bar(aes(x = plot), data = gvt, stat = "identity", colour="black") 
gvt.tb <- as.factor(d$Q5_vicroads_d)
gvt.tb <- summary(gvt.tb)
gvt.tb <- as.data.frame(gvt.tb)
gvt.tb$Var1 <- substring(row.names(gvt.tb), 1)
gvt.tb$answer <- revalue(as.character(gvt.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.gvt <- merge(gvt, gvt.tb, by = "answer")
plot.gvt <- plot.gvt[-c(2, 4, 6)]
plot.gvt <- setcolorder(plot.gvt, c("answer", "gvt.tb", "Freq"))
plot.gvt$order <- c(3, 2, 1)
plot.gvt <- plot.gvt %>% arrange(order)
plot.gvt <- plot.gvt[-c(4)]
colnames(plot.gvt) <- c("Response", "N", "Proportion")
kable(plot.gvt)| Response | N | Proportion |
|---|---|---|
| Yes | 216 | 75.5244755 |
| No | 69 | 24.1258741 |
| Don’t know | 1 | 0.3496503 |
Haultain St or Kings Rd
haultain <- prop.table(table(factor(d$Q5_vicroads_e, levels = c("1", "2", "77"))))*100
haultain <- as.data.frame(haultain)
haultain$answer <- substring(row.names(haultain), 1)
haultain$answer <- revalue(as.character(haultain$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
haultain$plot <- factor(haultain$answer, haultain$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
haultain.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
haultain.plot + geom_bar(aes(x = plot), data = haultain, stat = "identity", colour="black") 
haultain.tb <- as.factor(d$Q5_vicroads_e)
haultain.tb <- summary(haultain.tb)
haultain.tb <- as.data.frame(haultain.tb)
haultain.tb$Var1 <- substring(row.names(haultain.tb), 1)
haultain.tb$answer <- revalue(as.character(haultain.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.haultain <- merge(haultain, haultain.tb, by = "answer")
plot.haultain <- plot.haultain[-c(2, 4, 6)]
plot.haultain <- setcolorder(plot.haultain, c("answer", "haultain.tb", "Freq"))
plot.haultain$order <- c(3, 2, 1)
plot.haultain <- plot.haultain %>% arrange(order)
plot.haultain <- plot.haultain[-c(4)]
colnames(plot.haultain) <- c("Response", "N", "Proportion")
kable(plot.haultain)| Response | N | Proportion |
|---|---|---|
| Yes | 165 | 57.692308 |
| No | 115 | 40.209790 |
| Don’t know | 6 | 2.097902 |
Pandora Ave
pand <- prop.table(table(factor(d$Q5_vicroads_f, levels = c("1", "2", "77"))))*100
pand <- as.data.frame(pand)
pand$answer <- substring(row.names(pand), 1)
pand$answer <- revalue(as.character(pand$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
pand$plot <- factor(pand$answer, pand$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
pand.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pand.plot + geom_bar(aes(x = plot), data = pand, stat = "identity", colour="black") 
pand.tb <- as.factor(d$Q5_vicroads_f)
pand.tb <- summary(pand.tb)
pand.tb <- as.data.frame(pand.tb)
pand.tb$Var1 <- substring(row.names(pand.tb), 1)
pand.tb$answer <- revalue(as.character(pand.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.pand <- merge(pand, pand.tb, by = "answer")
plot.pand <- plot.pand[-c(2, 4, 6)]
plot.pand <- setcolorder(plot.pand, c("answer", "pand.tb", "Freq"))
plot.pand$order <- c(3, 2, 1)
plot.pand <- plot.pand %>% arrange(order)
plot.pand <- plot.pand[-c(4)]
colnames(plot.pand) <- c("Response", "N", "Proportion")
kable(plot.pand)| Response | N | Proportion |
|---|---|---|
| Yes | 245 | 85.6643357 |
| No | 40 | 13.9860140 |
| Don’t know | 1 | 0.3496503 |
Shelbourne St or Begbie St
shelb <- prop.table(table(factor(d$Q5_vicroads_g, levels = c("1", "2", "77"))))*100
shelb <- as.data.frame(shelb)
shelb$answer <- substring(row.names(shelb), 1)
shelb$answer <- revalue(as.character(shelb$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
shelb$plot <- factor(shelb$answer, shelb$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
shelb.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
shelb.plot + geom_bar(aes(x = plot), data = shelb, stat = "identity", colour="black") 
shelb.tb <- as.factor(d$Q5_vicroads_g)
shelb.tb <- summary(shelb.tb)
shelb.tb <- as.data.frame(shelb.tb)
shelb.tb$Var1 <- substring(row.names(shelb.tb), 1)
shelb.tb$answer <- revalue(as.character(shelb.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.shelb <- merge(shelb, shelb.tb, by = "answer")
plot.shelb <- plot.shelb[-c(2, 4, 6)]
plot.shelb <- setcolorder(plot.shelb, c("answer", "shelb.tb", "Freq"))
plot.shelb$order <- c(3, 2, 1)
plot.shelb <- plot.shelb %>% arrange(order)
plot.shelb <- plot.shelb[-c(4)]
colnames(plot.shelb) <- c("Response", "N", "Proportion")
kable(plot.shelb)| Response | N | Proportion |
|---|---|---|
| Yes | 117 | 40.909091 |
| No | 166 | 58.041958 |
| Don’t know | 3 | 1.048951 |
Harbour Rd, Wharf St or Belville St
bellv <- prop.table(table(factor(d$Q5_vicroads_h, levels = c("1", "2", "77"))))*100
bellv <- as.data.frame(bellv)
bellv$answer <- substring(row.names(bellv), 1)
bellv$answer <- revalue(as.character(bellv$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
bellv$plot <- factor(bellv$answer, bellv$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
bellv.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
bellv.plot + geom_bar(aes(x = plot), data = bellv, stat = "identity", colour="black") 
bellv.tb <- as.factor(d$Q5_vicroads_h)
bellv.tb <- summary(bellv.tb)
bellv.tb <- as.data.frame(bellv.tb)
bellv.tb$Var1 <- substring(row.names(bellv.tb), 1)
bellv.tb$answer <- revalue(as.character(bellv.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.bellv <- merge(bellv, bellv.tb, by = "answer")
plot.bellv <- plot.bellv[-c(2, 4, 6)]
plot.bellv <- setcolorder(plot.bellv, c("answer", "bellv.tb", "Freq"))
plot.bellv$order <- c(3, 2, 1)
plot.bellv <- plot.bellv %>% arrange(order)
plot.bellv <- plot.bellv[-c(4)]
colnames(plot.bellv) <- c("Response", "N", "Proportion")
kable(plot.bellv)| Response | N | Proportion |
|---|---|---|
| Yes | 224 | 78.321678 |
| No | 59 | 20.629371 |
| Don’t know | 3 | 1.048951 |
Galloping Goose or the E&N Trail
goose <- prop.table(table(factor(d$Q5_vicroads_i, levels = c("1", "2", "77"), exclude = NULL)))*100
goose <- as.data.frame(goose)
goose$answer <- substring(row.names(goose), 1)
goose$answer <- revalue(as.character(goose$answer), c("1" = "Yes", "2" = "No", "3" = "Don't know"))
goose$plot <- factor(goose$answer, goose$answer)
cols <- c("#31a354","#f03b20","grey","grey") #get colors for plot
goose.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
goose.plot + geom_bar(aes(x = plot), data = goose, stat = "identity", colour="black") 
goose.tb <- as.factor(d$Q5_vicroads_i)
goose.tb <- summary(goose.tb)
goose.tb <- as.data.frame(goose.tb)
goose.tb$Var1 <- substring(row.names(goose.tb), 1)
goose.tb$answer <- revalue(as.character(goose.tb$Var1), c("1" = "Yes", "2" = "No", "77" = "Don't know"))
plot.goose <- merge(goose, goose.tb, by = "answer")
plot.goose <- plot.goose[-c(2, 4, 6)]
plot.goose <- setcolorder(plot.goose, c("answer", "goose.tb", "Freq"))
plot.goose$order <- c(3, 2, 1)
plot.goose <- plot.goose %>% arrange(order)
plot.goose <- plot.goose[-c(4)]
colnames(plot.goose) <- c("Response", "N", "Proportion")
kable(plot.goose)| Response | N | Proportion |
|---|---|---|
| Yes | 251 | 87.7622378 |
| No | 34 | 11.8881119 |
| Don’t know | 1 | 0.3496503 |
Q6: How much would you agree with the following statement: “I would like to travel by bicycle more than I do now”?
bike.more <- prop.table(table(factor(d$Q6_bike_more, levels = c("1", "2", "3", "4", "77"))))*100
bike.more <- as.data.frame(bike.more)
bike.more$group <- substring(row.names(bike.more), 1)
bike.more$group <- revalue(as.character(bike.more$group), c("1" = "Strongly agree", "2" = "Somewhat agree", "3" = "Somewhat disagree", "4" = "Strongly disagree", "5" = "Don't know"))
bike.more$plot <- factor(bike.more$group, bike.more$group)
ch <- ggplot(bike.more, aes(x = group, y = Freq, fill = group)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
ch + geom_bar(aes(x = plot), data = bike.more, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn", direction=-1) +
ylab("Percent of total") +
xlab("Bicyle more") +
ggtitle("I would like to travel by bicycle more than I do now")
more.tb <- as.factor(d$Q6_bike_more)
more.tb <- summary(more.tb)
more.tb <- as.data.frame(more.tb)
more.tb$Var1 <- substring(row.names(more.tb), 1)
more.tb$group <- revalue(as.character(more.tb$Var1), c("1" = "Strongly agree", "2" = "Somewhat agree", "3" = "Somewhat disagree", "4" = "Strongly disagree", "77" = "Don't know"))
more.plot <- merge(bike.more, more.tb, by = "group")
more.plot <- more.plot[-c(1:2, 6)]
more.plot <- setcolorder(more.plot, c("plot", "more.tb", "Freq"))
more.plot$order <- c(5, 2, 3, 1, 4)
more.plot <- more.plot %>% arrange(order)
more.plot <- more.plot[-c(4)]
colnames(more.plot) <- c("Response", "N", "Proportion")
kable(more.plot)| Response | N | Proportion |
|---|---|---|
| Strongly agree | 111 | 38.811189 |
| Somewhat agree | 126 | 44.055944 |
| Somewhat disagree | 24 | 8.391608 |
| Strongly disagree | 17 | 5.944056 |
| Don’t know | 8 | 2.797203 |
Q7: How comfortable would you feel biking in the following places?
A path or trail that is physically separate from the street
path <- prop.table(table(factor(d$Q7_bike_comf_a, levels = c("1", "2", "3", "4", "77"))))*100
path <- as.data.frame(path)
path$answer <- substring(row.names(path), 1)
path$answer <- revalue(as.character(path$answer), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
path$plot <- factor(path$answer, path$answer)
path.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
path.plot + geom_bar(aes(x = plot), data = path, stat = "identity", colour="black") 
path.tb <- as.factor(d$Q7_bike_comf_a)
path.tb <- summary(path.tb)
path.tb <- as.data.frame(path.tb)
path.tb$Var1 <- substring(row.names(path.tb), 1)
nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$path.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("2", "5")
nval.df <- nval.df[-c(1)]
path.tb <- rbind(path.tb, nval.df)
path.tb$answer <- revalue(as.character(path.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
plot.path <- merge(path, path.tb, by = "answer")
plot.path <- plot.path[-c(2, 4, 6)]
plot.path <- setcolorder(plot.path, c("answer", "path.tb", "Freq"))
plot.path$order <- c(5, 3, 2, 4, 1)
plot.path <- plot.path %>% arrange(order)
plot.path <- plot.path[-c(4)]
colnames(plot.path) <- c("Response", "N", "Proportion")
kable(plot.path)| Response | N | Proportion |
|---|---|---|
| Very uncomfortable | 22 | 7.692308 |
| Somewhat uncomfortable | 0 | 0.000000 |
| Somewhat comfortable | 18 | 6.293706 |
| Very comfortable | 246 | 86.013986 |
| Don’t know | 0 | 0.000000 |
A quiet, residential street with traffic speeds of 30-40 km per hour
res <- prop.table(table(factor(d$Q7_bike_comf_b, levels = c("1", "2", "3", "4", "77"))))*100
res <- as.data.frame(res)
res$answer <- substring(row.names(res), 1)
res$answer <- revalue(as.character(res$answer), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
res$plot <- factor(res$answer, res$answer)
res.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
res.plot + geom_bar(aes(x = plot), data = res, stat = "identity", colour="black") 
res.tb <- as.factor(d$Q7_bike_comf_b)
res.tb <- summary(res.tb)
res.tb <- as.data.frame(res.tb)
res.tb$Var1 <- substring(row.names(res.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$res.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "5")
nval.df <- nval.df[-c(1)]
res.tb <- rbind(res.tb, nval.df)
res.tb$answer <- revalue(as.character(res.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
plot.res <- merge(res, res.tb, by = "answer")
plot.res <- plot.res[-c(2, 4, 6)]
plot.res <- setcolorder(plot.res, c("answer", "res.tb", "Freq"))
plot.res$order <- c(5, 3, 2, 4, 1)
plot.res <- plot.res %>% arrange(order)
plot.res <- plot.res[-c(4)]
colnames(plot.res) <- c("Response", "N", "Proportion")
kable(plot.res)| Response | N | Proportion |
|---|---|---|
| Very uncomfortable | 22 | 7.692308 |
| Somewhat uncomfortable | 20 | 6.993007 |
| Somewhat comfortable | 89 | 31.118881 |
| Very comfortable | 155 | 54.195804 |
| Don’t know | 0 | 0.000000 |
A quiet residential street, with a 30 km per hour speed limit, bicycle route markings, wide speed bumps, and other things that slow down and discourage car traffic
bkwy <- prop.table(table(factor(d$Q7_bike_comf_c, levels = c("1", "2", "3", "4", "77"))))*100
bkwy <- as.data.frame(bkwy)
bkwy$answer <- substring(row.names(bkwy), 1)
bkwy$answer <- revalue(as.character(bkwy$answer), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
bkwy$plot <- factor(bkwy$answer, bkwy$answer)
bkwy.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
bkwy.plot + geom_bar(aes(x = plot), data = bkwy, stat = "identity", colour="black") 
bkwy.tb <- as.factor(d$Q7_bike_comf_c)
bkwy.tb <- summary(bkwy.tb)
bkwy.tb <- as.data.frame(bkwy.tb)
bkwy.tb$Var1 <- substring(row.names(bkwy.tb), 1)
bkwy.tb$answer <- revalue(as.character(bkwy.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))
plot.bkwy <- merge(bkwy, bkwy.tb, by = "answer")
plot.bkwy <- plot.bkwy[-c(2, 4, 6)]
plot.bkwy <- setcolorder(plot.bkwy, c("answer", "bkwy.tb", "Freq"))
plot.bkwy$order <- c(5, 3, 2, 4, 1)
plot.bkwy <- plot.bkwy %>% arrange(order)
plot.bkwy <- plot.bkwy[-c(4)]
colnames(plot.bkwy) <- c("Response", "N", "Proportion")
kable(plot.bkwy)| Response | N | Proportion |
|---|---|---|
| Very uncomfortable | 23 | 8.0419580 |
| Somewhat uncomfortable | 4 | 1.3986014 |
| Somewhat comfortable | 42 | 14.6853147 |
| Very comfortable | 216 | 75.5244755 |
| Don’t know | 1 | 0.3496503 |
A major urban or suburban street with four lanes, on-street parking, traffic speeds of 50-60 km per hour, and no bike lane
major <- prop.table(table(factor(d$Q7_bike_comf_d, levels = c("1", "2", "3", "4", "77"))))*100
major <- as.data.frame(major)
major$answer <- substring(row.names(major), 1)
major$answer <- revalue(as.character(major$answer), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
major$plot <- factor(major$answer, major$answer)
major.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
major.plot + geom_bar(aes(x = plot), data = major, stat = "identity", colour="black") 
major.tb <- as.factor(d$Q7_bike_comf_d)
major.tb <- summary(major.tb)
major.tb <- as.data.frame(major.tb)
major.tb$Var1 <- substring(row.names(major.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$major.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
major.tb <- rbind(major.tb, nval.df)
major.tb$answer <- revalue(as.character(major.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))
plot.major <- merge(major, major.tb, by = "answer")
plot.major <- plot.major[-c(2, 4, 6)]
plot.major <- setcolorder(plot.major, c("answer", "major.tb", "Freq"))
plot.major$order <- c(5, 3, 2, 4, 1)
plot.major <- plot.major %>% arrange(order)
plot.major <- plot.major[-c(4)]
colnames(plot.major) <- c("Response", "N", "Proportion")
kable(plot.major)| Response | N | Proportion |
|---|---|---|
| Very uncomfortable | 127 | 44.405594 |
| Somewhat uncomfortable | 109 | 38.111888 |
| Somewhat comfortable | 39 | 13.636364 |
| Very comfortable | 11 | 3.846154 |
| Don’t know | 0 | 0.000000 |
A major urban or suburban street with four lanes, on-street parking, traffic speeds of 50-60 km per hour, but with a striped bike lane added
lane <- prop.table(table(factor(d$Q7_bike_comf_e, levels = c("1", "2", "3", "4", "77"))))*100
lane <- as.data.frame(lane)
lane$answer <- substring(row.names(lane), 1)
lane$answer <- revalue(as.character(lane$answer), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
lane$plot <- factor(lane$answer, lane$answer)
lane.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
lane.plot + geom_bar(aes(x = plot), data = lane, stat = "identity", colour="black") 
lane.tb <- as.factor(d$Q7_bike_comf_e)
lane.tb <- summary(lane.tb)
lane.tb <- as.data.frame(lane.tb)
lane.tb$Var1 <- substring(row.names(lane.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$lane.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c( "77")
nval.df <- nval.df[-c(1)]
lane.tb <- rbind(lane.tb, nval.df)
lane.tb$answer <- revalue(as.character(lane.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))
plot.lane <- merge(lane, lane.tb, by = "answer")
plot.lane <- plot.lane[-c(2, 4, 6)]
plot.lane <- setcolorder(plot.lane, c("answer", "lane.tb", "Freq"))
plot.lane$order <- c(5, 3, 2, 4, 1)
plot.lane <- plot.lane %>% arrange(order)
plot.lane <- plot.lane[-c(4)]
colnames(plot.lane) <- c("Response", "N", "Proportion")
kable(plot.lane)| Response | N | Proportion |
|---|---|---|
| Very uncomfortable | 26 | 9.090909 |
| Somewhat uncomfortable | 88 | 30.769231 |
| Somewhat comfortable | 131 | 45.804196 |
| Very comfortable | 41 | 14.335664 |
| Don’t know | 0 | 0.000000 |
A major urban or suburban street with four lanes, on-street parking, traffic speeds of 50-60 km per hour, but with a wide bike lane physically separated from traffic by a raised curb, planters, or parked cars
track <- prop.table(table(factor(d$Q7_bike_comf_f, levels = c("1", "2", "3", "4", "77"))))*100
track <- as.data.frame(track)
track$answer <- substring(row.names(track), 1)
track$answer <- revalue(as.character(track$answer), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "5" = "Don't know"))
track$plot <- factor(track$answer, track$answer)
track.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
track.plot + geom_bar(aes(x = plot), data = track, stat = "identity", colour="black") 
track.tb <- as.factor(d$Q7_bike_comf_f)
track.tb <- summary(track.tb)
track.tb <- as.data.frame(track.tb)
track.tb$Var1 <- substring(row.names(track.tb), 1)
track.tb$answer <- revalue(as.character(track.tb$Var1), c("1" = "Very uncomfortable", "2" = "Somewhat uncomfortable", "3" = "Somewhat comfortable", "4" = "Very comfortable", "77" = "Don't know"))
plot.track <- merge(track, track.tb, by = "answer")
plot.track <- plot.track[-c(2, 4, 6)]
plot.track <- setcolorder(plot.track, c("answer", "track.tb", "Freq"))
plot.track$order <- c(5, 3, 2, 4, 1)
plot.track <- plot.track %>% arrange(order)
plot.track <- plot.track[-c(4)]
colnames(plot.track) <- c("Response", "N", "Proportion")
kable(plot.track)| Response | N | Proportion |
|---|---|---|
| Very uncomfortable | 20 | 6.993007 |
| Somewhat uncomfortable | 10 | 3.496504 |
| Somewhat comfortable | 58 | 20.279720 |
| Very comfortable | 195 | 68.181818 |
| Don’t know | 3 | 1.048951 |
Q8: Have you ever heard of the All Ages and Abilities (AAA) Network (formerly known as Biketoria)?
AAA <- prop.table(table(factor(d$Q8aaa_familiarity, levels = c("1", "2")), exclude=NULL))*100
AAA <- as.data.frame(AAA)
AAA$answer <- substring(row.names(AAA), 1)
AAA$answer <- revalue(as.character(AAA$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
AAA$plot <- factor(AAA$answer, AAA$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
AAA.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
AAA.plot + geom_bar(aes(x = plot), data = AAA, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
AAA.tb <- as.factor(d$Q8aaa_familiarity)
AAA.tb <- summary(AAA.tb)
AAA.tb <- as.data.frame(AAA.tb)
AAA.tb$Var1 <- substring(row.names(AAA.tb), 1)
AAA.tb$answer <- revalue(as.character(AAA.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.AAA <- merge(AAA, AAA.tb, by = "answer")
plot.AAA <- plot.AAA[-c(2, 4, 6)]
plot.AAA <- setcolorder(plot.AAA, c("answer", "AAA.tb", "Freq"))
plot.AAA$order <- c(3, 2, 1)
plot.AAA <- plot.AAA %>% arrange(order)
plot.AAA <- plot.AAA[-c(4)]
colnames(plot.AAA) <- c("Response", "N", "Proportion")
kable(plot.AAA)| Response | N | Proportion |
|---|---|---|
| Yes | 192 | 67.1328671 |
| No | 92 | 32.1678322 |
| NA | 2 | 0.6993007 |
Q9: Do you think that the All Ages and Abilities (AAA) Network is a good or bad idea for Victoria? It is a…
aaa.idea <- prop.table(table(factor(d$Q9_aaa_idea, levels = c("1", "2", "3", "4", "77")), exclude=NULL))*100
aaa.idea <- as.data.frame(aaa.idea)
aaa.idea$answer <- substring(row.names(aaa.idea), 1)
aaa.idea$answer <- revalue(as.character(aaa.idea$answer), c("1" = "Very good idea", "2" = "Somewhat good idea", "3" = "Somewhat bad idea", "4" = "Very bad idea", "5" = "Don't know", "6" = "NA"))
aaa.idea$plot <- factor(aaa.idea$answer, aaa.idea$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
aaa.idea.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
aaa.idea.plot + geom_bar(aes(x = plot), data = aaa.idea, stat = "identity", colour="black") 
aaa.idea.tb <- as.factor(d$Q9_aaa_idea)
aaa.idea.tb <- summary(aaa.idea.tb)
aaa.idea.tb <- as.data.frame(aaa.idea.tb)
aaa.idea.tb$Var1 <- substring(row.names(aaa.idea.tb), 1)
aaa.idea.tb$answer <- revalue(as.character(aaa.idea.tb$Var1), c("1" = "Very good idea", "2" = "Somewhat good idea", "3" = "Somewhat bad idea", "4" = "Very bad idea", "77" = "Don't know", "NA's" = "NA"))
plot.aaa.idea <- merge(aaa.idea, aaa.idea.tb, by = "answer")
plot.aaa.idea <- plot.aaa.idea[-c(2, 4, 6)]
plot.aaa.idea <- setcolorder(plot.aaa.idea, c("answer", "aaa.idea.tb", "Freq"))
plot.aaa.idea$order <- c(5, 6, 3, 2, 4, 1)
plot.aaa.idea <- plot.aaa.idea %>% arrange(order)
plot.aaa.idea <- plot.aaa.idea[-c(4)]
colnames(plot.aaa.idea) <- c("Response", "N", "Proportion")
kable(plot.aaa.idea)| Response | N | Proportion |
|---|---|---|
| Very good idea | 244 | 85.3146853 |
| Somewhat good idea | 33 | 11.5384615 |
| Somewhat bad idea | 3 | 1.0489510 |
| Very bad idea | 1 | 0.3496503 |
| Don’t know | 3 | 1.0489510 |
| NA | 2 | 0.6993007 |
Q10:Will you likely cycle more in the future once the All Ages and Abilities (AAA) Network is built?
bike.more <- prop.table(table(factor(d$Q10_aaa_bike_more, levels = c("1", "2")), exclude=NULL))*100
bike.more <- as.data.frame(bike.more)
bike.more$answer <- substring(row.names(bike.more), 1)
bike.more$answer <- revalue(as.character(bike.more$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
bike.more$plot <- factor(bike.more$answer, bike.more$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
bike.more.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
bike.more.plot + geom_bar(aes(x = plot), data = bike.more, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
bike.more.tb <- as.factor(d$Q10_aaa_bike_more)
bike.more.tb <- summary(bike.more.tb)
bike.more.tb <- as.data.frame(bike.more.tb)
bike.more.tb$Var1 <- substring(row.names(bike.more.tb), 1)
bike.more.tb$answer <- revalue(as.character(bike.more.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.bike.more <- merge(bike.more, bike.more.tb, by = "answer")
plot.bike.more <- plot.bike.more[-c(2, 4, 6)]
plot.bike.more <- setcolorder(plot.bike.more, c("answer", "bike.more.tb", "Freq"))
plot.bike.more$order <- c(3, 2, 1)
plot.bike.more <- plot.bike.more %>% arrange(order)
plot.bike.more <- plot.bike.more[-c(4)]
colnames(plot.bike.more) <- c("Response", "N", "Proportion")
kable(plot.bike.more)| Response | N | Proportion |
|---|---|---|
| Yes | 223 | 77.9720280 |
| No | 61 | 21.3286713 |
| NA | 2 | 0.6993007 |
Q11:Do you currently have a valid driver’s license?
license <- prop.table(table(factor(d$Q11_license, levels = c("1", "2")), exclude=NULL))*100
license <- as.data.frame(license)
license$answer <- substring(row.names(license), 1)
license$answer <- revalue(as.character(license$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
license$plot <- factor(license$answer, license$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
license.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
license.plot + geom_bar(aes(x = plot), data = license, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
license.tb <- as.factor(d$Q11_)
license.tb <- summary(license.tb)
license.tb <- as.data.frame(license.tb)
license.tb$Var1 <- substring(row.names(license.tb), 1)
license.tb$answer <- revalue(as.character(license.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.license <- merge(license, license.tb, by = "answer")
plot.license <- plot.license[-c(2, 4, 6)]
plot.license <- setcolorder(plot.license, c("answer", "license.tb", "Freq"))
plot.license$order <- c(3, 2, 1)
plot.license <- plot.license %>% arrange(order)
plot.license <- plot.license[-c(4)]
colnames(plot.license) <- c("Response", "N", "Proportion")
kable(plot.license)| Response | N | Proportion |
|---|---|---|
| Yes | 275 | 96.1538462 |
| No | 9 | 3.1468531 |
| NA | 2 | 0.6993007 |
Q12:Do you have access to a car?
access <- prop.table(table(factor(d$Q12_car_access, levels = c("1", "2")), exclude=NULL))*100
access <- as.data.frame(access)
access$answer <- substring(row.names(access), 1)
access$answer <- revalue(as.character(access$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
access$plot <- factor(access$answer, access$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
access.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
access.plot + geom_bar(aes(x = plot), data = access, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
access.tb <- as.factor(d$Q12_car_access)
access.tb <- summary(access.tb)
access.tb <- as.data.frame(access.tb)
access.tb$Var1 <- substring(row.names(access.tb), 1)
access.tb$answer <- revalue(as.character(access.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.access <- merge(access, access.tb, by = "answer")
plot.access <- plot.access[-c(2, 4, 6)]
plot.access <- setcolorder(plot.access, c("answer", "access.tb", "Freq"))
plot.access$order <- c(3, 2, 1)
plot.access <- plot.access %>% arrange(order)
plot.access <- plot.access[-c(4)]
colnames(plot.access) <- c("Response", "N", "Proportion")
kable(plot.access)| Response | N | Proportion |
|---|---|---|
| Yes | 257 | 89.860140 |
| No | 18 | 6.293706 |
| NA | 11 | 3.846154 |
Q13:How many cars, trucks, or vans are kept in your household?
ggplot(d, aes(x = d$Q13_cars_household)) + geom_bar(na.rm = TRUE) + xlab("Number of cars, trucks or vans in household")
summary(d$Q13_cars_household)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 1.000 1.319 2.000 4.000 29Q14: How much do you enjoy using each transportation mode?
Walking
pref.walk <- prop.table(table(factor(d$Q14_preferred_mode_a, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
pref.walk <- as.data.frame(pref.walk)
pref.walk$answer <- substring(row.names(pref.walk), 1)
pref.walk$answer <- revalue(as.character(pref.walk$answer), c("1" = "A lot", "4" = "Not at all", "5" = "Not applicable", "6" = "NA"))
pref.walk$plot <- factor(pref.walk$answer, pref.walk$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
pref.walk.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pref.walk.plot + geom_bar(aes(x = plot), data = pref.walk, stat = "identity", colour="black") 
pref.walk.tb <- as.factor(d$Q14_preferred_mode_a)
pref.walk.tb <- summary(pref.walk.tb)
pref.walk.tb <- as.data.frame(pref.walk.tb)
pref.walk.tb$Var1 <- substring(row.names(pref.walk.tb), 1)
pref.walk.tb$answer <- revalue(as.character(pref.walk.tb$Var1), c("1" = "A lot", "2" = "2", "3" = "3", "4" = "Not at all", "5" = "Not applicable", "NA's" = "NA"))
plot.pref.walk <- merge(pref.walk, pref.walk.tb, by = "answer")
plot.pref.walk <- plot.pref.walk[-c(2, 4, 6)]
plot.pref.walk <- setcolorder(plot.pref.walk, c("answer", "pref.walk.tb", "Freq"))
plot.pref.walk$order <- c(2, 3, 1, 6, 5, 4)
plot.pref.walk <- plot.pref.walk %>% arrange(order)
plot.pref.walk <- plot.pref.walk[-c(4)]
colnames(plot.pref.walk) <- c("Response", "N", "Proportion")
kable(plot.pref.walk)| Response | N | Proportion |
|---|---|---|
| A lot | 159 | 55.5944056 |
| 2 | 94 | 32.8671329 |
| 3 | 21 | 7.3426573 |
| Not at all | 7 | 2.4475524 |
| Not applicable | 3 | 1.0489510 |
| NA | 2 | 0.6993007 |
Biking
pref.bike <- prop.table(table(factor(d$Q14_preferred_mode_b, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
pref.bike <- as.data.frame(pref.bike)
pref.bike$answer <- substring(row.names(pref.bike), 1)
pref.bike$answer <- revalue(as.character(pref.bike$answer), c("1" = "A lot", "4" = "Not at all", "5" = "Not applicable", "6" = "NA"))
pref.bike$plot <- factor(pref.bike$answer, pref.bike$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
pref.bike.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pref.bike.plot + geom_bar(aes(x = plot), data = pref.bike, stat = "identity", colour="black") 
pref.bike.tb <- as.factor(d$Q14_preferred_mode_b)
pref.bike.tb <- summary(pref.bike.tb)
pref.bike.tb <- as.data.frame(pref.bike.tb)
pref.bike.tb$Var1 <- substring(row.names(pref.bike.tb), 1)
nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$pref.bike.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("4", "5")
nval.df <- nval.df[-c(1)]
pref.bike.tb <- rbind(pref.bike.tb, nval.df)
pref.bike.tb$answer <- revalue(as.character(pref.bike.tb$Var1), c("1" = "A lot", "2" = "2", "3" = "3", "4" = "Not at all", "5" = "Not applicable", "NA's" = "NA"))
plot.pref.bike <- merge(pref.bike, pref.bike.tb, by = "answer")
plot.pref.bike <- plot.pref.bike[-c(2, 4, 6)]
plot.pref.bike <- setcolorder(plot.pref.bike, c("answer", "pref.bike.tb", "Freq"))
plot.pref.bike$order <- c(2, 3, 1, 6, 5, 4)
plot.pref.bike <- plot.pref.bike %>% arrange(order)
plot.pref.bike <- plot.pref.bike[-c(4)]
colnames(plot.pref.bike) <- c("Response", "N", "Proportion")
kable(plot.pref.bike)| Response | N | Proportion |
|---|---|---|
| A lot | 250 | 87.4125874 |
| 2 | 32 | 11.1888112 |
| 3 | 2 | 0.6993007 |
| Not at all | 0 | 0.0000000 |
| Not applicable | 0 | 0.0000000 |
| NA | 2 | 0.6993007 |
Public Transit
pref.transit <- prop.table(table(factor(d$Q14_preferred_mode_c, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
pref.transit <- as.data.frame(pref.transit)
pref.transit$answer <- substring(row.names(pref.transit), 1)
pref.transit$answer <- revalue(as.character(pref.transit$answer), c("1" = "A lot", "4" = "Not at all", "5" = "Not applicable", "6" = "NA"))
pref.transit$plot <- factor(pref.transit$answer, pref.transit$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
pref.transit.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pref.transit.plot + geom_bar(aes(x = plot), data = pref.transit, stat = "identity", colour="black") 
pref.transit.tb <- as.factor(d$Q14_preferred_mode_c)
pref.transit.tb <- summary(pref.transit.tb)
pref.transit.tb <- as.data.frame(pref.transit.tb)
pref.transit.tb$Var1 <- substring(row.names(pref.transit.tb), 1)
pref.transit.tb$answer <- revalue(as.character(pref.transit.tb$Var1), c("1" = "A lot", "2" = "2", "3" = "3", "4" = "Not at all", "5" = "Not applicable", "NA's" = "NA"))
plot.pref.transit <- merge(pref.transit, pref.transit.tb, by = "answer")
plot.pref.transit <- plot.pref.transit[-c(2, 4, 6)]
plot.pref.transit <- setcolorder(plot.pref.transit, c("answer", "pref.transit.tb", "Freq"))
plot.pref.transit$order <- c(2, 3, 1, 6, 5, 4)
plot.pref.transit <- plot.pref.transit %>% arrange(order)
plot.pref.transit <- plot.pref.transit[-c(4)]
colnames(plot.pref.transit) <- c("Response", "N", "Proportion")
kable(plot.pref.transit)| Response | N | Proportion |
|---|---|---|
| A lot | 11 | 3.8461538 |
| 2 | 64 | 22.3776224 |
| 3 | 131 | 45.8041958 |
| Not at all | 68 | 23.7762238 |
| Not applicable | 10 | 3.4965035 |
| NA | 2 | 0.6993007 |
Car
pref.car <- prop.table(table(factor(d$Q14_preferred_mode_d, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
pref.car <- as.data.frame(pref.car)
pref.car$answer <- substring(row.names(pref.car), 1)
pref.car$answer <- revalue(as.character(pref.car$answer), c("1" = "A lot", "4" = "Not at all", "5" = "Not applicable", "6" = "NA"))
pref.car$plot <- factor(pref.car$answer, pref.car$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
pref.car.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pref.car.plot + geom_bar(aes(x = plot), data = pref.car, stat = "identity", colour="black") 
pref.car.tb <- as.factor(d$Q14_preferred_mode_d)
pref.car.tb <- summary(pref.car.tb)
pref.car.tb <- as.data.frame(pref.car.tb)
pref.car.tb$Var1 <- substring(row.names(pref.car.tb), 1)
pref.car.tb$answer <- revalue(as.character(pref.car.tb$Var1), c("1" = "A lot", "2" = "2", "3" = "3", "4" = "Not at all", "5" = "Not applicable", "NA's" = "NA"))
plot.pref.car <- merge(pref.car, pref.car.tb, by = "answer")
plot.pref.car <- plot.pref.car[-c(2, 4, 6)]
plot.pref.car <- setcolorder(plot.pref.car, c("answer", "pref.car.tb", "Freq"))
plot.pref.car$order <- c(2, 3, 1, 6, 5, 4)
plot.pref.car <- plot.pref.car %>% arrange(order)
plot.pref.car <- plot.pref.car[-c(4)]
colnames(plot.pref.car) <- c("Response", "N", "Proportion")
kable(plot.pref.car)| Response | N | Proportion |
|---|---|---|
| A lot | 31 | 10.8391608 |
| 2 | 101 | 35.3146853 |
| 3 | 111 | 38.8111888 |
| Not at all | 35 | 12.2377622 |
| Not applicable | 6 | 2.0979021 |
| NA | 2 | 0.6993007 |
Motorcycle or scooter
pref.motor <- prop.table(table(factor(d$Q14_preferred_mode_e, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
pref.motor <- as.data.frame(pref.motor)
pref.motor$answer <- substring(row.names(pref.motor), 1)
pref.motor$answer <- revalue(as.character(pref.motor$answer), c("1" = "A lot", "4" = "Not at all", "5" = "Not applicable", "6" = "NA"))
pref.motor$plot <- factor(pref.motor$answer, pref.motor$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
pref.motor.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pref.motor.plot + geom_bar(aes(x = plot), data = pref.motor, stat = "identity", colour="black") 
pref.motor.tb <- as.factor(d$Q14_preferred_mode_e)
pref.motor.tb <- summary(pref.motor.tb)
pref.motor.tb <- as.data.frame(pref.motor.tb)
pref.motor.tb$Var1 <- substring(row.names(pref.motor.tb), 1)
pref.motor.tb$answer <- revalue(as.character(pref.motor.tb$Var1), c("1" = "A lot", "2" = "2", "3" = "3", "4" = "Not at all", "5" = "Not applicable", "NA's" = "NA"))
plot.pref.motor <- merge(pref.motor, pref.motor.tb, by = "answer")
plot.pref.motor <- plot.pref.motor[-c(2, 4, 6)]
plot.pref.motor <- setcolorder(plot.pref.motor, c("answer", "pref.motor.tb", "Freq"))
plot.pref.motor$order <- c(2, 3, 1, 6, 5, 4)
plot.pref.motor <- plot.pref.motor %>% arrange(order)
plot.pref.motor <- plot.pref.motor[-c(4)]
colnames(plot.pref.motor) <- c("Response", "N", "Proportion")
kable(plot.pref.motor)| Response | N | Proportion |
|---|---|---|
| A lot | 8 | 2.7972028 |
| 2 | 15 | 5.2447552 |
| 3 | 8 | 2.7972028 |
| Not at all | 30 | 10.4895105 |
| Not applicable | 223 | 77.9720280 |
| NA | 2 | 0.6993007 |
Other
pref.other <- prop.table(table(factor(d$Q14_preferred_mode_a, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
pref.other <- as.data.frame(pref.other)
pref.other$answer <- substring(row.names(pref.other), 1)
pref.other$answer <- revalue(as.character(pref.other$answer), c("1" = "A lot", "4" = "Not at all", "5" = "Not applicable", "6" = "NA"))
pref.other$plot <- factor(pref.other$answer, pref.other$answer)
cols <- c(rev(brewer.pal(4,"RdYlGn")),"grey", "grey")
pref.other.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pref.other.plot + geom_bar(aes(x = plot), data = pref.other, stat = "identity", colour="black")
pref.other.tb <- as.factor(d$Q14_preferred_mode_a)
pref.other.tb <- summary(pref.other.tb)
pref.other.tb <- as.data.frame(pref.other.tb)
pref.other.tb$Var1 <- substring(row.names(pref.other.tb), 1)
pref.other.tb$answer <- revalue(as.character(pref.other.tb$Var1), c("1" = "A lot", "2" = "2", "3" = "3", "4" = "Not at all", "5" = "Not applicable", "NA's" = "NA"))
plot.pref.other <- merge(pref.other, pref.other.tb, by = "answer")
plot.pref.other <- plot.pref.other[-c(2, 4, 6)]
plot.pref.other <- setcolorder(plot.pref.other, c("answer", "pref.other.tb", "Freq"))
plot.pref.other$order <- c(2, 3, 1, 6, 5, 4)
plot.pref.other <- plot.pref.other %>% arrange(order)
plot.pref.other <- plot.pref.other[-c(4)]
colnames(plot.pref.other) <- c("Response", "N", "Proportion")
kable(plot.pref.other)| Response | N | Proportion |
|---|---|---|
| A lot | 159 | 55.5944056 |
| 2 | 94 | 32.8671329 |
| 3 | 21 | 7.3426573 |
| Not at all | 7 | 2.4475524 |
| Not applicable | 3 | 1.0489510 |
| NA | 2 | 0.6993007 |
carshare.df <- c("Car2Go", "Modo", "ZipCar", "Other") #insert missing values
carshare.df <- as.data.frame(carshare.df)
carshare.df$Members <- c("21", "46", "5", "7")
as.numeric(carshare.df$Members)## [1] 21 46 5 7colnames(carshare.df) <- c("Carshare", "N Respondents")Q15:Which car share services are you a member of??
noshare<- prop.table(table(factor(d$Q15_car_share_None, levels = c("0", "1")), exclude=NULL))*100
noshare <- as.data.frame(noshare)
noshare$answer <- substring(row.names(noshare), 1)
noshare$answer <- revalue(as.character(noshare$answer), c("1" = "Member", "2" = "Non-member", "3" = "NA"))
noshare$plot <- factor(noshare$answer, noshare$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
noshare.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
noshare.plot + geom_bar(aes(x = plot), data = noshare, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
noshare.tb <- as.factor(d$Q15_car_share_None)
noshare.tb <- summary(noshare.tb)
noshare.tb <- as.data.frame(noshare.tb)
noshare.tb$Var1 <- substring(row.names(noshare.tb), 1)
noshare.tb$answer <- revalue(as.character(noshare.tb$Var1), c("0" = "Member", "1" = "Non-member", "NA's" = "NA"))
plot.noshare <- merge(noshare, noshare.tb, by = "answer")
plot.noshare <- plot.noshare[-c(2, 4, 6)]
plot.noshare <- setcolorder(plot.noshare, c("answer", "noshare.tb", "Freq"))
plot.noshare$order <- c(1, 3, 2)
plot.noshare <- plot.noshare %>% arrange(order)
plot.noshare <- plot.noshare[-c(4)]
colnames(plot.noshare) <- c("Response", "N", "Proportion")
kable(plot.noshare)| Response | N | Proportion |
|---|---|---|
| Member | 60 | 20.9790210 |
| Non-member | 224 | 78.3216783 |
| NA | 2 | 0.6993007 |
kable(carshare.df)| Carshare | N Respondents |
|---|---|
| Car2Go | 21 |
| Modo | 46 |
| ZipCar | 5 |
| Other | 7 |
-Some respondents are members of more than one carshare service Other carshare companies:
Evo (Vancouver, n=6)
Pogo (Edmonton, n=1)
Q16:Are you a member of a bike advocacy group (e.g., Greater Victoria Cycling Coalition)?
advocacy <- prop.table(table(factor(d$Q16_bike_advocacy, levels = c("1", "2")), exclude=NULL))*100
advocacy <- as.data.frame(advocacy)
advocacy$answer <- substring(row.names(advocacy), 1)
advocacy$answer <- revalue(as.character(advocacy$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
advocacy$plot <- factor(advocacy$answer, advocacy$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
advocacy.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
advocacy.plot + geom_bar(aes(x = plot), data = advocacy, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
advocacy.tb <- as.factor(d$Q16_bike_advocacy)
advocacy.tb <- summary(advocacy.tb)
advocacy.tb <- as.data.frame(advocacy.tb)
advocacy.tb$Var1 <- substring(row.names(advocacy.tb), 1)
advocacy.tb$answer <- revalue(as.character(advocacy.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.advocacy <- merge(advocacy, advocacy.tb, by = "answer")
plot.advocacy <- plot.advocacy[-c(2, 4, 6)]
plot.advocacy <- setcolorder(plot.advocacy, c("answer", "advocacy.tb", "Freq"))
plot.advocacy$order <- c(3, 2, 1)
plot.advocacy <- plot.advocacy %>% arrange(order)
plot.advocacy <- plot.advocacy[-c(4)]
colnames(plot.advocacy) <- c("Response", "N", "Proportion")
kable(plot.advocacy)| Response | N | Proportion |
|---|---|---|
| Yes | 65 | 22.7272727 |
| No | 219 | 76.5734266 |
| NA | 2 | 0.6993007 |
Q17:Do you ride with a recreational cycling group or club (e.g., Tripleshot, TriStars)?
group <- prop.table(table(factor(d$Q17_cycling_club, levels = c("1", "2")), exclude=NULL))*100
group <- as.data.frame(group)
group$answer <- substring(row.names(group), 1)
group$answer <- revalue(as.character(group$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
group$plot <- factor(group$answer, group$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
group.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
group.plot + geom_bar(aes(x = plot), data = group, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
group.tb <- as.factor(d$Q17_cycling_club)
group.tb <- summary(group.tb)
group.tb <- as.data.frame(group.tb)
group.tb$Var1 <- substring(row.names(group.tb), 1)
group.tb$answer <- revalue(as.character(group.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.group <- merge(group, group.tb, by = "answer")
plot.group <- plot.group[-c(2, 4, 6)]
plot.group <- setcolorder(plot.group, c("answer", "group.tb", "Freq"))
plot.group$order <- c(3, 2, 1)
plot.group <- plot.group %>% arrange(order)
plot.group <- plot.group[-c(4)]
colnames(plot.group) <- c("Response", "N", "Proportion")
kable(plot.group)| Response | N | Proportion |
|---|---|---|
| Yes | 46 | 16.0839161 |
| No | 238 | 83.2167832 |
| NA | 2 | 0.6993007 |
Section 2: Housing
Q18: Thinking about where you live now, are you .
tenure <- prop.table(table(factor(d$Q18_house_tenure, levels = c("1", "2", "3", "4", "5", "77")), exclude=NULL))*100
tenure <- as.data.frame(tenure)
tenure$answer <- substring(row.names(tenure), 1)
tenure$answer <- revalue(as.character(tenure$answer), c("1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "6" = "Don't know", "7" = "No anwer"))
tenure$plot <- factor(tenure$answer, tenure$answer)
tenure.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
tenure.plot + geom_bar(aes(x = plot), data = tenure, stat = "identity", colour="black") 
tenure.tb <- as.factor(d$Q18_house_tenure)
tenure.tb <- summary(tenure.tb)
tenure.tb <- as.data.frame(tenure.tb)
tenure.tb$Var1 <- substring(row.names(tenure.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$tenure.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("77")
nval.df <- nval.df[-c(1)]
tenure.tb <- rbind(tenure.tb, nval.df)
tenure.tb$answer <- revalue(as.character(tenure.tb$Var1), c("1" = "An owner", "2" = "A tenant", "3" = "Resident in a relative or friend's home", "4" = "Resident other than in a relative or friend's home", "5" = "Other", "77" = "Don't know", "NA's" = "No anwer"))
plot.tenure <- merge(tenure, tenure.tb, by = "answer")
plot.tenure <- plot.tenure[-c(2, 4, 6)]
plot.tenure <- setcolorder(plot.tenure, c("answer", "tenure.tb", "Freq"))
plot.tenure$order <- c(2, 1, 6, 7, 5, 3, 4)
plot.tenure <- plot.tenure %>% arrange(order)
plot.tenure <- plot.tenure[-c(4)]
colnames(plot.tenure) <- c("Response", "N", "Proportion")
kable(plot.tenure)| Response | N | Proportion |
|---|---|---|
| An owner | 179 | 62.5874126 |
| A tenant | 90 | 31.4685315 |
| Resident in a relative or friend’s home | 11 | 3.8461538 |
| Resident other than in a relative or friend’s home | 2 | 0.6993007 |
| Other | 2 | 0.6993007 |
| Don’t know | 0 | 0.0000000 |
| No anwer | 2 | 0.6993007 |
Q19: In what type of dwelling to you currently live? Is it.
dwelling <- prop.table(table(factor(d$Q19_dwelling_type, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "10", "77")), exclude=NULL))*100
dwelling <- as.data.frame(dwelling)
dwelling$answer <- substring(row.names(dwelling), 1)
dwelling$answer <- revalue(as.character(dwelling$answer), c("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", "10" = "Don't know", "11" = "NA"))
dwelling$plot <- factor(dwelling$answer, dwelling$answer)
dwelling.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn") +
ylab("Percent of total") +
xlab("")
dwelling.plot + geom_bar(aes(x = plot), data = dwelling, stat = "identity", colour="black") 
dwelling.tb <- as.factor(d$Q19_dwelling_type)
dwelling.tb <- summary(dwelling.tb)
dwelling.tb <- as.data.frame(dwelling.tb)
dwelling.tb$Var1 <- substring(row.names(dwelling.tb), 1)
nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$dwelling.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("8", "77")
nval.df <- nval.df[-c(1)]
dwelling.tb <- rbind(dwelling.tb, nval.df)
dwelling.tb$answer <- revalue(as.character(dwelling.tb$Var1), c("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", "10" = "Other", "77" = "Don't know", "NA's" = "NA"))
plot.dwelling <- merge(dwelling, dwelling.tb, by = "answer")
plot.dwelling <- plot.dwelling[-c(2, 4, 6)]
plot.dwelling <- setcolorder(plot.dwelling, c("answer", "dwelling.tb", "Freq"))
plot.dwelling$order <- c(4, 5, 6, 10, 7, 11, 9, 3, 2, 8, 1)
plot.dwelling <- plot.dwelling %>% arrange(order)
plot.dwelling <- plot.dwelling[-c(4)]
colnames(plot.dwelling) <- c("Response", "N", "Proportion")
kable(plot.dwelling)| Response | N | Proportion |
|---|---|---|
| Single detached house | 169 | 59.0909091 |
| Semi-detached house | 15 | 5.2447552 |
| Row house | 16 | 5.5944056 |
| An apartment (or condo) in a duplex or triplex | 15 | 5.2447552 |
| Apartment (or condo) in building with fewer than 5 storeys | 43 | 15.0349650 |
| Apartment (or condo) in building with more than 5 storeys | 19 | 6.6433566 |
| Mobile home/movable dwelling | 1 | 0.3496503 |
| Senior’s home | 0 | 0.0000000 |
| Other | 6 | 2.0979021 |
| Don’t know | 0 | 0.0000000 |
| NA | 2 | 0.6993007 |
Section 3: Physical Activity-Job
Q20: During the last 7 days, on how many days did you do vigorous physical activities as part of your work?
ggplot(d, aes(x = d$Q20_work_vigpa)) + geom_bar(na.rm = TRUE) + xlab("N days vigorous job-related n/ physical activity")
summary(d$Q20_work_vigpa)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0000 0.0000 0.0000 0.9225 1.0000 7.0000 2Q21: How much time did you usually spend on one of those days doing vigorous physical activities as part of your work?
ggplot(d, aes(x = d$Q21_work_vigpa_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20) + xlab("Minutes vigorous job-related n/ physical activity") 
summary(d$Q21_work_vigpa_freq, na.rm = TRUE)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 60.0 120.0 153.4 240.0 480.0 213Section 3: Physical Activity-Transportation
Q22: During the last 7 days, on how many days did you travel in a motor vehicle like a train, bus, car, or tram?
ggplot(d, aes(x = d$Q22_travel_motor)) + geom_bar(na.rm = TRUE) + xlab("N days")
summary(d$Q22_travel_motor)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 2.000 3.000 2.982 4.000 7.000 3Q23: How much time did you usually spend on one of those days traveling in a train, bus, car, tram, or other kind of motor vehicle?
ggplot(d, aes(x = d$Q23_travel_motor_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20) + xlab("Minutes travel time") 
summary(d$Q23_travel_motor_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 30.00 40.00 58.54 60.00 900.00 32Q24: During the last 7 days, on how many days did you bicycle for at least 10 minutes at a time to go from place to place?
ggplot(d, aes(x = d$Q24_travel_bike)) + geom_bar(na.rm = TRUE) + xlab("N days")
summary(d$Q24_travel_bike)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 4.000 5.000 4.801 6.000 7.000 4Q25: How much time did you usually spend on one of those days bicycling from place to place?
ggplot(d, aes(x = d$Q25_travel_bike_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20) + xlab("Minutes travel by bicycle") 
summary(d$Q25_travel_bike_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.00 30.00 45.00 58.61 60.00 300.00 14Q26: During the last 7 days, on how many days did you walk for at least 10 minutes at a time to go from place to place?
ggplot(d, aes(x = d$Q26_travel_walk)) + geom_bar(na.rm = TRUE) + xlab("N days")
summary(d$Q26_travel_walk)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 3.511 6.000 7.000 4Q27: How much time did you usually spend on one of those days bicycling from place to place?
ggplot(d, aes(x = d$Q27_travel_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 10) + xlab("Minutes travel by walking") 
summary(d$Q27_travel_walk_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 20.00 30.00 41.46 60.00 300.00 46Section 3: Physical Activity-Recreation, sport & leisure
Q28: Not counting any walking for transportation that you have already mentioned, during the last 7 days, on how many days did you walk for at least 10 minutes at a time in your leisure time?
ggplot(d, aes(x = d$Q28_leisure_walk)) + geom_bar(na.rm = TRUE) + xlab("N days")
summary(d$Q28_leisure_walk)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 2.000 2.805 5.000 7.000 4Q29: How much time did you usually spend on one of those days walking in your leisure time?
ggplot(d, aes(x = d$Q29_leisure_walk_freq)) + geom_histogram(na.rm = TRUE, binwidth = 20) + xlab("Minutes leisure time walking") 
summary(d$Q29_leisure_walk_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 30.00 45.00 65.02 60.00 900.00 65Q30:During the last 7 days, on how many days did you do vigorous physical activities like aerobics, running, fast bicycling, or fast swimming in your leisure time?
ggplot(d, aes(x = d$Q30_leisure_vigpa)) + geom_bar(na.rm = TRUE) + xlab("N days")
summary(d$Q30_leisure_vigpa)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 0.00 2.00 2.23 4.00 7.00 431:How much time did you usually spend on one of those days doing vigorous physical activities in your leisure time?
ggplot(d, aes(x = d$Q31_leisure_vigpa_freq)) + geom_histogram(na.rm = TRUE) + xlab("Minutes leisure time vigorous PA") ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(d$Q31_leisure_vigpa_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 45.00 60.00 76.95 120.00 300.00 90Q32:During the last 7 days, on how many days did you do moderate physical activities like bicycling at a regular pace, swimming at a regular pace, and doubles tennis in your leisure time?
ggplot(d, aes(x = d$Q32_modpa_leisure)) + geom_bar(na.rm = TRUE) + xlab("N days")
summary(d$Q32_modpa_leisure)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 0.000 1.000 1.868 3.000 7.000 533:How much time did you usually spend on one of those days doing moderate physical activities in your leisure time?
ggplot(d, aes(x = d$Q33_modpa_leisure_freq)) + geom_histogram(na.rm = TRUE) + xlab("Minutes leisure time moderate PA") ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(d$Q33_modpa_leisure_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 30.00 60.00 68.57 60.00 360.00 115Q34:During the last 7 days, how much time did you usually spend sitting on a weekday?
ggplot(d, aes(x = d$Q34_sit_weekday/60)) + geom_histogram(na.rm = TRUE, binwidth = 1) + xlab("Hours sitting, weekday") 
summary(d$Q34_sit_weekday/60)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.3333 4.0000 6.0000 6.2760 9.0000 14.0000 835:During the last 7 days, how much time did you usually spend sitting on a weekend day?
ggplot(d, aes(x = d$Q35_sit_weekend/60)) + geom_histogram(na.rm = TRUE, binwidth = 1) + xlab("Hours sitting, weekend") 
summary(d$Q35_sit_weekend/60)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.03333 2.00000 4.00000 3.82300 5.00000 14.00000 8Section 3: Physical Activity-Dog Ownership
Q36:Do you live with a dog?
dog <- prop.table(table(factor(d$Q36_dog, levels = c("1", "2")), exclude=NULL))*100
dog <- as.data.frame(dog)
dog$answer <- substring(row.names(dog), 1)
dog$answer <- revalue(as.character(dog$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
dog$plot <- factor(dog$answer, dog$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
dog.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
dog.plot + geom_bar(aes(x = plot), data = dog, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
dog.tb <- as.factor(d$Q36_dog)
dog.tb <- summary(dog.tb)
dog.tb <- as.data.frame(dog.tb)
dog.tb$Var1 <- substring(row.names(dog.tb), 1)
dog.tb$answer <- revalue(as.character(dog.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.dog <- merge(dog, dog.tb, by = "answer")
plot.dog <- plot.dog[-c(2, 4, 6)]
plot.dog <- setcolorder(plot.dog, c("answer", "dog.tb", "Freq"))
plot.dog$order <- c(3, 2, 1)
plot.dog <- plot.dog %>% arrange(order)
plot.dog <- plot.dog[-c(4)]
colnames(plot.dog) <- c("Response", "N", "Proportion")
kable(plot.dog)| Response | N | Proportion |
|---|---|---|
| Yes | 57 | 19.930070 |
| No | 223 | 77.972028 |
| NA | 6 | 2.097902 |
Q37:Do you walk the dog regularly?
dog.walk <- prop.table(table(factor(d$Q37_dog_walk, levels = c("1", "2"))))*100
dog.walk <- as.data.frame(dog.walk)
dog.walk$answer <- substring(row.names(dog.walk), 1)
dog.walk$answer <- revalue(as.character(dog.walk$answer), c("1" = "Yes", "2" = "No"))
dog.walk$plot <- factor(dog.walk$answer, dog.walk$answer)
cols <- c("#31a354","#f03b20") #get colors for plot
dog.walk.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
dog.walk.plot + geom_bar(aes(x = plot), data = dog.walk, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
dog.walk.tb <- as.factor(d$Q37_dog_walk)
dog.walk.tb <- summary(dog.walk.tb)
dog.walk.tb <- as.data.frame(dog.walk.tb)
dog.walk.tb$Var1 <- substring(row.names(dog.walk.tb), 1)
dog.walk.tb$answer <- revalue(as.character(dog.walk.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.dog.walk <- merge(dog.walk, dog.walk.tb, by = "answer")
plot.dog.walk <- merge(dog.walk, plot.dog.walk, by = "answer")
plot.dog.walk <- plot.dog.walk[-c(2, 4:7, 9)]
plot.dog.walk <- setcolorder(plot.dog.walk, c("answer", "dog.walk.tb", "Freq.x"))
plot.dog.walk$order <- c(2, 1)
plot.dog.walk <- plot.dog.walk %>% arrange(order)
plot.dog.walk <- plot.dog.walk[-c(4)]
colnames(plot.dog.walk) <- c("Response", "N", "Proportion")
kable(plot.dog.walk)| Response | N | Proportion |
|---|---|---|
| Yes | 43 | 75.4386 |
| No | 14 | 24.5614 |
Q38:How many hours or minutes a day on average do you walk the dog?
ggplot(d, aes(x = d$Q38_dog_walk_freq)) + geom_histogram(na.rm = TRUE) + xlab("Minutes dog walking") ## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
summary(d$Q38_dog_walk_freq)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 10.00 30.00 40.00 52.09 60.00 120.00 243Section 4: General Health
Q39: How tall are you? (Choose either feet or centimetres)
ggplot(d, aes(x = d$Q39_height)) + geom_histogram(na.rm = TRUE, binwidth = 1) + xlab("Height (cm)") 
summary(d$Q39_height)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 155.0 165.0 170.0 172.3 178.5 201.0 6Q40: How much do you weigh? (Choose either pounds or kilograms)
ggplot(d, aes(x = d$Q40_weight)) + geom_histogram(na.rm = TRUE, binwidth = 2) + xlab("Weight (kg)") 
summary(d$Q40_weight)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 45.00 61.00 70.00 71.75 80.25 150.00 6Q41: In general, would you say your health is:
health <- prop.table(table(factor(d$Q41, levels = c("1", "2", "3", "4", "5")), exclude=NULL))*100
health <- as.data.frame(health)
health$answer <- substring(row.names(health), 1)
health$answer <- revalue(as.character(health$answer), c("1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor", "6" = "No anwer"))
health$plot <- factor(health$answer, health$answer)
health.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("")
health.plot + geom_bar(aes(x = plot), data = health, stat = "identity", colour="black") 
health.tb <- as.factor(d$Q41_sf1)
health.tb <- summary(health.tb)
health.tb <- as.data.frame(health.tb)
health.tb$Var1 <- substring(row.names(health.tb), 1)
health.tb$answer <- revalue(as.character(health.tb$Var1), c("1" = "Excellent", "2" = "Very good", "3" = "Good", "4" = "Fair", "5" = "Poor", "NA's" = "No anwer"))
plot.health <- merge(health, health.tb, by = "answer")
plot.health <- plot.health[-c(2, 4, 6)]
plot.health <- setcolorder(plot.health, c("answer", "health.tb", "Freq"))
plot.health$order <- c(1, 4, 3, 6, 5, 2)
plot.health <- plot.health %>% arrange(order)
plot.health <- plot.health[-c(4)]
colnames(plot.health) <- c("Response", "N", "Proportion")
kable(plot.health)| Response | N | Proportion |
|---|---|---|
| Excellent | 69 | 24.1258741 |
| Very good | 138 | 48.2517483 |
| Good | 59 | 20.6293706 |
| Fair | 13 | 4.5454545 |
| Poor | 1 | 0.3496503 |
| No anwer | 6 | 2.0979021 |
Q42:The following items are about activities you might do during a typical day. Does your health now limit you in these activities? If so, how much?
Moderate activities such as moving a table, pushing a vacuum cleaner, bowling, or playing golf
mod <- prop.table(table(factor(d$Q42_sf2, levels = c("1", "2", "3")), exclude = NULL))*100
mod <- as.data.frame(mod)
mod$answer <- substring(row.names(mod), 1)
mod$answer <- revalue(as.character(mod$answer), c("1" = "Yes, limited a lot", "2" = "Yes, limited a little", "3" = "No, not at all", "4" = "NA"))
mod$plot <- factor(mod$answer, mod$answer)
cols <- c(brewer.pal(3,"RdYlGn"),"grey")
mod.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
mod.plot + geom_bar(aes(x = plot), data = mod, stat = "identity", colour="black") 
mod.tb <- as.factor(d$Q42_sf2)
mod.tb <- summary(mod.tb)
mod.tb <- as.data.frame(mod.tb)
mod.tb$Var1 <- substring(row.names(mod.tb), 1)
mod.tb$answer <- revalue(as.character(mod.tb$Var1), c("1" = "Yes, limited a lot", "2" = "Yes, limited a little", "3" = "No, not at all", "NA's" = "NA"))
plot.mod <- merge(mod, mod.tb, by = "answer")
plot.mod <- plot.mod[-c(2, 4, 6)]
plot.mod <- setcolorder(plot.mod, c("answer", "mod.tb", "Freq"))
plot.mod$order <- c(4, 3, 2, 1)
plot.mod <- plot.mod %>% arrange(order)
plot.mod <- plot.mod[-c(4)]
colnames(plot.mod) <- c("Response", "N", "Proportion")
kable(plot.mod)| Response | N | Proportion |
|---|---|---|
| Yes, limited a lot | 3 | 1.048951 |
| Yes, limited a little | 20 | 6.993007 |
| No, not at all | 257 | 89.860140 |
| NA | 6 | 2.097902 |
Climbing several flights of stairs
stairs <- prop.table(table(factor(d$Q42_sf3, levels = c("1", "2", "3")), exclude = NULL))*100
stairs <- as.data.frame(stairs)
stairs$answer <- substring(row.names(stairs), 1)
stairs$answer <- revalue(as.character(stairs$answer), c("1" = "Yes, limited a lot", "2" = "Yes, limited a little", "3" = "No, not at all", "4" = "NA"))
stairs$plot <- factor(stairs$answer, stairs$answer)
cols <- c(brewer.pal(3,"RdYlGn"),"grey")
stairs.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
stairs.plot + geom_bar(aes(x = plot), data = stairs, stat = "identity", colour="black") 
stairs.tb <- as.factor(d$Q42_sf3)
stairs.tb <- summary(stairs.tb)
stairs.tb <- as.data.frame(stairs.tb)
stairs.tb$Var1 <- substring(row.names(stairs.tb), 1)
stairs.tb$answer <- revalue(as.character(stairs.tb$Var1), c("1" = "Yes, limited a lot", "2" = "Yes, limited a little", "3" = "No, not at all", "NA's" = "NA"))
plot.stairs <- merge(stairs, stairs.tb, by = "answer")
plot.stairs <- plot.stairs[-c(2, 4, 6)]
plot.stairs <- setcolorder(plot.stairs, c("answer", "stairs.tb", "Freq"))
plot.stairs$order <- c(4, 3, 2, 1)
plot.stairs <- plot.stairs %>% arrange(order)
plot.stairs <- plot.stairs[-c(4)]
colnames(plot.stairs) <- c("Response", "N", "Proportion")
kable(plot.stairs)| Response | N | Proportion |
|---|---|---|
| Yes, limited a lot | 5 | 1.748252 |
| Yes, limited a little | 20 | 6.993007 |
| No, not at all | 255 | 89.160839 |
| NA | 6 | 2.097902 |
Q43: During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of your physical health?
Accomplished less than you would like
Q43.a <- prop.table(table(factor(d$Q43_sf4, levels = c("1", "2")), exclude = NULL))*100
Q43.a <- as.data.frame(Q43.a)
Q43.a$answer <- substring(row.names(Q43.a), 1)
Q43.a$answer <- revalue(as.character(Q43.a$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
Q43.a$plot <- factor(Q43.a$answer, Q43.a$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
Q43.a.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
Q43.a.plot + geom_bar(aes(x = plot), data = Q43.a, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
Q43.a.tb <- as.factor(d$Q43_sf4)
Q43.a.tb <- summary(Q43.a.tb)
Q43.a.tb <- as.data.frame(Q43.a.tb)
Q43.a.tb$Var1 <- substring(row.names(Q43.a.tb), 1)
Q43.a.tb$answer <- revalue(as.character(Q43.a.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.Q43.a <- merge(Q43.a, Q43.a.tb, by = "answer")
plot.Q43.a <- plot.Q43.a[-c(2, 4, 6)]
plot.Q43.a <- setcolorder(plot.Q43.a, c("answer", "Q43.a.tb", "Freq"))
plot.Q43.a$order <- c(3, 2, 1)
plot.Q43.a <- plot.Q43.a %>% arrange(order)
plot.Q43.a <- plot.Q43.a[-c(4)]
colnames(plot.Q43.a) <- c("Response", "N", "Proportion")
kable(plot.Q43.a)| Response | N | Proportion |
|---|---|---|
| Yes | 58 | 20.279720 |
| No | 222 | 77.622378 |
| NA | 6 | 2.097902 |
Were limited in the kind of work or other activities
Q43.b <- prop.table(table(factor(d$Q43_sf5, levels = c("1", "2")), exclude = NULL))*100
Q43.b <- as.data.frame(Q43.b)
Q43.b$answer <- substring(row.names(Q43.b), 1)
Q43.b$answer <- revalue(as.character(Q43.b$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
Q43.b$plot <- factor(Q43.b$answer, Q43.b$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
Q43.b.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
Q43.b.plot + geom_bar(aes(x = plot), data = Q43.b, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
Q43.b.tb <- as.factor(d$Q43_sf5)
Q43.b.tb <- summary(Q43.b.tb)
Q43.b.tb <- as.data.frame(Q43.b.tb)
Q43.b.tb$Var1 <- substring(row.names(Q43.b.tb), 1)
Q43.b.tb$answer <- revalue(as.character(Q43.b.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.Q43.b <- merge(Q43.b, Q43.b.tb, by = "answer")
plot.Q43.b <- plot.Q43.b[-c(2, 4, 6)]
plot.Q43.b <- setcolorder(plot.Q43.b, c("answer", "Q43.b.tb", "Freq"))
plot.Q43.b$order <- c(3, 2, 1)
plot.Q43.b <- plot.Q43.b %>% arrange(order)
plot.Q43.b <- plot.Q43.b[-c(4)]
colnames(plot.Q43.b) <- c("Response", "N", "Proportion")
kable(plot.Q43.b)| Response | N | Proportion |
|---|---|---|
| Yes | 48 | 16.783217 |
| No | 232 | 81.118881 |
| NA | 6 | 2.097902 |
Q44: During the past 4 weeks, have you had any of the following problems with your work or other regular daily activities as a result of any emotional problems (such as feeling depressed or anxious)?
Accomplished less than you would like
Q44.a <- prop.table(table(factor(d$Q44_sf6, levels = c("1", "2")), exclude = NULL))*100
Q44.a <- as.data.frame(Q44.a)
Q44.a$answer <- substring(row.names(Q44.a), 1)
Q44.a$answer <- revalue(as.character(Q44.a$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
Q44.a$plot <- factor(Q44.a$answer, Q44.a$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
Q44.a.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
Q44.a.plot + geom_bar(aes(x = plot), data = Q44.a, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
Q44.a.tb <- as.factor(d$Q44_sf6)
Q44.a.tb <- summary(Q44.a.tb)
Q44.a.tb <- as.data.frame(Q44.a.tb)
Q44.a.tb$Var1 <- substring(row.names(Q44.a.tb), 1)
Q44.a.tb$answer <- revalue(as.character(Q44.a.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.Q44.a <- merge(Q44.a, Q44.a.tb, by = "answer")
plot.Q44.a <- plot.Q44.a[-c(2, 4, 6)]
plot.Q44.a <- setcolorder(plot.Q44.a, c("answer", "Q44.a.tb", "Freq"))
plot.Q44.a$order <- c(3, 2, 1)
plot.Q44.a <- plot.Q44.a %>% arrange(order)
plot.Q44.a <- plot.Q44.a[-c(4)]
colnames(plot.Q44.a) <- c("Response", "N", "Proportion")
kable(plot.Q44.a)| Response | N | Proportion |
|---|---|---|
| Yes | 62 | 21.678322 |
| No | 218 | 76.223776 |
| NA | 6 | 2.097902 |
Did work or activities less carefully than usual
Q44.b <- prop.table(table(factor(d$Q44_sf7, levels = c("1", "2")), exclude = NULL))*100
Q44.b <- as.data.frame(Q44.b)
Q44.b$answer <- substring(row.names(Q44.b), 1)
Q44.b$answer <- revalue(as.character(Q44.b$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
Q44.b$plot <- factor(Q44.b$answer, Q44.b$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
Q44.b.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
Q44.b.plot + geom_bar(aes(x = plot), data = Q44.b, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
Q44.b.tb <- as.factor(d$Q44_sf7)
Q44.b.tb <- summary(Q44.b.tb)
Q44.b.tb <- as.data.frame(Q44.b.tb)
Q44.b.tb$Var1 <- substring(row.names(Q44.b.tb), 1)
Q44.b.tb$answer <- revalue(as.character(Q44.b.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.Q44.b <- merge(Q44.b, Q44.b.tb, by = "answer")
plot.Q44.b <- plot.Q44.b[-c(2, 4, 6)]
plot.Q44.b <- setcolorder(plot.Q44.b, c("answer", "Q44.b.tb", "Freq"))
plot.Q44.b$order <- c(3, 2, 1)
plot.Q44.b <- plot.Q44.b %>% arrange(order)
plot.Q44.b <- plot.Q44.b[-c(4)]
colnames(plot.Q44.b) <- c("Response", "N", "Proportion")
kable(plot.Q44.b)| Response | N | Proportion |
|---|---|---|
| Yes | 50 | 17.482518 |
| No | 230 | 80.419580 |
| NA | 6 | 2.097902 |
Q45:During the past 4 weeks, how much did pain interfere with your normal work (including work outside the home and housework)?
pain <- prop.table(table(factor(d$Q45_sf8, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100
pain <- as.data.frame(pain)
pain$answer <- substring(row.names(pain), 1)
pain$answer <- revalue(as.character(pain$answer), c("1" = "Not at all", "2" = "Slightly", "3" = "Moderately", "4" = "Quite a bit", "5" = "Extremely", "6" = "NA"))
pain$plot <- factor(pain$answer, pain$answer)
pain.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("")
pain.plot + geom_bar(aes(x = plot), data = pain, stat = "identity", colour="black") 
pain.tb <- as.factor(d$Q45_sf8)
pain.tb <- summary(pain.tb)
pain.tb <- as.data.frame(pain.tb)
pain.tb$Var1 <- substring(row.names(pain.tb), 1)
pain.tb$answer <- revalue(as.character(pain.tb$Var1), c("1" = "Not at all", "2" = "Slightly", "3" = "Moderately", "4" = "Quite a bit", "5" = "Extremely", "NA's" = "NA"))
plot.pain <- merge(pain, pain.tb, by = "answer")
plot.pain <- plot.pain[-c(2, 4, 6)]
plot.pain <- setcolorder(plot.pain, c("answer", "pain.tb", "Freq"))
plot.pain$order <- c(5, 3, 6, 1, 4, 2)
plot.pain <- plot.pain %>% arrange(order)
plot.pain <- plot.pain[-c(4)]
colnames(plot.pain) <- c("Response", "N", "Proportion")
kable(plot.pain)| Response | N | Proportion |
|---|---|---|
| Not at all | 176 | 61.538461 |
| Slightly | 74 | 25.874126 |
| Moderately | 18 | 6.293706 |
| Quite a bit | 9 | 3.146853 |
| Extremely | 3 | 1.048951 |
| NA | 6 | 2.097902 |
Q46:How much of the time during the past 4 weeks…
Have you felt calm and peaceful?
peace <- prop.table(table(factor(d$Q46_sf9, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100
peace <- as.data.frame(peace)
peace$answer <- substring(row.names(peace), 1)
peace$answer <- revalue(as.character(peace$answer), c("1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time", "7" = "NA"))
peace$plot <- factor(peace$answer, peace$answer)
peace.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("")
peace.plot + geom_bar(aes(x = plot), data = peace, stat = "identity", colour="black") 
peace.tb <- as.factor(d$Q46_sf9)
peace.tb <- summary(peace.tb)
peace.tb <- as.data.frame(peace.tb)
peace.tb$Var1 <- substring(row.names(peace.tb), 1)
peace.tb$answer <- revalue(as.character(peace.tb$Var1), c("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", "NA's" = "NA"))
plot.peace <- merge(peace, peace.tb, by = "answer")
plot.peace <- plot.peace[-c(2, 4, 6)]
plot.peace <- setcolorder(plot.peace, c("answer", "peace.tb", "Freq"))
plot.peace$order <- c(3, 5, 1, 2, 7, 6 ,4)
plot.peace <- plot.peace %>% arrange(order)
plot.peace <- plot.peace[-c(4)]
colnames(plot.peace) <- c("Response", "N", "Proportion")
kable(plot.peace)| Response | N | Proportion |
|---|---|---|
| All of the time | 7 | 2.4475524 |
| Most of the time | 103 | 36.0139860 |
| A good bit of the time | 92 | 32.1678322 |
| Some of the time | 59 | 20.6293706 |
| A little of the time | 17 | 5.9440559 |
| None of the time | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
Did you have a lot of energy?
energy <- prop.table(table(factor(d$Q46_sf10, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100
energy <- as.data.frame(energy)
energy$answer <- substring(row.names(energy), 1)
energy$answer <- revalue(as.character(energy$answer), c("1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time", "7" = "NA"))
energy$plot <- factor(energy$answer, energy$answer)
energy.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("")
energy.plot + geom_bar(aes(x = plot), data = energy, stat = "identity", colour="black") 
energy.tb <- as.factor(d$Q46_sf10)
energy.tb <- summary(energy.tb)
energy.tb <- as.data.frame(energy.tb)
energy.tb$Var1 <- substring(row.names(energy.tb), 1)
energy.tb$answer <- revalue(as.character(energy.tb$Var1), c("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", "NA's" = "NA"))
plot.energy <- merge(energy, energy.tb, by = "answer")
plot.energy <- plot.energy[-c(2, 4, 6)]
plot.energy <- setcolorder(plot.energy, c("answer", "energy.tb", "Freq"))
plot.energy$order <- c(3, 5, 1, 2, 7, 6 ,4)
plot.energy <- plot.energy %>% arrange(order)
plot.energy <- plot.energy[-c(4)]
colnames(plot.energy) <- c("Response", "N", "Proportion")
kable(plot.energy)| Response | N | Proportion |
|---|---|---|
| All of the time | 11 | 3.8461538 |
| Most of the time | 137 | 47.9020979 |
| A good bit of the time | 68 | 23.7762238 |
| Some of the time | 46 | 16.0839161 |
| A little of the time | 14 | 4.8951049 |
| None of the time | 2 | 0.6993007 |
| NA | 8 | 2.7972028 |
Have you felt downhearted and blue?
blue <- prop.table(table(factor(d$Q46_sf11, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100
blue <- as.data.frame(blue)
blue$answer <- substring(row.names(blue), 1)
blue$answer <- revalue(as.character(blue$answer), c("1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time", "7" = "NA"))
blue$plot <- factor(blue$answer, blue$answer)
blue.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("")
blue.plot + geom_bar(aes(x = plot), data = blue, stat = "identity", colour="black") 
blue.tb <- as.factor(d$Q46_sf11)
blue.tb <- summary(blue.tb)
blue.tb <- as.data.frame(blue.tb)
blue.tb$Var1 <- substring(row.names(blue.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$blue.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("1")
nval.df <- nval.df[-c(1)]
blue.tb <- rbind(blue.tb, nval.df)
blue.tb$answer <- revalue(as.character(blue.tb$Var1), c("1" = "All of the time", "2" = "Most of the time", "3" = "A good bit of the time", "4" = "Some of the time", "5" = "A little of the time", "6" = "None of the time", "NA's" = "NA"))
plot.blue <- merge(blue, blue.tb, by = "answer")
plot.blue <- plot.blue[-c(2, 4, 6)]
plot.blue <- setcolorder(plot.blue, c("answer", "blue.tb", "Freq"))
plot.blue$order <- c(3, 5, 1, 2, 7, 6 ,4)
plot.blue <- plot.blue %>% arrange(order)
plot.blue <- plot.blue[-c(4)]
colnames(plot.blue) <- c("Response", "N", "Proportion")
kable(plot.blue)| Response | N | Proportion |
|---|---|---|
| All of the time | 0 | 0.0000000 |
| Most of the time | 2 | 0.6993007 |
| A good bit of the time | 14 | 4.8951049 |
| Some of the time | 58 | 20.2797203 |
| A little of the time | 135 | 47.2027972 |
| None of the time | 69 | 24.1258741 |
| NA | 8 | 2.7972028 |
Q47: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.)?
social <- prop.table(table(factor(d$Q47_sf12, levels = c("1", "2", "3", "4", "5", "6")), exclude = NULL))*100
social <- as.data.frame(social)
social$answer <- substring(row.names(social), 1)
social$answer <- revalue(as.character(social$answer), c("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", "7" = "NA"))
social$plot <- factor(social$answer, social$answer)
social.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "BuGn", direction=-1) +
ylab("Percent of total") +
xlab("")
social.plot + geom_bar(aes(x = plot), data = social, stat = "identity", colour="black") 
social.tb <- as.factor(d$Q47_sf12)
social.tb <- summary(social.tb)
social.tb <- as.data.frame(social.tb)
social.tb$Var1 <- substring(row.names(social.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$social.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("2")
nval.df <- nval.df[-c(1)]
social.tb <- rbind(social.tb, nval.df)
social.tb$answer <- revalue(as.character(social.tb$Var1), c("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", "NA's" = "NA"))
plot.social <- merge(social, social.tb, by = "answer")
plot.social <- plot.social[-c(2, 4, 6)]
plot.social <- setcolorder(plot.social, c("answer", "social.tb", "Freq"))
plot.social$order <- c(3, 5, 1, 2, 7, 6 ,4)
plot.social <- plot.social %>% arrange(order)
plot.social <- plot.social[-c(4)]
colnames(plot.social) <- c("Response", "N", "Proportion")
kable(plot.social)| Response | N | Proportion |
|---|---|---|
| All of the time | 2 | 0.6993007 |
| Most of the time | 0 | 0.0000000 |
| A good bit of the time | 12 | 4.1958042 |
| Some of the time | 23 | 8.0419580 |
| A little of the time | 66 | 23.0769231 |
| None of the time | 176 | 61.5384615 |
| NA | 7 | 2.4475524 |
Section 5:Personal Wellbeing
Q48:Thinking about your own life and personal circumstances, how satisfied are you…
With your life as a whole?
life <- prop.table(table(factor(d$Q48_pwb_a, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
life <- as.data.frame(life)
life$answer <- substring(row.names(life), 1)
life$answer <- revalue(as.character(life$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
life$plot <- factor(life$answer, life$answer)
life.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
life.plot + geom_bar(aes(x = plot), data = life, stat = "identity", colour="black") 
life.tb <- as.factor(d$Q48_pwb_a)
life.tb <- summary(life.tb)
life.tb <- as.data.frame(life.tb)
life.tb$Var1 <- substring(row.names(life.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$life.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("10")
nval.df <- nval.df[-c(1)]
life.tb <- rbind(life.tb, nval.df)
life.tb$answer <- revalue(as.character(life.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.life <- merge(life, life.tb, by = "answer")
plot.life <- plot.life[-c(2, 4, 6)]
plot.life <- setcolorder(plot.life, c("answer", "life.tb", "Freq"))
plot.life$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.life <- plot.life %>% arrange(order)
plot.life <- plot.life[-c(4)]
colnames(plot.life) <- c("Response", "N", "Proportion")
kable(plot.life)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 32 | 11.1888112 |
| 2 | 119 | 41.6083916 |
| 3 | 63 | 22.0279720 |
| 4 | 27 | 9.4405594 |
| Neutral | 14 | 4.8951049 |
| 6 | 10 | 3.4965035 |
| 7 | 9 | 3.1468531 |
| 8 | 3 | 1.0489510 |
| 9 | 2 | 0.6993007 |
| Completely dissatisfied | 0 | 0.0000000 |
| NA | 7 | 2.4475524 |
With your standard of living?
living <- prop.table(table(factor(d$Q48_pwb_b, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
living <- as.data.frame(living)
living$answer <- substring(row.names(living), 1)
living$answer <- revalue(as.character(living$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
living$plot <- factor(living$answer, living$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
living.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
living.plot + geom_bar(aes(x = plot), data = living, stat = "identity", colour="black") 
living.tb <- as.factor(d$Q48_pwb_b)
living.tb <- summary(living.tb)
living.tb <- as.data.frame(living.tb)
living.tb$Var1 <- substring(row.names(living.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$living.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("9")
nval.df <- nval.df[-c(1)]
living.tb <- rbind(living.tb, nval.df)
living.tb$answer <- revalue(as.character(living.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.living <- merge(living, living.tb, by = "answer")
plot.living <- plot.living[-c(2, 4, 6)]
plot.living <- setcolorder(plot.living, c("answer", "living.tb", "Freq"))
plot.living$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.living <- plot.living %>% arrange(order)
plot.living <- plot.living[-c(4)]
colnames(plot.living) <- c("Response", "N", "Proportion")
kable(plot.living)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 83 | 29.0209790 |
| 2 | 99 | 34.6153846 |
| 3 | 50 | 17.4825175 |
| 4 | 22 | 7.6923077 |
| Neutral | 12 | 4.1958042 |
| 6 | 3 | 1.0489510 |
| 7 | 4 | 1.3986014 |
| 8 | 4 | 1.3986014 |
| 9 | 0 | 0.0000000 |
| Completely dissatisfied | 2 | 0.6993007 |
| NA | 7 | 2.4475524 |
With your health?
healthsat <- prop.table(table(factor(d$Q48_pwb_c, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
healthsat <- as.data.frame(healthsat)
healthsat$answer <- substring(row.names(healthsat), 1)
healthsat$answer <- revalue(as.character(healthsat$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
healthsat$plot <- factor(healthsat$answer, healthsat$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
healthsat.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
healthsat.plot + geom_bar(aes(x = plot), data = healthsat, stat = "identity", colour="black") 
healthsat.tb <- as.factor(d$Q48_pwb_c)
healthsat.tb <- summary(healthsat.tb)
healthsat.tb <- as.data.frame(healthsat.tb)
healthsat.tb$Var1 <- substring(row.names(healthsat.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$healthsat.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("10")
nval.df <- nval.df[-c(1)]
healthsat.tb <- rbind(healthsat.tb, nval.df)
healthsat.tb$answer <- revalue(as.character(healthsat.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.healthsat <- merge(healthsat, healthsat.tb, by = "answer")
plot.healthsat <- plot.healthsat[-c(2, 4, 6)]
plot.healthsat <- setcolorder(plot.healthsat, c("answer", "healthsat.tb", "Freq"))
plot.healthsat$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.healthsat <- plot.healthsat %>% arrange(order)
plot.healthsat <- plot.healthsat[-c(4)]
colnames(plot.healthsat) <- c("Response", "N", "Proportion")
kable(plot.healthsat)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 39 | 13.6363636 |
| 2 | 118 | 41.2587413 |
| 3 | 63 | 22.0279720 |
| 4 | 14 | 4.8951049 |
| Neutral | 11 | 3.8461538 |
| 6 | 14 | 4.8951049 |
| 7 | 13 | 4.5454545 |
| 8 | 6 | 2.0979021 |
| 9 | 1 | 0.3496503 |
| Completely dissatisfied | 0 | 0.0000000 |
| NA | 7 | 2.4475524 |
With what you are achieving in life?
ach <- prop.table(table(factor(d$Q48_pwb_d, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
ach <- as.data.frame(ach)
ach$answer <- substring(row.names(ach), 1)
ach$answer <- revalue(as.character(ach$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
ach$plot <- factor(ach$answer, ach$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
ach.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
ach.plot + geom_bar(aes(x = plot), data = ach, stat = "identity", colour="black") 
ach.tb <- as.factor(d$Q48_pwb_d)
ach.tb <- summary(ach.tb)
ach.tb <- as.data.frame(ach.tb)
ach.tb$Var1 <- substring(row.names(ach.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$ach.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("10")
nval.df <- nval.df[-c(1)]
ach.tb <- rbind(ach.tb, nval.df)
ach.tb$answer <- revalue(as.character(ach.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.ach <- merge(ach, ach.tb, by = "answer")
plot.ach <- plot.ach[-c(2, 4, 6)]
plot.ach <- setcolorder(plot.ach, c("answer", "ach.tb", "Freq"))
plot.ach$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.ach <- plot.ach %>% arrange(order)
plot.ach <- plot.ach[-c(4)]
colnames(plot.ach) <- c("Response", "N", "Proportion")
kable(plot.ach)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 43 | 15.034965 |
| 2 | 91 | 31.818182 |
| 3 | 70 | 24.475524 |
| 4 | 25 | 8.741259 |
| Neutral | 21 | 7.342657 |
| 6 | 11 | 3.846154 |
| 7 | 4 | 1.398601 |
| 8 | 7 | 2.447552 |
| 9 | 7 | 2.447552 |
| Completely dissatisfied | 0 | 0.000000 |
| NA | 7 | 2.447552 |
With your personal relationships?
relat <- prop.table(table(factor(d$Q48_pwb_e, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
relat <- as.data.frame(relat)
relat$answer <- substring(row.names(relat), 1)
relat$answer <- revalue(as.character(relat$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
relat$plot <- factor(relat$answer, relat$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
relat.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
relat.plot + geom_bar(aes(x = plot), data = relat, stat = "identity", colour="black") 
relat.tb <- as.factor(d$Q48_pwb_e)
relat.tb <- summary(relat.tb)
relat.tb <- as.data.frame(relat.tb)
relat.tb$Var1 <- substring(row.names(relat.tb), 1)
relat.tb$answer <- revalue(as.character(relat.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.relat <- merge(relat, relat.tb, by = "answer")
plot.relat <- plot.relat[-c(2, 4, 6)]
plot.relat <- setcolorder(plot.relat, c("answer", "relat.tb", "Freq"))
plot.relat$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.relat <- plot.relat %>% arrange(order)
plot.relat <- plot.relat[-c(4)]
colnames(plot.relat) <- c("Response", "N", "Proportion")
kable(plot.relat)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 54 | 18.8811189 |
| 2 | 105 | 36.7132867 |
| 3 | 44 | 15.3846154 |
| 4 | 30 | 10.4895105 |
| Neutral | 15 | 5.2447552 |
| 6 | 15 | 5.2447552 |
| 7 | 5 | 1.7482517 |
| 8 | 5 | 1.7482517 |
| 9 | 4 | 1.3986014 |
| Completely dissatisfied | 2 | 0.6993007 |
| NA | 7 | 2.4475524 |
With how safe you feel?
safe <- prop.table(table(factor(d$Q48_pwb_f, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
safe <- as.data.frame(safe)
safe$answer <- substring(row.names(safe), 1)
safe$answer <- revalue(as.character(safe$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
safe$plot <- factor(safe$answer, safe$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
safe.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
safe.plot + geom_bar(aes(x = plot), data = safe, stat = "identity", colour="black") 
safe.tb <- as.factor(d$Q48_pwb_f)
safe.tb <- summary(safe.tb)
safe.tb <- as.data.frame(safe.tb)
safe.tb$Var1 <- substring(row.names(safe.tb), 1)
nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$safe.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("7", "8")
nval.df <- nval.df[-c(1)]
safe.tb <- rbind(safe.tb, nval.df)
safe.tb$answer <- revalue(as.character(safe.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.safe <- merge(safe, safe.tb, by = "answer")
plot.safe <- plot.safe[-c(2, 4, 6)]
plot.safe <- setcolorder(plot.safe, c("answer", "safe.tb", "Freq"))
plot.safe$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.safe <- plot.safe %>% arrange(order)
plot.safe <- plot.safe[-c(4)]
colnames(plot.safe) <- c("Response", "N", "Proportion")
kable(plot.safe)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 123 | 43.0069930 |
| 2 | 97 | 33.9160839 |
| 3 | 24 | 8.3916084 |
| 4 | 17 | 5.9440559 |
| Neutral | 9 | 3.1468531 |
| 6 | 4 | 1.3986014 |
| 7 | 0 | 0.0000000 |
| 8 | 0 | 0.0000000 |
| 9 | 2 | 0.6993007 |
| Completely dissatisfied | 3 | 1.0489510 |
| NA | 7 | 2.4475524 |
With feeling part of your community?
com <- prop.table(table(factor(d$Q48_pwb_g, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
com <- as.data.frame(com)
com$answer <- substring(row.names(com), 1)
com$answer <- revalue(as.character(com$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
com$plot <- factor(com$answer, com$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
com.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
com.plot + geom_bar(aes(x = plot), data = com, stat = "identity", colour="black") 
com.tb <- as.factor(d$Q48_pwb_g)
com.tb <- summary(com.tb)
com.tb <- as.data.frame(com.tb)
com.tb$Var1 <- substring(row.names(com.tb), 1)
com.tb$answer <- revalue(as.character(com.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.com <- merge(com, com.tb, by = "answer")
plot.com <- plot.com[-c(2, 4, 6)]
plot.com <- setcolorder(plot.com, c("answer", "com.tb", "Freq"))
plot.com$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.com <- plot.com %>% arrange(order)
plot.com <- plot.com[-c(4)]
colnames(plot.com) <- c("Response", "N", "Proportion")
kable(plot.com)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 50 | 17.482518 |
| 2 | 79 | 27.622378 |
| 3 | 60 | 20.979021 |
| 4 | 28 | 9.790210 |
| Neutral | 30 | 10.489510 |
| 6 | 17 | 5.944056 |
| 7 | 4 | 1.398601 |
| 8 | 3 | 1.048951 |
| 9 | 5 | 1.748252 |
| Completely dissatisfied | 3 | 1.048951 |
| NA | 7 | 2.447552 |
With your future security?
sec <- prop.table(table(factor(d$Q48_pwb_h, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
sec <- as.data.frame(sec)
sec$answer <- substring(row.names(sec), 1)
sec$answer <- revalue(as.character(sec$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
sec$plot <- factor(sec$answer, sec$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
sec.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
sec.plot + geom_bar(aes(x = plot), data = sec, stat = "identity", colour="black") 
sec.tb <- as.factor(d$Q48_pwb_h)
sec.tb <- summary(sec.tb)
sec.tb <- as.data.frame(sec.tb)
sec.tb$Var1 <- substring(row.names(sec.tb), 1)
sec.tb$answer <- revalue(as.character(sec.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.sec <- merge(sec, sec.tb, by = "answer")
plot.sec <- plot.sec[-c(2, 4, 6)]
plot.sec <- setcolorder(plot.sec, c("answer", "sec.tb", "Freq"))
plot.sec$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.sec <- plot.sec %>% arrange(order)
plot.sec <- plot.sec[-c(4)]
colnames(plot.sec) <- c("Response", "N", "Proportion")
kable(plot.sec)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 49 | 17.132867 |
| 2 | 86 | 30.069930 |
| 3 | 49 | 17.132867 |
| 4 | 27 | 9.440559 |
| Neutral | 22 | 7.692308 |
| 6 | 20 | 6.993007 |
| 7 | 10 | 3.496504 |
| 8 | 6 | 2.097902 |
| 9 | 5 | 1.748252 |
| Completely dissatisfied | 5 | 1.748252 |
| NA | 7 | 2.447552 |
With your spirituality or religion?
spirit <- prop.table(table(factor(d$Q48_pwb_i, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10")), exclude = NULL))*100
spirit <- as.data.frame(spirit)
spirit$answer <- substring(row.names(spirit), 1)
spirit$answer <- revalue(as.character(spirit$answer), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "11" = "NA"))
spirit$plot <- factor(spirit$answer, spirit$answer)
cols <- c(rev(brewer.pal(10,"RdYlGn")),"grey")
spirit.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
spirit.plot + geom_bar(aes(x = plot), data = spirit, stat = "identity", colour="black") 
spirit.tb <- as.factor(d$Q48_pwb_i)
spirit.tb <- summary(spirit.tb)
spirit.tb <- as.data.frame(spirit.tb)
spirit.tb$Var1 <- substring(row.names(spirit.tb), 1)
nval.df <- c("0", "0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$spirit.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("9", "10")
nval.df <- nval.df[-c(1)]
spirit.tb <- rbind(spirit.tb, nval.df)
spirit.tb$answer <- revalue(as.character(spirit.tb$Var1), c("1" = "Completely satisfied", "5" = "Neutral", "10" = "Completely dissatisfied", "NA's" = "NA"))
plot.spirit <- merge(spirit, spirit.tb, by = "answer")
plot.spirit <- plot.spirit[-c(2, 4, 6)]
plot.spirit <- setcolorder(plot.spirit, c("answer", "spirit.tb", "Freq"))
plot.spirit$order <- c(2, 3, 4, 6, 7, 8, 9, 10, 1, 11, 5)
plot.spirit <- plot.spirit %>% arrange(order)
plot.spirit <- plot.spirit[-c(4)]
colnames(plot.spirit) <- c("Response", "N", "Proportion")
kable(plot.spirit)| Response | N | Proportion |
|---|---|---|
| Completely satisfied | 82 | 28.6713287 |
| 2 | 53 | 18.5314685 |
| 3 | 23 | 8.0419580 |
| 4 | 13 | 4.5454545 |
| Neutral | 97 | 33.9160839 |
| 6 | 5 | 1.7482517 |
| 7 | 4 | 1.3986014 |
| 8 | 2 | 0.6993007 |
| 9 | 0 | 0.0000000 |
| Completely dissatisfied | 0 | 0.0000000 |
| NA | 7 | 2.4475524 |
Section 6: Neighbourhood
Q49:Please indicate your satisfaction with each item
How satisfied are you with your neighbourhood as a good place to live? Are you…
nhds <- prop.table(table(factor(d$Q49_neighb_a, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100
nhds <- as.data.frame(nhds)
nhds$answer <- substring(row.names(nhds), 1)
nhds$answer <- revalue(as.character(nhds$answer), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "6" = "NA"))
nhds$plot <- factor(nhds$answer, nhds$answer)
cols <- c(rev(brewer.pal(5,"RdYlGn")),"grey")
nhds.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
nhds.plot + geom_bar(aes(x = plot), data = nhds, stat = "identity", colour="black") 
nhds.tb <- as.factor(d$Q49_neighb_a)
nhds.tb <- summary(nhds.tb)
nhds.tb <- as.data.frame(nhds.tb)
nhds.tb$Var1 <- substring(row.names(nhds.tb), 1)
nhds.tb$answer <- revalue(as.character(nhds.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "NA's" = "NA"))
plot.nhds <- merge(nhds, nhds.tb, by = "answer")
plot.nhds <- plot.nhds[-c(2, 4, 6)]
plot.nhds <- setcolorder(plot.nhds, c("answer", "nhds.tb", "Freq"))
plot.nhds$order <- c(4, 6, 3, 2, 5, 1)
plot.nhds <- plot.nhds %>% arrange(order)
plot.nhds <- plot.nhds[-c(4)]
colnames(plot.nhds) <- c("Response", "N", "Proportion")
kable(plot.nhds)| Response | N | Proportion |
|---|---|---|
| Strongly satisfied | 130 | 45.4545455 |
| Satisfied | 120 | 41.9580420 |
| Neither satisfied nor dissatisfied | 19 | 6.6433566 |
| Dissatisfied | 9 | 3.1468531 |
| Strongly dissatisfied | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
How satisfied are you with the number of people you know in your neighbourhood? Are you.
know <- prop.table(table(factor(d$Q49_neighb_b, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100
know <- as.data.frame(know)
know$answer <- substring(row.names(know), 1)
know$answer <- revalue(as.character(know$answer), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "6" = "NA"))
know$plot <- factor(know$answer, know$answer)
cols <- c(rev(brewer.pal(5,"RdYlGn")),"grey")
know.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
know.plot + geom_bar(aes(x = plot), data = know, stat = "identity", colour="black") 
know.tb <- as.factor(d$Q49_neighb_b)
know.tb <- summary(know.tb)
know.tb <- as.data.frame(know.tb)
know.tb$Var1 <- substring(row.names(know.tb), 1)
know.tb$answer <- revalue(as.character(know.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "NA's" = "NA"))
plot.know <- merge(know, know.tb, by = "answer")
plot.know <- plot.know[-c(2, 4, 6)]
plot.know <- setcolorder(plot.know, c("answer", "know.tb", "Freq"))
plot.know$order <- c(4, 6, 3, 2, 5, 1)
plot.know <- plot.know %>% arrange(order)
plot.know <- plot.know[-c(4)]
colnames(plot.know) <- c("Response", "N", "Proportion")
kable(plot.know)| Response | N | Proportion |
|---|---|---|
| Strongly satisfied | 40 | 13.986014 |
| Satisfied | 111 | 38.811189 |
| Neither satisfied nor dissatisfied | 84 | 29.370629 |
| Dissatisfied | 40 | 13.986014 |
| Strongly dissatisfied | 4 | 1.398601 |
| NA | 7 | 2.447552 |
How satisfied are you with the ethnic diversity of your neighbourhood? Are you…
div <- prop.table(table(factor(d$Q49_neighb_c, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100
div <- as.data.frame(div)
div$answer <- substring(row.names(div), 1)
div$answer <- revalue(as.character(div$answer), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "6" = "NA"))
div$plot <- factor(div$answer, div$answer)
cols <- c(rev(brewer.pal(5,"RdYlGn")),"grey")
div.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
div.plot + geom_bar(aes(x = plot), data = div, stat = "identity", colour="black") 
div.tb <- as.factor(d$Q49_neighb_c)
div.tb <- summary(div.tb)
div.tb <- as.data.frame(div.tb)
div.tb$Var1 <- substring(row.names(div.tb), 1)
div.tb$answer <- revalue(as.character(div.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "NA's" = "NA"))
plot.div <- merge(div, div.tb, by = "answer")
plot.div <- plot.div[-c(2, 4, 6)]
plot.div <- setcolorder(plot.div, c("answer", "div.tb", "Freq"))
plot.div$order <- c(4, 6, 3, 2, 5, 1)
plot.div <- plot.div %>% arrange(order)
plot.div <- plot.div[-c(4)]
colnames(plot.div) <- c("Response", "N", "Proportion")
kable(plot.div)| Response | N | Proportion |
|---|---|---|
| Strongly satisfied | 27 | 9.440559 |
| Satisfied | 79 | 27.622378 |
| Neither satisfied nor dissatisfied | 126 | 44.055944 |
| Dissatisfied | 39 | 13.636364 |
| Strongly dissatisfied | 8 | 2.797203 |
| NA | 7 | 2.447552 |
How satisfied are you with your neighbourhood as a good place to raise children? Are you…
rsch <- prop.table(table(factor(d$Q49_neighb_d, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100
rsch <- as.data.frame(rsch)
rsch$answer <- substring(row.names(rsch), 1)
rsch$answer <- revalue(as.character(rsch$answer), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "6" = "NA"))
rsch$plot <- factor(rsch$answer, rsch$answer)
cols <- c(rev(brewer.pal(5,"RdYlGn")),"grey")
rsch.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
rsch.plot + geom_bar(aes(x = plot), data = rsch, stat = "identity", colour="black") 
rsch.tb <- as.factor(d$Q49_neighb_d)
rsch.tb <- summary(rsch.tb)
rsch.tb <- as.data.frame(rsch.tb)
rsch.tb$Var1 <- substring(row.names(rsch.tb), 1)
rsch.tb$answer <- revalue(as.character(rsch.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "NA's" = "NA"))
plot.rsch <- merge(rsch, rsch.tb, by = "answer")
plot.rsch <- plot.rsch[-c(2, 4, 6)]
plot.rsch <- setcolorder(plot.rsch, c("answer", "rsch.tb", "Freq"))
plot.rsch$order <- c(4, 6, 3, 2, 5, 1)
plot.rsch <- plot.rsch %>% arrange(order)
plot.rsch <- plot.rsch[-c(4)]
colnames(plot.rsch) <- c("Response", "N", "Proportion")
kable(plot.rsch)| Response | N | Proportion |
|---|---|---|
| Strongly satisfied | 79 | 27.6223776 |
| Satisfied | 115 | 40.2097902 |
| Neither satisfied nor dissatisfied | 73 | 25.5244755 |
| Dissatisfied | 10 | 3.4965035 |
| Strongly dissatisfied | 2 | 0.6993007 |
| NA | 7 | 2.4475524 |
How satisfied are you with your neighbourhood as a good place to raise children? Are you…
rsch <- prop.table(table(factor(d$Q49_neighb_d, levels = c("1", "2", "3", "4", "5")), exclude = NULL))*100
rsch <- as.data.frame(rsch)
rsch$answer <- substring(row.names(rsch), 1)
rsch$answer <- revalue(as.character(rsch$answer), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "6" = "NA"))
rsch$plot <- factor(rsch$answer, rsch$answer)
cols <- c(rev(brewer.pal(5,"RdYlGn")),"grey")
rsch.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
rsch.plot + geom_bar(aes(x = plot), data = rsch, stat = "identity", colour="black")
rsch.tb <- as.factor(d$Q49_neighb_d)
rsch.tb <- summary(rsch.tb)
rsch.tb <- as.data.frame(rsch.tb)
rsch.tb$Var1 <- substring(row.names(rsch.tb), 1)
rsch.tb$answer <- revalue(as.character(rsch.tb$Var1), c("1" = "Strongly satisfied", "2" = "Satisfied", "3" = "Neither satisfied nor dissatisfied", "4" = "Dissatisfied", "5" = "Strongly dissatisfied", "NA's" = "NA"))
plot.rsch <- merge(rsch, rsch.tb, by = "answer")
plot.rsch <- plot.rsch[-c(2, 4, 6)]
plot.rsch <- setcolorder(plot.rsch, c("answer", "rsch.tb", "Freq"))
plot.rsch$order <- c(4, 6, 3, 2, 5, 1)
plot.rsch <- plot.rsch %>% arrange(order)
plot.rsch <- plot.rsch[-c(4)]
colnames(plot.rsch) <- c("Response", "N", "Proportion")
kable(plot.rsch)| Response | N | Proportion |
|---|---|---|
| Strongly satisfied | 79 | 27.6223776 |
| Satisfied | 115 | 40.2097902 |
| Neither satisfied nor dissatisfied | 73 | 25.5244755 |
| Dissatisfied | 10 | 3.4965035 |
| Strongly dissatisfied | 2 | 0.6993007 |
| NA | 7 | 2.4475524 |
Section 7: Neighbourhood Selection
Q50:Before moving into your current dwelling, when you were looking for a neighbourhood to live in, to what extent were the following characteristics important?
Good access to public transportation
bus <- prop.table(table(factor(d$Q50_neigh_pref_a, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
bus <- as.data.frame(bus)
bus$answer <- substring(row.names(bus), 1)
bus$answer <- revalue(as.character(bus$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
bus$plot <- factor(bus$answer, bus$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
bus.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
bus.plot + geom_bar(aes(x = plot), data = bus, stat = "identity", colour="black") 
bus.tb <- as.factor(d$Q50_neigh_pref_a)
bus.tb <- summary(bus.tb)
bus.tb <- as.data.frame(bus.tb)
bus.tb$Var1 <- substring(row.names(bus.tb), 1)
bus.tb$answer <- revalue(as.character(bus.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.bus <- merge(bus, bus.tb, by = "answer")
plot.bus <- plot.bus[-c(2, 4, 6)]
plot.bus <- setcolorder(plot.bus, c("answer", "bus.tb", "Freq"))
plot.bus$order <- c(5, 6, 4, 3, 2, 1)
plot.bus <- plot.bus %>% arrange(order)
plot.bus <- plot.bus[-c(4)]
colnames(plot.bus) <- c("Response", "N", "Proportion")
kable(plot.bus)| Response | N | Proportion |
|---|---|---|
| Very important | 99 | 34.6153846 |
| Somewhat important | 101 | 35.3146853 |
| Not very important | 52 | 18.1818182 |
| Not important at all | 26 | 9.0909091 |
| Don’t know | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
Sufficient parks and green spaces
parks <- prop.table(table(factor(d$Q50_neigh_pref_b, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
parks <- as.data.frame(parks)
parks$answer <- substring(row.names(parks), 1)
parks$answer <- revalue(as.character(parks$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
parks$plot <- factor(parks$answer, parks$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
parks.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
parks.plot + geom_bar(aes(x = plot), data = parks, stat = "identity", colour="black") 
parks.tb <- as.factor(d$Q50_neigh_pref_b)
parks.tb <- summary(parks.tb)
parks.tb <- as.data.frame(parks.tb)
parks.tb$Var1 <- substring(row.names(parks.tb), 1)
parks.tb$answer <- revalue(as.character(parks.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.parks <- merge(parks, parks.tb, by = "answer")
plot.parks <- plot.parks[-c(2, 4, 6)]
plot.parks <- setcolorder(plot.parks, c("answer", "parks.tb", "Freq"))
plot.parks$order <- c(5, 6, 4, 3, 2, 1)
plot.parks <- plot.parks %>% arrange(order)
plot.parks <- plot.parks[-c(4)]
colnames(plot.parks) <- c("Response", "N", "Proportion")
kable(plot.parks)| Response | N | Proportion |
|---|---|---|
| Very important | 172 | 60.1398601 |
| Somewhat important | 91 | 31.8181818 |
| Not very important | 11 | 3.8461538 |
| Not important at all | 4 | 1.3986014 |
| Don’t know | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
Sufficient shops and services
shops <- prop.table(table(factor(d$Q50_neigh_pref_c, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
shops <- as.data.frame(shops)
shops$answer <- substring(row.names(shops), 1)
shops$answer <- revalue(as.character(shops$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
shops$plot <- factor(shops$answer, shops$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
shops.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
shops.plot + geom_bar(aes(x = plot), data = shops, stat = "identity", colour="black") 
shops.tb <- as.factor(d$Q50_neigh_pref_c)
shops.tb <- summary(shops.tb)
shops.tb <- as.data.frame(shops.tb)
shops.tb$Var1 <- substring(row.names(shops.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$shops.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("77")
nval.df <- nval.df[-c(1)]
shops.tb <- rbind(shops.tb, nval.df)
shops.tb$answer <- revalue(as.character(shops.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.shops <- merge(shops, shops.tb, by = "answer")
plot.shops <- plot.shops[-c(2, 4, 6)]
plot.shops <- setcolorder(plot.shops, c("answer", "shops.tb", "Freq"))
plot.shops$order <- c(5, 6, 4, 3, 2, 1)
plot.shops <- plot.shops %>% arrange(order)
plot.shops <- plot.shops[-c(4)]
colnames(plot.shops) <- c("Response", "N", "Proportion")
kable(plot.shops)| Response | N | Proportion |
|---|---|---|
| Very important | 108 | 37.762238 |
| Somewhat important | 130 | 45.454546 |
| Not very important | 32 | 11.188811 |
| Not important at all | 9 | 3.146853 |
| Don’t know | 0 | 0.000000 |
| NA | 7 | 2.447552 |
Proximity to doctors, a pharmacy or other health services
docs <- prop.table(table(factor(d$Q50_neigh_pref_d, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
docs <- as.data.frame(docs)
docs$answer <- substring(row.names(docs), 1)
docs$answer <- revalue(as.character(docs$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
docs$plot <- factor(docs$answer, docs$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
docs.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
docs.plot + geom_bar(aes(x = plot), data = docs, stat = "identity", colour="black") 
docs.tb <- as.factor(d$Q50_neigh_pref_d)
docs.tb <- summary(docs.tb)
docs.tb <- as.data.frame(docs.tb)
docs.tb$Var1 <- substring(row.names(docs.tb), 1)
docs.tb$answer <- revalue(as.character(docs.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.docs <- merge(docs, docs.tb, by = "answer")
plot.docs <- plot.docs[-c(2, 4, 6)]
plot.docs <- setcolorder(plot.docs, c("answer", "docs.tb", "Freq"))
plot.docs$order <- c(5, 6, 4, 3, 2, 1)
plot.docs <- plot.docs %>% arrange(order)
plot.docs <- plot.docs[-c(4)]
colnames(plot.docs) <- c("Response", "N", "Proportion")
kable(plot.docs)| Response | N | Proportion |
|---|---|---|
| Very important | 27 | 9.4405594 |
| Somewhat important | 107 | 37.4125874 |
| Not very important | 105 | 36.7132867 |
| Not important at all | 39 | 13.6363636 |
| Don’t know | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
A good knowledge of the neighbourhood
know <- prop.table(table(factor(d$Q50_neigh_pref_e, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
know <- as.data.frame(know)
know$answer <- substring(row.names(know), 1)
know$answer <- revalue(as.character(know$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
know$plot <- factor(know$answer, know$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
know.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
know.plot + geom_bar(aes(x = plot), data = know, stat = "identity", colour="black") 
know.tb <- as.factor(d$Q50_neigh_pref_e)
know.tb <- summary(know.tb)
know.tb <- as.data.frame(know.tb)
know.tb$Var1 <- substring(row.names(know.tb), 1)
know.tb$answer <- revalue(as.character(know.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.know <- merge(know, know.tb, by = "answer")
plot.know <- plot.know[-c(2, 4, 6)]
plot.know <- setcolorder(plot.know, c("answer", "know.tb", "Freq"))
plot.know$order <- c(5, 6, 4, 3, 2, 1)
plot.know <- plot.know %>% arrange(order)
plot.know <- plot.know[-c(4)]
colnames(plot.know) <- c("Response", "N", "Proportion")
kable(plot.know)| Response | N | Proportion |
|---|---|---|
| Very important | 56 | 19.5804196 |
| Somewhat important | 131 | 45.8041958 |
| Not very important | 69 | 24.1258741 |
| Not important at all | 21 | 7.3426573 |
| Don’t know | 2 | 0.6993007 |
| NA | 7 | 2.4475524 |
Presence of relatives, friends or acquaintances
pres <- prop.table(table(factor(d$Q50_neigh_pref_f, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
pres <- as.data.frame(pres)
pres$answer <- substring(row.names(pres), 1)
pres$answer <- revalue(as.character(pres$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
pres$plot <- factor(pres$answer, pres$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
pres.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
pres.plot + geom_bar(aes(x = plot), data = pres, stat = "identity", colour="black") 
pres.tb <- as.factor(d$Q50_neigh_pref_f)
pres.tb <- summary(pres.tb)
pres.tb <- as.data.frame(pres.tb)
pres.tb$Var1 <- substring(row.names(pres.tb), 1)
pres.tb$answer <- revalue(as.character(pres.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.pres <- merge(pres, pres.tb, by = "answer")
plot.pres <- plot.pres[-c(2, 4, 6)]
plot.pres <- setcolorder(plot.pres, c("answer", "pres.tb", "Freq"))
plot.pres$order <- c(5, 6, 4, 3, 2, 1)
plot.pres <- plot.pres %>% arrange(order)
plot.pres <- plot.pres[-c(4)]
colnames(plot.pres) <- c("Response", "N", "Proportion")
kable(plot.pres)| Response | N | Proportion |
|---|---|---|
| Very important | 40 | 13.986014 |
| Somewhat important | 96 | 33.566434 |
| Not very important | 79 | 27.622378 |
| Not important at all | 60 | 20.979021 |
| Don’t know | 4 | 1.398601 |
| NA | 7 | 2.447552 |
A neighbourhood where it is pleasant to walk
walkpleas <- prop.table(table(factor(d$Q50_neigh_pref_g, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
walkpleas <- as.data.frame(walkpleas)
walkpleas$answer <- substring(row.names(walkpleas), 1)
walkpleas$answer <- revalue(as.character(walkpleas$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
walkpleas$plot <- factor(walkpleas$answer, walkpleas$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
walkpleas.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
walkpleas.plot + geom_bar(aes(x = plot), data = walkpleas, stat = "identity", colour="black") 
walkpleas.tb <- as.factor(d$Q50_neigh_pref_g)
walkpleas.tb <- summary(walkpleas.tb)
walkpleas.tb <- as.data.frame(walkpleas.tb)
walkpleas.tb$Var1 <- substring(row.names(walkpleas.tb), 1)
walkpleas.tb$answer <- revalue(as.character(walkpleas.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.walkpleas <- merge(walkpleas, walkpleas.tb, by = "answer")
plot.walkpleas <- plot.walkpleas[-c(2, 4, 6)]
plot.walkpleas <- setcolorder(plot.walkpleas, c("answer", "walkpleas.tb", "Freq"))
plot.walkpleas$order <- c(5, 6, 4, 3, 2, 1)
plot.walkpleas <- plot.walkpleas %>% arrange(order)
plot.walkpleas <- plot.walkpleas[-c(4)]
colnames(plot.walkpleas) <- c("Response", "N", "Proportion")
kable(plot.walkpleas)| Response | N | Proportion |
|---|---|---|
| Very important | 179 | 62.5874126 |
| Somewhat important | 85 | 29.7202797 |
| Not very important | 9 | 3.1468531 |
| Not important at all | 5 | 1.7482517 |
| Don’t know | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
A neighbourhood where it is practical to move around by car (ease of parking, low traffic, good access by car)
carprac <- prop.table(table(factor(d$Q50_neigh_pref_h, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
carprac <- as.data.frame(carprac)
carprac$answer <- substring(row.names(carprac), 1)
carprac$answer <- revalue(as.character(carprac$answer), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "5" = "Don't know", "6" = "NA"))
carprac$plot <- factor(carprac$answer, carprac$answer)
cols <- c(rev(brewer.pal(4,"PRGn")),"grey", "grey")
carprac.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("")
carprac.plot + geom_bar(aes(x = plot), data = carprac, stat = "identity", colour="black") 
carprac.tb <- as.factor(d$Q50_neigh_pref_h)
carprac.tb <- summary(carprac.tb)
carprac.tb <- as.data.frame(carprac.tb)
carprac.tb$Var1 <- substring(row.names(carprac.tb), 1)
carprac.tb$answer <- revalue(as.character(carprac.tb$Var1), c("1" = "Very important", "2" = "Somewhat important", "3" = "Not very important", "4" = "Not important at all", "77" = "Don't know", "NA's" = "NA"))
plot.carprac <- merge(carprac, carprac.tb, by = "answer")
plot.carprac <- plot.carprac[-c(2, 4, 6)]
plot.carprac <- setcolorder(plot.carprac, c("answer", "carprac.tb", "Freq"))
plot.carprac$order <- c(5, 6, 4, 3, 2, 1)
plot.carprac <- plot.carprac %>% arrange(order)
plot.carprac <- plot.carprac[-c(4)]
colnames(plot.carprac) <- c("Response", "N", "Proportion")
kable(plot.carprac)| Response | N | Proportion |
|---|---|---|
| Very important | 18 | 6.2937063 |
| Somewhat important | 75 | 26.2237762 |
| Not very important | 97 | 33.9160839 |
| Not important at all | 88 | 30.7692308 |
| Don’t know | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
Section 8:Demographics
Q51:What is your marital status? Are you.
marital <- prop.table(table(factor(d$Q51_marital_status, levels = c("1", "2", "3", "4")), exclude = NULL))*100
marital <- as.data.frame(marital)
marital$answer <- substring(row.names(marital), 1)
marital$answer <- revalue(as.character(marital$answer), c("1" = "Single", "2" = "Married/commonlaw", "3" = "Separated/divorced", "4" = "Widowed", "5" = "NA"))
marital$plot <- factor(marital$answer, marital$answer)
marital.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "Set3") +
ylab("Percent of total") +
xlab("")
marital.plot + geom_bar(aes(x = plot), data = marital, stat = "identity", colour="black") 
marital.tb <- as.factor(d$Q51_marital_status)
marital.tb <- summary(marital.tb)
marital.tb <- as.data.frame(marital.tb)
marital.tb$Var1 <- substring(row.names(marital.tb), 1)
marital.tb$answer <- revalue(as.character(marital.tb$Var1), c("1" = "Single", "2" = "Married/commonlaw", "3" = "Separated/divorced", "4" = "Widowed", "NA's" = "NA"))
plot.marital <- merge(marital, marital.tb, by = "answer")
plot.marital <- plot.marital[-c(2, 4, 6)]
plot.marital <- setcolorder(plot.marital, c("answer", "marital.tb", "Freq"))
plot.marital$order <- c(2, 5, 3, 1, 4)
plot.marital <- plot.marital %>% arrange(order)
plot.marital <- plot.marital[-c(4)]
colnames(plot.marital) <- c("Response", "N", "Proportion")
kable(plot.marital)| Response | N | Proportion |
|---|---|---|
| Single | 54 | 18.8811189 |
| Married/commonlaw | 200 | 69.9300699 |
| Separated/divorced | 23 | 8.0419580 |
| Widowed | 2 | 0.6993007 |
| NA | 7 | 2.4475524 |
Q52:Do you have children?
kids <- prop.table(table(factor(d$Q52_children, levels = c("1", "2")), exclude=NULL))*100
kids <- as.data.frame(kids)
kids$answer <- substring(row.names(kids), 1)
kids$answer <- revalue(as.character(kids$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
kids$plot <- factor(kids$answer, kids$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
kids.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
kids.plot + geom_bar(aes(x = plot), data = kids, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
kids.tb <- as.factor(d$Q52_children)
kids.tb <- summary(kids.tb)
kids.tb <- as.data.frame(kids.tb)
kids.tb$Var1 <- substring(row.names(kids.tb), 1)
kids.tb$answer <- revalue(as.character(kids.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.kids <- merge(kids, kids.tb, by = "answer")
plot.kids <- plot.kids[-c(2, 4, 6)]
plot.kids <- setcolorder(plot.kids, c("answer", "kids.tb", "Freq"))
plot.kids$order <- c(3, 2, 1)
plot.kids <- plot.kids %>% arrange(order)
plot.kids <- plot.kids[-c(4)]
colnames(plot.kids) <- c("Response", "N", "Proportion")
kable(plot.kids)| Response | N | Proportion |
|---|---|---|
| Yes | 149 | 52.097902 |
| No | 130 | 45.454546 |
| NA | 7 | 2.447552 |
Q53:How many children do you have?
nkids <- prop.table(table(factor(d$Q53_living_children, levels = c("1", "2", "3", "4", "5", "6")), exclude=NULL))*100
nkids <- as.data.frame(nkids)
nkids <- as.data.frame(nkids)
nkids$answer <- substring(row.names(nkids), 1)
nkids$answer <- revalue(as.character(nkids$answer), c("7" = "No children"))
nkids$plot <- factor(nkids$answer, nkids$answer)
cols <- c(brewer.pal(6,"YlGnBu"),"grey")
nkids.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
nkids.plot + geom_bar(aes(x = plot), data = nkids, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
nkids.tb <- as.factor(d$Q53_living_children)
nkids.tb <- summary(nkids.tb)
nkids.tb <- as.data.frame(nkids.tb)
nkids.tb$Var1 <- substring(row.names(nkids.tb), 1)
nkids.tb$answer <- revalue(as.character(nkids.tb$Var1), c("NA's" = "No children"))
plot.nkids <- merge(nkids, nkids.tb, by = "answer")
plot.nkids <- plot.nkids[-c(2, 4, 6)]
plot.nkids <- setcolorder(plot.nkids, c("answer", "nkids.tb", "Freq"))
colnames(plot.nkids) <- c("Response", "N", "Proportion")
kable(plot.nkids)| Response | N | Proportion |
|---|---|---|
| 1 | 39 | 13.6363636 |
| 2 | 82 | 28.6713287 |
| 3 | 23 | 8.0419580 |
| 4 | 2 | 0.6993007 |
| 5 | 2 | 0.6993007 |
| 6 | 1 | 0.3496503 |
| No children | 137 | 47.9020979 |
Q54:What is your current living arrangement? Do you live.
livewith <- prop.table(table(factor(d$Q54_living_arrange, levels = c("[1]", "[2]", "[3]", "[4]", "[5]", "[6]", "[7]", "[2, 3, 4, 7, 6]", "[2, 3, 4]", "[2, 3, 5]", "[2, 3]", "[2, 5]", "[2, 6]", "[2, 7]")), exclude=NULL))*100
livewith <- as.data.frame(livewith)
livewith <- as.data.frame(livewith)
livewith$answer <- substring(row.names(livewith), 1)
livewith$answer <- revalue(as.character(livewith$answer), c("1" = "Alone", "2" = "With a spouse/partner", "3" = "With children", "4" = "With grandchildren", "5" = "With relatives/siblings", "6" = "With friends", "7" = "With other people", "8" = "With spouse, children, grandchildren, friends & others", "9" = "With spouse, children & grandchildren", "10" = "With spouse, children & relatives/siblings", "11" = "With spouse & children", "12" = "With spouse & relatives/siblings", "13" = "With spouse & friends", "14" = "With spouse & others", "15" = "NA"))
livewith$plot <- factor(livewith$answer, livewith$answer)
livewith.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
livewith.plot + geom_bar(aes(x = plot), data = livewith, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "Set3") +
ylab("Percent of total") +
xlab("Response")
livewith.tb <- as.factor(d$Q54_living_arrange)
livewith.tb <- summary(livewith.tb)
livewith.tb <- as.data.frame(livewith.tb)
livewith.tb$Var1 <- substring(row.names(livewith.tb), 1)
nval.df <- c("0", "7") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$livewith.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("[4]", "NA")
nval.df <- nval.df[-c(1)]
livewith.tb <- rbind(livewith.tb, nval.df)
livewith.tb$answer <- revalue(as.character(livewith.tb$Var1), c("[1]" = "Alone", "[2]" = "With a spouse/partner", "[3]" = "With children", "[4]" = "With grandchildren", "[5]" = "With relatives/siblings", "[6]" = "With friends", "[7]" = "With other people", "[2, 3, 4, 7, 6]" = "With spouse, children, grandchildren, friends & others", "[2, 3, 4]" = "With spouse, children & grandchildren", "[2, 3, 5]" = "With spouse, children & relatives/siblings", "[2, 3]" = "With spouse & children", "[2, 5]" = "With spouse & relatives/siblings", "[2, 6]" = "With spouse & friends", "[2, 7]" = "With spouse & others"))
plot.livewith <- merge(livewith, livewith.tb, by = "answer")
plot.livewith <- plot.livewith[-c(2, 4, 6)]
plot.livewith <- setcolorder(plot.livewith, c("answer", "livewith.tb", "Freq"))
plot.livewith$order <- c(3, 15, 1, 7, 4, 13, 6, 5, 2, 10, 9, 11, 12, 8, 14)
plot.livewith <- plot.livewith %>% arrange(order)
plot.livewith <- plot.livewith[-c(4)]
colnames(plot.livewith) <- c("Response", "N", "Proportion")
kable(plot.livewith)| Response | N | Proportion |
|---|---|---|
| With a spouse/partner | 120 | 41.9580420 |
| With spouse & children | 77 | 26.9230769 |
| Alone | 43 | 15.0349650 |
| With friends | 11 | 3.8461538 |
| With relatives/siblings | 8 | 2.7972028 |
| With other people | 7 | 2.4475524 |
| With children | 4 | 1.3986014 |
| With spouse, children & relatives/siblings | 3 | 1.0489510 |
| With spouse & others | 2 | 0.6993007 |
| With spouse & friends | 1 | 0.3496503 |
| With spouse & relatives/siblings | 1 | 0.3496503 |
| With spouse, children & grandchildren | 1 | 0.3496503 |
| With grandchildren | 0 | 0.0000000 |
| With spouse, children, grandchildren, friends & others | 1 | 0.3496503 |
| NA | 7 | 2.4475524 |
Q57:How many adults aged 16 or older live in your household including yourself?
ggplot(d, aes(x = d$Q57_adults_household)) + geom_bar(na.rm = TRUE) + xlab("Number of adults in household")
summary(d$Q57_adults_household)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 2.000 2.202 2.000 16.000 23Q58:When did you move to your current residence?
ggplot(d, aes(x = d$Q58_residence.time)) + geom_bar(na.rm = TRUE, binwidth = 1) + xlab("Number of years at residence")
summary(d$Q58_residence.time)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.000 2.000 5.000 7.711 10.500 38.000 23Q59:Were you born in Canada?
can <- prop.table(table(factor(d$Q59_born_can, levels = c("1", "2")), exclude=NULL))*100
can <- as.data.frame(can)
can$answer <- substring(row.names(can), 1)
can$answer <- revalue(as.character(can$answer), c("1" = "Yes", "2" = "No", "3" = "NA"))
can$plot <- factor(can$answer, can$answer)
cols <- c("#31a354","#f03b20", "grey") #get colors for plot
can.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
can.plot + geom_bar(aes(x = plot), data = can, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_manual(values = cols) +
ylab("Percent of total") +
xlab("Response")
can.tb <- as.factor(d$Q59_born_can)
can.tb <- summary(can.tb)
can.tb <- as.data.frame(can.tb)
can.tb$Var1 <- substring(row.names(can.tb), 1)
can.tb$answer <- revalue(as.character(can.tb$Var1), c("1" = "Yes", "2" = "No", "NA's" = "NA"))
plot.can <- merge(can, can.tb, by = "answer")
plot.can <- plot.can[-c(2, 4, 6)]
plot.can <- setcolorder(plot.can, c("answer", "can.tb", "Freq"))
plot.can$order <- c(3, 2, 1)
plot.can <- plot.can %>% arrange(order)
plot.can <- plot.can[-c(4)]
colnames(plot.can) <- c("Response", "N", "Proportion")
kable(plot.can)| Response | N | Proportion |
|---|---|---|
| Yes | 194 | 67.832168 |
| No | 69 | 24.125874 |
| NA | 23 | 8.041958 |
imm <- d %>%
select(Q59_born_can, Q60_move_can, Q60_numYears) %>%
filter(Q59_born_can == "2")
ggplot(imm, aes(x = Q60_move_can)) + geom_bar(na.rm = TRUE, binwidth = 1) + xlab("Moved to Canada")
summary(d$Q60_move_can)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1946 1972 1988 1987 2002 2017 217summary(d$Q60_numYears)## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 15.0 29.0 30.3 45.0 71.0 217Q61:To which ethnic or cultural groups did your ancestors belong? (Check all that apply)
ethnic <- prop.table(table(factor(d$Q61_group_id, levels = c("[1]", "[1, 2, 4]", "[1, 4]", "[2]", "[2, 4]", "[3]", "[4]", "[4, 6]", "[5]", "[6]", "[77]")), exclude=NULL))*100
ethnic <- as.data.frame(ethnic)
ethnic <- as.data.frame(ethnic)
ethnic$answer <- substring(row.names(ethnic), 1)
ethnic$answer <- revalue(as.character(ethnic$answer), c("1" = "Aboriginal", "2" = "Aboriginal/Asian/Caucasian", "3" = "Aboriginal/Caucasian", "4" = "Asian", "5" = "Asian/Caucasian", "6" = "Black", "7" = "Caucasian", "8" = "Caucasian/Middle Eastern", "9" = "Latin American", "10" = "Middle Eastern", "11" = "Don't know/prefer no answer", "12" = "NA"))
ethnic$plot <- factor(ethnic$answer, ethnic$answer)
ethnic.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) #order responses as in t5
ethnic.plot + geom_bar(aes(x = plot), data = ethnic, stat = "identity", colour="black") +
guides(fill = FALSE) +
scale_fill_brewer(palette = "Set3") +
ylab("Percent of total") +
xlab("Response")
ethnic.tb <- as.factor(d$Q61_group_id)
ethnic.tb <- summary(ethnic.tb)
ethnic.tb <- as.data.frame(ethnic.tb)
ethnic.tb$Var1 <- substring(row.names(ethnic.tb), 1)
nval.df <- c("0", "0", "0", "23") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$ethnic.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("[1]", "[3]","[6]", "[99]")
nval.df <- nval.df[-c(1)]
ethnic.tb <- rbind(ethnic.tb, nval.df)
ethnic.tb$answer <- revalue(as.character(ethnic.tb$Var1), c("[1]" = "Aboriginal", "[1, 2, 4]" = "Aboriginal/Asian/Caucasian", "[1, 4]" = "Aboriginal/Caucasian", "[2]" = "Asian", "[2, 4]" = "Asian/Caucasian", "[3]" = "Black", "[4]" = "Caucasian", "[4, 6]" = "Caucasian/Middle Eastern", "[5]" = "Latin American", "[6]" = "Middle Eastern", "[77]" = "Don't know/prefer no answer", "[99]" = "NA"))
plot.ethnic <- merge(ethnic, ethnic.tb, by = "answer")
plot.ethnic <- plot.ethnic[-c(2, 4, 6)]
plot.ethnic <- setcolorder(plot.ethnic, c("answer", "ethnic.tb", "Freq"))
plot.ethnic$order <- c(1, 2, 3, 4, 5, 6, 7, 8, 11, 9, 10, 12)
plot.ethnic <- plot.ethnic %>% arrange(order)
plot.ethnic <- plot.ethnic[-c(4)]
colnames(plot.ethnic) <- c("Response", "N", "Proportion")
kable(plot.ethnic)| Response | N | Proportion |
|---|---|---|
| Aboriginal | 0 | 0.0000000 |
| Aboriginal/Asian/Caucasian | 1 | 0.3496503 |
| Aboriginal/Caucasian | 3 | 1.0489510 |
| Asian | 15 | 5.2447552 |
| Asian/Caucasian | 2 | 0.6993007 |
| Black | 0 | 0.0000000 |
| Caucasian | 228 | 79.7202797 |
| Caucasian/Middle Eastern | 1 | 0.3496503 |
| Latin American | 4 | 1.3986014 |
| Middle Eastern | 0 | 0.0000000 |
| Don’t know/prefer no answer | 9 | 3.1468531 |
| NA | 23 | 8.0419580 |
Q62: Which category best describes your annual household income, taking into account all sources of income?
income <- prop.table(table(factor(d$Q62_income, levels = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "77")), exclude = NULL))*100
income <- as.data.frame(income)
income$answer <- substring(row.names(income), 1)
income$answer <- revalue(as.character(income$answer), c("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" = "$200,000 or more", "12" = "Don't know/prefer no answer", "13" = "NA"))
income$plot <- factor(income$answer, income$answer)
income.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = (rev("YlGn"))) +
ylab("Percent of total") +
xlab("")
income.plot + geom_bar(aes(x = plot), data = income, stat = "identity", colour="black") 
income.tb <- as.factor(d$Q62_income)
income.tb <- summary(income.tb)
income.tb <- as.data.frame(income.tb)
income.tb$Var1 <- substring(row.names(income.tb), 1)
nval.df <- c("0") #insert missing values
nval.df <- as.data.frame(nval.df)
nval.df$income.tb <- as.factor(nval.df$nval.df)
nval.df$Var1 <- c("1")
nval.df <- nval.df[-c(1)]
income.tb <- rbind(income.tb, nval.df)
income.tb$answer <- revalue(as.character(income.tb$Var1), c("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" = "$200,000 or more", "77" = "Don't know/prefer no answer", "NA's" = "NA"))
plot.income <- merge(income, income.tb, by = "answer")
plot.income <- plot.income[-c(2, 4, 6)]
plot.income <- setcolorder(plot.income, c("answer", "income.tb", "Freq"))
plot.income$order <- c(10, 2, 3, 9, 4, 5, 11, 6, 7, 8, 12, 13, 1)
plot.income <- plot.income %>% arrange(order)
plot.income <- plot.income[-c(4)]
colnames(plot.income) <- c("Response", "N", "Proportion")
kable(plot.income)| Response | N | Proportion |
|---|---|---|
| No income | 0 | 0.0000000 |
| $1 to $9,999 | 3 | 1.0489510 |
| $10,000 to $14,999 | 2 | 0.6993007 |
| $15,000 to $19,999 | 5 | 1.7482517 |
| $20,000 to $29,999 | 9 | 3.1468531 |
| $30,000 to $39,999 | 11 | 3.8461538 |
| $40,000 to $49,999 | 15 | 5.2447552 |
| $50,000 to $99,999 | 99 | 34.6153846 |
| $100,000 to $149,999 | 61 | 21.3286713 |
| $150,000 to $199,999 | 31 | 10.8391608 |
| $200,000 or more | 9 | 3.1468531 |
| Don’t know/prefer no answer | 18 | 6.2937063 |
| NA | 23 | 8.0419580 |
Q63: To what extent does this annual household income allow you to satisfy your household’s needs?
needs <- prop.table(table(factor(d$Q63_income_needs, levels = c("1", "2", "3", "4", "77")), exclude = NULL))*100
needs <- as.data.frame(needs)
needs$answer <- substring(row.names(needs), 1)
needs$answer <- revalue(as.character(needs$answer), c("1" = "Very well", "2" = "Well", "3" = "Not so well", "4" = "Not at all", "5" = "Don't know/prefer no answer", "6" = "NA"))
needs$plot <- factor(needs$answer, needs$answer)
needs.plot <- ggplot(plot, aes(x = answer, y = Freq, fill = plot)) + theme(axis.text.x = element_text(angle=90, vjust=.6)) +
guides(fill = FALSE) +
scale_fill_brewer(palette = "YlGn", direction=-1) +
ylab("Percent of total") +
xlab("")
needs.plot + geom_bar(aes(x = plot), data = needs, stat = "identity", colour="black") 
needs.tb <- as.factor(d$Q63_income_needs)
needs.tb <- summary(needs.tb)
needs.tb <- as.data.frame(needs.tb)
needs.tb$Var1 <- substring(row.names(needs.tb), 1)
needs.tb$answer <- revalue(as.character(needs.tb$Var1), c("1" = "Very well", "2" = "Well", "3" = "Not so well", "4" = "Not at all", "77" = "Don't know/prefer no answer", "NA's" = "NA"))
plot.needs <- merge(needs, needs.tb, by = "answer")
plot.needs <- plot.needs[-c(2, 4, 6)]
plot.needs <- setcolorder(plot.needs, c("answer", "needs.tb", "Freq"))
plot.needs$order <- c(5, 6, 4, 3, 1, 2)
plot.needs <- plot.needs %>% arrange(order)
plot.needs <- plot.needs[-c(4)]
colnames(plot.needs) <- c("Response", "N", "Proportion")
kable(plot.needs)| Response | N | Proportion |
|---|---|---|
| Very well | 104 | 36.363636 |
| Well | 113 | 39.510489 |
| Not so well | 29 | 10.139860 |
| Not at all | 5 | 1.748252 |
| Don’t know/prefer no answer | 11 | 3.846154 |
| NA | 24 | 8.391608 |