Challenge 5

Introduction to Visualization

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

library(readr)
aus_marriage <- read_csv("_data/australian_marriage_tidy.csv")
print(aus_marriage)

# A tibble: 16 × 4
   territory                       resp    count percent
   <chr>                           <chr>   <dbl>   <dbl>
 1 New South Wales                 yes   2374362    57.8
 2 New South Wales                 no    1736838    42.2
 3 Victoria                        yes   2145629    64.9
 4 Victoria                        no    1161098    35.1
 5 Queensland                      yes   1487060    60.7
 6 Queensland                      no     961015    39.3
 7 South Australia                 yes    592528    62.5
 8 South Australia                 no     356247    37.5
 9 Western Australia               yes    801575    63.7
10 Western Australia               no     455924    36.3
11 Tasmania                        yes    191948    63.6
12 Tasmania                        no     109655    36.4
13 Northern Territory(b)           yes     48686    60.6
14 Northern Territory(b)           no      31690    39.4
15 Australian Capital Territory(c) yes    175459    74  
16 Australian Capital Territory(c) no      61520    26  

Briefly describe the data

Data set contains the results of the Australian Marriage Law Postal Survey (2017), designed to gauge support for legalizing same-sex marriage in Australia.

Tidy Data (as needed)

#simplify territory values
aus_marriage <- mutate(aus_marriage, territory = recode(territory, "New South Wales" = "NSW",
                                                                    "Victoria" = "Vic",
                                                                    "Queensland" = "Qld",
                                                                    "South Australia" = "SA",
                                                                    "Western Australia" = "WA",
                                                                    "Tasmania" = "Tas",
                                                                    "Northern Territory(b)" = "NT",
                                                                    "Australian Capital Territory(c)" = "ACT"))
aus_marriage

# A tibble: 16 × 4
   territory resp    count percent
   <chr>     <chr>   <dbl>   <dbl>
 1 NSW       yes   2374362    57.8
 2 NSW       no    1736838    42.2
 3 Vic       yes   2145629    64.9
 4 Vic       no    1161098    35.1
 5 Qld       yes   1487060    60.7
 6 Qld       no     961015    39.3
 7 SA        yes    592528    62.5
 8 SA        no     356247    37.5
 9 WA        yes    801575    63.7
10 WA        no     455924    36.3
11 Tas       yes    191948    63.6
12 Tas       no     109655    36.4
13 NT        yes     48686    60.6
14 NT        no      31690    39.4
15 ACT       yes    175459    74  
16 ACT       no      61520    26  

Univariate Visualizations

The simplest univariate vizualization for this data set seems to be a bar graph. I decided to display the number of “yes” votes for each territory.

#top 10 territories voting "yes"
yes_vote <- aus_marriage %>% filter(resp == "yes")
ggplot(yes_vote, aes(x = reorder(territory, -count), y = count))+
  geom_bar(stat = "identity")

Bivariate Visualization(s)

For the bivariate visualization, I chose a stacked bar chart to show the proportion of yes to no votes in each territory.

#Stacked Bar Chart
ggplot(aus_marriage, aes(fill=resp, y=percent, x=territory)) +
  geom_bar(position = "fill", stat = "identity")