Bivariate Visualizations

James Normington

Announcements

• Assignment 2 was due last night
• Tidy Tuesday 2 (TT2) is due tomorrow
  • Remember: you must submit one before TT5!

Learning Goals

• Identify appropriate types of bivariate visualizations, depending on the type of variables (categorical, quantitative)
• Create basic bivariate visualizations based on real data

Bivariate Visualizations

In this activity we will analyze data from the 2016 presidential election.

We’ll explore county-level election outcomes and demographics.

Template File

Download a template .Rmd of this activity. Put the file in a Day_04 folder within your COMP_STAT_112 folder.

• This .Rmd only contains exercises that we’ll work on in class and you’ll finish for Assignment 3.

Getting to know the dataset

Loading in the Data

elect <- read_csv("https://jamesnormington.github.io/112_spring_2023/data/electionDemographics16.csv")

Getting to know the dataset

Check out the first rows of elect. What are the units of observation?

head(elect)

# A tibble: 6 × 34
  county total…¹ dem_2…² gop_2…³ oth_2…⁴ total…⁵ dem_2…⁶ gop_2…⁷ oth_2…⁸ total…⁹
  <chr>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>   <dbl>
1 Walke…   28652    7420   20722     510   28497    6551   21633     313   29243
2 Bullo…    5415    4011    1391      13    5318    4058    1250      10    4701
3 Calho…   49242   16334   32348     560   46240   15500   30272     468   47376
4 Barbo…   11630    5697    5866      67   11459    5873    5539      47   10390
5 Fayet…    7957    1994    5883      80    7912    1803    6034      75    8196
6 Baldw…   81413   19386   61271     756   84988   18329   65772     887   94090
# … with 24 more variables: dem_2016 <dbl>, gop_2016 <dbl>, oth_2016 <dbl>,
#   perdem_2016 <dbl>, perrep_2016 <dbl>, winrep_2016 <lgl>, perdem_2012 <dbl>,
#   perrep_2012 <dbl>, winrep_2012 <lgl>, perdem_2008 <dbl>, perrep_2008 <dbl>,
#   winrep_2008 <lgl>, region <dbl>, total_population <dbl>,
#   percent_white <dbl>, percent_black <dbl>, percent_asian <dbl>,
#   percent_hispanic <dbl>, per_capita_income <dbl>, median_rent <dbl>,
#   median_age <dbl>, polyname <chr>, abb <chr>, StateColor <chr>, and …

Getting to know the dataset

How much data do we have?

dim(elect)

[1] 3112   34

Getting to know the dataset

What are the names of the variables?

names(elect)

 [1] "county"            "total_2008"        "dem_2008"         
 [4] "gop_2008"          "oth_2008"          "total_2012"       
 [7] "dem_2012"          "gop_2012"          "oth_2012"         
[10] "total_2016"        "dem_2016"          "gop_2016"         
[13] "oth_2016"          "perdem_2016"       "perrep_2016"      
[16] "winrep_2016"       "perdem_2012"       "perrep_2012"      
[19] "winrep_2012"       "perdem_2008"       "perrep_2008"      
[22] "winrep_2008"       "region"            "total_population" 
[25] "percent_white"     "percent_black"     "percent_asian"    
[28] "percent_hispanic"  "per_capita_income" "median_rent"      
[31] "median_age"        "polyname"          "abb"              
[34] "StateColor"       

Review: Univariate Viz

Categorical Variable: Bar Plot

ggplot(data = elect, aes(x = winrep_2016)) + 
    geom_bar()

Review: Univariate Viz

Quantitative Variable: Histogram or Density plot

ggplot(data = elect, aes(x = perrep_2016)) + 
    geom_histogram()

ggplot(data = elect, aes(x = perrep_2016)) + 
    geom_density()

Preview: Bivariate Viz

Quantitative + Quantitative Variable: Scatterplot

Preview: Bivariate Viz

Quantitative + Categorical Variable: Density Plots, Boxplots, etc.

Preview: Bivariate Viz

Categorical + Categorical Variable: side-by-side, proportion Bar plots, etc.

In Class

Work on the activity, checking in with your mates at your table.

Notice patterns! Feel free to make visualizations more effective as you go along.

After Class

• TT2 due tomorrow night!

• Assignment 3 due Wednesday 2/8 @ 11:59pm