Matching Visualization to Data

Sections: 1.4-1.5: Tuesday, Week 2

Load libraries

  library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.2     ✔ readr     2.1.4
✔ forcats   1.0.0     ✔ stringr   1.5.0
✔ ggplot2   3.4.3     ✔ tibble    3.2.1
✔ lubridate 1.9.2     ✔ tidyr     1.3.0
✔ purrr     1.0.2     
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

Save your work in scripts

In RStudio create new R-scripts with File > New File > R script. You can save R-commands and include documentation in a script. This is useful for class work and/or homework. You can also share scripts with others. Make sure that scripts work before you move on to other things.

Load the storms data, included with the Tidyverse

Either view the data in the editor window or glimpse it in the console.

data()

Here are 6 Ways to Visualize Data

Categorical Distributions

If you want to see how many things there are in each category you can use a bar plot.

Create a bar plot with geom_bar(). Compare two categorical variables - one category is represented bar a bar and we fill each bar with another variable.

Use dataset gss_cat, A sample of categorical variables from the General Social survey.

    glimpse(gss_cat)

Rows: 21,483
Columns: 9
$ year    <int> 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 2000, 20…
$ marital <fct> Never married, Divorced, Widowed, Never married, Divorced, Mar…
$ age     <int> 26, 48, 67, 39, 25, 25, 36, 44, 44, 47, 53, 52, 52, 51, 52, 40…
$ race    <fct> White, White, White, White, White, White, White, White, White,…
$ rincome <fct> $8000 to 9999, $8000 to 9999, Not applicable, Not applicable, …
$ partyid <fct> "Ind,near rep", "Not str republican", "Independent", "Ind,near…
$ relig   <fct> Protestant, Protestant, Protestant, Orthodox-christian, None, …
$ denom   <fct> "Southern baptist", "Baptist-dk which", "No denomination", "No…
$ tvhours <int> 12, NA, 2, 4, 1, NA, 3, NA, 0, 3, 2, NA, 1, NA, 1, 7, NA, 3, 3…

    ggplot(gss_cat, aes(x = race)) +
      geom_bar(aes(fill = marital)) +
      theme(axis.text.x = element_text(angle = 60, hjust = 1 ))

Numerical Distrubutions

If you want to see how many things there are having roughly the same numerical value using a histogram is useful. You can set binwidth to different values to reflect what roughly means. This would be good for test scores, etc.

Leave + signs at the end of the line, use indentation and alignment. Break you code into chunks, - start with the main 3 ingredients and build up slowly.

ggplot(storms, aes(x = lat)) + 
  geom_histogram(binwidth =  .5, aes(fill = status)) +
  labs(x = "Latitude", y = "Number of storms", 
       title = "Latitude Distribution", 
       subtitle = "Half degree intervals")

Relationships b/w a Numerical & Categorical Variable

In the storms data how do the latitudes (a numerical value) vary among the status categories?

Box plot

ggplot(storms, aes(x = status, y = lat)) +   
  geom_boxplot() + 
  theme(axis.text.x = element_text(angle = 60, hjust = 1 ))

To explore these box plots, use filter to look at some actual data entries. How do the numbers in the data reflect what you see in the boxplot above?

Make the same histogram above, but with a subset of the data.

Vary binwidth and use scale_x_continuous to change the labels on the x-axis. Also, notice that we filled each bar with a fill = age aesthetic and we got capital Age in the legend in the labs() function.

hi_storms <- filter(storms, lat > median(lat))
ggplot(hi_storms, aes(x = lat)) + 
  geom_histogram(binwidth =  .5, aes(fill = status)) +
  scale_x_continuous(breaks = seq(7,70,by = 0.5)) + 
  labs(x = "Latitude", y = "Number of storms", 
       title = "Latitude of Storms", 
       fill = "Storm Type") +
  theme(axis.text.x = element_text(angle = 60, hjust = 1 ))

Exercise: Improve the previous plot

1. Change the scale on the x-axis to improve readability.

Smooth Histograms

Use a density plot to plot a smooth histogram,

ggplot(storms, aes(x = lat, 
  color = status, fill = status)) +
  geom_density(alpha = 0.5)

Another view of the data through a box plot. What conclusions can you draw about the differences between the kinds of storms from the data below?

ggplot(storms, aes(x = status, y = lat)) +   
  geom_boxplot()

Three or more numerical values

It’s possible, but be careful not to over-do it. Too much information on a graphic is not good.

youth <- filter(gss_cat, age < 30)
ggplot(youth,
       aes(x = age, y = rincome)) +
       geom_point(aes(color = denom, shape = marital))

Assignment 4: Due in the Samba folder - Midnight, Tuesday

Answer all exercises in 1.4.3

https://r4ds.hadley.nz/data-visualize.html#exercises-2

and 1.6

https://r4ds.hadley.nz/data-visualize.html#exercises-3