library(tidyverse)AE 02: Visualizing the prognosticators
Suggested answers
Application exercise
Answers
Important
These are suggested answers. This document should be used as reference only, it’s not designed to be an exhaustive key.
Note
Below are the contents of the YAML header. You normally do not see this in the rendered file - we include it so you can reproduce the figure dimensions in the document.
---
title: "AE 02: Visualizing the prognosticators"
subtitle: "Suggested answers"
editor: visual
execute:
fig-width: 8
fig-asp: 4
fig-align: center
warning: false
---For all analyses, we’ll use the tidyverse packages.
The dataset we will visualize is called seers.1 It contains summary statistics for all known Groundhog Day forecasters. 2 Let’s glimpse() at it .
1 I would prefer prognosticators, but I had way too many typos preparing these materials to make you all use it.
2 Source: Countdown to Groundhog Day. Application exercise inspired by Groundhogs Do Not Make Good Meteorologists originally published on FiveThirtyEight.
# import data using readr::read_csv()
seers <- read_csv("data/prognosticators-sum-stats.csv")
glimpse(seers)Rows: 138
Columns: 18
$ name <chr> "Allen McButterpants", "Arboretum Annie", "Beard…
$ forecaster_type <chr> "Groundhog", "Groundhog", "Stuffed Prairie Dog",…
$ forecaster_simple <chr> "Groundhog", "Groundhog", "Other", "Other", "Oth…
$ alive <lgl> TRUE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, TRUE,…
$ climate_region <chr> "Northeast", "South", "Northeast", "South", "Ohi…
$ town <chr> "Hampton Bays", "Dallas", "Bridgeport", "Bee Cav…
$ state <chr> "NY", "TX", "CT", "TX", "OH", "TX", "TX", "AL", …
$ preds_n <dbl> 1, 3, 12, 13, 8, 7, 1, 1, 12, 2, 4, 13, 10, 1, 4…
$ preds_long_winter <dbl> 0, 1, 1, 3, 4, 5, 1, 1, 6, 2, 2, 8, 9, 0, 12, 6,…
$ preds_long_winter_pct <dbl> 0.00000000, 0.33333333, 0.08333333, 0.23076923, …
$ preds_correct <dbl> 1, 2, 9, 9, 5, 3, 0, 0, 5, 0, 2, 5, 2, 0, 23, 0,…
$ preds_rate <dbl> 1.0000000, 0.6666667, 0.7500000, 0.6923077, 0.62…
$ temp_mean <dbl> 32.40000, 50.18333, 31.13333, 51.30769, 40.29375…
$ temp_hist <dbl> 30.14667, 51.32000, 29.56639, 50.97615, 38.88542…
$ temp_sd <dbl> 4.120782, 3.890902, 4.120782, 3.890902, 4.706475…
$ precip_mean <dbl> 2.395000, 2.768333, 3.012500, 2.593462, 4.088125…
$ precip_hist <dbl> 2.9743333, 2.5588889, 3.0695000, 2.5643846, 3.37…
$ precip_sd <dbl> 0.9620579, 0.9029786, 0.9620579, 0.9029786, 1.18…The variables are:
name- name of the prognosticatorforecaster_type- what kind of animal or thing is the prognosticator?forecaster_simple- a simplified version that lumps together the least-frequently appearing types of prognosticatorsalive- is the prognosticator an animate (alive) being?3climate_region- the NOAA climate region in which the prognosticator is located.town- self-explanatorystate- state (or territory) where prognosticator is locatedpreds_n- number of predictions in the databasepreds_long_winter- number of predictions for a “Late Winter” (as opposed to “Early Spring”)preds_long_winter_pct- percentage of predictions for a “Late Winter”preds_correct- number of correct predictions4preds_rate- proportion of predictions that are correcttemp_mean- average temperature (in Fahrenheit) in February and March in the climate region across all prognostication yearstemp_hist- average of the rolling 15-year historic average temperature in February and March across all prognostication yearstemp_sd- standard deviation of average February and March temperatures across all prognostication yearsprecip_mean- average amount of precipitation in February and March across all prognostication years (measured in rainfall inches)precip_histaverage of the rolling 15-year historic average precipitation in February and March across all prognostication yearsprecip_sd- standard deviation of average February and March precipitation across all prognostication years
3 Prognosticators labeled as Animatronic/Puppet/Statue/Stuffed/Taxidermied are classified as not alive.
4 We adopt the same definition as FiveThirtyEight. An “Early Spring” is defined as any year in which the average temperature in either February or March was higher than the historic average. A “Late Winter” was when the average temperature in both months was lower than or the same as the historical average.
Visualizing prediction success rate - Demo
Single variable
Note
Analyzing the a single variable is called univariate analysis.
Create visualizations of the distribution of preds_rate for the prognosticators.
- Make a histogram. Set an appropriate binwidth.
ggplot(seers, aes(x = preds_rate)) +
geom_histogram(binwidth = 0.02)
- Make a boxplot.
ggplot(seers, aes(x = preds_rate)) +
geom_boxplot()
Two variables - Your turn
Note
Analyzing the relationship between two variables is called bivariate analysis.
Create visualizations of the distribution of preds_rate by alive (whether or not the prognosticator is alive).
- Make a single histogram. Set an appropriate binwidth.
ggplot(
seers,
aes(x = preds_rate, fill = alive)
) +
geom_histogram(binwidth = 0.02, alpha = 0.5, color = "black")
- Use multiple histograms via faceting, one for each type. Set an appropriate binwidth, add color as you see fit, and turn off legends if not needed.
ggplot(
seers,
aes(x = preds_rate, fill = alive)
) +
geom_histogram(binwidth = 0.02, show.legend = FALSE) +
facet_wrap(vars(alive), ncol = 1)
- Use side-by-side box plots. Add color as you see fit and turn off legends if not needed.
ggplot(
seers,
aes(x = alive, y = preds_rate, fill = alive)
) +
geom_boxplot(show.legend = FALSE)
- Use density plots. Add color as you see fit.
ggplot(
seers,
aes(x = preds_rate, fill = alive)
) +
geom_density(alpha = 0.5)
- Use violin plots. Add color as you see fit and turn off legends if not needed.
ggplot(
seers,
aes(x = alive, y = preds_rate, fill = alive)
) +
geom_violin(alpha = 0.5, show.legend = FALSE)
- Make a jittered scatter plot. Add color as you see fit and turn off legends if not needed.
ggplot(
seers,
aes(x = alive, y = preds_rate, color = alive)
) +
geom_jitter(show.legend = FALSE)
- Use beeswarm plots. Add color as you see fit and turn off legends if not needed.
library(ggbeeswarm)
ggplot(
seers,
aes(x = alive, y = preds_rate, color = alive)
) +
geom_beeswarm(show.legend = FALSE)
- Demonstration: Use multiple geoms on a single plot. Be deliberate about the order of plotting. Change the theme and the color scale of the plot. Finally, add informative labels.
ggplot(
seers,
aes(x = alive, y = preds_rate, color = alive)
) +
geom_beeswarm(show.legend = FALSE) +
geom_boxplot(show.legend = FALSE, alpha = 0.5) +
scale_color_viridis_d(option = "D", end = 0.8) +
scale_y_continuous(labels = scales::label_percent()) +
theme_minimal() +
labs(
x = "Is the prognosticator alive?",
y = "Prediction accuracy for late winter/early spring",
title = "Accuracy of prognosticators predicting the coming season",
subtitle = "By living status of prognosticator"
)
Multiple variables - Demo
Note
Analyzing the relationship between three or more variables is called multivariate analysis.
- Facet the plot you created in the previous exercise by
forecaster_simple. Adjust labels accordingly.
ggplot(
seers,
aes(x = alive, y = preds_rate, color = alive)
) +
geom_beeswarm(show.legend = FALSE) +
geom_boxplot(show.legend = FALSE, alpha = 0.5) +
scale_color_viridis_d(option = "D", end = 0.8) +
scale_y_continuous(labels = scales::label_percent()) +
facet_wrap(vars(forecaster_simple)) +
theme_minimal() +
labs(
x = "Is the prognosticator alive?",
y = "Prediction accuracy for late winter/early spring",
title = "Accuracy of prognosticators predicting the coming season",
subtitle = "By type and living status of prognosticator"
)
Before you continue, let’s turn off all warnings the code chunks generate and resize all figures. We’ll do this by editing the YAML.
Visualizing other variables - Your turn!
- Pick a single categorical variable from the data set and make a bar plot of its distribution.
# add code here- Pick two categorical variables and make a visualization to visualize the relationship between the two variables. Along with your code and output, provide an interpretation of the visualization.
# add code hereInterpretation goes here…
- Make another plot that uses at least three variables. At least one should be numeric and at least one categorical. In 1-2 sentences, describe what the plot shows about the relationships between the variables you plotted. Don’t forget to label your code chunk.
# add code hereInterpretation goes here…
Session information
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date 2023-09-05
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