SIDS Ch 3 - Data Wrangling

tidyverse
R
Author

Colin Madland

Published

May 18, 2025

Ismay, C., Kim, A. Y., & Valdivia, A. (2025). Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (2nd ed.). Chapman and Hall/CRC.

library(nycflights23)
library(ggplot2)
library(moderndive)
library(tibble)
library(viridis)
Loading required package: viridisLite
library(dplyr)

Attaching package: 'dplyr'
The following objects are masked from 'package:stats':

    filter, lag
The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

3 components of the Grammar or Graphiics

  • data: the dataset containing the variables of interest.
  • geom: the geometric object in question. This refers to the type of object we can observe in a plot. For example: points, lines, and bars.
  • aes: aesthetic attributes of the geometric object. For example, x/y position, color, shape, and size. Aesthetic attributes are mapped to variables in the dataset.

Other components

  • facet to break up a plot into several plots split by the values of another variable (Section 2.6)
  • position adjustments for barplots (Section 2.8)

Five Named graphs - 5NG

  1. Scatterplots
  2. line graphs
  3. bar graphs
  4. histograms
  5. boxplots

Scatterplots

Used to see the relationship between two numerical variables.

View(envoy_flights)
ggplot(data = envoy_flights, mapping = aes(x = dep_delay, y = arr_delay)) + 
  geom_point()
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).

  • there is a problem of the points overlapping
    • change transparency with alpha argument
ggplot(data = envoy_flights, mapping = aes(x = dep_delay, y = arr_delay)) + 
  geom_point(alpha = 0.2)
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).

  • add jitter
ggplot(data = envoy_flights, mapping = aes(x = dep_delay, y = arr_delay)) + 
  geom_jitter(width = 30, height = 30)
Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).

Linegraphs

Show the relationship between two variables when the x-axis (explanatory variable) is sequential.

View(weather)
glimpse(weather)
Rows: 26,207
Columns: 15
$ origin     <chr> "JFK", "JFK", "JFK", "JFK", "JFK", "JFK", "JFK", "JFK", "JF…
$ year       <int> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023,…
$ month      <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ day        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ hour       <int> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1…
$ temp       <dbl> 48.0, 48.2, 49.0, 49.0, 49.0, 48.0, 46.4, 46.0, 48.0, 47.0,…
$ dewp       <dbl> 48.0, 48.2, 49.0, 49.0, 49.0, 48.0, 46.4, 46.0, 48.0, 47.0,…
$ humid      <dbl> 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100…
$ wind_dir   <dbl> 0, 190, 190, 250, 170, 0, 250, 230, 260, 250, 240, 260, 260…
$ wind_speed <dbl> 0.00000, 4.60312, 5.75390, 5.75390, 8.05546, 0.00000, 9.206…
$ wind_gust  <dbl> 0.000000, 5.297178, 6.621473, 6.621473, 9.270062, 0.000000,…
$ precip     <dbl> 1e-02, 1e-02, 1e-04, 2e-02, 1e-04, 1e-04, 0e+00, 0e+00, 0e+…
$ pressure   <dbl> 1010.2, 1009.2, 1009.0, 1008.0, 1007.8, 1007.6, 1007.3, 100…
$ visib      <dbl> 0.25, 2.50, 0.25, 4.00, 0.75, 0.75, 0.24, 0.50, 8.00, 5.00,…
$ time_hour  <dttm> 2023-01-01 00:00:00, 2023-01-01 01:00:00, 2023-01-01 02:00…
ggplot(data = early_january_2023_weather, 
       mapping = aes(x = time_hour, y = wind_speed)) +
  geom_line()

Histograms

Shows the distribution of a variable.

ggplot(data = weather, mapping = aes(x = wind_speed)) +
  geom_histogram()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_bin()`).

ggplot(data = weather, mapping = aes(x = wind_speed)) +
  geom_histogram(color = "white", fill = "steelblue")
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_bin()`).

adjusting bins
  • number of bins
ggplot(data = weather, mapping = aes(x = wind_speed)) +
  geom_histogram(bins = 20, color = "white")
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_bin()`).

  • binwidth
ggplot(data = weather, mapping = aes(x = wind_speed)) +
  geom_histogram(binwidth = 5, color = "white")
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_bin()`).

Facets

Dividing plots by subcategories in the data.

ggplot(data = weather, mapping = aes(x = wind_speed)) +
  geom_histogram(binwidth = 5, color = "white") +
  facet_wrap(~ month)
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_bin()`).

Boxplots

Display the distribution of data and include five numbers to summarize the data:

  • minimum
  • first quartile (25th percentile)
  • median (2nd quartile, 50th percentile)
  • third quartile (75th percentile)
  • maximum
  • also shows the IQR (middle 50%)
    • whiskers extend no more than 1.5 IQR units beyond 25th and 75 percentile.
    • points beyond 1.5 IQR units may be considered outliers.
ggplot(data = weather, mapping = aes(x = month, y = wind_speed)) +
  geom_boxplot()
Warning: Continuous x aesthetic
ℹ did you forget `aes(group = ...)`?
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_boxplot()`).

Note

Returns an invalid plot and a warning.

ggplot(data = weather, mapping = aes(x = month, y = wind_speed, group = month)) +
  geom_boxplot() 
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_boxplot()`).

OR

  • represent months as factors and use geom(violin) for a more detailed plot.
ggplot(data = weather, mapping = aes(x = factor(month), y = wind_speed)) +
  geom_violin()
Warning: Removed 1041 rows containing non-finite outside the scale range
(`stat_ydensity()`).

Barplots

Simpler representation of the distribution known as frequencxies - depends on whether the items are counted or not.

fruits <- tibble(fruit = c("apple", "apple", "orange", "apple", "orange"))
fruits_counted <- tibble(
  fruit = c("apple", "orange"),
  number = c(3, 2))
ggplot(data = fruits, mapping = aes(x = fruit)) +
  geom_bar()

ggplot(data = fruits_counted, mapping = aes(x = fruit)) +
  geom_bar()

ggplot(data = fruits_counted, mapping = aes(x = fruit, y = number)) +
  geom_col()

  • when items are not counted, use geom_bar() with fruit mapped to the x aes
  • when items are counted, we add number to the y aes and use geom_col()
ggplot(flights,  aes(x = carrier)) +
  geom_bar()

  • geom_histogram() has bars that touch, but geom_bar() has bars with white space between
View(airlines)
Important

The best pie-chart is a bar-chart.

Stacked Barplot
ggplot(data = flights, mapping = aes(x = carrier, fill = origin)) +
  geom_bar()

Dodged Barplot
ggplot(data = flights, mapping = aes(x = carrier, fill = origin)) +
  geom_bar(position = "dodge") +
  scale_fill_viridis_d(option = "plasma")

Summary

Named graph Shows Geometric object Notes
1 Scatterplot Relationship between 2 numerical variables geom_point()
2 Linegraph Relationship between 2 numerical variables geom_line() Used when there is a sequential order to x-variable, e.g., time
3 Histogram Distribution of 1 numerical variable geom_histogram() Facetted histograms show the distribution of 1 numerical variable split by the values of another variable
4 Boxplot Distribution of 1 numerical variable split by the values of another variable geom_boxplot()
5 Barplot Distribution of 1 categorical variable geom_bar() when counts are not pre-counted, geom_col() when counts are pre-counted Stacked, side-by-side, and faceted barplots show the joint distribution of 2 categorical variables