Exploratory Data Analysis II

Lecture 5

Katie Solarz

Duke University
STA 199 Summer 2026: Session I

May 10, 2026

Announcements/Reminders

  • Lab is due TONIGHT at 11:59PM ➞ submit PDF to Gradescope

  • Come to office hours and/or post on Ed for help!

  • Kenna’s office hours are 11:00am - 1:00pm today on Zoom (find link on Canvas)

  • Always double check your project (top right-hand corner of screen in your RStudio) & be sure you are in the correct project for the task at hand (AE in lecture, lab in lab / at home when working on assignments)

Exam Intel

  • Midterm Date: Monday, 6/1 (confirmed)

  • Exam will take place during both lecture & lab time slots (i.e., you will have from 9:30am - 12:15pm)

  • I am NOT writing an exam I expect to take the full 2hr 45min timeframe; having the exam during both lecture & lab is meant to take some of the time pressure away from the exam & allow buffer time in case of any technical issues

  • You are of course entitled to the full 2hr 45min if you need it!

  • Format: written portion (mult. choice) & live coding / free response (think about this as a short lab assignment) on your laptop

  • Once you are finished with the written portion, you will turn it in and clone an exam repo

  • Cheatsheet (standard printer paper, both sides, can be hand-written/typed/combo, idc) allowed for both portions of the exam

  • The coding portion of the exam is open-assignment in that you will be working in your containers, so you have access to all of your AEs & lab assignments by changing projects (of course, switching between projects and looking through these materials eats away at time, so I wouldn’t be entirely reliant on this)

  • The coding portion of the exam will be closed-internet (i.e., you cannot open any other tabs on your browser aside from GitHub & your container); myself, Kenna, and potentially one additional person will be proctoring and actively monitoring adherence to this policy (breaking this policy will result in a score of 0 for this portion of the exam)

  • I will provide review materials for both portions ahead of time (likely by next Thursday) – your labs are the best preparatory materials, so make sure you feel confident with all lab content & review your graded feedback!

  • If you need accommodations and I have not received a letter, please get it to me by EOD today; make your appointment in the testing center!

Questions??

Lab: Narrative

  • Boxplots, histograms, density plots:

    • Center: Give an idea of a ‘typical value’ (median or mean, most commonly; choose which you think is more appropriate for the data, or mention both!);

    • Spread: Does the data have a lot of variability (“wide, flat” distribution), or just a little (“narrow” distribution)? What is the primary range of the data? (IQR, outliers)

    • Shape: Is there skew, or is the distribution symmetric? If the data are skewed, in which direction? Is the distribution unimodal, bimodal, or multimodal?

    • Outliers: Are there any unsual data points (whether via visual or mathematical assessment (i.e., “dots” on a boxplot))

Lab: Narrative

  • Scatter Plots

    • Is there a relationship between the two variables? Is it positive or negative?

    • If there is a relationship, does it seem to be strong or weak?

    • Is the relationship linear or nonlinear (i.e., quadratic, exponential, logarithmic)?

Lab: Advice

  • Be Specific in your narrative. Don’t just say “the spread is small and the center is low” What does that mean??? Give numbers, units (if available), etc.

  • Be Specific in your plot labels

  • Answer all parts of the question!! Statements like “Compare…” mean to do so in the written answer!

  • Written answers should be text outside of code chunks, not comments

  • Make sure you can render to PDF early & often :) This will help you identify / troubleshoot issues with your code that may not be apparent in isolation

Outline

  • Monday: Started learning about data transformation & intro to piping

  • Yesterday: More (gg)plotting & describing distributions

  • Today:

    • Review from last time + finish AE04

    • More about the pipe

    • Transformation + plotting combo

AE-04

Quick Review: Data Viz

After a while this is more art than science, but you can narrow down the options based on the number and type of variables:

Variable combo Options
1 numerical histogram, density, box plot
1 categorical bar chart, pie chart
2 numerical scatterplot
numerical/categorical side-by-side box or violin plots, stacked densities or histograms
2 categorical stacked bar plot (today)

For three or more variables, the human mind is limited, and you have to start getting creative (play with color, texture, etc).

Quick Review: Row operations

  • slice(): chooses rows based on location
  • filter(): chooses rows based on column values
  • arrange(): changes the order of the rows
  • sample_n(): take a random subset of the rows
X1 X2 X3
1 a yes
3 b no
5 a yes
7 b yes
9 a yes

Quick Review: Column operations

  • select(): changes whether or not a column is included
  • rename(): changes the name of columns
  • mutate(): changes the values of columns and creates new columns
X1 X2 X3
1 a yes
3 b no
5 a yes
7 b yes
9 a yes

Quick Review: Groups of rows

  • summarize(): collapses a group into a single row
  • count(): count unique values of one or more variables
  • group_by(): perform calculations separately for each value of a variable
X1 X2 X3
1 a yes
3 b no
5 a yes
7 b yes
9 a yes

Drilling down:
group_by(),
summarize(),
count()

What does group_by() do?

What does group_by() do in the following pipeline?

gerrymander |>
  count(flip18, gerry) |>
  group_by(flip18) |>
  mutate(prop = n / sum(n))
# A tibble: 8 × 4
# Groups:   flip18 [3]
  flip18 gerry     n  prop
   <dbl> <fct> <int> <dbl>
1     -1 low       2 0.4  
2     -1 mid       3 0.6  
3      0 low      52 0.133
4      0 mid     242 0.617
5      0 high     98 0.25 
6      1 low       8 0.211
7      1 mid      25 0.658
8      1 high      5 0.132

What does group_by() do?

What does group_by() do in the following pipeline?

gerrymander |>
  count(flip18, gerry) |>
  #group_by(flip18) |>
  mutate(prop = n / sum(n))
# A tibble: 8 × 4
  flip18 gerry     n    prop
   <dbl> <fct> <int>   <dbl>
1     -1 low       2 0.00460
2     -1 mid       3 0.00690
3      0 low      52 0.120  
4      0 mid     242 0.556  
5      0 high     98 0.225  
6      1 low       8 0.0184 
7      1 mid      25 0.0575 
8      1 high      5 0.0115

Let’s simplify!

What does group_by() do in the following pipeline?

gerrymander |>
  group_by(state) |>
  summarize(mean_trump16 = mean(trump16))
# A tibble: 50 × 2
   state mean_trump16
   <chr>        <dbl>
 1 AK            52.8
 2 AL            62.6
 3 AR            60.9
 4 AZ            46.9
 5 CA            31.7
 6 CO            43.6
 7 CT            41.0
 8 DE            41.9
 9 FL            47.9
10 GA            51.3
# ℹ 40 more rows

Let’s simplify!

What does group_by() do in the following pipeline?

gerrymander |>
  #group_by(state) |>
  summarize(mean_trump16 = mean(trump16))
# A tibble: 1 × 1
  mean_trump16
         <dbl>
1         45.9

group_by()

  • it converts a data frame to a grouped data frame, where subsequent operations are performed once per group

  • ungroup() removes grouping

gerrymander |>
  group_by(state)
# A tibble: 435 × 12
# Groups:   state [50]
   district last_name first_name party16 clinton16 trump16 dem16 state
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr>
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK   
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL   
 3 AL-02    Roby      Martha     R            33      64.9     0 AL   
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL   
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL   
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL   
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL   
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL   
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR   
10 AR-02    Hill      French     R            41.7    52.4     0 AR   
# ℹ 425 more rows
# ℹ 4 more variables: party18 <chr>, dem18 <dbl>, flip18 <dbl>,
#   gerry <fct>

group_by()

  • it converts a data frame to a grouped data frame, where subsequent operations are performed once per group

  • ungroup() removes grouping

gerrymander |>
  group_by(state) |>
  ungroup()
# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr>
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK   
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL   
 3 AL-02    Roby      Martha     R            33      64.9     0 AL   
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL   
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL   
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL   
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL   
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL   
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR   
10 AR-02    Hill      French     R            41.7    52.4     0 AR   
# ℹ 425 more rows
# ℹ 4 more variables: party18 <chr>, dem18 <dbl>, flip18 <dbl>,
#   gerry <fct>

group_by() |> summarize()

A common pipeline is group_by() and then summarize() to calculate summary statistics for each group:

gerrymander |>
  group_by(state) |>
  summarize(
    mean_trump16 = mean(trump16),
    median_trump16 = median(trump16)
  )
# A tibble: 50 × 3
   state mean_trump16 median_trump16
   <chr>        <dbl>          <dbl>
 1 AK            52.8           52.8
 2 AL            62.6           64.9
 3 AR            60.9           63.0
 4 AZ            46.9           47.7
 5 CA            31.7           28.4
 6 CO            43.6           41.3
 7 CT            41.0           40.4
 8 DE            41.9           41.9
 9 FL            47.9           49.6
10 GA            51.3           56.6
# ℹ 40 more rows

group_by() |> summarize()

This pipeline can also be used to count number of observations for each group:

gerrymander |>
  group_by(state) |>
  summarize(n = n())
# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 AK        1
 2 AL        7
 3 AR        4
 4 AZ        9
 5 CA       53
 6 CO        7
 7 CT        5
 8 DE        1
 9 FL       27
10 GA       14
# ℹ 40 more rows

summarize()

... |>
  summarize(
    name_of_summary_statistic = summary_function(variable)
  )
  • name_of_summary_statistic: Anything you want to call it!
    • Recommendation: Keep it short and evocative
  • summary_function():

summarize()

Recall from yesterday…

gerrymander |>
  summarize(
    mean_trump_perc = mean(trump16),
    median_trump_perc = median(trump16),
    sd = sd(trump16),
    iqr = IQR(trump16),
    q25 = quantile(trump16, 0.25),
    q75 = quantile(trump16, 0.75)
  )
# A tibble: 1 × 6
  mean_trump_perc median_trump_perc    sd   iqr   q25   q75
            <dbl>             <dbl> <dbl> <dbl> <dbl> <dbl>
1            45.9              48.7  16.8  23.3  34.8  58.1

summarize()

Or, more concisely

summary(gerrymander$trump16)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   4.90   34.85   48.70   45.86   58.10   80.40

summarize()

Getting to the point…

gerrymander |>
  summarize(
    gimme_my_mean = mean(trump16),
    gimme_my_median = median(trump16)
  )
  • summarize() creates a new data frame that stores the summaries;
  • You can compute as many summaries as you want, and whichever you want, in a single call to summarize();
  • To the left of the equal signs are your choice of column names in the new data frame you are creating. You can type whatever you want here (within reason);
  • To the right of the equal signs is R code that computes the summaries. You must use the correct command names (case sensitive): mean, median, quantile, sd, var, etc;
  • If you want to learn what these do, read the documentation (eg ?quantile).

Spot the difference

What’s the difference between the following two pipelines?

gerrymander |>
  group_by(state) |>
  summarize(n = n())
# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 AK        1
 2 AL        7
 3 AR        4
 4 AZ        9
 5 CA       53
 6 CO        7
 7 CT        5
 8 DE        1
 9 FL       27
10 GA       14
# ℹ 40 more rows
gerrymander |>
  count(state)
# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 AK        1
 2 AL        7
 3 AR        4
 4 AZ        9
 5 CA       53
 6 CO        7
 7 CT        5
 8 DE        1
 9 FL       27
10 GA       14
# ℹ 40 more rows

Spot the difference

count()

... |>
  count(variable)
... |>
  count(variable1, variable2)

  • Count the number of observations in each level of variable(s); if you supply more than 1 variable, count the number of observations in each unique combination of levels across all variables

  • Place the counts in a variable called n; you can access this variable in downstream code

count() and sort

What does the following pipeline do? Rewrite it with count() instead.

gerrymander |>
  group_by(state) |>
  summarize(n = n()) |>
  arrange(desc(n))
# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

count() and sort

What does the following pipeline do? Rewrite it with count() instead.

gerrymander |>
  count(state) |>
  arrange(desc(n))
# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

count() and sort

What does the following pipeline do? Rewrite it with count() instead.

gerrymander |>
  count(state, sort = TRUE)
# A tibble: 50 × 2
   state     n
   <chr> <int>
 1 CA       53
 2 TX       36
 3 FL       27
 4 NY       27
 5 IL       18
 6 PA       18
 7 OH       16
 8 GA       14
 9 MI       14
10 NC       13
# ℹ 40 more rows

mutate()

Flip the question

Note

From AE-04: Is a Congressional District more likely to have high prevalence of gerrymandering if a Democrat was able to flip the seat in the 2018 election?

vs.

Note

Is a Congressional District more likely to be flipped to a Democratic seat if it has high prevalence of gerrymandering or low prevalence of gerrymandering?

Flipping vs. gerrymandering prevalence

The following code should produce a visualization that answers the question “Is a Congressional District more likely to be flipped to a Democratic seat if it has high prevalence of gerrymandering or low prevalence of gerrymandering?” However, it produces a warning and an unexpected plot. What’s going on?

ggplot(
  gerrymander, 
  aes(x = gerry, fill = flip18)
  ) +
  geom_bar(position = "fill")
Warning: The following aesthetics were dropped during statistical
transformation: fill.
ℹ This can happen when ggplot fails to infer the correct grouping
  structure in the data.
ℹ Did you forget to specify a `group` aesthetic or to convert a
  numerical variable into a factor?

Another glimpse at gerrymander 

glimpse(gerrymander)
Rows: 435
Columns: 12
$ district   <chr> "AK-AL", "AL-01", "AL-02", "AL-03", "AL-04", "AL-…
$ last_name  <chr> "Young", "Byrne", "Roby", "Rogers", "Aderholt", "…
$ first_name <chr> "Don", "Bradley", "Martha", "Mike D.", "Rob", "Mo…
$ party16    <chr> "R", "R", "R", "R", "R", "R", "R", "D", "R", "R",…
$ clinton16  <dbl> 37.6, 34.1, 33.0, 32.3, 17.4, 31.3, 26.1, 69.8, 3…
$ trump16    <dbl> 52.8, 63.5, 64.9, 65.3, 80.4, 64.7, 70.8, 28.6, 6…
$ dem16      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0…
$ state      <chr> "AK", "AL", "AL", "AL", "AL", "AL", "AL", "AL", "…
$ party18    <chr> "R", "R", "R", "R", "R", "R", "R", "D", "R", "R",…
$ dem18      <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0…
$ flip18     <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0…
$ gerry      <fct> mid, high, high, high, high, high, high, high, mi…

mutate()

  • We want to use flip18 as a categorical variable

  • But it’s stored as a numeric

  • So we need to change its type first, before we can use it as a categorical variable (more on this tomorrow!)

  • The mutate() function transforms (mutates) a data frame by creating a new column or updating an existing one

In this case, we want to use it to update an existing column, flip18.

mutate() in action

gerrymander |>
  mutate(flip18 = as_factor(flip18))
# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr>
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK   
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL   
 3 AL-02    Roby      Martha     R            33      64.9     0 AL   
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL   
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL   
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL   
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL   
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL   
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR   
10 AR-02    Hill      French     R            41.7    52.4     0 AR   
# ℹ 425 more rows
# ℹ 4 more variables: party18 <chr>, dem18 <dbl>, flip18 <fct>,
#   gerry <fct>

mutate() in action

gerrymander |>
  mutate(flip18 = as_factor(flip18)) |>
  relocate(flip18) ## make flip18 the first column in gerrymander
# A tibble: 435 × 12
   flip18 district last_name first_name party16 clinton16 trump16
   <fct>  <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl>
 1 0      AK-AL    Young     Don        R            37.6    52.8
 2 0      AL-01    Byrne     Bradley    R            34.1    63.5
 3 0      AL-02    Roby      Martha     R            33      64.9
 4 0      AL-03    Rogers    Mike D.    R            32.3    65.3
 5 0      AL-04    Aderholt  Rob        R            17.4    80.4
 6 0      AL-05    Brooks    Mo         R            31.3    64.7
 7 0      AL-06    Palmer    Gary       R            26.1    70.8
 8 0      AL-07    Sewell    Terri      D            69.8    28.6
 9 0      AR-01    Crawford  Rick       R            30.2    65  
10 0      AR-02    Hill      French     R            41.7    52.4
# ℹ 425 more rows
# ℹ 5 more variables: dem16 <dbl>, state <chr>, party18 <chr>,
#   dem18 <dbl>, gerry <fct>

Revisit the plot

“Is a Congressional District more likely to be flipped to a Democratic seat if it has high prevalence of gerrymandering or low prevalence of gerrymandering?”

gerrymander |>
  mutate(flip18 = as_factor(flip18)) |>
  ggplot(aes(x = gerry, fill = flip18)) +
  geom_bar(position = "fill")

More about the pipe

  • The pipe operator passes what comes before it into the function that comes after it as the first argument in that function.
sum(1, 2)
[1] 3


More about the pipe

  • The pipe operator passes what comes before it into the function that comes after it as the first argument in that function.
sum(1, 2)
[1] 3


1 |> 
  sum(2)
[1] 3

Pipe + ggplot() !!

ggplot(gerrymander, aes(x = trump16)) +
  geom_boxplot()

Pipe + ggplot() !!

gerrymander |>
  ggplot(aes(x = trump16)) +
  geom_boxplot()

Plot + data transform

We can do data transformation immediately followed by a plot!

gerrymander
# A tibble: 435 × 12
   district last_name first_name party16 clinton16 trump16 dem16 state
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr>
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK   
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL   
 3 AL-02    Roby      Martha     R            33      64.9     0 AL   
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL   
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL   
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL   
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL   
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL   
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR   
10 AR-02    Hill      French     R            41.7    52.4     0 AR   
# ℹ 425 more rows
# ℹ 4 more variables: party18 <chr>, dem18 <dbl>, flip18 <dbl>,
#   gerry <fct>

Plot + data transform

We can do data transformation immediately followed by a plot!

gerrymander |>
  mutate(trump16_prop = trump16/100)
# A tibble: 435 × 13
   district last_name first_name party16 clinton16 trump16 dem16 state
   <chr>    <chr>     <chr>      <chr>       <dbl>   <dbl> <dbl> <chr>
 1 AK-AL    Young     Don        R            37.6    52.8     0 AK   
 2 AL-01    Byrne     Bradley    R            34.1    63.5     0 AL   
 3 AL-02    Roby      Martha     R            33      64.9     0 AL   
 4 AL-03    Rogers    Mike D.    R            32.3    65.3     0 AL   
 5 AL-04    Aderholt  Rob        R            17.4    80.4     0 AL   
 6 AL-05    Brooks    Mo         R            31.3    64.7     0 AL   
 7 AL-06    Palmer    Gary       R            26.1    70.8     0 AL   
 8 AL-07    Sewell    Terri      D            69.8    28.6     1 AL   
 9 AR-01    Crawford  Rick       R            30.2    65       0 AR   
10 AR-02    Hill      French     R            41.7    52.4     0 AR   
# ℹ 425 more rows
# ℹ 5 more variables: party18 <chr>, dem18 <dbl>, flip18 <dbl>,
#   gerry <fct>, trump16_prop <dbl>

Plot + data transform

We can do data transformation immediately followed by a plot!

gerrymander |>
  mutate(trump16_prop = trump16/100) |>
  ggplot(aes(x = trump16_prop))

Plot + data transform

We can do data transformation immediately followed by a plot!

gerrymander |>
  mutate(trump16_prop = trump16/100) |>
  ggplot(aes(x = trump16_prop)) +
  geom_boxplot()

Exploratory Data Analysis

What is exploratory data analysis (EDA)??

  • Basically everything we have done so far

  • Making plots and computing summary statistics (proportions, means, IQR, etc.) to help explore the data

  • Generally, EDA precedes some sort of more formal statistical analysis (i.e., hypothesis testing, modeling, etc.); compare your findings from statistical analyses to key takeaways from EDA (do your findings agree? did you discern something that wasn’t visually evident (perhaps obscured by other factors not visualized)?)

  • You cannot make any sort of causal claim / statement from EDA alone (i.e., don’t say something like, “from this scatterploy, we see that some change in x causes some change in y)

  • You can observe relationships & make hypotheses!

Assignment

Let’s make a tiny data frame to use as an example:

library(tidyverse)
df <- tibble(x = c(1, 2, 3, 4, 5), y = c("a", "a", "b", "c", "c"))
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     1 a    
2     2 a    
3     3 b    
4     4 c    
5     5 c

Assignment

Do something and print to screen

df |>
  mutate(x = x * 2)
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c    
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     1 a    
2     2 a    
3     3 b    
4     4 c    
5     5 c

Do something and save result

df <- df |>
  mutate(x = x * 2)
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c

Assignment

Do something, save result, overwriting original

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  mutate(x = x * 2)
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c

Do something, save result, not overwriting original

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df_new <- df |>
  mutate(x = x * 2)
df_new
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     2 a    
2     4 a    
3     6 b    
4     8 c    
5    10 c

Assignment

Do something, save result, overwriting original when you shouldn’t

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  group_by(y) |>
  summarize(mean_x = mean(x))
df
# A tibble: 3 × 2
  y     mean_x
  <chr>  <dbl>
1 a        1.5
2 b        3  
3 c        4.5

Do something, save result, overwriting original when you shouldn’t

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  ggplot(aes(x = x)) +
  geom_boxplot()
class(df)
[1] "ggplot2::ggplot" "ggplot"          "ggplot2::gg"    
[4] "S7_object"       "gg"             
typeof(df)
[1] "object"

Assignment

Do something, save result, overwriting original
data frame

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  mutate(z = x + 2)
df
# A tibble: 5 × 3
      x y         z
  <dbl> <chr> <dbl>
1     1 a         3
2     2 a         4
3     3 b         5
4     4 c         6
5     5 c         7

Do something, save result, overwriting original
column

df <- tibble(
  x = c(1, 2, 3, 4, 5), 
  y = c("a", "a", "b", "c", "c")
)
df <- df |>
  mutate(x = x + 2)
df
# A tibble: 5 × 2
      x y    
  <dbl> <chr>
1     3 a    
2     4 a    
3     5 b    
4     6 c    
5     7 c

AE-05