`dplyr` 2: Pipes and Groups

Author

Affiliation

Prof Amanda Luby

Carleton College
Stat 220 - Fall 2026

library(tidyverse)
library(ggthemes)
library(nycflights23)

Wrangling the `nycflights23` data

`summarize()`

Using the flights data from the {nycflights23} package, use summarize() to compute statistics about the data:

The lowest and highest distance traveled
The lowest and highest air_time
The median dep_delay

# include your code here

`summarize()` II

Using the flights data from the {nycflights23} package, extract the rows for Delta flights. (Hint: the carrier code is “DL”)

Then use summarize() and a summary function to find:

The number of flights in this subset
The median dep_delay. How does it compare to the overall median?

# include your code here

`group_by()`

Use group_by(), summarize(), and slice_max() to display the five dest airports with the most flights from NYC airports in 2023. Your display should include the median flight delay for these airports.

# include your code here

Intro to Tidy Data

bakeoff_ratings <- read_csv("https://stat220-f26.github.io/data/bakeoff_messy_ratings.csv")
bakeoff_ratings

# A tibble: 8 × 11
  series    e1    e2    e3    e4    e5    e6    e7    e8    e9   e10
   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1      1  2.24  3     3     2.6   3.03  2.75 NA    NA    NA    NA   
2      2  3.1   3.53  3.82  3.6   3.83  4.25  4.42  5.06 NA    NA   
3      3  3.85  4.6   4.53  4.71  4.61  4.82  5.1   5.35  5.7   6.74
4      4  6.6   6.65  7.17  6.82  6.95  7.32  7.76  7.41  7.41  9.45
5      5  8.51  8.79  9.28 10.2   9.95 10.1  10.3   9.02 10.7  13.5 
6      6 11.6  11.6  12.0  12.4  12.4  12    12.4  11.1  12.6  15.0 
7      7 13.6  13.4  13.0  13.3  13.1  13.1  13.4  13.3  13.4  15.9 
8      8  9.46  9.23  8.68  8.55  8.61  8.61  9.01  8.95  9.03 10.0

Discuss

Is this dataset in tidy format? Why or why not?

If not, what would a tidy data set look like? Sketch out the first few rows of this data set in tidy format

`tidy`ing bakeoff ratings

Follow along with the slides to tidy the bakeoff_ratings dataset

`messyratings2`

messy_ratings2 <- read_csv("https://stat220-f26.github.io/data/messy_ratings2.csv")
messy_ratings2

# A tibble: 8 × 21
  series e1_7day e1_28day e2_7day e2_28day e3_7day e3_28day e4_7day e4_28day
   <dbl>   <dbl>    <dbl>   <dbl>    <dbl>   <dbl>    <dbl>   <dbl>    <dbl>
1      1    2.24    NA       3       NA       3       NA       2.6     NA   
2      2    3.1     NA       3.53    NA       3.82    NA       3.6     NA   
3      3    3.85    NA       4.6     NA       4.53    NA       4.71    NA   
4      4    6.6     NA       6.65    NA       7.17    NA       6.82    NA   
5      5    8.51    NA       8.79    NA       9.28    NA      10.2     NA   
6      6   11.6     11.7    11.6     11.8    12.0     NA      12.4     12.7 
7      7   13.6     13.9    13.4     13.7    13.0     13.4    13.3     13.9 
8      8    9.46     9.72    9.23     9.53    8.68     9.06    8.55     8.87
# ℹ 12 more variables: e5_7day <dbl>, e5_28day <dbl>, e6_7day <dbl>,
#   e6_28day <dbl>, e7_7day <dbl>, e7_28day <dbl>, e8_7day <dbl>,
#   e8_28day <dbl>, e9_7day <dbl>, e9_28day <dbl>, e10_7day <dbl>,
#   e10_28day <dbl>

Task 1

Tidy this data set by

Selecting the series and e*_7day columns
Pivoting the data to add a column for episode and a column for rating (we’ll clean up the episode column later)

# include your code here

Task 2

Clean the episode and period column
Make a line plot with episode on the x-axis, rating on the y-axis, colored by series. (You will also need to map the group aesthetic to series)

Wrangling the nycflights23 data

summarize()

summarize() II

group_by()