dplyr group_by + summarise Exercises in R: 18 Aggregation Practice Problems

Eighteen runnable practice problems that drill the most common dplyr aggregation pattern: group_by() |> summarise(). The mix covers counts and means, multi-column groups, across() for column-wise stats, NA handling, group shares, and per-group ranking. Each problem hides a fully worked solution so you can try first and verify after.

The exercises share state: every variable you define (ex_1_1, ex_2_3, etc.) persists across blocks on this page, so feel free to reuse earlier results in later problems if you want.

Section 1. Counts and means: the basics (3 problems)

The first three exercises drill the bread-and-butter pattern: split the rows, then count or average inside each group. Every later problem is a variation on this same skeleton.

Exercise 1.1: Count cars per cylinder in mtcars

Task: Use group_by() and summarise() on the built-in mtcars dataset to count how many rows belong to each cyl value (the engine cylinder count). The output should have two columns, cyl and n. Save the result to ex_1_1.

Expected result:

#> # A tibble: 3 x 2
#>     cyl     n
#>   <dbl> <int>
#> 1     4    11
#> 2     6     7
#> 3     8    14

Difficulty: Beginner

Exercise 1.2: Average mpg per cylinder, rounded to one decimal

Task: A junior analyst is preparing a one-line summary of fuel economy by engine size for a status email. Compute the mean of mpg grouped by cyl in mtcars, round to one decimal, and save the two-column result to ex_1_2.

Expected result:

#> # A tibble: 3 x 2
#>     cyl avg_mpg
#>   <dbl>   <dbl>
#> 1     4    26.7
#> 2     6    19.7
#> 3     8    15.1

Difficulty: Intermediate

Exercise 1.3: Average ozone per month in airquality

Task: The reporting team at an air-quality office wants the monthly mean of Ozone from the built-in airquality dataset for the New York summer of 1973. Group by Month and produce one row per month with avg_ozone rounded to one decimal. Save the result to ex_1_3.

Expected result:

#> # A tibble: 5 x 2
#>   Month avg_ozone
#>   <int>     <dbl>
#> 1     5      23.6
#> 2     6      29.4
#> 3     7      59.1
#> 4     8      59.9
#> 5     9      31.4

Difficulty: Intermediate

Section 2. Multi-column grouping and rollups (3 problems)

Real summaries usually slice by two or three variables at once. The shape of the output changes the moment you add a second grouping column, and the .groups argument controls how dplyr leaves the grouping after the summary.

Exercise 2.1: Mean horsepower by cylinder count and gear count

Task: Aggregate mtcars by two columns at once, cyl and gear, and compute avg_hp = mean(hp). The output should have one row per existing cyl-and-gear combination, sorted by cyl then gear. Save to ex_2_1.

Expected result:

#> # A tibble: 8 x 3
#>     cyl  gear avg_hp
#>   <dbl> <dbl>  <dbl>
#> 1     4     3   97
#> 2     4     4   76
#> 3     4     5  102
#> 4     6     3  108
#> 5     6     4  116.
#> 6     6     5  175
#> 7     8     3  194.
#> 8     8     5  300.

Difficulty: Intermediate

Exercise 2.2: Median price per cut and color in diamonds

Task: A jeweller preparing a quarterly price review wants the median list price of stones in the diamonds dataset broken down by cut and color. Compute med_price per (cut, color) cell. Save the long-form tibble (one row per combination) to ex_2_2.

Expected result:

#> # A tibble: 35 x 3
#>    cut       color med_price
#>    <ord>     <ord>     <dbl>
#>  1 Fair      D         3730 
#>  2 Fair      E         2956.
#>  3 Fair      F         3035 
#>  4 Fair      G         3057 
#>  5 Fair      H         3816 
#>  6 Fair      I         3246 
#>  7 Fair      J         4234 
#>  8 Good      D         1838 
#>  9 Good      E         1994 
#> 10 Good      F         2281 
#> # 25 more rows hidden

Difficulty: Intermediate

Exercise 2.3: Hierarchical rollup of diamond inventory by cut and clarity

Task: The category manager needs a rollup of diamonds showing both the row count and the total revenue per (cut, clarity) combination, sorted high-to-low by total revenue. Compute n_stones and total_price per group, then arrange so the most valuable combinations are on top. Save to ex_2_3.

Expected result:

#> # A tibble: 40 x 4
#>    cut       clarity n_stones total_price
#>    <ord>     <ord>      <int>       <int>
#>  1 Ideal     VS2         5071    25719226
#>  2 Premium  SI1          3575    21477705
#>  3 Premium  VS2          3357    20063910
#>  4 Ideal     SI1         4282    19500488
#>  5 Ideal     VS1         3589    16769747
#> # 35 more rows hidden

Difficulty: Advanced

Section 3. across() for column-wise summaries (3 problems)

across() lets one call summarise many columns at once. It is the cleanest way to compute the same statistic across a panel of numeric columns, and it composes with column selection helpers like where() and starts_with().

Exercise 3.1: Mean of every numeric column in mtcars by cylinder

Task: Compute the mean of every numeric column in mtcars grouped by cyl. Use across(where(is.numeric), mean) so the code automatically picks up all numeric columns (every column in mtcars is numeric). Save the wide result to ex_3_1.

Expected result:

#> # A tibble: 3 x 11
#>     cyl   mpg  disp    hp  drat    wt  qsec    vs    am  gear  carb
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4  26.7  105.  82.6  4.07  2.29  19.1 0.909 0.727  4.09  1.55
#> 2     6  19.7  183. 122.   3.59  3.12  18.0 0.571 0.429  3.86  3.43
#> 3     8  15.1  353. 209.   3.23  4.00  16.8 0     0.143  3.29  3.5

Difficulty: Intermediate

Exercise 3.2: Multiple statistics across selected columns

Task: For each cyl group of mtcars, compute the minimum, mean, and maximum of mpg, hp, and wt in a single summarise(across(...)) call. Use a named list of functions so column names come out as mpg_min, mpg_mean, mpg_max, etc. Save the 10-column tibble to ex_3_2.

Expected result:

#> # A tibble: 3 x 10
#>     cyl mpg_min mpg_mean mpg_max hp_min hp_mean hp_max wt_min wt_mean wt_max
#>   <dbl>   <dbl>    <dbl>   <dbl>  <dbl>   <dbl>  <dbl>  <dbl>   <dbl>  <dbl>
#> 1     4    21.4     26.7    33.9     52    82.6    113   1.51    2.29   3.19
#> 2     6    17.8     19.7    21.4    105   122.    175   2.62    3.12   3.46
#> 3     8    10.4     15.1    19.2    150   209.    335   3.17    4.00   5.42

Difficulty: Advanced

Exercise 3.3: Mean of all measurement columns in airquality per month, ignoring NA

Task: From airquality, compute the per-month mean of every numeric column except Month and Day. Use across() with a column selector that excludes those two, and pass na.rm = TRUE so missing readings are ignored. Save the result to ex_3_3.

Expected result:

#> # A tibble: 5 x 5
#>   Month Ozone Solar.R  Wind  Temp
#>   <int> <dbl>   <dbl> <dbl> <dbl>
#> 1     5  23.6    182.  11.6  65.5
#> 2     6  29.4    190.  10.3  79.1
#> 3     7  59.1    216.   8.94 83.9
#> 4     8  59.9    172.   8.79 84.0
#> 5     9  31.4    167.  10.2  76.9

Difficulty: Intermediate

Section 4. Handling NA in group-wise aggregations (3 problems)

Missing values silently break aggregations: a single NA in a group turns mean() into NA for that whole row. These exercises drill the three coping strategies: skip, count, and replace.

Exercise 4.1: Naive mean produces NA, see what happens

Task: Compute the per-month mean of Solar.R in airquality WITHOUT setting na.rm = TRUE. The result will contain at least one NA row because Solar.R has missing readings. Save the result to ex_4_1 so you can inspect which months are affected.

Expected result:

#> # A tibble: 5 x 2
#>   Month avg_solar
#>   <int>     <dbl>
#> 1     5       NA 
#> 2     6      190.
#> 3     7      216.
#> 4     8       NA 
#> 5     9      167.

Difficulty: Beginner

Exercise 4.2: Count missing readings per month

Task: The data-quality reviewer needs to know how many Ozone readings are missing in each month of airquality. Compute n_missing = sum(is.na(Ozone)) and n_total = n() per Month so the team can see both the absolute count and the denominator. Save to ex_4_2.

Expected result:

#> # A tibble: 5 x 3
#>   Month n_missing n_total
#>   <int>     <int>   <int>
#> 1     5         5      31
#> 2     6        21      30
#> 3     7         5      31
#> 4     8         5      31
#> 5     9         1      30

Difficulty: Intermediate

Exercise 4.3: Replace NA with the column median before aggregating

Task: For airquality, impute missing Ozone and Solar.R values with each column's median (computed across the full dataset, not within month), then take the per-month mean. Use mutate(across(...)) for the impute, then group_by() + summarise() for the aggregate. Save to ex_4_3.

Expected result:

#> # A tibble: 5 x 3
#>   Month avg_ozone avg_solar
#>   <int>     <dbl>     <dbl>
#> 1     5      27.4     187. 
#> 2     6      30.4     190. 
#> 3     7      54.6     214. 
#> 4     8      56.7     180. 
#> 5     9      31.3     167. 

Difficulty: Advanced

Section 5. Group shares, ratios, and percentages (3 problems)

Counts and means are starting points; stakeholders usually want shares. These problems drill the move from raw aggregate to percent-of-total, which requires combining summarise() with a follow-up mutate() or doing both inside one call.

Exercise 5.1: Share of each cut grade in diamonds inventory

Task: A retail manager wants the percentage breakdown of stones in diamonds by cut grade. Count stones per cut, then add a pct column showing each row as a percentage of the 53,940 total, rounded to one decimal. Save to ex_5_1.

Expected result:

#> # A tibble: 5 x 3
#>   cut           n   pct
#>   <ord>     <int> <dbl>
#> 1 Fair       1610   3  
#> 2 Good       4906   9.1
#> 3 Very Good 12082  22.4
#> 4 Premium   13791  25.6
#> 5 Ideal     21551  40  

Difficulty: Intermediate

Exercise 5.2: Share of cylinder counts within each gear bucket

Task: A used-car analyst wants to see, for each gear bucket in mtcars, what percentage of cars have 4, 6, and 8 cylinders. Group by (gear, cyl), count, then compute pct_within_gear as the percentage WITHIN each gear group (not the dataset total). Save to ex_5_2.

Expected result:

#> # A tibble: 8 x 4
#> # Groups:   gear [3]
#>    gear   cyl     n pct_within_gear
#>   <dbl> <dbl> <int>           <dbl>
#> 1     3     4     1            6.67
#> 2     3     6     2           13.3 
#> 3     3     8    12           80   
#> 4     4     4     8           66.7 
#> 5     4     6     4           33.3 
#> 6     5     4     2           40   
#> 7     5     6     1           20   
#> 8     5     8     2           40   

Difficulty: Intermediate

Exercise 5.3: Daily ozone share of monthly total

Task: The environmental analyst wants a day-level table from airquality showing each day's Ozone reading as a percentage of that month's total Ozone. For each (Month, Day) keep Ozone and add pct_of_month. Drop rows where Ozone is missing first to avoid NA denominators. Save to ex_5_3.

Expected result:

#> # A tibble: 116 x 4
#>    Month   Day Ozone pct_of_month
#>    <int> <int> <int>        <dbl>
#>  1     5     1    41         5.61
#>  2     5     2    36         4.92
#>  3     5     3    12         1.64
#>  4     5     4    18         2.46
#>  5     5     6    28         3.83
#> # 111 more rows hidden

Difficulty: Advanced

Section 6. Per-group rankings and top-N (3 problems)

The final section drills lookups: given groups, which row inside each group wins on some criterion. These exercises move between summarise() (when you just need the winning value) and slice_max() (when you need the whole row).

Exercise 6.1: Maximum price stone in each cut grade

Task: A jeweller building a "premium picks" page needs the single most expensive stone of each cut grade in diamonds, returning every column of that winning row. Use slice_max() to pick the top row per group by price, breaking ties with n = 1, with_ties = FALSE. Save to ex_6_1.

Expected result:

#> # A tibble: 5 x 10
#>   carat cut       color clarity depth table price     x     y     z
#>   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1  2.01 Fair      G     SI1      70.6    64 18574  7.43  6.64  4.69
#> 2  2.8  Good      F     SI2      63.5    56 18707  8.78  8.62  5.51
#> 3  2    Very Good G     SI1      63.5    56 18818  7.9   7.97  5.04
#> 4  2.29 Premium   I     VS2      60.8    60 18823  8.5   8.47  5.16
#> 5  1.51 Ideal     G     IF       61.7    55 18806  7.37  7.41  4.56

Difficulty: Intermediate

Exercise 6.2: Top-3 heaviest cars per cylinder count

Task: From mtcars, return the 3 heaviest cars (highest wt) in each cyl group. Use tibble::rownames_to_column() first so the car names become a column called model, then group, then slice. Save the 9-row result to ex_6_2.

Expected result:

#> # A tibble: 9 x 4
#>   model                cyl    wt   mpg
#>   <chr>              <dbl> <dbl> <dbl>
#> 1 Volvo 142E             4 2.78   21.4
#> 2 Toyota Corona          4 2.46   21.5
#> 3 Datsun 710             4 2.32   22.8
#> 4 Merc 280C              6 3.44   17.8
#> 5 Merc 280               6 3.44   19.2
#> 6 Valiant                6 3.46   18.1
#> 7 Lincoln Continental    8 5.42   10.4
#> 8 Cadillac Fleetwood     8 5.25   10.4
#> 9 Chrysler Imperial      8 5.34   14.7

Difficulty: Advanced

Exercise 6.3: Distinct color count per cut grade

Task: A merchandising lead wants to know how many distinct color grades exist within each cut grade in diamonds, plus the total stone count, plus the median carat. Use n_distinct() for the color count and combine three statistics in one summarise() call. Save to ex_6_3.

Expected result:

#> # A tibble: 5 x 4
#>   cut       n_colors n_stones med_carat
#>   <ord>        <int>    <int>     <dbl>
#> 1 Fair             7     1610      1   
#> 2 Good             7     4906      0.82
#> 3 Very Good        7    12082      0.71
#> 4 Premium          7    13791      0.86
#> 5 Ideal            7    21551      0.54

Difficulty: Advanced

What to do next

• Drill multi-table aggregation on the dplyr Joins Exercises hub.
• Practise the reshape side of summaries on the tidyr pivot_longer + pivot_wider Exercises.
• Revisit the parent lesson dplyr group_by + summarise if any pattern above felt unfamiliar.
• For column-wise transformations beyond across(), see dplyr Exercises in R.