In this tutorial, you will learn how summarize a dataset by group with the dplyr library. In this tutorial, you will learn
Summarise()
Group_by vs no group_by
Function in summarise()
Basic function
Subsetting
Sum
Standard deviation
Minimum and maximum
Count
First and last
nth observation
Multiple groups
Filter
Ungroup
For this tutorial, you will use the batting dataset. The original dataset contains 102816 observations and 22 variables. You will only use 20 percent of this dataset and use the following variables:
playerID: Player ID code. Factor yearID: Year. Factor teamID: Team. factor lgID: League. Factor: AA AL FL NL PL UA AB: At bats. Numeric G: Games: number of games by a player. Numeric R: Runs. Numeric HR: Homeruns. Numeric SH: Sacrifice hits. Numeric
Before you perform summary, you will do the following steps to prepare the data:
Step 1: Import the data Step 2: Select the relevant variables Step 3: Sort the data
library(dplyr)
Step 1
data <- read.csv(“https://raw.githubusercontent.com/guru99-edu/R-Programming/master/lahman-batting.csv") % > %
Step 2
select(c(playerID, yearID, AB, teamID, lgID, G, R, HR, SH)) % > %
Step 3
arrange(playerID, teamID, yearID)
A good practice when you import a dataset is to use the glimpse() function to have an idea about the structure of the dataset.
Structure of the data
glimpse(data)
Output:
Observations: 104,324
Variables: 9
$ playerID
Summarise()
The syntax of summarise() is basic and consistent with the other verbs included in the dplyr library.
summarise(df, variable_name=condition) arguments:
df: Dataset used to construct the summary statisticsvariable_name=condition: Formula to create the new variable
Look at the code below:
summarise(data, mean_run =mean(R))
Code Explanation
summarise(data, mean_run = mean(R)): Creates a variable named mean_run which is the average of the column run from the dataset data.
Output:
mean_run
1 19.20114
You can add as many variables as you want. You return the average games played and the average sacrifice hits.
summarise(data, mean_games = mean(G), mean_SH = mean(SH, na.rm = TRUE))
Code Explanation
mean_SH = mean(SH, na.rm = TRUE): Summarize a second variable. You set na.rm = TRUE because the column SH contains missing observations.
Output:
mean_games mean_SH
1 51.98361 2.340085
Group_by vs no group_by
The function summerise() without group_by() does not make any sense. It creates summary statistic by group. The library dplyr applies a function automatically to the group you passed inside the verb group_by. Note that, group_by works perfectly with all the other verbs (i.e. mutate(), filter(), arrange(), …). It is convenient to use the pipeline operator when you have more than one step. You can compute the average homerun by baseball league.
data % > % group_by(lgID) % > % summarise(mean_run = mean(HR))
Code Explanation
data: Dataset used to construct the summary statistics group_by(lgID): Compute the summary by grouping the variable `lgID summarise(mean_run = mean(HR)): Compute the average homerun
Output:
A tibble: 7 x 2
lgID mean_run
1 AA 0.9166667
2 AL 3.1270988
3 FL 1.3131313
4 NL 2.8595953
5 PL 2.5789474
6 UA 0.6216216
7 0.2867133
The pipe operator works with ggplot() as well. You can easily show the summary statistic with a graph. All the steps are pushed inside the pipeline until the grap is plot. It seems more visual to see the average homerun by league with a bar char. The code below demonstrates the power of combining group_by(), summarise() and ggplot() together.
Step 1: Select data frame Step 2: Group data Step 3: Summarize the data Step 4: Plot the summary statistics
library(ggplot2)
Step 1
data % > % #Step 2 group_by(lgID) % > % #Step 3 summarise(mean_home_run = mean(HR)) % > % #Step 4 ggplot(aes(x = lgID, y = mean_home_run, fill = lgID)) + geom_bar(stat = “identity”) + theme_classic() + labs( x = “baseball league”, y = “Average home run”, title = paste( “Example group_by() with summarise()” ) )
Output:
Function in summarise()
The verb summarise() is compatible with almost all the functions in R. Here is a short list of useful functions you can use together with summarise(): We will see examples for every functions of table 1.
Basic function
In the previous example, you didn’t store the summary statistic in a data frame. You can proceed in two steps to generate a date frame from a summary:
Step 1: Store the data frame for further use Step 2: Use the dataset to create a line plot
Step 1) You compute the average number of games played by year.
Mean
ex1 <- data % > % group_by(yearID) % > % summarise(mean_game_year = mean(G)) head(ex1)
Code Explanation
The summary statistic of batting dataset is stored in the data frame ex1.
Output:
# A tibble: 6 x 2
yearID mean_game_year
1 1871 23.42308
2 1872 18.37931
3 1873 25.61538
4 1874 39.05263
5 1875 28.39535
6 1876 35.90625
Step 2) You show the summary statistic with a line plot and see the trend.
Plot the graph
ggplot(ex1, aes(x = yearID, y = mean_game_year)) + geom_line() + theme_classic() + labs( x = “Year”, y = “Average games played”, title = paste( “Average games played from 1871 to 2016” ) )
Output:
Subsetting
The function summarise() is compatible with subsetting.
Subsetting + Median
data % > % group_by(lgID) % > % summarise(median_at_bat_league = median(AB), #Compute the median without the zero median_at_bat_league_no_zero = median(AB[AB > 0]))
Code Explanation
median_at_bat_league_no_zero = median(AB[AB > 0]): The variable AB contains lots of 0. You can compare the median of the at bat variable with and without 0.
Output:
# A tibble: 7 x 3
lgID median_at_bat_league median_at_bat_league_no_zero
1 AA 130 131
2 AL 38 85
3 FL 88 97
4 NL 56 67
5 PL 238 238
6 UA 35 35
7 101 101
Sum
Another useful function to aggregate the variable is sum(). You can check which leagues have the more homeruns.
Sum
data % > % group_by(lgID) % > % summarise(sum_homerun_league = sum(HR))
Output:
# A tibble: 7 x 2
lgID sum_homerun_league
1 AA 341
2 AL 29426
3 FL 130
4 NL 29817
5 PL 98
6 UA 46
7 41
Standard deviation
Spread in the data is computed with the standard deviation or sd() in R.
Spread
data % > % group_by(teamID) % > % summarise(sd_at_bat_league = sd(HR))
Output:
# A tibble: 148 x 2
teamID sd_at_bat_league
1 ALT NA
2 ANA 8.7816395
3 ARI 6.0765503
4 ATL 8.5363863
5 BAL 7.7350173
6 BFN 1.3645163
7 BFP 0.4472136
8 BL1 0.6992059
9 BL2 1.7106757
10 BL3 1.0000000
# … with 138 more rows
There are lots of inequality in the quantity of homerun done by each team.
Minimum and maximum
You can access the minimum and the maximum of a vector with the function min() and max(). The code below returns the lowest and highest number of games in a season played by a player.
Min and max
data % > % group_by(playerID) % > % summarise(min_G = min(G), max_G = max(G))
Output:
# A tibble: 10,395 x 3
playerID min_G max_G
1 aardsda01 53 73
2 aaronha01 120 156
3 aasedo01 24 66
4 abadfe01 18 18
5 abadijo01 11 11
6 abbated01 3 153
7 abbeybe01 11 11
8 abbeych01 80 132
9 abbotgl01 5 23
10 abbotji01 13 29
# … with 10,385 more rows
Count
Count observations by group is always a good idea. With R, you can aggregate the the number of occurence with n(). For instance, the code below computes the number of years played by each player.
count observations
data % > % group_by(playerID) % > % summarise(number_year = n()) % > % arrange(desc(number_year))
Output:
# A tibble: 10,395 x 2
playerID number_year
1 pennohe01 11
2 joosted01 10
3 mcguide01 10
4 rosepe01 10
5 davisha01 9
6 johnssi01 9
7 kaatji01 9
8 keelewi01 9
9 marshmi01 9
10 quirkja01 9
# … with 10,385 more rows
First and last
You can select the first, last or nth position of a group. For instance, you can find the first and last year of each player.
first and last
data % > % group_by(playerID) % > % summarise(first_appearance = first(yearID), last_appearance = last(yearID))
Output:
# A tibble: 10,395 x 3
playerID first_appearance last_appearance
1 aardsda01 2009 2010
2 aaronha01 1973 1975
3 aasedo01 1986 1990
4 abadfe01 2016 2016
5 abadijo01 1875 1875
6 abbated01 1905 1897
7 abbeybe01 1894 1894
8 abbeych01 1895 1897
9 abbotgl01 1973 1979
10 abbotji01 1992 1996
# … with 10,385 more rows
nth observation
The fonction nth() is complementary to first() and last(). You can access the nth observation within a group with the index to return. For instance, you can filter only the second year that a team played.
nth
data % > % group_by(teamID) % > % summarise(second_game = nth(yearID, 2)) % > % arrange(second_game)
Output:
# A tibble: 148 x 2
teamID second_game
1 BS1 1871
2 CH1 1871
3 FW1 1871
4 NY2 1871
5 RC1 1871
6 BR1 1872
7 BR2 1872
8 CL1 1872
9 MID 1872
10 TRO 1872
# … with 138 more rows
Distinct number of observation
The function n() returns the number of observations in a current group. A closed function to n() is n_distinct(), which count the number of unique values. In the next example, you add up the total of players a team recruited during the all periods.
distinct values
data % > % group_by(teamID) % > % summarise(number_player = n_distinct(playerID)) % > % arrange(desc(number_player))
Code Explanation
group_by(teamID): Group by year and team summarise(number_player = n_distinct(playerID)): Count the distinct number of players by team arrange(desc(number_player)): Sort the data by the number of player
Output:
# A tibble: 148 x 2
teamID number_player
1 CHN 751
2 SLN 729
3 PHI 699
4 PIT 683
5 CIN 679
6 BOS 647
7 CLE 646
8 CHA 636
9 DET 623
10 NYA 612
# … with 138 more rows
Multiple groups
A summary statistic can be realized among multiple groups.
Multiple groups
data % > % group_by(yearID, teamID) % > % summarise(mean_games = mean(G)) % > % arrange(desc(teamID, yearID))
Code Explanation
group_by(yearID, teamID): Group by year and team summarise(mean_games = mean(G)): Summarize the number of game player arrange(desc(teamID, yearID)): Sort the data by team and year
Output:
# A tibble: 2,829 x 3
# Groups: yearID [146]
yearID teamID mean_games
1 1884 WSU 20.41667
2 1891 WS9 46.33333
3 1886 WS8 22.00000
4 1887 WS8 51.00000
5 1888 WS8 27.00000
6 1889 WS8 52.42857
7 1884 WS7 8.00000
8 1875 WS6 14.80000
9 1873 WS5 16.62500
10 1872 WS4 4.20000
# … with 2,819 more rows
Filter
Before you intend to do an operation, you can filter the dataset. The dataset starts in 1871, and the analysis does not need the years prior to 1980.
Filter
data % > % filter(yearID > 1980) % > % group_by(yearID) % > % summarise(mean_game_year = mean(G))
Code Explanation
filter(yearID > 1980): Filter the data to show only the relevant years (i.e. after 1980) group_by(yearID): Group by year summarise(mean_game_year = mean(G)): Summarize the data
Output:
# A tibble: 36 x 2
yearID mean_game_year
1 1981 40.64583
2 1982 56.97790
3 1983 60.25128
4 1984 62.97436
5 1985 57.82828
6 1986 58.55340
7 1987 48.74752
8 1988 52.57282
9 1989 58.16425
10 1990 52.91556
# … with 26 more rows
Ungroup
Last but not least, you need to remove the grouping before you want to change the level of the computation.
Ungroup the data
data % > % filter(HR > 0) % > % group_by(playerID) % > % summarise(average_HR_game = sum(HR) / sum(G)) % > % ungroup() % > % summarise(total_average_homerun = mean(average_HR_game))
Code Explanation
filter(HR >0) : Exclude zero homerun group_by(playerID): group by player summarise(average_HR_game = sum(HR)/sum(G)): Compute average homerun by player ungroup(): remove the grouping summarise(total_average_homerun = mean(average_HR_game)): Summarize the data
Output:
# A tibble: 1 x 1
total_average_homerun
1 0.06882226
Summary
When you want to return a summary by group, you can use:
group by X1, X2, X3
group(df, X1, X2, X3)
you need to ungroup the data with:
ungroup(df)
The table below summarizes the function you learnt with summarise()
