Major League Baseball Player Hitting Statistics for 2010.
Format
A data frame with 1199 observations on the following 19 variables.
- name
Player name
- team
Team abbreviation
- position
Player position
- game
Number of games
- at_bat
Number of at bats
- run
Number of runs
- hit
Number of hits
- double
Number of doubles
- triple
Number of triples
- home_run
Number of home runs
- rbi
Number of runs batted in
- total_base
Total bases, computed as 3HR + 23B + 1*2B + H
- walk
Number of walks
- strike_out
Number of strikeouts
- stolen_base
Number of stolen bases
- caught_stealing
Number of times caught stealing
- obp
On base percentage
- slg
Slugging percentage (total_base / at_bat)
- bat_avg
Batting average
Source
https://www.mlb.com, retrieved 2011-04-22.
Examples
library(ggplot2)
library(dplyr)
library(scales)
mlbbat10_200 <- mlbbat10 |>
filter(mlbbat10$at_bat > 200)
# On-base percentage across positions
ggplot(mlbbat10_200, aes(x = position, y = obp, fill = position)) +
geom_boxplot(show.legend = FALSE) +
scale_y_continuous(labels = label_number(suffix = "%", accuracy = 0.01)) +
labs(
title = "On-base percentage across positions",
y = "On-base percentage across positions",
x = "Position"
)
# Batting average across positions
ggplot(mlbbat10_200, aes(x = bat_avg, fill = position)) +
geom_density(alpha = 0.5) +
labs(
title = "Batting average across positions",
fill = NULL,
y = "Batting average",
x = "Position"
)
# Mean number of home runs across positions
mlbbat10_200 |>
group_by(position) |>
summarise(mean_home_run = mean(home_run)) |>
ggplot(aes(x = position, y = mean_home_run, fill = position)) +
geom_col(show.legend = FALSE) +
labs(
title = "Mean number of home runs across positions",
y = "Home runs",
x = "Position"
)
# Runs batted in across positions
ggplot(mlbbat10_200, aes(x = run, y = obp, fill = position)) +
geom_boxplot(show.legend = FALSE) +
labs(
title = "Runs batted in across positions",
y = "Runs",
x = "Position"
)
