柱状图中 geom_col 中的位置 = 闪避

Question

here is a dataset of soccer players that I need to visualise the total number of yellow cards received next to the number of games played per country in one bar plot. SO I need to calculate the total number of yellow cards and the total number of games per league country and bring the data into long format.

 dput(head(new_soccer_referee)) structure(list(playerShort = c("lucas-wilchez", "john-utaka", "abdon-prats", "pablo-mari", "ruben-pena", "aaron-hughes"), player = c("Lucas Wilchez", "John Utaka", " Abdón Prats", " Pablo Marí", " Rubén Peña", "Aaron Hughes" ), club = c("Real Zaragoza", "Montpellier HSC", "RCD Mallorca", "RCD Mallorca", "Real Valladolid", "Fulham FC"), leagueCountry = c("Spain", "France", "Spain", "Spain", "Spain", "England"), birthday = structure(c(4990, 4390, 8386, 8643, 7868, 3598), class = "Date"), height = c(177L, 179L, 181L, 191L, 172L, 182L), weight = c(72L, 82L, 79L, 87L, 70L, 71L), position = c("Attacking Midfielder", "Right Winger", NA, "Center Back", "Right Midfielder", "Center Back"), games = c(1L, 1L, 1L, 1L, 1L, 1L), victories = c(0L, 0L, 0L, 1L, 1L, 0L), ties = c(0L, 0L, 1L, 0L, 0L, 0L), defeats = c(1L, 1L, 0L, 0L, 0L, 1L), goals = c(0L, 0L, 0L, 0L, 0L, 0L), yellowCards = c(0L, 1L, 1L, 0L, 0L, 0L), yellowReds = c(0L, 0L, 0L, 0L, 0L, 0L), redCards = c(0L, 0L, 0L, 0L, 0L, 0L), photoID = c("95212.jpg", "1663.jpg", NA, NA, NA, "3868.jpg"), rater1 = c(0.25, 0.75, NA, NA, NA, 0.25), rater2 = c(0.5, 0.75, NA, NA, NA, 0), refNum = c(1L, 2L, 3L, 3L, 3L, 4L), refCountry = c(1L, 2L, 3L, 3L, 3L, 4L ), Alpha_3 = c("GRC", "ZMB", "ESP", "ESP", "ESP", "LUX"), meanIAT = c(0.326391469021736, 0.203374724564378, 0.369893594187172, 0.369893594187172, 0.369893594187172, 0.325185154120009), nIAT = c(712L, 40L, 1785L, 1785L, 1785L, 127L), seIAT = c(0.000564112354334542, 0.0108748941063986, 0.000229489640866464, 0.000229489640866464, 0.000229489640866464, 0.00329680952361961), meanExp = c(0.396, -0.204081632653061, 0.588297311544544, 0.588297311544544, 0.588297311544544, 0.538461538461538), nExp = c(750L, 49L, 1897L, 1897L, 1897L, 130L), seExp = c(0.0026964901062936, 0.0615044043187379, 0.00100164730649311, 0.00100164730649311, 0.00100164730649311, 0.013752210497518), BMI = c(22.98190175237, 25.5922099809619, 24.1140380330271, 23.8480304816206, 23.6614386154678, 21.4346093466973), position_new = c("Offense", "Offense", "Goalkeeper", "Defense", "Midfield", "Defense"), rater_mean = c(0.375, 0.75, NA, NA, NA, 0.125), ageinyear = c(28, 30, 19, 18, 20, 32), ageinyears = c(28, 30, 19, 18, 20, 32)), row.names = c(NA, 6L), class = "data.frame")

Use the data to draw a bar plot with the following characteristics:

– The x-axis displays the league country while the y-axis displays the number of games and the number of cards

– For each country there are two bars next to each other: one for the games played and one for the cards received

barplot <- ggplot(new_soccer_referee,aes(x=leagueCountry,y=number))

 barplot + geom_bar(fill=c("games","yellowCards")) + geom_col(Position="dodge") + labels(x="leagueCountry", y="number") ggplot

`

I know it is pretty messy but I am really confused how to build up the layers with ggplot and how to work out the long format, can anyone help?

Answer 1

One option would be to first aggregate your data to compute the number of yellowCards and games by leagueCountry . Afterwards you could convert to long which makes it easy to plot via ggplot2 .

Using some fake random example data to mimic your real data:

 set.seed(123) new_soccer_referee <- data.frame( player = sample(letters, 20), leagueCountry = sample(c("Spain", "France", "England", "Italy"), 20, replace = TRUE), yellowCards = sample(1:5, 20, replace = TRUE), games = sample(1:20, 20, replace = TRUE) ) library(dplyr) library(tidyr) library(ggplot2) new_soccer_referee_long <- new_soccer_referee %>% group_by(leagueCountry) %>% summarise(across(c(yellowCards, games), sum)) %>% pivot_longer(-leagueCountry, names_to = "variable", values_to = "number") ggplot(new_soccer_referee_long, aes(leagueCountry, number, fill = variable)) + geom_col(position = "dodge")

Answer 2

Something like this:

 library(tidyverse) new_soccer_referee %>% select(leagueCountry, games, yellowCards) %>% group_by(leagueCountry) %>% summarise(games = sum(games), yellowCars = sum(yellowCards) ) %>% pivot_longer(-leagueCountry) %>% ggplot(aes(x=leagueCountry, fill=name, y=value)) + geom_col(position = position_dodge())