20180409-D · NFL Positional Salaries · ggplot2 ggbeeswarm 绘制抖动散点图 · R 语言数据可视化 案例 源码
所有作品合集传送门： Tidy Tuesday
2018 年合集传送门： 2018
NFL Positional Salaries
NFL Positional Salaries
Tidy Tuesday 在 GitHub 上的传送地址：
Thomas Mock (2022). Tidy Tuesday: A weekly data project aimed at the R ecosystem. https://github.com/rfordatascience/tidytuesday
1. 一些环境设置
# 设置为国内镜像, 方便快速安装模块
options("repos" = c(CRAN = "https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
2. 设置工作路径
wkdir <- '/home/user/R_workdir/TidyTuesday/2018/2018-04-09_NFL_Positional_Salaries/src-d'
setwd(wkdir)
3. 加载 R 包
library(tidyverse)
library(ggbeeswarm)
library(showtext)
# 在 Ubuntu 系统上测试的, 不加这个我画出来的汉字会乱码 ~
showtext_auto()
4. 加载数据
df_input <- readxl::read_excel("../data/nfl_salary.xlsx")
# 简要查看数据内容
glimpse(df_input)
## Rows: 800
## Columns: 11
## $ year 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 20…
## $ Cornerback 11265916, 11000000, 10000000, 10000000, 10000000, …
## $ `Defensive Lalbert` 17818000, 16200000, 12476000, 11904706, 11762782, …
## $ Linebacker 16420000, 15623000, 11825000, 10083333, 10020000, …
## $ `Offensive Lineman` 15960000, 12800000, 11767500, 10358200, 10000000, …
## $ Quarterback 17228125, 16000000, 14400000, 14100000, 13510000, …
## $ `Running Back` 12955000, 10873833, 9479000, 7700000, 7500000, 703…
## $ Safety 8871428, 8787500, 8282500, 8000000, 7804333, 76527…
## $ `Special Teamer` 4300000, 3725000, 3556176, 3500000, 3250000, 32250…
## $ `Tight End` 8734375, 8591000, 8290000, 7723333, 6974666, 61333…
## $ `Wide Receiver` 16250000, 14175000, 11424000, 11415000, 10800000, …
# 检查数据的列名
colnames(df_input)
## [1] "year" "Cornerback" "Defensive Lineman"
## [4] "Linebacker" "Offensive Lineman" "Quarterback"
## [7] "Running Back" "Safety" "Special Teamer"
## [10] "Tight End" "Wide Receiver"
5. 数据预处理
# 整理数据, 从宽数据透视到长数据转换
df_plot <- df_input %>%
# pivot_longer() 从宽数据透视到长数据转换
pivot_longer(cols = -year,
names_to = "position",
values_to = "salary_position") %>%
# 去除缺失值
filter(!is.na(salary_position))
# 简要查看数据内容
glimpse(df_plot)
## Rows: 7,944
## Columns: 3
## $ year 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, …
## $ position "Cornerback", "Defensive Lineman", "Linealbert", "Offe…
## $ salary_position 11265916, 17818000, 16420000, 15960000, 17228125, 1295…
6. 利用 ggplot2 绘图
# PS: 方便讲解, 我这里进行了拆解, 具体使用时可以组合在一起
gg <- ggplot(df_plot, aes(year, salary_position / 1000000, group = year))
# geom_quasirandom() 绘制抖动散点图
gg <- gg + geom_quasirandom(size = 0.7, alpha = .3, colour = "#FF7F50")
# facet_wrap() 可视化分面图, ncol = 5 表示有五列
gg <- gg + facet_wrap( ~ position, ncol = 5)
# scale_y_continuous() 对连续变量设置坐标轴显示范围
gg <- gg + scale_y_continuous(labels = scales::dollar_format(suffix = "m"))
# labs() 对图形添加注释和标签（包含标题、子标题、坐标轴和引用等注释)
gg <- gg + labs(title = "NFL中不同位置的工资情况",
subtitle = NULL,
x = NULL,
y = '薪资',
caption = "NFL Quarterback Salaries · graph by 数绘小站")
# theme_minimal() 去坐标轴边框的最小化主题
gg <- gg + theme_minimal()
# theme() 实现对非数据元素的调整, 对结果进行进一步渲染, 使之更加美观
gg <- gg + theme(
# panel.grid.major 主网格线, 这一步表示删除主要网格线
panel.grid.major = element_line("grey", size = 0.2),
# panel.grid.minor 次网格线, 这一步表示删除次要网格线
panel.grid.minor = element_blank(),
# axis.text 坐标轴刻度文本
axis.text = element_text(color = "black", size = 9),
# axis.title 坐标轴标题
axis.title = element_text(color = "black", size = 12),
# axis.ticks 坐标轴刻度线
axis.ticks = element_blank(),
# plot.title 主标题
plot.title = element_text(hjust = 0.5, color = "black", size = 16, face = "bold"),
# plot.background 图片背景
plot.background = element_rect(fill = "white"),
# strip.text 自定义分面图每个分面标题的文字
strip.text = element_text(face = "bold", size = rel(0.8), vjust = -.2),
# strip.background 自定义分面图每个分面的背景颜色
strip.background = element_blank())
7. 保存图片到 PDF 和 PNG
gg
filename = '20180409-D-01'
ggsave(filename = paste0(filename, ".pdf"), width = 8.6, height = 5, device = cairo_pdf)
ggsave(filename = paste0(filename, ".png"), width = 8.6, height = 5, dpi = 100, device = "png")
8. session-info
sessionInfo()
## R version 4.2.1 (2022-06-23)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 LTS
##
## Matrix products: albert
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] showtext_0.9-5 showtextdb_3.0 sysfonts_0.8.8 ggbeeswarm_0.6.0
## [5] forcats_0.5.2 stringr_1.4.1 dplyr_1.0.10 purrr_0.3.4
## [9] readr_2.1.2 tidyr_1.2.1 tibble_3.1.8 ggplot2_3.3.6
## [13] tidyverse_1.3.2
##
## loaded via a namespace (and not attached):
## [1] lubridate_1.8.0 assertthat_0.2.1 digest_0.6.29
## [4] utf8_1.2.2 R6_2.5.1 cellranger_1.1.0
## [7] backports_1.4.1 reprex_2.0.2 evaluate_0.16
## [10] highr_0.9 httr_1.4.4 pillar_1.8.1
## [13] rlang_1.0.5 googlesheets4_1.0.1 readxl_1.4.1
## [16] rstudioapi_0.14 jquerylib_0.1.4 rmarkdown_2.16
## [19] textshaping_0.3.6 labeling_0.4.2 googledrive_2.0.0
## [22] munsell_0.5.0 broom_1.0.1 compiler_4.2.1
## [25] vipor_0.4.5 modelr_0.1.9 xfun_0.32
## [28] systemfonts_1.0.4 pkgconfig_2.0.3 htmltools_0.5.3
## [31] tidyselect_1.1.2 fansi_1.0.3 crayon_1.5.1
## [34] tzdb_0.3.0 dbplyr_2.2.1 withr_2.5.0
## [37] grid_4.2.1 jsonlite_1.8.0 gtable_0.3.1
## [40] lifecycle_1.0.1 DBI_1.1.3 magrittr_2.0.3
## [43] scales_1.2.1 cli_3.3.0 stringi_1.7.8
## [46] cachem_1.0.6 farver_2.1.1 fs_1.5.2
## [49] xml2_1.3.3 bslib_0.4.0 ragg_1.2.3
## [52] ellipsis_0.3.2 generics_0.1.3 vctrs_0.4.1
## [55] tools_4.2.1 glue_1.6.2 beeswarm_0.4.0
## [58] hms_1.1.2 fastmap_1.1.0 yaml_2.3.5
## [61] colorspace_2.0-3 gargle_1.2.1 rvest_1.0.3
## [64] knitr_1.40 haven_2.5.1 sass_0.4.2
测试数据
配套数据下载：nfl_salary.xlsx