引言
在进行数据分析时，免不了对结果进行可视化。那么，什么样的图形才最适合自己的数据呢？一个有效的图形应具备以下特点：
- 能正确传递信息，而不会产生歧义；
- 样式简单，但是易于理解；
- 添加的图形美学应辅助理解信息；
- 图形上不应出现冗余无用的信息。
本系列推文，小编将汇总可视化中常用 7 大类型图形，供读者参考。
每类制作成一篇推文，主要参考资料为：Top 50 ggplot2 Visualizations[1]。其他类似功能网站，资料包括：
- 庄闪闪的可视化笔记——常用图形[2]
- R Graph Gallery[3]
- 《R 语言教程》——ggplot 的各种图形[4]
系列目录
本文主要介绍第五部分：组成成分图形。
5 组成
5.1 华夫饼图
华夫图可以显示总体的组成成分。
var <- mpg$class # the categorical data ## Prep data (nothing to change here) nrows <- 10 df <- expand.grid(y = 1:nrows, x = 1:nrows) categ_table <- round(table(var) * ((nrows*nrows)/(length(var)))) df$category <- factor(rep(names(categ_table), categ_table)) # NOTE: if sum(categ_table) is not 100 (i.e. nrows^2), it will need adjustment to make the sum to 100. ## Plot ggplot(df, aes(x = x, y = y, fill = category)) + geom_tile(color = "black", size = 0.5) + scale_x_continuous(expand = c(0, 0)) + scale_y_continuous(expand = c(0, 0), trans = 'reverse') + scale_fill_brewer(palette = "Set3") + labs(title="Waffle Chart", subtitle="'Class' of vehicles", caption="Source: mpg") + theme(panel.border = element_rect(size = 2), plot.title = element_text(size = rel(1.2)), axis.text = element_blank(), axis.title = element_blank(), axis.ticks = element_blank(), legend.title = element_blank(), legend.position = "right")
华夫饼图
5.2 饼图
library(ggplot2) theme_set(theme_classic()) # Source: Frequency table df <- as.data.frame(table(mpg$class)) colnames(df) <- c("class", "freq") pie <- ggplot(df, aes(x = "", y=freq, fill = factor(class))) + geom_bar(width = 1, stat = "identity") + theme(axis.line = element_blank(), plot.title = element_text(hjust=0.5)) + labs(fill="class", x=NULL, y=NULL, title="Pie Chart of class", caption="Source: mpg") pie + coord_polar(theta = "y", start=0)
饼图
5.3 条形图
#prep frequency table freqtable <- table(mpg$manufacturer) df <- as.data.frame.table(freqtable) head(df)
library(ggplot2) theme_set(theme_classic()) # Plot g <- ggplot(df, aes(Var1, Freq)) g + geom_bar(stat="identity", width = 0.5, fill="tomato2") + labs(title="Bar Chart", subtitle="Manufacturer of vehicles", caption="Source: Frequency of Manufacturers from 'mpg' dataset") + theme(axis.text.x = element_text(angle=65, vjust=0.6))
条形图
# From on a categorical column variable g <- ggplot(mpg, aes(manufacturer)) g + geom_bar(aes(fill=class), width = 0.5) + theme(axis.text.x = element_text(angle=65, vjust=0.6)) + labs(title="Categorywise Bar Chart", subtitle="Manufacturer of vehicles", caption="Source: Manufacturers from 'mpg' dataset")
条形图
参考资料
[1]
Top 50 ggplot2 Visualizations: http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html
[2]
庄闪闪的可视化笔记——常用图形: https://liangliangzhuang.github.io/R-tutorial/main-diagram-types.html
[3]
R Graph Gallery: https://www.r-graph-gallery.com/ggplot2-package.html
[4]
R 语言教程——ggplot 的各种图形: https://www.math.pku.edu.cn/teachers/lidf/docs/Rbook/html/_Rbook/ggplotvis.html
