将不同类型的图组合在一起 (R) [英] Combining Different Types of Graphs Together (R)

问题描述

我正在尝试学习如何在 R 编程语言中将不同类型的图形组合在一起.假设我有以下数据:

I am trying to learn how to combine different types of graphs together in the R programming language. Suppose I have the following data:

library(dplyr)
library(ggplot2)

date= seq(as.Date("2014/1/1"), as.Date("2016/1/1"),by="day")

var <- rnorm(731,10,10)


group <- sample( LETTERS[1:4], 731, replace=TRUE, prob=c(0.25, 0.22, 0.25, 0.25) )

data = data.frame(date, var, group)

data$year = as.numeric(format(data$date,'%Y'))
data$year = as.factor(data$year)

我总结了这些数据，制作了不同类型的图表.例如:

I summarized this data making different types of graphs. For example:

1) Pie Charts:

    ###Pie
    
    Pie_2014 <- data %>% filter((data$year == "2014"))
    Pie_2014 %>% 
      group_by(group) %>% 
      summarise(n = n())
    
    Pie_2014_graph = ggplot(Pie_2014, aes(x="", y=n, fill=group)) +
      geom_bar(stat="identity", width=1) +
      coord_polar("y", start=0) +ggtitle( "Pie Chart 2014") 
    
    
    Pie_2015 <- data %>% filter((data$year == "2015"))
    Pie_2015 %>% 
      group_by(group) %>% 
      summarise(n = n())
    
    Pie_2015_graph = ggplot(Pie_2015, aes(x="", y=n, fill=group)) +
      geom_bar(stat="identity", width=1) +
      coord_polar("y", start=0) +ggtitle( "Pie Chart 2015") 
    
    
    Pie_total = data %>% 
      group_by(group) %>% 
      summarise(n = n())
    
    Pie_total_graph = ggplot(data, aes(x="", y=n, fill=group)) +
      geom_bar(stat="identity", width=1) +
      coord_polar("y", start=0) +ggtitle( "Pie Chart Average") 

1. 条形图:

1. Bar Plots:

Bar_years = data %>% 
  group_by(year, group) %>% 
  summarise(mean = mean(var))

Bar_years_plot = ggplot(Bar_years, aes(fill=group, y=mean, x=year)) + 
    geom_bar(position="dodge", stat="identity") + ggtitle("Bar Plot All Years")

Bar_total = data %>% 
  group_by(group) %>% 
  summarise(mean = n())

Bar_total_plot = ggplot(Bar_total, aes(x=group, y=mean, fill=group)) +
  geom_bar(stat="identity")+theme_minimal() + ggtitle("Bar Plot Average")

时间序列图:

Time Series Plots:

New <- data %>%
  mutate(date = as.Date(date)) %>%
  group_by(group, month = format(date, "%Y-%m")) %>%
  summarise( Mean = mean(var, na.rm = TRUE), Count = n())

#Plot
ts_1 <- ggplot(New) +
  geom_line(aes(x=month, y=Mean, colour=group,group=1))+
  scale_colour_manual(values=c("red","green","blue", "purple"))+
  theme(axis.text.x = element_text(angle=90))  + ggtitle("time seres 1")

ts_2 <- ggplot(New) +
  geom_line(aes(x=month, y=Count, colour=group,group=1))+
  scale_colour_manual(values=c("red","green","blue", "purple"))+
  theme(axis.text.x = element_text(angle=90)) + ggtitle("time seres 2")

所有这些图表都能完美运行.现在我正在寻找一种更好的方式来呈现它们.我的问题:是否可以使用 R 和 ggplot2 将所有这些图形整齐地排列到一个窗口中?

All these graphs work perfectly. Now I am looking for a better way to present them. My question: Is it possible to neatly arrange all these graphs into a window using R and ggplot2?

例如:

第 1 行:所有饼图(Pie_2014_graph、Pie_2015_graph、pie_total_graph)

Row 1: All Pie Charts (Pie_2014_graph, Pie_2015_graph, pie_total_graph)

第 2 行:所有条形图(Bar_years_plot、Bar_total_plot)

Row 2: All Bar Graphs (Bar_years_plot, Bar_total_plot)

第 3 行:所有时间序列图(ts_1、ts_2)

Row 3: All Time Series Graphs (ts_1, ts_2)

现在，我单独创建所有这些图形，将它们粘贴到 MS Paint 中并手动重新排列它们.

Right now, I creating all these graphs individually, pasting them into MS Paint and manually rearranging them.

有这样的吗?

非常感谢所有帮助.谢谢

All help is greatly appreciated. Thanks

推荐答案

您上面发布的代码失败，因为您尝试使用变量 n 但没有在 之后的任何位置分配数据>summarise(n = n()) 饼图数据的步骤.

The code you posted above fails because you are trying to use the variable n but have not assigned the data anywhere after your summarise(n = n()) step for your pie chart data.

您可以将汇总数据直接通过管道传输到 ggplot 中，否则您必须使用类似的方式分配中间步骤；

You can either pipe the summarised data straight into ggplot or otherwise you must assign the intermediary steps with something like this;

Pie_2014 <- data %>% 
  filter((data$year == "2014")) %>% 
  group_by(group) %>% 
  summarise(n = n())

Pie_2014_graph = ggplot(Pie_2014, aes(x="", y=n, fill=group)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) +ggtitle( "Pie Chart 2014") 


Pie_2015 <- data %>% 
  filter((data$year == "2015")) %>% 
  group_by(group) %>% 
  summarise(n = n())

Pie_2015_graph = ggplot(Pie_2015, aes(x="", y=n, fill=group)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) +ggtitle( "Pie Chart 2015") 


Pie_total = data %>% 
  group_by(group) %>% 
  summarise(n = n())

Pie_total_graph = ggplot(Pie_total, aes(x="", y=n, fill=group)) +
  geom_bar(stat="identity", width=1) +
  coord_polar("y", start=0) +ggtitle( "Pie Chart Average") 

之后，使用 patchwork 包将子图排列在一起非常简单.例如这样的事情会让你接近;

After that arranging the subplots together is pretty straightforward with the patchwork package. e.g. something like this will get you close;

# combine plots

# install.packages('patchwork')
library(patchwork)

(Pie_2014_graph | Pie_2015_graph | Pie_total_graph) /
  (Bar_years_plot | Bar_total_plot) / 
  (ts_1 | ts_2)

根据对非拼凑替代方案的要求，这里有一个让您开始使用cowplot的版本:

Following request for a non-patchwork alternative, here is a version to get you started using cowplot:

library(cowplot)

# arrange subplots in rows
top_row <- plot_grid(Pie_2014_graph, Pie_2015_graph, Pie_total_graph, nrow = 1)
middle_row <- plot_grid(Bar_years_plot, Bar_total_plot)
bottom_row <- plot_grid(ts_1, ts_2)

# arrange our new rows into combined plot
p <- plot_grid(top_row, middle_row, bottom_row, nrow = 3)
p

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