ggplot2 - Combining Different Types of Graphs Together (R)

Question

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)

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") 

2. 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")
   
   

3. 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")

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?

For example:

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

Row 2: All Bar Graphs (Bar_years_plot, Bar_total_plot)

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

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

Something like this?

All help is greatly appreciated. Thanks

Answer 1

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.

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") 

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)

EDIT: 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