用GGPLOT aes_string循环Freq图 [英] loop Freq plot with GGPLOT aes_string

问题描述

我回顾了上一个关于堆栈溢出的问题，这个问题与我的ggplot问题有关，但我找不到明显有用的东西。

问题：如何修改下面的代码，为数据框中的每列（变量）生成单独的频率图（直方图）一个循环。即ID x每个变量？

数据：

example.xlsx p>

  ID a1.sum b3.sum c6.sum d9.sum 
四月阵雨10 5 15 0 
 Anita Job 2 3 1 14 
 Candy Cain 4 7 14 17 
水晶球6 8 16 12 
点阵Matricks 15 9 1 
 Kay Largo 4 10 5 13 
  

代码：



  #set work DIR 
 setwd（C：/ A）
 
 library（rJava）
 options（java.parameters =-Xmx2048m） ##内存设置为2 GB 
 
库（xlsx）
 
＃读入.xlsx文件并应用编码UTF-8（法语口音）
 DAT< -  read.xlsx（example.xlsx，1，encoding =UTF-8）
 
 
 #plot data 
 library（ggplot2）
 
p <-ggplot（子集（DAT，a1.sum> 1），aes（ID，a1.sum，y = a1.sum））
p <-p + geom_bar（stat =identity， fill =blue，color =green）
p < -  p + theme（plot.background = element_rect（fill =白色），
 panel.background = element_rect（fill =white），
 panel.grid.major = element_line（color =white，size = 0.25），
面板。 grid.minor = element_blank（））
p < -  p + theme（axis.text.x = element_text（size = 10，angle = 90，hjust = 1，face =plain，family =serif ））
p < -  p + theme（axis.text.y = element_text（size = 10，hjust = 1，face =plain，family =serif））
p <-p + theme（axis.line.x = element_line（color =black，size = 0.50），
 axis.line.y = element_line（color =black，size = 0.5））
p 
 ggsave（filename =a1.png，plot = p）
  

输出：

a1.sum

更新后的答案 - 创建单独的ggplot对象

为了创建一个 ggplot 项目的列表，我从这个问题。你创建一个函数，然后你可以传递给 lapply 来制作图。

首先，制作函数：

$ p $ make $ plots = function（data ，列）{
ggplot（data，aes_string（x =ID，y = column））+
geom_bar（stat =identity，fill =blue，color =green） +
主题（plot.background = element_rect（fill =white），
panel.background = element_rect（fill =white），
panel.grid.major = element_line（color =white，size = 0.25），
panel.grid.minor = element_blank（），
axis.text.x = element_text（size = 10，angle = 90，hjust = 1，
face =plain，family =serif），
axis.text.y = element_text（size = 10，hjust = 1，face =plain，family =serif），
axis.line.x = element_line（color =black，size = 0.50），
axis.line.y = element_line（color =black，size = 0.5））
}


函数使用 data 和列参数。在这个分析中，只有第二到最后一列将被用来制作单个图。所以我们调用 lapply 如下：

  myplots<  -  lapply colnames（DAT [2：ncol（DAT）]），make_plots，data = DAT）
  

myplots 现在是一个 list of ggplot 可以访问的对象 myplots [1] ， myplots [2] ，...或者再次以 lapply 。


I have reviewed the previous question on stack overflow that relate to my ggplot question, but I was unable to find something that clearly helps.

Question: How can I modify the code below to generate separate frequency plots (histograms) for each column (variable) in the data frame using a loop. i.e. ID x each variable?

Data:

example.xlsx

ID  a1.sum  b3.sum  c6.sum  d9.sum
April Showers   10  5   15  0
Anita Job   2   3   1   14
Candy Cain  4   7   14  17
Crystal Ball    6   8   16  12
Dot Matricks    15  9       1
Kay Largo   4   10  5   13

Code:

#set work DIR
setwd("C:/A")

library(rJava)
options(java.parameters = "-Xmx2048m")  ## memory set to 2 GB

library(xlsx)

#read in .xlsx file and apply encoding UTF-8 (French accents)
DAT <- read.xlsx("example.xlsx", 1, encoding="UTF-8")


#plot data
library(ggplot2)   

p <- ggplot(subset(DAT, a1.sum>1), aes(ID, a1.sum, y=a1.sum))    
p <- p + geom_bar(stat="identity", fill="blue", color="green")
p <- p + theme(plot.background = element_rect(fill = "white"),
           panel.background = element_rect(fill = "white"),        
           panel.grid.major = element_line(colour = "white",size=0.25),
           panel.grid.minor = element_blank())
p <- p + theme(axis.text.x=element_text(size=10,angle=90, hjust=1, face="plain", family="serif"))  
p <- p + theme(axis.text.y=element_text(size=10, hjust=1, face="plain", family="serif"))
p <- p + theme(axis.line.x = element_line(color="black", size = 0.50),
           axis.line.y = element_line(color="black", size = 0.5))
p
ggsave(filename="a1.png", plot=p)

Output:

Plot of a1.sum Example of plot output

Trying to create a loop to generate the same plot for variables b3, c6, and d9.

I have tried several different approaches using aes_string. The following is how I am trying to setup the loop:

#get variable names that end in .sum
n <- names(DAT[grep("*.sum",names(DAT))])

#loop through variable names
for (i in 1:length(n)){
  in_dat <- c(n[i])

   ...ggplot...

print(p[i]);

}

解决方案

Original Answer - Using Facet Wrap

This sounds like an opportunity to use facet_wrap in ggplot2. You can first gather your data using tidyr in order to go from a wide format to a narrow format. Also, I used read.table based on your data and one row was missing a value so I filled that with 0.

DAT <- read.table(text = "ID  a1.sum  b3.sum  c6.sum  d9.sum
April_Showers   10  5   15  0
Anita_Job   2   3   1   14
Candy_Cain  4   7   14  17
Crystal_Ball    6   8   16  12
Dot_Matricks    15  9   0    1
Kay_Largo   4   10  5   13", 
                 header = TRUE, stringsAsFactors = FALSE)

    library(tidyr)
#gather data with
df2 <- gather(DAT, key, value, -ID)

This gives us:

> df2
              ID    key value
1  April_Showers a1.sum    10
2      Anita_Job a1.sum     2
3     Candy_Cain a1.sum     4
4   Crystal_Ball a1.sum     6
5   Dot_Matricks a1.sum    15
6      Kay_Largo a1.sum     4
7  April_Showers b3.sum     5
8      Anita_Job b3.sum     3
9     Candy_Cain b3.sum     7
10  Crystal_Ball b3.sum     8
11  Dot_Matricks b3.sum     9
12     Kay_Largo b3.sum    10
13 April_Showers c6.sum    15
14     Anita_Job c6.sum     1
15    Candy_Cain c6.sum    14
16  Crystal_Ball c6.sum    16
17  Dot_Matricks c6.sum     0
18     Kay_Largo c6.sum     5
19 April_Showers d9.sum     0
20     Anita_Job d9.sum    14
21    Candy_Cain d9.sum    17
22  Crystal_Ball d9.sum    12
23  Dot_Matricks d9.sum     1
24     Kay_Largo d9.sum    13

Then we make the same plot as before but it will be split by the key column. I have noted where I made changed below.

library(ggplot2)

p <- ggplot(df2, aes(x = ID, y=value))    ###Change made here
p <- p + geom_bar(stat="identity", fill="blue", color="green")
p <- p + theme(plot.background = element_rect(fill = "white"),
               panel.background = element_rect(fill = "white"),        
               panel.grid.major = element_line(colour = "white",size=0.25),
               panel.grid.minor = element_blank())
p <- p + theme(axis.text.x=element_text(size=10,angle=90, hjust=1, face="plain", family="serif"))  
p <- p + theme(axis.text.y=element_text(size=10, hjust=1, face="plain", family="serif"))
p <- p + theme(axis.line.x = element_line(color="black", size = 0.50),
               axis.line.y = element_line(color="black", size = 0.5)) +
  facet_wrap(~key) #facet added here

Updated Answer - Creating Separate ggplot Objects

In order to create a list of ggplot items, I borrowed heavily from this question. You create a function which you can then pass to lapply to make the plots.

First, make the function:

make_plots = function(data, column){
  ggplot(data, aes_string(x = "ID", y=column)) +
  geom_bar(stat="identity", fill="blue", color="green") +
  theme(plot.background = element_rect(fill = "white"),
      panel.background = element_rect(fill = "white"),        
      panel.grid.major = element_line(colour = "white",size=0.25),
      panel.grid.minor = element_blank(),
      axis.text.x=element_text(size=10,angle=90, hjust=1, 
                               face="plain", family="serif"),
      axis.text.y=element_text(size=10, hjust=1, face="plain", family="serif"), 
      axis.line.x = element_line(color="black", size = 0.50), 
      axis.line.y = element_line(color="black", size = 0.5))
}

The function takes data and column arguments. In this analysis, only the second through last columns will be used to make individual plots. So we call lapply as follows:

myplots <- lapply(colnames(DAT[2:ncol(DAT)]), make_plots, data = DAT)

myplots is now a list of ggplot objects which you can access with myplots[1], myplots[2],...or again with lapply.

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