在循环中动态命名变量以创建30个ggplots [英] Dynamically naming variables in a loop to create 30 ggplots

问题描述

我有一个包含30列的数据框，我想基于这些列创建30个(gg)图.通过ggplot创建图时，必须创建一个变量，该图的所有信息都将添加到该变量中.

I have a dataframe with 30 columns and I would like to create 30 (gg)plots based on these columns. When creating a plot through ggplot, you have to create a variable to which all the information of the plot is added.

有没有一种方法可以在for循环中创建30个这样的变量名(这样我就不必在本地创建和存储它们了?

Is there a way how I can create 30 of such variable names in a for loop (so that I don't have to create and store them all locally?

在先前的代码中，我重复了以下步骤30次:

In earlier code I repeated the below steps 30 times:

在以前的代码中，我有以下内容:

In earlier code, I had the following:

a1 = ggplot(data = results_round_one,
            aes(results_round_one$`R-0,01`)) 
a1 = a1 + geom_histogram()
a1 = a1 + xlim(0.46, 0.55)
a1 = a1 + geom_vline(xintercept= mean(results_round_one$`R-0,01`),
                     col = 'blue')
a1 = a1 + geom_vline(xintercept = max(results_round_one$`R-0,01`),
                     col = 'red')
a1= a1 + labs(y = 'Frequency', 
              x= 'Validated accuracy', 
              title = 'Optimizer = RMSProp', 
              subtitle = 'Learning rate = 0.01')

但是，由于我只需要更改aes和标签，我认为我也应该能够在for循环中完成此过程.

However, since I only have to change the aes and the labels, I think I should be able to do this process in a for loop as well.

推荐答案

在缺少示例数据的情况下，以下代码将循环遍历iris列，从而创建密度图:

In absence of some example data, here is some code that would loop through iris columns, creating density plots:

library(purrr)
library(dplyr)

df <- iris %>%
  select(Sepal.Length:Petal.Width)

df %>% 
  map2(names(df), ~ .x %>% 
         as.data.frame %>% 
         set_names(.y) %>% 
         ggplot(aes_string(.y)) + geom_density() + ggtitle(.y))

使用您的代码，类似以下内容:

Using your code, something along the lines of:

results_round_one %>% 
  map2(names(results_round_one), ~ .x %>% 
         as.data.frame %>% 
         set_names(.y) %>% 
         ggplot(aes_string(.y)) +
           geom_histogram() +
           xlim(0.46, 0.55) +
           geom_vline(xintercept = mean(.x), col = 'blue') +
           geom_vline(xintercept = max(.x), col = 'red') +
           labs(y = 'Frequency', 
               x= 'Validated accuracy',
               title = 'Optimizer = RMSProp', 
               subtitle = 'Learning rate = 0.01'))   

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