Loop graphs ggplot y for x for different categories with linear regression, How to consequetively plot categories?

Question

EDITED: I have a large data base trying to reapeatedly assess energy expenditue over time with the aim to compare multiple different variables (0/1, eg presence of severe head trauma vs. no such). The graph analysis should be repeated for all available variables in the database. All tables should be exported to a PDF File.

Currently I'm using the following code:

library(tidyverse)
library(ggpmisc)
my_data %>%
pdf(file="Plots.pdf" )
print(colnames(my_data) %>%
        map(function(x) my_data%>%
              ggplot(aes(x = Day, 
                         y = REE,
                         color=as_factor(x)))+
              scale_x_continuous(breaks = c(0,2,4,6,8,10,12,14,16,18,20,22,24,26,28))+
              scale_y_continuous(limits= c(0000,4000))+
              geom_point()+
              geom_smooth(method=lm,
                          se=TRUE,
                          size=2/10,
                          aes(group=as_factor(x)))+
              stat_poly_eq(aes(label = paste(after_stat(eq.label),
                                             after_stat(rr.label), 
                                             after_stat(p.value.label),
                                             sep = "*\", \"*")),
                           label.y="bottom", label.x="right")+
              labs(x="Time [d]",
                   y="Resting Energy Expenditure [kcal]")+
              scale_colour_grey(start=0.7,
                                  end=0.3)+
              theme_bw()
))
dev.off()

It generates the PDF File with all graphs. However, it does not group/color according to the as_factor(x) and all data points are categorised into the same group.

Does anyone have a possible explanation on how to resolve this problem that the categorising according to the factor variable doesn't work?

Answer 1

The issue is that you loop over column names which are character strings. Doing color=as.factor(x) you are mapping a constant character string on the color aes, ie you are doing something like color="foo" . To tell ggplot2 that you want to map the data column whose name stored in x on the color aes you have to use eg the .data pronoun, ie do color=as.factor(.data[[x]]) .

Using a minimal reprex based on mtcars :

Note: Personally I would suggest to put your plotting code in a separate function ainstead of passing it as an anonymous function to purrr::map as I do below. Makes debugging easier and your code cleaner.

library(tidyverse)
library(ggpmisc)

my_data <- mtcars

plot_fun <- function(x) {
  ggplot(my_data, aes(
    x = mpg,
    y = hp,
    color = as_factor(.data[[x]])
  )) +
    #scale_x_continuous(breaks = c(0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28)) +
    #scale_y_continuous(limits = c(0000, 4000)) +
    geom_point() +
    geom_smooth(
      method = lm,
      se = TRUE,
      size = 2 / 10,
      aes(group = as_factor(x))
    ) +
    stat_poly_eq(aes(label = paste(after_stat(eq.label),
      after_stat(rr.label),
      after_stat(p.value.label),
      sep = "*\", \"*"
    )),
    label.y = "bottom", label.x = "right"
    ) +
    labs(
      x = "Time [d]",
      y = "Resting Energy Expenditure [kcal]"
    ) +
    scale_colour_grey(
      start = 0.7,
      end = 0.3
    ) +
    theme_bw()
}

cols <- c("cyl", "am", "gear") # colnames(my_data)

#pdf(file = "Plots.pdf")
purrr::map(cols, plot_fun)
#> [[1]]
#> `geom_smooth()` using formula 'y ~ x'

#> 
#> [[2]]
#> `geom_smooth()` using formula 'y ~ x'

#> 
#> [[3]]
#> `geom_smooth()` using formula 'y ~ x'

#dev.off()