Plot histograms per row using gt tables

I want to create a gt table where I see some metrics like number of observations, mean and median, and I want a column with its histogram. For this question I will use the iris dataset.

I have recently learned how to put a plot in a tibble using this code:

library(dplyr)
library(tidyr)
library(purrr)
library(gt)
my_tibble <- iris %>%
  pivot_longer(-Species, 
               names_to = "Vars", 
               values_to = "Values") %>%
  group_by(Vars) %>%
  summarise(obs = n(),
            mean = round(mean(Values),2),
            median = round(median(Values),2), 
            plots = list(ggplot(cur_data(), aes(Values))   geom_histogram()))

Now I want to use the plots column for plotting an histogram per variable, so I have tried this:

my_tibble %>%
  mutate(ggplot = NA) %>%
  gt() %>%
  text_transform(
    locations = cells_body(vars(ggplot)),
    fn = function(x) {
      map(.$plots,ggplot_image)
    }
  )

But it returns me an error:

Error in body[[col]][stub_df$rownum_i %in% loc$rows] <- fn(body[[col]][stub_df$rownum_i %in%  : 
  replacement has length zero

The gt table should be like this:

Any help will be greatly appreciated.

CodePudding user response：

Update: See comments:

For your purposes in accordance with a shiny app you may use summarytools see here:

Try this:

library(skimr)
skim(iris)

  skim_variable n_missing complete_rate  mean    sd    p0   p25   p50   p75  p100 hist 
* <chr>             <int>         <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
1 Sepal.Length          0             1  5.84 0.828   4.3   5.1  5.8    6.4   7.9 ▆▇▇▅▂
2 Sepal.Width           0             1  3.06 0.436   2     2.8  3      3.3   4.4 ▁▆▇▂▁
3 Petal.Length          0             1  3.76 1.77    1     1.6  4.35   5.1   6.9 ▇▁▆▇▂
4 Petal.Width           0             1  1.20 0.762   0.1   0.3  1.3    1.8   2.5 ▇▁▇▅▃

CodePudding user response：

After reviewing the excellent ideas from @akrun and @TarJae, I have this solution that gives the required gt table:

plots <- iris %>% 
  pivot_longer(-Species, 
               names_to = "Vars", 
               values_to = "Values") %>%
  group_by(Vars) %>%
  nest() %>%
  mutate(plot = map(data, 
                    function(df) df %>% 
                      ggplot(aes(Values))   
                      geom_histogram())) %>%
  select(-data)

iris %>%
  pivot_longer(-Species, 
               names_to = "Vars", 
               values_to = "Values") %>%
  group_by(Vars) %>%
  summarise(obs = n(),
            mean = round(mean(Values),2),
            median = round(median(Values),2)) %>%
  mutate(ggplot = NA) %>%
  gt() %>%
  text_transform(
    locations = cells_body(vars(ggplot)),
    fn = function(x) {
      map(plots$plot, ggplot_image, height = px(100))
    }
  )

And this is the table:

I had to create the plot outside the output table, so I could call it in the gt table.

CodePudding user response：

We need to loop over the plots

library(dplyr)
library(tidyr)
library(purrr)
library(gt)
library(ggplot2)
iris %>%
  pivot_longer(-Species, 
               names_to = "Vars", 
               values_to = "Values") %>%
  nest_by(Vars) %>%
  mutate(n = nrow(data),
         mean = round(mean(data$Values), 2), 
         median = round(median(data$Values), 2), 
         plots = list(ggplot(data, aes(Values))   geom_histogram()), .keep = "unused") %>%
  ungroup %>%
  mutate(ggplot = NA) %>%
  {dat <- .
  dat %>%
    select(-plots) %>%
    gt() %>%
  text_transform(locations = cells_body(c(ggplot)),
                 fn = function(x) {
                  map(dat$plots, ggplot_image, height = px(100))
                 }
                 
                 
                 )
  }

-check for the output