In arthurwelle/codehover: Hoverable HTML Table for Displaying Intermediary Code Results (Pipes)

library(codehover)
library(knitr)
library(data.table)
suppressPackageStartupMessages(library(tidyverse))
#library(magick)

library(codehover)
library(htmltools)
library(data.table)
library(tidyverse)

#install.packages("flextable")
#install.packages("webshot")
#remotes::install_github("rstudio/webshot2")
require(flextable)
require(officer)
#library(webshot)
#library(webshot2)
#webshot::install_phantomjs()

f_save_img <- function(x, text){
  flextable::save_as_image(x, path = paste0("./IMG/", text, ".png"), webshot = "webshot")
}

dados <- data.frame(Year = c(rep(c(2000, 2010, 2020),3), 2010),
                    Country = c(rep("Brazil", 3), rep("India", 3), rep("Russia", 3), "China"),
                    Continent = c(rep("S.America", 3), rep("Asia", 7)),
                    GDP = c(655, 2209, 1430, 468, 1676, 2710, 278, 1630, 1470, 6087), 
                    Pop = c(172, 194, 211, 1060, 1230, 1390, 146, 142, 147, 1338)  )

# schuffle
dados <- dados[ sample(nrow(dados)), ]

# save
write.csv2(dados, "./dados.csv", row.names = FALSE)

# reload
df <- read.csv2("./dados.csv" )

df$Year <- format(df$Year, big.mark = "")  

f_table <- function(data){
t <- flextable::flextable(data) %>%
     flextable::font(fontname = "Liberation Serif") %>%
     flextable::align(align = "center", part = "all") %>%
     flextable::bg(bg = "white", part = "all") %>% 
     flextable::border_remove() 
return(t)
}
t <- f_table(df)
t

#f_save_img(t, "limpo")

df %>% 
  mutate(GDP_PC = GDP / Pop) %>% 
  mutate(Group_GDP_PC_Level = case_when(GDP_PC < 3 ~ "Low",
                             GDP_PC >= 3 & GDP_PC < 10 ~ "Medium",
                             GDP_PC >= 10 ~ "High")) %>% 
  filter(Country != "China") %>% 
  group_by(Group_GDP_PC_Level) %>% 
  summarise(Average_GDP_PC_per_group = mean(GDP_PC))

df %>% as.data.table() %>% 
  .[, GDP_PC := GDP / Pop] %>% 
  .[, Group_GDP_PC_Level := fcase(GDP_PC < 3 , "Low",
                             GDP_PC >= 3 & GDP_PC < 10 , "Medium",
                             GDP_PC >= 10 , "High")] %>% 
  .[Country != "China", ] %>% 
  .[, .(Average_GDP_PC_per_group = mean(GDP_PC)), by = .(Group_GDP_PC_Level) ]

d <- df %>% 
  mutate(GDP_PC = GDP / Pop) %>% 
  mutate(Group_GDP_PC_Level = case_when(GDP_PC < 3 ~ "Low",
                             GDP_PC >= 3 & GDP_PC < 10 ~ "Medium",
                             GDP_PC >= 10 ~ "High")) %>% 
  filter(Country != "China") %>% 
  group_by(Group_GDP_PC_Level) %>% 
  summarise(Average_GDP_PC_per_group = mean(GDP_PC)) %>% 
  f_table(.) %>% 
  f_save_img(., "t4")

d <- df %>% 
  mutate(GDP_PC = GDP / Pop) %>% 
  mutate(Group_GDP_PC_Level = case_when(GDP_PC < 3 ~ "Low",
                             GDP_PC >= 3 & GDP_PC < 10 ~ "Medium",
                             GDP_PC >= 10 ~ "High")) %>% 
  filter(Country != "China") %>% 
  f_table(.) %>% 
  f_save_img(., "t3")

d <- df %>% 
  mutate(GDP_PC = GDP / Pop) %>% 
  mutate(Group_GDP_PC_Level = case_when(GDP_PC < 3 ~ "Low",
                             GDP_PC >= 3 & GDP_PC < 10 ~ "Medium",
                             GDP_PC >= 10 ~ "High")) %>% 
  f_table(.) %>% 
  f_save_img(., "t2")

d <- df %>% 
  mutate(GDP_PC = GDP / Pop) %>% 
  f_table(.) %>% 
  f_save_img(., "t1")

d <- df %>% 
  f_table(.) %>% 
  f_save_img(., "t0")

f_colage_img <- function(img){
f <- magick::image_blank(1940, 1010, "white")
d <- magick::image_read(paste0("./IMG/",img,".png"))
a <- magick::image_mosaic(c(f, d))

magick::image_write(a, 
                    path = paste0("./IMG/",img,"B.png"), 
                    format = "png")
}
f_colage_img("t0")

l_img <- c("t0","t1","t2","t3","t4")
lapply(l_img, f_colage_img)

tidyerse

result <- ch_int(type = "incremental") %>% 
          ch_row(text = "df %>% ",
                   img ="./IMG/fixed_dimentions/t0B.png") %>%   
          ch_row(text = "mutate(GDP_PC = GDP / Pop) %>%",
                   img ="./IMG/fixed_dimentions/t1B.png") %>% 
          ch_row(text = "mutate(Group_GDP_PC_Level = case_when(GDP_PC < 3 ~ 'Low',
                            <br><tab3> GDP_PC >= 3 & GDP_PC < 10 ~ 'Medium',<tab3>
                            <br><tab3>GDP_PC >= 10 ~ 'High')) %>%</tab3>",
                   img ="./IMG/fixed_dimentions/t2B.png") %>%   
          ch_row(text = " filter(Country != 'China') %>% ",
                   img ="./IMG/fixed_dimentions/t3B.png") %>% 
          ch_row(text = "group_by(Group_GDP_PC_Level) %>%",
                   img ="./IMG/fixed_dimentions/t4B.png") %>%   
          ch_row(text = "summarise(Average_GDP_PC_per_group = mean(GDP_PC))",
                   img ="./IMG/fixed_dimentions/t4B.png") %>% 
          ch_out(img = "./IMG/fixed_dimentions/t0B.png") 

htmltools::HTML(result)

data.table

result <- ch_int(type = "incremental") %>% 
          ch_row(text = "dt %>% ",
                   img ="./IMG/fixed_dimentions/t0B.png") %>%   
          ch_row(text = ".[, GDP_PC := GDP / Pop] %>% ",
                   img ="./IMG/fixed_dimentions/t1B.png") %>% 
          ch_row(text = ".[, Group_GDP_PC_Level := fcase(GDP_PC < 3 , 'Low',
                             <br><tab3>GDP_PC >= 3 & GDP_PC < 10 , 'Medium',</tab3>
                             <br><tab3>GDP_PC >= 10 , 'High')] %>%</tab3>",
                   img ="./IMG/fixed_dimentions/t2B.png") %>%   
          ch_row(text = ".[Country != 'China', ] %>% ",
                   img ="./IMG/fixed_dimentions/t3B.png") %>% 
          ch_row(text = ".[, .(Average_GDP_PC_per_group = mean(GDP_PC)), by = .(Group_GDP_PC_Level) ]",
                   img ="./IMG/fixed_dimentions/t4B.png") %>%   
          ch_out(img = "./IMG/fixed_dimentions/t0B.png") 

htmltools::HTML(result)

codehover code to produce the above

tidyerse

result <- ch_int(type = "incremental") %>% 
          ch_row(text = "df %>% ",
                   img ="./IMG/fixed_dimentions/t0B.png") %>%   
          ch_row(text = "mutate(GDP_PC = GDP / Pop) %>%",
                   img ="./IMG/fixed_dimentions/t1B.png") %>% 
          ch_row(text = "mutate(Group_GDP_PC_Level = case_when(GDP_PC < 3 ~ 'Low',
                            <br><tab3> GDP_PC >= 3 & GDP_PC < 10 ~ 'Medium',<tab3>
                            <br><tab3>GDP_PC >= 10 ~ 'High')) %>%</tab3>",
                   img ="./IMG/fixed_dimentions/t2B.png") %>%   
          ch_row(text = " filter(Country != 'China') %>% ",
                   img ="./IMG/fixed_dimentions/t3B.png") %>% 
          ch_row(text = "group_by(Group_GDP_PC_Level) %>%",
                   img ="./IMG/fixed_dimentions/t4B.png") %>%   
          ch_row(text = "summarise(Average_GDP_PC_per_group = mean(GDP_PC))",
                   img ="./IMG/fixed_dimentions/t4B.png") %>% 
          ch_out(img = "./IMG/fixed_dimentions/t0B.png") 

htmltools::HTML(result)

data.table

result <- ch_int(type = "incremental") %>% 
          ch_row(text = "dt %>% ",
                   img ="./IMG/fixed_dimentions/t0B.png") %>%   
          ch_row(text = ".[, GDP_PC := GDP / Pop] %>% ",
                   img ="./IMG/fixed_dimentions/t1B.png") %>% 
          ch_row(text = ".[, Group_GDP_PC_Level := fcase(GDP_PC < 3 , 'Low',
                             <br><tab3>GDP_PC >= 3 & GDP_PC < 10 , 'Medium',</tab3>
                             <br><tab3>GDP_PC >= 10 , 'High')] %>%</tab3>",
                   img ="./IMG/fixed_dimentions/t2B.png") %>%   
          ch_row(text = ".[Country != 'China', ] %>% ",
                   img ="./IMG/fixed_dimentions/t3B.png") %>% 
          ch_row(text = ".[, .(Average_GDP_PC_per_group = mean(GDP_PC)), by = .(Group_GDP_PC_Level) ]",
                   img ="./IMG/fixed_dimentions/t4B.png") %>%   
          ch_out(img = "./IMG/fixed_dimentions/t0B.png") 

htmltools::HTML(result)

arthurwelle/codehover documentation built on Dec. 19, 2021, 4:43 a.m.