In konradsemsch/ggrapid: Create neat & complete ggplot visualizations with as little code as possible

library(tidyverse)
library(ggrapid)

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/",
  warning = FALSE,
  message = FALSE
)

ggrapid: Create neat & complete ggplot visualizations with as little code as possible

Overview

ggrapid enables creation of the most common ggplot-based visualizations fast and with just a few lines of code. In practice the package offers wrappers of some of the most common ggplot geoms such as: geom_density, geom_boxplot, geom_bar etc. ggrapid comes handy when you'd like to do an initial and quick EDA (Exploratory Data Analysis) over various columns of your dataset programatically, without the need of writing a lot of custom ggplot code.

Installation

# Install development version from GitHub
devtools::install_github("konradsemsch/ggrapid")

Main functions

ggrapid offers a couple wrappers around the most commonly used functions in the course of doing an EDA:

• plot_density
• plot_boxplot
• plot_deciles (with calculate_decile_table)
• plot_correlation
• plot_bars
• plot_line

Density plot

diamonds %>%
  plot_density(x = carat)

Box-plot

diamonds %>%
  plot_boxplot(x = cut,
               y = carat,
               fill = cut)

Decile plot

diamonds %>% 
  filter(cut %in% c("Ideal", "Premium")) %>% 
  calculate_decile_table(price, cut, "Ideal") %>%
  plot_deciles()

Correlation

diamonds %>%
  plot_correlation()

Barplot

diamonds %>%
  plot_bars(x = carat,
            x_type = "num",
            fill = cut)

Lineplot

tibble(
  time = 1:20,
  value = rnorm(20, 0.5, 2)
  ) %>%
  plot_line(
    x = time,
    y = value
  )

Main arguments

The most commonly implemented ggplot2 arguments across all main ggrapid functions ensure that you can build your basic EDA file without making additional changes or custom functions. Those arguments are mainly (might slightly differ across functions):

• fill
• facet
• position
• ticks
• angle
• title
• subtitle
• caption
• lab_x
• lab_y
• legend
• vline/ hline
• alpha
• quantile_low
• quantile_high
• theme_type
• palette

diamonds %>%
  plot_density(x = carat)

diamonds %>%
  plot_density(x = carat,
               fill = cut,
               position = "stack")

diamonds %>%
  plot_density(x = carat,
               fill = cut,
               position = "fill")

diamonds %>%
  plot_density(x = carat,
               fill = cut,
               facet = cut,
               title = "Write your title here",
               subtitle = "Write your subtitle here",
               caption = "Write your caption here",
               lab_x = "Carat",
               alpha = .5,
               vline = 1)

Complete usage

You can easily iterate across selected columns and create a set of plots for your EDA file:

library(recipes)

credit_data_nested <- credit_data %>% 
  select(-one_of("Home", "Marital", "Records", "Job")) %>% # removing categorical variables
  gather(variable, variable_value,
         one_of("Seniority", "Time", "Age", "Expenses", # selecting variables to gather
                "Income", "Assets", "Debt", "Amount", "Price")) %>% 
  nest(-variable) %>% 
  mutate(
    decile_table = map(data, 
                       ~calculate_decile_table(
                         .x,
                         binning = variable_value,
                         grouping = Status,
                         top_level = "bad",
                         format = FALSE
                         )
    ),
    plot_deciles  = pmap(list(x = decile_table, y = variable),
                         ~plot_deciles(
                           .x,
                           title = glue::glue("Decile plot of {.y}"),
                           quantile_low = 0, 
                           quantile_high = 1, 
                           lab_x = "Decile",
                           lab_y = "Bad rate, %"
                           )
    ),
    plot_boxplot  = pmap(list(x = data, y = variable),
                         ~plot_boxplot(
                           .x,
                           x = Status,
                           y = variable_value,
                           fill = Status,
                           title = glue::glue("Box plot of {.y} by Status"),
                           quantile_low = 0.01,
                           quantile_high = 0.99,
                           lab_x = "Performance",
                           caption = "Removed 1% of observations from each side",
                           palette = "inv_binary"
                           )
    ),
    plot_density  = pmap(list(x = data, y = variable),
                     ~plot_density(
                       .x,
                       x = variable_value,
                       fill = Status,
                       title = glue::glue("Box plot of {.y} by Status"),
                       quantile_low = 0.01,
                       quantile_high = 0.99,
                       lab_x = "Performance",
                       caption = "Removed 1% of observations from each side",
                       palette = "inv_binary"
                       )
    )
  )

This will give you the following structure. Each row represents an individual variable and columns are the different plots you would like to inspect:

credit_data_nested[1:3, ]

Exemplary EDA format

Creating a standardised EDA file is just as easy as doing something like this:

r glue::glue("Variable: {credit_data_nested$variable[[1]]}") {.tabset .tabset-fade .tabset-pills}

Decile analysis

credit_data_nested$decile_table[[1]]

credit_data_nested$plot_deciles[[1]]

Aditional plots

credit_data_nested$plot_boxplot[[1]]

credit_data_nested$plot_density[[1]]

r glue::glue("Variable: {credit_data_nested$variable[[2]]}") {.tabset .tabset-fade .tabset-pills}

Decile analysis

credit_data_nested$decile_table[[2]]

credit_data_nested$plot_deciles[[2]]

Aditional plots

credit_data_nested$plot_boxplot[[2]]

credit_data_nested$plot_density[[2]]

konradsemsch/ggrapid documentation built on Nov. 4, 2019, 3:56 p.m.