plot_outlier: Plot outlier information of numerical data diagnosis

Description

The plot_outlier() visualize outlier information for diagnosing the quality of the numerical data.

Usage

plot_outlier(.data, ...)
# S3 method for data.frame
plot_outlier(.data, ..., col = "lightblue")

Arguments

.data

a data.frame or a tbl_df.

...

one or more unquoted expressions separated by commas. You can treat variable names like they are positions. Positive values select variables; negative values to drop variables. If the first expression is negative, plot_outlier() will automatically start with all variables. These arguments are automatically quoted and evaluated in a context where column names represent column positions. They support unquoting and splicing.

col

a color to be used to fill the bars. The default is "lightblue".

Outlier diagnostic information

The plot derived from the numerical data diagnosis is as follows.

With outliers box plot
Without outliers box plot
With outliers histogram
Without outliers histogram

See vignette("diagonosis") for an introduction to these concepts.

Details

The scope of the diagnosis is the provide a outlier information. Since the plot is drawn for each variable, if you specify more than one variable in the ... argument, the specified number of plots are drawn.

Examples

Run this code

# NOT RUN {
# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA

# Visualization of all numerical variables
plot_outlier(carseats)

# Select the variable to diagnose
plot_outlier(carseats, Sales, Price)
plot_outlier(carseats, -Sales, -Price)
plot_outlier(carseats, "Sales", "Price")
plot_outlier(carseats, 6)

# Using the col argument
plot_outlier(carseats, Sales, col = "gray")

# Using pipes ---------------------------------
library(dplyr)

# Visualization of all numerical variables
carseats %>%
  plot_outlier()
# Positive values select variables
carseats %>%
  plot_outlier(Sales, Price)
# Negative values to drop variables
carseats %>%
  plot_outlier(-Sales, -Price)
# Positions values select variables
carseats %>%
  plot_outlier(6)
# Positions values select variables
carseats %>%
  plot_outlier(-1, -5)

# Using pipes & dplyr -------------------------
# Visualization of numerical variables with a ratio of
# outliers greater than 1%
carseats %>%
  plot_outlier(carseats %>%
      diagnose_outlier() %>%
      filter(outliers_ratio > 1) %>%
      select(variables) %>%
      pull())
  
# }