dlookr (version 0.3.9)

diagnose_outlier: Diagnose outlier of numerical variables

Description

The diagnose_outlier() produces outlier information for diagnosing the quality of the numerical data.

Usage

diagnose_outlier(.data, ...)
# S3 method for data.frame
diagnose_outlier(.data, ...)

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, diagnose_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.

Value

an object of tbl_df.

Outlier Diagnostic information

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

• variables : variable names

• outliers_cnt : count of outliers

• outliers_ratio : percent of outliers

• outliers_mean : arithmetic average of outliers

• with_mean : arithmetic average of with outliers

• without_mean : arithmetic average of without outliers

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

Details

The scope of the diagnosis is the provide a outlier information. If the number of outliers is small and the difference between the averages including outliers and the averages not including them is large, it is necessary to eliminate or replace the outliers.

See Also

diagnose_outlier.tbl_dbi, diagnose.data.frame, diagnose_category.data.frame, diagnose_numeric.data.frame.

Examples

# 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

# Diagnosis of numerical variables
diagnose_outlier(carseats)

# Select the variable to diagnose
diagnose_outlier(carseats, Sales, Income)
diagnose_outlier(carseats, -Sales, -Income)
diagnose_outlier(carseats, "Sales", "Income")
diagnose_outlier(carseats, 5)

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

# Diagnosis of all numerical variables
carseats %>%
  diagnose_outlier()
# Positive values select variables
carseats %>%
  diagnose_outlier(Sales, Income)
# Negative values to drop variables
carseats %>%
  diagnose_outlier(-Sales, -Income)
# Positions values select variables
carseats %>%
  diagnose_outlier(5)
# Positions values select variables
carseats %>%
  diagnose_outlier(-1, -5)

# Using pipes & dplyr -------------------------
# outlier_ratio is more than 1%
carseats %>%
  diagnose_outlier()  %>%
  filter(outliers_ratio > 1)
# }