dlookr (version 0.3.9)

diagnose_category: Diagnose data quality of categorical variables

Description

The diagnose_category() produces information for diagnosing the quality of the variables of data.frame or tbl_df.

Usage

diagnose_category(.data, ...)
# S3 method for data.frame
diagnose_category(.data, ..., top = 10,
  add_character = TRUE)

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_category() 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.

top

an integer. Specifies the upper top rank to extract. Default is 10.

add_character

logical. Decide whether to include text variables in the diagnosis of categorical data. The default value is TRUE, which also includes character variables.

Value

an object of tbl_df.

Categorical diagnostic information

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

• variables : variable names

• levels: level names

• N : number of observation

• freq : number of observation at the levles

• ratio : percentage of observation at the levles

• rank : rank of occupancy ratio of levels

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

Details

The scope of the diagnosis is the occupancy status of the levels in categorical data. If a certain level of occupancy is close to 100 then the removal of this variable in the forecast model will have to be considered. Also, if the occupancy of all levels is close to 0 variable is likely to be an identifier.

See Also

diagnose_category.tbl_dbi, diagnose.data.frame, diagnose_numeric.data.frame, diagnose_outlier.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 categorical variables
diagnose_category(carseats)

# Select the variable to diagnose
diagnose_category(carseats, ShelveLoc, Urban)
diagnose_category(carseats, -ShelveLoc, -Urban)
diagnose_category(carseats, "ShelveLoc", "Urban")
diagnose_category(carseats, 7)

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

# Diagnosis of all categorical variables
carseats %>%
  diagnose_category()
# Positive values select variables
carseats %>%
  diagnose_category(Urban, US)
# Negative values to drop variables
carseats %>%
  diagnose_category(-Urban, -US)
# Positions values select variables
carseats %>%
  diagnose_category(7)
# Positions values select variables
carseats %>%
  diagnose_category(-7)
# Top rank levels with top argument
carseats %>%
  diagnose_category(top = 2)

# Using pipes & dplyr -------------------------
# Extraction of level that is more than 60% of categorical data
carseats %>%
  diagnose_category()  %>%
  filter(ratio >= 60)
# }