dlookr (version 0.6.3)

diagnose: Diagnose data quality of variables

Description

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

Usage

diagnose(.data, ...)
# S3 method for data.frame
diagnose(.data, ...)
# S3 method for grouped_df
diagnose(.data, ...)

Value

An object of tbl_df.

Arguments

.data

a data.frame or a tbl_df or a grouped_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() 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.

Diagnostic information

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

• variables : variable names

• types : data type of the variable or to select a variable to be corrected or removed through data diagnosis.

  • integer, numeric, factor, ordered, character, etc.

• missing_count : number of missing values

• missing_percent : percentage of missing values

• unique_count : number of unique values

• unique_rate : ratio of unique values. unique_count / number of observation

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

Details

The scope of data quality diagnosis is information on missing values and unique value information. Data quality diagnosis can determine variables that require missing value processing. Also, the unique value information can determine the variable to be removed from the data analysis.

See Also

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

Examples

# \donttest{
# Diagnosis of all variables
diagnose(jobchange)

# Select the variable to diagnose
diagnose(jobchange, gender, experience, training_hours)
diagnose(jobchange, -gender, -experience, -training_hours)
diagnose(jobchange, "gender", "experience", "training_hours")
diagnose(jobchange, 4, 9, 13)

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

# Diagnosis of all variables
jobchange %>%
  diagnose()
# Positive values select variables
jobchange %>%
  diagnose(gender, experience, training_hours)
# Negative values to drop variables
jobchange %>%
  diagnose(-gender, -experience, -training_hours)
# Positions values select variables
jobchange %>%
  diagnose(4, 9, 13)
# Negative values to drop variables
jobchange %>%
  diagnose(-8, -9, -10)
  
# Using pipes & dplyr -------------------------
# Diagnosis of missing variables
jobchange %>%
  diagnose() %>%
  filter(missing_count > 0)
   
# Using group_by ------------------------------
# Calculate the diagnosis of all variables by 'job_chnge' using group_by()
jobchange %>%
  group_by(job_chnge) %>% 
  diagnose() 
# }