dlookr (version 0.3.9)

diagnose_numeric: Diagnose data quality of numerical variables

Description

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

Usage

diagnose_numeric(.data, ...)
# S3 method for data.frame
diagnose_numeric(.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_numeric() 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.

Numerical diagnostic information

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

• variables : variable names

• min : minimum

• Q1 : 25 percentile

• mean : arithmetic average

• median : median. 50 percentile

• Q3 : 75 percentile

• max : maximum

• zero : count of zero values

• minus : count of minus values

• outlier : count of outliers

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

Details

The scope of the diagnosis is the calculate a statistic that can be used to understand the distribution of numerical data. min, Q1, mean, median, Q3, max can be used to estimate the distribution of data. If the number of zero or minus is large, it is necessary to suspect the error of the data. If the number of outliers is large, a strategy of eliminating or replacing outliers is needed.

See Also

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

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

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

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

# Using pipes & dplyr -------------------------
# Information records of zero variable more than 0
carseats %>%
  diagnose_numeric()  %>%
  filter(zero > 0)
# }