normality.data.frame: Performs the Shapiro-Wilk test of normality

In dlookr: Tools for Data Diagnosis, Exploration, Transformation

Performs the Shapiro-Wilk test of normality

Description

The normality() performs Shapiro-Wilk test of normality of numerical values.

Usage

normality(.data, ...)

## S3 method for class 'data.frame'
normality(.data, ..., sample = 5000)

## S3 method for class 'grouped_df'
normality(.data, ..., sample = 5000)

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, normality() 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.
sample	the number of samples to perform the test. See vignette("EDA") for an introduction to these concepts.

Details

This function is useful when used with the group_by function of the dplyr package. If you want to test by level of the categorical data you are interested in, rather than the whole observation, you can use group_tf as the group_by function. This function is computed shapiro.test function.

Value

An object of the same class as .data.

Normality test information

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

• statistic : the value of the Shapiro-Wilk statistic.

• p_value : an approximate p-value for the test. This is said in Roystion(1995) to be adequate for p_value < 0.1.

• sample : the number of samples to perform the test. The number of observations supported by the stats::shapiro.test function is 3 to 5000.

See Also

normality.tbl_dbi, diagnose_numeric.data.frame, describe.data.frame, plot_normality.data.frame.

Examples

# Normality test of numerical variables
normality(heartfailure)

# Select the variable to describe
normality(heartfailure, platelets, sodium, sample = 200)

# death_eventing dplyr::grouped_dt
library(dplyr)

gdata <- group_by(heartfailure, smoking, death_event)
normality(gdata, "platelets")
normality(gdata, sample = 250)

# Positive values select variables
heartfailure %>%
  normality(platelets, sodium)

# death_eventing pipes & dplyr -------------------------
# Test all numerical variables by 'smoking' and 'death_event',
# and extract only those with 'smoking' variable level is "No".
heartfailure %>%
  group_by(smoking, death_event) %>%
  normality() %>%
  filter(smoking == "No")

# extract only those with 'sex' variable level is "Male",
# and test 'platelets' by 'smoking' and 'death_event'
heartfailure %>%
  filter(sex == "Male") %>%
  group_by(smoking, death_event) %>%
  normality(platelets)

# Test log(platelets) variables by 'smoking' and 'death_event',
# and extract only p.value greater than 0.01.
heartfailure %>%
  mutate(platelets_income = log(platelets)) %>%
  group_by(smoking, death_event) %>%
  normality(platelets_income) %>%
  filter(p_value > 0.01)

dlookr documentation built on May 29, 2024, 2 a.m.