dlookr (version 0.3.9)

normality.tbl_dbi: Performs the Shapiro-Wilk test of normality

Description

The normality() performs Shapiro-Wilk test of normality of numerical(INTEGER, NUMBER, etc.) column of the DBMS table through tbl_dbi.

Usage

# S3 method for tbl_dbi
normality(.data, ..., sample = 5000,
  in_database = FALSE, collect_size = Inf)

Arguments

.data

a tbl_dbi.

...

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 numer of samples to perform the test.

in_database

Specifies whether to perform in-database operations. If TRUE, most operations are performed in the DBMS. if FALSE, table data is taken in R and operated in-memory. Not yet supported in_database = TRUE.

collect_size

a integer. The number of data samples from the DBMS to R. Applies only if in_database = FALSE.

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

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

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.

See Also

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

Examples

# NOT RUN {
library(dplyr)

# Generate data for the example
carseats <- ISLR::Carseats
carseats[sample(seq(NROW(carseats)), 20), "Income"] <- NA
carseats[sample(seq(NROW(carseats)), 5), "Urban"] <- NA

# connect DBMS
con_sqlite <- DBI::dbConnect(RSQLite::SQLite(), ":memory:")

# copy carseats to the DBMS with a table named TB_CARSEATS
copy_to(con_sqlite, carseats, name = "TB_CARSEATS", overwrite = TRUE)

# Using pipes ---------------------------------
# Normality test of all numerical variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  normality()

# Positive values select variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  normality(Sales, Price, collect_size  = 200)

# Positions values select variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  normality(1)

# Using pipes & dplyr -------------------------
# Test all numerical variables by 'ShelveLoc' and 'US',
# and extract only those with 'ShelveLoc' variable level is "Good".
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  group_by(ShelveLoc, US) %>%
  normality() %>%
  filter(ShelveLoc == "Good")

# extract only those with 'Urban' variable level is "Yes",
# and test 'Sales' by 'ShelveLoc' and 'US'
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  filter(Urban == "Yes") %>%
  group_by(ShelveLoc, US) %>%
  normality(Sales)

# Test log(Income) variables by 'ShelveLoc' and 'US',
# and extract only p.value greater than 0.01.

# SQLite extension functions for log
RSQLite::initExtension(con_sqlite)

con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  mutate(log_income = log(Income)) %>%
  group_by(ShelveLoc, US) %>%
  normality(log_income) %>%
  filter(p_value > 0.01)
# }
# NOT RUN {
# }