dlookr (version 0.5.0)

plot_normality.tbl_dbi: Plot distribution information of numerical data

Description

The plot_normality() visualize distribution information for normality test of the numerical(INTEGER, NUMBER, etc.) column of the DBMS table through tbl_dbi.

Usage

# S3 method for tbl_dbi
plot_normality(
  .data,
  ...,
  in_database = FALSE,
  collect_size = Inf,
  left = c("log", "sqrt", "log+1", "1/x", "x^2", "x^3", "Box-Cox", "Yeo-Johnson"),
  right = c("sqrt", "log", "log+1", "1/x", "x^2", "x^3", "Box-Cox", "Yeo-Johnson"),
  col = "steelblue",
  typographic = TRUE
)

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, plot_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.

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

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.

left

character. Specifies the data transformation method to draw the histogram in the lower left corner. The default is "log".

right

character. Specifies the data transformation method to draw the histogram in the lower right corner. The default is "sqrt".

col

a color to be used to fill the bars. The default is "steelblue".

typographic

logical. Whether to apply focuses on typographic elements to ggplot2 visualization.

Distribution information

The plot derived from the numerical data visualization is as follows.

• histogram by original data

• q-q plot by original data

• histogram by log transfer data

• histogram by square root transfer data

Details

The scope of the visualization is the provide a distribution information. Since the plot is drawn for each variable, if you specify more than one variable in the ... argument, the specified number of plots are drawn.

The argument values that left and right can have are as follows.:

• "log" : log transformation. log(x)

• "log+1" : log transformation. log(x + 1). Used for values that contain 0.

• "sqrt" : square root transformation.

• "1/x" : 1 / x transformation

• "x^2" : x square transformation

• "x^3" : x^3 square transformation

• "Box-Cox" : Box-Box transformation

• "Yeo-Johnson" : Yeo-Johnson transformation

See Also

plot_normality.data.frame, plot_outlier.tbl_dbi.

Examples

# NOT RUN {
library(dplyr)

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

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

# Using pipes ---------------------------------
# Visualization of all numerical variables
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  plot_normality()

# Positive values select variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  plot_normality(platelets, sodium, collect_size = 200)

# Positions values select variables
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  plot_normality(1)

# Not allow the typographic elements
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  plot_normality(1, typographic = FALSE)
  
# Using pipes & dplyr -------------------------
# Plot 'sodium' variable by 'smoking' and 'death_event'
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  group_by(smoking, death_event) %>%
  plot_normality(sodium)

# Plot using left and right arguments
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  group_by(smoking, death_event) %>%
  plot_normality(sodium, left = "Box-Cox", right = "log")

# extract only those with 'smoking' variable level is "Yes",
# and plot 'sodium' by 'death_event'
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  filter(smoking == "Yes") %>%
  group_by(death_event) %>%
  plot_normality(sodium)
  
# Disconnect DBMS   
DBI::dbDisconnect(con_sqlite)
# }
# NOT RUN {
# }