dlookr (version 0.3.9)

diagnose_category.tbl_dbi: Diagnose data quality of categorical variables in the DBMS

Description

The diagnose_category() produces information for diagnosing the quality of the character(CHAR, VARCHAR, VARCHAR2, etc.) column of the DBMS table through tbl_dbi.

Usage

# S3 method for tbl_dbi
diagnose_category(.data, ..., top = 10,
  in_database = TRUE, 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, diagnose_category() 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.

top

an integer. Specifies the upper top rank to extract. Default is 10.

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.

collect_size

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

Value

an object of tbl_df.

Categorical diagnostic information

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

• variables : variable names

• levels: level names

• N : number of observation

• freq : number of observation at the levles

• ratio : percentage of observation at the levles

• rank : rank of occupancy ratio of levels

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

Details

The scope of the diagnosis is the occupancy status of the levels in categorical data. If a certain level of occupancy is close to 100 then the removal of this variable in the forecast model will have to be considered. Also, if the occupancy of all levels is close to 0 variable is likely to be an identifier.

See Also

diagnose_category.data.frame, diagnose.tbl_dbi, diagnose_category.tbl_dbi, diagnose_numeric.tbl_dbi, diagnose_outlier.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 ---------------------------------
# Diagnosis of all categorical variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category()
  
# Positive values select variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(Urban, US)
  
# Negative values to drop variables, and In-memory mode
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(-Urban, -US, in_database = FALSE)
  
# Positions values select variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(7, in_database = FALSE, collect_size = 200) 
  
# Positions values select variables
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(-7)
  
# Top rank levels with top argument
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category(top = 2)

# Using pipes & dplyr -------------------------
# Extraction of level that is more than 60% of categorical data
con_sqlite %>% 
  tbl("TB_CARSEATS") %>% 
  diagnose_category()  %>%
  filter(ratio >= 60)
  
# }