Diagnose data quality of numerical variables in the DBMS

The diagnose_numeric() produces information for diagnosing the quality of the numerical(INTEGER, NUMBER, etc.) column of the DBMS table through tbl_dbi.

# S3 method for tbl_dbi
diagnose_numeric(.data, ..., 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, 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.

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.

Value

an object of tbl_df.

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. You can use grouped_df as the group_by() function.

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.

See also

diagnose_numeric.data.frame, diagnose.tbl_dbi, diagnose_category.tbl_dbi, diagnose_outlier.tbl_dbi.

Examples

# If you have the 'DBI' and 'RSQLite' packages installed, perform the code block:
if (FALSE) {
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 ---------------------------------
# Diagnosis of all numerical variables
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_numeric()
  
# Positive values select variables, and In-memory mode and collect size is 200
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_numeric(age, sodium, collect_size = 200)
  
# Negative values to drop variables
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_numeric(-age, -sodium)
  
# Positions values select variables
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_numeric(5)
  
# Negative values to drop variables
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_numeric(-1, -5)

# Using pipes & dplyr -------------------------
# List of variables containing outliers
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  diagnose_numeric()  %>%
  filter(outlier > 0)

# Using group_by() ---------------------------- 
con_sqlite %>% 
  tbl("TB_HEARTFAILURE") %>% 
  group_by(death_event) %>% 
  diagnose_numeric() 
  
# Disconnect DBMS   
DBI::dbDisconnect(con_sqlite)
}