The diagnose() produces information for diagnosing the quality of the column of the DBMS table through tbl_dbi.
# S3 method for tbl_dbi diagnose(.data, ..., in_database = TRUE, collect_size = Inf)
| .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() 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 | a logical. 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. |
An object of tbl_df.
The scope of data quality diagnosis is information on missing values and unique value information. Data quality diagnosis can determine variables that require missing value processing. Also, the unique value information can determine the variable to be removed from the data analysis.
The information derived from the data diagnosis is as follows.:
variables : column names
types : data type of the variable or to select a variable to be corrected or removed through data diagnosis.
integer, numeric, factor, ordered, character, etc.
missing_count : number of missing values
missing_percent : percentage of missing values
unique_count : number of unique values
unique_rate : ratio of unique values. unique_count / number of observation
See vignette("diagonosis") for an introduction to these concepts.
# \donttest{ library(dplyr) # connect DBMS con_sqlite <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") # copy jobchange to the DBMS with a table named TB_JOBCHANGE copy_to(con_sqlite, jobchange, name = "TB_JOBCHANGE", overwrite = TRUE) # Using pipes --------------------------------- # Diagnosis of all columns con_sqlite %>% tbl("TB_JOBCHANGE") %>% diagnose()#> # A tibble: 14 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <dbl> <dbl> <int> <dbl> #> 1 enrollee_id charac… 0 0 19158 1 #> 2 city charac… 0 0 123 0.00642 #> 3 city_dev_index double 0 0 93 0.00485 #> 4 gender charac… 4508 23.5 4 0.000209 #> 5 relevent_expe… charac… 0 0 2 0.000104 #> 6 enrolled_univ… charac… 386 2.01 4 0.000209 #> 7 education_lev… charac… 460 2.40 6 0.000313 #> 8 major_discipl… charac… 2813 14.7 7 0.000365 #> 9 experience charac… 65 0.339 23 0.00120 #> 10 company_size charac… 5938 31.0 9 0.000470 #> 11 company_type charac… 6140 32.0 7 0.000365 #> 12 last_new_job charac… 423 2.21 7 0.000365 #> 13 training_hours integer 0 0 241 0.0126 #> 14 job_chnge charac… 0 0 2 0.000104# Positive values select columns con_sqlite %>% tbl("TB_JOBCHANGE") %>% diagnose(gender, education_level, company_size)#> # A tibble: 3 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <dbl> <dbl> <int> <dbl> #> 1 gender charact… 4508 23.5 4 0.000209 #> 2 education_lev… charact… 460 2.40 6 0.000313 #> 3 company_size charact… 5938 31.0 9 0.000470# Negative values to drop columns con_sqlite %>% tbl("TB_JOBCHANGE") %>% diagnose(-gender, -education_level, -company_size)#> # A tibble: 11 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <dbl> <dbl> <int> <dbl> #> 1 enrollee_id charac… 0 0 19158 1 #> 2 city charac… 0 0 123 0.00642 #> 3 city_dev_index double 0 0 93 0.00485 #> 4 relevent_expe… charac… 0 0 2 0.000104 #> 5 enrolled_univ… charac… 386 2.01 4 0.000209 #> 6 major_discipl… charac… 2813 14.7 7 0.000365 #> 7 experience charac… 65 0.339 23 0.00120 #> 8 company_type charac… 6140 32.0 7 0.000365 #> 9 last_new_job charac… 423 2.21 7 0.000365 #> 10 training_hours integer 0 0 241 0.0126 #> 11 job_chnge charac… 0 0 2 0.000104# Positions values select columns, and In-memory mode con_sqlite %>% tbl("TB_JOBCHANGE") %>% diagnose(1, 3, 8, in_database = FALSE)#> # A tibble: 3 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <int> <dbl> <int> <dbl> #> 1 enrollee_id charac… 0 0 19158 1 #> 2 city_dev_index numeric 0 0 93 0.00485 #> 3 major_discipli… charac… 2813 14.7 7 0.000365# Positions values select columns, and In-memory mode and collect size is 200 con_sqlite %>% tbl("TB_JOBCHANGE") %>% diagnose(-8, -9, -10, in_database = FALSE, collect_size = 200)#> # A tibble: 11 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <int> <dbl> <int> <dbl> #> 1 enrollee_id charac… 0 0 200 1 #> 2 city charac… 0 0 49 0.245 #> 3 city_dev_index numeric 0 0 40 0.2 #> 4 gender charac… 48 24 4 0.02 #> 5 relevent_expe… charac… 0 0 2 0.01 #> 6 enrolled_univ… charac… 5 2.5 4 0.02 #> 7 education_lev… charac… 6 3 5 0.025 #> 8 company_type charac… 62 31 7 0.035 #> 9 last_new_job charac… 5 2.5 7 0.035 #> 10 training_hours integer 0 0 101 0.505 #> 11 job_chnge charac… 0 0 2 0.01# Using pipes & dplyr ------------------------- # Diagnosis of missing variables con_sqlite %>% tbl("TB_JOBCHANGE") %>% diagnose() %>% filter(missing_count > 0)#> # A tibble: 8 x 6 #> variables types missing_count missing_percent unique_count unique_rate #> <chr> <chr> <dbl> <dbl> <int> <dbl> #> 1 gender charac… 4508 23.5 4 0.000209 #> 2 enrolled_unive… charac… 386 2.01 4 0.000209 #> 3 education_level charac… 460 2.40 6 0.000313 #> 4 major_discipli… charac… 2813 14.7 7 0.000365 #> 5 experience charac… 65 0.339 23 0.00120 #> 6 company_size charac… 5938 31.0 9 0.000470 #> 7 company_type charac… 6140 32.0 7 0.000365 #> 8 last_new_job charac… 423 2.21 7 0.000365