Scans data for checking columns that are constant, one-to-one, missing or all unique. Users can also try to identify the columns that uniquely index the observational unit and whether some columns are nested or complete.
The goal of datascan is to provide a set of functions to perform data quality checks and data exploration in R. It includes functions for checking missing values, identifying nested columns, and other common data quality issues.
You can install the development version of datascan from GitHub with:
# install.packages("pak")
pak::pak("emitanaka/datascan")
This is a basic example which shows you how to solve a common problem:
library(datascan)
head(CO2)
#> Plant Type Treatment conc uptake
#> 1 Qn1 Quebec nonchilled 95 16.0
#> 2 Qn1 Quebec nonchilled 175 30.4
#> 3 Qn1 Quebec nonchilled 250 34.8
#> 4 Qn1 Quebec nonchilled 350 37.2
#> 5 Qn1 Quebec nonchilled 500 35.3
#> 6 Qn1 Quebec nonchilled 675 39.2
cols_identify_all(CO2)
#> → No columns with all unique values.
#> → No bijective columns.
#> → No constant columns.
#> → No identified columns with missing proportion.
#>
#> ── Checking if all values are unique in a column ───────────────────────────────
#> [1] NA
#>
#> ── Checking if any two columns are bijective ───────────────────────────────────
#> list()
#>
#> ── Checking if any column is all constant ──────────────────────────────────────
#> [1] NA
#>
#> ── Checking if columns with preset cut-off in missing values ───────────────────
#> [1] NA
cols_nested(CO2)
#> [[1]]
#> [1] "Plant" "Type"
#>
#> [[2]]
#> [1] "Plant" "Treatment"
concurrence_matrix(CO2, Plant, Type)
#> • Diagonal shows the total number of levels for Plant at the corresponding
#> level for Type
#> • Off-diagonal shows the number of common levels for Plant between the two
#> levels for Type
#>
#> Quebec Mississippi
#> Quebec 6 0
#> Mississippi 0 6
#>
#> ℹ Concurrence matrix: 2 × 2 Type.