read_spss_data() is designed to seamlessly import data from an SPSS data (.sav or .zsav) files. It converts labelled variables into factors, a crucial step that enhances the ease of data manipulation and analysis within the R programming environment.
See also
read_stata_data() which reads Stata data file and converts labelled variables into factors.
Examples
# Read an SPSS data file without converting variable labels as column names
file_path <- system.file("extdata", "Wages.sav", package = "bulkreadr")
data <- read_spss_data(file = file_path)
data
#> # A tibble: 400 × 9
#> id educ south sex exper wage occup marr ed
#> <dbl> <dbl> <fct> <fct> <dbl> <dbl> <fct> <fct> <fct>
#> 1 3 12 does not live in South Male 17 7.5 Other Married High …
#> 2 4 13 does not live in South Male 9 13.1 Other Not married Some …
#> 3 5 10 lives in South Male 27 4.45 Other Not married Less …
#> 4 12 9 lives in South Male 30 6.25 Other Not married Less …
#> 5 13 9 lives in South Male 29 20.0 Other Married Less …
#> 6 14 12 does not live in South Male 37 7.3 Other Married High …
#> 7 17 11 does not live in South Male 16 3.65 Other Not married Less …
#> 8 20 12 does not live in South Male 9 3.75 Other Not married High …
#> 9 21 11 lives in South Male 14 4.5 Other Married Less …
#> 10 23 6 lives in South Male 45 5.75 Other Married Less …
#> # ℹ 390 more rows
# Read an SPSS data file and convert variable labels as column names
data <- read_spss_data(file = file_path, label = TRUE)
data
#> # A tibble: 400 × 9
#> `Worker ID` `Number of years of education` `Live in south` Gender
#> <dbl> <dbl> <fct> <fct>
#> 1 3 12 does not live in South Male
#> 2 4 13 does not live in South Male
#> 3 5 10 lives in South Male
#> 4 12 9 lives in South Male
#> 5 13 9 lives in South Male
#> 6 14 12 does not live in South Male
#> 7 17 11 does not live in South Male
#> 8 20 12 does not live in South Male
#> 9 21 11 lives in South Male
#> 10 23 6 lives in South Male
#> # ℹ 390 more rows
#> # ℹ 5 more variables: `Number of years of work experience` <dbl>,
#> # `Wage (dollars per hour)` <dbl>, Occupation <fct>, `Marital status` <fct>,
#> # `Highest education level` <fct>