Select a subset of the dataset.
Usage
wid_subset(db = NULL, ..., variables = NULL, surveys = NULL,
min_year = -Inf, max_year = Inf, selection = NULL)Arguments
- db
The dataset to subset; an
Datasetas returned fromwid_open.- ...
Conditions in the form of
variable operator value(e.g.,age > 50).- variables
A character vector containing the variables you want to require. That is, only observations with values for these variables will be included.
- surveys
A character vector of survey abbreviations to include.
- min_year
Earliest year to include.
- max_year
Latest year to include.
- selection
A character vector with country_year_survey IDs to be selected, or a matrix-like object consisting of 3 columns containing country, year, and survey IDs in that order.
Value
A filtered version of db if it is provided, or a list with entries
for remaining variables, surveys, and years.
Examples
# see what's available in a given year
wid_subset(max_year = 1998)
#> $variables
#> [1] "age" "country" "education" "id_ind"
#> [5] "main_activity" "main_job_ind" "marital_status" "n_child_5"
#> [9] "rural" "sex" "survey" "survey_year"
#> [13] "wgt" "work" "work_search" "year"
#>
#> $surveys
#> [1] "BHPS" "CASEN" "CPS-IPUMS" "GLSS" "IPUMS" "KLIPS"
#> [7] "LFS" "NSS"
#>
#> $years
#> [1] 1960 1962 1963 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
#> [16] 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
#> [31] 1994 1995 1996 1997 1998
#>
# see which variables and years are in a particular survey
wid_subset(surveys = "CASEN")
#> $variables
#> [1] "age" "country" "education" "id_ind"
#> [5] "main_activity" "main_job_ind" "marital_status" "n_child_5"
#> [9] "rural" "sex" "survey" "wgt"
#> [13] "work" "work_search" "year"
#>
#> $surveys
#> [1] "CASEN"
#>
#> $years
#> [1] 1990 1992 1994 1996 1998 2000 2003 2006 2009 2011 2013 2015 2017
#>
# see which surveys and years have a particular variable
wid_subset(variables = "work_search")[-1]
#> $surveys
#> [1] "BHPS" "CASEN" "CFPS" "CPS-IPUMS" "ECAM" "ECE"
#> [7] "ECH" "ECVMA" "EH" "EHCVM" "EICV" "EMICOV"
#> [13] "ENAHO" "ENEMDU" "ENES" "ENOE" "ENPE" "EULFS"
#> [19] "GEIH" "GLSS" "HILDA" "HLFS" "IBEP" "IHS"
#> [25] "ILCS" "ILFS" "IPUMS" "KCHSP" "KLIPS" "LFCLS"
#> [31] "LFS" "LSMS" "NBHS" "NLFS" "NSS" "PLFS"
#> [37] "PNAD" "PSLM" "QLFS" "RLMS" "SILC"
#>
#> $years
#> [1] 1960 1962 1963 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978
#> [16] 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993
#> [31] 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
#> [46] 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
#>
# filter to a subset of data
db_dir <- "../../../gender_growth_gap"
if (dir.exists(db_dir)) {
wid_open(db_dir) |> wid_subset(age > 200) |> dplyr::collect()
}
#> # A tibble: 5 × 15
#> age country education id_ind main_activity main_job_ind marital_status
#> <int> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 205 IND Primary 41499210202 Out of Workfo… NA Married / In-…
#> 2 262 IND Graduate 41962220204 Out of Workfo… NA Married / In-…
#> 3 302 IND NA 43285110203 Industry 31_class_20… Married / In-…
#> 4 810 IND NA 43977220104 Out of Workfo… NA Never Married
#> 5 653 IND None 45727110201 Agriculture 31_class_111 Never Married
#> # ℹ 8 more variables: n_child_5 <int>, rural <lgl>, sex <lgl>, survey <chr>,
#> # wgt <dbl>, work <lgl>, work_search <lgl>, year <int>