Reads a dataset downloaded from the IPUMS extract system. For IPUMS projects with microdata, it relies on a downloaded DDI codebook and a fixed-width file. Loads the data with value labels (using labelled format) and variable labels. See 'Details' for more information on how record types are handled by the ipumsr package.

read_ipums_micro(ddi, vars = NULL, n_max = Inf, data_file = NULL,
  verbose = TRUE, var_attrs = c("val_labels", "var_label", "var_desc"),
  lower_vars = FALSE)

read_ipums_micro_list(ddi, vars = NULL, n_max = Inf,
  data_file = NULL, verbose = TRUE, var_attrs = c("val_labels",
  "var_label", "var_desc"), lower_vars = FALSE)

Arguments

ddi

Either a filepath to a DDI xml file downloaded from the website, or a ipums_ddi object parsed by read_ipums_ddi

vars

Names of variables to load. Accepts a character vector of names, or dplyr_select_style conventions. For hierarchical data, the rectype id variable will be added even if it is not specified.

n_max

The maximum number of records to load.

data_file

Specify a directory to look for the data file. If left empty, it will look in the same directory as the DDI file.

verbose

Logical, indicating whether to print progress information to console.

var_attrs

Variable attributes to add from the DDI, defaults to adding all (val_labels, var_label and var_desc). See set_ipums_var_attributes for more details.

lower_vars

If reading a DDI from a file, a logical indicating whether to convert variable names to lowercase (default is FALSE due to tradition)

Value

read_ipums_micro returns a single tbl_df data frame, and read_ipums_micro_list returns a list of data frames, named by the Record Type. See 'Details' for more information.

Details

Some IPUMS projects have data for multiple types of records (eg Household and Person). When downloading data from many of these projects you have the option for the IPUMS extract system to "rectangularize" the data, meaning that the data is transformed so that each row of data represents only one type of record.

There also is the option to download "hierarchical" extracts, which are a single file with record types mixed in the rows. The ipumsr package offers two methods for importing this data.

read_ipums_micro loads this data into a "long" format where the record types are mixed in the rows, but the variables are NA for the record types that they do not apply to.

read_ipums_micro_list loads the data into a list of data frames objects, where each data frame contains only one record type. The names of the data frames in the list are the text from the record type labels without 'Record' (often 'HOUSEHOLD' for Household and 'PERSON' for Person).

See also

Examples

# Rectangular example file cps_rect_ddi_file <- ipums_example("cps_00006.xml") cps <- read_ipums_micro(cps_rect_ddi_file)
#> Use of data from IPUMS-CPS is subject to conditions including that users should #> cite the data appropriately. Use command `ipums_conditions()` for more details.
# Or load DDI separately to keep the metadata ddi <- read_ipums_ddi(cps_rect_ddi_file) cps <- read_ipums_micro(ddi)
#> Use of data from IPUMS-CPS is subject to conditions including that users should #> cite the data appropriately. Use command `ipums_conditions()` for more details.
# Hierarchical example file cps_hier_ddi_file <- ipums_example("cps_00010.xml") # Read in "long" format and you get 1 data frame cps_long <- read_ipums_micro(cps_hier_ddi_file)
#> Use of data from IPUMS-CPS is subject to conditions including that users should #> cite the data appropriately. Use command `ipums_conditions()` for more details.
head(cps_long)
#> # A tibble: 6 x 9 #> RECTYPE YEAR SERIAL HWTSUPP STATEFIP MONTH PERNUM WTSUPP INCTOT #> <chr+lbl> <dbl> <dbl> <dbl> <int+lbl> <int+lbl> <dbl> <dbl> <dbl+lbl> #> 1 H [House~ 1962 80 1476. 55 [Wisc~ 3 [Marc~ NA NA NA #> 2 P [Perso~ 1962 80 NA NA NA 1 1476. 4.88e34883 #> 3 P [Perso~ 1962 80 NA NA NA 2 1471. 5.80e35800 #> 4 P [Perso~ 1962 80 NA NA NA 3 1579. 10.00e7 [Mis~ #> 5 H [House~ 1962 82 1598. 27 [Minn~ 3 [Marc~ NA NA NA #> 6 P [Perso~ 1962 82 NA NA NA 1 1598. 1.40e414015
# Read in "list" format and you get a list of multiple data frames cps_list <- read_ipums_micro_list(cps_hier_ddi_file)
#> Use of data from IPUMS-CPS is subject to conditions including that users should #> cite the data appropriately. Use command `ipums_conditions()` for more details.
head(cps_list$PERSON)
#> # A tibble: 6 x 6 #> RECTYPE YEAR SERIAL PERNUM WTSUPP INCTOT #> <chr+lbl> <dbl> <dbl> <dbl> <dbl> <dbl+lbl> #> 1 P [Person Record] 1962 80 1 1476. 48834883 #> 2 P [Person Record] 1962 80 2 1471. 58005800 #> 3 P [Person Record] 1962 80 3 1579. 99999998 [Missing.] #> 4 P [Person Record] 1962 82 1 1598. 1401514015 #> 5 P [Person Record] 1962 83 1 1707. 1655216552 #> 6 P [Person Record] 1962 84 1 1790. 63756375
head(cps_list$HOUSEHOLD)
#> # A tibble: 6 x 6 #> RECTYPE YEAR SERIAL HWTSUPP STATEFIP MONTH #> <chr+lbl> <dbl> <dbl> <dbl> <int+lbl> <int+lbl> #> 1 H [Household Record] 1962 80 1476. 55 [Wisconsin] 3 [March] #> 2 H [Household Record] 1962 82 1598. 27 [Minnesota] 3 [March] #> 3 H [Household Record] 1962 83 1707. 27 [Minnesota] 3 [March] #> 4 H [Household Record] 1962 84 1790. 27 [Minnesota] 3 [March] #> 5 H [Household Record] 1962 107 4355. 19 [Iowa] 3 [March] #> 6 H [Household Record] 1962 108 1479. 19 [Iowa] 3 [March]
# Or you can use the \code{%<-%} operator from zeallot to unpack c(household, person) %<-% read_ipums_micro_list(cps_hier_ddi_file)
#> Use of data from IPUMS-CPS is subject to conditions including that users should #> cite the data appropriately. Use command `ipums_conditions()` for more details.
head(person)
#> # A tibble: 6 x 6 #> RECTYPE YEAR SERIAL PERNUM WTSUPP INCTOT #> <chr+lbl> <dbl> <dbl> <dbl> <dbl> <dbl+lbl> #> 1 P [Person Record] 1962 80 1 1476. 48834883 #> 2 P [Person Record] 1962 80 2 1471. 58005800 #> 3 P [Person Record] 1962 80 3 1579. 99999998 [Missing.] #> 4 P [Person Record] 1962 82 1 1598. 1401514015 #> 5 P [Person Record] 1962 83 1 1707. 1655216552 #> 6 P [Person Record] 1962 84 1 1790. 63756375
head(household)
#> # A tibble: 6 x 6 #> RECTYPE YEAR SERIAL HWTSUPP STATEFIP MONTH #> <chr+lbl> <dbl> <dbl> <dbl> <int+lbl> <int+lbl> #> 1 H [Household Record] 1962 80 1476. 55 [Wisconsin] 3 [March] #> 2 H [Household Record] 1962 82 1598. 27 [Minnesota] 3 [March] #> 3 H [Household Record] 1962 83 1707. 27 [Minnesota] 3 [March] #> 4 H [Household Record] 1962 84 1790. 27 [Minnesota] 3 [March] #> 5 H [Household Record] 1962 107 4355. 19 [Iowa] 3 [March] #> 6 H [Household Record] 1962 108 1479. 19 [Iowa] 3 [March]