HILDA survey data is a large panel survey 20 waves (2001 - 2020) and counting! Some waves have more than 5000 variables, which means reading them into R is a little challenging (personally I think it is wayyyyyyyy too slow).
The goal of this package is to provide a quick and easy way to query
HILDA data into R. This is possible by converting each wave of HILDA
from its STATA file (.dta), one of the three formats HILDA provides,
to fst format. fst is a binary
format and can be read much much quicker than .dta in R.
| Function name | Description |
|---|---|
hil_setup() |
Setup HILDA fst files for hil_fetch() to use. |
hil_fetch() |
Fetches HILDA records based on query options. |
hil_dict() |
Shows HILDA data glossary and waves each variable is available in. This provides a convenient way to select multiple variables based on their description by passing it to hil_fetch(). |
hil_vars() |
Returns all variables where their variable names match a regular expression. |
hil_labs() |
Returns all variables where their labels match a regular expression. |
hil_browse() |
Opens up the HILDA data dictionary page on your default web browser. |
hil_crosswave_info() |
Takes a variable name and search for its cross wave information. |
hil_var_details() |
Similar to hil_crosswave_info() but it searches for a variable’s details. |
The development version from GitHub with:
# install.packages("remotes")
remotes::install_github("asiripanich/hildar")Use hil_setup() to read HILDA STATA (.dta) files and save them as
.fst files. .fst is a binary data format that can be read very
quickly, a lot faster than .dta. An additional benefit of
hil_setup() is that it creates a HILDA dictionary file that you can
later call using hil_dict(). Having a functional hil_dict() allows
the user to use hil_vars() and hil_labs() for searching variable
names using a regular expression.
This will allow you to fast query HILDA data from all waves using
hil_fetch().
hil_setup(
read_dir = "/path/to/your/hilda-stata-files",
save_dir = "/path/to/save/hilda-fst-files"
)To speed up the setup process, you can select a future parallel
backend and call it before running your hil_setup().
library(future)
plan(multisession, workers = 2)
# `hil_setup()` can take several minutes to finish.
# To monitor its progress, you can wrap the function in
# `progressr::with_progress({...}}` like below.
progressr::with_progress({
hil_setup(read_dir = "...", save_dir = "...")
})hil_fetch() requires the user to specify where the HILDA fst files
generated in the previous step are stored. You can either set this
HILDA_FST as a global option or an R environment variable. Setting
this as a persistent option for all your R sessions will make
hil_fetch() more convinient to use. Alternatively, you can manually
set it in each call using hilda_fst_dir argument in hil_fetch().
Once the setup is completed, you can now start fetching HILDA data with
hildar!
library(hildar)
# fetch removes the HILDA year prefix from all the selected variable
# (e.g. axxx = 2001, bxxx = 2002).
hil_fetch(years = 2001:2003, add_geography = T) %>%
summary()
#> ℹ `HILDAR_USER_DEFAULT_VARS` in Renviron: `hhid`
#> xwaveid hhid wave hhwth hhwtrp
#> Length:55899 Length:55899 Min. :1.00 Min. : 0.0 Min. : -10.0
#> Class :character Class :character 1st Qu.:1.00 1st Qu.: 732.7 1st Qu.: -10.0
#> Mode :character Mode :character Median :2.00 Median : 909.4 Median : 821.9
#> Mean :1.96 Mean : 1021.9 Mean : 809.4
#> 3rd Qu.:3.00 3rd Qu.: 1148.8 3rd Qu.: 1122.0
#> Max. :3.00 Max. :14094.0 Max. :16000.0
#> hgage hgsex mrcurr hhrih hhsgcc
#> Min. : 0.00 Length:55899 Length:55899 Length:55899 Length:55899
#> 1st Qu.: 15.00 Class :character Class :character Class :character Class :character
#> Median : 34.00 Mode :character Mode :character Mode :character Mode :character
#> Mean : 34.82
#> 3rd Qu.: 51.00
#> Max. :100.00There is a quick option to add basic demographic variables to the data, which is set to TRUE by default.
hil_fetch(years = 2001, add_basic_vars = T) %>%
names()
#> ℹ `HILDAR_USER_DEFAULT_VARS` in Renviron: `hhid`
#> [1] "xwaveid" "hhid" "wave" "hhwth" "hhwtrp" "hgage" "hgsex" "mrcurr" "hhrih"How about doing a quick search to find variables that you want? Use
hil_dict which is a data.table that you can search or view HILDA
variables without going to their documentation webpage.
hilda_dictionary <- hil_dict()
head(hilda_dictionary)
#> var wave label
#> 1: xwaveid NA XW Cross wave ID
#> 2: hhid 1,2,3,4,5,6,... HF Household ID
#> 3: hhpno 1,2,3,4,5,6,... HF Person number
#> 4: hhpid 1,2,3,4,5,6,... HF Person ID
#> 5: hhrpid 1,2,3,4,5,6,... DV: Randomised person id
#> 6: hhstate 1,2,3,4,5,6,... HF StateLet say we want to select all variables that are related to
‘employment’. Here is how we can easily use the selected employment
variables in hil_fetch().
hilda_data <- hil_fetch(years = 2001:2003, vars = hil_labs("employment"))
#> ℹ `HILDAR_USER_DEFAULT_VARS` in Renviron: `hhid`
#> ! These variables: `cnpu_fd`, `cnpu_np`, `cnpu_o1`, `cnpu_o2`, `cnpu_na`, `cnph_o1`, `cnph_o2`, `cnpc_ps`, `cnpc_fd`, `cnpc_o1`, `cnpc_o2`, `cnsu_ps`, `cnsu_fd`, `cnsu_kp`, `cnsu_np`, `cnsu_o1`, `cnsu_o2`, `cnsu_na`, `cnsh_bs`, `cnsh_ru`, `cnsh_re`, `cnsh_ps`, `cnsh_fd`, `cnsh_kp`, `cnsh_o1`, `cnsh_o2`, `cnsc_bs`, `cnsc_ru`, `cnsc_ps`, `cnsc_fd`, `cnsc_kp`, `cnsc_o1`, `cnsc_o2`, `chkb12`, `pjothru`, `pjothra`, `pjotcnt`, `fmfempn`, `fmmempn`, `lshremp`, `lsmnemp`, `lsmncom`, `fisemr`, `lsemp`, `lscom`, `jbtremp`, `ujtros`, `ncesop`, `rcesop`, `rtgwage`, `cnsh_au`, `hepuwrk`, `herjob`, `herhour`, `hechjob`, `hetowrk`, `heonas`, `hespeq`, `heothed`, `nsu1_fd`, `nsu1_o1`, `nsu1_o2`, `nsu1_na`, `nsu1_np`, `nsu2_fd`, `nsu2_o1`, `nsu2_o2`, `nsu2_na`, `nsu2_np`, `nsu3_fd`, `nsu3_o1`, `nsu3_o2`, `nsu3_na`, `nsu3_np`, `nsu4_fd`, `nsu4_o1`, `nsu4_o2`, `nsu4_na`, `nsu4_np`, `nsu5_fd`, `nsu5_o1`, `nsu5_o2`, `nsu5_na`, `nsu5_np`, `nsu6_fd`, `nsu6_o1`, `nsu6_o2`, `nsu6_na`, `nsu6_np`, `nsh1_ps`, `nsh2_ps`, `nsh3_ps`, `nsh4_ps`, `nsh5_ps`, `nsh6_ps`, `nsh1_fd`, `nsh2_fd`, `nsh3_fd`, `nsh4_fd`, `nsh5_fd`, … don't exist in '/Users/amarin/OneDrive - UNSW/data/HIlDA20-fst/Combined_a200u.fst'.
#> ! These variables: `fmfempo`, `fmmempo`, `jbempst`, `loimpew`, `jbtremp`, `ujtros`, `ncesop`, `rcesop`, `rtgwage`, `cnsh_au`, `hepuwrk`, `herjob`, `herhour`, `hechjob`, `hetowrk`, `heonas`, `hespeq`, `heothed`, `nsu1_fd`, `nsu1_o1`, `nsu1_o2`, `nsu1_na`, `nsu1_np`, `nsu2_fd`, `nsu2_o1`, `nsu2_o2`, `nsu2_na`, `nsu2_np`, `nsu3_fd`, `nsu3_o1`, `nsu3_o2`, `nsu3_na`, `nsu3_np`, `nsu4_fd`, `nsu4_o1`, `nsu4_o2`, `nsu4_na`, `nsu4_np`, `nsu5_fd`, `nsu5_o1`, `nsu5_o2`, `nsu5_na`, `nsu5_np`, `nsu6_fd`, `nsu6_o1`, `nsu6_o2`, `nsu6_na`, `nsu6_np`, `nsh1_ps`, `nsh2_ps`, `nsh3_ps`, `nsh4_ps`, `nsh5_ps`, `nsh6_ps`, `nsh1_fd`, `nsh2_fd`, `nsh3_fd`, `nsh4_fd`, `nsh5_fd`, `nsh6_fd`, `nsh1_o1`, `nsh2_o1`, `nsh3_o1`, `nsh4_o1`, `nsh5_o1`, `nsh6_o1`, `nsh1_o2`, `nsh2_o2`, `nsh3_o2`, `nsh4_o2`, `nsh5_o2`, `nsh6_o2`, `npu1_o1`, `npu1_o2`, `npu1_na`, `npu1_np`, `npu2_o1`, `npu2_o2`, `npu2_na`, `npu2_np`, `npu3_o1`, `npu3_o2`, `npu3_na`, `npu3_np`, `npu4_o1`, `npu4_o2`, `npu4_na`, `npu4_np`, `npu5_o1`, `npu5_o2`, `npu5_na`, `npu5_np`, `npu6_o1`, `npu6_o2`, `npu6_na`, `npu6_np`, `nph1_fd`, `nph2_fd`, `nph3_fd`, `nph4_fd`, … don't exist in '/Users/amarin/OneDrive - UNSW/data/HIlDA20-fst/Combined_b200u.fst'.
#> ! These variables: `fmfempo`, `fmmempo`, `jbempst`, `loimpew`, `fisemr`, `cnsh_au`, `hepuwrk`, `herjob`, `herhour`, `hechjob`, `hetowrk`, `heonas`, `hespeq`, `heothed`, `nsu1_fd`, `nsu1_o1`, `nsu1_o2`, `nsu1_na`, `nsu1_np`, `nsu2_fd`, `nsu2_o1`, `nsu2_o2`, `nsu2_na`, `nsu2_np`, `nsu3_fd`, `nsu3_o1`, `nsu3_o2`, `nsu3_na`, `nsu3_np`, `nsu4_fd`, `nsu4_o1`, `nsu4_o2`, `nsu4_na`, `nsu4_np`, `nsu5_fd`, `nsu5_o1`, `nsu5_o2`, `nsu5_na`, `nsu5_np`, `nsu6_fd`, `nsu6_o1`, `nsu6_o2`, `nsu6_na`, `nsu6_np`, `nsh1_ps`, `nsh2_ps`, `nsh3_ps`, `nsh4_ps`, `nsh5_ps`, `nsh6_ps`, `nsh1_fd`, `nsh2_fd`, `nsh3_fd`, `nsh4_fd`, `nsh5_fd`, `nsh6_fd`, `nsh1_o1`, `nsh2_o1`, `nsh3_o1`, `nsh4_o1`, `nsh5_o1`, `nsh6_o1`, `nsh1_o2`, `nsh2_o2`, `nsh3_o2`, `nsh4_o2`, `nsh5_o2`, `nsh6_o2`, `npu1_o1`, `npu1_o2`, `npu1_na`, `npu1_np`, `npu2_o1`, `npu2_o2`, `npu2_na`, `npu2_np`, `npu3_o1`, `npu3_o2`, `npu3_na`, `npu3_np`, `npu4_o1`, `npu4_o2`, `npu4_na`, `npu4_np`, `npu5_o1`, `npu5_o2`, `npu5_na`, `npu5_np`, `npu6_o1`, `npu6_o2`, `npu6_na`, `npu6_np`, `nph1_fd`, `nph2_fd`, `nph3_fd`, `nph4_fd`, `nph5_fd`, `nph6_fd`, `nph1_o1`, `nph2_o1`, … don't exist in '/Users/amarin/OneDrive - UNSW/data/HIlDA20-fst/Combined_c200u.fst'.
dim(hilda_data)
#> [1] 55899 79Or if you know the prefix of a subject area that you like to query, you
can use hil_vars(pattern) to query all variable names that match the
pattern. For example, hil_vars("^ff") will get all variables in
subject area ‘Health’ and nested area ‘Heath - diet’.
hilda_data <- hil_fetch(years = 2001:2003, vars = hil_vars("^ff"))
#> ℹ `HILDAR_USER_DEFAULT_VARS` in Renviron: `hhid`
#> ! These variables: `ffmilk`, `ffveg`, `ffvegs`, `fffrt`, `fffrts`, `ffbf`, `ffsalt`, `ffbrfr`, `fflunr`, `ffdinr`, `ffcdiet`, `ffdietf`, `ffsrw`, `ffscw`, `ffleg`, `ffcake`, `ffpasta`, `ffsnack`, `ffcerl`, `ffconf`, `ffbread`, `ffspud`, `ffrmeat`, `ffprocm`, `ffpoult`, and `fffish` don't exist in '/Users/amarin/OneDrive - UNSW/data/HIlDA20-fst/Combined_a200u.fst'.
#> ! These variables: `ffmilk`, `ffveg`, `ffvegs`, `fffrt`, `fffrts`, `ffbf`, `ffsalt`, `ffbrfr`, `fflunr`, `ffdinr`, `ffcdiet`, `ffdietf`, `ffsrw`, `ffscw`, `ffleg`, `ffcake`, `ffpasta`, `ffsnack`, `ffcerl`, `ffconf`, `ffbread`, `ffspud`, `ffrmeat`, `ffprocm`, `ffpoult`, and `fffish` don't exist in '/Users/amarin/OneDrive - UNSW/data/HIlDA20-fst/Combined_b200u.fst'.
#> ! These variables: `ffmilk`, `ffveg`, `ffvegs`, `fffrt`, `fffrts`, `ffbf`, `ffsalt`, `ffbrfr`, `fflunr`, `ffdinr`, `ffcdiet`, `ffdietf`, `ffsrw`, `ffscw`, `ffleg`, `ffcake`, `ffpasta`, `ffsnack`, `ffcerl`, `ffconf`, `ffbread`, `ffspud`, `ffrmeat`, `ffprocm`, `ffpoult`, and `fffish` don't exist in '/Users/amarin/OneDrive - UNSW/data/HIlDA20-fst/Combined_c200u.fst'.
dim(hilda_data)
#> [1] 55899 9To set default variables to be loaded every time you call hil_fetch(),
see hil_user_default_vars().
Here is a summary of the dimensions of our HILDA data files.
# the number of variables and rows in each wave
nrows_by_wave <-
hil_fetch(years = 2001:2020, add_basic_vars = F) %>%
.[, .(number_of_rows = .N), by = wave]
#> ℹ `HILDAR_USER_DEFAULT_VARS` in Renviron: `hhid`
hilda_dictionary[, unlist(wave), by = .(var, label)] %>%
data.table::setnames("V1", "wave") %>%
.[!is.na(wave), .(number_of_variables = .N), by = wave] %>%
merge(nrows_by_wave, by = "wave")
#> wave number_of_variables number_of_rows
#> 1: 1 4289 19914
#> 2: 2 5220 18295
#> 3: 3 5214 17690
#> 4: 4 5081 17209
#> 5: 5 5893 17467
#> 6: 6 6140 17453
#> 7: 7 6044 17280
#> 8: 8 6186 17144
#> 9: 9 6202 17632
#> 10: 10 6488 17855
#> 11: 11 6691 23415
#> 12: 12 6530 23182
#> 13: 13 6455 23299
#> 14: 14 6719 23114
#> 15: 15 6800 23305
#> 16: 16 6433 23507
#> 17: 17 6697 23442
#> 18: 18 7093 23267
#> 19: 19 7278 23256
#> 20: 20 6864 22932