You can install the development version of tidylfs from GitHub with:
# install.packages("devtools")
devtools::install_github("wklimowicz/tidylfs")Copy the LFS .sav files into a directory (in my example
lfs_data_folder). They should look like this:
.
└── lfs_data_folder/
├── 1992 Q1.sav
├── 1992 Q2.sav
├── ...
└── 2020 Q2.sav
You can then run:
library(tidylfs)
# Converts `.sav` files to `.Rds` files, to save
# space and for quicker loading
lfs_convert(lfs_directory = "lfs_data_folder/",
output_directory = "lfs_rds_folder/")
# Compiles into one file.
lfs <- lfs_compile(lfs_directory = "lfs_rds_folder/")To reproduce official ONS publications, such as
- Unemployent UNEM01 NSA
lfs %>%
lfs_summarise_unemployment(QUARTER)- Salary by occupation EARN06
lfs %>%
dplyr::filter(
!is.na(OCCUPATION_MAJOR), # Filter out NA's
FTPTWK == "Full-time" # Take only full-time employees
) %>%
lfs_summarise_salary(QUARTER, OCCUPATION_MAJOR)Extending them is easy:
- Unemployment by quarter, sex, and age category
lfs %>%
lfs_summarise_unemployment(QUARTER, SEX, AGES)For more information on the ONS variables, see the LFS Guidance.
Some of the variables included by default are:
| Variable Name | Definition |
|---|---|
| YEAR | Year |
| QUARTER | Quarter |
| SEX | Sex |
| GOVTOR | Government Office Region |
| AGE | Age |
| FTPTWK | Part-Time/Full-time Status |
| EDAGE | Age when completed full time education |
| TTACHR | Actual hours worked |
| UNION | In union? |
| HIQUALD | Highest Qualification |
| DEGREE_SUBJECT | Degree Subject |
| OCCUPATION | Occupation in main job |
| PARENTAL_OCCUPATION | Parental Occupation at 14 |
| ETHNICITY | Ethnicity |
To avoid storing multiple copies of the compiled dataset, you can set
the DATA_DIRECTORY environment variable before compiling. This can be
done on the system or in .Rprofile:
Sys.setenv(DATA_DIRECTORY = "path/to/folder")When compiling the dataset include the save_to_folder option as TRUE:
lfs_compile(lfs_directory = "lfs_rds_folder/", save_to_folder = TRUE)The compiled dataset will be saved to the directory as an fst file, and you can load it from anywhere using this command:
lfs <- lfs_load(data.table = FALSE)