View source: R/get_dataverse.R
get_cces_dataverse | R Documentation |
Get the data from dataverse into the current R environment. You must use the development version of IQSS/dataverse-client-r. The function also does
get_cces_dataverse(
name = "cumulative",
year_subset = NULL,
std_index = TRUE,
dataverse_paths = ccesMRPprep::cces_dv_ids
)
name |
The name of the dataset as defined in |
year_subset |
The year (or years, a vector) to subset too. If |
std_index |
Whether to standardize the unique case identifier. These
have different column names in different datasets, but setting this to |
dataverse_paths |
A dataframe where one row represents metadata for one CCES dataset. Built-in data cces_dv_ids is used as a default and should not be changed. |
The current dataverse package downloads the raw data, so this function writes the raw binary into a tempfile and loads it into a tibble with the appropriate file data type. We find it convenient to loop over this function for all values in cces_dv_ids and populate the MRP directory with all datasets (about 2GB in total). Each dataset has slightly different formats; using get_cces_question will standardize, for example, the name of the case ID.
# read in cumulative common content, subsetted to 2018, into environemt
## Not run:
ccc <- get_cces_dataverse("cumulative", year_subset = 2018)
## End(Not run)
# Example code to read _and_ write a series of common content datasets
# in a directory "data/input/cces/
## Not run:
dir_create("data/cces")
for (d in c("cumulative", "2018")) {
if (file_exists(glue("data/input/cces/cces_{d}.rds")))
next
write_rds(get_cces_dataverse(d), glue("data/input/cces/cces_{d}.rds")) # takes a few minutes
}
## End(Not run)
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