Depending on your analysis, you may wish to download a number of data files to your hard disk. This can save time when working on large scale projects. In addition, downloading and saving the raw data files used in your analysis can support future reproduction. Should files on the server be revised or modified, you will have a copy of the files as they existed at the time of your original analysis.
You can also download the dictionary files that accompany each
complete data file. While iped_dict() provides useful
information, the full data files may contain more detailed definitions
and distributional information (e.g., frequencies, means,
etc.).
ipeds_download_to_disk()
To download either complete data files or the accompanying dictionary
files, use the ipeds_download_to_disk() function.
There are two basic ways to download the files:
- Using file names directly
- Using results from the helper function
ipeds_file_table()
Using file names
If you know the file names you need, you can use them directly. Be
sure to give a local directory for the download. If the directory
doesn’t exist, R will try to create it, unless you set
create_dir = FALSE.
## single file to current working directory
ipeds_download_to_disk(to_dir = ".", files = "IC2022")Note that you do not need to specify the file ending, only the file
stub name. Whichever files you choose, you will end up with a zip file
(<file_stub_name.zip>: IC2022.zip) that contains:
- The CSV file (typically lowercase):
ic2022.csv - A revised version of the file, if one exists, ending in
*_rv:ic2022_rv.csv
You can pass an object with vector of names.
## multiple files stored in object
files_to_get <- c("IC2020", "IC2021", "IC2022")
ipeds_download_to_disk(to_dir = ".", files = files_to_get)The zipped version of each of these files will be downloaded to the
to_dir.
Using results from helper function
ipeds_file_table()
If you don’t know the specific file names or simply want to download
a larger number of files without listing them all, you can use results
from ipeds_file_table().
For example, if you want to download all files associated with 2020,
you can filter the results of ipeds_file_table() to that
year:
## save results from ipeds_file_table() to object
dict <- ipeds_file_table()
## pull all files from 2020
ipeds_download_to_disk(".", use_ipeds_dict = dict[dict$year == 2020,])Note that for files with academic year naming conventions (e.g., 9899), the fall semester is the year associated with the file: 2020 will select 20-21 files.
You can also filter on other values. Broadly, IPEDS survey components fall into a few categories. You can filter to return all Institutional Characteristics survey files:
## download all surveys under the Institutional Characteristics category
ipeds_download_to_disk(".", use_ipeds_dict = dict[dict$survey == "Institutional Characteristics",])You can filter by key words in the survey titles. For example, if you want to save all files related to directory information:
## download using regular expressions to grab files with "Directory" in title
ipeds_download_to_disk(".", use_ipeds_dict = dict[grepl("Directory", dict$title),])Finally, you can download the entirety of IPEDS if you use
ipeds_file_table() with no filter:
## download all files
ipeds_download_to_disk(".", use_ipeds_dict = dict)Note, however, that IPEDS is very large. It both will take a while to download and take up substantial disk space (> 1GB as of January 2025).
Dictionary files
Download accompanying dictionary files by setting
type = "dictionary":
## get dictionary file
ipeds_download_to_disk(".", "IC2020", type = "dictionary")These files will be named using the stub name plus
*_Dict ending and also stored inside a zip directory:
e.g., IC2020_Dict.zip.
Overwrite
By default, ipeds_download_to_disk() won’t overwrite
existing files. This is to save time and bandwidth. If you wish to
download all input files, set overwrite = TRUE:
## overwrite
files_to_get <- c("IC2020", "IC2021", "IC2022")
ipeds_download_to_disk(to_dir = ".", files = files_to_get, overwrite = TRUE)