heidi

The heidi package is a collection of R functions to access the HEIDI API from HESA, which provides access to higher education datasets for HEIDI subscribers.

The main benefits of using the API in R, rather than the HEIDI web interface are

• Easy to select data broken down by several dimensions (no more repetitive drilling through the data explorer)
• The data is provided in a 'long' rather than 'wide' format, making it easier to use later, e.g. in Excel pivot tables
• Column headings are shorter and easier to understand
• Data downloads can be scripted, for example to download multiple years of data

Installing

Note the heidi package requires an older version of httr to be installed (any version prior to httr 1.0). You can install an older version by running the following commands.

packageurl <- "https://cran.r-project.org/src/contrib/Archive/httr/httr_0.6.1.tar.gz"
install.packages(packageurl, repos=NULL, type="source")

To install the heidi package run the following commands in an R session.

# If you don't already have the devtools package, install it
install.packages("devtools")
# Use devtools to install heidi directly from github
# If you are behind a proxy server uncomment and run the two lines below
# library(httr)
# set_config(use_proxy(url = 'myproxy.ac.uk', 8080))
devtools::install_github("grahamrp/heidi", build_vignettes = TRUE)

# For more details on how to use the package view the quickstart vignette
browseVignettes(package = "heidi")

Usage

Use browseVignettes(package = "heidi") for an introduction to the package and a detailed example.

This is a shorter example that downloads a dataset of HE student counts in 2012/13 for each institution and subject.

library(heidi)
# Setup authentication using your organisation's HEIDI API key
base64userpass <- setup('1234', '1f1f1f1f-2e2e-3d3d-4c4c-5b5b5b5b5b5b')

# Retrieve identifiers for Row Type, Value Type, Year and Field
id_rowtype <- subset(ListRowTypes(), 
                     Label == 'Institution', select = 'Id', drop = TRUE)
id_year <- subset(ListYears(rowtype = id_rowtype),
                  Label == '2012/13', select = 'Id', drop = TRUE)
id_valtype <- subset(ListValueTypes(rowtype = id_rowtype),
             Label == 'HE students Full-person equivalent', select = 'Id', drop = TRUE)
id_field <- subset(ListFields(rowtype = id_rowtype, year = id_year, valuetype = id_valtype),
                   Label == 'JACS subject area', select = 'Id', drop = TRUE)

# Get the data
heidi_data <- GetData(rowtype = id_rowtype,
                      year = id_year,
                      valuetype = id_valtype,
                      field1 = id_field)

# Write the data to a file
write.csv(heidi_data, file = 'fpe_by_inst_and_subject_12-13.csv')