This repository contains the functions that clean and check the raw data files, which eventually create the final working dataset (
PROMISE). It also contains the data dictionary and a description of the methods.
Important: If you want to contribute, please read the
CONTRIBUTING.mdfile before contributing to the PROMISE dataset.
This repository stores mainly the code involved in creating the final merged PROMISE dataset (and the individual datasets), as well as documentation on the dataset. It does not contain the data collection forms, the scrubbing and other functions, and questionnaires, nor does it contain the Access database with the original raw data.
I would suggest you install
PROMISE.data using this method:
# install.packages("devtools") promise_gzip <- list.files(pattern = "PROMISE-v0.3.0.*.tar.gz") untar(promise_gzip) promise_pkg <- sub("\\.tar\\.gz", "", promise_gzip) devtools::install(promise_pkg, dependencies = TRUE, upgrade_dependencies = TRUE)
One of the main goals of this
PROMISE.data package was to allow the data and manuals to be easily accessible to the end user (us graduate students!). So here are some of the basic commands to use:
To access the dataset, it’s simple!
Or to load it into your environment:
There are also other datasets available. See
?PROMISE for the other datasets or the
datasets vignette (see below for viewing it).
If you want to combine datasets together, do something like:
combine_datasets(msd, ogtt) combine_datasets(PROMISE, dhq) combine_datasets(PROMISE, form012) # Or specific variables from specific datasets library(dplyr) combine_datasets( PROMISE, form012 %>% select(SID, VN, matches("Neuro")) # for Neuropathy measures )
To view manuals:
# See a list of manuals available: list_manuals view_manual('dictionary') view_manual('methods') view_manual('datasets')
If you need the dataset as a different format (e.g. if you want to import it into SAS or some other program), you can export it as a
If you want to add new data to the dataset or want to run some quality control commands or to add a new variable to the dataset, see the