chchpd

The goal of the chchpd package is to allow NZBRI researchers to access data from the New Zealand Parkinson’s Progression (NZP³) study. Rather than deal directly with data in spreadsheets or databases, functions like import_participants() and import_motor_scores() are provided, to abstract away dealing with the raw data source. Currently, these access data from shared online Google Sheets. Some of those are periodically exported from NZBRI’s Alice and REDCap Parkinson’s databases, while (for the time being) some contain manually-entered data. While the data source might change, the function used to obtain will appear unchanged to the user, which is the benefit of this layer of abstraction.

Data is returned as a separate dataframe for each data set. Users are responsible for linking them together as required.

The package was originally developed by Michael MacAskill, with contributions from Daniel Myall, Toni Pitcher, and Reza Shoorangiz. Please direct queries to Michael in the first instance.

Installation

This package is only of interest and utility internally at NZBRI and hence can’t be made available via CRAN. Instead, it is hosted in an online Github at https://github.com/nzbri/chchpd.

Therefore, the usual installation route using install.packages('chchpd') is not possible, and will yield a not-useful error message that the package is not available for your version of R. Instead, install chchpd from its development repository on Github as follows:

# install.packages('remotes')
remotes::install_github('nzbri/chchpd')

If install_github('nzbri/chchpd') is invoked subsequently, the package will be downloaded and installed only if the version on Github is newer than the one installed locally.

If problems arise in a new release, you can downgrade to a previous version by specifying the name of a particular release to revert to, e.g.

devtools::install_github('nzbri/chchpd@v0.1.5')

The list of releases is here: https://github.com/nzbri/chchpd/releases

Please use the issue tracker at https://github.com/nzbri/chchpd/issues to report any problems. Bear in mind that this repository is public to the world, so if reporting data-related issues, be careful not to post anything that contains identifiers.

Example usage

Records across the various tables must be joined using either subject_id as an index (e.g. for linking to the participant table, as it includes information, such as sex, that is constant for a subject), or session_id (to join various measures gathered at approximately the same assessment session for a given participant), as these might change at different time points.

The ‘glue’ between participants and the data gathered about them is generally the ‘sessions’ table. For each participant, this lists the various assessment sessions they have had. In the raw data, these sessions were often described by idiosyncratic labels, such as 999BIO_F2 (indicating the follow-up session two years after the baseline recruitment session in the Progression study). But the same session might also have served as the baseline in the more selective PET study, and been labelled, say, 999BIO_PET0. This often idiosyncratic labelling is cured by the subject session mapping table, which would have a record for both the 999BIO_F2 and 999BIO_PET0 sessions, linking them to the same standardised session code, which has a form like 999BIO_2016-01-28. When importing various data sources (like HADS or UPDRS), their idiosyncratic session labels are replaced by this standardised form. Thus it is easy to join multiple tables together systematically, as below. Often a key step is to specify just a restricted set of sessions, by specifying a particular study to filter them by. In the example code below, we select just those sessions in the ‘PET’ study.

# load necessary packages:
library(chchpd)
library(dplyr)    # to join dataframes

# first establish your rights to view the data:
google_authenticate()

# then import datasets of interest:
participants = import_participants()
sessions     = import_sessions(from_study = 'PET')
np           = import_neuropsyc()
updrs        = import_motor_scores()

# bind the records together, linked by subject or session IDs:
dat = right_join(participants, sessions, by = 'subject_id') %>% 
  left_join(np, by = 'session_id') %>% 
  left_join(updrs, by = 'session_id')

Data caching

To speed up development of your analysis scripts, data caching is now enabled by default. That is, it can be quite slow to import data from a Google spreadsheet, so a copy of the data will be cached for you. If you subsequently attempt to re-import that data, if it is within a certain interval (60 minutes by default), then the data won’t be downloaded afresh: you will just instantly be returned the cached version.

If you want to force a fresh download of the data, or change other options, you can do the following:

options('chchpd_use_cached' = FALSE)
options('chchpd_cache_duration' = 30) # duration in minutes
options('chchpd_suppress_warnings' = FALSE) # reduce warnings from googlesheets