Working groups sometimes need to handle user-specific information in their code. For example, if your group stores your data in the cloud (e.g., in Box, in Dropbox, etc.) each user will have a different “absolute file path” to the synced version of the data folder on their personal computer. Similarly, groups may find it valuable to use their email address in the code. While you could simply have each group member add their information (file path, email, etc.) and comment out all but one of them when you work in that script, there is a better option: user-specific JSON files!
The main advantage of this method is that you and your group members do not have to manually change any user-specific information in scripts just because a different person runs them!
To follow along with this tutorial you will need to take the following steps:
Feel free to skip any steps that you have already completed!
As in so many facets of collaborative work, the first step is to discuss with your group. While JSONs are useful for storing user-specific information, you and your group still need to agree on two pieces of information:
We recommend keeping it simple so consider naming the file “user.json”. Similarly, for the ‘column names’ consider short, all-lowercase names that are succinct and clear (e.g., dropbox_path for each user’s path to a local Dropbox sync, email for each user’s email address, etc.).
We have developed the ltertools R package–in part–to store content that is useful to collaborative teams. Among the functions included in that package is one called make_json that will make the JSON for each user. It accepts a “named” vector which is straightforward to create.
# Create the named vector
my_info <- c("dropbox_path" = "~/Users/lyon/Dropbox/LTER Data/",
"email" = "lyon@nceas.ucsb.edu")
# Look at it
my_info dropbox_path email
"~/Users/lyon/Dropbox/LTER Data/" "lyon@nceas.ucsb.edu" Once you have that vector prepared, it’s time to use make_json to actually create the file that stores user-specific information. If you are working in GitHub, we recommend setting the git_ignore argument to TRUE so that the JSON you create is automatically ignored by Git. This will prevent someone from accidentally sending sensitive (or at least user-specific) information to GitHub. For a deeper dive on this topic, see our GitHub workshop here
# Load package
library(ltertools)
# Make JSON
make_json(x = my_info, file = "user.json", git_ignore = TRUE)Now that all group members have made a JSON with the same internal components, all that is left is to reap the rewards of your forward thinking!
See an example below:
# Load needed library
library(RJSONIO)
# Read in the JSON file
user_info <- RJSONIO::fromJSON(content = "user.json")
# Use its contents!
my_data <- read.csv(file = file.path(user_info$dropbox_path, "2024_data.csv"))
# Syntax is just like how you'd access a column in a dataframe
googledrive::drive_auth(email = user_info$email)Now everyone in your group can use the same script because their personal file paths are readily accessible without needing to be hard-coded! The same theory applies to any other piece of information your group finds it valuable to store in the JSON.
Identifying and manually writing out the file path you want to preserve in a JSON can be cumbersome so we’ve found a nice work-around (at least for Mac users) that you may find useful.
make_json
