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: creating a small file to store that information!
The main advantage of this method is that you and your group members will 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 creating this small file is useful, you and your group still need to agree on two pieces of information:
We recommend keeping it simple so consider naming the file “user.csv”. 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.).
Creating the file does unfortunately need to be done manually but it can be done either in script form or via Microsoft Excel/etc. depending on each group member’s preference. For the purposes of this tutorial, we’ll demonstrate how to do this with code.
# Create the data frame
my_info <- data.frame("dropbox_path" = "~/Users/lyon/Dropbox/LTER Data/",
"email" = "lyon@nceas.ucsb.edu")
# Look at it
my_info dropbox_path email
1 ~/Users/lyon/Dropbox/LTER Data/ lyon@nceas.ucsb.eduIf your group is using GitHub, we strongly recommend that you add this file name to the .gitignore file. That file tells Git what not to track changes to which is ideal because each member of your group will have a different version of user.csv (but they’ll all share the same name).
This is especially critical if you want to store sensitive information in the user-specific file (e.g., passwords, tokens, etc.).
You can edit the .gitignore by clicking it in RStudio’s “Files” pane and editing it as you would any other file. For a deeper dive on this topic, see our GitHub workshop here.
Once you’re happy with your data frame, save a local copy as a CSV with the file name that your group agreed on.
# Export CSV to your computer
write.csv(x = my_info, file = "user.csv", na = '', row.names = F)Now that all group members have their own version of user.csv, your project scripts can read it in and use it to handle any actions that require differences among users! See an example below:
# Read in user file
user_info <- read.csv(file = "user.csv")
# Use its contents!
tidy_v1 <- read.csv(file = file.path(user_info$dropbox_path, "harmonized-data.csv"))
# Syntax is the same as accessing a column in any other dataframe
googledrive::drive_auth(email = user_info$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.
Identifying and manually writing out the file path you want to preserve in this kind of file can be cumbersome so we’ve found a nice work-around (at least for Mac users) that you may find useful.
