Downloading OSF Data

The data for winter’s Statistics for Linguistics textbook is all available on the Open Science Framework at https://osf.io/34mq9/ You can download the files as you need them and upload them into your blog files, or you can download the whole thing at once with this quarto notebook and the osfr package.

I’m effectively just running through their sample documentation from the about page.

Step 1 - Install osfr and load it

This will check to see of osfr is installed. If it is, it will load it. If not, it will install it, then load it. This may start printing out scary looking things like g++ -std=gnu++14 -I"/usr/share/R/include" and so forth. That is ok.

osfr_exists <- require("osfr")

Loading required package: osfr

if(!osfr_exists){
  install.packages("osfr")
  library(osfr)
}

Step 2 - Download the data

The little string of letters and numbers comes from the osf link. I’ve included some if()s in there to be defensive, just in case you re-run the code so you won’t accidentally delete or overwrite anything.

# I'm just being defensive here,
# in case you've run the script before.
data_exists <- dir.exists("data")
if(!data_exists){
  dir.create(path = "data")
}

data_files <- list.files("data")
if(length(data_files) != 17){
  osf_retrieve_node('34mq9') |> 
    osf_ls_files(
      "materials/data", 
      n_max = Inf
    ) |> 
    osf_download(
      path = "data",
      conflicts = "skip"
    )
}

Step 3 - Load the data into your blog post.

There are two ways to go about loading the data into a quarto notebook for your work-through blog post.

Way 1

I’ve assumed you’ve run the download_data.qmd code from the main workthrough blog directory. So, from any given blog post in posts/XX_chapter/index.qmd file, you’d need to type

nettle_df <- read.csv("../../data/nettle_1999_climate.csv")

Way 2

In the file browser pane in RStudio, if you navigate to the data directory, click on the “⚙️ More” drop down menu. Then select Copy Folder Path to Clipboard. For me, this copies ~/work_through_blog/data to the clipboard. You can then use this inside read.csv() like so:

nettle_df <- read.csv("~/work_through_blog/data/nettle_1999_climate.csv")

Reuse