1 Problem

Problem: You want to read in a data file in an R code chunk in an R Markdown post. But:

• Where should you save the data file?
• What file path will work to run the code chunks in the console?
• What file path will work when you serve site?

Solution: Read on.

This post will show you how to add local data files to your blogdown site, and the file paths to read those data files in an R code chunk. (If you came here looking for how to add static images and use file paths, please see this post.)

2 TL;DR

Let’s say you have a data file called "mazes.csv", and you want to read in that CSV file in an R chunk. The below table summarizes where the file should live in your blogdown site directory, and the file paths to use.

More detail on each of these scenarios below. We’ll use readr::read_csv(), so you’ll need to install and load the readr package:

# install.packages("readr")
library(readr)

mazes <- read_csv("mazes.csv")

mazes <- read_csv("../../static/mazes.csv")
# or
library(here)
mazes <- read_csv(here("static", "mazes.csv"))

mazes <- read_csv("../../static/data/mazes.csv")
# or
library(here)
mazes <- read_csv(here("static", "data", "mazes.csv"))

mazes_gist <- "https://gist.githubusercontent.com/kylebgorman/77ce12c9167554ade560af9d34565c11/raw/c5d653fb146821ecd96a9aa085263c3f17480dd5/McFarlaneEtAl_MazeData-Deidentified.csv"
mazes <- read_csv(mazes_gist)

3 HERE

How did here work?

# use here to build the file path
library(here)

## here() starts at /Users/hillali/Documents/Projects/r-projects/blogdown-demo

# read in the file
mazes <- read_csv(here("static", "data", "mazes.csv"))

## Parsed with column specification:
## cols(
##   Study.ID = col_character(),
##   CA = col_double(),
##   VIQ = col_double(),
##   DX = col_character(),
##   Activity = col_character(),
##   Content = col_double(),
##   Filler = col_double(),
##   REP = col_double(),
##   REV = col_double(),
##   FS = col_double(),
##   Cued = col_double(),
##   Not.Cued = col_double()
## )

mazes

## # A tibble: 381 x 12
##    Study.ID    CA   VIQ DX    Activity    Content Filler   REP   REV    FS
##    <chr>    <dbl> <dbl> <chr> <chr>         <dbl>  <dbl> <dbl> <dbl> <dbl>
##  1 CSLU-001  5.67   124 TD    Conversati…   24.0   31.0   2.00  5.00 17.0 
##  2 CSLU-001  5.67   124 TD    Picture De…    1.00   2.00  0     0     1.00
##  3 CSLU-001  5.67   124 TD    Play          21.0    6.00  3.00  8.00 10.0 
##  4 CSLU-001  5.67   124 TD    Wordless P…    8.00   2.00  0     4.00  4.00
##  5 CSLU-002  6.50   124 TD    Conversati…    3.00  10.0   3.00  0     0   
##  6 CSLU-002  6.50   124 TD    Picture De…    5.00   3.00  2.00  1.00  2.00
##  7 CSLU-002  6.50   124 TD    Play           8.00   8.00  3.00  2.00  3.00
##  8 CSLU-002  6.50   124 TD    Wordless P…    2.00   2.00  0     0     2.00
##  9 CSLU-007  7.50   108 TD    Conversati…   25.0   21.0   4.00  4.00 17.0 
## 10 CSLU-007  7.50   108 TD    Picture De…   10.0   13.0   0     2.00  8.00
## # ... with 371 more rows, and 2 more variables: Cued <dbl>, Not.Cued <dbl>

Now that is some serious black magic. Let’s break down what the here package did.

# where are we?
here() 

## [1] "/Users/hillali/Documents/Projects/r-projects/blogdown-demo"

# if mazes.csv were in static, this is the file path
here("static", "mazes.csv")

## [1] "/Users/hillali/Documents/Projects/r-projects/blogdown-demo/static/mazes.csv"

# but it is not! it is in /static/data
here("static", "data", "mazes.csv")

## [1] "/Users/hillali/Documents/Projects/r-projects/blogdown-demo/static/data/mazes.csv"

# you can read in directly
mazes1 <- read_csv(here("static", "data", "mazes.csv"))

## Parsed with column specification:
## cols(
##   Study.ID = col_character(),
##   CA = col_double(),
##   VIQ = col_double(),
##   DX = col_character(),
##   Activity = col_character(),
##   Content = col_double(),
##   Filler = col_double(),
##   REP = col_double(),
##   REV = col_double(),
##   FS = col_double(),
##   Cued = col_double(),
##   Not.Cued = col_double()
## )

# you can save the file path 
mazes_file <- here("static", "data", "mazes.csv")

# then read that in
mazes2 <- read_csv(mazes_file)

## Parsed with column specification:
## cols(
##   Study.ID = col_character(),
##   CA = col_double(),
##   VIQ = col_double(),
##   DX = col_character(),
##   Activity = col_character(),
##   Content = col_double(),
##   Filler = col_double(),
##   REP = col_double(),
##   REV = col_double(),
##   FS = col_double(),
##   Cued = col_double(),
##   Not.Cued = col_double()
## )

For more of a breakdown, see here, here by Jenny Bryan.