I often work with comma separated values, and was curious to the differences between read_csv() and read.csv().
Are there any practical differences that could shine light on the situational usage of both?
Quoted from the introduction page.
11.2.1 Compared to base R
If you’ve used R before, you might wonder why we’re not using read.csv(). There are a few good reasons to favour readr functions over the base equivalents:
They are typically much faster (~10x) than their base equivalents. Long running jobs have a progress bar, so you can see what’s happening. If you’re looking for raw speed, try data.table::fread(). It doesn’t fit quite so well into the tidyverse, but it can be quite a bit faster.
They produce tibbles, they don’t convert character vectors to factors*, use row names, or munge the column names. These are common sources of frustration with the base R functions.
They are more reproducible. Base R functions inherit some behaviour from your operating system and environment variables, so import code that works on your computer might not work on someone else’s.
*Note that from R 4.0.0
R [...] uses a
stringsAsFactors = FALSEdefault, and hence by default no longer converts strings to factors in calls todata.frame()andread.table().