Read.csv vs read_csv in R: Key Differences and Usage

read.csv is a base R function for reading CSV files, while read_csv comes from the readr package and is faster with better defaults. Use read_csv for improved performance and modern features, and read.csv for simple, base R needs.

Quick Comparison

Here is a quick side-by-side comparison of read.csv and read_csv functions in R.

Feature	read.csv	read_csv
Package	Base R (utils)	readr (tidyverse)
Speed	Slower	Faster (C++ backend)
Default Separator	Comma (,)	Comma (,)
Returns	Data frame	Tibble (modern data frame)
Handling of Strings	Converts to factors by default (older R versions)	Keeps as character by default
NA Strings	Default is "NA"	Default is "NA"
Progress Display	No	Yes (for large files)

Key Differences

read.csv is part of base R and has been around for a long time. It reads CSV files into a standard data frame and is simple to use without extra packages. However, it can be slower on large files and has some older defaults like converting strings to factors (in older R versions).

read_csv is from the readr package, which is part of the tidyverse. It is designed for speed and efficiency, using C++ under the hood. It returns a tibble, which is a modern and more user-friendly version of a data frame. It also keeps strings as characters by default and shows a progress bar when reading large files.

Another difference is in how missing values are handled and the flexibility of parsing options. read_csv offers more control and better parsing diagnostics, making it preferred for data science workflows.

Code Comparison

Here is how you read a CSV file named data.csv using read.csv in base R.

r

data <- read.csv("data.csv")
print(head(data))

Output

[[Output depends on data.csv content, but head(data) shows first 6 rows]]

read_csv Equivalent

Here is the equivalent code using read_csv from the readr package.

r

library(readr)
data <- read_csv("data.csv")
print(head(data))

Output

[[Output depends on data.csv content, but head(data) shows first 6 rows as tibble]]

When to Use Which

Choose read.csv when you want a quick, no-dependency way to read CSV files in base R, especially for small or simple datasets. It is good for quick scripts or when you want to avoid installing packages.

Choose read_csv when working with larger datasets, needing faster reading, or when you want modern features like tibbles, better parsing, and progress bars. It is ideal for data analysis workflows using the tidyverse.

Key Takeaways

read.csv is base R, simple but slower and older defaults.

read_csv is faster, from readr, returns tibbles and has modern defaults.

Use read_csv for large data and tidyverse workflows.

Use read.csv for quick, dependency-free CSV reading.

read_csv provides better parsing diagnostics and progress display.