You can use the fread() function from the data.table package in R to import files quickly and conveniently.
This function uses the following basic syntax:
library(data.table) df <- fread("C:\\Users\\Path\\To\\My\\data.csv")
For large files, this function has been shown to be significantly faster than functions like read.csv from base R.
And in most cases, this function can also automatically detect the delimiter and column types for the dataset you’re importing.
The following example shows how to use this function in practice.
Example: How to Use fread() to Import Files in R
Suppose I have a CSV file called data.csv saved in the following location:
And suppose the CSV file contains the following data:
team, points, assists 'A', 78, 12 'B', 85, 20 'C', 93, 23 'D', 90, 8 'E', 91, 14
I can use the fread() function from the data.table package to import this file into my current R environment:
library(data.table) #import data df <- fread("C:\\Users\\Bob\\Desktop\\data.csv") #view data df team points assists 1 A 78 12 2 B 85 20 3 C 93 23 4 D 90 8 5 E 91 14
We’re able to successfully import the CSV file using the fread() function.
Note: We used double backslashes (\\) in the file path to avoid a common import error.
Notice that we didn’t have to specify the delimiter either since the fread() function automatically detected that it was a comma.
If we use the str() function to view the structure of the data frame, we can see that the fread() function automatically identified the object type for each column as well:
#view structure of data str(df) Classes 'data.table' and 'data.frame': 5 obs. of 3 variables: $ team : chr "'A'" "'B'" "'C'" "'D'" ... $ points : int 78 85 93 90 91 $ assists: int 12 20 23 8 14
From the output we can see:
- The team variable is a character.
- The points variable is an integer.
- The assists variable is an integer.
In this example we used a small data frame for simplicity (5 rows x 3 columns) but in practice the fread() function is able to quickly and efficiently import data frames with tens of thousands of rows, which makes it the preferred import method for large-scale datasets.
The following tutorials explain how to import specific file types into R: