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If your CSV is extremely large, the fastest way to import it into R is with the fread function from the data.table package: library(data.table)Ĭlasses 'data.table' and 'ame': 5 obs. If you’re working with larger files, you can use the read_csv function from the readr package: library(readr) The following code shows how to use read.csv to import this CSV file into R: #import dataĭata1 <- read.csv(" C:\\Users\\Bob\\Desktop\\data.csv", header= TRUE, stringsAsFactors= FALSE) When using this method, be sure to specify stringsAsFactors=FALSE so that R doesn’t convert character or categorical variables into factors. If your CSV file is reasonably small, you can just use the read.csv function from Base R to import it. This tutorial shows an example of how to use each of these methods to import the CSV file into R. Use fread from data.table package (2-3x faster than read_csv) library(data.table)ĭata3 <- fread(" C:\\Users\\Bob\\Desktop\\data.csv") Use read_csv from readr package (2-3x faster than read.csv) library(readr)ĭata2 <- read_csv(" C:\\Users\\Bob\\Desktop\\data.csv")ģ.
However, reading the XLSX and XLS extensions is possible in R: > install.packages (readxl) > library (readxl) > dataEXCEL <- readexcel (survey.xlsx, sheet 1) > dataEXCEL.It may be easier to use Excel to save individual sheets as CSV files and then read the CSV files into R. Use read.csv from base R (Slowest method, but works fine for smaller datasets) data1 <- read.csv(" C:\\Users\\Bob\\Desktop\\data.csv", header= TRUE, stringsAsFactors= FALSE)Ģ. Sheet: The name of the sheet or its location number. There are three common ways to import this CSV file into R:ġ. Suppose I have a CSV file called data.csv saved in the following location:Īnd suppose the CSV file contains the following data: team, points, assists
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