CODEWITHSUNDEEP
rr-factorread.csv
Joe
Joe

Reputation: 991

Imported a csv-dataset to R but the values becomes factors

I am very new to R and I am having trouble accessing a dataset I've imported. I'm using RStudio and used the Import Dataset function when importing my csv-file and pasted the line from the console-window to the source-window. The code looks as follows:

setwd("c:/kalle/R")
stuckey <- read.csv("C:/kalle/R/stuckey.csv")
point <- stuckey$PTS
time <- stuckey$MP

However, the data isn't integer or numeric as I am used to but factors so when I try to plot the variables I only get histograms, not the usual plot. When checking the data it seems to be in order, just that I'm unable to use it since it's in factor form.

Upvotes: 77

Views: 195187

Answers (8)

Daniel
Daniel

Reputation: 97

When importing csv data files the import command should reflect both the data separation between each column (;) and the float-number separator for your numeric values (for numerical variable = 2,5 this would be ",").

The command for importing a csv, therefore, has to be a bit more comprehensive with more commands:

stuckey <- read.csv2("C:/kalle/R/stuckey.csv", header=TRUE, sep=";", dec=",")

This should import all variables as either integers or numeric.

Upvotes: 8

Braide
Braide

Reputation: 167

None of these answers mention the colClasses argument which is another way to specify the variable classes in read.csv.

 stuckey <- read.csv("C:/kalle/R/stuckey.csv", colClasses = "numeric") # all variables to numeric

or you can specify which columns to convert:

stuckey <- read.csv("C:/kalle/R/stuckey.csv", colClasses = c("PTS" = "numeric", "MP" = "numeric") # specific columns to numeric

Note that if a variable can't be converted to numeric then it will be converted to factor as default which makes it more difficult to convert to number. Therefore, it can be advisable just to read all variables in as 'character' colClasses = "character" and then convert the specific columns to numeric once the csv is read in:

stuckey <- read.csv("C:/kalle/R/stuckey.csv", colClasses = "character")
point <- as.numeric(stuckey$PTS)
time <- as.numeric(stuckey$MP)

Upvotes: 4

Sam
Sam

Reputation: 8182

You can set this globally for all read.csv/read.* commands with options(stringsAsFactors=F)

Then read the file as follows: my.tab <- read.table( "filename.csv", as.is=T )

Upvotes: 23

Tymo
Tymo

Reputation: 259

for me the solution was to include skip = 0 (number of rows to skip at the top of the file. Can be set >0)

mydata <- read.csv(file = "file.csv", header = TRUE, sep = ",", skip = 22)

Upvotes: 0

Helen
Helen

Reputation: 367

This only worked right for me when including strip.white = TRUE in the read.csv command.

(I found the solution here.)

Upvotes: 1

Rishal
Rishal

Reputation: 21

I'm new to R as well and faced the exact same problem. But then I looked at my data and noticed that it is being caused due to the fact that my csv file was using a comma separator (,) in all numeric columns (Ex: 1,233,444.56 instead of 1233444.56).

I removed the comma separator in my csv file and then reloaded into R. My data frame now recognises all columns as numbers.

I'm sure there's a way to handle this within the read.csv function itself.

Upvotes: 2

Hong Ooi
Hong Ooi

Reputation: 57686

By default, read.csv checks the first few rows of your data to see whether to treat each variable as numeric. If it finds non-numeric values, it assumes the variable is character data, and character variables are converted to factors.

It looks like the PTS and MP variables in your dataset contain non-numerics, which is why you're getting unexpected results. You can force these variables to numeric with

point <- as.numeric(as.character(point))
time <- as.numeric(as.character(time))

But any values that can't be converted will become missing. (The R FAQ gives a slightly different method for factor -> numeric conversion but I can never remember what it is.)

Upvotes: 29

Dirk is no longer here
Dirk is no longer here

Reputation: 368241

Both the data import function (here: read.csv()) as well as a global option offer you to say stringsAsFactors=FALSE which should fix this.

Upvotes: 76

Related Questions