read.csv and skip last column in R

Question

I have read several other posts about how to import csv files with read.csv but skipping specific columns. However, all the examples I have found had very few columns, and so it was easy to do something like:

 columnHeaders <- c("column1", "column2", "column_to_skip")
 columnClasses <- c("numeric", "numeric", "NULL")
 data <- read.csv(fileCSV, header = FALSE, sep = ",", col.names = 
 columnHeaders, colClasses = columnClasses)

I have 201 columns, without column labels. I would like to skip the last column. How would it be possible to do this without naming all the other columns to keep? Many thanks.

Or just read all the columns in and then eliminate the columns you don't like afterwards? `data — leerssej, Nov 15 '17 at 17:23

Andrew Haynes · Answer 1 · 2017-11-15T17:30:28.040

0

Bit hacky but, I usually read in a small number of the rows of the dataset I want, then use sapply(..., class) to find the column types and set the last one to "NULL".

data<-read.table("test.csv", sep=',', nrows = 100)
colClasses<-sapply(data, class)
colClasses[length(colClasses)]<-"NULL"

Then you can pass this colClasses to your read.csv() function

edited Nov 15 '17 at 17:30

answered Nov 15 '17 at 17:25

Andrew Haynes

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score 0 · Answer 2 · answered Nov 15 '17 at 17:37

You can just read in all the data and then eliminate the offenders afterwards.

data <- read.csv("../CAASPP_clustering/ca2016_1_csv_v3.zip")
data_trimmed <- data[,1:(ncol(data)-1)]

If you prefer to screen the classes more programmatically then you could do something like this:

class_list <- lapply(data, class)
chosen_cols <- names(class_list[class_list != "NULL"])
data_trimmed <- data[chosen_cols]

read.csv and skip last column in R

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