I am trying to specify the colClasses
options in the read.csv
function in R. In my data, the first column \"time\" is basically a character vector
You can specify the colClasse for only one columns.
So in your example you should use:
data <- read.csv('test.csv', colClasses=c("time"="character"))
If we combine what @Hendy and @Oddysseus Ithaca contributed, we get cleaner and a more general (i.e., adaptable?) chunk of code.
data <- read.csv("test.csv", head = F, colClasses = c(V36 = "character", V38 = "character"))
For multiple datetime columns with no header, and a lot of columns, say my datetime fields are in columns 36 and 38, and I want them read in as character fields:
data<-read.csv("test.csv", head=FALSE, colClasses=c("V36"="character","V38"="character"))
I know OP asked about the utils::read.csv
function, but let me provide an answer for these that come here searching how to do it using readr::read_csv
from the tidyverse.
read_csv ("test.csv", col_names=FALSE, col_types = cols (.default = "c", time = "i"))
This should set the default type for all columns as character, while time would be parsed as integer.
Assuming your 'time' column has at least one observation with a non-numeric character and all your other columns only have numbers, then 'read.csv's default will be to read in 'time' as a 'factor' and all the rest of the columns as 'numeric'. Therefore setting 'stringsAsFactors=F' will have the same result as setting the 'colClasses' manually i.e.,
data <- read.csv('test.csv', stringsAsFactors=F)
If you want to refer to names from the header rather than column numbers, you can use something like this:
fname <- "test.csv"
headset <- read.csv(fname, header = TRUE, nrows = 10)
classes <- sapply(headset, class)
classes[names(classes) %in% c("time")] <- "character"
dataset <- read.csv(fname, header = TRUE, colClasses = classes)