I'm using R and have loaded data into a dataframe using read.csv(). How do I determine the data type of each column in the data frame?

3

11 Answers

Your best bet to start is to use ?str(). To explore some examples, let's make some data:

set.seed(3221) # this makes the example exactly reproducible
my.data <- data.frame(y=rnorm(5), x1=c(1:5), x2=c(TRUE, TRUE, FALSE, FALSE, FALSE), X3=letters[1:5])

@Wilmer E Henao H's solution is very streamlined:

sapply(my.data, class) y x1 x2 X3
"numeric" "integer" "logical" "factor" 

Using str() gets you that information plus extra goodies (such as the levels of your factors and the first few values of each variable):

str(my.data)
'data.frame': 5 obs. of 4 variables:
$ y : num 1.03 1.599 -0.818 0.872 -2.682
$ x1: int 1 2 3 4 5
$ x2: logi TRUE TRUE FALSE FALSE FALSE
$ X3: Factor w/ 5 levels "a","b","c","d",..: 1 2 3 4 5

@Gavin Simpson's approach is also streamlined, but provides slightly different information than class():

sapply(my.data, typeof) y x1 x2 X3
"double" "integer" "logical" "integer"

For more information about class, typeof, and the middle child, mode, see this excellent SO thread: A comprehensive survey of the types of things in R. 'mode' and 'class' and 'typeof' are insufficient.

4

sapply(yourdataframe, class)

Where yourdataframe is the name of the data frame you're using

1

I would suggest

sapply(foo, typeof)

if you need the actual types of the vectors in the data frame. class() is somewhat of a different beast.

If you don't need to get this information as a vector (i.e. you don't need it to do something else programmatically later), just use str(foo).

In both cases foo would be replaced with the name of your data frame.

For small data frames:

library(tidyverse)
as_tibble(mtcars)

gives you a print out of the df with data types

# A tibble: 32 x 11 mpg cyl disp hp drat wt qsec vs am gear carb * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1

For large data frames:

glimpse(mtcars)

gives you a structured view of data types:

Observations: 32
Variables: 11
$ mpg <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8, 16.4, 17....
$ cyl <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8, 8, 8, 8, ...
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 167.6, 167.6...
$ hp <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180, 205, 215...
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92, 3.07, 3.0...
$ wt <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.440, 3.440...
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18.30, 18.90...
$ vs <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, ...
$ am <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, ...
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3, 3, 3, 3, ...
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2, 2, 4, 2, ...

To get a list of the columns' data type (as said by @Alexandre above):

map(mtcars, class)

gives a list of data types:

$mpg
[1] "numeric"
$cyl
[1] "numeric"
$disp
[1] "numeric"
$hp
[1] "numeric"

To change data type of a column:

library(hablar)
mtcars %>% convert(chr(mpg, am), int(carb))

converts columns mpg and am to character and the column carb to integer:

# A tibble: 32 x 11 mpg cyl disp hp drat wt qsec vs am gear carb <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl> <int> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1

Simply pass your data frame into the following function:

data_types <- function(frame) { res <- lapply(frame, class) res_frame <- data.frame(unlist(res)) barplot(table(res_frame), main="Data Types", col="steelblue", ylab="Number of Features")
}

to produce a plot of all data types in your data frame. For the iris dataset we get the following:

data_types(iris)

Another option is using the map function of the purrr package.

library(purrr)
map(df,class)

Since it wasn't stated clearly, I just add this:

I was looking for a way to create a table which holds the number of occurrences of all the data types.

Say we have a data.frame with two numeric and one logical column

dta <- data.frame(a = c(1,2,3), b = c(4,5,6), c = c(TRUE, FALSE, TRUE))

You can summarize the number of columns of each data type with that

table(unlist(lapply(dta, class)))
# logical numeric
# 1 2 

This comes extremely handy, if you have a lot of columns and want to get a quick overview.

To give credit: This solution was inspired by the answer of @Cybernetic.

For a convenient dataframe, here's a simple function in base

col_classes <- function(df) { data.frame( variable = names(df), class = unname(sapply(df, class)) )
}
col_classes(my.data) variable class
1 y numeric
2 x1 integer
3 x2 logical
4 X3 character

Here is a function that is part of the helpRFunctions package that will return a list of all of the various data types in your data frame, as well as the specific variable names associated with that type.

install.package('devtools') # Only needed if you dont have this installed.
library(devtools)
install_github('adam-m-mcelhinney/helpRFunctions')
library(helpRFunctions)
my.data <- data.frame(y=rnorm(5), x1=c(1:5), x2=c(TRUE, TRUE, FALSE, FALSE, FALSE), X3=letters[1:5])
t <- list.df.var.types(my.data)
t$factor
t$integer
t$logical
t$numeric

You could then do something like var(my.data[t$numeric]).

Hope this is helpful!

1

If you import the csv file as a data.frame (and not matrix), you can also use summary.default

summary.default(mtcars) Length Class Mode
mpg 32 -none- numeric
cyl 32 -none- numeric
disp 32 -none- numeric
hp 32 -none- numeric
drat 32 -none- numeric
wt 32 -none- numeric
qsec 32 -none- numeric
vs 32 -none- numeric
am 32 -none- numeric
gear 32 -none- numeric
carb 32 -none- numeric

To get a nice Tibble with types and classes:

 purrr::map2_df(mtcars,names(mtcars), ~ { tibble( field = .y, type = typeof(.x), class_1 = class(.x)[1], class_2 = class(.x)[2] ) })

Your Answer

Sign up or log in

Sign up using Google Sign up using Facebook Sign up using Email and Password Submit

Post as a guest

Post Your Answer Discard

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy