[r] Determine the data types of a data frame's columns

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?

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!

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.

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)

sapply(yourdataframe, class)

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

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

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

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.