How to convert a data frame column to numeric type

Question

How do you convert a data frame column to a numeric type?

Answer 1

While your question is strictly on numeric, there are many conversions that are difficult to understand when beginning R. I'll aim to address methods to help. This question is similar to This Question.

Type conversion can be a pain in R because (1) factors can't be converted directly to numeric, they need to be converted to character class first, (2) dates are a special case that you typically need to deal with separately, and (3) looping across data frame columns can be tricky. Fortunately, the "tidyverse" has solved most of the issues.

This solution uses mutate_each() to apply a function to all columns in a data frame. In this case, we want to apply the type.convert() function, which converts strings to numeric where it can. Because R loves factors (not sure why) character columns that should stay character get changed to factor. To fix this, the mutate_if() function is used to detect columns that are factors and change to character. Last, I wanted to show how lubridate can be used to change a timestamp in character class to date-time because this is also often a sticking block for beginners.

library(tidyverse) 
library(lubridate)

# Recreate data that needs converted to numeric, date-time, etc
data_df
#> # A tibble: 5 × 9
#>             TIMESTAMP SYMBOL    EX  PRICE  SIZE  COND   BID BIDSIZ   OFR
#>                 <chr>  <chr> <chr>  <chr> <chr> <chr> <chr>  <chr> <chr>
#> 1 2012-05-04 09:30:00    BAC     T 7.8900 38538     F  7.89    523  7.90
#> 2 2012-05-04 09:30:01    BAC     Z 7.8850   288     @  7.88  61033  7.90
#> 3 2012-05-04 09:30:03    BAC     X 7.8900  1000     @  7.88   1974  7.89
#> 4 2012-05-04 09:30:07    BAC     T 7.8900 19052     F  7.88   1058  7.89
#> 5 2012-05-04 09:30:08    BAC     Y 7.8900 85053     F  7.88 108101  7.90

# Converting columns to numeric using "tidyverse"
data_df %>%
    mutate_all(type.convert) %>%
    mutate_if(is.factor, as.character) %>%
    mutate(TIMESTAMP = as_datetime(TIMESTAMP, tz = Sys.timezone()))
#> # A tibble: 5 × 9
#>             TIMESTAMP SYMBOL    EX PRICE  SIZE  COND   BID BIDSIZ   OFR
#>                <dttm>  <chr> <chr> <dbl> <int> <chr> <dbl>  <int> <dbl>
#> 1 2012-05-04 09:30:00    BAC     T 7.890 38538     F  7.89    523  7.90
#> 2 2012-05-04 09:30:01    BAC     Z 7.885   288     @  7.88  61033  7.90
#> 3 2012-05-04 09:30:03    BAC     X 7.890  1000     @  7.88   1974  7.89
#> 4 2012-05-04 09:30:07    BAC     T 7.890 19052     F  7.88   1058  7.89
#> 5 2012-05-04 09:30:08    BAC     Y 7.890 85053     F  7.88 108101  7.90

Answer 2

In my PC (R v.3.2.3), apply or sapply give error. lapply works well.

dt[,2:4] <- lapply(dt[,2:4], function (x) as.factor(as.numeric(x)))

Answer 3

Since (still) nobody got check-mark, I assume that you have some practical issue in mind, mostly because you haven't specified what type of vector you want to convert to numeric. I suggest that you should apply transform function in order to complete your task.

Now I'm about to demonstrate certain "conversion anomaly":

# create dummy data.frame
d <- data.frame(char = letters[1:5], 
                fake_char = as.character(1:5), 
                fac = factor(1:5), 
                char_fac = factor(letters[1:5]), 
                num = 1:5, stringsAsFactors = FALSE)

Let us have a glance at data.frame

> d
  char fake_char fac char_fac num
1    a         1   1        a   1
2    b         2   2        b   2
3    c         3   3        c   3
4    d         4   4        d   4
5    e         5   5        e   5

and let us run:

> sapply(d, mode)
       char   fake_char         fac    char_fac         num 
"character" "character"   "numeric"   "numeric"   "numeric" 
> sapply(d, class)
       char   fake_char         fac    char_fac         num 
"character" "character"    "factor"    "factor"   "integer" 

Now you probably ask yourself "Where's an anomaly?" Well, I've bumped into quite peculiar things in R, and this is not the most confounding thing, but it can confuse you, especially if you read this before rolling into bed.

Here goes: first two columns are character. I've deliberately called 2^nd one fake_char. Spot the similarity of this character variable with one that Dirk created in his reply. It's actually a numerical vector converted to character. 3^rd and 4^th column are factor, and the last one is "purely" numeric.

If you utilize transform function, you can convert the fake_char into numeric, but not the char variable itself.

> transform(d, char = as.numeric(char))
  char fake_char fac char_fac num
1   NA         1   1        a   1
2   NA         2   2        b   2
3   NA         3   3        c   3
4   NA         4   4        d   4
5   NA         5   5        e   5
Warning message:
In eval(expr, envir, enclos) : NAs introduced by coercion

but if you do same thing on fake_char and char_fac, you'll be lucky, and get away with no NA's:

> transform(d, fake_char = as.numeric(fake_char), 
               char_fac = as.numeric(char_fac))

  char fake_char fac char_fac num
1    a         1   1        1   1
2    b         2   2        2   2
3    c         3   3        3   3
4    d         4   4        4   4
5    e         5   5        5   5

If you save transformed data.frame and check for mode and class, you'll get:

> D <- transform(d, fake_char = as.numeric(fake_char), 
                    char_fac = as.numeric(char_fac))

> sapply(D, mode)
       char   fake_char         fac    char_fac         num 
"character"   "numeric"   "numeric"   "numeric"   "numeric" 
> sapply(D, class)
       char   fake_char         fac    char_fac         num 
"character"   "numeric"    "factor"   "numeric"   "integer"

So, the conclusion is: Yes, you can convert character vector into a numeric one, but only if it's elements are "convertible" to numeric. If there's just one character element in vector, you'll get error when trying to convert that vector to numerical one.

And just to prove my point:

> err <- c(1, "b", 3, 4, "e")
> mode(err)
[1] "character"
> class(err)
[1] "character"
> char <- as.numeric(err)
Warning message:
NAs introduced by coercion 
> char
[1]  1 NA  3  4 NA

And now, just for fun (or practice), try to guess the output of these commands:

> fac <- as.factor(err)
> fac
???
> num <- as.numeric(fac)
> num
???

Kind regards to Patrick Burns! =)

Answer 4

With the following code you can convert all data frame columns to numeric (X is the data frame that we want to convert it's columns):

as.data.frame(lapply(X, as.numeric))

and for converting whole matrix into numeric you have two ways: Either:

mode(X) <- "numeric"

or:

X <- apply(X, 2, as.numeric)

Alternatively you can use data.matrix function to convert everything into numeric, although be aware that the factors might not get converted correctly, so it is safer to convert everything to character first:

X <- sapply(X, as.character)
X <- data.matrix(X)

I usually use this last one if I want to convert to matrix and numeric simultaneously

Answer 5

If you run into problems with:

as.numeric(as.character(dat$x))

Take a look to your decimal marks. If they are "," instead of "." (e.g. "5,3") the above won't work.

A potential solution is:

as.numeric(gsub(",", ".", dat$x))

I believe this is quite common in some non English speaking countries.

Answer 6

Tim is correct, and Shane has an omission. Here are additional examples:

R> df <- data.frame(a = as.character(10:15))
R> df <- data.frame(df, num = as.numeric(df$a), 
                        numchr = as.numeric(as.character(df$a)))
R> df
   a num numchr
1 10   1     10
2 11   2     11
3 12   3     12
4 13   4     13
5 14   5     14
6 15   6     15
R> summary(df)
  a          num           numchr    
 10:1   Min.   :1.00   Min.   :10.0  
 11:1   1st Qu.:2.25   1st Qu.:11.2  
 12:1   Median :3.50   Median :12.5  
 13:1   Mean   :3.50   Mean   :12.5  
 14:1   3rd Qu.:4.75   3rd Qu.:13.8  
 15:1   Max.   :6.00   Max.   :15.0  
R> 

Our data.frame now has a summary of the factor column (counts) and numeric summaries of the as.numeric() --- which is wrong as it got the numeric factor levels --- and the (correct) summary of the as.numeric(as.character()).