How do you convert a data frame column to a numeric type?
18 Answers
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 2nd 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. 3rd and 4th 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! =)
Something that has helped me: if you have ranges of variables to convert (or just more than one), you can use sapply.
A bit nonsensical but just for example:
data(cars)
cars[, 1:2] <- sapply(cars[, 1:2], as.factor)
Say columns 3, 6-15 and 37 of you dataframe need to be converted to numeric one could:
dat[, c(3,6:15,37)] <- sapply(dat[, c(3,6:15,37)], as.numeric)
if x is the column name of dataframe dat, and x is of type factor, use:
as.numeric(as.character(dat$x))
Tim is correct, and Shane has an omission. Here are additional examples:
R> df <- data.frame(a = as.character(10:15))
R> df <- data.frame(df, num = as.numeric(df$a),
numchr = as.numeric(as.character(df$a)))
R> df
a num numchr
1 10 1 10
2 11 2 11
3 12 3 12
4 13 4 13
5 14 5 14
6 15 6 15
R> summary(df)
a num numchr
10:1 Min. :1.00 Min. :10.0
11:1 1st Qu.:2.25 1st Qu.:11.2
12:1 Median :3.50 Median :12.5
13:1 Mean :3.50 Mean :12.5
14:1 3rd Qu.:4.75 3rd Qu.:13.8
15:1 Max. :6.00 Max. :15.0
R>
Our data.frame now has a summary of the factor column (counts) and numeric summaries of the as.numeric() --- which is wrong as it got the numeric factor levels --- and the (correct) summary of the as.numeric(as.character()).
If the dataframe has multiple types of columns, some characters, some numeric try the following to convert just the columns that contain numeric values to numeric:
for (i in 1:length(data[1,])){
if(length(as.numeric(data[,i][!is.na(data[,i])])[!is.na(as.numeric(data[,i][!is.na(data[,i])]))])==0){}
else {
data[,i]<-as.numeric(data[,i])
}
}
with hablar::convert
To easily convert multiple columns to different data types you can use hablar::convert. Simple syntax: df %>% convert(num(a)) converts the column a from df to numeric.
Detailed example
Lets convert all columns of mtcars to character.
df <- mtcars %>% mutate_all(as.character) %>% as_tibble()
> df
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
1 21 6 160 110 3.9 2.62 16.46 0 1 4 4
2 21 6 160 110 3.9 2.875 17.02 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
With hablar::convert:
library(hablar)
# Convert columns to integer, numeric and factor
df %>%
convert(int(cyl, vs),
num(disp:wt),
fct(gear))
results in:
# A tibble: 32 x 11
mpg cyl disp hp drat wt qsec vs am gear carb
<chr> <int> <dbl> <dbl> <dbl> <dbl> <chr> <int> <chr> <fct> <chr>
1 21 6 160 110 3.9 2.62 16.46 0 1 4 4
2 21 6 160 110 3.9 2.88 17.02 0 1 4 4
3 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
4 21.4 6 258 110 3.08 3.22 19.44 1 0 3 1
df ist your dataframe. x is a column of df you want to convert
as.numeric(factor(df$x))
If you don't care about preserving the factors, and want to apply it to any column that can get converted to numeric, I used the script below. if df is your original dataframe, you can use the script below.
df[] <- lapply(df, as.character)
df <- data.frame(lapply(df, function(x) ifelse(!is.na(as.numeric(x)), as.numeric(x), x)))
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