R Change All Columns of Type Factor to Numeric

R change all columns of type factor to numeric

Applying the wisdom from Carl Witthoft above:

asNumeric <- function(x) as.numeric(as.character(x))
factorsNumeric <- function(d) modifyList(d, lapply(d[, sapply(d, is.factor)],   
                                                   asNumeric))

Example:

d <- data.frame(x=factor(1:3), y=factor(2:4), z=factor(3:5),
                r=c("a", "b", "c"), stringsAsFactors=FALSE)
> f <- factorsNumeric(d)
> class(f$x)
[1] "numeric"
> class(f$r)
[1] "character"

Change all columns from factor to numeric in R

This works but I'm thinking your data has an odd character or space, something that makes it read in as factor. You can try reading in with the argument stringsAsFactors = FALSE. But still wouldn't address character vs numeric read in. Here's a fix:

data[] <- lapply(data, function(x) as.numeric(as.character(x)))

## > str(data)
## 'data.frame':   8 obs. of  4 variables:
##  $ v1: num  22.39 43.72 58.54 56.88 1.66 ...
##  $ v2: num  144.4 72.3 119.4 112.4 35.8 ...
##  $ v3: num  7 4 7 10 18 5 10 13
##  $ v4: num  5 0 3 4 18 3 4 7

How to convert data.frame column from Factor to numeric

breast$class <- as.numeric(as.character(breast$class))

If you have many columns to convert to numeric

indx <- sapply(breast, is.factor)
breast[indx] <- lapply(breast[indx], function(x) as.numeric(as.character(x)))

Another option is to use stringsAsFactors=FALSE while reading the file using read.table or read.csv

Just in case, other options to create/change columns

 breast[,'class'] <- as.numeric(as.character(breast[,'class']))

 breast <- transform(breast, class=as.numeric(as.character(breast)))

Change the class from factor to numeric of many columns in a data frame

Further to Ramnath's answer, the behaviour you are experiencing is that due to as.numeric(x) returning the internal, numeric representation of the factor x at the R level. If you want to preserve the numbers that are the levels of the factor (rather than their internal representation), you need to convert to character via as.character() first as per Ramnath's example.

Your for loop is just as reasonable as an apply call and might be slightly more readable as to what the intention of the code is. Just change this line:

stats[,i] <- as.numeric(stats[,i])

to read

stats[,i] <- as.numeric(as.character(stats[,i]))

This is FAQ 7.10 in the R FAQ.

HTH

How to convert a data frame column to numeric type?

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! =)

Convert multiple columns from factor to numeric but obtaining NAs in R

as.character/as.numeric expects a vector as input. With df[, cols] you are passing a dataframe to it (check class(df[, cols])).

If you are talking about the accepted answer in the link it says to change the code in for loop and doesn't suggest to pass entire dataframe. To change class of multiple columns you can use for loop, apply or lapply.

df[cols] <- lapply(df[cols], function(x) as.numeric(as.character(x)))

converting multiple columns from character to numeric format in r

You could try

DF <- data.frame("a" = as.character(0:5),
                 "b" = paste(0:5, ".1", sep = ""),
                 "c" = letters[1:6],
                 stringsAsFactors = FALSE)

# Check columns classes
sapply(DF, class)

#           a           b           c 
# "character" "character" "character" 

cols.num <- c("a","b")
DF[cols.num] <- sapply(DF[cols.num],as.numeric)
sapply(DF, class)

#          a           b           c 
#  "numeric"   "numeric" "character"

How to convert a factor to integer\numeric without loss of information?

See the Warning section of ?factor:

In particular, as.numeric applied to
a factor is meaningless, and may
happen by implicit coercion. To
transform a factor f to
approximately its original numeric
values, as.numeric(levels(f))[f] is
recommended and slightly more
efficient than
as.numeric(as.character(f)).

The FAQ on R has similar advice.

Why is as.numeric(levels(f))[f] more efficent than as.numeric(as.character(f))?

as.numeric(as.character(f)) is effectively as.numeric(levels(f)[f]), so you are performing the conversion to numeric on length(x) values, rather than on nlevels(x) values. The speed difference will be most apparent for long vectors with few levels. If the values are mostly unique, there won't be much difference in speed. However you do the conversion, this operation is unlikely to be the bottleneck in your code, so don't worry too much about it.

Some timings

library(microbenchmark)
microbenchmark(
  as.numeric(levels(f))[f],
  as.numeric(levels(f)[f]),
  as.numeric(as.character(f)),
  paste0(x),
  paste(x),
  times = 1e5
)
## Unit: microseconds
##                         expr   min    lq      mean median     uq      max neval
##     as.numeric(levels(f))[f] 3.982 5.120  6.088624  5.405  5.974 1981.418 1e+05
##     as.numeric(levels(f)[f]) 5.973 7.111  8.352032  7.396  8.250 4256.380 1e+05
##  as.numeric(as.character(f)) 6.827 8.249  9.628264  8.534  9.671 1983.694 1e+05
##                    paste0(x) 7.964 9.387 11.026351  9.956 10.810 2911.257 1e+05
##                     paste(x) 7.965 9.387 11.127308  9.956 11.093 2419.458 1e+05

R Change All Columns of Type Factor to Numeric