Changing Class and Mode from Character to Numeric

The lines

as.factor(df$StudyAreaVisitNote)
as.numeric(df$Year)
as.numeric(df$Session)

do not permanently change the values in df. They return transformed vectors that are printed to the console, then, because you do not save them anywhere, they disappear as soon as that line in done being called. Generally objects in R are not updated via referece, you must alwayts re-assign the returned result to wherevver you would like to store it. So try

df$Year <- as.numeric(df$Year)
df$Session <- as.numeric(df$Session)

instead

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

Convert a column of character mode into numeric in R?

x <- "24,31"
y <- as.numeric(gsub(",", ".", x))
y
# [1] 24.31
class(y)
# [1] "numeric"

A side note

I think depending on the data file you have, you might even want to prevent this to happen in the first place defining dec. Be careful if your sep is a comma as well though. It still can be an option, so you do not get your values as a character and therefor no need to do any replacements.

fread(file, header = T, sep = ";", dec = ",") # fread is data.table, but I think read.csv and any others support it as well

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! =)

Converting Character to Numeric without NA Coercion in R

As Anando pointed out, the problem is somewhere in your data, and we can't really help you much without a reproducible example. That said, here's a code snippet to help you pin down the records in your data that are causing you problems:

test = as.character(c(1,2,3,4,'M'))
v = as.numeric(test) # NAs intorduced by coercion
ix.na = is.na(v)
which(ix.na) # row index of our problem = 5
test[ix.na]  # shows the problematic record, "M"

Instead of guessing as to why NAs are being introduced, pull out the records that are causing the problem and address them directly/individually until the NAs go away.

UPDATE: Looks like the problem is in your call to str_replace_all. I don't know the stringr library, but I think you can accomplish the same thing with gsub like this:

v2 = c("1.00","2.00","3.00")
gsub("\\.00", "", v2)

[1] "1" "2" "3"

I'm not entirely sure what this accomplishes though:

sum(as.numeric(v2)!=as.numeric(gsub("\\.00", "", v2))) # Illustrate that vectors are equivalent.

[1] 0

Unless this achieves some specific purpose for you, I'd suggest dropping this step from your preprocessing entirely, as it doesn't appear necessary and seems to be giving you problems.

Changing class from 'character' to other type which supports rowSums in r

You could change the storage mode of your matrix:

mmat <- matrix(c("2","3","7","0"), ncol = 2)
storage.mode(mmat) <- "double"  # changed from "numeric"
rowSums(mmat)
# [1] 9 3

Converting from character to numeric in a matrix

Answered by @Sophia.

Her solution:
How about converting your matrix to data.frame before you add the character columns?

Changing Class and Mode from Character to Numeric