将数据框架中许多列的类从 factor 更改为 numeric

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You have to be careful while changing factors to numeric. Here is a line of code that would change a set of columns from factor to numeric. I am assuming here that the columns to be changed to numeric are 1, 3, 4 and 5 respectively. You could change it accordingly

cols = c(1, 3, 4, 5);
df[,cols] = apply(df[,cols], 2, function(x) as.numeric(as.character(x)));

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最佳答案

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

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I think that ucfagls found why your loop is not working.

In case you still don't want use a loop here is solution with lapply:

factorToNumeric <- function(f) as.numeric(levels(f))[as.integer(f)]
cols <- c(1, 3:ncol(stats))
stats[cols] <- lapply(stats[cols], factorToNumeric)

Edit. I found simpler solution. It seems that as.matrix convert to character. So

stats[cols] <- as.numeric(as.matrix(stats[cols]))

should do what you want.

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This can be done in one line, there's no need for a loop, be it a for-loop or an apply. Use unlist() instead :

# testdata
Df <- data.frame(
x = as.factor(sample(1:5,30,r=TRUE)),
y = as.factor(sample(1:5,30,r=TRUE)),
z = as.factor(sample(1:5,30,r=TRUE)),
w = as.factor(sample(1:5,30,r=TRUE))
)
##


Df[,c("y","w")] <- as.numeric(as.character(unlist(Df[,c("y","w")])))


str(Df)

Edit : for your code, this becomes :

id <- c(1,3:ncol(stats)))
stats[,id] <- as.numeric(as.character(unlist(stats[,id])))

Obviously, if you have a one-column data frame and you don't want the automatic dimension reduction of R to convert it to a vector, you'll have to add the drop=FALSE argument.

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lapply is pretty much designed for this

unfactorize<-c("colA","colB")
df[,unfactorize]<-lapply(unfactorize, function(x) as.numeric(as.character(df[,x])))

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I had problems converting all columns to numeric with an apply() call:

apply(data, 2, as.numeric)

The problem turns out to be because some of the strings had a comma in them -- e.g. "1,024.63" instead of "1024.63" -- and R does not like this way of formatting numbers. So I removed them and then ran as.numeric():

data = as.data.frame(apply(data, 2, function(x) {
y = str_replace_all(x, ",", "") #remove commas
return(as.numeric(y)) #then convert
}))

Note that this requires the stringr package to be loaded.

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I would like to point out that if you have NAs in any column, simply using subscripts will not work. If there are NAs in the factor, you must use the apply script provided by Ramnath.

E.g.

Df <- data.frame(
x = c(NA,as.factor(sample(1:5,30,r=T))),
y = c(NA,as.factor(sample(1:5,30,r=T))),
z = c(NA,as.factor(sample(1:5,30,r=T))),
w = c(NA,as.factor(sample(1:5,30,r=T)))
)


Df[,c(1:4)] <- as.numeric(as.character(Df[,c(1:4)]))

Returns the following:

Warning message:
NAs introduced by coercion


> head(Df)
x  y  z  w
1 NA NA NA NA
2 NA NA NA NA
3 NA NA NA NA
4 NA NA NA NA
5 NA NA NA NA
6 NA NA NA NA

But:

Df[,c(1:4)]= apply(Df[,c(1:4)], 2, function(x) as.numeric(as.character(x)))

Returns:

> head(Df)
x  y  z  w
1 NA NA NA NA
2  2  3  4  1
3  1  5  3  4
4  2  3  4  1
5  5  3  5  5
6  4  2  4  4

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I know this question is long resolved, but I recently had a similar issue and think I've found a little more elegant and functional solution, although it requires the magrittr package.

library(magrittr)
cols = c(1, 3, 4, 5)
df[,cols] %<>% lapply(function(x) as.numeric(as.character(x)))

The %<>% operator pipes and reassigns, which is very useful for keeping data cleaning and transformation simple. Now the list apply function is much easier to read, by only specifying the function you wish to apply.

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That's what's worked for me. The apply() function tries to coerce df to matrix and it returns NA's.

numeric.df <- as.data.frame(sapply(df, 2, as.numeric))

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I found this function on a couple other duplicate threads and have found it an elegant and general way to solve this problem. This thread shows up first on most searches on this topic, so I am sharing it here to save folks some time. I take no credit for this just so see the original posts here and here for details.

df <- data.frame(x = 1:10,
y = rep(1:2, 5),
k = rnorm(10, 5,2),
z = rep(c(2010, 2012, 2011, 2010, 1999), 2),
j = c(rep(c("a", "b", "c"), 3), "d"))


convert.magic <- function(obj, type){
FUN1 <- switch(type,
character = as.character,
numeric = as.numeric,
factor = as.factor)
out <- lapply(obj, FUN1)
as.data.frame(out)
}


str(df)
str(convert.magic(df, "character"))
str(convert.magic(df, "factor"))
df[, c("x", "y")] <- convert.magic(df[, c("x", "y")], "factor")

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you can use unfactor() function from "varhandle" package form CRAN:

library("varhandle")


my_iris <- data.frame(Sepal.Length = factor(iris$Sepal.Length),
sample_id = factor(1:nrow(iris)))


my_iris <- unfactor(my_iris)

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I like this code because it's pretty handy:

  data[] <- lapply(data, function(x) type.convert(as.character(x), as.is = TRUE)) #change all vars to their best fitting data type

It is not exactly what was asked for (convert to numeric), but in many cases even more appropriate.

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Based on @SDahm's answer, this was an "optimal" solution for my tibble:

data %<>% lapply(type.convert) %>% as.data.table()

This requires dplyr and magrittr.

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I tried a bunch of these on a similar problem and kept getting NAs. Base R has some really irritating coercion behaviors, which are generally fixed in Tidyverse packages. I used to avoid them because I didn't want to create dependencies, but they make life so much easier that now I don't even bother trying to figure out the Base R solution most of the time.

Here's the Tidyverse solution, which is extremely simple and elegant:

library(purrr)


mydf <- data.frame(
x1 = factor(c(3, 5, 4, 2, 1)),
x2 = factor(c("A", "C", "B", "D", "E")),
x3 = c(10, 8, 6, 4, 2))


map_df(mydf, as.numeric)

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Here are some dplyr options:

# by column type:
df %>%
mutate_if(is.factor, ~as.numeric(as.character(.)))


# by specific columns:
df %>%
mutate_at(vars(x, y, z), ~as.numeric(as.character(.)))


# all columns:
df %>%
mutate_all(~as.numeric(as.character(.)))

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df$colname <- as.numeric(df$colname)

I tried this way for changing one column type and I think it is better than many other versions, if you are not going to change all column types

df$colname <- as.character(df$colname)

for the vice versa.