Converting Multiple Columns from Character to Numeric Format in R

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"

Convert multiple columns of a data frame from string to numeric in R

We can use lapply to loop through the columns and apply as.numeric

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

where

cols <- names(df)[4:10] # or column index (change the index if needed)

Permanently convert multiple columns to numeric - dplyr

Use this instead -- across did not exist 7 years ago when the link in the question was written:

df %>% mutate(across(.fns = type.convert))

or with only base R:

replace(df, TRUE, lapply(df, type.convert))

Coercing all but two columns from character into numeric

Try this base R solution:

df[,3:43] <- apply(df[,3:43],2,as.numeric)

Convert multiple character columns with numbers and , symbols into numeric in R

You can apply this approach to whichever columns you want. In this case, if you want to apply to columns 1 through 3, you can specify as labor_df[1:3]. If you want to apply to specific columns based on the column name, then create a cols vector containing the names of columns to apply this to and use labor_df[cols] instead.

The first gsub will remove the greater than sign, and keep the value unchanged. The ifelse is vectorized and will apply to all values in the column. It will first check with grepl if less than sign is present; if it is, remove it, convert to a numeric value, and then divide by 2. Otherwise leave as is.

labor_df[1:3] <- lapply(labor_df[1:3], function(x) {
  x <- gsub(">", "", x)
  x <- ifelse(grepl("<", x), as.numeric(gsub("<", "", x)) / 2, x)
  as.numeric(x)
})
labor_df

Output

   a  b     c
1  1  9 20.00
2  5 14  1.99
3 12  5 14.50

Data

labor_df <- structure(list(a = c("1", "<10", "12"), b = c("9", "14", ">5"
), c = c("20", "1.99", "14.5")), class = "data.frame", row.names = c(NA, 
-3L))

How do I convert all numeric columns to character type in my dataframe?

In base R, we may either use one of the following i.e. loop over all the columns, create an if/else conditon to change it

dataframe[] <- lapply(dataframe, function(x) if(is.numeric(x)) 
           as.character(x) else x)

Or create an index for numeric columns and loop only on those columns and assign

i1 <- sapply(dataframe, is.numeric)
dataframe[i1] <- lapply(dataframe[i1], as.character)

---

It may be more flexible in dplyr

library(dplyr)
dataframe <- dataframe %>%
        mutate(across(where(is.numeric), as.character))

Convert multiple character columns into numeric in R

Probably, you were trying to do :

df[cols] <- lapply(df[cols], function(x) as.integer(factor(x)))

You can also use :

df[cols] <- lapply(df[cols], function(x) match(x, unique(x)))

Converting multiple columns to double type in R using dplyr

It would be better to do this with type.convert from base R which automatically correct the type based on the value in each column

df1 <- type.convert(df, as.is = TRUE)

---

In dplyr, it can be done with across and specify the range of columns with either numeric index

df %>%
   mutate(across(2:4, as.numeric))

Or the column names range

df %>%
   mutate(across(X11:P3, as.numeric))

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