How to Convert Entire Dataframe to Numeric While Preserving Decimals

How to convert entire dataframe to numeric while preserving decimals?

You might need to do some checking. You cannot safely convert factors directly to numeric. as.character must be applied first. Otherwise, the factors will be converted to their numeric storage values. I would check each column with is.factor then coerce to numeric as necessary.

df1[] <- lapply(df1, function(x) {
    if(is.factor(x)) as.numeric(as.character(x)) else x
})
sapply(df1, class)
#         a         b 
# "numeric" "numeric" 

How to convert an entire data.frame to numeric

In base R we can do :

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

or

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

Here's a benchmark of the solutions (ignoring the considerations about factors) :

DF <- data.frame(a = 1:10000, b = letters[1:10000],
                 c = seq(as.Date("2004-01-01"), by = "week", len = 10000),
                 stringsAsFactors = TRUE)
DF <- setNames(do.call(cbind,replicate(50,DF,simplify = F)),paste0("V",1:150))

dim(DF)
# [1] 10000   150

library(dplyr)
n1tk  <- function(x) data.frame(data.matrix(x))
mm    <- function(x) {x[] <- lapply(x,as.numeric); x}
akrun <- function(x) mutate_all(x, as.numeric)
mo    <- function(x)  {for(i in 1:150){ x[, i] <- as.numeric(x[, i])}}

microbenchmark::microbenchmark(
  akrun = akrun(DF),
  n1tk  = n1tk(DF),
  mo    = mo(DF),
  mm    = mm(DF)
)

# Unit: milliseconds
#   expr      min        lq       mean    median        uq      max neval
#  akrun 152.9837 177.48150 198.292412 190.38610 206.56800 432.2679   100
#   n1tk  10.8700  14.48015  22.632782  17.43660  21.68520  89.4694   100
#     mo   9.3512  11.41880  15.313889  14.71970  17.66530  37.6390   100
#     mm   4.8294   5.91975   8.906348   7.80095  10.11335  71.2647   100

R data frame: convert all data frame elements from characters to numerics while keeping decimals

Do you mean something like below?

df[] <- lapply(
  df,
  function(x) {
    eval(str2lang(gsub("d","e",x)))
  }
)

and you will see

> df
  V1    V2   V3    V4     V5   V6
1  1 0.007 0.73 4.165 1438.8 6050

> str(df)
'data.frame':   1 obs. of  6 variables:
 $ V1: num 1
 $ V2: num 0.007
 $ V3: num 0.73
 $ V4: num 4.16
 $ V5: num 1439
 $ V6: num 6050

data

> dput(df)
structure(list(V1 = 1L, V2 = "0.007d0", V3 = "0.73d0", V4 = "4.165d0",
    V5 = "1438.8d0", V6 = 6050L), class = "data.frame", row.names = "1")

Dataframe column: Remove quotes, change decimals and turn into numeric

Using chartr():

as.numeric(chartr('",', ' .', test$v1))
# [1]    2.60    1.30  850.00 1000.00   57.25   98.00

Decimal places deleted in R

try running this option first

options(digits = 15)

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