Converting data from one to other format

Using dplyr, I would use:

library(dplyr)
df %>% 
    mutate(
        across(
            .cols = where(is.character), 
            .fns = as.numeric
        )
    )

That also assumes that you want to change to numeric ALL character variables.

You need to apply it, i.e.

df[1:18]<-lapply(df[1:18], as.numeric)

You could try type.convert like below

> type.convert(df, as.is = TRUE)
# A tibble: 10 × 18
   Y_O_13_Males Y_O_13_Females Y_O_13_Unknown Y14_17_Males Y14_17_Females
          <int>          <int>          <int>        <int>          <int>
 1           10              1              7            2              0
 2            0              0              0            0              0
 3            0              0              0            0              0
 4            0              0              0            0              0
 5            0              0              0            0              0
 6            0              0              0            0              0
 7            0              0              0            0              0
 8            0              0              0            0              0
 9            0              0              0            0              0
10            0              0              0            0              0
# … with 13 more variables: Y14_17_Unknown <int>, Y18_34_Males <int>,
#   Y18_34_Females <int>, Y18_34_Unknown <int>, Y35_64_Males <int>,
#   Y35_64_Females <int>, Y35_64_Unknown <int>, Y65_And_Over_Males <int>,
#   Y65_And_Over_Females <int>, Y65_And_Over_Unknown <int>,
#   Unknown_And_Over_Males <int>, Unknown_And_Over_Females <int>,
#   Unknown_And_Over_Unknown <int>

Simple base R assuming you only have those 18 columns:

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

The class can be changed from character to numeric with:

for(i in which(sapply(df, class) == "character")) class(df[[i]]) <- "numeric"

str(df)
#Classes ‘tbl_df’, ‘tbl’ and 'data.frame':       10 obs. of  18 variables:
# $ Y_O_13_Males            : num  10 0 0 0 0 0 0 0 0 0
# $ Y_O_13_Females          : num  1 0 0 0 0 0 0 0 0 0
# $ Y_O_13_Unknown          : num  7 0 0 0 0 0 0 0 0 0
# $ Y14_17_Males            : num  2 0 0 0 0 0 0 0 0 0
# $ Y14_17_Females          : num  0 0 0 0 0 0 0 0 0 0
# $ Y14_17_Unknown          : num  0 0 0 0 0 0 0 0 0 0
# $ Y18_34_Males            : num  0 0 0 0 0 0 0 0 0 0
# $ Y18_34_Females          : num  0 0 0 0 0 0 0 0 0 0
# $ Y18_34_Unknown          : num  0 0 0 0 0 0 0 0 0 0
# $ Y35_64_Males            : num  0 0 0 0 0 0 0 0 0 0
# $ Y35_64_Females          : num  0 0 0 0 0 0 0 0 0 0
# $ Y35_64_Unknown          : num  0 0 0 0 0 0 0 0 0 0
# $ Y65_And_Over_Males      : num  0 0 0 0 0 0 0 0 0 0
# $ Y65_And_Over_Females    : num  0 0 0 0 0 0 0 0 0 0
# $ Y65_And_Over_Unknown    : num  0 0 0 0 0 0 0 0 0 0
# $ Unknown_And_Over_Males  : num  0 0 0 0 0 0 0 0 0 0
# $ Unknown_And_Over_Females: num  0 0 0 0 0 0 0 0 0 0
# $ Unknown_And_Over_Unknown: num  0 0 0 0 0 0 0 0 0 0

In this case integer might fit better than numeric.