Expand dataframe based on column values

Question

I have a dataframe like so:

    Title Female Male Asian HispanicLatino White
1   Title1      2    3     1              1     3
2   Title2      1    5    NA              1     5
3   Title3     NA    2    NA             NA     2

I want to expand this out so that there are two rows for Female in Title 1, and three Male, as well as expanding the other columns (I have many more columns).

I've tried various different things, and the following technically works but is not ideal.

    df[is.na(df)] <- 0

    dfM <- uncount(df, df$Male)
    dfM$Sex <- "M"


    dfF <- uncount(df, df$Female)
    dfF$Sex <- "F"

    df <- rbind.fill(dfF, dfM)

Which produces

   Title Female Male Asian  HispanicLatino   White Sex
1    Title1      2    3     1              1     3   F
2    Title1      2    3     1              1     3   F
3    Title2      1    5     0              1     5   F
4    Title1      2    3     1              1     3   M
5    Title1      2    3     1              1     3   M
6    Title1      2    3     1              1     3   M
7    Title2      1    5     0              1     5   M
8    Title2      1    5     0              1     5   M
9    Title2      1    5     0              1     5   M
10   Title2      1    5     0              1     5   M
11   Title2      1    5     0              1     5   M
12   Title3      0    2     0              0     2   M
13   Title3      0    2     0              0     2   M

Curious to see if there is a much easier way to do this.

Here is some data:

dput(df)
structure(list(Title = structure(1:3, .Label = c("Title1", 
"Title2", "Title3"), class = "factor"), Female = c(2L, 1L, NA
), Male = c(3L, 5L, 2L), Asian = c(1L, NA, NA), HispanicLatino = c(1L, 
1L, NA), White = c(3L, 5L, 2L)), .Names = c("Title", "Female", 
"Male", "Asian", "HispanicLatino", "White"), class = "data.frame", row.names = c(NA, 
-3L))

Answer 1

An option would be map to loop over the columns to expand after replaceing the NA with 0 while creating a .id column ('Sex')

library(tidyverse)
map_df(setNames(c("Female", "Male"), c("F", "M")), ~ 
       df %>%
           mutate_at(vars(.x), replace_na, 0) %>% 
           uncount(!! rlang::sym(.x), .remove = FALSE), .id = 'Sex') %>%
       mutate_at(3:6, replace_na, 0)
#   Sex  Title Female Male Asian HispanicLatino White
#1    F Title1      2    3     1              1     3
#2    F Title1      2    3     1              1     3
#3    F Title2      1    5     0              1     5
#4    M Title1      2    3     1              1     3
#5    M Title1      2    3     1              1     3
#6    M Title1      2    3     1              1     3
#7    M Title2      1    5     0              1     5
#8    M Title2      1    5     0              1     5
#9    M Title2      1    5     0              1     5
#10   M Title2      1    5     0              1     5
#11   M Title2      1    5     0              1     5
#12   M Title3      0    2     0              0     2
#13   M Title3      0    2     0              0     2