Make a For Loop more Efficient - r

Question

I have two dataframes for men and women working in a company. One is 15000 rows x 1000 columns and the other is 150 x 1000. Each column represents an attribute (e.g, Salary, Height, etc...). I am comparing female and male employees within each Bracket (there are five in total).
Below I created some dummy data and the for loop.
#Create the data
num_of_employee = 100
f <- rep(c("Female"), 15)
m <- rep(c("Male"), 85)
Employee = paste("Employee",seq(1:num_of_employee))
Bracket = sample(seq(1,5,1),num_of_employee, replace = TRUE)
Height = sample(seq(65,100, 1),num_of_employee, replace = TRUE)
Weight = sample(seq(120,220, 1),num_of_employee, replace = TRUE)
Years_Employed = sample(seq(1,13, 1),num_of_employee, replace = TRUE)
Income = sample(seq(50000,200000, 1000),num_of_employee, replace = TRUE)
gender <- sample(append(f,m), replace = FALSE)
df1 = data.frame(Employee, Height, Weight, Years_Employed, Income, Bracket, gender)
women <-df1[df1$gender == 'Female',]
men <- df1[df1$gender == 'Male',]
So that's all the data. Now, this for-loop essentially compares both the men and women dataframes column by column. So for example, Income from df1 will be compared to Income from df2, likewise for Height, Years_Employed etc...
v <-c()
runs <- 1000
for(j in 1:runs){
male_vector <- c()
female_vector <- c()
#loop through each of the 5 Brackets
for(z in 1:5){
#print out number of rows in each bracket.
number_of_rows <- length(which(women$Bracket == z))
#compare attributes of men and women within each bracket.
male_vector <- append(male_vector, men[sample(which(men$Bracket == z), number_of_rows), ]$Height)
female_vector <- append(female_vector, women[which(women$Bracket == z), ]$Height)
}
#Ask, are men and women different?
v <- append(v, sum(male_vector) > sum(female_vector))
}
#How many times are the men>women out of 1000?
as.numeric(sum(v))
[1] 70
So this code works, but I want to compare each column - meaning Height, Weight, Years_Employed and Income.
Edit
I would like to input the two dataframes and the output be the following:
"Height " 0.223
"Salary: " 0.994
"Weight: " 0.006
"Years_Employed:" 0.325
.
.
.
"1000th column :" 0.013
Note, my actual data consists of 1000 columns, so hard-coding anything (the way I originally did it), won't work.

Answer 1

The following is much simpler than your code.
Note that there are loops in disguise, namely split and sapply. But the code is cleaner and it avoids repeating the same computations over and over again.
If you call set.seed(4358) just before running your code the result will be exactly the same as the result of mean(v) in the end of this.
set.seed(4358) # Needed because of the call to sample()
runs <- 1000
v <- logical(runs)
df1_br <- split(df1, df1$Bracket)
df2_br <- split(df2, df2$Bracket)
female_vector <- sapply(df2_br, function(x) sum(x$Income))
sum_female_vector <- sum(female_vector)
number_of_rows <- sapply(df2_br, nrow)
for(j in 1:runs){
male_vector <- sapply(seq_along(df1_br), function(i) sum(sample(df1_br[[i]]$Income, number_of_rows[i], TRUE)))
v[j] <- sum(male_vector) > sum_female_vector
}
mean(v)
#[1] 0.933
Sample data.
I have recreated the datasets by first calling set.seed().
set.seed(6736)
num_of_employee = 15000
#Create their attributes
Employee <- paste("Employee", 1:num_of_employee)
Bracket <- sample(1:5, num_of_employee, replace = TRUE)
Height <- sample(65:100, num_of_employee, replace = TRUE)
Weight <- sample(120:220, num_of_employee, replace = TRUE)
Years_Employed <- sample(1:13, num_of_employee, replace = TRUE)
Income <- sample(seq(50000, 200000, 1000), num_of_employee, replace = TRUE)
gender <- sample(c("Female", "Male"), num_of_employee, prob = c(150, 14850)/15000, replace = TRUE)
#Finally make a dataframe for all their data
df1 = data.frame(Employee, Height, Weight, Years_Employed, Income, Bracket, gender)
#Split the dataframe by gender
df2 <- df1[df1$gender == 'Female', ]
df1 <- df1[df1$gender == 'Male', ]
Edit.
To have the code above accept any column, rewrite it as a function.
compareGender <- function(Female, Male, what = "Income", Runs = 1000){
v <- logical(Runs)
Male_br <- split(Male, Male[["Bracket"]])
Female_br <- split(Female, Female[["Bracket"]])
female_vector <- sapply(Female_br, function(x) sum(x[[what]]))
sum_female_vector <- sum(female_vector)
number_of_rows <- sapply(Female_br, nrow)
for(j in seq_len(Runs)){
male_vector <- sapply(seq_along(Male_br), function(i) sum(sample(Male_br[[i]][[what]], number_of_rows[i], TRUE)))
v[j] <- sum(male_vector) > sum_female_vector
}
c(what = mean(v))
}
set.seed(4358) # To compare the result with the result above
compareGender(Female = df2, Male = df1)
#[1] 0.933
compareGender(Female = df2, Male = df1, what = "Height")
#[1] 0.012
compareGender(Female = df2, Male = df1, what = "Years_Employed")
#[1] 0.815
If you want to apply the function to several columns automatically, you can use the *apply functions.
In this case I will sapply the function to columns 2 to 5, or to names(df1)[2:5].
res <- sapply(names(df1)[2:5], function(x) compareGender(df2, df1, x))
names(res) <- sub("\\.what$", "", names(res))
res
#Height Weight Years_Employed Income
#0.012 0.211 0.827 0.948
Now, you can transform this output into a data.frame. There are two ways you can do it. The first creates a df with one column and the names attribute as the row names. The second creates a df with two columns, the original column names and the mean values returned by compareGender.
final1 <- data.frame(Mean = res)
final1
# Mean
#Height 0.012
#Weight 0.211
#Years_Employed 0.827
#Income 0.948
final2 <- data.frame(Variable = names(res), Mean = res)
row.names(final2) <- NULL
final2
# Variable Mean
#1 Height 0.012
#2 Weight 0.211
#3 Years_Employed 0.827
#4 Income 0.948