Raise to any power

Make a function that uses two arguments, x for a number, and power for the power. Call it raise().

raise <- function() {
  
}

# test with
raise(x = 2, power = 4)
# should give you
2^4

Default arguments

Change your raise() function to default to squaring x when the user doesn’t enter a value for power.

# test
raise(x = 5)
# should give you
5^2

Functions for data

Write a function to calculate the stratified mean income for grouping variable var. In other words, write a function such that mean_group_inc(var = "sex") and mean_group_inc(var = "glasses") produce the results above.

Look at the function from the slides for help:

var_q <- function(q, var) {
  quant <- nlsy %>%
    rename(new_var = var) %>% #<<
    summarize(q_var = quantile(new_var, probs = q))
  return(quant)
}
var_q(q = 0.5, var = "income")

Write your function here:

mean_group_inc <- function(var) {
  
}

# test with
mean_group_inc(var = "glasses")
mean_group_inc(var = "sex")

Rewrite your function to accept two arguments: group_var to determine what the grouping variable is, and mean_var to determine what variable you want to take the mean of (e.g., mean_group(group_var = "sex", mean_var = "income") should give you the same results as above).

mean_group <- function(group_var, mean_var) {
  
}

# test with
mean_group(group_var = "sex", mean_var = "income")

Write a for loop

We used this function:

var_q_new <- function(q, var) {
  quant <- nlsy %>%
    rename(new_var = var) %>%
    summarize(q_var = quantile(new_var, probs = q)) %>%
    pull(q_var)
  return(quant)
}
var_q_new(q = 0.5, var = "income")

#>    50% 
#>  11155

inside of a for loop in order to calculate each decile of income:

qs <- seq(0.1, 0.9, by = 0.1)
deciles <- rep(NA, length(qs))
for (i in seq_along(qs)) {
  deciles[[i]] <- var_q_new(q = qs[[i]], 
                        var = "income")
}
deciles

#>  [1]  3177.2  5025.6  6907.2  9000.0 11155.0 14000.0 18053.6 23800.0 33024.0

Change the for loop above to loop over different variables instead of different quantiles. That is, calculate the 0.25 quantile for each of c("income", "age_bir", "nsibs") in a for loop.

vars <- c("income", "age_bir", "nsibs")
q_25s <- ...

Nested loops

You can nest for loops inside each other, as long as you use different iteration variables. Write a nested for loop to iterate over variables (with i) and quantiles (with j). You’ll need to start with an empty matrix instead of a vector, with rows indexed by i and columns by j. Calculate each of the deciles for each of the above variables.

vars <- c("income", "age_bir", "nsibs")
qs <- qs <- seq(0.1, 0.9, by = 0.1)
results_mat <- matrix(NA, ncol = length(qs), nrow = length(vars))

# helpful to print to see what's going on
for (i in vars) {
  for (j in qs) {
    print(c(i, j))
  }
}

for (i in seq_along(vars)) {
  for (j in seq_along(qs)) {
    print(var_q_new(q = qs[[j]], var = vars[[i]]))
  }
}

for (i in seq_along(vars)) {
  for (j in seq_along(qs)) {
    results_mat[i, j] <- var_q_new(q = qs[[j]], var = vars[[i]])
  }
}

results_mat

rownames(results_mat) <- vars
colnames(results_mat) <- qs

results_mat