Using a for loop, write a function to calculate the
number of zeroes in a numeric vector. Before entering the loop, set up a
counter variable counter <- 0. Inside the loop, add 1 to
counter each time you have a zero in the vector. Finally,
use return(counter) for the output.
Use subsetting instead of a loop to rewrite the function as a single line of code.
Write a function that takes as input two integers representing the number of rows and columns in a matrix. The output is a matrix of these dimensions in which each element is the product of the row number x the column number.
Now let’s practice calling custom functions within a for loops. Use the code from previous lectures on loops and functions to complete the following steps:
Simulate a dataset with 3 groups of data, each group drawn from a distribution with a different mean. The final data frame should have 1 column for group and 1 column for the response variable.
Write a custom function that 1) reshuffles the response variable, and 2) calculates the mean of each group in the reshuffled data. Store the means in a vector of length 3.
Use a for loop to repeat the function in b 100 times. Store the results in a data frame that has 1 column indicating the replicate number and 1 column for each new group mean, for a total of 4 columns.
Use qplot() to create a histogram of the means for each reshuffled group. Or, if you want a challenge, use ggplot() to overlay all 3 histograms in the same figure. How do the distributions of reshuffled means compare to the original means?