Challenge 9

Creating a function

library(tidyverse)
library(ggplot2)

knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)

Challenge Overview

Today’s challenge is simple. Create a function, and use it to perform a data analysis / cleaning / visualization task:

Examples of such functions are: 1) A function that reads in and cleans a dataset.
2) A function that computes summary statistics (e.g., computes the z score for a variable).
3) A function that plots a histogram.

That’s it!

get_summary_statistics <- function(x) {
    mean <- mean(x)
    median <- median(x)
    std_dev <- sd(x)
    min_val <- min(x)
    max_val <- max(x)
    result <- c(mean, median, std_dev, min_val, max_val)
    names(result) <- c("Mean", "Median", "Standard Deviation", "Minimum", "Maximum")
  return(result)
}
test_scores <- c(90, 85, 77, 69, 71, 80, 65, 83, 53, 95, 94, 90, 87, 71, 70, 65, 58, 59, 98, 90, 87, 76)
get_summary_statistics(test_scores)

              Mean             Median Standard Deviation            Minimum 
          77.86364           78.50000           13.07231           53.00000 
           Maximum 
          98.00000 

condition <- x >= 50

Error in eval(expr, envir, enclos): object 'x' not found

exclude_outliers <- function(test_scores, condition) {
  no_outliers_test_scores <- keep(test_scores, condition)
  return(no_outliers_test_scores)
}

At the top of the file, we define our function “get_summary_statistics”. Then we code how we want it to interpret the input, and what that input is supposed to be, in this case a group of test scores. Our function computes the mean, median, standard dev., min, and max of our test scores, and returns the results to the console for viewing. Voila!