Challenge 9 Post: Creating Functions for the Cereal Dataset

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!

Solution

For Challenge 9, I am going to read in the cereal.csv dataset. Since cereal.csv is already meets the qualifications for being a tidy dataset, I’ll aim to create the two latter functions described in the instructions–creating a function that computes the z-score for a variable and creating a function that plots a histogram.

Reading in the data

# Reading in cereal.csv
cereal_1 <- read_csv("_data/cereal.csv")
cereal_1

Creating function, calculate_z_score, to compute z-scores for Sodium and Sugar variables

# Creating function to compute z-scores
calculate_z_score <- function(col_name) {
  z_score <- (col_name - mean(col_name)) / sd(col_name)
  return(z_score)
}

# Using z-score function for Sodium variable in cereal_1 dataset
calculate_z_score(cereal_1$Sodium)

 [1] -2.16146621  2.23912368 -1.25546241 -0.34945861  0.42711608  0.16825785
 [7]  0.55654519 -0.22002949 -0.86717507 -0.47888772 -0.34945861  0.16825785
[13]  0.29768696 -0.09060038  1.59197810 -1.51432064  0.68597431  0.16825785
[19]  0.42711608  0.55654519

# Using z-score function for Sugar variable in cereal_1 dataset
calculate_z_score(cereal_1$Sugar)

 [1]  0.42293060  1.73871469 -0.70488433  0.98683807  0.61089976 -1.45676096
 [7]  0.23496144  1.36277638 -1.64473011  0.61089976  0.98683807 -0.32894602
[13]  0.04699229 -0.51691518 -1.08082265  1.17480722 -0.89285349 -0.89285349
[19] -1.08082265  0.42293060

Creating function, build_histogram, to make histograms for the Sodium variable

# Creating a function, build_histogram, to make a histogram
build_histogram <- function(data, col_name, title, fill="orange", xlab="x", ylab= "Frequency", x_max= 350, y_max= 4) {
  data %>%
    ggplot(aes({{col_name}})) + geom_histogram(fill= fill)+ labs(x=xlab, y=ylab, title= title) + xlim(0,x_max)+ ylim(0,y_max)
}

# Using histogram function for Sodium variable in cereal_1 dataset
build_histogram(cereal_1, Sodium, "Histogram of Sodium Content in Cereals", xlab = "Sodium content(mg)", ylab = "Frequency")