Overview

This file contains a set of tasks that you need to complete in R for the lab assignment. The tasks may require you to add a code chuck, type code into a chunk, and/or execute code. Don’t forget that you need to acknowledge if you used any resources beyond class materials or got help to complete the assignment.

Additional information and examples relevant to this assignment can be found in the file “VisualizingAmountsandProportionsTutorial.html”.

The data set you will use is different than the one used in the instructions. Pay attention to the differences in the Excel files name, any variable names, and/or object names. You will need to adjust your code accordingly.

Once you have completed the assignment, you will need to knit this R Markdown file to produce an html file. You will then need to upload the .html file and this .Rmd file to AsULearn.

1. Add your Name and the Date

The first thing you need to do in this file is to add your name and date in the lines underneath this document’s title (see the code in lines 9 and 10).

2. Identify and Set Your Working Directory

You need to identify and set your working directory in this section. If you are working in the cloud version of RStudio, enter a note here to tell us that you did not need to change the working directory because you are working in the cloud.

getwd()

## [1] "/cloud/project/VisualizingAmountsProportionsFall2025"

setwd("/cloud/project/VisualizingAmountsProportionsFall2025")

3. Installing and Loading Packages and Data Set

You load the packages and data set you’ll use for the lab assignment in this section. In this lab we will use the packages: dyplr, tidyverse, forcats, ggplot2, and openxlsx.

library(tidyverse)

## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.1     ✔ stringr   1.5.2
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors

library(openxlsx)

library(dplyr)

library(forcats)
library(ggplot2)

VisualzingVariablesData <- read.xlsx("BBQData_Assignment7.xlsx")

4. Amounts – Bar Graph 1

Create a bar graph that plots respondents’ favorite sauce.

The graph should be based on the favorite sauce variable stored as a numeric variable.
Change the labels on the x-axis to reflect respondents’ favorite sauces in words.
Change the numerical labels on each bar to words that correspond to sauce type. Make sure your labels are readable on the graph, to do this you will need to shorten the labels. When abbreviating labels you want to do so in a way that someone looking at the graph will still know what you are referencing.
Change the color of the bars to purple.

ggplot(data = VisualzingVariablesData,  #"data", argument
      mapping = aes(x = Favorite.Sauce)) + #"mapping" and "aesthetics" arguments
  geom_bar(fill= "purple") + #geom" argument
  labs(x="Favorite Sauce", y="Total") + 
  scale_x_continuous(breaks = c(1,2,3,4,5,6,7), label = c ("Eastern no tOM", "Western/w tOM", "KS /w molasses", "Dry Rub", "SC Mustard", "korean Style", "other"))

5. Amounts – Bar Graph 2

Create a bar graph that plots respondents’ favorite sauce.

Recode the numeric format of the variable into a variable with a character format.
Use the character format version of the variable to construct the graph.
Change the labels on the x-axis to reflect respondents’ favorite sauces in words.
Change the color of the bars to orange.
Order respondents’ favorite sauces from most to least

VisualzingVariablesData %>%
  mutate(Favorite.Sauce.Label=NA) %>%
  mutate(Favorite.sauce.Label=replace(Favorite.Sauce.Label,Favorite.Sauce==1,"Eastern style (with no tomato)")) %>%
  mutate(Favorite.Sauce.Label=replace(Favorite.sauce.Label,Favorite.Sauce==2, "Western style (with tomato")) %>%
  mutate(Favorite.sauce.Label=replace(Favorite.sauce.Label,Favorite.Sauce==3, "Kansas style (with molasses")) %>% 
  mutate(Favorite.sauce.Label=replace(Favorite.sauce.Label,Favorite.Sauce==4, "Dry Rub")) %>%
  mutate(Favorite.sauce.Label=replace(Favorite.sauce.Label,Favorite.Sauce==5, "South Carolina Mustard")) %>%
  mutate(Favorite.sauce.Label=replace(Favorite.sauce.Label,Favorite.Sauce==6, "Korean style")) %>%
  mutate(Favorite.sauce.Label=replace(Favorite.sauce.Label,Favorite.Sauce==7, "Other")) -> VisualzingVariablesData

ggplot(data = VisualzingVariablesData, 
       mapping = aes(x = fct_infreq(Favorite.sauce.Label))) + 
  geom_bar(fill = "orange") + 
  labs (x= "Favorite Sauce", y="Total")

6. Total respondents that perfer each type of suace.

Identify how many total respondents prefer each type of sauce. Save the count as a data object. Use the head() command to display the data.

VisualzingVariablesData %>%
  count(Favorite.sauce.Label)

##             Favorite.sauce.Label   n
## 1                        Dry Rub  46
## 2 Eastern style (with no tomato) 162
## 3    Kansas style (with molasses  26
## 4                   Korean style  27
## 5                          Other  30
## 6         South Carolina Mustard  25
## 7                           <NA>  63

VisualzingVariablesData %>%
  count(Favorite.sauce.Label) -> FavoriteSauceData

head(FavoriteSauceData)

##             Favorite.sauce.Label   n
## 1                        Dry Rub  46
## 2 Eastern style (with no tomato) 162
## 3    Kansas style (with molasses  26
## 4                   Korean style  27
## 5                          Other  30
## 6         South Carolina Mustard  25

7. Proportions - Pie Chart 1

Create a pie chart that plots the respondents’ favorite sauce. You will need to transform the counts you calculated above into percentages and save these as a new data object before you can graph the data.

The pie chart should identify the percentage of respondents who prefer each type of sauce.
Round to 1 decimal place.
The chart should have a “blank” or “clean” background.
Change the title on the legend to “Favorite Sauce”.

ggplot(FavoriteSauceData,             #data argument
     aes(x="", y=n, fill=Favorite.sauce.Label)) +  #mapping and aesthetics arguments
  geom_bar(stat="identity", width=1, color="white") + #geom to create bar graph of proportions 
  coord_polar("y", start=0)   #geom argument to transform bar graph to pie chart

FavoriteSauceData %>% 
  mutate (Percentage.Respondents = n/sum(n)*100) -> FavoriteSauceData

head(FavoriteSauceData)

##             Favorite.sauce.Label   n Percentage.Respondents
## 1                        Dry Rub  46              12.137203
## 2 Eastern style (with no tomato) 162              42.744063
## 3    Kansas style (with molasses  26               6.860158
## 4                   Korean style  27               7.124011
## 5                          Other  30               7.915567
## 6         South Carolina Mustard  25               6.596306

ggplot(FavoriteSauceData, 
       aes(x="", y=Percentage.Respondents, fill=Favorite.sauce.Label)) + 
  geom_bar(stat="identity", width=1, color="white") +
  coord_polar("y",start=0)

ggplot(FavoriteSauceData, 
       aes(x="", y=Percentage.Respondents, fill=Favorite.sauce.Label)) + 
  geom_bar(stat="identity", width=1, color="white") + 
  coord_polar("y", start=0) + 
  geom_text(aes(label = Percentage.Respondents), position = position_stack(vjust = 0.5)) + 
  guides(fill=guide_legend("Favorite BBQ Sauce")) + 
  theme_void()

8. Proportions - Pie Chart 2

Create a pie chart that plots the respondents’ preferred level of sweetness. You need to identify how many total respondents prefer each type of sauce. Save the count as a data object. Then transform the count into percentages and save these as a new data object before you can graph the data.

The pie chart should identify the percentage of respondents who prefer each level of sweetness.
Round to whole numbers (no decimal places).
The chart should have a “blank” or “clean” background.

VisualzingVariablesData %>% 
  count(Sweetness) ->SweetnessData

head(SweetnessData)

##   Sweetness   n
## 1         1  30
## 2         2  83
## 3         3 163
## 4         4  85
## 5         5  18

SweetnessData %>%
  mutate(Sweet.Percent = n/sum(n)*100) -> SweetnessData

ggplot(SweetnessData, 
       aes(x="", y=Sweet.Percent, fill=Sweetness)) +
  geom_bar(stat="identity", width=1, color="white") + 
  coord_polar("y",start=0) + 
  geom_text(aes(label = Sweet.Percent), position = position_stack(vjust = 0.5)) + 
  guides(fill=guide_legend("Sweetness Prefernce")) + 
  theme_void()

9. Did you recieve help?

Enter the names of anyone one that assisted you with completing this lab. If no one helped you complete the assignment, just type out that no one helped you

10. Did you provide anyone help with completing this lab?

Enter the names of anyone that you assisted with completing this lab. If you did not help anyone, then just type out that you didn’t help anyone.

11. Knit the Document

Click the “Knit” button to publish your work as an html document. This document or file will appear in the folder specified by your working directory. You will need to upload both this RMarkdown file and the html file it produces to AsU Learn to get all of the lab points for this week.

CJ/PS 3115 Fall 2025 Visualizing Amounts and Proportions Lab Assignment

(Kennedi Croker)

(10/1/25)