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. Some tasks may also ask you to answer specific questions. 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 “PlayingWithDataTutorial.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. Additionally, for this assignment you will upload the Excel file you created.

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 10 and 11).

2. Getting started

Insert a chunk of code in this section to identify and set your working directory and load packages. We will use the same three packages we did in the last lab: openxlsx, dplyr and tidyverse.
setwd(“/Users/corddoss/Desktop/Research methods class/”) library(dplyr) Attaching package: ‘dplyr’

The following objects are masked from ‘package:stats’:

filter, lag

The following objects are masked from ‘package:base’:

intersect, setdiff, setequal, union
 library(tidyverse)

── Attaching core tidyverse packages ─────────── ✔ forcats 1.0.1 ✔ readr 2.1.5 ✔ ggplot2 4.0.0 ✔ stringr 1.5.2 ✔ lubridate 1.9.4 ✔ tibble 3.3.0 ✔ purrr 1.1.0 ✔ tidyr 1.3.1 ── Conflicts ────────── tidyverse_conflicts() ── ✖ dplyr::filter() masks stats::filter() ✖ dplyr::lag() masks stats::lag() ℹ Use the conflicted package to force all conflicts to become errors library(openxlsx) # 3. Load Two Data Sets Insert a chunk of code in this section to load your data. The Excel file for this assignment has two sheets: grades and attendance. Sheet 1 contains the grades data and Sheet 2 contains the attendance data. You will want to load each sheet into R as separate data objects. The name of the Excel file is different than what is in the instructions. Accordingly, you will need to adjust the code to read in the Excel file that was downloaded as part of the zip file. GradeBook <- read.xlsx(“GradeBook.xlsx” , sheet = 1) > head(GradeBook, 10) X1 Midterm.1 Midterm.2 Assignment.1 1 Noah 15.00000 12.00000 5.000000 2 Jack 11.00478 15.00000 6.771172 3 Emily 20.00000 20.00000 8.000000 4 Colin 20.00000 17.00000 8.000000 5 Hannah 10.00000 17.00000 6.802136 6 Aubrie 20.00000 14.00000 5.000000 7 Olivia 14.00000 17.72971 10.000000 8 Duncan 9.62783 16.00000 7.000000 9 Katie 19.00000 12.00000 9.000000 10 Jackson 17.00000 15.00000 8.000000 Assignment.2 Assignment.3 Final 1 8.000000 5.661442 30.00000 2 10.000000 8.000000 26.00000 3 8.000000 8.154995 20.00000 4 5.000000 8.673615 25.00000 5 9.604730 10.000000 20.00000 6 6.000000 6.000000 17.78453 7 7.000000 6.000000 26.00000 8 8.065708 8.000000 18.95910 9 8.000000 8.967217 20.00000 10 6.000000 2.549882 25.00000 Attendance <- read.xlsx(“GradeBook.xlsx” , sheet = 2) > head(Attendance, 10) Name 1 2 3 4 5 1 Noah 1 1 1 1 1 2 Jack 0 1 1 1 1 3 Emily 1 1 0 0 1 4 Colin 1 0 1 1 1 5 Hannah 1 1 0 1 1 6 Aubrie 1 1 1 1 1 7 Olivia 1 1 1 1 1 8 Duncan 0 1 0 0 1 9 Katie 1 1 1 1 1 10 Jackson 1 0 1 1 1 # 4. Take a look at your data Insert a chunk of code in this section and display the first 15 observations of each data set. head(Attendance, 15) Name 1 2 3 4 5 1 Noah 1 1 1 1 1 2 Jack 0 1 1 1 1 3 Emily 1 1 0 0 1 4 Colin 1 0 1 1 1 5 Hannah 1 1 0 1 1 6 Aubrie 1 1 1 1 1 7 Olivia 1 1 1 1 1 8 Duncan 0 1 0 0 1 9 Katie 1 1 1 1 1 10 Jackson 1 0 1 1 1 11 Victoria 1 1 1 1 1 12 Matthew 1 1 0 1 0 13 Michael 0 1 1 1 1 14 Olivia 1 1 1 1 1 15 Samantha 1 0 1 1 1 # 5. Rename Variables You will need to insert chunks of code and rename variables in your data sets in this section. I recommend trying to do only one thing per chunk of code. Attendance %>% + rename(Class1 = “1”, + Class2 = “2”, + Class3 = “3”, + Class4 = “4”, + Class5 = “5”) -> Attendance In the attendance data set, you will need to rename the variables that are currently numbers into text. In the instructions, I called each variable Class and then the number of that class, for example Class1. Instead of using the same variable name as I did, you should call each variable a Meeting. Attendance %>% + rename(Meeting1 = “Class1”, + Meeting2 = “Class2”, + Meeting3 = “Class3”, + Meeting4 = “Class4”, + Meeting5 = “Class5”) -> Attendance head(Attendance, 15) Name Meeting1 Meeting2 Meeting3 Meeting4 1 Noah 1 1 1 1 2 Jack 0 1 1 1 3 Emily 1 1 0 0 4 Colin 1 0 1 1 5 Hannah 1 1 0 1 6 Aubrie 1 1 1 1 7 Olivia 1 1 1 1 8 Duncan 0 1 0 0 9 Katie 1 1 1 1 10 Jackson 1 0 1 1 11 Victoria 1 1 1 1 12 Matthew 1 1 0 1 13 Michael 0 1 1 1 14 Olivia 1 1 1 1 15 Samantha 1 0 1 1 Meeting5 1 1 2 1 3 1 4 1 5 1 6 1 7 1 8 1 9 1 10 1 11 1 12 0 13 1 14 1 15 1 In the grade book data set, rename the variables so that they do not have a . in their names. GradeBook %>% + rename(Name = “X1”, + Midterm1 = “Midterm.1”, + Midterm2 = “Midterm.2”, + Assignment1 = “Assignment.1”, + Assignment2 = “Assignment.2”, + Assignment3 = “Assignment.3”, + Final = “Final”) -> GradeBook After renaming the variables, look at the first 15 observations for each data set. head(GradeBook, 15) Name Midterm1 Midterm2 Assignment1 1 Noah 15.00000 12.00000 5.000000 2 Jack 11.00478 15.00000 6.771172 3 Emily 20.00000 20.00000 8.000000 4 Colin 20.00000 17.00000 8.000000 5 Hannah 10.00000 17.00000 6.802136 6 Aubrie 20.00000 14.00000 5.000000 7 Olivia 14.00000 17.72971 10.000000 8 Duncan 9.62783 16.00000 7.000000 9 Katie 19.00000 12.00000 9.000000 10 Jackson 17.00000 15.00000 8.000000 11 Victoria 11.00000 9.93236 8.000000 12 Matthew 10.00000 13.00000 10.000000 13 Michael 7.00000 11.00000 8.000000 14 Olivia 14.00000 12.00000 6.000000 15 Samantha 6.00000 10.00000 6.000000 Assignment2 Assignment3 Final 1 8.000000 5.661442 30.00000 2 10.000000 8.000000 26.00000 3 8.000000 8.154995 20.00000 4 5.000000 8.673615 25.00000 5 9.604730 10.000000 20.00000 6 6.000000 6.000000 17.78453 7 7.000000 6.000000 26.00000 8 8.065708 8.000000 18.95910 9 8.000000 8.967217 20.00000 10 6.000000 2.549882 25.00000 11 10.000000 6.701154 26.00000 12 9.000000 10.000000 26.00000 13 10.000000 10.000000 18.00000 14 7.000000 8.000000 29.00000 15 8.000000 6.000000 19.00000 # 6. Creating New Attendance Variables In this section, insert chunks and create the following variables in your attendance data set. Attendance %>% + mutate(Present = (Meeting1 + Meeting2 + Meeting3 + Meeting4 + Meeting5)) -> Attendance - Total number of classes attended.

Total number of classes absent. There are a total of 5 classes that students could potentially attend.
Attendance %>%

mutate(Absent = ((Meeting1 + Meeting2 + Meeting3 + Meeting4 + Meeting5) -5) *-1) -> Attendance

Attendance$Unexcused <- 0 Attendance$Unexcused[8] <- 1

Total number of unexcused absences. Students are allowed up to 2 excused absences. Attendance %>%

mutate(GradePenalty = Unexcused * 0.5) -> Attendance

Penalty on grade for unexcused absences. For each unexcused absence, a student’s grade will be penalized 0.5 points. Based on the number of unexcused absences, calculate the total penalty that should be applied to their grade. head(Attendance, 15) Name Meeting1 Meeting2 Meeting3 Meeting4 1 Noah 1 1 1 1 2 Jack 0 1 1 1 3 Emily 1 1 0 0 4 Colin 1 0 1 1 5 Hannah 1 1 0 1 6 Aubrie 1 1 1 1 7 Olivia 1 1 1 1 8 Duncan 0 1 0 0 9 Katie 1 1 1 1 10 Jackson 1 0 1 1 11 Victoria 1 1 1 1 12 Matthew 1 1 0 1 13 Michael 0 1 1 1 14 Olivia 1 1 1 1 15 Samantha 1 0 1 1 Meeting5 Present Absent Unexcused 1 1 5 0 0 2 1 4 1 0 3 1 3 2 0 4 1 4 1 0 5 1 4 1 0 6 1 5 0 0 7 1 5 0 0 8 1 2 3 1 9 1 5 0 0 10 1 4 1 0 11 1 5 0 0 12 0 3 2 0 13 1 4 1 0 14 1 5 0 0 15 1 4 1 0 GradePenalty 1 0.0 2 0.0 3 0.0 4 0.0 5 0.0 6 0.0 7 0.0 8 0.5 9 0.0 10 0.0 11 0.0 12 0.0 13 0.0 14 0.0 15 0.0 After you have completed these calculations, take a look at the first 15 observations in your data set.

7. Create New Grade Variables

In this section, insert chunks and create the following variables in your grade book data set.

Each assignment in class is worth 10 points. Create a new variable for each assignment where you convert the raw score into a percentage. GradeBook %>%

mutate(PerA1 = (Assignment1/10)*100) -> GradeBook

Each midterm in the class is worth 20 points. Create a new variable for each midterm where you convert the raw score into a percentage. GradeBook %>%

mutate(PerA2 = (Assignment2/10)*100) -> GradeBook

The final exam is worth 30 points. Create a new variable for the final where you convert the raw score into a percentage.
GradeBook %>%
```
mutate(PerA3 = (Assignment3/10)*100) -> GradeBook
```
There are multiple ways one can calculate the overall grade for the class. You are going to calculate the final grade in two different ways.
GradeBook %>%

mutate(PerMT1 = (Midterm1/20)*100) -> GradeBook

You should provide equal weight to each item in the class regardless of the number of points it was originally worth. To do this, you should add together the percentage grades that you calculated and divide by 600 (you have 6 assignments, each one is worth up to 100 points once the grades were converted to percents). GradeBook %>%

mutate(PerMT2 = (Midterm2/20)*100) -> GradeBook

You should weight items based on the number of points each was originally worth. The most straightforward way to do this is to add together the raw scores for each item and then divide by the total number of points possible. You already have the information you need to calculate the total number of points possible because you know how many points each type of assignment is worth and you know how many of each type of assignment is in the grade book.
GradeBook %>%

```
mutate(PerF = (Final/30)*100) -> GradeBook
```
After you have completed these calucations, take a look at the first 15 observations in your data set. GradeBook %>%
```
mutate(FinalGrade1 = (PerA1 + PerA2 + PerA3 + PerMT1 + PerMT2 + PerF) / 6) -> GradeBook
```
8. Create Objects Containing a Single Value

In this section, insert chunks and calculate the mean, minimum, and maximum for 3 different variables (midterm 2, assignment 3, and the final exam) in the grade book data set. Use the variables that report the scores as a percentage that you created.
head(GradeBook, 15) Name Midterm1 Midterm2 Assignment1 1 Noah 15.00000 12.00000 5.000000 2 Jack 11.00478 15.00000 6.771172 3 Emily 20.00000 20.00000 8.000000 4 Colin 20.00000 17.00000 8.000000 5 Hannah 10.00000 17.00000 6.802136 6 Aubrie 20.00000 14.00000 5.000000 7 Olivia 14.00000 17.72971 10.000000 8 Duncan 9.62783 16.00000 7.000000 9 Katie 19.00000 12.00000 9.000000 10 Jackson 17.00000 15.00000 8.000000 11 Victoria 11.00000 9.93236 8.000000 12 Matthew 10.00000 13.00000 10.000000 13 Michael 7.00000 11.00000 8.000000 14 Olivia 14.00000 12.00000 6.000000 15 Samantha 6.00000 10.00000 6.000000 Assignment2 Assignment3 Final PerA1 1 8.000000 5.661442 30.00000 50.00000 2 10.000000 8.000000 26.00000 67.71172 3 8.000000 8.154995 20.00000 80.00000 4 5.000000 8.673615 25.00000 80.00000 5 9.604730 10.000000 20.00000 68.02136 6 6.000000 6.000000 17.78453 50.00000 7 7.000000 6.000000 26.00000 100.00000 8 8.065708 8.000000 18.95910 70.00000 9 8.000000 8.967217 20.00000 90.00000 10 6.000000 2.549882 25.00000 80.00000 11 10.000000 6.701154 26.00000 80.00000 12 9.000000 10.000000 26.00000 100.00000 13 10.000000 10.000000 18.00000 80.00000 14 7.000000 8.000000 29.00000 60.00000 15 8.000000 6.000000 19.00000 60.00000 PerA2 PerA3 PerMT1 PerMT2 1 80.00000 56.61442 75.00000 60.00000 2 100.00000 80.00000 55.02392 75.00000 3 80.00000 81.54995 100.00000 100.00000 4 50.00000 86.73615 100.00000 85.00000 5 96.04730 100.00000 50.00000 85.00000 6 60.00000 60.00000 100.00000 70.00000 7 70.00000 60.00000 70.00000 88.64856 8 80.65708 80.00000 48.13915 80.00000 9 80.00000 89.67217 95.00000 60.00000 10 60.00000 25.49882 85.00000 75.00000 11 100.00000 67.01154 55.00000 49.66180 12 90.00000 100.00000 50.00000 65.00000 13 100.00000 100.00000 35.00000 55.00000 14 70.00000 80.00000 70.00000 60.00000 15 80.00000 60.00000 30.00000 50.00000 PerF FinalGrade1 FinalGrade2 1 100.00000 70.26907 75.66144 2 86.66667 77.40038 76.77596 3 66.66667 84.70277 84.15500 4 83.33333 80.84491 83.67361 5 66.66667 77.62255 73.40687 6 59.28176 66.54696 68.78453 7 86.66667 79.21920 80.72971 8 63.19701 70.33221 67.65264 9 66.66667 80.22314 76.96722 10 83.33333 68.13869 73.54988 11 86.66667 73.05667 71.63351 12 86.66667 81.94444 78.00000 13 60.00000 71.66667 64.00000 14 96.66667 72.77778 76.00000 15 63.33333 57.22222 55.00000 # 9. Create Objects Containing Multiple Values In this section, insert chunks and produce the following objects that will contain values for each variable in the data set. mean_PerA2 <- mean(GradeBook$PerA2) > min_PerA2 <- min(GradeBook$PerA2) > max_PerA2 <- max(GradeBook$PerA2) print(mean_PerA2) [1] 79.16902 print(min_PerA2) [1] 50 print(max_PerA2) [1] 100 mean_PerA3 <- mean(GradeBook$PerA3) min_PerA3 <- min(GradeBook$PerA3) max_PerA3 <- max(GradeBook$PerA3) print(mean_PerA3) [1] 74.81769 print(min_PerA3) [1] 25.49882 print(max_PerA3) [1] 100 mean_PerF <- mean(GradeBook$PerF) > min_PerF <- min(GradeBook$PerF) > max_PerF <- max(GradeBook$PerF) > print(mean_PerF) [1] 78.07159 print(min_PerF) [1] 59.28176 print(max_PerF) [1] 100
Using the attendance data, create two objects. One object should contain the total number of students attending a class session. One object should contain the mean number of students attending a class session.
Using the grade book data, create three objects for the mean, min, and max grades for each of the variable in the data set.

10. Combining Objects

In this section, insert chunks of code that will combine objects together.

Combine the two objects you created using the attendance data set in the last question into a single object. Print the object where you stored these objects.
AttendanceSummary <- rbind(attend_sum,attend_mean) > print(AttendanceSummary) Meeting1 Meeting2 Meeting3 Meeting4 attend_sum 13.0000 13.0000 12.00 14.000 attend_mean 0.8125 0.8125 0.75 0.875 Meeting5 attend_sum 15.0000 attend_mean 0.9375 >
Combine the three objects you created using the grade book data set in the last question into a single object. Print the object where you stored these objects.
GradeSummary <- rbind(mean_grades,min_grades,max_grades) > print(GradeSummary) PerA1 PerA2 PerA3 mean_grades 75.35832 79.16902 74.81769 min_grades 50.00000 50.00000 25.49882 max_grades 100.00000 100.00000 100.00000 PerMT1 PerMT2 PerF mean_grades 67.07269 69.8944 78.07159 min_grades 30.00000 49.6618 59.28176 max_grades 100.00000 100.0000 100.00000 # 11. Export Data Sets In this section, insert a chunk of code to export the grade book data, the attendance data, the summary grade book, and the summary attendance as one Excel file. Make sure to name your data file something different than the Excel file that had the original data that you loaded into R for this assignment.
sheets<- list(“Grades” = GradeBook, “Attendance2” = Attendance, “AttendanceSummary” = AttendanceSummary, “GradeSummary” = GradeSummary) > write.xlsx(sheets, file = “CombinedSummary.xlsx”) # 12. 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

13. 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. no # 14. 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. Additionally, you need to upload the Excel file that you exported when completing the assignment to get all of the lab points for this week.

PS/CJ3115 Fall 2025: Playing with Data

(Cord Doss)