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.
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).
getwd()## [1] "/Users/Saniyaaa/Desktop/PlayingWithDataFall2025"setwd("/Users/Saniyaaa/Desktop/PlayingWithDataFall2025")setwd("/Users/Saniyaaa/Desktop/PlayingWithDataFall2025")
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, unionInsert 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.
library(openxlsx)
library(dplyr)
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ readr 2.1.5
## ✔ ggplot2 3.5.2 ✔ stringr 1.5.1
## ✔ 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 (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsInsert 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 Assignment.2 Assignment.3 Final
## 1 Noah 15.00000 12.00000 5.000000 8.000000 5.661442 30.00000
## 2 Jack 11.00478 15.00000 6.771172 10.000000 8.000000 26.00000
## 3 Emily 20.00000 20.00000 8.000000 8.000000 8.154995 20.00000
## 4 Colin 20.00000 17.00000 8.000000 5.000000 8.673615 25.00000
## 5 Hannah 10.00000 17.00000 6.802136 9.604730 10.000000 20.00000
## 6 Aubrie 20.00000 14.00000 5.000000 6.000000 6.000000 17.78453
## 7 Olivia 14.00000 17.72971 10.000000 7.000000 6.000000 26.00000
## 8 Duncan 9.62783 16.00000 7.000000 8.065708 8.000000 18.95910
## 9 Katie 19.00000 12.00000 9.000000 8.000000 8.967217 20.00000
## 10 Jackson 17.00000 15.00000 8.000000 6.000000 2.549882 25.00000Attendance <- 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 1Insert a chunk of code in this section and display the first 15 observations of each data set.
GradeBook <- read.xlsx("GradeBook.xlsx" , sheet = 1)
head(GradeBook, 15)## X1 Midterm.1 Midterm.2 Assignment.1 Assignment.2 Assignment.3 Final
## 1 Noah 15.00000 12.00000 5.000000 8.000000 5.661442 30.00000
## 2 Jack 11.00478 15.00000 6.771172 10.000000 8.000000 26.00000
## 3 Emily 20.00000 20.00000 8.000000 8.000000 8.154995 20.00000
## 4 Colin 20.00000 17.00000 8.000000 5.000000 8.673615 25.00000
## 5 Hannah 10.00000 17.00000 6.802136 9.604730 10.000000 20.00000
## 6 Aubrie 20.00000 14.00000 5.000000 6.000000 6.000000 17.78453
## 7 Olivia 14.00000 17.72971 10.000000 7.000000 6.000000 26.00000
## 8 Duncan 9.62783 16.00000 7.000000 8.065708 8.000000 18.95910
## 9 Katie 19.00000 12.00000 9.000000 8.000000 8.967217 20.00000
## 10 Jackson 17.00000 15.00000 8.000000 6.000000 2.549882 25.00000
## 11 Victoria 11.00000 9.93236 8.000000 10.000000 6.701154 26.00000
## 12 Matthew 10.00000 13.00000 10.000000 9.000000 10.000000 26.00000
## 13 Michael 7.00000 11.00000 8.000000 10.000000 10.000000 18.00000
## 14 Olivia 14.00000 12.00000 6.000000 7.000000 8.000000 29.00000
## 15 Samantha 6.00000 10.00000 6.000000 8.000000 6.000000 19.00000Attendance <- read.xlsx("Gradebook.xlsx" , sheet = 2)
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 1You 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.
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 = "1",
Meeting2 = "2",
Meeting3 = "3",
Meeting4 = "4",
Meeting5 = "5") -> Attendancehead(Attendance, 10)## Name Meeting1 Meeting2 Meeting3 Meeting4 Meeting5
## 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 1In 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, 10)## Name Midterm1 Midterm2 Assignment1 Assignment2 Assignment3 Final
## 1 Noah 15.00000 12.00000 5.000000 8.000000 5.661442 30.00000
## 2 Jack 11.00478 15.00000 6.771172 10.000000 8.000000 26.00000
## 3 Emily 20.00000 20.00000 8.000000 8.000000 8.154995 20.00000
## 4 Colin 20.00000 17.00000 8.000000 5.000000 8.673615 25.00000
## 5 Hannah 10.00000 17.00000 6.802136 9.604730 10.000000 20.00000
## 6 Aubrie 20.00000 14.00000 5.000000 6.000000 6.000000 17.78453
## 7 Olivia 14.00000 17.72971 10.000000 7.000000 6.000000 26.00000
## 8 Duncan 9.62783 16.00000 7.000000 8.065708 8.000000 18.95910
## 9 Katie 19.00000 12.00000 9.000000 8.000000 8.967217 20.00000
## 10 Jackson 17.00000 15.00000 8.000000 6.000000 2.549882 25.00000In this section, insert chunks and create the following variables in your attendance data set.
Total number of classes attended.
Total number of classes absent. There are a total of 5 classes that students could potentially attend.
Total number of unexcused absences. Students are allowed up to 2 excused absences.
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.
Attendance %>%
mutate(Present = ("class1 + class2 + class3 + class4 + class5")) -> Attendancehead(Attendance, 15)## Name Meeting1 Meeting2 Meeting3 Meeting4 Meeting5
## 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
## Present
## 1 class1 + class2 + class3 + class4 + class5
## 2 class1 + class2 + class3 + class4 + class5
## 3 class1 + class2 + class3 + class4 + class5
## 4 class1 + class2 + class3 + class4 + class5
## 5 class1 + class2 + class3 + class4 + class5
## 6 class1 + class2 + class3 + class4 + class5
## 7 class1 + class2 + class3 + class4 + class5
## 8 class1 + class2 + class3 + class4 + class5
## 9 class1 + class2 + class3 + class4 + class5
## 10 class1 + class2 + class3 + class4 + class5
## 11 class1 + class2 + class3 + class4 + class5
## 12 class1 + class2 + class3 + class4 + class5
## 13 class1 + class2 + class3 + class4 + class5
## 14 class1 + class2 + class3 + class4 + class5
## 15 class1 + class2 + class3 + class4 + class5After you have completed these calculations, take a look at the first 15 observations in your data set.
In this section, insert chunks and create the following variables in your grade book data set.
GradeBook %>%
mutate(PerA.1 = (Assignment1/10)*100) -> GradeBookprint(GradeBook$Assignment1)## [1] 5.000000 6.771172 8.000000 8.000000 6.802136 5.000000 10.000000
## [8] 7.000000 9.000000 8.000000 8.000000 10.000000 8.000000 6.000000
## [15] 6.000000 9.000000GradeBook %>%
mutate(PerA.2 = (Assignment2/10)*100) -> GradeBookprint(GradeBook$Assignment2)## [1] 8.000000 10.000000 8.000000 5.000000 9.604730 6.000000 7.000000
## [8] 8.065708 8.000000 6.000000 10.000000 9.000000 10.000000 7.000000
## [15] 8.000000 7.000000GradeBook %>%
mutate(PerA.3 = (Assignment3/10)*100) -> GradeBookprint(GradeBook$Assignment3)## [1] 5.661442 8.000000 8.154995 8.673615 10.000000 6.000000 6.000000
## [8] 8.000000 8.967217 2.549882 6.701154 10.000000 10.000000 8.000000
## [15] 6.000000 7.000000GradeBook %>%
mutate(PerMT.1 = (Midterm1/20)*100) -> GradeBookprint(GradeBook$Midterm1)## [1] 15.00000 11.00478 20.00000 20.00000 10.00000 20.00000 14.00000 9.62783
## [9] 19.00000 17.00000 11.00000 10.00000 7.00000 14.00000 6.00000 11.00000GradeBook %>%
mutate(PerMT.2 = (Midterm2/20)*100) -> GradeBookprint(GradeBook$Midterm2)## [1] 12.00000 15.00000 20.00000 17.00000 17.00000 14.00000 17.72971 16.00000
## [9] 12.00000 15.00000 9.93236 13.00000 11.00000 12.00000 10.00000 12.00000print(GradeBook$PerMT.1)## [1] 75.00000 55.02392 100.00000 100.00000 50.00000 100.00000 70.00000
## [8] 48.13915 95.00000 85.00000 55.00000 50.00000 35.00000 70.00000
## [15] 30.00000 55.00000GradeBook %>%
mutate(PerF = (Final/30)*100) -> GradeBookprint(GradeBook$PerF)## [1] 100.00000 86.66667 66.66667 83.33333 66.66667 59.28176 86.66667
## [8] 63.19701 66.66667 83.33333 86.66667 86.66667 60.00000 96.66667
## [15] 63.33333 93.33333There 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(OverallGrade = ((PerA.1+PerA.2+PerA.3+PerMT.1+PerMT.2+PerF)/600*100)) -> GradeBookYou 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).
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.
After you have completed these calucations, take a look at the first 15 observations in your data set.
head(GradeBook, 15)## Name Midterm1 Midterm2 Assignment1 Assignment2 Assignment3 Final
## 1 Noah 15.00000 12.00000 5.000000 8.000000 5.661442 30.00000
## 2 Jack 11.00478 15.00000 6.771172 10.000000 8.000000 26.00000
## 3 Emily 20.00000 20.00000 8.000000 8.000000 8.154995 20.00000
## 4 Colin 20.00000 17.00000 8.000000 5.000000 8.673615 25.00000
## 5 Hannah 10.00000 17.00000 6.802136 9.604730 10.000000 20.00000
## 6 Aubrie 20.00000 14.00000 5.000000 6.000000 6.000000 17.78453
## 7 Olivia 14.00000 17.72971 10.000000 7.000000 6.000000 26.00000
## 8 Duncan 9.62783 16.00000 7.000000 8.065708 8.000000 18.95910
## 9 Katie 19.00000 12.00000 9.000000 8.000000 8.967217 20.00000
## 10 Jackson 17.00000 15.00000 8.000000 6.000000 2.549882 25.00000
## 11 Victoria 11.00000 9.93236 8.000000 10.000000 6.701154 26.00000
## 12 Matthew 10.00000 13.00000 10.000000 9.000000 10.000000 26.00000
## 13 Michael 7.00000 11.00000 8.000000 10.000000 10.000000 18.00000
## 14 Olivia 14.00000 12.00000 6.000000 7.000000 8.000000 29.00000
## 15 Samantha 6.00000 10.00000 6.000000 8.000000 6.000000 19.00000
## PerA.1 PerA.2 PerA.3 PerMT.1 PerMT.2 PerF OverallGrade
## 1 50.00000 80.00000 56.61442 75.00000 60.00000 100.00000 70.26907
## 2 67.71172 100.00000 80.00000 55.02392 75.00000 86.66667 77.40038
## 3 80.00000 80.00000 81.54995 100.00000 100.00000 66.66667 84.70277
## 4 80.00000 50.00000 86.73615 100.00000 85.00000 83.33333 80.84491
## 5 68.02136 96.04730 100.00000 50.00000 85.00000 66.66667 77.62255
## 6 50.00000 60.00000 60.00000 100.00000 70.00000 59.28176 66.54696
## 7 100.00000 70.00000 60.00000 70.00000 88.64856 86.66667 79.21920
## 8 70.00000 80.65708 80.00000 48.13915 80.00000 63.19701 70.33221
## 9 90.00000 80.00000 89.67217 95.00000 60.00000 66.66667 80.22314
## 10 80.00000 60.00000 25.49882 85.00000 75.00000 83.33333 68.13869
## 11 80.00000 100.00000 67.01154 55.00000 49.66180 86.66667 73.05667
## 12 100.00000 90.00000 100.00000 50.00000 65.00000 86.66667 81.94444
## 13 80.00000 100.00000 100.00000 35.00000 55.00000 60.00000 71.66667
## 14 60.00000 70.00000 80.00000 70.00000 60.00000 96.66667 72.77778
## 15 60.00000 80.00000 60.00000 30.00000 50.00000 63.33333 57.22222In 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.
mean_PerMT1 <- mean(GradeBook$PerMT.1)print(mean_PerMT1)## [1] 67.07269min_PerMT1 <- min(GradeBook$PerMT.1)
print(min_PerMT1)## [1] 30max_PerMT1 <- max(GradeBook$PerMT.1)
print(max_PerMT1)## [1] 100In this section, insert chunks and produce the following objects that will contain values for each variable in the data set.
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.
attendsum <- sapply(Attendance[ , c('Meeting1', 'Meeting2','Meeting3', 'Meeting4', 'Meeting5')], sum)attendmean <- sapply(Attendance[ , c('Meeting1', 'Meeting2','Meeting3', 'Meeting4', 'Meeting5')], mean)attendmean <- sapply(GradeBook[ , c('Assignment1', 'Assignment2','Assignment3', 'Midterm1', 'Midterm2', 'Final')], mean)attendmin <- sapply(GradeBook[ , c('Assignment1', 'Assignment2','Assignment3', 'Midterm1', 'Midterm2', 'Final')], min)attendmax <- sapply(GradeBook[ , c('Assignment1', 'Assignment2','Assignment3', 'Midterm1', 'Midterm2', 'Final')], max)In this section, insert chunks of code that will combine objects together.
AttedanceSummary <- rbind(attendsum,attendmean)## Warning in rbind(attendsum, attendmean): number of columns of result is not a
## multiple of vector length (arg 1)print(AttedanceSummary)## Assignment1 Assignment2 Assignment3 Midterm1 Midterm2 Final
## attendsum 13.000000 13.000000 12.000000 14.00000 15.00000 13.00000
## attendmean 7.535832 7.916902 7.481769 13.41454 13.97888 23.42148GradeBookSummary <- rbind(attendmin, attendmean, attendmax)print(GradeBookSummary)## Assignment1 Assignment2 Assignment3 Midterm1 Midterm2 Final
## attendmin 5.000000 5.000000 2.549882 6.00000 9.93236 17.78453
## attendmean 7.535832 7.916902 7.481769 13.41454 13.97888 23.42148
## attendmax 10.000000 10.000000 10.000000 20.00000 20.00000 30.00000In 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.
write.xlsx(AttedanceSummary, file = "AttendanceSummary.xlsx")write.xlsx(GradeBookSummary, file = "GradesSummary.xlsx")sheets<- list("Grades" = GradeBook, "Attendance" = Attendance, AttendanceSummary = "AttedanceSummary")write.xlsx(sheets, file = "combined.xlsx")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
Caitlyn Fiocchi
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.
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.