RStat2101: Active Learning
Task I - Version III
Name:Ruqaia Hussain Ali Al-sawaii
ID:142162
Section:40
2024-03-23
YAML Header
Question 1. [1 point] Edit the header of the document by stating your name, ID and section.
Reading Dataset
Question 2. [2 points] Read the dataset
`AL_S2101_Grade.csv’ and assign it to an object called
s2101_grade.
#Answer-Question 2
## type the code here
s2101_grade <- read.csv ("AL_S2101_Grade.csv")
s2101_grade## College Gender Section Total Grade
## 1 SCI Female 10 98 A
## 2 SCI Female 10 97 A
## 3 EDU Female 40 96 A
## 4 EDU Female 40 96 A
## 5 SCI Female 50 95 A
## 6 SCI Female 40 94 A
## 7 EDU Female 70 94 A
## 8 SCI Female 20 93 A
## 9 SCI Female 40 93 A
## 10 EDU Female 10 93 A
## 11 EDU Female 20 92 A
## 12 MED Female 50 92 A
## 13 SCI Female 20 91 A
## 14 SCI Female 40 91 A
## 15 EDU Female 40 91 A
## 16 EDU Female 10 90 A
## 17 SCI Female 30 90 A
## 18 SCI Male 30 90 A
## 19 SCI Female 10 90 A
## 20 SCI Female 50 90 A
## 21 SCI Female 70 90 A
## 22 EDU Female 80 90 A
## 23 SCI Male 30 89 A-
## 24 EDU Female 50 89 A-
## 25 EDU Female 80 89 A-
## 26 MED Female 80 89 A-
## 27 EDU Female 20 88 A-
## 28 SCI Female 20 88 A-
## 29 SCI Female 60 88 A-
## 30 SCI Female 70 88 A-
## 31 SCI Male 80 88 A-
## 32 SCI Female 80 88 A-
## 33 SCI Male 10 87 A-
## 34 SCI Male 10 87 A-
## 35 SCI Male 10 87 A-
## 36 SCI Female 10 87 A-
## 37 SCI Female 60 87 A-
## 38 SCI Male 60 86 A-
## 39 SCI Female 10 85 A-
## 40 SCI Female 10 85 A-
## 41 SCI Female 30 85 A-
## 42 SCI Female 40 85 A-
## 43 EDU Female 70 85 A-
## 44 EDU Female 70 85 A-
## 45 SCI Male 80 85 A-
## 46 SCI Female 20 84 B+
## 47 SCI Male 20 84 B+
## 48 SCI Female 20 84 B+
## 49 SCI Female 50 84 B+
## 50 EDU Female 80 84 B+
## 51 SCI Female 10 83 B+
## 52 SCI Female 10 83 B+
## 53 SCI Female 10 83 B+
## 54 SCI Male 20 83 B+
## 55 SCI Male 20 83 B+
## 56 SCI Female 40 83 B+
## 57 SCI Female 70 83 B+
## 58 SCI Female 10 82 B+
## 59 SCI Female 10 82 B+
## 60 SCI Female 20 82 B+
## 61 SCI Female 20 82 B+
## 62 SCI Male 20 82 B+
## 63 EDU Male 30 82 B+
## 64 SCI Female 30 82 B+
## 65 EDU Male 40 82 B+
## 66 SCI Female 40 82 B+
## 67 SCI Female 60 82 B+
## 68 SCI Female 70 82 B+
## 69 SCI Male 40 81 B+
## 70 EDU Female 80 81 B+
## 71 SCI Female 20 80 B+
## 72 SCI Male 20 80 B+
## 73 EDU Female 40 80 B+
## 74 MED Male 50 80 B+
## 75 SCI Male 50 80 B+
## 76 SCI Female 80 80 B+
## 77 EDU Male 10 79 B
## 78 SCI Female 10 79 B
## 79 SCI Female 10 79 B
## 80 SCI Female 10 79 B
## 81 SCI Female 20 79 B
## 82 SCI Female 40 79 B
## 83 EDU Female 40 79 B
## 84 SCI Male 30 78 B
## 85 SCI Male 30 78 B
## 86 SCI Female 30 78 B
## 87 SCI Female 80 78 B
## 88 SCI Male 10 77 B
## 89 SCI Female 10 77 B
## 90 SCI Female 30 77 B
## 91 SCI Male 30 77 B
## 92 SCI Female 30 77 B
## 93 SCI Male 30 77 B
## 94 EDU Male 50 77 B
## 95 EDU Female 50 77 B
## 96 SCI Female 70 77 B
## 97 SCI Male 80 77 B
## 98 SCI Male 80 77 B
## 99 SCI Male 10 76 B
## 100 SCI Female 10 76 B
## 101 SCI Female 20 76 B
## 102 SCI Female 40 76 B
## 103 SCI Female 50 76 B
## 104 SCI Female 60 76 B
## 105 SCI Female 30 75 B-
## 106 SCI Male 40 75 B-
## 107 EDU Male 50 75 B-
## 108 EDU Female 50 75 B-
## 109 EDU Female 60 75 B-
## 110 SCI Female 60 75 B-
## 111 EDU Female 70 75 B-
## 112 SCI Female 80 75 B-
## 113 SCI Female 10 74 B-
## 114 SCI Female 20 74 B-
## 115 SCI Female 30 74 B-
## 116 SCI Female 30 74 B-
## 117 SCI Female 40 74 B-
## 118 EDU Female 60 74 B-
## 119 SCI Male 70 74 B-
## 120 SCI Female 10 73 B-
## 121 SCI Female 20 73 B-
## 122 SCI Female 30 73 B-
## 123 SCI Female 50 73 B-
## 124 SCI Male 60 73 B-
## 125 SCI Female 70 73 B-
## 126 SCI Male 20 72 B-
## 127 SCI Male 30 72 B-
## 128 SCI Female 30 72 B-
## 129 EDU Male 40 72 B-
## 130 SCI Female 40 72 B-
## 131 SCI Female 60 72 B-
## 132 SCI Female 40 71 C+
## 133 SCI Male 40 71 C+
## 134 SCI Female 50 71 C+
## 135 SCI Female 70 71 C+
## 136 SCI Male 70 71 C+
## 137 AGR Female 70 71 C+
## 138 SCI Female 80 71 C+
## 139 AGR Female 80 71 C+
## 140 SCI Male 10 70 C+
## 141 SCI Male 10 70 C+
## 142 SCI Male 30 70 C+
## 143 SCI Female 50 70 C+
## 144 EDU Male 50 70 C+
## 145 SCI Female 70 70 C+
## 146 SCI Female 80 70 C+
## 147 SCI Male 10 69 C+
## 148 EDU Male 40 69 C+
## 149 SCI Male 40 69 C+
## 150 SCI Female 50 69 C+
## 151 SCI Female 70 69 C+
## 152 SCI Female 10 68 C+
## 153 SCI Female 20 68 C+
## 154 SCI Female 30 68 C+
## 155 SCI Male 30 68 C+
## 156 SCI Male 40 68 C+
## 157 SCI Female 50 68 C+
## 158 SCI Male 50 68 C+
## 159 EDU Female 50 68 C+
## 160 SCI Female 70 68 C+
## 161 SCI Female 80 68 C+
## 162 SCI Female 80 68 C+
## 163 SCI Female 80 68 C+
## 164 SCI Male 10 67 C+
## 165 SCI Female 10 67 C+
## 166 SCI Female 30 67 C+
## 167 SCI Male 50 67 C+
## 168 SCI Male 60 67 C+
## 169 SCI Male 60 67 C+
## 170 SCI Male 60 67 C+
## 171 SCI Female 30 66 C
## 172 SCI Male 30 66 C
## 173 SCI Female 40 66 C
## 174 SCI Male 50 66 C
## 175 SCI Female 50 66 C
## 176 EDU Female 70 66 C
## 177 ART Male 80 66 C
## 178 SCI Female 80 66 C
## 179 SCI Female 10 65 C
## 180 SCI Male 20 65 C
## 181 SCI Male 30 65 C
## 182 SCI Male 30 65 C
## 183 SCI Male 30 65 C
## 184 EDU Male 40 65 C
## 185 SCI Female 40 65 C
## 186 SCI Female 60 65 C
## 187 SCI Male 60 65 C
## 188 SCI Male 10 64 C
## 189 SCI Male 20 64 C
## 190 SCI Male 60 64 C
## 191 EDU Male 80 64 C
## 192 SCI Male 10 63 C
## 193 SCI Female 10 63 C
## 194 SCI Female 20 63 C
## 195 SCI Male 30 63 C
## 196 SCI Male 40 63 C
## 197 SCI Male 50 63 C
## 198 SCI Female 70 63 C
## 199 SCI Male 10 62 C
## 200 EDU Male 70 62 C
## 201 SCI Male 80 62 C
## 202 SCI Male 10 61 C-
## 203 SCI Male 30 61 C-
## 204 SCI Male 40 61 C-
## 205 SCI Male 50 61 C-
## 206 EDU Female 50 61 C-
## 207 SCI Female 60 61 C-
## 208 AGR Female 60 61 C-
## 209 SCI Male 70 61 C-
## 210 SCI Male 10 60 C-
## 211 SCI Female 10 60 C-
## 212 AGR Male 30 60 C-
## 213 SCI Male 70 60 C-
## 214 SCI Female 10 59 C-
## 215 SCI Male 30 59 C-
## 216 EDU Female 40 59 C-
## 217 EDU Male 50 59 C-
## 218 SCI Female 60 59 C-
## 219 AGR Male 60 59 C-
## 220 SCI Male 30 58 C-
## 221 SCI Male 60 58 C-
## 222 SCI Female 60 58 C-
## 223 SCI Male 20 57 C-
## 224 SCI Male 20 57 C-
## 225 SCI Male 30 57 C-
## 226 SCI Female 70 57 C-
## 227 SCI Male 10 56 D+
## 228 SCI Female 20 55 D+
## 229 SCI Male 70 55 D+
## 230 ART Male 80 55 D+
## 231 SCI Female 10 54 D+
## 232 EDU Male 40 54 D+
## 233 AGR Male 60 54 D+
## 234 SCI Male 80 54 D+
## 235 SCI Male 80 54 D+
## 236 SCI Male 20 53 D+
## 237 SCI Male 40 53 D+
## 238 SCI Male 60 53 D+
## 239 SCI Male 50 51 D
## 240 SCI Male 20 49 F
## 241 SCI Male 80 44 F
## 242 SCI Male 50 38 F
## 243 SCI Male 10 33 FData Extraction
Question 3. [2 points] Extract the dataset of
sections 10 and 40. Assign the result to an object called
grade_10_40 . [Hint: use a logical statement
s2101_grade$Section %in% c(10,40)]
#Answer-Q3
## type the code here
grade_10_40 <-s2101_grade[s2101_grade$Section %in% c(10,40), ]
grade_10_40## College Gender Section Total Grade
## 1 SCI Female 10 98 A
## 2 SCI Female 10 97 A
## 3 EDU Female 40 96 A
## 4 EDU Female 40 96 A
## 6 SCI Female 40 94 A
## 9 SCI Female 40 93 A
## 10 EDU Female 10 93 A
## 14 SCI Female 40 91 A
## 15 EDU Female 40 91 A
## 16 EDU Female 10 90 A
## 19 SCI Female 10 90 A
## 33 SCI Male 10 87 A-
## 34 SCI Male 10 87 A-
## 35 SCI Male 10 87 A-
## 36 SCI Female 10 87 A-
## 39 SCI Female 10 85 A-
## 40 SCI Female 10 85 A-
## 42 SCI Female 40 85 A-
## 51 SCI Female 10 83 B+
## 52 SCI Female 10 83 B+
## 53 SCI Female 10 83 B+
## 56 SCI Female 40 83 B+
## 58 SCI Female 10 82 B+
## 59 SCI Female 10 82 B+
## 65 EDU Male 40 82 B+
## 66 SCI Female 40 82 B+
## 69 SCI Male 40 81 B+
## 73 EDU Female 40 80 B+
## 77 EDU Male 10 79 B
## 78 SCI Female 10 79 B
## 79 SCI Female 10 79 B
## 80 SCI Female 10 79 B
## 82 SCI Female 40 79 B
## 83 EDU Female 40 79 B
## 88 SCI Male 10 77 B
## 89 SCI Female 10 77 B
## 99 SCI Male 10 76 B
## 100 SCI Female 10 76 B
## 102 SCI Female 40 76 B
## 106 SCI Male 40 75 B-
## 113 SCI Female 10 74 B-
## 117 SCI Female 40 74 B-
## 120 SCI Female 10 73 B-
## 129 EDU Male 40 72 B-
## 130 SCI Female 40 72 B-
## 132 SCI Female 40 71 C+
## 133 SCI Male 40 71 C+
## 140 SCI Male 10 70 C+
## 141 SCI Male 10 70 C+
## 147 SCI Male 10 69 C+
## 148 EDU Male 40 69 C+
## 149 SCI Male 40 69 C+
## 152 SCI Female 10 68 C+
## 156 SCI Male 40 68 C+
## 164 SCI Male 10 67 C+
## 165 SCI Female 10 67 C+
## 173 SCI Female 40 66 C
## 179 SCI Female 10 65 C
## 184 EDU Male 40 65 C
## 185 SCI Female 40 65 C
## 188 SCI Male 10 64 C
## 192 SCI Male 10 63 C
## 193 SCI Female 10 63 C
## 196 SCI Male 40 63 C
## 199 SCI Male 10 62 C
## 202 SCI Male 10 61 C-
## 204 SCI Male 40 61 C-
## 210 SCI Male 10 60 C-
## 211 SCI Female 10 60 C-
## 214 SCI Female 10 59 C-
## 216 EDU Female 40 59 C-
## 227 SCI Male 10 56 D+
## 231 SCI Female 10 54 D+
## 232 EDU Male 40 54 D+
## 237 SCI Male 40 53 D+
## 243 SCI Male 10 33 FData Tabulation
Question 4. [3 points] Use grade_10_40
dataset to create a relative frequency table called
grade_table of Grade variable. Print the
table.
#Answer-Question 4
## type the code here
grade_table<-transform(table(grade_10_40$Grade))
grade_table$relative_freq<-prop.table(grade_table$Freq)*100
grade_table## Var1 Freq relative_freq
## 1 A 11 14.473684
## 2 A- 7 9.210526
## 3 B 11 14.473684
## 4 B- 6 7.894737
## 5 B+ 10 13.157895
## 6 C 9 11.842105
## 7 C- 6 7.894737
## 8 C+ 11 14.473684
## 9 D+ 4 5.263158
## 10 F 1 1.315789Data Visualization
Question 5. [3 points] Use histogram with 5 breaks to graphically represent the total scores of Science students in sections 10 and 40.
#Answer-Question 5
## type the code here
par(mfrow=c(1,2)) ## keep this to show two graphs at the same row
grade_science<-grade_10_40[grade_10_40$College %in% c("SCI"), ]
grade_science_hist<- hist(grade_science$Total,
breaks= 5 ,
xlab="Total",
ylab="frequncy",
col="darkblue",
main="total scores of Science students in sections 10 and 40")
Question 6. [3 points] Customize the obtained graphs by adding meaningful title, labels and distinct colors.
#Answer-Question 6
## type the code here
grade_science_hist<- hist(grade_science$Total,
breaks= 5 ,
xlab="grade",
ylab="frequncy",
col="purple",
main="grades of Science students in sections 10 and 40")
Question 7. [2 points] Identify any patterns or differences between the two sections’ grades.
the grads of section 40 has a symmetric shape of data
the grads of section 10 has a left-Skewed shape of data
grade_10<-s2101_grade[s2101_grade$Section %in% c(10), ]
boxplot(grade_10$Total,
horizontal = TRUE,
col = "darkgreen")
grade_40<-s2101_grade[s2101_grade$Section %in% c(40), ]
boxplot(grade_40$Total,
horizontal = TRUE,
col = "darkred")
Data Description
Question 8. [2 points] Find the five number summary for the scores of Science students in each section.
#Answer-Question 8
## type the code here
grade_sci<-s2101_grade[s2101_grade$College %in% c("SCI"), ]
fivenum(grade_sci$Total)## [1] 33 65 72 82 98Question 9. [2 points] Interpret the third quartile value for each section.
section 10 = 83.50
section 20 = 84
section 30 = 77.75
section 40 = 82.25
section 50 = 77
section 60 = 75.0
section 70 = 82.25
section 80 = 82.5
grade_10<-s2101_grade[s2101_grade$Section %in% c(10), ]
quantile(grade_10$Total)## 0% 25% 50% 75% 100%
## 33.00 64.75 76.50 83.50 98.00grade_20<-s2101_grade[s2101_grade$Section %in% c(20), ]
quantile(grade_20$Total)## 0% 25% 50% 75% 100%
## 49 65 80 84 93grade_30<-s2101_grade[s2101_grade$Section %in% c(30), ]
quantile(grade_30$Total)## 0% 25% 50% 75% 100%
## 57.00 65.25 72.50 77.75 90.00grade_40<-s2101_grade[s2101_grade$Section %in% c(40), ]
quantile(grade_40$Total)## 0% 25% 50% 75% 100%
## 53.00 67.50 74.50 82.25 96.00grade_50<-s2101_grade[s2101_grade$Section %in% c(50), ]
quantile(grade_50$Total)## 0% 25% 50% 75% 100%
## 38 66 70 77 95grade_60<-s2101_grade[s2101_grade$Section %in% c(60), ]
quantile(grade_60$Total)## 0% 25% 50% 75% 100%
## 53.0 60.5 67.0 75.0 88.0grade_70<-s2101_grade[s2101_grade$Section %in% c(70), ]
quantile(grade_70$Total)## 0% 25% 50% 75% 100%
## 55.00 65.25 71.00 82.25 94.00grade_80<-s2101_grade[s2101_grade$Section %in% c(80), ]
quantile(grade_80$Total)## 0% 25% 50% 75% 100%
## 44.0 66.0 71.0 82.5 90.0