Assignment 2
2025-09-04
Flipped Assignment 2
Reading csv files
f1<-read.csv("https://raw.githubusercontent.com/tmatis12/datafiles/main/normtemp.csv")
f1## Temp Sex Beats
## 1 96.3 1 70
## 2 96.7 1 71
## 3 96.9 1 74
## 4 97.0 1 80
## 5 97.1 1 73
## 6 97.1 1 75
## 7 97.1 1 82
## 8 97.2 1 64
## 9 97.3 1 69
## 10 97.4 1 70
## 11 97.4 1 68
## 12 97.4 1 72
## 13 97.4 1 78
## 14 97.5 1 70
## 15 97.5 1 75
## 16 97.6 1 74
## 17 97.6 1 69
## 18 97.6 1 73
## 19 97.7 1 77
## 20 97.8 1 58
## 21 97.8 1 73
## 22 97.8 1 65
## 23 97.8 1 74
## 24 97.9 1 76
## 25 97.9 1 72
## 26 98.0 1 78
## 27 98.0 1 71
## 28 98.0 1 74
## 29 98.0 1 67
## 30 98.0 1 64
## 31 98.0 1 78
## 32 98.1 1 73
## 33 98.1 1 67
## 34 98.2 1 66
## 35 98.2 1 64
## 36 98.2 1 71
## 37 98.2 1 72
## 38 98.3 1 86
## 39 98.3 1 72
## 40 98.4 1 68
## 41 98.4 1 70
## 42 98.4 1 82
## 43 98.4 1 84
## 44 98.5 1 68
## 45 98.5 1 71
## 46 98.6 1 77
## 47 98.6 1 78
## 48 98.6 1 83
## 49 98.6 1 66
## 50 98.6 1 70
## 51 98.6 1 82
## 52 98.7 1 73
## 53 98.7 1 78
## 54 98.8 1 78
## 55 98.8 1 81
## 56 98.8 1 78
## 57 98.9 1 80
## 58 99.0 1 75
## 59 99.0 1 79
## 60 99.0 1 81
## 61 99.1 1 71
## 62 99.2 1 83
## 63 99.3 1 63
## 64 99.4 1 70
## 65 99.5 1 75
## 66 96.4 2 69
## 67 96.7 2 62
## 68 96.8 2 75
## 69 97.2 2 66
## 70 97.2 2 68
## 71 97.4 2 57
## 72 97.6 2 61
## 73 97.7 2 84
## 74 97.7 2 61
## 75 97.8 2 77
## 76 97.8 2 62
## 77 97.8 2 71
## 78 97.9 2 68
## 79 97.9 2 69
## 80 97.9 2 79
## 81 98.0 2 76
## 82 98.0 2 87
## 83 98.0 2 78
## 84 98.0 2 73
## 85 98.0 2 89
## 86 98.1 2 81
## 87 98.2 2 73
## 88 98.2 2 64
## 89 98.2 2 65
## 90 98.2 2 73
## 91 98.2 2 69
## 92 98.2 2 57
## 93 98.3 2 79
## 94 98.3 2 78
## 95 98.3 2 80
## 96 98.4 2 79
## 97 98.4 2 81
## 98 98.4 2 73
## 99 98.4 2 74
## 100 98.4 2 84
## 101 98.5 2 83
## 102 98.6 2 82
## 103 98.6 2 85
## 104 98.6 2 86
## 105 98.6 2 77
## 106 98.7 2 72
## 107 98.7 2 79
## 108 98.7 2 59
## 109 98.7 2 64
## 110 98.7 2 65
## 111 98.7 2 82
## 112 98.8 2 64
## 113 98.8 2 70
## 114 98.8 2 83
## 115 98.8 2 89
## 116 98.8 2 69
## 117 98.8 2 73
## 118 98.8 2 84
## 119 98.9 2 76
## 120 99.0 2 79
## 121 99.0 2 81
## 122 99.1 2 80
## 123 99.1 2 74
## 124 99.2 2 77
## 125 99.2 2 66
## 126 99.3 2 68
## 127 99.4 2 77
## 128 99.9 2 79
## 129 100.0 2 78
## 130 100.8 2 77Sorting and analyzing the data by gender
#male = 1
males<-f1[f1$Sex == 1,]
males## Temp Sex Beats
## 1 96.3 1 70
## 2 96.7 1 71
## 3 96.9 1 74
## 4 97.0 1 80
## 5 97.1 1 73
## 6 97.1 1 75
## 7 97.1 1 82
## 8 97.2 1 64
## 9 97.3 1 69
## 10 97.4 1 70
## 11 97.4 1 68
## 12 97.4 1 72
## 13 97.4 1 78
## 14 97.5 1 70
## 15 97.5 1 75
## 16 97.6 1 74
## 17 97.6 1 69
## 18 97.6 1 73
## 19 97.7 1 77
## 20 97.8 1 58
## 21 97.8 1 73
## 22 97.8 1 65
## 23 97.8 1 74
## 24 97.9 1 76
## 25 97.9 1 72
## 26 98.0 1 78
## 27 98.0 1 71
## 28 98.0 1 74
## 29 98.0 1 67
## 30 98.0 1 64
## 31 98.0 1 78
## 32 98.1 1 73
## 33 98.1 1 67
## 34 98.2 1 66
## 35 98.2 1 64
## 36 98.2 1 71
## 37 98.2 1 72
## 38 98.3 1 86
## 39 98.3 1 72
## 40 98.4 1 68
## 41 98.4 1 70
## 42 98.4 1 82
## 43 98.4 1 84
## 44 98.5 1 68
## 45 98.5 1 71
## 46 98.6 1 77
## 47 98.6 1 78
## 48 98.6 1 83
## 49 98.6 1 66
## 50 98.6 1 70
## 51 98.6 1 82
## 52 98.7 1 73
## 53 98.7 1 78
## 54 98.8 1 78
## 55 98.8 1 81
## 56 98.8 1 78
## 57 98.9 1 80
## 58 99.0 1 75
## 59 99.0 1 79
## 60 99.0 1 81
## 61 99.1 1 71
## 62 99.2 1 83
## 63 99.3 1 63
## 64 99.4 1 70
## 65 99.5 1 75#female =2
females<-f1[f1$Sex == 2,]
females## Temp Sex Beats
## 66 96.4 2 69
## 67 96.7 2 62
## 68 96.8 2 75
## 69 97.2 2 66
## 70 97.2 2 68
## 71 97.4 2 57
## 72 97.6 2 61
## 73 97.7 2 84
## 74 97.7 2 61
## 75 97.8 2 77
## 76 97.8 2 62
## 77 97.8 2 71
## 78 97.9 2 68
## 79 97.9 2 69
## 80 97.9 2 79
## 81 98.0 2 76
## 82 98.0 2 87
## 83 98.0 2 78
## 84 98.0 2 73
## 85 98.0 2 89
## 86 98.1 2 81
## 87 98.2 2 73
## 88 98.2 2 64
## 89 98.2 2 65
## 90 98.2 2 73
## 91 98.2 2 69
## 92 98.2 2 57
## 93 98.3 2 79
## 94 98.3 2 78
## 95 98.3 2 80
## 96 98.4 2 79
## 97 98.4 2 81
## 98 98.4 2 73
## 99 98.4 2 74
## 100 98.4 2 84
## 101 98.5 2 83
## 102 98.6 2 82
## 103 98.6 2 85
## 104 98.6 2 86
## 105 98.6 2 77
## 106 98.7 2 72
## 107 98.7 2 79
## 108 98.7 2 59
## 109 98.7 2 64
## 110 98.7 2 65
## 111 98.7 2 82
## 112 98.8 2 64
## 113 98.8 2 70
## 114 98.8 2 83
## 115 98.8 2 89
## 116 98.8 2 69
## 117 98.8 2 73
## 118 98.8 2 84
## 119 98.9 2 76
## 120 99.0 2 79
## 121 99.0 2 81
## 122 99.1 2 80
## 123 99.1 2 74
## 124 99.2 2 77
## 125 99.2 2 66
## 126 99.3 2 68
## 127 99.4 2 77
## 128 99.9 2 79
## 129 100.0 2 78
## 130 100.8 2 77summary(males$Beats)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 58.00 70.00 73.00 73.37 78.00 86.00summary(females$Beats)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 57.00 68.00 76.00 74.15 80.00 89.00The Minimum and the 1st quartile of Males is greater than females.
Histogram plot for males
h_male<-hist(males$Beats, col="blue",main = "Histogram of Males", xlab ="Beats", ylab= "Frequency")
Histogram plot for females
h_female<-hist(females$Beats, col="pink", main = "Histogram of Females", xlab ="Beats", ylab= "Frequency")
The resting heart rate of males’ are normally distributed as compared to females’ resting heart rate.
Normal Probability Plot for Male
qqnorm(males$Beats) #normal probability plot
Normal Probability Plot for Female
#female
qqnorm(females$Beats) #normal probability plot
Boxplot Comparison
boxplot(males$Beats, females$Beats,names = c("Male", "Female"), col = c("blue", "pink"),main = "Resting heart rate by gender", xlab = "gender", ylab = "Resting heart rate")
The interquartile range of the female 12 whereas the interquartile range of male is 8 which shows that females values are more spread out around the median.