COMP4033 Assignment 1 Sleep Data Analysis
Jun and COMP4033 Group 5
2025-07-03
Print the structure of your dataset.
Sleep=read.csv("Sleep.csv")
str(Sleep)## 'data.frame': 99 obs. of 13 variables:
## $ Person.ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Gender : chr "Male" "Male" "Male" "Male" ...
## $ Age : int 27 28 28 28 28 28 29 29 29 29 ...
## $ Occupation : chr "Software Engineer" "Doctor" "Doctor" "Sales Representative" ...
## $ Sleep.Duration : num 6.1 6.2 6.2 5.9 5.9 5.9 6.3 7.8 7.8 7.8 ...
## $ Quality.of.Sleep : int 6 6 6 4 4 4 6 7 7 7 ...
## $ Physical.Activity.Level: int 42 60 60 30 30 30 40 75 75 75 ...
## $ Stress.Level : int 6 8 8 8 8 8 7 6 6 6 ...
## $ BMI.Category : chr "Overweight" "Normal" "Normal" "Obese" ...
## $ Blood.Pressure : chr "126/83" "125/80" "125/80" "140/90" ...
## $ Heart.Rate : int 77 75 75 85 85 85 82 70 70 70 ...
## $ Daily.Steps : int 4200 10000 10000 3000 3000 3000 3500 8000 8000 8000 ...
## $ Sleep.Disorder : chr "None" "None" "None" "Sleep Apnea" ...List the variables in your dataset.
ls(Sleep)## [1] "Age" "Blood.Pressure"
## [3] "BMI.Category" "Daily.Steps"
## [5] "Gender" "Heart.Rate"
## [7] "Occupation" "Person.ID"
## [9] "Physical.Activity.Level" "Quality.of.Sleep"
## [11] "Sleep.Disorder" "Sleep.Duration"
## [13] "Stress.Level"Print the top 15 rows of your dataset.
head(Sleep,15)## Person.ID Gender Age Occupation Sleep.Duration Quality.of.Sleep
## 1 1 Male 27 Software Engineer 6.1 6
## 2 2 Male 28 Doctor 6.2 6
## 3 3 Male 28 Doctor 6.2 6
## 4 4 Male 28 Sales Representative 5.9 4
## 5 5 Male 28 Sales Representative 5.9 4
## 6 6 Male 28 Software Engineer 5.9 4
## 7 7 Male 29 Teacher 6.3 6
## 8 8 Male 29 Doctor 7.8 7
## 9 9 Male 29 Doctor 7.8 7
## 10 10 Male 29 Doctor 7.8 7
## 11 11 Male 29 Doctor 6.1 6
## 12 12 Male 29 Doctor 7.8 7
## 13 13 Male 29 Doctor 6.1 6
## 14 14 Male 29 Doctor 6.0 6
## 15 15 Male 29 Doctor 6.0 6
## Physical.Activity.Level Stress.Level BMI.Category Blood.Pressure Heart.Rate
## 1 42 6 Overweight 126/83 77
## 2 60 8 Normal 125/80 75
## 3 60 8 Normal 125/80 75
## 4 30 8 Obese 140/90 85
## 5 30 8 Obese 140/90 85
## 6 30 8 Obese 140/90 85
## 7 40 7 Obese 140/90 82
## 8 75 6 Normal 120/80 70
## 9 75 6 Normal 120/80 70
## 10 75 6 Normal 120/80 70
## 11 30 8 Normal 120/80 70
## 12 75 6 Normal 120/80 70
## 13 30 8 Normal 120/80 70
## 14 30 8 Normal 120/80 70
## 15 30 8 Normal 120/80 70
## Daily.Steps Sleep.Disorder
## 1 4200 None
## 2 10000 None
## 3 10000 None
## 4 3000 Sleep Apnea
## 5 3000 Sleep Apnea
## 6 3000 Insomnia
## 7 3500 Insomnia
## 8 8000 None
## 9 8000 None
## 10 8000 None
## 11 8000 None
## 12 8000 None
## 13 8000 None
## 14 8000 None
## 15 8000 NoneWrite a user defined function using any of the variables from the data set.
x='Sleep Duration'
y='Quality of Sleep'
SleepEffectiveness=function(x,y){x*y}Use data manipulation techniques and filter rows based on any logical criteria that exist in your dataset.
HealthyMale=Sleep %>% filter(`Sleep.Duration`>6,`Quality.of.Sleep`>5,Gender=="Male")Identify the dependent & independent variables and use reshaping techniques and create a new data frame by joining those variables from your dataset.
"ID"=Sleep$`Person.ID`
"Duration"=Sleep$`Sleep.Duration` #independent variable
"Stress"=Sleep$`Stress.Level` #dependent variable
DurationStress=cbind(ID,Duration,Stress)
DurationStress=as.data.frame(DurationStress)Remove missing values in your dataset.
na.omit(Sleep)## Person.ID Gender Age Occupation Sleep.Duration Quality.of.Sleep
## 1 1 Male 27 Software Engineer 6.1 6
## 2 2 Male 28 Doctor 6.2 6
## 3 3 Male 28 Doctor 6.2 6
## 4 4 Male 28 Sales Representative 5.9 4
## 5 5 Male 28 Sales Representative 5.9 4
## 6 6 Male 28 Software Engineer 5.9 4
## 7 7 Male 29 Teacher 6.3 6
## 8 8 Male 29 Doctor 7.8 7
## 9 9 Male 29 Doctor 7.8 7
## 10 10 Male 29 Doctor 7.8 7
## 11 11 Male 29 Doctor 6.1 6
## 12 12 Male 29 Doctor 7.8 7
## 13 13 Male 29 Doctor 6.1 6
## 14 14 Male 29 Doctor 6.0 6
## 15 15 Male 29 Doctor 6.0 6
## 16 16 Male 29 Doctor 6.0 6
## 17 17 Female 29 Nurse 6.5 5
## 18 18 Male 29 Doctor 6.0 6
## 19 19 Female 29 Nurse 6.5 5
## 20 20 Male 30 Doctor 7.6 7
## 21 21 Male 30 Doctor 7.7 7
## 22 22 Male 30 Doctor 7.7 7
## 23 23 Male 30 Doctor 7.7 7
## 24 24 Male 30 Doctor 7.7 7
## 25 25 Male 30 Doctor 7.8 7
## 26 26 Male 30 Doctor 7.9 7
## 27 27 Male 30 Doctor 7.8 7
## 28 28 Male 30 Doctor 7.9 7
## 29 29 Male 30 Doctor 7.9 7
## 30 30 Male 30 Doctor 7.9 7
## 31 31 Female 30 Nurse 6.4 5
## 32 32 Female 30 Nurse 6.4 5
## 33 33 Female 31 Nurse 7.9 8
## 34 34 Male 31 Doctor 6.1 6
## 35 35 Male 31 Doctor 7.7 7
## 36 36 Male 31 Doctor 6.1 6
## 37 37 Male 31 Doctor 6.1 6
## 38 38 Male 31 Doctor 7.6 7
## 39 39 Male 31 Doctor 7.6 7
## 40 40 Male 31 Doctor 7.6 7
## 41 41 Male 31 Doctor 7.7 7
## 42 42 Male 31 Doctor 7.7 7
## 43 43 Male 31 Doctor 7.7 7
## 44 44 Male 31 Doctor 7.8 7
## 45 45 Male 31 Doctor 7.7 7
## 46 46 Male 31 Doctor 7.8 7
## 47 47 Male 31 Doctor 7.7 7
## 48 48 Male 31 Doctor 7.8 7
## 49 49 Male 31 Doctor 7.7 7
## 50 50 Male 31 Doctor 7.7 7
## 51 51 Male 32 Engineer 7.5 8
## 52 52 Male 32 Engineer 7.5 8
## 53 53 Male 32 Doctor 6.0 6
## 54 54 Male 32 Doctor 7.6 7
## 55 55 Male 32 Doctor 6.0 6
## 56 56 Male 32 Doctor 6.0 6
## 57 57 Male 32 Doctor 7.7 7
## 58 58 Male 32 Doctor 6.0 6
## 59 59 Male 32 Doctor 6.0 6
## 60 60 Male 32 Doctor 7.7 7
## 61 61 Male 32 Doctor 6.0 6
## 62 62 Male 32 Doctor 6.0 6
## 63 63 Male 32 Doctor 6.2 6
## 64 64 Male 32 Doctor 6.2 6
## 65 65 Male 32 Doctor 6.2 6
## 66 66 Male 32 Doctor 6.2 6
## 67 67 Male 32 Accountant 7.2 8
## 68 68 Male 33 Doctor 6.0 6
## 69 69 Female 33 Scientist 6.2 6
## 70 70 Female 33 Scientist 6.2 6
## 71 71 Male 33 Doctor 6.1 6
## 72 72 Male 33 Doctor 6.1 6
## 73 73 Male 33 Doctor 6.1 6
## 74 74 Male 33 Doctor 6.1 6
## 75 75 Male 33 Doctor 6.0 6
## 76 76 Male 33 Doctor 6.0 6
## 77 77 Male 33 Doctor 6.0 6
## 78 78 Male 33 Doctor 6.0 6
## 79 79 Male 33 Doctor 6.0 6
## 80 80 Male 33 Doctor 6.0 6
## 81 81 Female 34 Scientist 5.8 4
## 82 82 Female 34 Scientist 5.8 4
## 83 83 Male 35 Teacher 6.7 7
## 84 84 Male 35 Teacher 6.7 7
## 85 85 Male 35 Software Engineer 7.5 8
## 86 86 Female 35 Accountant 7.2 8
## 87 87 Male 35 Engineer 7.2 8
## 88 88 Male 35 Engineer 7.2 8
## 89 89 Male 35 Engineer 7.3 8
## 90 90 Male 35 Engineer 7.3 8
## 91 91 Male 35 Engineer 7.3 8
## 92 92 Male 35 Engineer 7.3 8
## 93 93 Male 35 Software Engineer 7.5 8
## 94 94 Male 35 Lawyer 7.4 7
## 95 95 Female 36 Accountant 7.2 8
## 96 96 Female 36 Accountant 7.1 8
## 97 97 Female 36 Accountant 7.2 8
## 98 98 Female 36 Accountant 7.1 8
## 99 99 Female 36 Teacher 7.1 8
## Physical.Activity.Level Stress.Level BMI.Category Blood.Pressure Heart.Rate
## 1 42 6 Overweight 126/83 77
## 2 60 8 Normal 125/80 75
## 3 60 8 Normal 125/80 75
## 4 30 8 Obese 140/90 85
## 5 30 8 Obese 140/90 85
## 6 30 8 Obese 140/90 85
## 7 40 7 Obese 140/90 82
## 8 75 6 Normal 120/80 70
## 9 75 6 Normal 120/80 70
## 10 75 6 Normal 120/80 70
## 11 30 8 Normal 120/80 70
## 12 75 6 Normal 120/80 70
## 13 30 8 Normal 120/80 70
## 14 30 8 Normal 120/80 70
## 15 30 8 Normal 120/80 70
## 16 30 8 Normal 120/80 70
## 17 40 7 Normal Weight 132/87 80
## 18 30 8 Normal 120/80 70
## 19 40 7 Normal Weight 132/87 80
## 20 75 6 Normal 120/80 70
## 21 75 6 Normal 120/80 70
## 22 75 6 Normal 120/80 70
## 23 75 6 Normal 120/80 70
## 24 75 6 Normal 120/80 70
## 25 75 6 Normal 120/80 70
## 26 75 6 Normal 120/80 70
## 27 75 6 Normal 120/80 70
## 28 75 6 Normal 120/80 70
## 29 75 6 Normal 120/80 70
## 30 75 6 Normal 120/80 70
## 31 35 7 Normal Weight 130/86 78
## 32 35 7 Normal Weight 130/86 78
## 33 75 4 Normal Weight 117/76 69
## 34 30 8 Normal 125/80 72
## 35 75 6 Normal 120/80 70
## 36 30 8 Normal 125/80 72
## 37 30 8 Normal 125/80 72
## 38 75 6 Normal 120/80 70
## 39 75 6 Normal 120/80 70
## 40 75 6 Normal 120/80 70
## 41 75 6 Normal 120/80 70
## 42 75 6 Normal 120/80 70
## 43 75 6 Normal 120/80 70
## 44 75 6 Normal 120/80 70
## 45 75 6 Normal 120/80 70
## 46 75 6 Normal 120/80 70
## 47 75 6 Normal 120/80 70
## 48 75 6 Normal 120/80 70
## 49 75 6 Normal 120/80 70
## 50 75 6 Normal 120/80 70
## 51 45 3 Normal 120/80 70
## 52 45 3 Normal 120/80 70
## 53 30 8 Normal 125/80 72
## 54 75 6 Normal 120/80 70
## 55 30 8 Normal 125/80 72
## 56 30 8 Normal 125/80 72
## 57 75 6 Normal 120/80 70
## 58 30 8 Normal 125/80 72
## 59 30 8 Normal 125/80 72
## 60 75 6 Normal 120/80 70
## 61 30 8 Normal 125/80 72
## 62 30 8 Normal 125/80 72
## 63 30 8 Normal 125/80 72
## 64 30 8 Normal 125/80 72
## 65 30 8 Normal 125/80 72
## 66 30 8 Normal 125/80 72
## 67 50 6 Normal Weight 118/76 68
## 68 30 8 Normal 125/80 72
## 69 50 6 Overweight 128/85 76
## 70 50 6 Overweight 128/85 76
## 71 30 8 Normal 125/80 72
## 72 30 8 Normal 125/80 72
## 73 30 8 Normal 125/80 72
## 74 30 8 Normal 125/80 72
## 75 30 8 Normal 125/80 72
## 76 30 8 Normal 125/80 72
## 77 30 8 Normal 125/80 72
## 78 30 8 Normal 125/80 72
## 79 30 8 Normal 125/80 72
## 80 30 8 Normal 125/80 72
## 81 32 8 Overweight 131/86 81
## 82 32 8 Overweight 131/86 81
## 83 40 5 Overweight 128/84 70
## 84 40 5 Overweight 128/84 70
## 85 60 5 Normal Weight 120/80 70
## 86 60 4 Normal 115/75 68
## 87 60 4 Normal 125/80 65
## 88 60 4 Normal 125/80 65
## 89 60 4 Normal 125/80 65
## 90 60 4 Normal 125/80 65
## 91 60 4 Normal 125/80 65
## 92 60 4 Normal 125/80 65
## 93 60 5 Normal Weight 120/80 70
## 94 60 5 Obese 135/88 84
## 95 60 4 Normal 115/75 68
## 96 60 4 Normal 115/75 68
## 97 60 4 Normal 115/75 68
## 98 60 4 Normal 115/75 68
## 99 60 4 Normal 115/75 68
## Daily.Steps Sleep.Disorder
## 1 4200 None
## 2 10000 None
## 3 10000 None
## 4 3000 Sleep Apnea
## 5 3000 Sleep Apnea
## 6 3000 Insomnia
## 7 3500 Insomnia
## 8 8000 None
## 9 8000 None
## 10 8000 None
## 11 8000 None
## 12 8000 None
## 13 8000 None
## 14 8000 None
## 15 8000 None
## 16 8000 None
## 17 4000 Sleep Apnea
## 18 8000 Sleep Apnea
## 19 4000 Insomnia
## 20 8000 None
## 21 8000 None
## 22 8000 None
## 23 8000 None
## 24 8000 None
## 25 8000 None
## 26 8000 None
## 27 8000 None
## 28 8000 None
## 29 8000 None
## 30 8000 None
## 31 4100 Sleep Apnea
## 32 4100 Insomnia
## 33 6800 None
## 34 5000 None
## 35 8000 None
## 36 5000 None
## 37 5000 None
## 38 8000 None
## 39 8000 None
## 40 8000 None
## 41 8000 None
## 42 8000 None
## 43 8000 None
## 44 8000 None
## 45 8000 None
## 46 8000 None
## 47 8000 None
## 48 8000 None
## 49 8000 None
## 50 8000 Sleep Apnea
## 51 8000 None
## 52 8000 None
## 53 5000 None
## 54 8000 None
## 55 5000 None
## 56 5000 None
## 57 8000 None
## 58 5000 None
## 59 5000 None
## 60 8000 None
## 61 5000 None
## 62 5000 None
## 63 5000 None
## 64 5000 None
## 65 5000 None
## 66 5000 None
## 67 7000 None
## 68 5000 Insomnia
## 69 5500 None
## 70 5500 None
## 71 5000 None
## 72 5000 None
## 73 5000 None
## 74 5000 None
## 75 5000 None
## 76 5000 None
## 77 5000 None
## 78 5000 None
## 79 5000 None
## 80 5000 None
## 81 5200 Sleep Apnea
## 82 5200 Sleep Apnea
## 83 5600 None
## 84 5600 None
## 85 8000 None
## 86 7000 None
## 87 5000 None
## 88 5000 None
## 89 5000 None
## 90 5000 None
## 91 5000 None
## 92 5000 None
## 93 8000 None
## 94 3300 Sleep Apnea
## 95 7000 Insomnia
## 96 7000 None
## 97 7000 None
## 98 7000 None
## 99 7000 NoneIdentify and remove duplicated data from your dataset.
duplicated(Sleep) #identify duplicated data## [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [25] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [37] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [49] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [61] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [73] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [85] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## [97] FALSE FALSE FALSESleep[!duplicated(Sleep), ] #remove duplicated data## Person.ID Gender Age Occupation Sleep.Duration Quality.of.Sleep
## 1 1 Male 27 Software Engineer 6.1 6
## 2 2 Male 28 Doctor 6.2 6
## 3 3 Male 28 Doctor 6.2 6
## 4 4 Male 28 Sales Representative 5.9 4
## 5 5 Male 28 Sales Representative 5.9 4
## 6 6 Male 28 Software Engineer 5.9 4
## 7 7 Male 29 Teacher 6.3 6
## 8 8 Male 29 Doctor 7.8 7
## 9 9 Male 29 Doctor 7.8 7
## 10 10 Male 29 Doctor 7.8 7
## 11 11 Male 29 Doctor 6.1 6
## 12 12 Male 29 Doctor 7.8 7
## 13 13 Male 29 Doctor 6.1 6
## 14 14 Male 29 Doctor 6.0 6
## 15 15 Male 29 Doctor 6.0 6
## 16 16 Male 29 Doctor 6.0 6
## 17 17 Female 29 Nurse 6.5 5
## 18 18 Male 29 Doctor 6.0 6
## 19 19 Female 29 Nurse 6.5 5
## 20 20 Male 30 Doctor 7.6 7
## 21 21 Male 30 Doctor 7.7 7
## 22 22 Male 30 Doctor 7.7 7
## 23 23 Male 30 Doctor 7.7 7
## 24 24 Male 30 Doctor 7.7 7
## 25 25 Male 30 Doctor 7.8 7
## 26 26 Male 30 Doctor 7.9 7
## 27 27 Male 30 Doctor 7.8 7
## 28 28 Male 30 Doctor 7.9 7
## 29 29 Male 30 Doctor 7.9 7
## 30 30 Male 30 Doctor 7.9 7
## 31 31 Female 30 Nurse 6.4 5
## 32 32 Female 30 Nurse 6.4 5
## 33 33 Female 31 Nurse 7.9 8
## 34 34 Male 31 Doctor 6.1 6
## 35 35 Male 31 Doctor 7.7 7
## 36 36 Male 31 Doctor 6.1 6
## 37 37 Male 31 Doctor 6.1 6
## 38 38 Male 31 Doctor 7.6 7
## 39 39 Male 31 Doctor 7.6 7
## 40 40 Male 31 Doctor 7.6 7
## 41 41 Male 31 Doctor 7.7 7
## 42 42 Male 31 Doctor 7.7 7
## 43 43 Male 31 Doctor 7.7 7
## 44 44 Male 31 Doctor 7.8 7
## 45 45 Male 31 Doctor 7.7 7
## 46 46 Male 31 Doctor 7.8 7
## 47 47 Male 31 Doctor 7.7 7
## 48 48 Male 31 Doctor 7.8 7
## 49 49 Male 31 Doctor 7.7 7
## 50 50 Male 31 Doctor 7.7 7
## 51 51 Male 32 Engineer 7.5 8
## 52 52 Male 32 Engineer 7.5 8
## 53 53 Male 32 Doctor 6.0 6
## 54 54 Male 32 Doctor 7.6 7
## 55 55 Male 32 Doctor 6.0 6
## 56 56 Male 32 Doctor 6.0 6
## 57 57 Male 32 Doctor 7.7 7
## 58 58 Male 32 Doctor 6.0 6
## 59 59 Male 32 Doctor 6.0 6
## 60 60 Male 32 Doctor 7.7 7
## 61 61 Male 32 Doctor 6.0 6
## 62 62 Male 32 Doctor 6.0 6
## 63 63 Male 32 Doctor 6.2 6
## 64 64 Male 32 Doctor 6.2 6
## 65 65 Male 32 Doctor 6.2 6
## 66 66 Male 32 Doctor 6.2 6
## 67 67 Male 32 Accountant 7.2 8
## 68 68 Male 33 Doctor 6.0 6
## 69 69 Female 33 Scientist 6.2 6
## 70 70 Female 33 Scientist 6.2 6
## 71 71 Male 33 Doctor 6.1 6
## 72 72 Male 33 Doctor 6.1 6
## 73 73 Male 33 Doctor 6.1 6
## 74 74 Male 33 Doctor 6.1 6
## 75 75 Male 33 Doctor 6.0 6
## 76 76 Male 33 Doctor 6.0 6
## 77 77 Male 33 Doctor 6.0 6
## 78 78 Male 33 Doctor 6.0 6
## 79 79 Male 33 Doctor 6.0 6
## 80 80 Male 33 Doctor 6.0 6
## 81 81 Female 34 Scientist 5.8 4
## 82 82 Female 34 Scientist 5.8 4
## 83 83 Male 35 Teacher 6.7 7
## 84 84 Male 35 Teacher 6.7 7
## 85 85 Male 35 Software Engineer 7.5 8
## 86 86 Female 35 Accountant 7.2 8
## 87 87 Male 35 Engineer 7.2 8
## 88 88 Male 35 Engineer 7.2 8
## 89 89 Male 35 Engineer 7.3 8
## 90 90 Male 35 Engineer 7.3 8
## 91 91 Male 35 Engineer 7.3 8
## 92 92 Male 35 Engineer 7.3 8
## 93 93 Male 35 Software Engineer 7.5 8
## 94 94 Male 35 Lawyer 7.4 7
## 95 95 Female 36 Accountant 7.2 8
## 96 96 Female 36 Accountant 7.1 8
## 97 97 Female 36 Accountant 7.2 8
## 98 98 Female 36 Accountant 7.1 8
## 99 99 Female 36 Teacher 7.1 8
## Physical.Activity.Level Stress.Level BMI.Category Blood.Pressure Heart.Rate
## 1 42 6 Overweight 126/83 77
## 2 60 8 Normal 125/80 75
## 3 60 8 Normal 125/80 75
## 4 30 8 Obese 140/90 85
## 5 30 8 Obese 140/90 85
## 6 30 8 Obese 140/90 85
## 7 40 7 Obese 140/90 82
## 8 75 6 Normal 120/80 70
## 9 75 6 Normal 120/80 70
## 10 75 6 Normal 120/80 70
## 11 30 8 Normal 120/80 70
## 12 75 6 Normal 120/80 70
## 13 30 8 Normal 120/80 70
## 14 30 8 Normal 120/80 70
## 15 30 8 Normal 120/80 70
## 16 30 8 Normal 120/80 70
## 17 40 7 Normal Weight 132/87 80
## 18 30 8 Normal 120/80 70
## 19 40 7 Normal Weight 132/87 80
## 20 75 6 Normal 120/80 70
## 21 75 6 Normal 120/80 70
## 22 75 6 Normal 120/80 70
## 23 75 6 Normal 120/80 70
## 24 75 6 Normal 120/80 70
## 25 75 6 Normal 120/80 70
## 26 75 6 Normal 120/80 70
## 27 75 6 Normal 120/80 70
## 28 75 6 Normal 120/80 70
## 29 75 6 Normal 120/80 70
## 30 75 6 Normal 120/80 70
## 31 35 7 Normal Weight 130/86 78
## 32 35 7 Normal Weight 130/86 78
## 33 75 4 Normal Weight 117/76 69
## 34 30 8 Normal 125/80 72
## 35 75 6 Normal 120/80 70
## 36 30 8 Normal 125/80 72
## 37 30 8 Normal 125/80 72
## 38 75 6 Normal 120/80 70
## 39 75 6 Normal 120/80 70
## 40 75 6 Normal 120/80 70
## 41 75 6 Normal 120/80 70
## 42 75 6 Normal 120/80 70
## 43 75 6 Normal 120/80 70
## 44 75 6 Normal 120/80 70
## 45 75 6 Normal 120/80 70
## 46 75 6 Normal 120/80 70
## 47 75 6 Normal 120/80 70
## 48 75 6 Normal 120/80 70
## 49 75 6 Normal 120/80 70
## 50 75 6 Normal 120/80 70
## 51 45 3 Normal 120/80 70
## 52 45 3 Normal 120/80 70
## 53 30 8 Normal 125/80 72
## 54 75 6 Normal 120/80 70
## 55 30 8 Normal 125/80 72
## 56 30 8 Normal 125/80 72
## 57 75 6 Normal 120/80 70
## 58 30 8 Normal 125/80 72
## 59 30 8 Normal 125/80 72
## 60 75 6 Normal 120/80 70
## 61 30 8 Normal 125/80 72
## 62 30 8 Normal 125/80 72
## 63 30 8 Normal 125/80 72
## 64 30 8 Normal 125/80 72
## 65 30 8 Normal 125/80 72
## 66 30 8 Normal 125/80 72
## 67 50 6 Normal Weight 118/76 68
## 68 30 8 Normal 125/80 72
## 69 50 6 Overweight 128/85 76
## 70 50 6 Overweight 128/85 76
## 71 30 8 Normal 125/80 72
## 72 30 8 Normal 125/80 72
## 73 30 8 Normal 125/80 72
## 74 30 8 Normal 125/80 72
## 75 30 8 Normal 125/80 72
## 76 30 8 Normal 125/80 72
## 77 30 8 Normal 125/80 72
## 78 30 8 Normal 125/80 72
## 79 30 8 Normal 125/80 72
## 80 30 8 Normal 125/80 72
## 81 32 8 Overweight 131/86 81
## 82 32 8 Overweight 131/86 81
## 83 40 5 Overweight 128/84 70
## 84 40 5 Overweight 128/84 70
## 85 60 5 Normal Weight 120/80 70
## 86 60 4 Normal 115/75 68
## 87 60 4 Normal 125/80 65
## 88 60 4 Normal 125/80 65
## 89 60 4 Normal 125/80 65
## 90 60 4 Normal 125/80 65
## 91 60 4 Normal 125/80 65
## 92 60 4 Normal 125/80 65
## 93 60 5 Normal Weight 120/80 70
## 94 60 5 Obese 135/88 84
## 95 60 4 Normal 115/75 68
## 96 60 4 Normal 115/75 68
## 97 60 4 Normal 115/75 68
## 98 60 4 Normal 115/75 68
## 99 60 4 Normal 115/75 68
## Daily.Steps Sleep.Disorder
## 1 4200 None
## 2 10000 None
## 3 10000 None
## 4 3000 Sleep Apnea
## 5 3000 Sleep Apnea
## 6 3000 Insomnia
## 7 3500 Insomnia
## 8 8000 None
## 9 8000 None
## 10 8000 None
## 11 8000 None
## 12 8000 None
## 13 8000 None
## 14 8000 None
## 15 8000 None
## 16 8000 None
## 17 4000 Sleep Apnea
## 18 8000 Sleep Apnea
## 19 4000 Insomnia
## 20 8000 None
## 21 8000 None
## 22 8000 None
## 23 8000 None
## 24 8000 None
## 25 8000 None
## 26 8000 None
## 27 8000 None
## 28 8000 None
## 29 8000 None
## 30 8000 None
## 31 4100 Sleep Apnea
## 32 4100 Insomnia
## 33 6800 None
## 34 5000 None
## 35 8000 None
## 36 5000 None
## 37 5000 None
## 38 8000 None
## 39 8000 None
## 40 8000 None
## 41 8000 None
## 42 8000 None
## 43 8000 None
## 44 8000 None
## 45 8000 None
## 46 8000 None
## 47 8000 None
## 48 8000 None
## 49 8000 None
## 50 8000 Sleep Apnea
## 51 8000 None
## 52 8000 None
## 53 5000 None
## 54 8000 None
## 55 5000 None
## 56 5000 None
## 57 8000 None
## 58 5000 None
## 59 5000 None
## 60 8000 None
## 61 5000 None
## 62 5000 None
## 63 5000 None
## 64 5000 None
## 65 5000 None
## 66 5000 None
## 67 7000 None
## 68 5000 Insomnia
## 69 5500 None
## 70 5500 None
## 71 5000 None
## 72 5000 None
## 73 5000 None
## 74 5000 None
## 75 5000 None
## 76 5000 None
## 77 5000 None
## 78 5000 None
## 79 5000 None
## 80 5000 None
## 81 5200 Sleep Apnea
## 82 5200 Sleep Apnea
## 83 5600 None
## 84 5600 None
## 85 8000 None
## 86 7000 None
## 87 5000 None
## 88 5000 None
## 89 5000 None
## 90 5000 None
## 91 5000 None
## 92 5000 None
## 93 8000 None
## 94 3300 Sleep Apnea
## 95 7000 Insomnia
## 96 7000 None
## 97 7000 None
## 98 7000 None
## 99 7000 NoneReorder multiple rows in descending order.
Sleep %>% arrange(desc(`Person.ID`))## Person.ID Gender Age Occupation Sleep.Duration Quality.of.Sleep
## 1 99 Female 36 Teacher 7.1 8
## 2 98 Female 36 Accountant 7.1 8
## 3 97 Female 36 Accountant 7.2 8
## 4 96 Female 36 Accountant 7.1 8
## 5 95 Female 36 Accountant 7.2 8
## 6 94 Male 35 Lawyer 7.4 7
## 7 93 Male 35 Software Engineer 7.5 8
## 8 92 Male 35 Engineer 7.3 8
## 9 91 Male 35 Engineer 7.3 8
## 10 90 Male 35 Engineer 7.3 8
## 11 89 Male 35 Engineer 7.3 8
## 12 88 Male 35 Engineer 7.2 8
## 13 87 Male 35 Engineer 7.2 8
## 14 86 Female 35 Accountant 7.2 8
## 15 85 Male 35 Software Engineer 7.5 8
## 16 84 Male 35 Teacher 6.7 7
## 17 83 Male 35 Teacher 6.7 7
## 18 82 Female 34 Scientist 5.8 4
## 19 81 Female 34 Scientist 5.8 4
## 20 80 Male 33 Doctor 6.0 6
## 21 79 Male 33 Doctor 6.0 6
## 22 78 Male 33 Doctor 6.0 6
## 23 77 Male 33 Doctor 6.0 6
## 24 76 Male 33 Doctor 6.0 6
## 25 75 Male 33 Doctor 6.0 6
## 26 74 Male 33 Doctor 6.1 6
## 27 73 Male 33 Doctor 6.1 6
## 28 72 Male 33 Doctor 6.1 6
## 29 71 Male 33 Doctor 6.1 6
## 30 70 Female 33 Scientist 6.2 6
## 31 69 Female 33 Scientist 6.2 6
## 32 68 Male 33 Doctor 6.0 6
## 33 67 Male 32 Accountant 7.2 8
## 34 66 Male 32 Doctor 6.2 6
## 35 65 Male 32 Doctor 6.2 6
## 36 64 Male 32 Doctor 6.2 6
## 37 63 Male 32 Doctor 6.2 6
## 38 62 Male 32 Doctor 6.0 6
## 39 61 Male 32 Doctor 6.0 6
## 40 60 Male 32 Doctor 7.7 7
## 41 59 Male 32 Doctor 6.0 6
## 42 58 Male 32 Doctor 6.0 6
## 43 57 Male 32 Doctor 7.7 7
## 44 56 Male 32 Doctor 6.0 6
## 45 55 Male 32 Doctor 6.0 6
## 46 54 Male 32 Doctor 7.6 7
## 47 53 Male 32 Doctor 6.0 6
## 48 52 Male 32 Engineer 7.5 8
## 49 51 Male 32 Engineer 7.5 8
## 50 50 Male 31 Doctor 7.7 7
## 51 49 Male 31 Doctor 7.7 7
## 52 48 Male 31 Doctor 7.8 7
## 53 47 Male 31 Doctor 7.7 7
## 54 46 Male 31 Doctor 7.8 7
## 55 45 Male 31 Doctor 7.7 7
## 56 44 Male 31 Doctor 7.8 7
## 57 43 Male 31 Doctor 7.7 7
## 58 42 Male 31 Doctor 7.7 7
## 59 41 Male 31 Doctor 7.7 7
## 60 40 Male 31 Doctor 7.6 7
## 61 39 Male 31 Doctor 7.6 7
## 62 38 Male 31 Doctor 7.6 7
## 63 37 Male 31 Doctor 6.1 6
## 64 36 Male 31 Doctor 6.1 6
## 65 35 Male 31 Doctor 7.7 7
## 66 34 Male 31 Doctor 6.1 6
## 67 33 Female 31 Nurse 7.9 8
## 68 32 Female 30 Nurse 6.4 5
## 69 31 Female 30 Nurse 6.4 5
## 70 30 Male 30 Doctor 7.9 7
## 71 29 Male 30 Doctor 7.9 7
## 72 28 Male 30 Doctor 7.9 7
## 73 27 Male 30 Doctor 7.8 7
## 74 26 Male 30 Doctor 7.9 7
## 75 25 Male 30 Doctor 7.8 7
## 76 24 Male 30 Doctor 7.7 7
## 77 23 Male 30 Doctor 7.7 7
## 78 22 Male 30 Doctor 7.7 7
## 79 21 Male 30 Doctor 7.7 7
## 80 20 Male 30 Doctor 7.6 7
## 81 19 Female 29 Nurse 6.5 5
## 82 18 Male 29 Doctor 6.0 6
## 83 17 Female 29 Nurse 6.5 5
## 84 16 Male 29 Doctor 6.0 6
## 85 15 Male 29 Doctor 6.0 6
## 86 14 Male 29 Doctor 6.0 6
## 87 13 Male 29 Doctor 6.1 6
## 88 12 Male 29 Doctor 7.8 7
## 89 11 Male 29 Doctor 6.1 6
## 90 10 Male 29 Doctor 7.8 7
## 91 9 Male 29 Doctor 7.8 7
## 92 8 Male 29 Doctor 7.8 7
## 93 7 Male 29 Teacher 6.3 6
## 94 6 Male 28 Software Engineer 5.9 4
## 95 5 Male 28 Sales Representative 5.9 4
## 96 4 Male 28 Sales Representative 5.9 4
## 97 3 Male 28 Doctor 6.2 6
## 98 2 Male 28 Doctor 6.2 6
## 99 1 Male 27 Software Engineer 6.1 6
## Physical.Activity.Level Stress.Level BMI.Category Blood.Pressure Heart.Rate
## 1 60 4 Normal 115/75 68
## 2 60 4 Normal 115/75 68
## 3 60 4 Normal 115/75 68
## 4 60 4 Normal 115/75 68
## 5 60 4 Normal 115/75 68
## 6 60 5 Obese 135/88 84
## 7 60 5 Normal Weight 120/80 70
## 8 60 4 Normal 125/80 65
## 9 60 4 Normal 125/80 65
## 10 60 4 Normal 125/80 65
## 11 60 4 Normal 125/80 65
## 12 60 4 Normal 125/80 65
## 13 60 4 Normal 125/80 65
## 14 60 4 Normal 115/75 68
## 15 60 5 Normal Weight 120/80 70
## 16 40 5 Overweight 128/84 70
## 17 40 5 Overweight 128/84 70
## 18 32 8 Overweight 131/86 81
## 19 32 8 Overweight 131/86 81
## 20 30 8 Normal 125/80 72
## 21 30 8 Normal 125/80 72
## 22 30 8 Normal 125/80 72
## 23 30 8 Normal 125/80 72
## 24 30 8 Normal 125/80 72
## 25 30 8 Normal 125/80 72
## 26 30 8 Normal 125/80 72
## 27 30 8 Normal 125/80 72
## 28 30 8 Normal 125/80 72
## 29 30 8 Normal 125/80 72
## 30 50 6 Overweight 128/85 76
## 31 50 6 Overweight 128/85 76
## 32 30 8 Normal 125/80 72
## 33 50 6 Normal Weight 118/76 68
## 34 30 8 Normal 125/80 72
## 35 30 8 Normal 125/80 72
## 36 30 8 Normal 125/80 72
## 37 30 8 Normal 125/80 72
## 38 30 8 Normal 125/80 72
## 39 30 8 Normal 125/80 72
## 40 75 6 Normal 120/80 70
## 41 30 8 Normal 125/80 72
## 42 30 8 Normal 125/80 72
## 43 75 6 Normal 120/80 70
## 44 30 8 Normal 125/80 72
## 45 30 8 Normal 125/80 72
## 46 75 6 Normal 120/80 70
## 47 30 8 Normal 125/80 72
## 48 45 3 Normal 120/80 70
## 49 45 3 Normal 120/80 70
## 50 75 6 Normal 120/80 70
## 51 75 6 Normal 120/80 70
## 52 75 6 Normal 120/80 70
## 53 75 6 Normal 120/80 70
## 54 75 6 Normal 120/80 70
## 55 75 6 Normal 120/80 70
## 56 75 6 Normal 120/80 70
## 57 75 6 Normal 120/80 70
## 58 75 6 Normal 120/80 70
## 59 75 6 Normal 120/80 70
## 60 75 6 Normal 120/80 70
## 61 75 6 Normal 120/80 70
## 62 75 6 Normal 120/80 70
## 63 30 8 Normal 125/80 72
## 64 30 8 Normal 125/80 72
## 65 75 6 Normal 120/80 70
## 66 30 8 Normal 125/80 72
## 67 75 4 Normal Weight 117/76 69
## 68 35 7 Normal Weight 130/86 78
## 69 35 7 Normal Weight 130/86 78
## 70 75 6 Normal 120/80 70
## 71 75 6 Normal 120/80 70
## 72 75 6 Normal 120/80 70
## 73 75 6 Normal 120/80 70
## 74 75 6 Normal 120/80 70
## 75 75 6 Normal 120/80 70
## 76 75 6 Normal 120/80 70
## 77 75 6 Normal 120/80 70
## 78 75 6 Normal 120/80 70
## 79 75 6 Normal 120/80 70
## 80 75 6 Normal 120/80 70
## 81 40 7 Normal Weight 132/87 80
## 82 30 8 Normal 120/80 70
## 83 40 7 Normal Weight 132/87 80
## 84 30 8 Normal 120/80 70
## 85 30 8 Normal 120/80 70
## 86 30 8 Normal 120/80 70
## 87 30 8 Normal 120/80 70
## 88 75 6 Normal 120/80 70
## 89 30 8 Normal 120/80 70
## 90 75 6 Normal 120/80 70
## 91 75 6 Normal 120/80 70
## 92 75 6 Normal 120/80 70
## 93 40 7 Obese 140/90 82
## 94 30 8 Obese 140/90 85
## 95 30 8 Obese 140/90 85
## 96 30 8 Obese 140/90 85
## 97 60 8 Normal 125/80 75
## 98 60 8 Normal 125/80 75
## 99 42 6 Overweight 126/83 77
## Daily.Steps Sleep.Disorder
## 1 7000 None
## 2 7000 None
## 3 7000 None
## 4 7000 None
## 5 7000 Insomnia
## 6 3300 Sleep Apnea
## 7 8000 None
## 8 5000 None
## 9 5000 None
## 10 5000 None
## 11 5000 None
## 12 5000 None
## 13 5000 None
## 14 7000 None
## 15 8000 None
## 16 5600 None
## 17 5600 None
## 18 5200 Sleep Apnea
## 19 5200 Sleep Apnea
## 20 5000 None
## 21 5000 None
## 22 5000 None
## 23 5000 None
## 24 5000 None
## 25 5000 None
## 26 5000 None
## 27 5000 None
## 28 5000 None
## 29 5000 None
## 30 5500 None
## 31 5500 None
## 32 5000 Insomnia
## 33 7000 None
## 34 5000 None
## 35 5000 None
## 36 5000 None
## 37 5000 None
## 38 5000 None
## 39 5000 None
## 40 8000 None
## 41 5000 None
## 42 5000 None
## 43 8000 None
## 44 5000 None
## 45 5000 None
## 46 8000 None
## 47 5000 None
## 48 8000 None
## 49 8000 None
## 50 8000 Sleep Apnea
## 51 8000 None
## 52 8000 None
## 53 8000 None
## 54 8000 None
## 55 8000 None
## 56 8000 None
## 57 8000 None
## 58 8000 None
## 59 8000 None
## 60 8000 None
## 61 8000 None
## 62 8000 None
## 63 5000 None
## 64 5000 None
## 65 8000 None
## 66 5000 None
## 67 6800 None
## 68 4100 Insomnia
## 69 4100 Sleep Apnea
## 70 8000 None
## 71 8000 None
## 72 8000 None
## 73 8000 None
## 74 8000 None
## 75 8000 None
## 76 8000 None
## 77 8000 None
## 78 8000 None
## 79 8000 None
## 80 8000 None
## 81 4000 Insomnia
## 82 8000 Sleep Apnea
## 83 4000 Sleep Apnea
## 84 8000 None
## 85 8000 None
## 86 8000 None
## 87 8000 None
## 88 8000 None
## 89 8000 None
## 90 8000 None
## 91 8000 None
## 92 8000 None
## 93 3500 Insomnia
## 94 3000 Insomnia
## 95 3000 Sleep Apnea
## 96 3000 Sleep Apnea
## 97 10000 None
## 98 10000 None
## 99 4200 NoneRename some of the column names in your dataset.
names(Sleep)[names(Sleep) == "Person.ID"] = "Patient.ID" Add new variables in your data frame by using a mathematical function.
Sleep$`Stress Level 2` = Sleep$`Stress.Level` * 2Create a training set using a random number generator engine.
set.seed(1234)
Training.Set=Sleep %>% sample_n(5, replace = FALSE) Print the summary statistics of your dataset.
Sleep %>% group_by(Gender) %>% summarise(mean(`Quality.of.Sleep`))## # A tibble: 2 × 2
## Gender `mean(Quality.of.Sleep)`
## <chr> <dbl>
## 1 Female 6.4
## 2 Male 6.61Use any of the numerical variables from the dataset and perform the following statistical functions.
mean(Sleep$`Sleep.Duration`)## [1] 6.868687median(Sleep$`Quality.of.Sleep`)## [1] 7mode(Sleep$`Physical.Activity.Level`)## [1] "numeric"range(Sleep$`Stress.Level`)## [1] 3 8Plot a scatter plot for any 2 variables in your dataset.
ggplot(Sleep, aes(x = `Stress.Level`, y = `Sleep.Duration`)) +
geom_point(color = "steelblue") +
labs(title = "Sleep Duration vs. Stress Level",
x = "Stress Level",
y = "Sleep Duration (hours)")
Plot a bar plot for any 2 variables in your dataset
ggplot(data = Sleep,aes(x = `Stress.Level`))+geom_bar(fill = "aquamarine4")
ggplot(data = Sleep,aes(x = `Sleep.Duration`))+geom_bar(fill = "aquamarine4")
Find the correlation between any 2 variables by applying least square linear regression model.
Y = Sleep$`Stress.Level`
X = Sleep$`Sleep.Duration`
cor(Y,X, method="pearson") ## [1] -0.7279217