R Markdown
library(MASS)
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following object is masked from 'package:MASS':
##
## select
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
Examdata = survey
apply(is.na(Examdata), 2, sum)>=1
## Sex Wr.Hnd NW.Hnd W.Hnd Fold Pulse Clap Exer Smoke Height M.I
## TRUE TRUE TRUE TRUE FALSE TRUE TRUE FALSE TRUE TRUE TRUE
## Age
## FALSE
Examdata2 = na.omit(Examdata)
apply(is.na(Examdata2), 2, sum)>=1
## Sex Wr.Hnd NW.Hnd W.Hnd Fold Pulse Clap Exer Smoke Height M.I
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## Age
## FALSE
ExamdataSel = Examdata2 %>% reframe(Sex, W.Hnd, Height, Age, Smoke)
ExamFiltered = ExamdataSel %>% filter(Smoke=="Never")
ExamFiltered = arrange(ExamFiltered, desc(Age))
ExamFiltered
## Sex W.Hnd Height Age Smoke
## 1 Male Right 172.72 70.417 Never
## 2 Female Right 167.64 44.250 Never
## 3 Male Right 180.34 43.833 Never
## 4 Female Right 157.00 35.833 Never
## 5 Male Right 185.00 35.500 Never
## 6 Male Right 185.42 32.667 Never
## 7 Female Right 165.00 30.750 Never
## 8 Female Right 156.20 28.500 Never
## 9 Male Right 180.00 27.333 Never
## 10 Female Right 165.00 26.500 Never
## 11 Male Right 195.00 25.500 Never
## 12 Female Right 163.00 24.667 Never
## 13 Female Right 175.00 24.167 Never
## 14 Male Right 170.00 23.833 Never
## 15 Male Right 165.00 23.667 Never
## 16 Female Right 165.00 23.583 Never
## 17 Male Right 173.00 23.583 Never
## 18 Female Right 166.50 23.250 Never
## 19 Female Right 164.00 23.083 Never
## 20 Female Right 159.00 22.917 Never
## 21 Male Right 167.00 22.333 Never
## 22 Male Right 179.00 21.583 Never
## 23 Male Right 174.00 21.333 Never
## 24 Female Right 170.18 21.167 Never
## 25 Male Right 168.00 21.167 Never
## 26 Female Right 172.72 21.000 Never
## 27 Male Right 175.26 21.000 Never
## 28 Female Right 158.00 20.500 Never
## 29 Male Right 165.00 20.417 Never
## 30 Male Right 187.00 20.333 Never
## 31 Male Right 173.00 20.333 Never
## 32 Male Right 176.50 20.167 Never
## 33 Female Right 175.00 20.167 Never
## 34 Female Right 164.00 20.167 Never
## 35 Female Right 160.00 20.167 Never
## 36 Male Right 196.00 20.083 Never
## 37 Male Right 187.96 20.000 Never
## 38 Female Right 165.00 20.000 Never
## 39 Male Right 183.00 19.667 Never
## 40 Female Right 165.00 19.667 Never
## 41 Male Left 165.00 19.500 Never
## 42 Male Right 170.00 19.417 Never
## 43 Female Right 157.00 19.333 Never
## 44 Male Right 182.88 19.333 Never
## 45 Male Right 185.00 19.333 Never
## 46 Male Right 167.00 19.250 Never
## 47 Female Left 172.00 19.167 Never
## 48 Female Right 175.26 19.083 Never
## 49 Male Right 175.00 19.000 Never
## 50 Male Right 180.00 19.000 Never
## 51 Male Right 185.00 19.000 Never
## 52 Female Left 171.00 18.917 Never
## 53 Male Right 179.10 18.917 Never
## 54 Male Right 188.00 18.917 Never
## 55 Male Right 182.88 18.833 Never
## 56 Female Right 170.00 18.750 Never
## 57 Male Right 178.00 18.750 Never
## 58 Male Right 175.00 18.750 Never
## 59 Female Right 167.00 18.667 Never
## 60 Female Right 165.00 18.667 Never
## 61 Female Right 164.00 18.583 Never
## 62 Male Right 180.00 18.583 Never
## 63 Male Right 160.00 18.583 Never
## 64 Male Left 200.00 18.500 Never
## 65 Female Right 168.00 18.500 Never
## 66 Male Right 165.00 18.500 Never
## 67 Male Right 175.26 18.417 Never
## 68 Female Right 167.00 18.417 Never
## 69 Male Left 182.88 18.333 Never
## 70 Male Right 170.18 18.333 Never
## 71 Female Right 152.00 18.333 Never
## 72 Female Right 173.00 18.250 Never
## 73 Male Right 184.00 18.250 Never
## 74 Female Right 170.00 18.250 Never
## 75 Male Right 180.34 18.167 Never
## 76 Male Right 190.50 18.167 Never
## 77 Female Right 165.00 18.167 Never
## 78 Female Right 170.00 18.167 Never
## 79 Female Right 162.56 18.167 Never
## 80 Female Right 169.00 18.167 Never
## 81 Male Right 175.26 18.083 Never
## 82 Female Right 162.56 18.000 Never
## 83 Female Right 157.00 18.000 Never
## 84 Male Right 177.00 17.917 Never
## 85 Male Right 190.50 17.917 Never
## 86 Male Right 182.50 17.917 Never
## 87 Male Right 185.00 17.917 Never
## 88 Male Right 180.34 17.833 Never
## 89 Female Right 165.00 17.750 Never
## 90 Male Right 170.00 17.750 Never
## 91 Female Right 157.48 17.750 Never
## 92 Female Right 168.50 17.750 Never
## 93 Male Right 177.80 17.667 Never
## 94 Female Right 171.00 17.667 Never
## 95 Female Right 172.72 17.667 Never
## 96 Female Right 165.10 17.667 Never
## 97 Female Right 170.00 17.583 Never
## 98 Female Right 155.00 17.500 Never
## 99 Male Right 172.72 17.500 Never
## 100 Male Right 180.00 17.500 Never
## 101 Female Right 176.50 17.500 Never
## 102 Male Right 190.00 17.500 Never
## 103 Female Right 178.00 17.500 Never
## 104 Female Right 172.00 17.500 Never
## 105 Female Right 175.00 17.500 Never
## 106 Male Right 191.80 17.500 Never
## 107 Female Right 164.00 17.500 Never
## 108 Male Right 179.00 17.417 Never
## 109 Female Right 167.64 17.417 Never
## 110 Female Right 162.56 17.417 Never
## 111 Male Left 180.00 17.417 Never
## 112 Female Right 153.50 17.417 Never
## 113 Male Right 185.00 17.417 Never
## 114 Female Right 167.64 17.333 Never
## 115 Male Right 180.34 17.333 Never
## 116 Female Right 167.64 17.333 Never
## 117 Female Right 167.00 17.250 Never
## 118 Female Right 172.72 17.250 Never
## 119 Female Left 170.00 17.250 Never
## 120 Female Right 162.50 17.250 Never
## 121 Female Right 160.02 17.250 Never
## 122 Female Left 163.00 17.250 Never
## 123 Male Right 180.00 17.167 Never
## 124 Female Right 157.48 17.167 Never
## 125 Male Right 167.64 17.167 Never
## 126 Female Right 162.56 17.167 Never
## 127 Male Right 154.94 17.167 Never
## 128 Female Right 160.00 17.167 Never
## 129 Male Right 183.00 17.167 Never
## 130 Female Right 154.94 17.083 Never
## 131 Female Right 165.10 17.083 Never
## 132 Female Right 168.90 17.083 Never
## 133 Female Right 170.18 17.000 Never
## 134 Female Right 160.00 16.917 Never
ggplot(ExamFiltered, aes(y = Age, fill = Sex) ) + geom_histogram(color = "black", binwidth = 5, alpha = 0.7) +
facet_wrap(~Sex) + scale_fill_manual(values = c("blue", "red")) +
labs(
title = "No Smokers by Age",
x = "Frequency",
y = "Age"
)
