Function work flow example
Yi-Han Tang
2021/4/23
The package will used in this example
library(ggplot2)
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, unionlibrary(yihanfinalproject)Read the data and input it into R.
data<- read.csv(file.choose(),header = TRUE)
data## participants gender age height weight.1..Before. weight.2..after.
## 1 1 M 35 181 102 92
## 2 2 F 20 186 85 76
## 3 3 M 14 170 85 75
## 4 4 F 75 164 104 94
## 5 5 M 60 182 84 75
## 6 6 F 88 164 88 80
## 7 7 M 43 157 90 82
## 8 8 F 22 156 103 93
## 9 9 M 10 112 117 107
## 10 10 F 22 167 80 72
## 11 11 M 20 162 115 110
## 12 12 F 55 153 85 79
## 13 13 F 65 171 113 108
## 14 14 M 55 165 91 87
## 15 15 F 45 167 120 115
## 16 16 M 42 173 108 103
## 17 17 M 44 183 82 79
## 18 18 F 49 169 103 100
## 19 19 F 87 155 91 89
## 20 20 M 78 164 102 99
## 21 21 F 32 158 98 95
## 22 22 F 12 140 91 88
## 23 23 F 7 124 28 23
## 24 24 M 15 155 108 105
## 25 25 M 88 164 109 107
## 26 26 M 60 172 96 94
## 27 27 F 73 155 99 96
## 28 28 M 43 160 104 101
## 29 29 F 33 163 119 116
## 30 30 M 59 175 114 111
## 31 31 M 34 178 96 96
## 32 32 F 57 170 82 82
## 33 33 M 65 170 107 107
## 34 34 F 24 160 80 80
## 35 35 M 22 169 93 93
## 36 36 M 17 173 97 97
## 37 37 F 9 128 23 23
## 38 38 F 60 154 78 78
## 39 39 M 5 103 20 20
## 40 40 F 33 171 99 99
## drug_frequency
## 1 5
## 2 5
## 3 5
## 4 5
## 5 5
## 6 5
## 7 5
## 8 5
## 9 5
## 10 5
## 11 3
## 12 3
## 13 3
## 14 3
## 15 3
## 16 3
## 17 3
## 18 3
## 19 3
## 20 3
## 21 1
## 22 1
## 23 1
## 24 1
## 25 1
## 26 1
## 27 1
## 28 1
## 29 1
## 30 1
## 31 0
## 32 0
## 33 0
## 34 0
## 35 0
## 36 0
## 37 0
## 38 0
## 39 0
## 40 0Calculate the two BMI(before & after) of participants by using function cal_bmi() and insert new column for the results
data$first_BMI <- cal_bmi(data$height, data$weight.1..Before.)
data$second_BMI <-cal_bmi(data$height, data$weight.2..after.)
data## participants gender age height weight.1..Before. weight.2..after.
## 1 1 M 35 181 102 92
## 2 2 F 20 186 85 76
## 3 3 M 14 170 85 75
## 4 4 F 75 164 104 94
## 5 5 M 60 182 84 75
## 6 6 F 88 164 88 80
## 7 7 M 43 157 90 82
## 8 8 F 22 156 103 93
## 9 9 M 10 112 117 107
## 10 10 F 22 167 80 72
## 11 11 M 20 162 115 110
## 12 12 F 55 153 85 79
## 13 13 F 65 171 113 108
## 14 14 M 55 165 91 87
## 15 15 F 45 167 120 115
## 16 16 M 42 173 108 103
## 17 17 M 44 183 82 79
## 18 18 F 49 169 103 100
## 19 19 F 87 155 91 89
## 20 20 M 78 164 102 99
## 21 21 F 32 158 98 95
## 22 22 F 12 140 91 88
## 23 23 F 7 124 28 23
## 24 24 M 15 155 108 105
## 25 25 M 88 164 109 107
## 26 26 M 60 172 96 94
## 27 27 F 73 155 99 96
## 28 28 M 43 160 104 101
## 29 29 F 33 163 119 116
## 30 30 M 59 175 114 111
## 31 31 M 34 178 96 96
## 32 32 F 57 170 82 82
## 33 33 M 65 170 107 107
## 34 34 F 24 160 80 80
## 35 35 M 22 169 93 93
## 36 36 M 17 173 97 97
## 37 37 F 9 128 23 23
## 38 38 F 60 154 78 78
## 39 39 M 5 103 20 20
## 40 40 F 33 171 99 99
## drug_frequency first_BMI second_BMI
## 1 5 31.13458 28.08217
## 2 5 24.56931 21.96786
## 3 5 29.41176 25.95156
## 4 5 38.66746 34.94943
## 5 5 25.35926 22.64219
## 6 5 32.71862 29.74420
## 7 5 36.51264 33.26707
## 8 5 42.32413 38.21499
## 9 5 93.27168 85.29974
## 10 5 28.68514 25.81663
## 11 3 43.81954 41.91434
## 12 3 36.31082 33.74770
## 13 3 38.64437 36.93444
## 14 3 33.42516 31.95592
## 15 3 43.02772 41.23490
## 16 3 36.08540 34.41478
## 17 3 24.48565 23.58984
## 18 3 36.06316 35.01278
## 19 3 37.87721 37.04475
## 20 3 37.92385 36.80845
## 21 1 39.25653 38.05480
## 22 1 46.42857 44.89796
## 23 1 18.21020 14.95838
## 24 1 44.95317 43.70447
## 25 1 40.52647 39.78287
## 26 1 32.44997 31.77393
## 27 1 41.20708 39.95838
## 28 1 40.62500 39.45312
## 29 1 44.78904 43.65990
## 30 1 37.22449 36.24490
## 31 0 30.29920 30.29920
## 32 0 28.37370 28.37370
## 33 0 37.02422 37.02422
## 34 0 31.25000 31.25000
## 35 0 32.56189 32.56189
## 36 0 32.41004 32.41004
## 37 0 14.03809 14.03809
## 38 0 32.88919 32.88919
## 39 0 18.85192 18.85192
## 40 0 33.85657 33.85657Check the qualify of each participants (the weight should use weight 1). Increase a column to show the results. After that, using filter() to filter the participants who are “Eligile”.
data$checkstate <- check_check(data$age, data$height, data$weight.1..Before.)
newdata <- filter(data, data$checkstate == "Eligile")
newdata## participants gender age height weight.1..Before. weight.2..after.
## 1 1 M 35 181 102 92
## 2 4 F 75 164 104 94
## 3 5 M 60 182 84 75
## 4 6 F 88 164 88 80
## 5 7 M 43 157 90 82
## 6 8 F 22 156 103 93
## 7 10 F 22 167 80 72
## 8 12 F 55 153 85 79
## 9 13 F 65 171 113 108
## 10 14 M 55 165 91 87
## 11 15 F 45 167 120 115
## 12 16 M 42 173 108 103
## 13 18 F 49 169 103 100
## 14 19 F 87 155 91 89
## 15 20 M 78 164 102 99
## 16 21 F 32 158 98 95
## 17 25 M 88 164 109 107
## 18 26 M 60 172 96 94
## 19 27 F 73 155 99 96
## 20 28 M 43 160 104 101
## 21 29 F 33 163 119 116
## 22 30 M 59 175 114 111
## 23 31 M 34 178 96 96
## 24 32 F 57 170 82 82
## 25 33 M 65 170 107 107
## 26 34 F 24 160 80 80
## 27 35 M 22 169 93 93
## 28 38 F 60 154 78 78
## 29 40 F 33 171 99 99
## drug_frequency first_BMI second_BMI checkstate
## 1 5 31.13458 28.08217 Eligile
## 2 5 38.66746 34.94943 Eligile
## 3 5 25.35926 22.64219 Eligile
## 4 5 32.71862 29.74420 Eligile
## 5 5 36.51264 33.26707 Eligile
## 6 5 42.32413 38.21499 Eligile
## 7 5 28.68514 25.81663 Eligile
## 8 3 36.31082 33.74770 Eligile
## 9 3 38.64437 36.93444 Eligile
## 10 3 33.42516 31.95592 Eligile
## 11 3 43.02772 41.23490 Eligile
## 12 3 36.08540 34.41478 Eligile
## 13 3 36.06316 35.01278 Eligile
## 14 3 37.87721 37.04475 Eligile
## 15 3 37.92385 36.80845 Eligile
## 16 1 39.25653 38.05480 Eligile
## 17 1 40.52647 39.78287 Eligile
## 18 1 32.44997 31.77393 Eligile
## 19 1 41.20708 39.95838 Eligile
## 20 1 40.62500 39.45312 Eligile
## 21 1 44.78904 43.65990 Eligile
## 22 1 37.22449 36.24490 Eligile
## 23 0 30.29920 30.29920 Eligile
## 24 0 28.37370 28.37370 Eligile
## 25 0 37.02422 37.02422 Eligile
## 26 0 31.25000 31.25000 Eligile
## 27 0 32.56189 32.56189 Eligile
## 28 0 32.88919 32.88919 Eligile
## 29 0 33.85657 33.85657 EligileSummay each group(drug frequency) and input extra participants to each group have same participants
count(newdata, drug_frequency)## drug_frequency n
## 1 0 7
## 2 1 7
## 3 3 8
## 4 5 7data2 <-read.csv(file.choose(),header = TRUE)
#calculate BMI(before & after)
data2$first_BMI <- cal_bmi(data2$height, data2$weight.1..Before.)
data2$second_BMI <-cal_bmi(data2$height, data2$weight.2..after.)
#check qualify
data2$checkstate <- check_check(data2$age, data2$height, data2$weight.1..Before.)
#combine two dataframe
total <- rbind(newdata, data2)
head(total)## participants gender age height weight.1..Before. weight.2..after.
## 1 1 M 35 181 102 92
## 2 4 F 75 164 104 94
## 3 5 M 60 182 84 75
## 4 6 F 88 164 88 80
## 5 7 M 43 157 90 82
## 6 8 F 22 156 103 93
## drug_frequency first_BMI second_BMI checkstate
## 1 5 31.13458 28.08217 Eligile
## 2 5 38.66746 34.94943 Eligile
## 3 5 25.35926 22.64219 Eligile
## 4 5 32.71862 29.74420 Eligile
## 5 5 36.51264 33.26707 Eligile
## 6 5 42.32413 38.21499 Eligile# organize dataframe
arrange(total,drug_frequency)## participants gender age height weight.1..Before. weight.2..after.
## 1 31 M 34 178 96 96
## 2 32 F 57 170 82 82
## 3 33 M 65 170 107 107
## 4 34 F 24 160 80 80
## 5 35 M 22 169 93 93
## 6 38 F 60 154 78 78
## 7 40 F 33 171 99 99
## 8 49 M 76 173 107 107
## 9 50 M 66 161 80 80
## 10 51 M 27 170 96 96
## 11 21 F 32 158 98 95
## 12 25 M 88 164 109 107
## 13 26 M 60 172 96 94
## 14 27 F 73 155 99 96
## 15 28 M 43 160 104 101
## 16 29 F 33 163 119 116
## 17 30 M 59 175 114 111
## 18 46 F 79 156 70 68
## 19 47 M 81 154 71 68
## 20 48 M 56 179 109 105
## 21 12 F 55 153 85 79
## 22 13 F 65 171 113 108
## 23 14 M 55 165 91 87
## 24 15 F 45 167 120 115
## 25 16 M 42 173 108 103
## 26 18 F 49 169 103 100
## 27 19 F 87 155 91 89
## 28 20 M 78 164 102 99
## 29 44 F 32 166 80 75
## 30 45 M 56 173 95 90
## 31 1 M 35 181 102 92
## 32 4 F 75 164 104 94
## 33 5 M 60 182 84 75
## 34 6 F 88 164 88 80
## 35 7 M 43 157 90 82
## 36 8 F 22 156 103 93
## 37 10 F 22 167 80 72
## 38 41 M 47 182 110 100
## 39 42 F 26 185 115 105
## 40 43 M 37 171 95 85
## drug_frequency first_BMI second_BMI checkstate
## 1 0 30.29920 30.29920 Eligile
## 2 0 28.37370 28.37370 Eligile
## 3 0 37.02422 37.02422 Eligile
## 4 0 31.25000 31.25000 Eligile
## 5 0 32.56189 32.56189 Eligile
## 6 0 32.88919 32.88919 Eligile
## 7 0 33.85657 33.85657 Eligile
## 8 0 35.75128 35.75128 Eligile
## 9 0 30.86301 30.86301 Eligile
## 10 0 33.21799 33.21799 Eligile
## 11 1 39.25653 38.05480 Eligile
## 12 1 40.52647 39.78287 Eligile
## 13 1 32.44997 31.77393 Eligile
## 14 1 41.20708 39.95838 Eligile
## 15 1 40.62500 39.45312 Eligile
## 16 1 44.78904 43.65990 Eligile
## 17 1 37.22449 36.24490 Eligile
## 18 1 28.76397 27.94214 Eligile
## 19 1 29.93759 28.67263 Eligile
## 20 1 34.01891 32.77051 Eligile
## 21 3 36.31082 33.74770 Eligile
## 22 3 38.64437 36.93444 Eligile
## 23 3 33.42516 31.95592 Eligile
## 24 3 43.02772 41.23490 Eligile
## 25 3 36.08540 34.41478 Eligile
## 26 3 36.06316 35.01278 Eligile
## 27 3 37.87721 37.04475 Eligile
## 28 3 37.92385 36.80845 Eligile
## 29 3 29.03179 27.21730 Eligile
## 30 3 31.74179 30.07117 Eligile
## 31 5 31.13458 28.08217 Eligile
## 32 5 38.66746 34.94943 Eligile
## 33 5 25.35926 22.64219 Eligile
## 34 5 32.71862 29.74420 Eligile
## 35 5 36.51264 33.26707 Eligile
## 36 5 42.32413 38.21499 Eligile
## 37 5 28.68514 25.81663 Eligile
## 38 5 33.20855 30.18959 Eligile
## 39 5 33.60117 30.67933 Eligile
## 40 5 32.48863 29.06877 EligileUse total(dataset) to see the different results by different varies. First, to see the BMI distribution of participants of different ages by using finction plotage(). And using plotage_separate() to see in different drug frequency, the age distribution of participants.
Before1 <- plotage(total$age,total$height, total$weight.1..Before., total$drug_frequency)
After1 <- plotage(total$age, total$height, total$weight.2..after.,total$drug_frequency)
Before1
After1
Before_separte1 <- plotage_separate(total, total$age,total$height, total$weight.1..Before., total$drug_frequency)
After_separte1 <- plotage_separate(total, total$age, total$height, total$weight.2..after.,total$drug_frequency)
Before_separte1
After_separte1
Using plotgender() and plotgender_separate() to see the different results by gender.
genderbefore <-plotgender(total$age, total$height, total$weight.1..Before.)
genderafter <- plotgender(total$age, total$height, total$weight.2..after.)
genderbefore
genderafter
# using plotgender_separate() to separate in different plots
genderbeforese <-plotgender_separate(total, total$age, total$height, total$weight.1..Before., total$gender)
genderafterse <- plotgender_separate(total,total$age, total$height, total$weight.2..after., total$gender )
genderbeforese
genderafterse