Install and load the package dplyr package.
Given the metadata:
Wt: weight of the subject (kg).
Dose: dose of theophylline administered orally to the subject (mg/kg).
Time: time since drug administration when the sample was drawn (hr).
conc: theophylline concentration in the sample (mg/L).
Copy and paste this code to get df
install.packages("dplyr", repos="http://cran.rstudio.com/")## package 'dplyr' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\Baoco\AppData\Local\Temp\RtmpUfDrpr\downloaded_packagesdf=data.frame(Theoph)
library(dplyr, lib.loc="C:/Program Files/R/R-3.3.2/library")Use the names() function to get the column names of df.
names(df)## [1] "Subject" "Wt" "Dose" "Time" "conc"Let’s practice using the select() function. This allows you to work with just column names instead of indices. a) Select only the columns starting from Subject to Dose b) Only select the Wt and Dose columns now.
select(df, Subject:Dose)## Subject Wt Dose
## 1 1 79.6 4.02
## 2 1 79.6 4.02
## 3 1 79.6 4.02
## 4 1 79.6 4.02
## 5 1 79.6 4.02
## 6 1 79.6 4.02
## 7 1 79.6 4.02
## 8 1 79.6 4.02
## 9 1 79.6 4.02
## 10 1 79.6 4.02
## 11 1 79.6 4.02
## 12 2 72.4 4.40
## 13 2 72.4 4.40
## 14 2 72.4 4.40
## 15 2 72.4 4.40
## 16 2 72.4 4.40
## 17 2 72.4 4.40
## 18 2 72.4 4.40
## 19 2 72.4 4.40
## 20 2 72.4 4.40
## 21 2 72.4 4.40
## 22 2 72.4 4.40
## 23 3 70.5 4.53
## 24 3 70.5 4.53
## 25 3 70.5 4.53
## 26 3 70.5 4.53
## 27 3 70.5 4.53
## 28 3 70.5 4.53
## 29 3 70.5 4.53
## 30 3 70.5 4.53
## 31 3 70.5 4.53
## 32 3 70.5 4.53
## 33 3 70.5 4.53
## 34 4 72.7 4.40
## 35 4 72.7 4.40
## 36 4 72.7 4.40
## 37 4 72.7 4.40
## 38 4 72.7 4.40
## 39 4 72.7 4.40
## 40 4 72.7 4.40
## 41 4 72.7 4.40
## 42 4 72.7 4.40
## 43 4 72.7 4.40
## 44 4 72.7 4.40
## 45 5 54.6 5.86
## 46 5 54.6 5.86
## 47 5 54.6 5.86
## 48 5 54.6 5.86
## 49 5 54.6 5.86
## 50 5 54.6 5.86
## 51 5 54.6 5.86
## 52 5 54.6 5.86
## 53 5 54.6 5.86
## 54 5 54.6 5.86
## 55 5 54.6 5.86
## 56 6 80.0 4.00
## 57 6 80.0 4.00
## 58 6 80.0 4.00
## 59 6 80.0 4.00
## 60 6 80.0 4.00
## 61 6 80.0 4.00
## 62 6 80.0 4.00
## 63 6 80.0 4.00
## 64 6 80.0 4.00
## 65 6 80.0 4.00
## 66 6 80.0 4.00
## 67 7 64.6 4.95
## 68 7 64.6 4.95
## 69 7 64.6 4.95
## 70 7 64.6 4.95
## 71 7 64.6 4.95
## 72 7 64.6 4.95
## 73 7 64.6 4.95
## 74 7 64.6 4.95
## 75 7 64.6 4.95
## 76 7 64.6 4.95
## 77 7 64.6 4.95
## 78 8 70.5 4.53
## 79 8 70.5 4.53
## 80 8 70.5 4.53
## 81 8 70.5 4.53
## 82 8 70.5 4.53
## 83 8 70.5 4.53
## 84 8 70.5 4.53
## 85 8 70.5 4.53
## 86 8 70.5 4.53
## 87 8 70.5 4.53
## 88 8 70.5 4.53
## 89 9 86.4 3.10
## 90 9 86.4 3.10
## 91 9 86.4 3.10
## 92 9 86.4 3.10
## 93 9 86.4 3.10
## 94 9 86.4 3.10
## 95 9 86.4 3.10
## 96 9 86.4 3.10
## 97 9 86.4 3.10
## 98 9 86.4 3.10
## 99 9 86.4 3.10
## 100 10 58.2 5.50
## 101 10 58.2 5.50
## 102 10 58.2 5.50
## 103 10 58.2 5.50
## 104 10 58.2 5.50
## 105 10 58.2 5.50
## 106 10 58.2 5.50
## 107 10 58.2 5.50
## 108 10 58.2 5.50
## 109 10 58.2 5.50
## 110 10 58.2 5.50
## 111 11 65.0 4.92
## 112 11 65.0 4.92
## 113 11 65.0 4.92
## 114 11 65.0 4.92
## 115 11 65.0 4.92
## 116 11 65.0 4.92
## 117 11 65.0 4.92
## 118 11 65.0 4.92
## 119 11 65.0 4.92
## 120 11 65.0 4.92
## 121 11 65.0 4.92
## 122 12 60.5 5.30
## 123 12 60.5 5.30
## 124 12 60.5 5.30
## 125 12 60.5 5.30
## 126 12 60.5 5.30
## 127 12 60.5 5.30
## 128 12 60.5 5.30
## 129 12 60.5 5.30
## 130 12 60.5 5.30
## 131 12 60.5 5.30
## 132 12 60.5 5.30Let’s look at the sample with Dose greater than 5 mg/kg. Use the filter command() to return df with Dose>5’
filter(df, Dose > 5)## Subject Wt Dose Time conc
## 1 5 54.6 5.86 0.00 0.00
## 2 5 54.6 5.86 0.30 2.02
## 3 5 54.6 5.86 0.52 5.63
## 4 5 54.6 5.86 1.00 11.40
## 5 5 54.6 5.86 2.02 9.33
## 6 5 54.6 5.86 3.50 8.74
## 7 5 54.6 5.86 5.02 7.56
## 8 5 54.6 5.86 7.02 7.09
## 9 5 54.6 5.86 9.10 5.90
## 10 5 54.6 5.86 12.00 4.37
## 11 5 54.6 5.86 24.35 1.57
## 12 10 58.2 5.50 0.00 0.24
## 13 10 58.2 5.50 0.37 2.89
## 14 10 58.2 5.50 0.77 5.22
## 15 10 58.2 5.50 1.02 6.41
## 16 10 58.2 5.50 2.05 7.83
## 17 10 58.2 5.50 3.55 10.21
## 18 10 58.2 5.50 5.05 9.18
## 19 10 58.2 5.50 7.08 8.02
## 20 10 58.2 5.50 9.38 7.14
## 21 10 58.2 5.50 12.10 5.68
## 22 10 58.2 5.50 23.70 2.42
## 23 12 60.5 5.30 0.00 0.00
## 24 12 60.5 5.30 0.25 1.25
## 25 12 60.5 5.30 0.50 3.96
## 26 12 60.5 5.30 1.00 7.82
## 27 12 60.5 5.30 2.00 9.72
## 28 12 60.5 5.30 3.52 9.75
## 29 12 60.5 5.30 5.07 8.57
## 30 12 60.5 5.30 7.07 6.59
## 31 12 60.5 5.30 9.03 6.11
## 32 12 60.5 5.30 12.05 4.57
## 33 12 60.5 5.30 24.15 1.17Great. Now use filter command to return df with Dose>5 and Time greater than the mean Time.
filter(df, Dose > 5, Time > mean(Time))## Subject Wt Dose Time conc
## 1 5 54.6 5.86 7.02 7.09
## 2 5 54.6 5.86 9.10 5.90
## 3 5 54.6 5.86 12.00 4.37
## 4 5 54.6 5.86 24.35 1.57
## 5 10 58.2 5.50 7.08 8.02
## 6 10 58.2 5.50 9.38 7.14
## 7 10 58.2 5.50 12.10 5.68
## 8 10 58.2 5.50 23.70 2.42
## 9 12 60.5 5.30 7.07 6.59
## 10 12 60.5 5.30 9.03 6.11
## 11 12 60.5 5.30 12.05 4.57
## 12 12 60.5 5.30 24.15 1.17Now let’s try sorting the data. Use the arrange() function to 1) arrange df by weight (descending)
arrange(df, desc(Wt))## Subject Wt Dose Time conc
## 1 9 86.4 3.10 0.00 0.00
## 2 9 86.4 3.10 0.30 7.37
## 3 9 86.4 3.10 0.63 9.03
## 4 9 86.4 3.10 1.05 7.14
## 5 9 86.4 3.10 2.02 6.33
## 6 9 86.4 3.10 3.53 5.66
## 7 9 86.4 3.10 5.02 5.67
## 8 9 86.4 3.10 7.17 4.24
## 9 9 86.4 3.10 8.80 4.11
## 10 9 86.4 3.10 11.60 3.16
## 11 9 86.4 3.10 24.43 1.12
## 12 6 80.0 4.00 0.00 0.00
## 13 6 80.0 4.00 0.27 1.29
## 14 6 80.0 4.00 0.58 3.08
## 15 6 80.0 4.00 1.15 6.44
## 16 6 80.0 4.00 2.03 6.32
## 17 6 80.0 4.00 3.57 5.53
## 18 6 80.0 4.00 5.00 4.94
## 19 6 80.0 4.00 7.00 4.02
## 20 6 80.0 4.00 9.22 3.46
## 21 6 80.0 4.00 12.10 2.78
## 22 6 80.0 4.00 23.85 0.92
## 23 1 79.6 4.02 0.00 0.74
## 24 1 79.6 4.02 0.25 2.84
## 25 1 79.6 4.02 0.57 6.57
## 26 1 79.6 4.02 1.12 10.50
## 27 1 79.6 4.02 2.02 9.66
## 28 1 79.6 4.02 3.82 8.58
## 29 1 79.6 4.02 5.10 8.36
## 30 1 79.6 4.02 7.03 7.47
## 31 1 79.6 4.02 9.05 6.89
## 32 1 79.6 4.02 12.12 5.94
## 33 1 79.6 4.02 24.37 3.28
## 34 4 72.7 4.40 0.00 0.00
## 35 4 72.7 4.40 0.35 1.89
## 36 4 72.7 4.40 0.60 4.60
## 37 4 72.7 4.40 1.07 8.60
## 38 4 72.7 4.40 2.13 8.38
## 39 4 72.7 4.40 3.50 7.54
## 40 4 72.7 4.40 5.02 6.88
## 41 4 72.7 4.40 7.02 5.78
## 42 4 72.7 4.40 9.02 5.33
## 43 4 72.7 4.40 11.98 4.19
## 44 4 72.7 4.40 24.65 1.15
## 45 2 72.4 4.40 0.00 0.00
## 46 2 72.4 4.40 0.27 1.72
## 47 2 72.4 4.40 0.52 7.91
## 48 2 72.4 4.40 1.00 8.31
## 49 2 72.4 4.40 1.92 8.33
## 50 2 72.4 4.40 3.50 6.85
## 51 2 72.4 4.40 5.02 6.08
## 52 2 72.4 4.40 7.03 5.40
## 53 2 72.4 4.40 9.00 4.55
## 54 2 72.4 4.40 12.00 3.01
## 55 2 72.4 4.40 24.30 0.90
## 56 3 70.5 4.53 0.00 0.00
## 57 3 70.5 4.53 0.27 4.40
## 58 3 70.5 4.53 0.58 6.90
## 59 3 70.5 4.53 1.02 8.20
## 60 3 70.5 4.53 2.02 7.80
## 61 3 70.5 4.53 3.62 7.50
## 62 3 70.5 4.53 5.08 6.20
## 63 3 70.5 4.53 7.07 5.30
## 64 3 70.5 4.53 9.00 4.90
## 65 3 70.5 4.53 12.15 3.70
## 66 3 70.5 4.53 24.17 1.05
## 67 8 70.5 4.53 0.00 0.00
## 68 8 70.5 4.53 0.25 3.05
## 69 8 70.5 4.53 0.52 3.05
## 70 8 70.5 4.53 0.98 7.31
## 71 8 70.5 4.53 2.02 7.56
## 72 8 70.5 4.53 3.53 6.59
## 73 8 70.5 4.53 5.05 5.88
## 74 8 70.5 4.53 7.15 4.73
## 75 8 70.5 4.53 9.07 4.57
## 76 8 70.5 4.53 12.10 3.00
## 77 8 70.5 4.53 24.12 1.25
## 78 11 65.0 4.92 0.00 0.00
## 79 11 65.0 4.92 0.25 4.86
## 80 11 65.0 4.92 0.50 7.24
## 81 11 65.0 4.92 0.98 8.00
## 82 11 65.0 4.92 1.98 6.81
## 83 11 65.0 4.92 3.60 5.87
## 84 11 65.0 4.92 5.02 5.22
## 85 11 65.0 4.92 7.03 4.45
## 86 11 65.0 4.92 9.03 3.62
## 87 11 65.0 4.92 12.12 2.69
## 88 11 65.0 4.92 24.08 0.86
## 89 7 64.6 4.95 0.00 0.15
## 90 7 64.6 4.95 0.25 0.85
## 91 7 64.6 4.95 0.50 2.35
## 92 7 64.6 4.95 1.02 5.02
## 93 7 64.6 4.95 2.02 6.58
## 94 7 64.6 4.95 3.48 7.09
## 95 7 64.6 4.95 5.00 6.66
## 96 7 64.6 4.95 6.98 5.25
## 97 7 64.6 4.95 9.00 4.39
## 98 7 64.6 4.95 12.05 3.53
## 99 7 64.6 4.95 24.22 1.15
## 100 12 60.5 5.30 0.00 0.00
## 101 12 60.5 5.30 0.25 1.25
## 102 12 60.5 5.30 0.50 3.96
## 103 12 60.5 5.30 1.00 7.82
## 104 12 60.5 5.30 2.00 9.72
## 105 12 60.5 5.30 3.52 9.75
## 106 12 60.5 5.30 5.07 8.57
## 107 12 60.5 5.30 7.07 6.59
## 108 12 60.5 5.30 9.03 6.11
## 109 12 60.5 5.30 12.05 4.57
## 110 12 60.5 5.30 24.15 1.17
## 111 10 58.2 5.50 0.00 0.24
## 112 10 58.2 5.50 0.37 2.89
## 113 10 58.2 5.50 0.77 5.22
## 114 10 58.2 5.50 1.02 6.41
## 115 10 58.2 5.50 2.05 7.83
## 116 10 58.2 5.50 3.55 10.21
## 117 10 58.2 5.50 5.05 9.18
## 118 10 58.2 5.50 7.08 8.02
## 119 10 58.2 5.50 9.38 7.14
## 120 10 58.2 5.50 12.10 5.68
## 121 10 58.2 5.50 23.70 2.42
## 122 5 54.6 5.86 0.00 0.00
## 123 5 54.6 5.86 0.30 2.02
## 124 5 54.6 5.86 0.52 5.63
## 125 5 54.6 5.86 1.00 11.40
## 126 5 54.6 5.86 2.02 9.33
## 127 5 54.6 5.86 3.50 8.74
## 128 5 54.6 5.86 5.02 7.56
## 129 5 54.6 5.86 7.02 7.09
## 130 5 54.6 5.86 9.10 5.90
## 131 5 54.6 5.86 12.00 4.37
## 132 5 54.6 5.86 24.35 1.57arrange(df, Wt)## Subject Wt Dose Time conc
## 1 5 54.6 5.86 0.00 0.00
## 2 5 54.6 5.86 0.30 2.02
## 3 5 54.6 5.86 0.52 5.63
## 4 5 54.6 5.86 1.00 11.40
## 5 5 54.6 5.86 2.02 9.33
## 6 5 54.6 5.86 3.50 8.74
## 7 5 54.6 5.86 5.02 7.56
## 8 5 54.6 5.86 7.02 7.09
## 9 5 54.6 5.86 9.10 5.90
## 10 5 54.6 5.86 12.00 4.37
## 11 5 54.6 5.86 24.35 1.57
## 12 10 58.2 5.50 0.00 0.24
## 13 10 58.2 5.50 0.37 2.89
## 14 10 58.2 5.50 0.77 5.22
## 15 10 58.2 5.50 1.02 6.41
## 16 10 58.2 5.50 2.05 7.83
## 17 10 58.2 5.50 3.55 10.21
## 18 10 58.2 5.50 5.05 9.18
## 19 10 58.2 5.50 7.08 8.02
## 20 10 58.2 5.50 9.38 7.14
## 21 10 58.2 5.50 12.10 5.68
## 22 10 58.2 5.50 23.70 2.42
## 23 12 60.5 5.30 0.00 0.00
## 24 12 60.5 5.30 0.25 1.25
## 25 12 60.5 5.30 0.50 3.96
## 26 12 60.5 5.30 1.00 7.82
## 27 12 60.5 5.30 2.00 9.72
## 28 12 60.5 5.30 3.52 9.75
## 29 12 60.5 5.30 5.07 8.57
## 30 12 60.5 5.30 7.07 6.59
## 31 12 60.5 5.30 9.03 6.11
## 32 12 60.5 5.30 12.05 4.57
## 33 12 60.5 5.30 24.15 1.17
## 34 7 64.6 4.95 0.00 0.15
## 35 7 64.6 4.95 0.25 0.85
## 36 7 64.6 4.95 0.50 2.35
## 37 7 64.6 4.95 1.02 5.02
## 38 7 64.6 4.95 2.02 6.58
## 39 7 64.6 4.95 3.48 7.09
## 40 7 64.6 4.95 5.00 6.66
## 41 7 64.6 4.95 6.98 5.25
## 42 7 64.6 4.95 9.00 4.39
## 43 7 64.6 4.95 12.05 3.53
## 44 7 64.6 4.95 24.22 1.15
## 45 11 65.0 4.92 0.00 0.00
## 46 11 65.0 4.92 0.25 4.86
## 47 11 65.0 4.92 0.50 7.24
## 48 11 65.0 4.92 0.98 8.00
## 49 11 65.0 4.92 1.98 6.81
## 50 11 65.0 4.92 3.60 5.87
## 51 11 65.0 4.92 5.02 5.22
## 52 11 65.0 4.92 7.03 4.45
## 53 11 65.0 4.92 9.03 3.62
## 54 11 65.0 4.92 12.12 2.69
## 55 11 65.0 4.92 24.08 0.86
## 56 3 70.5 4.53 0.00 0.00
## 57 3 70.5 4.53 0.27 4.40
## 58 3 70.5 4.53 0.58 6.90
## 59 3 70.5 4.53 1.02 8.20
## 60 3 70.5 4.53 2.02 7.80
## 61 3 70.5 4.53 3.62 7.50
## 62 3 70.5 4.53 5.08 6.20
## 63 3 70.5 4.53 7.07 5.30
## 64 3 70.5 4.53 9.00 4.90
## 65 3 70.5 4.53 12.15 3.70
## 66 3 70.5 4.53 24.17 1.05
## 67 8 70.5 4.53 0.00 0.00
## 68 8 70.5 4.53 0.25 3.05
## 69 8 70.5 4.53 0.52 3.05
## 70 8 70.5 4.53 0.98 7.31
## 71 8 70.5 4.53 2.02 7.56
## 72 8 70.5 4.53 3.53 6.59
## 73 8 70.5 4.53 5.05 5.88
## 74 8 70.5 4.53 7.15 4.73
## 75 8 70.5 4.53 9.07 4.57
## 76 8 70.5 4.53 12.10 3.00
## 77 8 70.5 4.53 24.12 1.25
## 78 2 72.4 4.40 0.00 0.00
## 79 2 72.4 4.40 0.27 1.72
## 80 2 72.4 4.40 0.52 7.91
## 81 2 72.4 4.40 1.00 8.31
## 82 2 72.4 4.40 1.92 8.33
## 83 2 72.4 4.40 3.50 6.85
## 84 2 72.4 4.40 5.02 6.08
## 85 2 72.4 4.40 7.03 5.40
## 86 2 72.4 4.40 9.00 4.55
## 87 2 72.4 4.40 12.00 3.01
## 88 2 72.4 4.40 24.30 0.90
## 89 4 72.7 4.40 0.00 0.00
## 90 4 72.7 4.40 0.35 1.89
## 91 4 72.7 4.40 0.60 4.60
## 92 4 72.7 4.40 1.07 8.60
## 93 4 72.7 4.40 2.13 8.38
## 94 4 72.7 4.40 3.50 7.54
## 95 4 72.7 4.40 5.02 6.88
## 96 4 72.7 4.40 7.02 5.78
## 97 4 72.7 4.40 9.02 5.33
## 98 4 72.7 4.40 11.98 4.19
## 99 4 72.7 4.40 24.65 1.15
## 100 1 79.6 4.02 0.00 0.74
## 101 1 79.6 4.02 0.25 2.84
## 102 1 79.6 4.02 0.57 6.57
## 103 1 79.6 4.02 1.12 10.50
## 104 1 79.6 4.02 2.02 9.66
## 105 1 79.6 4.02 3.82 8.58
## 106 1 79.6 4.02 5.10 8.36
## 107 1 79.6 4.02 7.03 7.47
## 108 1 79.6 4.02 9.05 6.89
## 109 1 79.6 4.02 12.12 5.94
## 110 1 79.6 4.02 24.37 3.28
## 111 6 80.0 4.00 0.00 0.00
## 112 6 80.0 4.00 0.27 1.29
## 113 6 80.0 4.00 0.58 3.08
## 114 6 80.0 4.00 1.15 6.44
## 115 6 80.0 4.00 2.03 6.32
## 116 6 80.0 4.00 3.57 5.53
## 117 6 80.0 4.00 5.00 4.94
## 118 6 80.0 4.00 7.00 4.02
## 119 6 80.0 4.00 9.22 3.46
## 120 6 80.0 4.00 12.10 2.78
## 121 6 80.0 4.00 23.85 0.92
## 122 9 86.4 3.10 0.00 0.00
## 123 9 86.4 3.10 0.30 7.37
## 124 9 86.4 3.10 0.63 9.03
## 125 9 86.4 3.10 1.05 7.14
## 126 9 86.4 3.10 2.02 6.33
## 127 9 86.4 3.10 3.53 5.66
## 128 9 86.4 3.10 5.02 5.67
## 129 9 86.4 3.10 7.17 4.24
## 130 9 86.4 3.10 8.80 4.11
## 131 9 86.4 3.10 11.60 3.16
## 132 9 86.4 3.10 24.43 1.12arrange(df, Wt, desc(Time))## Subject Wt Dose Time conc
## 1 5 54.6 5.86 24.35 1.57
## 2 5 54.6 5.86 12.00 4.37
## 3 5 54.6 5.86 9.10 5.90
## 4 5 54.6 5.86 7.02 7.09
## 5 5 54.6 5.86 5.02 7.56
## 6 5 54.6 5.86 3.50 8.74
## 7 5 54.6 5.86 2.02 9.33
## 8 5 54.6 5.86 1.00 11.40
## 9 5 54.6 5.86 0.52 5.63
## 10 5 54.6 5.86 0.30 2.02
## 11 5 54.6 5.86 0.00 0.00
## 12 10 58.2 5.50 23.70 2.42
## 13 10 58.2 5.50 12.10 5.68
## 14 10 58.2 5.50 9.38 7.14
## 15 10 58.2 5.50 7.08 8.02
## 16 10 58.2 5.50 5.05 9.18
## 17 10 58.2 5.50 3.55 10.21
## 18 10 58.2 5.50 2.05 7.83
## 19 10 58.2 5.50 1.02 6.41
## 20 10 58.2 5.50 0.77 5.22
## 21 10 58.2 5.50 0.37 2.89
## 22 10 58.2 5.50 0.00 0.24
## 23 12 60.5 5.30 24.15 1.17
## 24 12 60.5 5.30 12.05 4.57
## 25 12 60.5 5.30 9.03 6.11
## 26 12 60.5 5.30 7.07 6.59
## 27 12 60.5 5.30 5.07 8.57
## 28 12 60.5 5.30 3.52 9.75
## 29 12 60.5 5.30 2.00 9.72
## 30 12 60.5 5.30 1.00 7.82
## 31 12 60.5 5.30 0.50 3.96
## 32 12 60.5 5.30 0.25 1.25
## 33 12 60.5 5.30 0.00 0.00
## 34 7 64.6 4.95 24.22 1.15
## 35 7 64.6 4.95 12.05 3.53
## 36 7 64.6 4.95 9.00 4.39
## 37 7 64.6 4.95 6.98 5.25
## 38 7 64.6 4.95 5.00 6.66
## 39 7 64.6 4.95 3.48 7.09
## 40 7 64.6 4.95 2.02 6.58
## 41 7 64.6 4.95 1.02 5.02
## 42 7 64.6 4.95 0.50 2.35
## 43 7 64.6 4.95 0.25 0.85
## 44 7 64.6 4.95 0.00 0.15
## 45 11 65.0 4.92 24.08 0.86
## 46 11 65.0 4.92 12.12 2.69
## 47 11 65.0 4.92 9.03 3.62
## 48 11 65.0 4.92 7.03 4.45
## 49 11 65.0 4.92 5.02 5.22
## 50 11 65.0 4.92 3.60 5.87
## 51 11 65.0 4.92 1.98 6.81
## 52 11 65.0 4.92 0.98 8.00
## 53 11 65.0 4.92 0.50 7.24
## 54 11 65.0 4.92 0.25 4.86
## 55 11 65.0 4.92 0.00 0.00
## 56 3 70.5 4.53 24.17 1.05
## 57 8 70.5 4.53 24.12 1.25
## 58 3 70.5 4.53 12.15 3.70
## 59 8 70.5 4.53 12.10 3.00
## 60 8 70.5 4.53 9.07 4.57
## 61 3 70.5 4.53 9.00 4.90
## 62 8 70.5 4.53 7.15 4.73
## 63 3 70.5 4.53 7.07 5.30
## 64 3 70.5 4.53 5.08 6.20
## 65 8 70.5 4.53 5.05 5.88
## 66 3 70.5 4.53 3.62 7.50
## 67 8 70.5 4.53 3.53 6.59
## 68 3 70.5 4.53 2.02 7.80
## 69 8 70.5 4.53 2.02 7.56
## 70 3 70.5 4.53 1.02 8.20
## 71 8 70.5 4.53 0.98 7.31
## 72 3 70.5 4.53 0.58 6.90
## 73 8 70.5 4.53 0.52 3.05
## 74 3 70.5 4.53 0.27 4.40
## 75 8 70.5 4.53 0.25 3.05
## 76 3 70.5 4.53 0.00 0.00
## 77 8 70.5 4.53 0.00 0.00
## 78 2 72.4 4.40 24.30 0.90
## 79 2 72.4 4.40 12.00 3.01
## 80 2 72.4 4.40 9.00 4.55
## 81 2 72.4 4.40 7.03 5.40
## 82 2 72.4 4.40 5.02 6.08
## 83 2 72.4 4.40 3.50 6.85
## 84 2 72.4 4.40 1.92 8.33
## 85 2 72.4 4.40 1.00 8.31
## 86 2 72.4 4.40 0.52 7.91
## 87 2 72.4 4.40 0.27 1.72
## 88 2 72.4 4.40 0.00 0.00
## 89 4 72.7 4.40 24.65 1.15
## 90 4 72.7 4.40 11.98 4.19
## 91 4 72.7 4.40 9.02 5.33
## 92 4 72.7 4.40 7.02 5.78
## 93 4 72.7 4.40 5.02 6.88
## 94 4 72.7 4.40 3.50 7.54
## 95 4 72.7 4.40 2.13 8.38
## 96 4 72.7 4.40 1.07 8.60
## 97 4 72.7 4.40 0.60 4.60
## 98 4 72.7 4.40 0.35 1.89
## 99 4 72.7 4.40 0.00 0.00
## 100 1 79.6 4.02 24.37 3.28
## 101 1 79.6 4.02 12.12 5.94
## 102 1 79.6 4.02 9.05 6.89
## 103 1 79.6 4.02 7.03 7.47
## 104 1 79.6 4.02 5.10 8.36
## 105 1 79.6 4.02 3.82 8.58
## 106 1 79.6 4.02 2.02 9.66
## 107 1 79.6 4.02 1.12 10.50
## 108 1 79.6 4.02 0.57 6.57
## 109 1 79.6 4.02 0.25 2.84
## 110 1 79.6 4.02 0.00 0.74
## 111 6 80.0 4.00 23.85 0.92
## 112 6 80.0 4.00 12.10 2.78
## 113 6 80.0 4.00 9.22 3.46
## 114 6 80.0 4.00 7.00 4.02
## 115 6 80.0 4.00 5.00 4.94
## 116 6 80.0 4.00 3.57 5.53
## 117 6 80.0 4.00 2.03 6.32
## 118 6 80.0 4.00 1.15 6.44
## 119 6 80.0 4.00 0.58 3.08
## 120 6 80.0 4.00 0.27 1.29
## 121 6 80.0 4.00 0.00 0.00
## 122 9 86.4 3.10 24.43 1.12
## 123 9 86.4 3.10 11.60 3.16
## 124 9 86.4 3.10 8.80 4.11
## 125 9 86.4 3.10 7.17 4.24
## 126 9 86.4 3.10 5.02 5.67
## 127 9 86.4 3.10 3.53 5.66
## 128 9 86.4 3.10 2.02 6.33
## 129 9 86.4 3.10 1.05 7.14
## 130 9 86.4 3.10 0.63 9.03
## 131 9 86.4 3.10 0.30 7.37
## 132 9 86.4 3.10 0.00 0.00The mutate() command allows you to create a new column using conditions and data derived from other columns. Use mutate() command to create a new column called trend that equals to Time-mean(Time). This will tell you how far each time value is from its mean. Set na.rm=TRUE.
mutate(df, trend=Time-mean(Time, na.rm=TRUE))## Subject Wt Dose Time conc trend
## 1 1 79.6 4.02 0.00 0.74 -5.8946212
## 2 1 79.6 4.02 0.25 2.84 -5.6446212
## 3 1 79.6 4.02 0.57 6.57 -5.3246212
## 4 1 79.6 4.02 1.12 10.50 -4.7746212
## 5 1 79.6 4.02 2.02 9.66 -3.8746212
## 6 1 79.6 4.02 3.82 8.58 -2.0746212
## 7 1 79.6 4.02 5.10 8.36 -0.7946212
## 8 1 79.6 4.02 7.03 7.47 1.1353788
## 9 1 79.6 4.02 9.05 6.89 3.1553788
## 10 1 79.6 4.02 12.12 5.94 6.2253788
## 11 1 79.6 4.02 24.37 3.28 18.4753788
## 12 2 72.4 4.40 0.00 0.00 -5.8946212
## 13 2 72.4 4.40 0.27 1.72 -5.6246212
## 14 2 72.4 4.40 0.52 7.91 -5.3746212
## 15 2 72.4 4.40 1.00 8.31 -4.8946212
## 16 2 72.4 4.40 1.92 8.33 -3.9746212
## 17 2 72.4 4.40 3.50 6.85 -2.3946212
## 18 2 72.4 4.40 5.02 6.08 -0.8746212
## 19 2 72.4 4.40 7.03 5.40 1.1353788
## 20 2 72.4 4.40 9.00 4.55 3.1053788
## 21 2 72.4 4.40 12.00 3.01 6.1053788
## 22 2 72.4 4.40 24.30 0.90 18.4053788
## 23 3 70.5 4.53 0.00 0.00 -5.8946212
## 24 3 70.5 4.53 0.27 4.40 -5.6246212
## 25 3 70.5 4.53 0.58 6.90 -5.3146212
## 26 3 70.5 4.53 1.02 8.20 -4.8746212
## 27 3 70.5 4.53 2.02 7.80 -3.8746212
## 28 3 70.5 4.53 3.62 7.50 -2.2746212
## 29 3 70.5 4.53 5.08 6.20 -0.8146212
## 30 3 70.5 4.53 7.07 5.30 1.1753788
## 31 3 70.5 4.53 9.00 4.90 3.1053788
## 32 3 70.5 4.53 12.15 3.70 6.2553788
## 33 3 70.5 4.53 24.17 1.05 18.2753788
## 34 4 72.7 4.40 0.00 0.00 -5.8946212
## 35 4 72.7 4.40 0.35 1.89 -5.5446212
## 36 4 72.7 4.40 0.60 4.60 -5.2946212
## 37 4 72.7 4.40 1.07 8.60 -4.8246212
## 38 4 72.7 4.40 2.13 8.38 -3.7646212
## 39 4 72.7 4.40 3.50 7.54 -2.3946212
## 40 4 72.7 4.40 5.02 6.88 -0.8746212
## 41 4 72.7 4.40 7.02 5.78 1.1253788
## 42 4 72.7 4.40 9.02 5.33 3.1253788
## 43 4 72.7 4.40 11.98 4.19 6.0853788
## 44 4 72.7 4.40 24.65 1.15 18.7553788
## 45 5 54.6 5.86 0.00 0.00 -5.8946212
## 46 5 54.6 5.86 0.30 2.02 -5.5946212
## 47 5 54.6 5.86 0.52 5.63 -5.3746212
## 48 5 54.6 5.86 1.00 11.40 -4.8946212
## 49 5 54.6 5.86 2.02 9.33 -3.8746212
## 50 5 54.6 5.86 3.50 8.74 -2.3946212
## 51 5 54.6 5.86 5.02 7.56 -0.8746212
## 52 5 54.6 5.86 7.02 7.09 1.1253788
## 53 5 54.6 5.86 9.10 5.90 3.2053788
## 54 5 54.6 5.86 12.00 4.37 6.1053788
## 55 5 54.6 5.86 24.35 1.57 18.4553788
## 56 6 80.0 4.00 0.00 0.00 -5.8946212
## 57 6 80.0 4.00 0.27 1.29 -5.6246212
## 58 6 80.0 4.00 0.58 3.08 -5.3146212
## 59 6 80.0 4.00 1.15 6.44 -4.7446212
## 60 6 80.0 4.00 2.03 6.32 -3.8646212
## 61 6 80.0 4.00 3.57 5.53 -2.3246212
## 62 6 80.0 4.00 5.00 4.94 -0.8946212
## 63 6 80.0 4.00 7.00 4.02 1.1053788
## 64 6 80.0 4.00 9.22 3.46 3.3253788
## 65 6 80.0 4.00 12.10 2.78 6.2053788
## 66 6 80.0 4.00 23.85 0.92 17.9553788
## 67 7 64.6 4.95 0.00 0.15 -5.8946212
## 68 7 64.6 4.95 0.25 0.85 -5.6446212
## 69 7 64.6 4.95 0.50 2.35 -5.3946212
## 70 7 64.6 4.95 1.02 5.02 -4.8746212
## 71 7 64.6 4.95 2.02 6.58 -3.8746212
## 72 7 64.6 4.95 3.48 7.09 -2.4146212
## 73 7 64.6 4.95 5.00 6.66 -0.8946212
## 74 7 64.6 4.95 6.98 5.25 1.0853788
## 75 7 64.6 4.95 9.00 4.39 3.1053788
## 76 7 64.6 4.95 12.05 3.53 6.1553788
## 77 7 64.6 4.95 24.22 1.15 18.3253788
## 78 8 70.5 4.53 0.00 0.00 -5.8946212
## 79 8 70.5 4.53 0.25 3.05 -5.6446212
## 80 8 70.5 4.53 0.52 3.05 -5.3746212
## 81 8 70.5 4.53 0.98 7.31 -4.9146212
## 82 8 70.5 4.53 2.02 7.56 -3.8746212
## 83 8 70.5 4.53 3.53 6.59 -2.3646212
## 84 8 70.5 4.53 5.05 5.88 -0.8446212
## 85 8 70.5 4.53 7.15 4.73 1.2553788
## 86 8 70.5 4.53 9.07 4.57 3.1753788
## 87 8 70.5 4.53 12.10 3.00 6.2053788
## 88 8 70.5 4.53 24.12 1.25 18.2253788
## 89 9 86.4 3.10 0.00 0.00 -5.8946212
## 90 9 86.4 3.10 0.30 7.37 -5.5946212
## 91 9 86.4 3.10 0.63 9.03 -5.2646212
## 92 9 86.4 3.10 1.05 7.14 -4.8446212
## 93 9 86.4 3.10 2.02 6.33 -3.8746212
## 94 9 86.4 3.10 3.53 5.66 -2.3646212
## 95 9 86.4 3.10 5.02 5.67 -0.8746212
## 96 9 86.4 3.10 7.17 4.24 1.2753788
## 97 9 86.4 3.10 8.80 4.11 2.9053788
## 98 9 86.4 3.10 11.60 3.16 5.7053788
## 99 9 86.4 3.10 24.43 1.12 18.5353788
## 100 10 58.2 5.50 0.00 0.24 -5.8946212
## 101 10 58.2 5.50 0.37 2.89 -5.5246212
## 102 10 58.2 5.50 0.77 5.22 -5.1246212
## 103 10 58.2 5.50 1.02 6.41 -4.8746212
## 104 10 58.2 5.50 2.05 7.83 -3.8446212
## 105 10 58.2 5.50 3.55 10.21 -2.3446212
## 106 10 58.2 5.50 5.05 9.18 -0.8446212
## 107 10 58.2 5.50 7.08 8.02 1.1853788
## 108 10 58.2 5.50 9.38 7.14 3.4853788
## 109 10 58.2 5.50 12.10 5.68 6.2053788
## 110 10 58.2 5.50 23.70 2.42 17.8053788
## 111 11 65.0 4.92 0.00 0.00 -5.8946212
## 112 11 65.0 4.92 0.25 4.86 -5.6446212
## 113 11 65.0 4.92 0.50 7.24 -5.3946212
## 114 11 65.0 4.92 0.98 8.00 -4.9146212
## 115 11 65.0 4.92 1.98 6.81 -3.9146212
## 116 11 65.0 4.92 3.60 5.87 -2.2946212
## 117 11 65.0 4.92 5.02 5.22 -0.8746212
## 118 11 65.0 4.92 7.03 4.45 1.1353788
## 119 11 65.0 4.92 9.03 3.62 3.1353788
## 120 11 65.0 4.92 12.12 2.69 6.2253788
## 121 11 65.0 4.92 24.08 0.86 18.1853788
## 122 12 60.5 5.30 0.00 0.00 -5.8946212
## 123 12 60.5 5.30 0.25 1.25 -5.6446212
## 124 12 60.5 5.30 0.50 3.96 -5.3946212
## 125 12 60.5 5.30 1.00 7.82 -4.8946212
## 126 12 60.5 5.30 2.00 9.72 -3.8946212
## 127 12 60.5 5.30 3.52 9.75 -2.3746212
## 128 12 60.5 5.30 5.07 8.57 -0.8246212
## 129 12 60.5 5.30 7.07 6.59 1.1753788
## 130 12 60.5 5.30 9.03 6.11 3.1353788
## 131 12 60.5 5.30 12.05 4.57 6.1553788
## 132 12 60.5 5.30 24.15 1.17 18.2553788Given the meta-data
76.2 kg Super-middleweight 72.57 kg Middleweight 69.85 kg Light-middleweight 66.68 kg Welterweight
Use the mutate function to classify the weight using the information above. For the purpose of this exercise, considering anything above 76.2 kg to be Super-middleweight and anything below 66.8 to be Welterweight. Anything below 76.2 to be middleweight and anything below 72.57 to be light-middleweight. Store the classifications under weight_cat. Hint: Use ifelse function() with mutate() to achieve this. Store this back into df.
df <- mutate(df, weight_cat=ifelse(Wt > 76.2, 'Super-middleweight', ifelse(Wt > 72.57, 'Middleweight', ifelse(Wt > 66.68, 'Light-middleweight', 'Welterweight'))))Use the group_by() command to group df by weight_cat. This allows us to use aggregated functions similar to group by in SQL. Store this in a df called weight_group
weight_group <- group_by(df, weight_cat)Use the summarize() command on the weight_group created in Question 9 to find the mean Time and sum of Dose received by each weight categories.
summarize(weight_group, mean.Time=mean(Time), sum.Dose=sum(Dose))## # A tibble: 4 × 3
## weight_cat mean.Time sum.Dose
## <chr> <dbl> <dbl>
## 1 Light-middleweight 5.888788 148.06
## 2 Middleweight 5.940000 48.40
## 3 Super-middleweight 5.902121 122.32
## 4 Welterweight 5.884545 291.83