Lab 2
Izzy Simon
2024-09-10
#Loading Packages
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(ggplot2)
Leaf<-read.csv("~/Biostats 2024/Data/LeafData_corrected.csv")
BP<-read.csv("~/Biostats 2024/Data/BP.csv")#Working on Columns in a Data Frame
Leaf$Length## [1] 8.2 8.4 6.0 6.5 5.9 14.4 22.2 15.8 19.2 28.6 8.8 8.8 8.5 8.6 8.8
## [16] 15.7 15.9 24.2 20.7 25.6 8.0 9.0 7.3 8.0 9.4 19.2 16.0 13.7 21.4 19.6
## [31] 6.2 5.2 5.5 6.1 4.2 13.7 26.4 19.1 18.2 22.8 6.2 6.7 7.5 6.8 6.9
## [46] 22.6 20.5 16.0 21.5 16.7 14.7 10.9 11.5 27.5 24.0 6.0 6.0 5.8 5.6 5.9
## [61] 7.6 8.0 8.5 7.4 7.6 24.0 26.5 25.0 14.0 15.3 8.9 8.9 8.9 8.8 8.8
## [76] 19.5 18.5 23.0 26.0 25.5 27.7 26.5 28.3 26.6 24.3 7.8 7.9 7.9 7.7 7.8
## [91] 24.9 16.6 18.0 16.2 25.2 8.4 8.1 7.3 5.2 5.5 8.5 8.7 8.6 8.6 8.8
## [106] 24.9 18.0 25.1 23.2 21.7Leaf$Size<-Leaf$Length*Leaf$Width
LfSize<-Leaf$Size
Leaf$Unique<-paste(Leaf$Name, Leaf$Leaf.ID, sep="_")
Leaf$Unique## [1] "MG_1" "MG_2" "MG_3" "MG_4" "MG_5" "MG_6" "MG_7" "MG_8"
## [9] "MG_9" "MG_10" "SA_1" "SA_2" "SA_3" "SA_4" "SA_5" "SA_6"
## [17] "SA_7" "SA_8" "SA_9" "SA_10" "MH_1" "MH_2" "MH_3" "MH_4"
## [25] "MH_5" "MH_6" "MH_7" "MH_8" "MH_9" "MH_10" "IS_1" "IS_2"
## [33] "IS_3" "IS_4" "IS_5" "IS_6" "IS_7" "IS_8" "IS_9" "IS_10"
## [41] "AR_1" "AR_2" "AR_3" "AR_4" "AR_5" "AR_6" "AR_7" "AR_8"
## [49] "AR_9" "AR_10" "JW_1" "JW_2" "JW_3" "JW_4" "JW_5" "JW_6"
## [57] "JW_7" "JW_8" "JW_9" "JW_10" "AVB_1" "AVB_2" "AVB_3" "AVB_4"
## [65] "AVB_5" "AVB_6" "AVB_7" "AVB_8" "AVB_9" "AVB_10" "EO_1" "EO_2"
## [73] "EO_3" "EO_4" "EO_5" "EO_6" "EO_7" "EO_8" "EO_9" "EO_10"
## [81] "STA_1" "STC_2" "STA_3" "STA_4" "STA_5" "STA_6" "STA_7" "STA_8"
## [89] "STA_9" "STA_10" "BS_1" "BS_2" "BS_3" "BS_4" "BS_5" "BS_6"
## [97] "BS_7" "BS_8" "BS_9" "BS_10" "AEW_1" "AEW_2" "AEW_3" "AEW_4"
## [105] "AEW_5" "AEW_6" "AEW_7" "AEW_8" "AEW_9" "AEW_10"SizeLeaf<-select(Leaf, Length, Width, Size)
Ratio<-mutate(SizeLeaf, ratio = "Lenth/Width")
Ratio## Length Width Size ratio
## 1 8.2 0.1 0.82 Lenth/Width
## 2 8.4 0.1 0.84 Lenth/Width
## 3 6.0 0.1 0.60 Lenth/Width
## 4 6.5 0.1 0.65 Lenth/Width
## 5 5.9 0.1 0.59 Lenth/Width
## 6 14.4 5.3 76.32 Lenth/Width
## 7 22.2 8.2 182.04 Lenth/Width
## 8 15.8 5.8 91.64 Lenth/Width
## 9 19.2 6.2 119.04 Lenth/Width
## 10 28.6 9.8 280.28 Lenth/Width
## 11 8.8 0.1 0.88 Lenth/Width
## 12 8.8 0.1 0.88 Lenth/Width
## 13 8.5 0.1 0.85 Lenth/Width
## 14 8.6 0.1 0.86 Lenth/Width
## 15 8.8 0.1 0.88 Lenth/Width
## 16 15.7 6.1 95.77 Lenth/Width
## 17 15.9 5.5 87.45 Lenth/Width
## 18 24.2 8.0 193.60 Lenth/Width
## 19 20.7 7.5 155.25 Lenth/Width
## 20 25.6 8.0 204.80 Lenth/Width
## 21 8.0 0.1 0.80 Lenth/Width
## 22 9.0 0.1 0.90 Lenth/Width
## 23 7.3 0.1 0.73 Lenth/Width
## 24 8.0 0.1 0.80 Lenth/Width
## 25 9.4 0.1 0.94 Lenth/Width
## 26 19.2 6.1 117.12 Lenth/Width
## 27 16.0 5.5 88.00 Lenth/Width
## 28 13.7 4.3 58.91 Lenth/Width
## 29 21.4 7.3 156.22 Lenth/Width
## 30 19.6 6.7 131.32 Lenth/Width
## 31 6.2 0.1 0.62 Lenth/Width
## 32 5.2 0.1 0.52 Lenth/Width
## 33 5.5 0.1 0.55 Lenth/Width
## 34 6.1 0.1 0.61 Lenth/Width
## 35 4.2 0.1 0.42 Lenth/Width
## 36 13.7 4.8 65.76 Lenth/Width
## 37 26.4 9.9 261.36 Lenth/Width
## 38 19.1 6.5 124.15 Lenth/Width
## 39 18.2 6.8 123.76 Lenth/Width
## 40 22.8 7.8 177.84 Lenth/Width
## 41 6.2 0.1 0.62 Lenth/Width
## 42 6.7 0.1 0.67 Lenth/Width
## 43 7.5 0.1 0.75 Lenth/Width
## 44 6.8 0.1 0.68 Lenth/Width
## 45 6.9 0.1 0.69 Lenth/Width
## 46 22.6 8.4 189.84 Lenth/Width
## 47 20.5 7.6 155.80 Lenth/Width
## 48 16.0 5.9 94.40 Lenth/Width
## 49 21.5 7.9 169.85 Lenth/Width
## 50 16.7 5.8 96.86 Lenth/Width
## 51 14.7 5.8 85.26 Lenth/Width
## 52 10.9 3.9 42.51 Lenth/Width
## 53 11.5 4.2 48.30 Lenth/Width
## 54 27.5 9.0 247.50 Lenth/Width
## 55 24.0 8.5 204.00 Lenth/Width
## 56 6.0 0.1 0.60 Lenth/Width
## 57 6.0 0.1 0.60 Lenth/Width
## 58 5.8 0.1 0.58 Lenth/Width
## 59 5.6 0.1 0.56 Lenth/Width
## 60 5.9 0.1 0.59 Lenth/Width
## 61 7.6 0.1 0.76 Lenth/Width
## 62 8.0 0.1 0.80 Lenth/Width
## 63 8.5 0.1 0.85 Lenth/Width
## 64 7.4 0.1 0.74 Lenth/Width
## 65 7.6 0.1 0.76 Lenth/Width
## 66 24.0 9.5 228.00 Lenth/Width
## 67 26.5 10.5 278.25 Lenth/Width
## 68 25.0 9.0 225.00 Lenth/Width
## 69 14.0 6.0 84.00 Lenth/Width
## 70 15.3 5.5 84.15 Lenth/Width
## 71 8.9 0.1 0.89 Lenth/Width
## 72 8.9 0.1 0.89 Lenth/Width
## 73 8.9 0.1 0.89 Lenth/Width
## 74 8.8 0.1 0.88 Lenth/Width
## 75 8.8 0.1 0.88 Lenth/Width
## 76 19.5 6.5 126.75 Lenth/Width
## 77 18.5 6.8 125.80 Lenth/Width
## 78 23.0 8.0 184.00 Lenth/Width
## 79 26.0 8.5 221.00 Lenth/Width
## 80 25.5 8.0 204.00 Lenth/Width
## 81 27.7 8.8 243.76 Lenth/Width
## 82 26.5 8.0 212.00 Lenth/Width
## 83 28.3 8.5 240.55 Lenth/Width
## 84 26.6 8.2 218.12 Lenth/Width
## 85 24.3 7.3 177.39 Lenth/Width
## 86 7.8 0.1 0.78 Lenth/Width
## 87 7.9 0.1 0.79 Lenth/Width
## 88 7.9 0.1 0.79 Lenth/Width
## 89 7.7 0.1 0.77 Lenth/Width
## 90 7.8 0.1 0.78 Lenth/Width
## 91 24.9 7.6 189.24 Lenth/Width
## 92 16.6 6.7 111.22 Lenth/Width
## 93 18.0 6.7 120.60 Lenth/Width
## 94 16.2 5.6 90.72 Lenth/Width
## 95 25.2 8.3 209.16 Lenth/Width
## 96 8.4 0.1 0.84 Lenth/Width
## 97 8.1 0.1 0.81 Lenth/Width
## 98 7.3 0.1 0.73 Lenth/Width
## 99 5.2 0.1 0.52 Lenth/Width
## 100 5.5 0.1 0.55 Lenth/Width
## 101 8.5 0.1 0.85 Lenth/Width
## 102 8.7 0.1 0.87 Lenth/Width
## 103 8.6 0.1 0.86 Lenth/Width
## 104 8.6 0.1 0.86 Lenth/Width
## 105 8.8 0.1 0.88 Lenth/Width
## 106 24.9 8.2 204.18 Lenth/Width
## 107 18.0 6.5 117.00 Lenth/Width
## 108 25.1 8.1 203.31 Lenth/Width
## 109 23.2 8.5 197.20 Lenth/Width
## 110 21.7 8.8 190.96 Lenth/Widthmean(BP$BMI)## [1] 27.26sd(BP$BMI)## [1] 3.073977#Y=27.26 S=3.07#More Practice with Data
data("iris")
flower<-iris
Sepal<-select(flower, Sepal.Length, Sepal.Width)
Sepal## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9
## 7 4.6 3.4
## 8 5.0 3.4
## 9 4.4 2.9
## 10 4.9 3.1
## 11 5.4 3.7
## 12 4.8 3.4
## 13 4.8 3.0
## 14 4.3 3.0
## 15 5.8 4.0
## 16 5.7 4.4
## 17 5.4 3.9
## 18 5.1 3.5
## 19 5.7 3.8
## 20 5.1 3.8
## 21 5.4 3.4
## 22 5.1 3.7
## 23 4.6 3.6
## 24 5.1 3.3
## 25 4.8 3.4
## 26 5.0 3.0
## 27 5.0 3.4
## 28 5.2 3.5
## 29 5.2 3.4
## 30 4.7 3.2
## 31 4.8 3.1
## 32 5.4 3.4
## 33 5.2 4.1
## 34 5.5 4.2
## 35 4.9 3.1
## 36 5.0 3.2
## 37 5.5 3.5
## 38 4.9 3.6
## 39 4.4 3.0
## 40 5.1 3.4
## 41 5.0 3.5
## 42 4.5 2.3
## 43 4.4 3.2
## 44 5.0 3.5
## 45 5.1 3.8
## 46 4.8 3.0
## 47 5.1 3.8
## 48 4.6 3.2
## 49 5.3 3.7
## 50 5.0 3.3
## 51 7.0 3.2
## 52 6.4 3.2
## 53 6.9 3.1
## 54 5.5 2.3
## 55 6.5 2.8
## 56 5.7 2.8
## 57 6.3 3.3
## 58 4.9 2.4
## 59 6.6 2.9
## 60 5.2 2.7
## 61 5.0 2.0
## 62 5.9 3.0
## 63 6.0 2.2
## 64 6.1 2.9
## 65 5.6 2.9
## 66 6.7 3.1
## 67 5.6 3.0
## 68 5.8 2.7
## 69 6.2 2.2
## 70 5.6 2.5
## 71 5.9 3.2
## 72 6.1 2.8
## 73 6.3 2.5
## 74 6.1 2.8
## 75 6.4 2.9
## 76 6.6 3.0
## 77 6.8 2.8
## 78 6.7 3.0
## 79 6.0 2.9
## 80 5.7 2.6
## 81 5.5 2.4
## 82 5.5 2.4
## 83 5.8 2.7
## 84 6.0 2.7
## 85 5.4 3.0
## 86 6.0 3.4
## 87 6.7 3.1
## 88 6.3 2.3
## 89 5.6 3.0
## 90 5.5 2.5
## 91 5.5 2.6
## 92 6.1 3.0
## 93 5.8 2.6
## 94 5.0 2.3
## 95 5.6 2.7
## 96 5.7 3.0
## 97 5.7 2.9
## 98 6.2 2.9
## 99 5.1 2.5
## 100 5.7 2.8
## 101 6.3 3.3
## 102 5.8 2.7
## 103 7.1 3.0
## 104 6.3 2.9
## 105 6.5 3.0
## 106 7.6 3.0
## 107 4.9 2.5
## 108 7.3 2.9
## 109 6.7 2.5
## 110 7.2 3.6
## 111 6.5 3.2
## 112 6.4 2.7
## 113 6.8 3.0
## 114 5.7 2.5
## 115 5.8 2.8
## 116 6.4 3.2
## 117 6.5 3.0
## 118 7.7 3.8
## 119 7.7 2.6
## 120 6.0 2.2
## 121 6.9 3.2
## 122 5.6 2.8
## 123 7.7 2.8
## 124 6.3 2.7
## 125 6.7 3.3
## 126 7.2 3.2
## 127 6.2 2.8
## 128 6.1 3.0
## 129 6.4 2.8
## 130 7.2 3.0
## 131 7.4 2.8
## 132 7.9 3.8
## 133 6.4 2.8
## 134 6.3 2.8
## 135 6.1 2.6
## 136 7.7 3.0
## 137 6.3 3.4
## 138 6.4 3.1
## 139 6.0 3.0
## 140 6.9 3.1
## 141 6.7 3.1
## 142 6.9 3.1
## 143 5.8 2.7
## 144 6.8 3.2
## 145 6.7 3.3
## 146 6.7 3.0
## 147 6.3 2.5
## 148 6.5 3.0
## 149 6.2 3.4
## 150 5.9 3.0#there are 150 rows
Setosa<- filter(flower, Species =="setosa")
Setosa## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
## 7 4.6 3.4 1.4 0.3 setosa
## 8 5.0 3.4 1.5 0.2 setosa
## 9 4.4 2.9 1.4 0.2 setosa
## 10 4.9 3.1 1.5 0.1 setosa
## 11 5.4 3.7 1.5 0.2 setosa
## 12 4.8 3.4 1.6 0.2 setosa
## 13 4.8 3.0 1.4 0.1 setosa
## 14 4.3 3.0 1.1 0.1 setosa
## 15 5.8 4.0 1.2 0.2 setosa
## 16 5.7 4.4 1.5 0.4 setosa
## 17 5.4 3.9 1.3 0.4 setosa
## 18 5.1 3.5 1.4 0.3 setosa
## 19 5.7 3.8 1.7 0.3 setosa
## 20 5.1 3.8 1.5 0.3 setosa
## 21 5.4 3.4 1.7 0.2 setosa
## 22 5.1 3.7 1.5 0.4 setosa
## 23 4.6 3.6 1.0 0.2 setosa
## 24 5.1 3.3 1.7 0.5 setosa
## 25 4.8 3.4 1.9 0.2 setosa
## 26 5.0 3.0 1.6 0.2 setosa
## 27 5.0 3.4 1.6 0.4 setosa
## 28 5.2 3.5 1.5 0.2 setosa
## 29 5.2 3.4 1.4 0.2 setosa
## 30 4.7 3.2 1.6 0.2 setosa
## 31 4.8 3.1 1.6 0.2 setosa
## 32 5.4 3.4 1.5 0.4 setosa
## 33 5.2 4.1 1.5 0.1 setosa
## 34 5.5 4.2 1.4 0.2 setosa
## 35 4.9 3.1 1.5 0.2 setosa
## 36 5.0 3.2 1.2 0.2 setosa
## 37 5.5 3.5 1.3 0.2 setosa
## 38 4.9 3.6 1.4 0.1 setosa
## 39 4.4 3.0 1.3 0.2 setosa
## 40 5.1 3.4 1.5 0.2 setosa
## 41 5.0 3.5 1.3 0.3 setosa
## 42 4.5 2.3 1.3 0.3 setosa
## 43 4.4 3.2 1.3 0.2 setosa
## 44 5.0 3.5 1.6 0.6 setosa
## 45 5.1 3.8 1.9 0.4 setosa
## 46 4.8 3.0 1.4 0.3 setosa
## 47 5.1 3.8 1.6 0.2 setosa
## 48 4.6 3.2 1.4 0.2 setosa
## 49 5.3 3.7 1.5 0.2 setosa
## 50 5.0 3.3 1.4 0.2 setosa#there are 50 rows
head(Sepal)## Sepal.Length Sepal.Width
## 1 5.1 3.5
## 2 4.9 3.0
## 3 4.7 3.2
## 4 4.6 3.1
## 5 5.0 3.6
## 6 5.4 3.9head(Setosa)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosastats<-flower%>%
group_by(Species) %>%
summarize(average=mean(Petal.Width))
stats## # A tibble: 3 × 2
## Species average
## <fct> <dbl>
## 1 setosa 0.246
## 2 versicolor 1.33
## 3 virginica 2.03t.test(Leaf$Length)$conf.int## [1] 12.70252 15.54839
## attr(,"conf.level")
## [1] 0.95#95% of the Leaf Length data falls between 12.7cm and 15.5cm
pine<-filter(Leaf, Species == "pine")
pine## Leaf.ID Name Species Deciduous Length Width Damage Size Unique
## 1 1 MG pine no 8.2 0.1 2 0.82 MG_1
## 2 2 MG pine no 8.4 0.1 2 0.84 MG_2
## 3 3 MG pine no 6.0 0.1 1 0.60 MG_3
## 4 4 MG pine no 6.5 0.1 1 0.65 MG_4
## 5 5 MG pine no 5.9 0.1 2 0.59 MG_5
## 6 1 SA pine no 8.8 0.1 0 0.88 SA_1
## 7 2 SA pine no 8.8 0.1 1 0.88 SA_2
## 8 3 SA pine no 8.5 0.1 0 0.85 SA_3
## 9 4 SA pine no 8.6 0.1 0 0.86 SA_4
## 10 5 SA pine no 8.8 0.1 0 0.88 SA_5
## 11 1 MH pine no 8.0 0.1 0 0.80 MH_1
## 12 2 MH pine no 9.0 0.1 0 0.90 MH_2
## 13 3 MH pine no 7.3 0.1 0 0.73 MH_3
## 14 4 MH pine no 8.0 0.1 0 0.80 MH_4
## 15 5 MH pine no 9.4 0.1 0 0.94 MH_5
## 16 1 IS pine no 6.2 0.1 0 0.62 IS_1
## 17 2 IS pine no 5.2 0.1 0 0.52 IS_2
## 18 3 IS pine no 5.5 0.1 0 0.55 IS_3
## 19 4 IS pine no 6.1 0.1 0 0.61 IS_4
## 20 5 IS pine no 4.2 0.1 0 0.42 IS_5
## 21 1 AR pine no 6.2 0.1 0 0.62 AR_1
## 22 2 AR pine no 6.7 0.1 0 0.67 AR_2
## 23 3 AR pine no 7.5 0.1 0 0.75 AR_3
## 24 4 AR pine no 6.8 0.1 0 0.68 AR_4
## 25 5 AR pine no 6.9 0.1 0 0.69 AR_5
## 26 6 JW pine no 6.0 0.1 0 0.60 JW_6
## 27 7 JW pine no 6.0 0.1 0 0.60 JW_7
## 28 8 JW pine no 5.8 0.1 0 0.58 JW_8
## 29 9 JW pine no 5.6 0.1 0 0.56 JW_9
## 30 10 JW pine no 5.9 0.1 0 0.59 JW_10
## 31 1 AVB pine no 7.6 0.1 1 0.76 AVB_1
## 32 2 AVB pine no 8.0 0.1 1 0.80 AVB_2
## 33 3 AVB pine no 8.5 0.1 0 0.85 AVB_3
## 34 4 AVB pine no 7.4 0.1 1 0.74 AVB_4
## 35 5 AVB pine no 7.6 0.1 1 0.76 AVB_5
## 36 1 EO pine no 8.9 0.1 0 0.89 EO_1
## 37 2 EO pine no 8.9 0.1 0 0.89 EO_2
## 38 3 EO pine no 8.9 0.1 0 0.89 EO_3
## 39 4 EO pine no 8.8 0.1 0 0.88 EO_4
## 40 5 EO pine no 8.8 0.1 0 0.88 EO_5
## 41 6 STA pine no 7.8 0.1 0 0.78 STA_6
## 42 7 STA pine no 7.9 0.1 0 0.79 STA_7
## 43 8 STA pine no 7.9 0.1 0 0.79 STA_8
## 44 9 STA pine no 7.7 0.1 0 0.77 STA_9
## 45 10 STA pine no 7.8 0.1 0 0.78 STA_10
## 46 6 BS pine no 8.4 0.1 0 0.84 BS_6
## 47 7 BS pine no 8.1 0.1 0 0.81 BS_7
## 48 8 BS pine no 7.3 0.1 0 0.73 BS_8
## 49 9 BS pine no 5.2 0.1 0 0.52 BS_9
## 50 10 BS pine no 5.5 0.1 0 0.55 BS_10
## 51 1 AEW pine no 8.5 0.1 0 0.85 AEW_1
## 52 2 AEW pine no 8.7 0.1 0 0.87 AEW_2
## 53 3 AEW pine no 8.6 0.1 0 0.86 AEW_3
## 54 4 AEW pine no 8.6 0.1 0 0.86 AEW_4
## 55 5 AEW pine no 8.8 0.1 0 0.88 AEW_5notPine<-filter(Leaf,Species == "buckeye")
notPine## Leaf.ID Name Species Deciduous Length Width Damage Size Unique
## 1 6 MH buckeye yes 19.2 6.1 2 117.12 MH_6
## 2 7 MH buckeye yes 16.0 5.5 2 88.00 MH_7
## 3 8 MH buckeye yes 13.7 4.3 1 58.91 MH_8
## 4 9 MH buckeye yes 21.4 7.3 2 156.22 MH_9
## 5 10 MH buckeye yes 19.6 6.7 1 131.32 MH_10
## 6 6 IS buckeye yes 13.7 4.8 3 65.76 IS_6
## 7 7 IS buckeye yes 26.4 9.9 4 261.36 IS_7
## 8 8 IS buckeye yes 19.1 6.5 1 124.15 IS_8
## 9 9 IS buckeye yes 18.2 6.8 2 123.76 IS_9
## 10 10 IS buckeye yes 22.8 7.8 3 177.84 IS_10
## 11 6 AR buckeye yes 22.6 8.4 1 189.84 AR_6
## 12 7 AR buckeye yes 20.5 7.6 3 155.80 AR_7
## 13 8 AR buckeye yes 16.0 5.9 1 94.40 AR_8
## 14 9 AR buckeye yes 21.5 7.9 1 169.85 AR_9
## 15 10 AR buckeye yes 16.7 5.8 1 96.86 AR_10
## 16 1 JW buckeye yes 14.7 5.8 2 85.26 JW_1
## 17 2 JW buckeye yes 10.9 3.9 0 42.51 JW_2
## 18 3 JW buckeye yes 11.5 4.2 2 48.30 JW_3
## 19 4 JW buckeye yes 27.5 9.0 1 247.50 JW_4
## 20 5 JW buckeye yes 24.0 8.5 1 204.00 JW_5
## 21 6 AVB buckeye yes 24.0 9.5 1 228.00 AVB_6
## 22 7 AVB buckeye yes 26.5 10.5 3 278.25 AVB_7
## 23 8 AVB buckeye yes 25.0 9.0 4 225.00 AVB_8
## 24 9 AVB buckeye yes 14.0 6.0 3 84.00 AVB_9
## 25 10 AVB buckeye yes 15.3 5.5 2 84.15 AVB_10
## 26 6 EO buckeye yes 19.5 6.5 2 126.75 EO_6
## 27 7 EO buckeye yes 18.5 6.8 1 125.80 EO_7
## 28 8 EO buckeye yes 23.0 8.0 0 184.00 EO_8
## 29 9 EO buckeye yes 26.0 8.5 0 221.00 EO_9
## 30 10 EO buckeye yes 25.5 8.0 1 204.00 EO_10
## 31 1 STA buckeye yes 27.7 8.8 5 243.76 STA_1
## 32 2 STC buckeye yes 26.5 8.0 1 212.00 STC_2
## 33 3 STA buckeye yes 28.3 8.5 1 240.55 STA_3
## 34 4 STA buckeye yes 26.6 8.2 2 218.12 STA_4
## 35 5 STA buckeye yes 24.3 7.3 1 177.39 STA_5
## 36 1 BS buckeye yes 24.9 7.6 1 189.24 BS_1
## 37 2 BS buckeye yes 16.6 6.7 2 111.22 BS_2
## 38 3 BS buckeye yes 18.0 6.7 0 120.60 BS_3
## 39 4 BS buckeye yes 16.2 5.6 2 90.72 BS_4
## 40 5 BS buckeye yes 25.2 8.3 0 209.16 BS_5
## 41 6 AEW buckeye yes 24.9 8.2 1 204.18 AEW_6
## 42 7 AEW buckeye yes 18.0 6.5 1 117.00 AEW_7
## 43 8 AEW buckeye yes 25.1 8.1 1 203.31 AEW_8
## 44 9 AEW buckeye yes 23.2 8.5 1 197.20 AEW_9
## 45 10 AEW buckeye yes 21.7 8.8 3 190.96 AEW_10t.test(pine$Length)$conf.int## [1] 7.124792 7.820662
## attr(,"conf.level")
## [1] 0.95# we are 95% confident that the interval between 7.12cm and 7.82cm contains the population parameter
t.test(notPine$Length)$conf.int## [1] 19.46355 22.33645
## attr(,"conf.level")
## [1] 0.95# we are 95% confident that the interval between 19.5cm and 22.3cm contains the population parameter #GGplot2 Demo
ggplot(flower, aes(x=Species)) + geom_bar(stat="count")
ggplot(flower, aes(x=Species, y=Petal.Width)) + geom_boxplot()
ggplot(flower, aes(x=Species, y=Petal.Width)) + geom_jitter()
ggplot(flower, aes(x=Species, y=Petal.Width)) + geom_jitter(position=position_jitter(0.1))
ggplot(flower, aes(x=Petal.Width, y=Petal.Length)) + geom_point() + xlab("Petal Width (cm)") + ylab("Petal Length (cm)") + theme_bw() + facet_grid(~Species)
ggplot(flower, aes(x=Sepal.Width, y=Sepal.Length)) + geom_point() + xlab("Petal Width (cm)") + ylab("Petal Length (cm)") + theme_bw() + facet_grid(~Species)
ggplot(Leaf, aes(x=Length, y=Width))+ geom_point() + xlab("Leaf Length (cm)") + ylab("Leaf Width (cm)") + theme_bw() + facet_grid(~Species)
#Density Plots
?sleep()## starting httpd help server ... donehonkshoo<-sleep
honkshoo## extra group ID
## 1 0.7 1 1
## 2 -1.6 1 2
## 3 -0.2 1 3
## 4 -1.2 1 4
## 5 -0.1 1 5
## 6 3.4 1 6
## 7 3.7 1 7
## 8 0.8 1 8
## 9 0.0 1 9
## 10 2.0 1 10
## 11 1.9 2 1
## 12 0.8 2 2
## 13 1.1 2 3
## 14 0.1 2 4
## 15 -0.1 2 5
## 16 4.4 2 6
## 17 5.5 2 7
## 18 1.6 2 8
## 19 4.6 2 9
## 20 3.4 2 10ggplot(honkshoo, aes(x=extra)) + geom_density()
ggplot(honkshoo, aes(x=extra)) + geom_density() + xlim(c(-10,10))
ggplot(honkshoo, aes(extra, fill=group)) + geom_density(alpha = 0.5) + xlim(c(-10,10))
#Density Plots for Our Data
ggplot(Leaf, aes(x=Length)) + geom_density()
ggplot(pine, aes(x=Length)) + geom_density()
ggplot(notPine, aes(x=Length)) + geom_density()
shapiro.test(pine$Length)##
## Shapiro-Wilk normality test
##
## data: pine$Length
## W = 0.91832, p-value = 0.001152shapiro.test(notPine$Length)##
## Shapiro-Wilk normality test
##
## data: notPine$Length
## W = 0.95147, p-value = 0.05776# the p-values are incredibly low which means the data is not normally distributed. #QQ Plots
normal_vector<-rnorm(n=100, mean=13, sd=4)
qqnorm(normal_vector, pch=1)
qqnorm(normal_vector, pch=2)
qqnorm(normal_vector, pch=3)
qqnorm(pine$Length, pch=1)
qqnorm(pine$Width, pch=1)
qqnorm(notPine$Length, pch=1)
qqnorm(notPine$Width, pch=1)