Module 6: Publishing an RMarkdown from Module 3 - Squids and Flowers
Katya Podkovyroff Lewis
2022-06-20
Iris data file: separating on species
Here, I’m opening and reading the file “iris.csv.” From there, I was able to factor the species types into a numeric value and then further specify each species (irisS being setosa, irisV being versicolor and irisVi being virginica).
read.csv("iris.csv")## Sepal.Length Sepal.Width Petal.Length Petal.Width Species Code
## 1 5.1 3.5 1.4 0.2 setosa 1
## 2 4.9 3.0 1.4 0.2 setosa 1
## 3 4.7 3.2 1.3 0.2 setosa 1
## 4 4.6 3.1 1.5 0.2 setosa 1
## 5 5.0 3.6 1.4 0.2 setosa 1
## 6 5.4 3.9 1.7 0.4 setosa 1
## 7 4.6 3.4 1.4 0.3 setosa 1
## 8 5.0 3.4 1.5 0.2 setosa 1
## 9 4.4 2.9 1.4 0.2 setosa 1
## 10 4.9 3.1 1.5 0.1 setosa 1
## 11 5.4 3.7 1.5 0.2 setosa 1
## 12 4.8 3.4 1.6 0.2 setosa 1
## 13 4.8 3.0 1.4 0.1 setosa 1
## 14 4.3 3.0 1.1 0.1 setosa 1
## 15 5.8 4.0 1.2 0.2 setosa 1
## 16 5.7 4.4 1.5 0.4 setosa 1
## 17 5.4 3.9 1.3 0.4 setosa 1
## 18 5.1 3.5 1.4 0.3 setosa 1
## 19 5.7 3.8 1.7 0.3 setosa 1
## 20 5.1 3.8 1.5 0.3 setosa 1
## 21 5.4 3.4 1.7 0.2 setosa 1
## 22 5.1 3.7 1.5 0.4 setosa 1
## 23 4.6 3.6 1.0 0.2 setosa 1
## 24 5.1 3.3 1.7 0.5 setosa 1
## 25 4.8 3.4 1.9 0.2 setosa 1
## 26 5.0 3.0 1.6 0.2 setosa 1
## 27 5.0 3.4 1.6 0.4 setosa 1
## 28 5.2 3.5 1.5 0.2 setosa 1
## 29 5.2 3.4 1.4 0.2 setosa 1
## 30 4.7 3.2 1.6 0.2 setosa 1
## 31 4.8 3.1 1.6 0.2 setosa 1
## 32 5.4 3.4 1.5 0.4 setosa 1
## 33 5.2 4.1 1.5 0.1 setosa 1
## 34 5.5 4.2 1.4 0.2 setosa 1
## 35 4.9 3.1 1.5 0.2 setosa 1
## 36 5.0 3.2 1.2 0.2 setosa 1
## 37 5.5 3.5 1.3 0.2 setosa 1
## 38 4.9 3.6 1.4 0.1 setosa 1
## 39 4.4 3.0 1.3 0.2 setosa 1
## 40 5.1 3.4 1.5 0.2 setosa 1
## 41 5.0 3.5 1.3 0.3 setosa 1
## 42 4.5 2.3 1.3 0.3 setosa 1
## 43 4.4 3.2 1.3 0.2 setosa 1
## 44 5.0 3.5 1.6 0.6 setosa 1
## 45 5.1 3.8 1.9 0.4 setosa 1
## 46 4.8 3.0 1.4 0.3 setosa 1
## 47 5.1 3.8 1.6 0.2 setosa 1
## 48 4.6 3.2 1.4 0.2 setosa 1
## 49 5.3 3.7 1.5 0.2 setosa 1
## 50 5.0 3.3 1.4 0.2 setosa 1
## 51 7.0 3.2 4.7 1.4 versicolor 2
## 52 6.4 3.2 4.5 1.5 versicolor 2
## 53 6.9 3.1 4.9 1.5 versicolor 2
## 54 5.5 2.3 4.0 1.3 versicolor 2
## 55 6.5 2.8 4.6 1.5 versicolor 2
## 56 5.7 2.8 4.5 1.3 versicolor 2
## 57 6.3 3.3 4.7 1.6 versicolor 2
## 58 4.9 2.4 3.3 1.0 versicolor 2
## 59 6.6 2.9 4.6 1.3 versicolor 2
## 60 5.2 2.7 3.9 1.4 versicolor 2
## 61 5.0 2.0 3.5 1.0 versicolor 2
## 62 5.9 3.0 4.2 1.5 versicolor 2
## 63 6.0 2.2 4.0 1.0 versicolor 2
## 64 6.1 2.9 4.7 1.4 versicolor 2
## 65 5.6 2.9 3.6 1.3 versicolor 2
## 66 6.7 3.1 4.4 1.4 versicolor 2
## 67 5.6 3.0 4.5 1.5 versicolor 2
## 68 5.8 2.7 4.1 1.0 versicolor 2
## 69 6.2 2.2 4.5 1.5 versicolor 2
## 70 5.6 2.5 3.9 1.1 versicolor 2
## 71 5.9 3.2 4.8 1.8 versicolor 2
## 72 6.1 2.8 4.0 1.3 versicolor 2
## 73 6.3 2.5 4.9 1.5 versicolor 2
## 74 6.1 2.8 4.7 1.2 versicolor 2
## 75 6.4 2.9 4.3 1.3 versicolor 2
## 76 6.6 3.0 4.4 1.4 versicolor 2
## 77 6.8 2.8 4.8 1.4 versicolor 2
## 78 6.7 3.0 5.0 1.7 versicolor 2
## 79 6.0 2.9 4.5 1.5 versicolor 2
## 80 5.7 2.6 3.5 1.0 versicolor 2
## 81 5.5 2.4 3.8 1.1 versicolor 2
## 82 5.5 2.4 3.7 1.0 versicolor 2
## 83 5.8 2.7 3.9 1.2 versicolor 2
## 84 6.0 2.7 5.1 1.6 versicolor 2
## 85 5.4 3.0 4.5 1.5 versicolor 2
## 86 6.0 3.4 4.5 1.6 versicolor 2
## 87 6.7 3.1 4.7 1.5 versicolor 2
## 88 6.3 2.3 4.4 1.3 versicolor 2
## 89 5.6 3.0 4.1 1.3 versicolor 2
## 90 5.5 2.5 4.0 1.3 versicolor 2
## 91 5.5 2.6 4.4 1.2 versicolor 2
## 92 6.1 3.0 4.6 1.4 versicolor 2
## 93 5.8 2.6 4.0 1.2 versicolor 2
## 94 5.0 2.3 3.3 1.0 versicolor 2
## 95 5.6 2.7 4.2 1.3 versicolor 2
## 96 5.7 3.0 4.2 1.2 versicolor 2
## 97 5.7 2.9 4.2 1.3 versicolor 2
## 98 6.2 2.9 4.3 1.3 versicolor 2
## 99 5.1 2.5 3.0 1.1 versicolor 2
## 100 5.7 2.8 4.1 1.3 versicolor 2
## 101 6.3 3.3 6.0 2.5 virginica 3
## 102 5.8 2.7 5.1 1.9 virginica 3
## 103 7.1 3.0 5.9 2.1 virginica 3
## 104 6.3 2.9 5.6 1.8 virginica 3
## 105 6.5 3.0 5.8 2.2 virginica 3
## 106 7.6 3.0 6.6 2.1 virginica 3
## 107 4.9 2.5 4.5 1.7 virginica 3
## 108 7.3 2.9 6.3 1.8 virginica 3
## 109 6.7 2.5 5.8 1.8 virginica 3
## 110 7.2 3.6 6.1 2.5 virginica 3
## 111 6.5 3.2 5.1 2.0 virginica 3
## 112 6.4 2.7 5.3 1.9 virginica 3
## 113 6.8 3.0 5.5 2.1 virginica 3
## 114 5.7 2.5 5.0 2.0 virginica 3
## 115 5.8 2.8 5.1 2.4 virginica 3
## 116 6.4 3.2 5.3 2.3 virginica 3
## 117 6.5 3.0 5.5 1.8 virginica 3
## 118 7.7 3.8 6.7 2.2 virginica 3
## 119 7.7 2.6 6.9 2.3 virginica 3
## 120 6.0 2.2 5.0 1.5 virginica 3
## 121 6.9 3.2 5.7 2.3 virginica 3
## 122 5.6 2.8 4.9 2.0 virginica 3
## 123 7.7 2.8 6.7 2.0 virginica 3
## 124 6.3 2.7 4.9 1.8 virginica 3
## 125 6.7 3.3 5.7 2.1 virginica 3
## 126 7.2 3.2 6.0 1.8 virginica 3
## 127 6.2 2.8 4.8 1.8 virginica 3
## 128 6.1 3.0 4.9 1.8 virginica 3
## 129 6.4 2.8 5.6 2.1 virginica 3
## 130 7.2 3.0 5.8 1.6 virginica 3
## 131 7.4 2.8 6.1 1.9 virginica 3
## 132 7.9 3.8 6.4 2.0 virginica 3
## 133 6.4 2.8 5.6 2.2 virginica 3
## 134 6.3 2.8 5.1 1.5 virginica 3
## 135 6.1 2.6 5.6 1.4 virginica 3
## 136 7.7 3.0 6.1 2.3 virginica 3
## 137 6.3 3.4 5.6 2.4 virginica 3
## 138 6.4 3.1 5.5 1.8 virginica 3
## 139 6.0 3.0 4.8 1.8 virginica 3
## 140 6.9 3.1 5.4 2.1 virginica 3
## 141 6.7 3.1 5.6 2.4 virginica 3
## 142 6.9 3.1 5.1 2.3 virginica 3
## 143 5.8 2.7 5.1 1.9 virginica 3
## 144 6.8 3.2 5.9 2.3 virginica 3
## 145 6.7 3.3 5.7 2.5 virginica 3
## 146 6.7 3.0 5.2 2.3 virginica 3
## 147 6.3 2.5 5.0 1.9 virginica 3
## 148 6.5 3.0 5.2 2.0 virginica 3
## 149 6.2 3.4 5.4 2.3 virginica 3
## 150 5.9 3.0 5.1 1.8 virginica 3iris = read.csv("iris.csv")
iris$Species <- factor(iris$Species)
setosa = iris$Species == "setosa"
irisS = iris[setosa, ]
iris$Species2 = as.numeric(factor(iris$Species))
versicolor = iris$Species == "versicolor"
virginica = iris$Species == "virginica"
irisV = iris[versicolor, ]
irisVi = iris[virginica, ]Iris data file: separation of species by color-coding and symbol size variations
Once this was completed, I was able to display each species on a plot with various color coding as well as well as variations in sepal width. For the specific parameters for sepal width, I took the average of the sepal width and plot above average versus below average to show two different types of dot sizes on the graph.
iris$Col1 = iris$Species2
iris$Col1 [iris$Species2 == 1] = 5
iris$Col1 [iris$Species2 == 2] = 6
iris$Col1 [iris$Species2 == 3] = 7
iris$Sepal.Width2 = as.numeric(factor(iris$Sepal.Width))
iris$Cex2 <- iris$Sepal.Width2
iris$Cex2 [iris$Sepal.Width2 <= 10] <- 1.5
iris$Cex2 [iris$Sepal.Width2 >= 10] <- 0.5plot(iris$Petal.Length, iris$Petal.Width, xlab = "Petal Width", ylab = "Petal Length", main = "Petal Length vs Width based on Sepal Width", pch = 16, cex = iris$Cex2, col = iris$Col1)
legend("bottomright", pch = 16, col = c(5,6,7), legend = c("setosa", "versicolor", "virginica"), title = "Species")
legend("topleft", pch = 16, pt.cex = c(1.5, 0.5), legend = c(">average sepal width", "<average sepal width"), title = "Sepal width (average = 10)")
Squid data file
The instructions first asked to create a histogram based on GSI which is related to body size.
squid = read.csv("squid.csv", stringsAsFactors = TRUE)
hist(squid$GSI, col = "coral", xlab = "GSI", ylab = "Frequency", main = "Squid GSI related to body size")
Squid data file: separating GSI based on Sex
First, I wanted to make sure I could separate between sex so I had to make ids to separate male and female.
maleID = squid$Sex == "M"
squidM = squid[ ,maleID]
femaleID = squid$Sex == "F"
squidF = squid[ ,femaleID]
squid$Sex <- factor(squid$Sex, labels = c("M", "F"))Then, I was able to create two histograms based on each sex.
hist(squid$GSI[squid$Sex == "M"], col = "coral", xlab = "GSI", ylab = "Frequency", main = "Squid GSI related to sex: Male")
hist(squid$GSI[squid$Sex == "F"], col = "lightblue", xlab = "GSI", ylab = "Frequency", main = "Squid GSI related to sex: Female")