a3-Marko Vila-Drew Fisher- Brian Fay - Dylan Shanes

.eslintrc.cjs

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+module.exports = {
+	root: true,
+	extends: ['eslint:recommended', 'prettier'],
+	plugins: ['svelte3'],
+	overrides: [{ files: ['*.svelte'], processor: 'svelte3/svelte3' }],
+	parserOptions: {
+		sourceType: 'module',
+		ecmaVersion: 2020
+	},
+	env: {
+		browser: true,
+		es2017: true,
+		node: true
+	}
+};

.gitignore

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+.DS_Store
+node_modules
+/build
+/.svelte-kit
+/package
+.env
+.env.*
+!.env.example
+.Rproj.user

.npmrc

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+engine-strict=true

.prettierrc

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+{
+	"useTabs": true,
+	"singleQuote": true,
+	"trailingComma": "none",
+	"printWidth": 100
+}

R/.Rhistory

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+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(echo = TRUE)
+summary(cars)
+plot(pressure)
+knitr::opts_chunk$set(echo = TRUE)
+data <- read.csv2('TestingData.csv')
+data <- read.csv2('TestingData.csv')
+print(data)
+data <- read.csv('TestingData.csv')
+print(data)
+ggplot(mtcars, aes(mpg, factor(cyl))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+ggplot(data, aes(mpg, factor(cyl))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+install.packages("ggplot2")
+library("ggplot2")
+ggplot(data, aes(mpg, factor(cyl))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+library("ggplot2")
+ggplot(data, aes(mpg, factor(Accuracy))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(cyl))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+data <- read.csv('TestingData.csv')
+print(data)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point() +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point(aes(size = 3)) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 2)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point(aes(size = 2)) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point(aes(size = 2)) +
+stat_summary(fun.data = "95_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point(aes(size = 2)) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+geom_point(aes(size = 0)) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+p <- ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+plotly:ggplot(p)
+library("ggplot2")
+p <- ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+plotly::ggplot(p)
+library("ggplot2")
+library("plotly")
+> install.packages("plotly")
+install.packages("plotly")
+library("ggplot2")
+library("plotly")
+p <- ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+plotly::ggplot(p)
+library("ggplot2")
+library("plotly")
+p <- ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+plotly::ggplot(p)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
++  scale_x_continuous(expand = c(0, 0), limits = c(0, 7)) +
+scale_y_continuous(expand = c(0, 0), limits = c(0, 7))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 7)) +
+scale_y_continuous(expand = c(0, 0), limits = c(0, 7))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 7)) +
+scale_y_continuous(expand = c(0, 0), limits = c(0, 7))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 7)) +
+scale_y_continuous(expand = d(0, 0), limits = d(0, 7))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 7)) +
+scale_y_continuous(expand = c(0, 0), limits = c(0, 7))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 7))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3))
+knitr::opts_chunk$set(echo = TRUE)
+data <- read.csv('GraphingData.csv')
+data <- read.csv('GraphingData.csv')
+data <- read.csv('GraphingData.csv')
+data <- read.csv('GraphingData.csv')
+knitr::opts_chunk$set(echo = TRUE)
+data <- read.csv('GraphingData.csv')
+data <- read.csv('GraphingData.csv')
+print(data)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3))
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3)) +
+theme(axis.text.y=element_blank(),)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3)) +
+theme(y = element_blank(),)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, Graph type) +
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3)) +
+theme(x = axis.text.yelement_blank(),)
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3)) +
+theme()
+library("ggplot2")
+library("plotly")
+ggplot(data, aes(Accuracy, factor(Category))) +
+stat_summary(fun.data = "mean_cl_boot", colour = "red", size = 1) +
+scale_x_continuous(expand = c(0, 0), limits = c(0, 3)) +
+theme(axis.title.y=element_blank(),)