daroczig · March 1, 2020 22:11
diff --git a/exercise.R b/exercise.R
 ## load data
 df <- read.csv('http://bit.ly/CEU-R-numbers')

 ## analyze the association between x and y
 summary(df)
 cor(df)
 ## ..

 ## visualize the association between x and y
 plot(df)
 ## ..
diff --git a/generate.R b/generate.R
 ## download data
 t <- tempfile()
 download.file(
    'http://phylopic.org/assets/images/submissions/202c2ad3-48a7-471d-87ef-c6d8406640e8.original.png',
    destfile = t, mode = 'wb')

 ## read data
 library(png)
 img <- readPNG(t)

 ## record the dimensions of the image
 height <- dim(img)[1]
 width  <- dim(img)[2]

 ## convert to 2d
 points <- as.data.frame(matrix(img, height * width))
 points$x <- rep(1:height)
 points$y <- rep(1:width, each = height)

 ## filter for gray points (contour)
 points <- subset(points, V4 > 0.5 & V4 < 0.8)

 ## sample polygon
 np <- 15 * 1e3
 points <- points[sample(nrow(points), np, replace = TRUE), ]

 ## flip axeses
 points <- data.frame(x = points$y, y = -points$x)

 ## render polygon
 ggplot(points, aes(x, y)) + geom_point()

 ## add noise
 nn <- 1e4 * 2
 noise <- round(data.frame(
    x = rnorm(nn, mean = width / 2, sd = width / 4),
    y = rnorm(nn, mean = -height / 2, sd = height / 4)
 ))

 ## render noise
 ggplot(noise, aes(x, y)) + geom_point()

 ## add noise to polygon
 df <- rbind(points[, c('x', 'y')], noise)

 ## visualize all points
 ggplot(df, aes(x, y)) + geom_point()
 ggplot(df, aes(x, y)) + geom_point(alpha = 0.05)
 ggplot(df, aes(x, y)) + geom_point(size = 0.2, alpha = 0.1)
 ggplot(df, aes(x, y)) + geom_hex(binwidth = 25)
diff --git a/plot-1.png b/plot-1.png
diff --git a/plot-2.png b/plot-2.png
diff --git a/plot-3.png b/plot-3.png
diff --git a/plot-4.png b/plot-4.png
diff --git a/points.csv b/points.csv
	## load data
	df <- read.csv('http://bit.ly/CEU-R-numbers')

	## analyze the association between x and y
	summary(df)
	cor(df)
	## ..

	## visualize the association between x and y
	plot(df)
	## ..
	## download data
	t <- tempfile()
	download.file(
	'http://phylopic.org/assets/images/submissions/202c2ad3-48a7-471d-87ef-c6d8406640e8.original.png',
	destfile = t, mode = 'wb')

	## read data
	library(png)
	img <- readPNG(t)

	## record the dimensions of the image
	height <- dim(img)[1]
	width <- dim(img)[2]

	## convert to 2d
	points <- as.data.frame(matrix(img, height * width))
	points$x <- rep(1:height)
	points$y <- rep(1:width, each = height)

	## filter for gray points (contour)
	points <- subset(points, V4 > 0.5 & V4 < 0.8)

	## sample polygon
	np <- 15 * 1e3
	points <- points[sample(nrow(points), np, replace = TRUE), ]

	## flip axeses
	points <- data.frame(x = points$y, y = -points$x)

	## render polygon
	ggplot(points, aes(x, y)) + geom_point()

	## add noise
	nn <- 1e4 * 2
	noise <- round(data.frame(
	x = rnorm(nn, mean = width / 2, sd = width / 4),
	y = rnorm(nn, mean = -height / 2, sd = height / 4)
	))

	## render noise
	ggplot(noise, aes(x, y)) + geom_point()

	## add noise to polygon
	df <- rbind(points[, c('x', 'y')], noise)

	## visualize all points
	ggplot(df, aes(x, y)) + geom_point()
	ggplot(df, aes(x, y)) + geom_point(alpha = 0.05)
	ggplot(df, aes(x, y)) + geom_point(size = 0.2, alpha = 0.1)
	ggplot(df, aes(x, y)) + geom_hex(binwidth = 25)