Stefan Jaspers

Graphs in R / Aesthetics in ggplot2

Stefan April 17, 2018 ggplot, R

ggplot2 – the aes() element in ggplot The aim is to have a clear plot of your data. Here some examples :

Aesthetics in ggplot2

R

library(ggplot2) mtcars$cyl <- as.factor(mtcars$cyl) mtcars$am <- as.factor(mtcars$am) # 1 ggplot(mtcars, aes(mpg, cyl)) + geom_point() + ggtitle("1 - Miles/gallon vs Cylinders") # 2 ggplot(mtcars, aes(cyl, mpg)) + geom_point() + ggtitle("2 - Cylinders vs Miles/gallon") # 3 ggplot(mtcars, aes(wt, mpg, col=cyl)) + geom_point() + ggtitle("3 - Weight vs Miles/Gallon, cylinders colorcoded, basic") # 4 ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) + geom_point(shape = 1, size = 4) + ggtitle("4 - Weight vs Miles/Gallon, cylinders colorcoded, shaped datapoints") # 5 ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(shape = 1, size = 4) + ggtitle("5 - Weight vs Miles/Gallon, cylinders colorfilled, shaped datapoints") # 6 ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(shape = 21, size = 4,alpha=0.6) + ggtitle("6 - Weight vs Miles/Gallon, cylinders colorfilled, shaped transparent datapoints") # 7 ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl,col=am)) + geom_point(shape = 21, size = 4,alpha=0.6) + ggtitle("7 - Weight vs Miles/Gallon, cylinders colorfilled, am (gear) bordercolored ") # 8 ggplot(mtcars,aes(wt,mpg,size=cyl))+ geom_point() + ggtitle("8 - Weight vs Miles/Gallon, cylinders size-coded") # 9 ggplot(mtcars,aes(wt,mpg,alpha=cyl))+ geom_point() + ggtitle("9 - Weight vs Miles/Gallon, cylinders opacity-coded") # 10 ggplot(mtcars,aes(wt,mpg,shape=cyl))+ geom_point() + ggtitle("10 - Weight vs Miles/Gallon, cylinders shape-coded") # 11 ggplot(mtcars,aes(wt,mpg,label=cyl))+ geom_text() + ggtitle("11 - Weight vs Miles/Gallon, cylinders label/text-coded")

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library(ggplot2) mtcars$cyl <- as.factor(mtcars$cyl) mtcars$am <- as.factor(mtcars$am)   # 1 ggplot(mtcars, aes(mpg, cyl)) + geom_point() +   ggtitle("1 - Miles/gallon vs Cylinders")   # 2 ggplot(mtcars, aes(cyl, mpg)) + geom_point() +     ggtitle("2 - Cylinders vs Miles/gallon")   # 3 ggplot(mtcars, aes(wt, mpg, col=cyl)) + geom_point() +   ggtitle("3 - Weight vs Miles/Gallon, cylinders colorcoded, basic")   # 4 ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) + geom_point(shape = 1, size = 4) +   ggtitle("4 - Weight vs Miles/Gallon, cylinders colorcoded, shaped datapoints")   # 5 ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) + geom_point(shape = 1, size = 4) +   ggtitle("5 - Weight vs Miles/Gallon, cylinders colorfilled, shaped datapoints")   # 6 ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl)) +   geom_point(shape = 21, size = 4,alpha=0.6) +   ggtitle("6 - Weight vs Miles/Gallon, cylinders colorfilled, shaped transparent datapoints")   # 7 ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl,col=am)) +   geom_point(shape = 21, size = 4,alpha=0.6) +   ggtitle("7 - Weight vs Miles/Gallon, cylinders colorfilled, am (gear) bordercolored ")   # 8 ggplot(mtcars,aes(wt,mpg,size=cyl))+  geom_point() +   ggtitle("8 - Weight vs Miles/Gallon, cylinders size-coded")   # 9 ggplot(mtcars,aes(wt,mpg,alpha=cyl))+  geom_point() +   ggtitle("9 - Weight vs Miles/Gallon, cylinders opacity-coded")   # 10 ggplot(mtcars,aes(wt,mpg,shape=cyl))+  geom_point() +   ggtitle("10 - Weight vs Miles/Gallon, cylinders shape-coded")   # 11 ggplot(mtcars,aes(wt,mpg,label=cyl))+  geom_text() +   ggtitle("11 - Weight vs Miles/Gallon, cylinders label/text-coded")

  Plots with a clear output Plot 1,2,3, 6 and 11   Plots with confusing elements Plot

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Graphs in R / Data formatting

Stefan April 16, 2018 ggplot, R

Working with tidyr package A dataset is ‘tidy’ when every row is 1 observation A dataset is ‘wide’ when a row contains more observations In R tidyr package ….  

Tidy vs Wide

R

# Load the tidyr package library(tidyr) # IRIS tidy iris.tidy <- iris %>% gather(key, Value, -Species) %>% separate(key, c("Part", "Measure"), "\\.") # IRIS wide iris$Flower <- 1:nrow(iris) iris.wide <- iris %>% gather(key, value, -Species, -Flower) %>% separate(key, c("Part", "Measure"), "\\.") %>% spread(Measure, value)

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# Load the tidyr package library(tidyr)   # IRIS tidy iris.tidy <- iris %>%   gather(key, Value, -Species) %>%   separate(key, c("Part", "Measure"), "\\.")   # IRIS wide iris$Flower <- 1:nrow(iris) iris.wide <- iris %>%   gather(key, value, -Species, -Flower) %>%   separate(key, c("Part", "Measure"), "\\.") %>%   spread(Measure, value)

Result

> head(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Species Flower 1 5.1 3.5 1.4 0.2 setosa 1 2 4.9 3.0 1.4 0.2 setosa 2 3 4.7 3.2 1.3 0.2 setosa 3 4 4.6 3.1 1.5 0.2 setosa 4 5 5.0 3.6 1.4 0.2 setosa 5 6 5.4 3.9 1.7 0.4 setosa 6 > head(iris.wide) Species Flower Part Length Width 1 setosa 1 Petal 1.4 0.2 2 setosa 1 Sepal 5.1 3.5 3 setosa 2 Petal 1.4 0.2 4 setosa 2 Sepal 4.9 3.0 5 setosa 3 Petal 1.3 0.2 6 setosa 3 Sepal 4.7 3.2 > head(iris.tidy) Species Part Measure Value 1 setosa Sepal Length 5.1 2 setosa Sepal Length 4.9 3 setosa Sepal Length 4.7 4 setosa Sepal Length 4.6 5 setosa Sepal Length 5.0 6 setosa Sepal Length 5.4

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> head(iris)   Sepal.Length Sepal.Width Petal.Length Petal.Width Species Flower 1          5.1         3.5          1.4         0.2  setosa      1 2          4.9         3.0          1.4         0.2  setosa      2 3          4.7         3.2          1.3         0.2  setosa      3 4          4.6         3.1          1.5         0.2  setosa      4 5          5.0         3.6          1.4         0.2  setosa      5 6          5.4         3.9          1.7         0.4  setosa      6 > head(iris.wide)   Species Flower  Part Length Width 1  setosa      1 Petal    1.4   0.2 2  setosa      1 Sepal    5.1   3.5 3  setosa      2 Petal    1.4   0.2 4  setosa      2 Sepal    4.9   3.0 5  setosa      3 Petal    1.3   0.2 6  setosa      3 Sepal    4.7   3.2 > head(iris.tidy)   Species  Part Measure Value 1  setosa Sepal  Length   5.1 2  setosa Sepal  Length   4.9 3  setosa Sepal  Length   4.7 4  setosa Sepal  Length   4.6 5  setosa Sepal  Length   5.0 6  setosa Sepal  Length   5.4

In Plots

R

library(ggplot2) ggplot(iris.wide, aes(x = Length, y = Width, color = Part)) + geom_jitter() + facet_grid(. ~ Species) ggplot(iris.tidy, aes(x = Species, y = Value, col = Part)) + geom_jitter() + facet_grid(. ~ Measure)

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library(ggplot2)   ggplot(iris.wide, aes(x = Length, y = Width, color = Part)) +   geom_jitter() +   facet_grid(. ~ Species)   ggplot(iris.tidy, aes(x = Species, y = Value, col = Part)) +   geom_jitter() +   facet_grid(. ~ Measure)

 With the wide dataset

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Graphs in R / Basic Plot vs ggplot2

Stefan April 14, 2018April 14, 2018 ggplot, R

Basic Plot with lm()

Basic Plot

R

# Convert cyl to factor mtcars$cyl <- as.factor(mtcars$cyl) # Basic Plot plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl) # Linear model on all data abline(lm(mpg ~ wt, data = mtcars), lty = 2) # Linear Model on subset per cylinder (mtcars$cyl) lapply(mtcars$cyl, function(x) { abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x) }) legend(x = 5, y = 33, legend = levels(mtcars$cyl), col = 1:3, pch = 1, bty = "n")

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# Convert cyl to factor mtcars$cyl <- as.factor(mtcars$cyl)   # Basic Plot plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl)   # Linear model on all data abline(lm(mpg ~ wt, data = mtcars), lty = 2)   # Linear Model on subset per cylinder (mtcars$cyl) lapply(mtcars$cyl, function(x) {   abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x) }) legend(x = 5, y = 33, legend = levels(mtcars$cyl),        col = 1:3, pch = 1, bty = "n")

  ggplot2 with geom_smooth() method lm

ggplot version with linear models

R

# ggplot version library(ggplot2) ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) + geom_point() + geom_smooth(method = "lm", se = FALSE) + geom_smooth(aes(group = 1), method = "lm", se = FALSE, linetype = 2)

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# ggplot version library(ggplot2)   ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +   geom_point() +   geom_smooth(method = "lm", se = FALSE) +   geom_smooth(aes(group = 1), method = "lm", se = FALSE, linetype = 2)

In ggplot the lm() is being composed within the geom_smooth() element, and choosing “lm” (linear model).  

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Graphs in R / Basic Plot function – linear model

Stefan April 14, 2018 R

Basic Plot – linear model with abline() Plot 1 – linear model all datapoints

Basic Plot with abline

R

# Build a linear model with lm() where wt (weight) relates to mpg (miles per gallon) carModel <- lm(mpg ~ wt, data = mtcars) # Basic plot ( factor the number of cylinders) mtcars$cyl <- as.factor(mtcars$cyl) plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl) # Call abline() to overlay the basic plot with the linear model abline(carModel, lty = 2)

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# Build a linear model with lm() where wt (weight) relates to mpg (miles per gallon) carModel <- lm(mpg ~ wt, data = mtcars)   # Basic plot ( factor the number of cylinders) mtcars$cyl <- as.factor(mtcars$cyl) plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl)   # Call abline() to overlay the basic plot with the linear model abline(carModel, lty = 2)

  Plot 2 – linear model per subset Add library(graphics) to the code for the legend() function

Basic plot witl linear model per subset

R

# Calculate per cylinder a lineair model with lapply() plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl) lapply(mtcars$cyl, function(x) { abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x) }) # For better understanding add a legend. legend(x = 5, y = 33, legend = levels(mtcars$cyl), col = 1:3, pch = 1, bty = "n")

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# Calculate per cylinder a lineair model with lapply() plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl) lapply(mtcars$cyl, function(x) {   abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x) })   # For better understanding add a legend. legend(x = 5, y = 33, legend = levels(mtcars$cyl),        col = 1:3, pch = 1, bty = "n")

Use of lapply –> take the vector

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Graphs in R / ggplot2 – Diamonds

Stefan April 14, 2018April 14, 2018 ggplot, R

Part 2 – Scatterplot and additional plots Scatterplot with smoothed line

Scatterplot with basic smoothed line

R

ggplot(diamonds, aes(x = carat, y = price)) + geom_point() + geom_smooth()

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ggplot(diamonds, aes(x = carat, y = price)) +   geom_point() +   geom_smooth()

Scatterplot with 3rd variable and based on smoothed line x-axis = carat / y-axis = price / 3rd variable = clarity

Scatterplot - 3 variables - with smoothed colored lines

R

ggplot(diamonds, aes(x = carat, y = price, col=clarity)) + geom_smooth()

1 2	ggplot(diamonds, aes(x = carat, y = price, col=clarity)) +   geom_smooth()

Scatterplot - 3 variables - colored points

R

ggplot(diamonds, aes(x = carat, y = price, col=clarity)) + geom_smooth()

1 2	ggplot(diamonds, aes(x = carat, y = price, col=clarity)) +   geom_smooth()

 

Working with Alpha

R

dia_plot <- ggplot(diamonds, aes(x = carat, y = price)) dia_plot <- dia_plot + geom_point(alpha=0.2) # 1 - Plot dia_plot with additional geom_smooth() with se set to FALSE dia_plot + geom_point(alpha=0.2)+ geom_smooth(se=FALSE) # 2 - Copy the command from above and add aes() with the correct mapping to geom_smooth() dia_plot + geom_smooth(aes(color=clarity),se=FALSE)

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dia_plot <- ggplot(diamonds, aes(x = carat, y = price)) dia_plot <- dia_plot +  geom_point(alpha=0.2)   # 1 - Plot dia_plot with additional geom_smooth() with se set to FALSE dia_plot + geom_point(alpha=0.2)+  geom_smooth(se=FALSE)   # 2 - Copy the command from above and add aes() with the correct mapping to geom_smooth() dia_plot + geom_smooth(aes(color=clarity),se=FALSE)

To omit the grey zone

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Graphs in R / ggplot2

Stefan April 13, 2018April 13, 2018 ggplot, R

First Graphs – Scatterplot 2 variables

Scatterplot 101

R

library(ggplot2) ggplot(mtcars, aes(x=cyl, y = mpg)) + geom_point()

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library(ggplot2) ggplot(mtcars, aes(x=cyl, y = mpg)) +   geom_point()

Scatterplot 1

R

library(ggplot2) # Change the command below so that cyl is treated as factor ggplot(mtcars, aes(x = factor(cyl), y = mpg)) + geom_point()

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library(ggplot2)   # Change the command below so that cyl is treated as factor ggplot(mtcars, aes(x = factor(cyl), y = mpg)) +   geom_point()

The x-axis is now handled as factor. Graphs – Scatterplot with 3 variables This can be done by either add a color or a size. But this is not optimal ( colors can

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