For each of the plots (scatter plot, histogram, boxplot, area chart, heat map, correlogram) explain what you see (including what is on the x- and y-axis) and try to transform what you see into insight about the data. All except the correlogram use ggplot2
for plotting. If you want to read more about the idea behind ggplot2
(grammar of graphics) Chapter 3 of R for Data Science is a good read.
install.packages("car")
install.packages("faraway")
install.packages("ggplot2")
install.packages("GGally")
install.packages("reshape")
install.packages("corrplot")
install.packages("corrgram")
Three different data sets are used - read descriptions in R:
SLID
: ?car::SLID
mtcars
: ?datasets::mtcars
ozone
: ?faraway::ozone
library(car)
library(ggplot2)
SLID = na.omit(SLID)
ggplot(SLID, aes(education, wages)) + geom_point() + labs(title = "Scatterplot") +
theme_minimal()
ggplot(SLID, aes(education, wages)) + geom_point(aes(color = language)) + scale_x_continuous("Education") +
scale_y_continuous("Wages") + theme_bw() + labs(title = "Scatterplot") +
facet_wrap(~language) + theme_minimal()
ggplot(SLID, aes(wages)) + geom_histogram(binwidth = 2) + labs(title = "Histogram") +
theme_minimal()
ggplot(SLID, aes(language, wages)) + geom_boxplot(fill = "skyblue") + labs(title = "Box Plot") +
theme_minimal()
library(GGally)
ggpairs(SLID) + theme_minimal()
ages = cut(SLID$age, breaks = 3)
SLID2 = cbind(SLID, ages)
ggplot(SLID, aes(x = wages, fill = ages)) + geom_area(stat = "bin") + theme_minimal()
library(reshape)
head(mtcars)
carsdf = data.frame(scale(mtcars))
carsdf$model = rownames(mtcars)
cars_melt = melt(carsdf, id.vars = "model")
ggplot(cars_melt, aes(x = variable, y = model)) + geom_raster(aes(fill = value)) +
labs(title = "Heat Map") + scale_fill_continuous(name = "Value") + theme_minimal()
## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
The ozone
data:
library(faraway)
data(ozone)
library(corrplot)
ozonecorr = cor(ozone)
corrplot(ozonecorr)
library(corrgram)
corrgram(ozone, upper.panel = panel.conf)