05_Exploratory_Data_Analysis

Question 1

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Answer 1

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Question 2

How do you compute grouped summary statistics quickly?

Answer 2

Use dplyr::group_by() + summarise().

Code:
library(dplyr)
mtcars %>% group_by(cyl) %>% summarise(across(mpg:hp, list(mean=mean, sd=sd)))

Question 3

How do you make a scatter plot with ggplot2?

Answer 3

Use geom_point().

Code:
library(ggplot2)
ggplot(mtcars, aes(wt, mpg)) + geom_point() + labs(x=’Weight’, y=’MPG’)

Question 4

How do you plot a histogram and a boxplot?

Answer 4

Use geom_histogram() for distributions; geom_boxplot() for spread/outliers.

Code:
library(ggplot2)
ggplot(mtcars, aes(mpg)) + geom_histogram(bins = 20)
ggplot(mtcars, aes(factor(cyl), mpg)) + geom_boxplot()

Question 5

How do you facet plots by a category?

Answer 5

Use facet_wrap(~ var).

Code:
library(ggplot2)
ggplot(iris, aes(Sepal.Length, Petal.Length)) + geom_point() + facet_wrap(~ Species)

Question 6

How do you compute and test correlations?

Answer 6

Use cor() for coefficient, cor.test() for inference.

Code:
cor(mtcars$mpg, mtcars$wt, method=’pearson’)
cor.test(mtcars$mpg, mtcars$wt)