library(gapminder) data(“gapminder”)

#Data overview

summary(gapminder) glimpse(gapminder) names(gapminder) head(gapminder) print(gapminder)

#Centrality

mean(gapminder\(pop) mean(gapminder\)gdpPercap) mean(gapminder$lifeExp)

median(gapminder\(pop) median(gapminder\)gdpPercap) median(gapminder\(lifeExp) var(gapminder\)pop) var(gapminder\(gdpPercap) var(gapminder\)lifeExp) summary(gapminder\(pop) cor.test(gapminder\)gdpPercap,gapminder$lifeExp)

#Create a subset of the data frame

library(gapminder) data(“gapminder”)

gapminder_3 <- gapminder[25:48,3:6, drop= FALSE] view(gapminder_3) print(gapminder_3) head(gapminder_3)

#Create new column names in the new data frame

library(tidyverse) gapminder_3 %>% mutate(Population= pop,Life_Expectancy=lifeExp,GDP=gdpPercap) %>% select(Population,Life_Expectancy,GDP)

head(gapminder_3) print(gapminder_3)

summary of new data frame

summary(gapminder_3)

Assigning new columns names

gapminder_4 <- c(gapminder_3 %>% mutate(Population= pop,Life_Expectancy=lifeExp,GDP=gdpPercap) %>% select(Population,Life_Expectancy,GDP)) print(gapminder_4) glimpse (gapminder_4) head

comparing Life Expectancy and GDP in new data frame

gapminder_4[c(“Life_Expectancy”,“GDP”)]

comparing lifeExp and gdpPercap in original data frame

gapminder[c(“lifeExp”,“gdpPercap”)]