Data Visualizatoin I(Quiz4)
library(gapminder)
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5 v purrr 0.3.4
## v tibble 3.1.4 v dplyr 1.0.7
## v tidyr 1.1.3 v stringr 1.4.0
## v readr 2.0.1 v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
gapminder %>%
filter(year=="2007") %>%
dplyr::select(-year) %>%
arrange(desc(pop)) %>%
mutate(country=factor(country,country)) %>%
ggplot(mapping=aes(x=gdpPercap, y=lifeExp,size=pop,fill=continent)) +
geom_point(alpha=0.5,shape=21,color="black")+
scale_size(range=c(.1,24),name="Population(M)") +
theme_minimal() +
theme(legend.position="right") +
labs(
subtitle="Life Expectancy vs. GDP per Capita",
y="Life Expectancy",
x="GDP per Capita",
title="Scatter Plot",
caption="Source:gapminder"
)

Quiz 4-1
gapminder %>%
filter(year=="2007") %>%
dplyr::select(-year) %>%
arrange(desc(pop)) %>%
mutate(country=factor(country,country)) %>%
ggplot(mapping=aes(lifeExp,size=pop,fill=continent))+geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Quiz 4-2
gapminder %>%
filter(year=="2007") %>%
dplyr::select(-year) %>%
arrange(desc(pop)) %>%
mutate(country=factor(country,country)) %>%
ggplot(mapping=aes(lifeExp,size=pop,fill=continent))+geom_histogram()+ facet_wrap(vars(continent))
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

Quiz 4-3
gapminder %>%
filter(year=="2007") %>%
dplyr::select(-year) %>%
arrange(desc(pop)) %>%
mutate(country=factor(country,country)) %>%
ggplot(mapping=aes(y=lifeExp,size=pop,fill=continent))+geom_boxplot()

Quiz 4-4
gapminder %>%
ggplot(mapping=aes(x=year, y=lifeExp,color=continent,group=country))+geom_line()

Quiz 4-5
gapminder %>%
ggplot(mapping=aes(x=year, y=lifeExp,color=continent,group=country))+geom_line() +
facet_wrap(vars(continent))

Quiz 4-6
## corrplot 0.90 loaded
gapminder %>%
filter(year=="2007") %>%
select(lifeExp, pop, gdpPercap) %>%
cor() %>%
corrplot.mixed()
