Bộ dữ liệu gapminder
data("gapminder")
names(gapminder)
## [1] "country" "continent" "year" "lifeExp" "pop" "gdpPercap"
p2 = ggplot(data = gapminder, mapping = aes(x =gdpPercap, y = lifeExp))
p2
p3 = p2 + geom_point(aes(color = continent))
p3
p4 = p3 + scale_x_log10()
p4
p5 = p4 + geom_smooth(method = "loess")
p5
## `geom_smooth()` using formula 'y ~ x'
p6 = p3 + geom_smooth(method = "loess")
p6
## `geom_smooth()` using formula 'y ~ x'
p7 = p4 + labs(x = "log GDP per Capita", y = "Life Expectancy") +
ggtitle("Association between GDP per Capita and Life Expectancy")
p7
p8 = p7 + theme(plot.title = element_text(lineheight = 0.6, face = "bold", hjust = 0.5))
p8
p9 = p5 + labs(x = "log GDP per Capita", y = "Life Expectancy") +
ggtitle("Association between GDP per Capita and Life Expectancy")
p9
## `geom_smooth()` using formula 'y ~ x'
p10 = p9 + theme_economist()
p10
## `geom_smooth()` using formula 'y ~ x'
Y2007 <- subset(gapminder, year == 2007)
dim(Y2007)
## [1] 142 6
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2007 43.8 31889923 975.
## 2 Albania Europe 2007 76.4 3600523 5937.
## 3 Algeria Africa 2007 72.3 33333216 6223.
## 4 Angola Africa 2007 42.7 12420476 4797.
## 5 Argentina Americas 2007 75.3 40301927 12779.
## 6 Australia Oceania 2007 81.2 20434176 34435.
## # A tibble: 3 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Yemen, Rep. Asia 2007 62.7 22211743 2281.
## 2 Zambia Africa 2007 42.4 11746035 1271.
## 3 Zimbabwe Africa 2007 43.5 12311143 470.
## tibble [142 x 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 4 1 1 2 5 4 3 3 4 ...
## $ year : int [1:142] 2007 2007 2007 2007 2007 2007 2007 2007 2007 2007 ...
## $ lifeExp : num [1:142] 43.8 76.4 72.3 42.7 75.3 ...
## $ pop : int [1:142] 31889923 3600523 33333216 12420476 40301927 20434176 8199783 708573 150448339 10392226 ...
## $ gdpPercap: num [1:142] 975 5937 6223 4797 12779 ...
## country continent year lifeExp
## Afghanistan: 1 Africa :52 Min. :2007 Min. :39.61
## Albania : 1 Americas:25 1st Qu.:2007 1st Qu.:57.16
## Algeria : 1 Asia :33 Median :2007 Median :71.94
## Angola : 1 Europe :30 Mean :2007 Mean :67.01
## Argentina : 1 Oceania : 2 3rd Qu.:2007 3rd Qu.:76.41
## Australia : 1 Max. :2007 Max. :82.60
## (Other) :136
## pop gdpPercap
## Min. :1.996e+05 Min. : 277.6
## 1st Qu.:4.508e+06 1st Qu.: 1624.8
## Median :1.052e+07 Median : 6124.4
## Mean :4.402e+07 Mean :11680.1
## 3rd Qu.:3.121e+07 3rd Qu.:18008.8
## Max. :1.319e+09 Max. :49357.2
##
y1 = ggplot(data = Y2007, mapping = aes(x =gdpPercap))
y2 = y1 + geom_histogram(fill = "lightgreen", color = "red") + labs(title = "ABA")
y2
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
y3 = y1 + geom_histogram(aes(y = ..density..), fill = "lightblue", color = "red")
y3
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
y4 = ggplot(data = Y2007, mapping = aes(gdpPercap, fill = continent)) +
geom_density(alpha = 0.4)
y4
y5 = ggplot(data = Y2007, mapping = aes(x = continent, fill = continent)) + geom_bar()
y5
y6 = y5 + theme(legend.position = "none")
y6
Y2007$level_age[Y2007$lifeExp < 60] <- "< 60 age"
## Warning: Unknown or uninitialised column: `level_age`.
Y2007$level_age[60 <= Y2007$lifeExp & Y2007$lifeExp < 80] <- "60 - 80 age"
Y2007$level_age[Y2007$lifeExp >= 80] <- "> 80 age"
y7 = ggplot(data = Y2007, mapping = aes(x = level_age, fill = level_age)) + geom_bar()
y7
y8 = ggplot(data = Y2007, mapping = aes(x = continent, fill = level_age)) + geom_bar()
y8
CA <- subset(gapminder, year == 2007 & continent == "Asia")
y9 = ggplot(data = CA, mapping = aes(x = country, y = lifeExp, fill = country)) + geom_bar(stat = "identity", width = 0.9)
y10 = y9 + coord_flip()
y10
y11 = y10 + theme(legend.position = "none")
y11
y12 = ggplot(data = CA, mapping = aes(x = reorder(country, lifeExp), y = lifeExp, fill = country)) + geom_bar(stat = "identity", width = 0.9)
y13 = y12 + coord_flip() + theme(legend.position = "none")
y13
y14 = ggplot(data = CA, mapping = aes(x = reorder(country, gdpPercap), y = gdpPercap, fill = country)) + geom_bar(stat = "identity", width = 0.9)
y15 = y14 + coord_flip() + theme(legend.position = "none")
y15
grid.arrange(y15,y13, ncol = 2)
y9 = ggplot(data = CA, mapping = aes(x = country, y = lifeExp, fill = country)) + geom_bar(stat = "identity", width = 0.9) +
theme(legend.position = "none")
y14 = ggplot(data = CA, mapping = aes(x = reorder(country, gdpPercap), y = gdpPercap, fill = country)) + geom_bar(stat = "identity", width = 0.9)
grid.arrange(y9,y14, ncol = 2)
g1 = ggplot(data = Y2007, mapping = aes(x = continent, y = gdpPercap, fill = continent))
g1
g2 = g1 + geom_boxplot(alpha = 0.9)
g2
g3 = g2 + geom_jitter(alpha = 0.3)
g3
