| title | author | output | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
Data visualization |
Yohan |
|
## gapminder
str(gapminder)## Classes 'tbl_df', 'tbl' and 'data.frame': 1704 obs. of 6 variables:
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num 28.8 30.3 32 34 36.1 ...
## $ pop : int 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num 779 821 853 836 740 ...
ggplot(data = gapminder, aes(x = year, y = lifeExp,
group = country, color = continent)) +
geom_line(alpha = 0.5) +
facet_wrap( ~ continent) +
xlab("Year") + ylab("Life expectancy") +
ggtitle("Life expectancy over time") + theme_bw()
ggplot(data = gapminder, aes(x = continent, y = year, color = continent)) +
geom_point()
ggplot(data = gapminder, aes(x = continent, y = year, color = continent)) +
geom_point(position = position_jitter(width = 0.5, height = 2))
ggplot(data = gapminder, aes(x = year, y = lifeExp, group = country)) +
geom_line(alpha = 0.5, aes(color = "Country", size = "Country")) +
geom_line(stat = "smooth", method = "loess",
aes(group = continent, color = "Continent", size = "Continent"),
alpha = 0.5) +
facet_wrap(~ continent, nrow = 2) +
scale_color_manual(name = "Life Exp. for:",
values = c("Country" = "black", "Continent" = "dodgerblue1")) +
scale_size_manual(name = "Life Exp. for:",
values = c("Country" = 0.25, "Continent" = 3)) +
theme_minimal(base_size = 14) +
ylab("Years") + xlab("") +
ggtitle("Life Expectancy, 1952-2007", subtitle = "By continent and country") +
theme(legend.position=c(0.75, 0.2), axis.text.x = element_text(angle = 45))
gapminder %>%
filter(year == 1952) %>%
group_by(continent) %>%
summarize(medianGdpPercap = median(gdpPercap)) %>%
ggplot(aes(x = continent, y = medianGdpPercap)) +
geom_col()
gapminder %>%
filter(year == 1952) %>%
ggplot(aes(x = pop)) +
geom_histogram() +
scale_x_log10()
gapminder %>%
filter(year == 1952) %>%
ggplot(aes(x = continent, y = gdpPercap, color = continent)) +
geom_boxplot() +
scale_y_log10() +
ggtitle("Comparing GDP per capita across continents")
## tidy
str(iris)## 'data.frame': 150 obs. of 5 variables:
## $ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
## $ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
## $ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
## $ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
## $ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...
iris.tidy <- iris %>%
gather(key, Value, -Species) %>%
separate(key, c("Part", "Measure"), "\\.")
iris$Flower <- 1:nrow(iris)
iris.wide <- iris %>%
gather(key, value, -Species, -Flower) %>%
separate(key, c("Part", "Measure"), "\\.") %>%
spread(Measure, value)
ggplot(iris.wide, aes(x = Length, y = Width, color = Part)) +
geom_jitter() +
facet_grid(. ~ Species)
## cluster analysis
ggpairs(iris[,-6],mapping=aes(color=Species))
ggpairs(iris, columns = 1:4,
aes(color=Species, alpha=0.4),
title="Scatterplot Matrix",
upper=list(continuous="density", combo="box"),
lower=list(continuous="smooth", combo="dot")) +
theme_light() +
theme(plot.title=element_text(size=10))
ggplot(iris, aes(x=Petal.Length, y=Sepal.Width, colour=Species) ) +
geom_point(size= 2.5) +
geom_smooth(method="lm") +
labs(title="Aggregated Data")
set.seed(123)
cluster=kmeans(iris[,1:4],3)
iris$cluster=as.factor(cluster$cluster)
ggpairs(iris,columns = 1:5, mapping=aes(color=cluster))
set.seed(456)
performance=c()
for (i in rep(1:100,times=30)) {
clust=kmeans(iris[,1:4],i)
performance=c(performance,1-clust$tot.withinss/clust$totss)
}
perf_df=data.frame(metrics=performance,number_of_center=rep(1:100,times=30))
ggplot(perf_df,aes(x=number_of_center,y=metrics)) +
geom_point(alpha=0.2) +
geom_vline(xintercept = 3,color='red')
## point
data(diamonds)
str(diamonds)## Classes 'tbl_df', 'tbl' and 'data.frame': 53940 obs. of 10 variables:
## $ carat : num 0.23 0.21 0.23 0.29 0.31 0.24 0.24 0.26 0.22 0.23 ...
## $ cut : Ord.factor w/ 5 levels "Fair"<"Good"<..: 5 4 2 4 2 3 3 3 1 3 ...
## $ color : Ord.factor w/ 7 levels "D"<"E"<"F"<"G"<..: 2 2 2 6 7 7 6 5 2 5 ...
## $ clarity: Ord.factor w/ 8 levels "I1"<"SI2"<"SI1"<..: 2 3 5 4 2 6 7 3 4 5 ...
## $ depth : num 61.5 59.8 56.9 62.4 63.3 62.8 62.3 61.9 65.1 59.4 ...
## $ table : num 55 61 65 58 58 57 57 55 61 61 ...
## $ price : int 326 326 327 334 335 336 336 337 337 338 ...
## $ x : num 3.95 3.89 4.05 4.2 4.34 3.94 3.95 4.07 3.87 4 ...
## $ y : num 3.98 3.84 4.07 4.23 4.35 3.96 3.98 4.11 3.78 4.05 ...
## $ z : num 2.43 2.31 2.31 2.63 2.75 2.48 2.47 2.53 2.49 2.39 ...
diamonds %>%
ggplot(aes(x = carat, y = price)) +
geom_point() +
geom_smooth()
ggplot(diamonds, aes(x = carat, y = price, color = clarity)) +
geom_point(alpha = 0.4)+
geom_smooth()
ggplot(diamonds, aes(x = carat, y = price)) +
geom_point(alpha = 0.4)+
geom_smooth(aes(color = clarity))
ggplot(diamonds, aes(x = clarity, y = carat, color = price)) +
geom_point(alpha = 0.5)
ggplot(diamonds, aes(x = clarity, y = carat, color = price)) +
geom_point(alpha = 0.5, position = "jitter")
data("mtcars")
str(mtcars)## 'data.frame': 32 obs. of 11 variables:
## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...
## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...
## $ disp: num 160 160 108 258 360 ...
## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...
## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...
## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...
## $ qsec: num 16.5 17 18.6 19.4 17 ...
## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...
## $ am : num 1 1 1 0 0 0 0 0 0 0 ...
## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...
## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...
mtcars <- mtcars %>%
mutate_at(vars(cyl, am), factor)
plot(mtcars$wt, mtcars$mpg, col = mtcars$cyl)
lapply(mtcars$cyl, function(x) {
abline(lm(mpg ~ wt, mtcars, subset = (cyl == x)), col = x)
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legend(x = 5, y = 33, legend = levels(mtcars$cyl),
col = 1:3, pch = 1, bty = "n")
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl)) +
geom_point() +
geom_smooth(method = "lm", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", se = FALSE, linetype = 2)
## aesthetics
### x, y, color, fill, size, alpha, labels, linetype, shape
### variable: continuous, discrete
ggplot(mtcars, aes(x = wt, y = mpg, fill = cyl, col = am)) +
geom_point(shape = 21, size = 4, alpha = 0.6)
ggplot(mtcars, aes(x = wt, y = mpg, size = cyl)) +
geom_point()
ggplot(mtcars, aes(x = wt, y = mpg, alpha = cyl)) +
geom_point()
ggplot(mtcars, aes(x = wt, y = mpg, shape = cyl)) +
geom_point()
ggplot(mtcars, aes(x = wt, y = mpg, label = cyl)) +
geom_text()
ggplot(mtcars, aes(x = wt, y = mpg)) +
geom_text(label = rownames(mtcars), color = 'red')
ggplot(mtcars, aes(x = mpg, y = qsec, col = cyl, shape = am,
size = (hp/wt))) +
geom_point()
ggplot(mtcars, aes(x = mpg, y = 0)) +
geom_jitter() +
scale_y_continuous(limits = c(-2,2))
ggplot(mtcars, aes(x = cyl, y = wt)) +
geom_jitter(width = 0.1, alpha = 0.6, shape = 1)
ggplot(mtcars, aes(x = cyl, y = wt)) +
geom_point(position = position_jitter(0.1))
### histogram
ggplot(mtcars, aes(mpg)) +
geom_histogram(aes(y = ..density..), binwidth = 1, fill = "#377EB8")
ggplot(mtcars, aes(mpg, fill = cyl)) +
geom_histogram(binwidth = 1)
ggplot(mtcars, aes(mpg, fill = cyl)) +
geom_histogram(binwidth = 1, position = "dodge")
ggplot(mtcars, aes(mpg, color = cyl)) +
geom_freqpoly(binwidth = 1)
ggplot(mtcars, aes(mpg, fill = cyl)) +
geom_histogram(binwidth = 1, position = "identity", alpha = 0.4)
### bar
ggplot(mtcars, aes(x = cyl, fill = am)) +
geom_bar(position = "stack")
ggplot(mtcars, aes(x = cyl, fill = am)) +
geom_bar(position = "fill") +
scale_fill_brewer()
ggplot(mtcars, aes(x = cyl, fill = am)) +
geom_bar(position = "dodge")
ggplot(mtcars, aes(x = cyl, fill = am)) +
geom_bar(position = position_dodge(0.2), alpha = 0.6)
ggplot(mtcars, aes(x = 1, fill = factor(cyl))) +
geom_bar() +
coord_polar(theta = "y")
ggplot(mtcars, aes(x = 1, fill = cyl)) +
geom_bar(width = .1) +
scale_x_continuous(limits = c(0.5,1.5)) +
coord_polar(theta = "y")
### facet
mtcars$cyl_am <- paste(mtcars$cyl, mtcars$am, sep = "_")
myCol <- rbind(brewer.pal(9, "Blues")[c(3,6,8)],
brewer.pal(9, "Reds")[c(3,6,8)])
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl_am)) +
geom_point() +
scale_color_manual(values = myCol)
ggplot(mtcars, aes(x = wt, y = mpg, col = cyl_am, size = disp)) +
geom_point() +
scale_color_manual(values = myCol) +
facet_grid(gear ~ vs)
### qplot
qplot(wt, mpg, data = mtcars)
qplot(wt, mpg, data = mtcars, size = cyl)
qplot(wt, mpg, data = mtcars, color = hp)
qplot(cyl, factor(vs), data = mtcars)
qplot(cyl, factor(vs), data = mtcars, geom = "jitter")
ggplot(mtcars, aes(cyl, wt, col = am)) +
geom_point(position = position_jitter(0.2, 0))
ggplot(mtcars, aes(cyl, wt, fill = am)) +
geom_dotplot(stackdir = "center", binaxis = "y")
qplot(
cyl, wt,
data = mtcars,
fill = am,
geom = "dotplot",
binaxis = "y",
stackdir = "center"
)
head(ChickWeight)## Grouped Data: weight ~ Time | Chick
## weight Time Chick Diet
## 1 42 0 1 1
## 2 51 2 1 1
## 3 59 4 1 1
## 4 64 6 1 1
## 5 76 8 1 1
## 6 93 10 1 1
ggplot(ChickWeight, aes(x = Time, y = weight)) +
geom_line(aes(group = Chick))
ggplot(ChickWeight, aes(x = Time, y = weight, color = Diet)) +
geom_line(aes(group = Chick))
ggplot(ChickWeight, aes(x = Time, y = weight, color = Diet)) +
geom_line(aes(group = Chick), alpha = 0.3) +
geom_smooth(lwd = 2, se = FALSE)