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11-dataviz_with_ggplot.R
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11-dataviz_with_ggplot.R
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##########################################################################
# Jose Cajide - @jrcajide
# Master Data Science: ggplot2
##########################################################################
library(ggplot2)
# El lenguaje ggplot ------------------------------------------------------
# https://www.rstudio.com/wp-content/uploads/2016/12/ggplot2-cheatsheet-2.1-Spanish.pdf
ggplot()
(vector_y <- sample(10)) # Variable dependiente
(vector_x <- sample(10)) # Variable independiente
ggplot(mapping = aes(y = vector_y, x = vector_x))
ggplot(mapping = aes(y = vector_y, x = vector_x)) + geom_point()
# https://www.youtube.com/watch?v=jbkSRLYSojo
library(gapminder)
gapminder
?gapminder
summary(gapminder)
glimpse(gapminder)
dim(gapminder)
View(gapminder)
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp))
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point()
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point() + scale_x_log10()
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point() + scale_x_log10() + geom_point(aes(color=continent))
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point() + scale_x_log10() + geom_point(aes(color=continent)) + geom_smooth()
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp)) + geom_point() + scale_x_log10() + geom_point(aes(color=continent)) + geom_smooth(lwd=1, se=FALSE, method="lm", col="black")
ggplot(gapminder, aes(x = gdpPercap, y = lifeExp, color = continent)) + geom_point() + scale_x_log10() + geom_smooth(se=F, lwd=1)
# Todo junto:
p <- ggplot(gapminder, aes(gdpPercap, lifeExp))
p <- p + aes(color = continent) + geom_point()
p <- p + scale_x_log10() + aes(color = continent) + geom_point() + geom_smooth(se=F, lwd=1)
p
gappminder_plot <- ggplot(gapminder) +
aes(x = gdpPercap, y = lifeExp, colour = continent,
size = pop, frame = year) +
geom_point(alpha = 0.4) +
scale_x_log10()
gappminder_plot
#' Histograma: distribución de una variable cuantitativa
ggplot(gapminder, aes(x = lifeExp)) +
geom_histogram()
ggplot(gapminder, aes(x = lifeExp)) +
geom_histogram(binwidth = 1)
#' Añadimos una nueva variable: continent
ggplot(gapminder, aes(x = lifeExp, fill = continent)) +
geom_histogram()
#' Gráfico de frecuencias con líneas
ggplot(gapminder, aes(x = lifeExp, color = continent)) +
geom_freqpoly()
#' Gráfico de densidad
ggplot(gapminder, aes(x = lifeExp)) + geom_density()
#' Gráfico de densidad con más de una variable
ggplot(gapminder, aes(x = lifeExp, color = continent)) + geom_density()
#' alpha
ggplot(gapminder, aes(x = lifeExp, fill = continent)) +
geom_density(alpha = 0.2)
#' facets
ggplot(gapminder, aes(x = lifeExp)) + geom_density() + facet_wrap(~ continent)
ggplot(gapminder,
aes(x = lifeExp, fill = continent)) + geom_histogram() +
facet_grid(continent ~ .)
#' boxplot de una variable cuantitativa y una discreta
#' ojo: year no es un factor
ggplot(gapminder, aes(x = year, y = lifeExp)) + geom_boxplot()
#' podemos solucinarlo
ggplot(gapminder, aes(x = year, y = lifeExp)) + geom_boxplot(aes(group = year))
#' geom_violin()
ggplot(gapminder, aes(x = year, y = lifeExp)) +
geom_violin(aes(group = year)) +
geom_jitter(alpha = 1/4) +
geom_smooth(se = FALSE)
#' stripplots: para una variable cualitativa y una cuantitativa
ggplot(gapminder, aes(x = continent, y = lifeExp)) + geom_point()
#' geom_jitter
ggplot(gapminder, aes(x = continent, y = lifeExp)) + geom_jitter()
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4)
#' boxplot
ggplot(gapminder, aes(x = continent, y = lifeExp)) + geom_boxplot()
#' raw data AND boxplots
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
geom_boxplot(outlier.colour = "hotpink") +
geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4)
#' superposición de información estadística
ggplot(gapminder, aes(x = continent, y = lifeExp)) +
geom_jitter(position = position_jitter(width = 0.1), alpha = 1/4) +
stat_summary(fun.y = median, colour = "red", geom = "point", size = 5)
#' podemos mejorarlo reordenando el eje
ggplot(gapminder, aes(reorder(x = continent, lifeExp), y = lifeExp)) +
geom_jitter(position = position_jitter(width = 0.1), alpha = 1/4) +
stat_summary(fun.y = median, colour = "red", geom = "point", size = 5)
# themes ------------------------------------------------------------------
gappminder_plot + theme_grey()
gappminder_plot + theme_bw()
gappminder_plot + theme_minimal()
#Crear nuestros propios temas
gappminder_plot + theme(panel.border=element_rect(color = 'white', fill = NA),
panel.background = element_rect(color = 'white', fill = NA) )
gappminder_plot + theme_grey() + theme_minimal() + theme(
axis.title.x = element_text(size = 13),
text = element_text(family="Arial", colour="grey50", size=12),
panel.grid.major.y = element_blank(),
panel.grid.minor.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank()
)
# Con ggthemes: library("ggthemes")
gappminder_plot + theme_excel() + scale_fill_excel()
gappminder_plot + theme_economist() + scale_fill_economist()
gappminder_plot + theme_wsj() + scale_fill_wsj(palette = "black_green")
(gappminder_plot <- gappminder_plot + theme_tufte() + scale_fill_tableau())
# labels and legends ------------------------------------------------------
(gappminder_plot <- gappminder_plot + ylab("Esperanza de vida") + xlab("PIB per cápita"))
gappminder_plot <- gappminder_plot + labs(title="Esperanza de vida y PIB per cápita",
subtitle= "Fuente: Gapminder",
caption = "Visualización: R + ggplot2")
gappminder_plot + theme(legend.position="bottom")
# guardar un gráfico ------------------------------------------------------
ggsave("gapminder.pdf", gappminder_plot)
ggsave("gapminder.png", width = 6, height = 4)
# animations --------------------------------------------------------------
# brew install imagemagick
# Source: https://github.com/dgrtwo/gganimate
# install.packages("cowplot") # a gganimate dependency
# devtools::install_github("dgrtwo/gganimate")
library(gganimate)
theme_set(theme_minimal()) # pre-set the bw theme.
g <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, frame = year)) +
geom_point() +
geom_smooth(aes(group = year),
method = "lm",
show.legend = FALSE) +
facet_wrap(~continent, scales = "free") +
scale_x_log10() # convert to log scale
gganimate(g, interval=0.2)
g2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color=continent, frame = year)) +
geom_point(show.legend = F) +
scale_x_log10() + # convert to log scale
theme_minimal() +
labs(title = "ffff") +
theme_tq() +
theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank()) +
labs(title = "Life expectancy, GDP per capita and population by country from 1952 to",
subtitle = "Color by continent and size by population",
ylab="Life expectancy",
xlab="GDP per capita")
gganimate(g2, interval=0.4, ani.width=800, ani.height=600)
# ggpairs ------------------------------------------------------------------
library(GGally)
gapminder %>%
filter(country %in% c("Spain", "Brazil", "Angola")) %>%
mutate(country = as.character(country)) %>%
ggpairs(mapping = aes(colour = country))
# googleVis ---------------------------------------------------------------
library(googleVis)
gg <- gvisMotionChart(gapminder, idvar='country', timevar='year', colorvar ='continent', xvar = 'gdpPercap', yvar = 'lifeExp', sizevar='pop')
plot(gg) # Necesita Flash