GGPLOTLY - gráphicos interactivos y dinámicos
library(dplyr) #
library(ggplot2) #
library(gganimate) #
library(gapminder) #
library(ggthemes) #
library(plotly) #
head(gapminder)
## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
ggplot(
gapminder,
aes(x = gdpPercap, y=lifeExp, size = pop, colour = country)
) +
geom_point(show.legend = FALSE, alpha = 0.7) +
scale_color_viridis_d() +
scale_size(range = c(2, 12)) +
scale_x_log10() +
labs(x = "GDP per capita", y = "Life expectancy") + transition_time(year) +
labs(title = "Year: {frame_time}")
ggplotly(ggplot(
gapminder,
aes(x = gdpPercap, y=lifeExp, size = pop, colour = country)
) +
geom_point(show.legend = FALSE, alpha = 0.7) +
scale_color_viridis_d() +
scale_size(range = c(2, 12)) +
scale_x_log10() +
labs(x = "GDP per capita", y = "Life expectancy") )
ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country)) +
geom_point(alpha = 0.7, show.legend = FALSE) +
scale_colour_manual(values = country_colors) +
scale_size(range = c(2, 12)) +
scale_x_log10() +
facet_wrap(~continent) +
# Here comes the gganimate specific bits
labs(title = 'Year: {frame_time}', x = 'GDP per capita', y = 'life expectancy') +
transition_time(year) +
ease_aes('linear')
