Interactive graphics and animated graphics
Data
# A tibble: 6 x 8
species island culmen_length culmen_depth flipper_length body_mass sex year
<fct> <fct> <dbl> <dbl> <int> <int> <fct> <int>
1 Adelie Torge… 39.1 18.7 181 3750 male 2007
2 Adelie Torge… 39.5 17.4 186 3800 fema… 2007
3 Adelie Torge… 40.3 18 195 3250 fema… 2007
4 Adelie Torge… 36.7 19.3 193 3450 fema… 2007
5 Adelie Torge… 39.3 20.6 190 3650 male 2007
6 Adelie Torge… 38.9 17.8 181 3625 fema… 2007
species island culmen_length culmen_depth flipper_length
Adelie :146 Biscoe :163 Min. :32.10 Min. :13.10 Min. :172
Chinstrap: 68 Dream :123 1st Qu.:39.50 1st Qu.:15.60 1st Qu.:190
Gentoo :119 Torgersen: 47 Median :44.50 Median :17.30 Median :197
Mean :43.99 Mean :17.16 Mean :201
3rd Qu.:48.60 3rd Qu.:18.70 3rd Qu.:213
Max. :59.60 Max. :21.50 Max. :231
body_mass sex year
Min. :2700 female:165 Min. :2007
1st Qu.:3550 male :168 1st Qu.:2007
Median :4050 Median :2008
Mean :4207 Mean :2008
3rd Qu.:4775 3rd Qu.:2009
Max. :6300 Max. :2009 Static graphic
library(tidyverse)
g <- ggplot(penguins, aes(x=flipper_length, y=body_mass, col=species))+
geom_point() + theme_bw(base_size=16)
g
Interactive graphics
allows the use to interact with the graphical information presented on the display.
Cross filtering
Zoom by selecting an area of interest
Hover the line to get exact information.
Creating Interactive plots
Method 1: ggplotly
ggplotly() converts your static plots to an interactive web-based version.
Example 1
library(plotly)
ggplotly(g)Example 2
p <- ggplot(penguins, aes(x=body_mass, color=sex)) +
geom_freqpoly(stat = "density") +
facet_wrap(~species)
ggplotly(p)Example 3
library(gapminder)
head(gapminder, 25)
# A tibble: 25 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.
7 Afghanistan Asia 1982 39.9 12881816 978.
8 Afghanistan Asia 1987 40.8 13867957 852.
9 Afghanistan Asia 1992 41.7 16317921 649.
10 Afghanistan Asia 1997 41.8 22227415 635.
# … with 15 more rows
p1 <- ggplot(gapminder, aes(gdpPercap, pop, color = continent, frame = year)) +
geom_point(aes(size = pop, ids = country)) +
geom_smooth(method = "lm", se=FALSE) +
scale_x_log10() +
theme_bw()
Warning: Ignoring unknown aesthetics: ids
ggplotly(p1)
`geom_smooth()` using formula 'y ~ x'Example 4
p2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, colour = country, frame=year)) +
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)
ggplotly(p2)Method 2: plotly
powered by the JavaScript library plotly.js
Mapping variables: Instead of
aesuse~plotly finds a suitable geometric representation for us.
Users can also define geometries.
Functions to encode data
- color: map each level to a different colour
- colors: specify a range of colours
- stroke
- span
- symbol
- linetype
Example 1 - 1
plot_ly(penguins, x = ~species)plot_ly(penguins, x = ~species, y= ~island)plot_ly(
penguins,
x = ~species,
color = "green"
) # Doesn't give green barsExample 2 - 1
library(magrittr)
plot_ly(penguins, x = ~flipper_length, y = ~body_mass, color=~species)Example 2 - 2
There are a family of add_* functions.
library(magrittr)
plot_ly(penguins, x = ~flipper_length, y = ~body_mass) %>% add_markers(color=~species)Example 3
library(magrittr)
plot_ly(penguins, x = ~flipper_length, y = ~body_mass, z = ~culmen_depth, color=~species)library(magrittr)
plot_ly(penguins, x = ~flipper_length, y = ~body_mass, z = ~culmen_depth) %>% add_markers(color=~species)library(magrittr)
df <- data.frame(x=1:100, y=rnorm(100))
plot_ly(df, x = ~x, y = ~y) library(magrittr)
df <- data.frame(x=1:100, y=rnorm(100))
plot_ly(df, x = ~x, y = ~y) %>%
add_lines(color = I("black"))plot_ly(df, x = ~x, y = ~y) %>%
add_trace(y = ~y, mode = 'markers')plot_ly(df, x = ~x, y = ~y) %>%
add_trace(y = ~y, mode = 'line')plot_ly(df, x = ~x, y = ~y) %>%
add_trace(y = ~y, mode = 'lines+markers')Modify graphs: add_*
add_histogram
penguins %>% plot_ly() %>% add_histogram(x=~body_mass)add_bars (requires pre calculated counts)
penguins %>% dplyr::count(species) %>%
plot_ly() %>% add_bars(x=~species, y=~n)library(dplyr)
penguins %>%
plot_ly(x = ~species) %>%
add_histogram() %>%
group_by(species) %>%
summarise(n = n()) %>%
add_text(
text = ~scales::comma(n), y = ~n,
textposition = "top middle",
cliponaxis = FALSE
)A Grammar of Animated Graphics
gganimate is an extension of the ggplot2 package for creating animated ggplots.
library(ggplot2)
library(gganimate)
g <- 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)
g
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')