Dynamic Visualization with Gapminder
Karol Orozco
11/3/2021
fert<-read_csv("https://raw.githubusercontent.com/kitadasmalley/FA2020_DataViz/main/data/gapminderFert.csv")
head(fert)## # A tibble: 6 x 6
## Country year life fert pop continent
## <chr> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 Afghanistan 1962 33.0 7.67 9.3 Asia
## 2 Afghanistan 1963 33.5 7.67 9.5 Asia
## 3 Afghanistan 1964 34.1 7.67 9.7 Asia
## 4 Afghanistan 1965 34.6 7.67 9.9 Asia
## 5 Afghanistan 1966 35.1 7.67 10.1 Asia
## 6 Afghanistan 1967 35.7 7.67 10.4 Asiap<- fert%>%
filter(year == 2015)%>%
ggplot(aes(fert, life, size = pop, color = continent)) +
labs(x="Fertility Rate", y = "Life expectancy at birth (years)",
caption = "(Based on data from Hans Rosling - gapminder.com)",
color = 'Continent',size = "Population (millions)") +
ylim(30,100) +
geom_point()
p
2) Add some interaction: plotly
The plotly package can be used to created interactive graphics. Although it is an entirely different paradigm of programming graphics, it does have a very nice function, ggplotly that can be added on to ggplot objects.
Use the tooltip to learn more about each point
p<- fert%>%
ggplot(aes(x=fert, y=life, size = pop, color = continent,frame = year)) +
labs(x="Fertility Rate", y = "Life expectancy at birth (years)",
caption = "(Based on data from Hans Rosling - gapminder.com)",
color = 'Continent',size = "Population (millions)") +
ylim(30,100) +
geom_point(aes(text=Country))
ggplotly(p)3) Now let’s animate! gganimate
#install.packages("gganimate")
#install.packages("gifski")
p1 <- ggplot(fert, aes(fert, life, size = pop, color = continent, frame = year)) +
labs(x="Fertility Rate", y = "Life expectancy at birth (years)",
caption = "(Based on data from Hans Rosling - gapminder.com)",
color = 'Continent',size = "Population (millions)") +
ylim(30,100) + # To prevent the y-axis from changing over time
geom_point() +
#scale_color_brewer(type = 'div', palette = 'Spectral') +
# gganimate code
ggtitle("Year: {frame_time}") +
transition_time(year) +
ease_aes("linear") +
enter_fade() + #to avoid flashing points
exit_fade()
animate(p1,fps = 4, width = 600, height = 400, renderer = gifski_renderer()) 
anim_save("output.gif")Notice that this animation is a little choppy, because there is one observation every year. You can use the tweener package to interpolate and make smoother transitions https://www.r-bloggers.com/2017/05/create-smooth-animations-in-r-with-the-tweenr-package/.
More Demos
1: Area Graph
# Load dataset from github
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/3_TwoNumOrdered.csv", header=T)
str(data)## 'data.frame': 1822 obs. of 2 variables:
## $ date : chr "2013-04-28" "2013-04-29" "2013-04-30" "2013-05-01" ...
## $ value: num 136 147 147 140 126 ...data$date <- as.Date(data$date)
# Usual area chart
p <- data %>%
ggplot( aes(x=date, y=value)) +
geom_area(fill="#69b3a2", alpha=0.5) +
geom_line(color="#69b3a2") +
ylab("bitcoin price ($)")
# Turn it interactive with ggplotly
p <- ggplotly(p)
pII. Example with heatmaply
Here is an example showing 8 general features like population or life expectancy for about 30 countries in 2015. Data come from the French National Institute of Demographic Studies https://www.ined.fr/en/everything_about_population/data/all-countries/?lst_continent=908&lst_pays=926
Heatmap (left to right): * Pop * Birth rate * Mortality rate * Life expectancy * Infant Mortality * Children per woman * Growth rate * Population aged 65
library(viridis)
#install.packages("heatmaply")
library(heatmaply)
# Load data
data <- read.table("https://raw.githubusercontent.com/holtzy/data_to_viz/master/Example_dataset/multivariate.csv", header=T, sep=";")
head(data)## Country Pop Birth.rate Mortality.rate Life.expectancy
## 1 Channel Islands 166 9.280 9.140 81.269
## 2 Denmark 5733 10.707 9.620 80.839
## 3 Estonia 1302 10.698 12.657 77.342
## 4 Finland 5556 10.672 9.865 81.576
## 5 Iceland 337 12.836 6.467 83.220
## 6 Ireland 4791 13.179 6.562 81.621
## Infant.mortality Children.per.woman Growth.rate Population.aged.65. Group
## 1 7.276 1.492 4.56 30 North
## 2 3.495 1.758 3.67 1132 North
## 3 2.843 1.657 -2.63 256 North
## 4 1.839 1.764 2.65 1212 North
## 5 1.752 1.893 7.83 50 North
## 6 2.385 1.998 8.84 682 North
## Continent
## 1 Europe
## 2 Europe
## 3 Europe
## 4 Europe
## 5 Europe
## 6 Europecolnames(data) <- gsub("\\.", " ", colnames(data)) # gsub, looking for anything that have a , and replace it with space
# Select a few country
data <- data %>%
filter(Country %in% c("France", "Sweden", "Italy", "Spain", "England", "Portugal", "Greece", "Peru", "Chile", "Brazil", "Argentina", "Bolivia", "Venezuela", "Australia", "New Zealand", "Fiji", "China", "India", "Thailand", "Afghanistan", "Bangladesh", "United States of America", "Canada", "Burundi", "Angola", "Kenya", "Togo")) %>%
arrange(Country) %>%
mutate(Country = factor(Country, Country))
# Matrix format
mat <- data
rownames(mat) <- mat[,1]
mat <- mat %>% dplyr::select(-Country, -Group, -Continent)
mat <- as.matrix(mat)
head(mat)## Pop Birth rate Mortality rate Life expectancy Infant mortality
## Afghanistan 34936 31.0 7.6 61.7 62.8
## Angola 27498 43.6 12.6 53.9 86.8
## Argentina 44692 16.8 7.5 77.0 11.5
## Australia 24967 12.9 6.7 83.1 3.3
## Bangladesh 166735 18.4 5.2 73.1 24.8
## Bolivia 11218 22.8 7.2 69.8 34.3
## Children per woman Growth rate Population aged 65
## Afghanistan 4.15 21.8 906
## Angola 5.75 31.0 658
## Argentina 2.26 9.3 507
## Australia 1.86 12.9 3945
## Bangladesh 2.06 11.4 8373
## Bolivia 2.81 14.7 767# Heatmap
p <- heatmaply(mat,
dendrogram = "none",
xlab = "", ylab = "",
main = "",
scale = "column",
margins = c(60,100,40,20),
grid_color = "white",
grid_width = 0.00001,
titleX = FALSE,
hide_colorbar = TRUE,
branches_lwd = 0.1,
label_names = c("Country", "Feature:", "Value"),
fontsize_row = 5, fontsize_col = 5,
labCol = colnames(mat),
labRow = rownames(mat),
heatmap_layers = theme(axis.line=element_blank())
)
p