Visualization with R workshop
Jeremy Buhler and Albina Gibadullina
October 8, 2021
Installing and loading packages
Install R packages
#install.packages("tidyverse")
#install.packages("gapminder")
#install.packages("ggridges")
#install.packages("ggrepel")
#install.packages("viridis")
#install.packages("RColorBrewer")
#install.packages("ggsci")
#install.packages("plotly")
#install.packages("gganimate")
#install.packages("gifski")Load R packages
library(tidyverse) # a collection of data manipulation and visualization packages
library(gapminder) # package with a gapminder dataset
library(ggridges) # extension of ggplot2 that makes ridgeline plots
library(ggrepel) # extension of ggplot2 that makes non-overlapping labels
library(viridis) # color palette package
library(RColorBrewer) # color palette package
library(ggsci) # color palette package
library(plotly) # package for interactive data visualizations
library(gganimate) # package for producing gifs, extension of ggplot2
library(gifski) # package for converting video frames to GIF animationsPreparing the data
Import data from the built-in "gapminder" dataset
countries <- gapminder # create a data frame
head(countries) #see the first six rows of the dataframe## # 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.Check the data structure
str(countries)## tibble [1,704 x 6] (S3: tbl_df/tbl/data.frame)
## $ 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 [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ... Data manipulation with dplyr
Filter function
Filter data of countries from 1997 and with Oceania continent
filter(countries, year==1997 & continent=="Oceania") # Use "&" for AND condition## # A tibble: 2 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Australia Oceania 1997 78.8 18565243 26998.
## 2 New Zealand Oceania 1997 77.6 3676187 21050.Filter observations from year 2002 or year 2007
filter(countries, year==2002 | year==2007) # Use "|" for OR condition## # A tibble: 284 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 2002 42.1 25268405 727.
## 2 Afghanistan Asia 2007 43.8 31889923 975.
## 3 Albania Europe 2002 75.7 3508512 4604.
## 4 Albania Europe 2007 76.4 3600523 5937.
## 5 Algeria Africa 2002 71.0 31287142 5288.
## 6 Algeria Africa 2007 72.3 33333216 6223.
## 7 Angola Africa 2002 41.0 10866106 2773.
## 8 Angola Africa 2007 42.7 12420476 4797.
## 9 Argentina Americas 2002 74.3 38331121 8798.
## 10 Argentina Americas 2007 75.3 40301927 12779.
## # ... with 274 more rowsSummarise function
Available sub-functions:
- Center: mean(), median()
- Spread: sd(), IQR(), mad()
- Range: min(), max(), quantile()
- Position: first(), last(), nth(),
- Count: n(), n_distinct()
- Logical: any(), all()
Find average value of the lifeExp variable
summarise(countries, mean_lifeExp=mean(lifeExp))## # A tibble: 1 x 1
## mean_lifeExp
## <dbl>
## 1 59.5Piping with multiple functions
What if you want to perform multiple functions in R? Use Pipe operator (%>%) in the dplyr package. It allows you to perform multiple functions without using nested parentheses.
This is how piping looks:
# DataFrame%>%
#function to execute first %>%
#function to execute second %>%
#function to execute thirdGroup_by function
Group_by function allows you to do operations by groups. E.g. find maximum life expectancy by continent
countries %>%
group_by(continent) %>%
summarise(max_lifeExp=max(lifeExp))## # A tibble: 5 x 2
## continent max_lifeExp
## <fct> <dbl>
## 1 Africa 76.4
## 2 Americas 80.7
## 3 Asia 82.6
## 4 Europe 81.8
## 5 Oceania 81.2 Data visualization with ggplot2
Barchart
Shows the distribution of a categorical variable
countries %>%
ggplot(aes(x=continent))+
geom_bar()
Add unique colour to each continent using fill function
countries %>%
ggplot(aes(x=continent, fill=continent))+
geom_bar()
Add title, subtitle, x and y labels, and caption
countries %>%
ggplot(aes(x=continent, fill=continent))+
geom_bar()+
labs(title="Distribution of observations by continents",
subtitle = "Measured as counts",
x="Continents",
y="Number of observations",
caption = "Source: Gapminder data")
Make a relative barchart by modifying y-values to measure the percentage of observations
countries %>%
ggplot(aes(x=continent, fill=continent))+
geom_bar(aes(y=stat(count)/sum(count)))+
labs(title="Data distribution by continents",
x="Continents",
y="Percent of observations")
Remove legend using guides command
countries %>%
ggplot(aes(x=continent, fill=continent))+
geom_bar(aes(y=stat(count)/sum(count)))+
labs(title="Data distribution by continents",
x="Continents",
y="Percent of observations")+
guides(fill = "none")
Histograms
Visualizes the distribution of one quantitative variable
countries %>%
ggplot(aes(x=lifeExp))+
geom_histogram()+
labs(title="Histogram of life expenctancy ",
x="Life expectancy",
y="Number of observations")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Use light-blue fill color with a black border
countries %>%
ggplot(aes(x=lifeExp))+
geom_histogram(color="black", fill="lightblue")+
labs(title="Histogram of life expenctancy ",
x="Life expectancy",
y="Number of observations")## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Change bin-width from 2 to 4
countries %>%
ggplot(aes(x=lifeExp))+
geom_histogram(binwidth=4, color="black", fill="lightblue")+
labs(title="Histogram of life expenctancy ",
x="Life expectancy",
y="Number of observations")
Make a relative histogram by adding y=..density.. (remember to change titles)
countries %>%
ggplot(aes(x=lifeExp,y=..density..))+
geom_histogram(binwidth=4, color="black", fill="lightblue")+
labs(title="Relative histogram of life expenctancy ",
x="Life expectancy",
y="Percent of observations")
Density plots
Visualizes the distribution of a quantitative variable
countries %>%
ggplot(aes(x=lifeExp))+
geom_density()+
labs(title="Density plot of life expenctancy",
x="Life expectancy")
Use pink fill color with a thicker border (size = 1)
countries %>%
ggplot(aes(x=lifeExp))+
geom_density(size=1, fill="pink")+
labs(title="Density plot of life expenctancy ",
x="Life expectancy")
Contrasting lifeExp distributions of continents using density plots
countries %>%
ggplot(aes(x=lifeExp, fill=continent))+
geom_density()+
labs(title="Density plot of life expenctancy by continent ",
x="Life expectancy")
Change alpha to make it more transparent, alpha measures color saturation (0 to 1)
countries %>%
ggplot(aes(x=lifeExp, fill=continent))+
geom_density(alpha=0.3)+
labs(title="Density plot of life expenctancy by continent ",
x="Life expectancy")
Or make a ridgeline plot usinggeom_density_ridges function (from ggridges package)
countries %>%
ggplot(aes(x=lifeExp, y=continent, fill=continent))+
geom_density_ridges(alpha=0.7)+
labs(title="Distribution of life expenctancy by continent ",
x="Life expectancy")## Picking joint bandwidth of 2.23
Boxplots
Boxplots are used to compare distributions of one quantitative variable across multiple categories (a visualization alternative to density plots)
Make a boxplot of life expectancy
countries %>%
ggplot(aes(y=lifeExp))+
geom_boxplot()+
labs(title="Boxplots of life expenctancy",
y="Life expectancy")
Comparing distribution of lifeExp variable by continent
countries %>%
ggplot(aes(x=continent, y=lifeExp))+
geom_boxplot()+
labs(title="Boxplots of life expenctancy by continent",
x="Continent",
y="Life expectancy")
Add unique colour to each continent
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_boxplot()+
labs(title="Boxplots of life expenctancy by continent",
x="Continent",
y="Life expectancy")
Reorder continents from smallest to highest life expectancy
countries %>%
ggplot(aes(x=reorder(continent,lifeExp), y=lifeExp, fill=continent))+
geom_boxplot()+
labs(title="Boxplots of life expenctancy by continent",
x="Continent",
y="Life expectancy")
Change the name of your legend title to "Continent"
countries %>%
ggplot(aes(x=reorder(continent,lifeExp), y=lifeExp, fill=continent))+
geom_boxplot()+
labs(title="Boxplots of life expenctancy by continent",
x="Continent",
y="Life expectancy",
fill="Continent")
Add average life expectancy values to each boxplot
countries %>%
ggplot(aes(x=reorder(continent,lifeExp), y=lifeExp, fill=continent))+
geom_boxplot()+
labs(title="Boxplots of life expenctancy by continent",
x="Continent",
y="Life expectancy",
fill="Continent")+
stat_summary(fun=mean, geom="point", shape=5, size=4)
Violin charts
Make a violin chart of life expectancy grouped by continent
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")
Add observations to violin plots using geom_jitter
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
geom_jitter(alpha=0.2, width=0.3)+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")
Small multiples
facet_wrap function
Usinf facet_wrap function to break down each violin chart by year
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
facet_wrap(~year)
facet_grid function
Stacking charts into columns with facet_grid
countries %>%
filter(year==1967 | year==2007) %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
facet_grid(.~year)
Stacking charts into rows with facet_grid
countries %>%
filter(year==1967 | year==2007) %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
facet_grid(year~.)
Scatterplots
Scatterplots show the relationship between two quantitative variables
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point()+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="GDP per capita",
y="Life expectancy")
Add color to represent different continents
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent))+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="GDP per capita",
y="Life expectancy")
Change the size of data points to measure the size of population
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent, size=pop))+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="GDP per capita",
y="Life expectancy")
Transforming x and y values
Transform the gdpPercap variable using a scale_x_log10()
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop))+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()
Specify ranges for x and y values using xlim and ylim functions
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop))+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()+
xlim(1000,10000)+
ylim(50,70)## Scale for 'x' is already present. Adding another scale for 'x', which will
## replace the existing scale.## Warning: Removed 1132 rows containing missing values (geom_point).
Best fit lines
Add a best fit line to the scatterplot
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop))+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()+
geom_smooth(method="lm", se=FALSE)## `geom_smooth()` using formula 'y ~ x'
Add a best fit line for each continent and reduce saturation for points
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop), alpha=0.3)+
labs(title="Scatterplot of GDP per capita and life expectancy",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()+
geom_smooth(aes(color=continent),method="lm", se=FALSE)## `geom_smooth()` using formula 'y ~ x'
Exercise #1a
Add facet_wrapto create a small-multiple for each continent
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop), alpha=0.3)+
labs(title="Best-fit lines between gdpPercap(log10) and lifeExp for each continent",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()+
geom_smooth(color="black",method="lm",se=FALSE)+
facet_wrap(~continent)## `geom_smooth()` using formula 'y ~ x'
Exercise #1b
Add facet_gridto create a small-multiple to compare continent between 1967 and 2007
- Use
facet_gridand make years rows and continents columns
countries %>%
filter(year==1967|year==2007) %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop), alpha=0.3)+
labs(title="Best-fit lines between gdpPercap(log10) and lifeExp for each continent",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()+
geom_smooth(color="black",method="lm",se=FALSE)+
facet_grid(year~continent)## `geom_smooth()` using formula 'y ~ x'
Labels
Use geom_text function to add labels your observations
countries %>%
filter(year==2007, continent=="Europe") %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(size=pop), color="blue", alpha=0.3)+
geom_text(aes(label=country), size = 3)+
labs(title="GDP per capita and life expectancy in Europe in 2007",
x="GDP per capita",
y="Life expectancy")
Use geom_text_repel function (from ggrepel package) to make sure labels do not overlap
countries %>%
filter(year==2007, continent=="Europe") %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(size=pop), color="blue", alpha=0.3)+
geom_text_repel(aes(label=country), size = 3)+
labs(title="GDP per capita and life expectancy in Europe in 2007",
x="GDP per capita",
y="Life expectancy")
Themes
theme_gray()- the signature ggplot2 theme with a grey background and white gridlines, designed to put the data forward yet make comparisons easy.theme_bw()- the classic dark-on-light ggplot2 theme. May work better for presentations displayed with a projector.theme_linedraw()- a theme with only black lines of various widths on white backgrounds, reminiscent of a line drawing. Serves a purpose similar to theme_bw. Note that this theme has some very thin lines (<< 1 pt) which some journals may refuse.theme_light()- a theme similar to theme_linedraw but with light grey lines and axes, to direct more attention towards the data.theme_dark()- the dark cousin of theme_light, with similar line sizes but a dark background. Useful to make thin coloured lines pop out.theme_minimal()- a minimalistic theme with no background annotations.theme_classic()- a classic-looking theme, with x and y axis lines and no gridlines.theme()- create your own theme by modifying theme components, see https://ggplot2.tidyverse.org/reference/theme.html
Exercise #2
Play around with a few themes - contrast and compare, let us know your favorite ones in the comments
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop), alpha=0.3)+
geom_smooth(aes(color=continent), method="lm",se=FALSE)+
scale_x_log10()+
labs(title="Best-fit lines between gdpPercap(log10) and lifeExp for each continent",
x="Log 10 of GDP per capita",
y="Life expectancy")+
theme_light()## `geom_smooth()` using formula 'y ~ x'
Modify graphs withtheme()
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop), alpha=0.3)+
geom_smooth(aes(color=continent), method="lm",se=FALSE)+
scale_x_log10()+
labs(title="Best-fit lines between gdpPercap(log10) and lifeExp for each continent",
x="Log 10 of GDP per capita",
y="Life expectancy")+
theme_light()+
theme( # change font size, font, and color for x-axis title
axis.title.x=element_text(size=20, family="serif", face="italic", color="red"))## `geom_smooth()` using formula 'y ~ x'
Colors
Set ggplot color manually
Use scale_fill_manual() for box plot, bar plot, violin plot, dot plot, etc
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
scale_fill_manual(values = c("#FFDB6D","#D16103","#52854C","#4E84C4","#293352"))
Use scale_color_manual() for lines and points
countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent,size=pop), alpha=0.3)+
geom_smooth(aes(color=continent), method="lm",se=FALSE)+
scale_x_log10()+
labs(title="Best-fit lines between gdpPercap(log10) and lifeExp for each continent",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_color_manual(values = c("#FFDB6D","#D16103","#52854C","#4E84C4","#293352"))## `geom_smooth()` using formula 'y ~ x'
Viridis color palettes
The viridis R package provides color palettes to make beautiful plots that are: printer-friendly, perceptually uniform and easy to read by those with colorblindness.
- Use
scale_fill_viridis()for box plot, bar plot, violin plot, dot plot - Use
scale_color_viridis()for lines and points
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
scale_fill_viridis(discrete = TRUE)
Rcolorbrewer palettes
RColorBrewer is an R package that contains a ready-to-use color palettes for creating beautiful graphics:
- Sequential palettes (first list of colors), which are suited to ordered data that progress from low to high (gradient)
- Qualitative palettes (second list of colors), which are best suited to represent nominal or categorical data
- Diverging palettes (third list of colors), which put equal emphasis on mid-range critical values and extremes at both ends of the data range
Display available palettes
display.brewer.all()
Display only colorblind-friendly brewer palettes
display.brewer.all(colorblindFriendly = TRUE)
Which type of color palette is appropriate to visualize continents?
- Use
scale_fill_brewer()for box plot, bar plot, violin plot, dot plot - Use
scale_color_brewer()for lines and points
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
scale_fill_brewer(palette = "Set2")
Grey color scales
- Use
scale_fill_grey()for box plot, bar plot, violin plot, dot plot - Use
scale_color_grey()for lines and points
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
scale_fill_grey(start = 1, end = 0.2) # ranges from 0 (black) to 1 (white)
Scientific journal color palettes
The R package ggsci contains a collection of high-quality color palettes inspired by colors used in scientific journals, data visualization libraries, and more.
- scale_color_npg() and scale_fill_npg(): Nature Publishing Group color palettes
- scale_color_aaas() and scale_fill_aaas(): American Association for the Advancement of Science color palettes
- scale_color_lancet() and scale_fill_lancet(): Lancet journal color palettes
- scale_color_jco() and scale_fill_jco(): Journal of Clinical Oncology color palettes
- scale_color_tron() and scale_fill_tron(): This palette is inspired by the colors used in Tron Legacy. It is suitable for displaying data when using a dark theme.
Exercise #3
Play around with different palettes - contrast and compare, let us know your favorite ones in the comments
Remember to use fill functions for box plot, bar plot, violin plot, dot plot
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
scale_fill_jco()
Annotations
Use geom_hline to add life expectancy in Canada as a reference line to your graph (replace for geom_vline for vertical lines):
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
geom_hline(color="red", yintercept=82.51)
Use annotate to add text and point to the line:
countries %>%
ggplot(aes(x=continent, y=lifeExp, fill=continent))+
geom_violin()+
labs(title="Distribution of life expenctancy by continent",
x="Continent",
y="Life expectancy")+
geom_hline(color="red", yintercept=82.51)+
annotate("text", x = "Americas", y = 85.51, label = "Canada 2020", color="red")+
annotate("point",x = "Americas", y = 82.51, color="black")
Piping in ggplot2
Visualize changes in annual average life expectancy by continent with points representing the size of populations
lifeExp_continent <- countries %>%
group_by(continent, year) %>%
summarise(lifeExp=mean(lifeExp), pop=sum(pop)) %>%
ggplot(aes(x=year, y=lifeExp, color=continent))+
geom_point(aes(size=pop))+
labs(title="Annual average life expectancy by continent",
y="Life expectancy")+
geom_line()## `summarise()` has grouped output by 'continent'. You can override using the `.groups` argument.lifeExp_continent
Save produced graphs
Use ggsave function to save the plot in your working directory
ggsave("lifeExp_continent.png", plot = lifeExp_continent, device="png", width = 8, height = 5)Interactive visualizations
ggplotly
Make an interactive plotly scatterplot of life expectancy and GDP per capita in 2007
plot1 <- countries %>%
filter(year==2007) %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(size=pop, color=continent, label=country))+
labs(title="Life expectancy and GDP per capita, 2007",
x="Log 10 of GDP per capita",
y="Life expectancy")+
scale_x_log10()+
geom_smooth(aes(color=continent), method="lm", se=FALSE)## Warning: Ignoring unknown aesthetics: labelggplotly(plot1)## `geom_smooth()` using formula 'y ~ x'Add year as a frame so that we can see changes over time
plot2 <- countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp, frame=year))+
geom_point(aes(color=continent, size=pop,label=country))+
geom_smooth(aes(color=continent), method="lm",se=FALSE)+
scale_x_log10()+
labs(title="Life exp. and GDP per capita, 1952-2007",
x="Log 10 of GDP per capita",
y="Life expectancy")## Warning: Ignoring unknown aesthetics: labelggplotly(plot2)## `geom_smooth()` using formula 'y ~ x'Note: You can make any static ggplot2 graph interactive with plotly
gganimate
Create an animation using transition_time function + show historical traces by adding shadow_wake function
gif_countries <- countries %>%
ggplot(aes(x=gdpPercap, y=lifeExp))+
geom_point(aes(color=continent, size=pop,label=country))+
scale_x_log10()+
transition_time(year)+
shadow_wake(wake_length = 0.1)+
labs(title="Life expectancy and GDP per capita in {frame_time}",
x="Log 10 of GDP per capita",
y="Life expectancy")## Warning: Ignoring unknown aesthetics: labelgif_countries
Save animation using anim_save function
anim_save("countries.gif", animation = gif_countries, path = NULL)