Gapminder gif with Rstudio

I decided to remake the Gapminder gif that I made the other day in Python, but in Rstudio this time. I’ll probably continue doing this for a while, as I try to figure out the advantages of using one program over the other. Here’s is a walk-through of what I did to recreate it:

#install these packages if you haven't already
install.packages(c("devtools", "dplyr", "ggplot2", "readr"))
devtools::install_github("dgrtwo/gganimate",force=TRUE)

library(devtools)
library(dplyr)
library(readr)
library(viridis)
library(ggplot2)
library(gganimate)
library(animation)

#Set up ImageMagick --for gifs
install.packages("installr",dependencies = TRUE)
library(installr)

#Configure your environment--change the location
Sys.setenv(PATH = paste("C:/Program Files/ImageMagick-7.0.7-Q16", Sys.getenv("PATH"), sep = ";")) #change the path to where you installed ImageMagick
#Again, change the location:
magickPath <- shortPathName("C:/Program Files/ImageMagick-7.0.7-Q16/magick.exe")
#ani.options(convert=magickPath)

If you need to download ImageMagick, go to this link

Load data and create plot

Once you’ve installed the appropriate packages and configured ImageMagick to work with Rstudio, you can load your data and plot as usual.

gapminder_data<-read.csv("https://python-graph-gallery.com/wp-content/uploads/gapminderData.csv", header=TRUE)

glimpse(gapminder_data) #print to make sure it loaded correctly

## Observations: 1,704
## Variables: 6
## $ country    Afghanistan, Afghanistan, Afghanistan, Afghanistan, ...
## $ year       1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992...
## $ pop        8425333, 9240934, 10267083, 11537966, 13079460, 1488...
## $ continent  Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia, Asia...
## $ lifeExp    28.801, 30.332, 31.997, 34.020, 36.088, 38.438, 39.8...
## $ gdpPercap  779.4453, 820.8530, 853.1007, 836.1971, 739.9811, 78...

# Helper function for string wrapping. 
# Default 20 character target width.
swr = function(string, nwrap=40) {
  paste(strwrap(string, width=nwrap), collapse="\n")
}
swr = Vectorize(swr)

gapminder_plot<-ggplot(gapminder_data) +
  aes(x = gdpPercap,
      y = lifeExp,
      colour = continent,
      size = pop, 
      frame=year) +
      scale_x_log10() +
  scale_size_continuous(guide =FALSE) + #suppresses the second legend (size=pop)
  geom_point() +
  scale_color_viridis(discrete=TRUE)+ #optional way to change colors of the plot
  theme_bw() +
  labs(title=swr("Relationship Between Life Expectancy and GDP per Capita"),
       x= "GDP Per Capita",
       y= "Life expectancy",
      caption="Data: Gapminder")
  theme(legend.position = "none",
        axis.title.x=element_text(size=.2),
        axis.title.y=element_text(size=.2),
        plot.caption = element_text(size=.1))</

#getOption("device") #try running this if your plot doesn't immediately show gapminder_plot

#if you want to save the plot:
ggsave("title.png", 
       plot = last_plot(), # or give ggplot object name as in myPlot,
       width = 5, height = 5, 
       units = "in", # other options c("in", "cm", "mm"), 
       dpi = 300)

Notice that I created the swr function to wrap the title text. If I don’t include that function, the title runs off the plot, like this:

Animate the plot

Now you can animate the plot using gganimate. Also, if you want to change any of the axis-titles or any other feature of the plot, I like to reference STHDA.

#remember to assign a working directory first:
#setwd() <--use this to change the working directory, if needed
gganimate(gapminder_plot,interval=.5,"gapminderplot.gif")

 

All in all, I’d say that creating the gif was equally easy in Python and R. Although I had more trouble initally configuring Python with ImageMagic–I might have found it easier in R simply because I used Python to figure this out the first time.  On the other hand, I like the way the Python gif looks much more than the gif that Rstudio rendered.

Looks like I’ll have to continue experimenting.

Data Visualization in Python

Sharing a visualization that I made with Python, in Jupyter Notebook.

First, import the following libraries:

# Set up libraries
%matplotlib notebook

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.set_style("white")
import pandas as pd
my_dpi=96

Then import data and make scatter plots for each year of life expectancy data, courtesy of Gapminder:

# Get Gapminder Life Expectancy data (csv file is hosted on the web)
url = 'https://python-graph-gallery.com/wp-content/uploads/gapminderData.csv'
data = pd.read_csv(url)
 
# Transform Continent into numerical values group1->1, group2->2...
data['continent']=pd.Categorical(data['continent'])
 
# For each year:
for i in data.year.unique():
 
# initialize a figure
fig = plt.figure(figsize=(680/my_dpi, 480/my_dpi), dpi=my_dpi)
 
# Change color for the x-axis values
tmp=data[ data.year == i ]
plt.scatter(tmp['lifeExp'], tmp['gdpPercap'] , s=tmp['pop']/200000 , c=tmp['continent'].cat.codes, cmap="Accent", alpha=0.6, edgecolors="white", linewidth=2)
 
# Add titles (main and on axis)
plt.yscale('log')
plt.xlabel("Life Expectancy")
plt.ylabel("GDP per Capita")
plt.title("Year: "+str(i) )
plt.ylim(0,100000)
plt.xlim(30, 90)
 
# Save the results
filename='Gapminder_step'+str(i)+'.png'
plt.savefig(filename, dpi=96)
plt.gca()

Next, download and install ImageMagick to make the following gif, by typing in your (Windows 10) command prompt:

magick convert.exeGapminder*.png animated_gapminder.gif

–If you have any issues configuring ImageMagick, like I did, you may find this link useful.

Note: You can make a gif using Matplotlib or moviepy, but I couldn’t quite figure it out. I will update once I do.