Matplotlib vs. ggplot: How to Use Both in R Shiny Apps | R-bloggers

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Matplotlib vs. ggplot: How to Use Both in R Shiny Apps
Posted on September 22, 2022 by Dario Radečić in R bloggers | 0 Comments
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Data Science has (unnecessarily) divided the world into two halves – R users and Python users. Irrelevant of the group you belong to, there’s one thing you have to admit – each language individually has libraries far superior to anything available in the alternative. For example, R Shiny is much easier for beginners than anything Python offers. But what about basic data visualization? That’s where this Matplotlib vs. ggplot article comes in.
Today we’ll see how R and Python compare in basic data visualization. We’ll compare their standard plotting libraries – Matplotlib and ggplot to see which one is easier to use and which looks better at the end. We’ll also show you how to include Matplotlib charts in R Shiny dashboards, as that’s been a common pain point for Python users. What’s even better, the chart will react to user input.
Want to use R and Python together? Here are 2 packages you get you started .
Table of contents:
Summary of Matplotlib vs. ggplot
Matplotlib vs. ggplot – Which is Better for Basic Plots?
There’s no denying that both Matplotlib and ggplot don’t look the best by default. There’s a lot you can change, of course, but we’ll get to that later. The aim of this section is to compare Matplotlib and ggplot in the realm of unstyled visualizations.
To keep things simple, we’ll only make a scatter plot of the well-known mtcars dataset, in which X-axis shows miles per gallon and Y-axis shows the corresponding horsepower.
There’s not a lot you have to do to produce this visualization in R ggplot:
library(ggplot2) ggplot(data = mtcars, aes(x = mpg, y = hp)) + geom_point()
Image 1 – Basic ggplot scatter plot
It’s a bit dull by default, but is Matplotlib better?
The mtcars dataset isn’t included in Python, so we have to download and parse the dataset from GitHub. After doing so, a simple call to ax.scatter() puts both variables on their respective axes:
import pandas as pd import matplotlib.pyplot as plt mtcars = pd.read_csv("https://gist.githubusercontent.com/ZeccaLehn/4e06d2575eb9589dbe8c365d61cb056c/raw/898a40b035f7c951579041aecbfb2149331fa9f6/mtcars.csv", index_col=[0]) fig, ax = plt.subplots(figsize=(13, 8)) ax.scatter(x=mtcars["mpg"], y=mtcars["hp"])
Image 2 – Basic matplotlib scatter plot
It would be unfair to call ggplot superior to Matplotlib, for the pure fact that the dataset comes included with R. Python requires an extra step.
From the visual point of view, things are highly subjective. Matplotlib figures have a lower resolution by default, so the whole thing looks blurry. Other than that, declaring a winner is near impossible.
Do you prefer Matplotlib or ggplot2 default stylings? Let us know in the comment section below.
Let’s add some styles to see which one is easier to customize.
Matplotlib vs. ggplot – Which is easier to customize?
To keep things simple, we’ll modify only a couple of things:
Change the point sizing by the qsec variable
Change the point color by the cyl variable
Add a custom color palette for three distinct color factors
Change the theme
Add title
In R ggplot, that boils down to adding a couple of lines of code:
ggplot(data = mtcars, aes(x = mpg, y = hp)) + geom_point(aes(size = qsec, color = factor(cyl))) + scale_color_manual(values = c("#3C6E71", "#70AE6E", "#BEEE62")) + theme_classic() + theme(legend.position = "none") + labs(title = "Miles per Gallon vs. Horse Power")
Image 3 – Customized ggplot scatter plot
The chart now actually looks usable, both for reporting and dashboarding purposes.
But how difficult it is to produce the same chart in Python? Let’s take a look. For starters, we’ll increase the DPI to get rid of the blurriness, and also remove the top and right lines around the figure.
Changing point size and color is a bit trickier to do in Matplotlib, but it’s just a matter of experience and preference. Also, Matplotlib doesn’t place labels on axes by default – consider this as a pro or a con. We’ll add them manually:
plt.rcParams["figure.dpi"] = 300 plt.rcParams["axes.spines.top"] = False plt.rcParams["axes.spines.right"] = False fig, ax = plt.subplots(figsize=(13, 8)) ax.scatter( x=mtcars["mpg"], y=mtcars["hp"], s=[s**1.8 for s in mtcars["qsec"].to_numpy()], c=["#3C6E71" if cyl == 4 else "#70AE6E" if cyl == 6 else "#BEEE62" for cyl in mtcars["cyl"].to_numpy()] ) ax.set_title("Miles per Gallon vs. Horse Power", size=18, loc="left") ax.set_xlabel("mpg", size=14) ax.set_ylabel("hp", size=14)
Image 4 – Customized matplotlib scatter plot
The figures look almost identical, so what’s the verdict? Is it better to use Python’s Matplotlib or R’s ggplot2?
Objectively speaking, Python’s Matplotlib requires more code to do the same thing when compared to R’s ggplot2. Further, Python’s code is harder to read, due to bracket notation for variable access and inline conditional statements.
So, does ggplot2 take the win here? Well, no. If you’re a Python user it will take you less time to create a chart in Matplotlib than it would to learn a whole new language/library. The same goes the other way.
Up next, we’ll see how easy it is to include this chart in an interactive dashboard.
How to Include ggplot Charts in R Shiny
Shiny is an R package for creating dashboards around your data. It’s built for R programming language, and hence integrates nicely with most of the other R packages – ggplot2 included.
We’ll now create a simple R Shiny dashboard that allows you to select columns for the X and Y axis and then updates the figure automatically. If you have more than 30 minutes of R Shiny experience, the code snippet below shouldn’t be difficult to read:
library(shiny) library(ggplot2) ui