R or Python: A Data-Driven Answer

Hakan Mehmetcik

Last updated on Jun 30, 2022 5 min read

R or Python: A Data-Driven Answer

The question of which programming language to learn is a popular subject among seasoned and aspiring data scientists. R vs. Python is one choice to evaluate among many others, and we have a short and definitive answer: R is considerably better for statistics and visualization, while Python is far better for data extraction, wrangling, and programming. Here is an extract from a short threat on this:

The main distinction between the two languages is in their approach to data science. Both open source programming languages are supported by large communities, continuously extending their libraries and tools. But while R is mainly used for statistical analysis, Python provides a more general approach to data wrangling.

Python vs. R

Yet, it would be much more ideal if you can build skills on both of the programming languages. To this end, recently Rstudio is building up tools and environments in/by which data scientist use both of the languages. Here is a short threat on that!

What I am interested, here, is though to provide a data-driven answer for the question of which language is better! For that purpose, I will bring an exert from datacamp projects “Rise and Fall of Programming Languages” by David Robinson.

Project: Rise and Fall of Programming Languages

The aim of the project is to look into the popularity of programming languages through a data derived from Stack Overflow. A list of such question and answer site available at Stack Exchange Data Explorer. With such data in hand, we can ask numerous questions on the popularity of programing languages; even makes much more sense in the discussion between pythonisters and Risters!

Let’s get the required libraries and data first. The data file is available here.

# required libraries 
library(tidyverse, ggplot2)

# download data and load it to RStudio 
by_tag_year <- read_csv("https://gist.githubusercontent.com/dgrtwo/a30d99baa9b7bfc9f2440b355ddd1f75/raw/700ab5bb0b5f8f5a14377f5103dbe921d4238216/by_tag_year.csv", col_names = T)

🚀Each Stack Overflow question has a tag, which marks a question to describe its topic or technology. Through the tags, we can find out which programming languages are most popular!

# to inspect the data 
glimpse(by_tag_year)

## Rows: 40,518
## Columns: 4
## $ year       <dbl> 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008, 2008,…
## $ tag        <chr> ".htaccess", ".net", ".net-2.0", ".net-3.5", ".net-4.0", ".…
## $ number     <dbl> 54, 5910, 289, 319, 6, 3, 1, 42, 19, 4, 73, 149, 10, 1, 5, …
## $ year_total <dbl> 58390, 58390, 58390, 58390, 58390, 58390, 58390, 58390, 583…

This data has one observation for each pair of a tag and a year, showing the number of questions asked in that tag in that year and the total number of questions asked in that year. For instance, there were 54 questions asked about the .htaccess tag in 2008, out of a total of 58390 questions in that year.

Rather than just the counts, we’re probably interested in a percentage: the fraction of questions that year that have that tag. So let’s add that to the table.

# create new column that is franction of whole year numbers 
by_tag_year_fraction <- by_tag_year %>% 
  mutate(fraction = number/year_total) 

#let's list the top 10 programming languages 
head(by_tag_year_fraction, 10)

## # A tibble: 10 × 5
##     year tag           number year_total  fraction
##    <dbl> <chr>          <dbl>      <dbl>     <dbl>
##  1  2008 .htaccess         54      58390 0.000925 
##  2  2008 .net            5910      58390 0.101    
##  3  2008 .net-2.0         289      58390 0.00495  
##  4  2008 .net-3.5         319      58390 0.00546  
##  5  2008 .net-4.0           6      58390 0.000103 
##  6  2008 .net-assembly      3      58390 0.0000514
##  7  2008 .net-core          1      58390 0.0000171
##  8  2008 2d                42      58390 0.000719 
##  9  2008 32-bit            19      58390 0.000325 
## 10  2008 32bit-64bit        4      58390 0.0000685

Has R or Python been growing or shrinking?

Let’s look at whether the fraction of Stack Overflow questions that are about R and Python has been increasing or decreasing over time.

r_over_years <- by_tag_year_fraction %>% 
  filter(tag %in% c("r", "python"))

Visualizing change over time

Rather than looking at the results in a table, we often want to create a visualization. Change over time is usually visualized with a line plot.

ggplot(r_over_years, aes(x=year, y=fraction, color=tag))+ 
  geom_line()

What are the most asked-about tags?

We might like to know which tags have the most questions overall, not just within a particular year. Right now, we have several rows for every tag, but we’ll be combining them into one. That means we want group_by() and summarize().

Let’s look at tags that have the most questions in history.

sorted_tags <- by_tag_year %>% 
  group_by(tag) %>% 
  summarise(tag_total=sum(number)) %>% 
  arrange(desc(tag_total)) 
#let's see those top 20 tags
head(sorted_tags, 20)

## # A tibble: 20 × 2
##    tag           tag_total
##    <chr>             <dbl>
##  1 javascript      1632049
##  2 java            1425961
##  3 c#              1217450
##  4 php             1204291
##  5 android         1110261
##  6 python           970768
##  7 jquery           915159
##  8 html             755341
##  9 c++              574263
## 10 ios              566075
## 11 css              539818
## 12 mysql            522287
## 13 sql              445419
## 14 asp.net          334479
## 15 ruby-on-rails    293432
## 16 objective-c      284451
## 17 c                279915
## 18 .net             269578
## 19 arrays           266578
## 20 angularjs        252951

There are many other alternatives to find out more by using this data set on programming langugaes! Give a try! On the question of R vs Python, as RStudio’s Chief Data Scientist Hadley Wickham expressed in a recent interview with Dan Kopf: Use whatever makes you happy. However, the data suggest only that Python is much more popular compare to R in terms of Stack Overflow search numbers. These numbers are only suggestive rather than being definitive, yet it can be assumed that there are more people using Python for various purposes and they often end up with searching helps on the website.

Hakan Mehmetcik

Assoc. Professor

My research interests include social data science with a speciality of International relations