R vs Python for Data Analysis — An Objective Comparison


r vs python for data science

R vs Python — Opinions vs Facts

There are dozens articles on the market that evaluate R vs. Python from a subjective, opinion-based perspective. Both Python and R are nice choices for information evaluation, or any work within the data science discipline.

But in case your purpose is to determine which language is true for you, studying the opinion of another person is probably not useful. One individual’s “easy” is one other individual’s “hard,” and vice versa.

In this text, we will do one thing totally different. We’ll take an goal take a look at how each languages deal with on a regular basis data science duties so to take a look at them side-by-side, and see which one appears higher for you.

Keep in thoughts, you need not really perceive all of this code to make a judgment right here! We’ll offer you R vs Python code snippets for every process — merely scan by the code and contemplate which one appears extra “readable” to you. Read the reasons, and see if one language holds extra enchantment than the opposite.

The excellent news? There’s no incorrect reply right here! If you are seeking to be taught some programming expertise for working with information, taking a Python course or an R course would each be nice choices.

Why You Should Trust Us

Since we’ll be presenting code side-by-side on this article, you do not actually need to “trust” something — you possibly can merely take a look at the code and make your personal judgments.

For the document, although, we do not take a aspect within the R vs Python debate! Both languages are nice for working with information, and each have their strengths and weaknesses. We train each, so we do not have an curiosity in steering you in the direction of one over the opposite.

R vs Python: Importing a CSV

Let’s soar proper into the real-world comparability, beginning with how R and Python deal with importing CSVs!

(As we’re evaluating the code, we’ll even be analyzing a knowledge set of NBA gamers and their efficiency within the 2013-2014 season. You can obtain the file right here if you would like to strive it for your self.)


nba <- read_csv("nba_2013.csv")


import pandas
nba = pandas.read_csv("nba_2013.csv")

In each languages, this code will load the CSV file nba_2013.csv, which accommodates information on NBA gamers from the 2013-2014 season, into the variable nba.

The solely actual distinction is that in Python, we have to import the pandas library to get entry to Dataframes. In R, whereas we may import the info utilizing the bottom R operate learn.csv(), utilizing the readr library operate read_csv() has the benefit of larger velocity and constant interpretation of knowledge varieties.

Dataframes can be found in each R and Python — they’re two-dimensional arrays (matrices) the place every column might be of a special datatype. You can consider them as being just like the programming model of a knowledge desk or a spreadsheet. At the tip of this step, the CSV file has been loaded by each languages right into a dataframe.

Finding the variety of rows



Although the syntax and formatting differ barely, we are able to see that in each languages, we are able to get the identical data very simply.

The output above tells us that this information set has 481 rows and 31 columns.

Inspecting the primary row of the info


head(nba, 1)
participant pos age bref_team_id
1 Quincy Acy SF 23 TOT[output truncated]


participant pos age bref_team_id
0 Quincy Acy SF 23 TOT[output truncated]

Again, we are able to see that though there are some slight syntax variations, the 2 languages are very comparable.

It’s price noting that Python is extra object-oriented right here — head is a technique on the dataframe object, whereas R has a separate head operate.

This is a typical theme we’ll see as we begin to do evaluation with these languages. Python is extra object-oriented, and R is extra purposeful.

Don’t fear in the event you do not perceive the distinction — these are merely two totally different approaches to programming, and within the context of working with information, each approaches can work very effectively!

R vs Python: Finding Averages for Each Statistic

Now let’s discover the typical values for every statistic in our information set!

The columns, as we are able to see, have names like fg (discipline targets made), and ast (assists). These are the season-long statistics and our information set tracks them for every row (every row represents a person participant).

If you’d like a fuller clarification of all of the stats, look right here. Let’s check out how R and Python deal with abstract statistics by discovering the typical values for every stat within the information:


nba %>%
  select_if(is.numeric) %>%
  map_dbl(imply, na.rm = TRUE)
participant NA
pos NA
age 26.5093555093555
bref_team_id NA
[output truncated]


age 26.509356
g 53.253638
gs 25.571726
[output truncated]

Now we are able to see some main variations within the approaches taken by R vs Python.

In each, we’re making use of a operate throughout the dataframe columns. In Python, utilizing the imply technique on a dataframe will discover the imply of every column by default.

In R, it is just a little extra difficult. We can use capabilities from two standard packages to pick out the columns we need to common and apply the imply operate to them. The %>% operator, known as “the pipe”, passes output of 1 operate as enter to the subsequent.

Taking the imply of string values (in different phrases, textual content information that can not be averaged) will simply lead to NA — not out there. We can take the imply of solely the numeric columns by utilizing select_if.

However, we do must ignore NA values once we take the imply (requiring us to go na.rm=TRUE into the imply operate). If we don’t, we find yourself with NA for the imply of columns like x3p.. This column is three level proportion. Some gamers didn’t take three level photographs, so their proportion is lacking. If we strive the imply operate in R, we get NA as a response, except we specify na.rm=TRUE, which ignores NA values when taking the imply.

In distinction, the .imply() technique in Python already ignores these values by default.

Making Pairwise Scatterplots

One frequent approach to discover a knowledge set is to see how totally different columns correlate to others. Let’s evaluate the ast, fg, and trb columns.


nba %>%
choose(ast, fg, trb) %>%
r vs python scatterplot


import seaborn as sns
import matplotlib.pyplot as plt
sns.pairplot(nba[["ast", "fg", "trb"]])
r vs python scatterplot 2

In the tip, each languages produce very comparable plots. But within the code, we are able to see how the R data science ecosystem has many smaller packages (GGally is a helper package deal for ggplot2, the most-used R plotting package deal), and extra visualization packages normally. 

In Python, matplotlib is the first plotting package deal, and seaborn is a extensively used layer over matplotlib.

With visualization in Python, there may be typically one most important approach to do one thing, whereas in R, there are numerous packages supporting totally different strategies of doing issues (there are at the least a half-dozen packages to make pair plots, for occasion).

Again, neither method is “better”, however R could supply extra flexibility simply when it comes to with the ability to decide and select the package deal that works finest for you.

Making Clusters of the Players

Another good approach to discover this type of information is to generate cluster plots. These will present which gamers are most comparable.

(For now, we’re simply going to make the clusters; we’ll plot them visually within the subsequent step.)


isGoodCol <- operate(col){
  sum(is.na(col)) == 0 && is.numeric(col)
goodCols <- sapply(nba, isGoodCol)
clusters <- kmeans(nba[,goodCols], facilities=5)
labels <- clusters$cluster


from sklearn.cluster import KMeans
kmeans_model = KMeans(n_clusters=5, random_state=1)
good_columns = nba._get_numeric_data().dropna(axis=1)
labels = kmeans_model.labels_

In order to cluster correctly, we have to take away any non-numeric columns and columns with lacking values (NA, Nan, and many others).

In R, we do that by making use of a operate throughout every column, and eradicating the column if it has any lacking values or isn’t numeric. We then use the cluster package deal to carry out k-means and discover 5 clusters in our information. We set a random seed utilizing set.seed to have the ability to reproduce our outcomes.

In Python, we use the primary Python machine learning package deal, scikit-learn, to suit a k-means clustering mannequin and get our cluster labels. We carry out very comparable strategies to organize the info that we utilized in R, besides we use the get_numeric_data and dropna strategies to take away non-numeric columns and columns with lacking values.

Plotting Players by Cluster

We can now plot out the gamers by cluster to find patterns. One approach to do that is to first use PCA to make our information  two-dimensional, then plot it, and shade every level in accordance with cluster affiliation.


nba2d <- prcomp(nba[,goodCols], middle=TRUE)
twoColumns <- nba2d$x[,1:2]
clusplot(twoColumns, labels)
r vs python cluster


from sklearn.decomposition import PCA
pca_2 = PCA(2)
plot_columns = pca_2.fit_transform(good_columns)
plt.scatter(x=plot_columns[:,0], y=plot_columns[:,1], c=labels)

Above, we made a scatter plot of our information, and shaded or modified the icon of every information level in accordance with its cluster.

In R, we used the clusplot operate, which is a part of the cluster library. We carried out PCA by way of the pccomp operate that’s constructed into R.

With Python, we used the PCA class within the scikit-learn library. We used matplotlib to create the plot.

Once once more, we are able to see that whereas each languages take barely totally different approaches, the ultimate outcome and the quantity of code required to get it’s fairly comparable.

Splitting Data into Training and Testing Sets

If we need to use R or Python for supervised machine learning, it’s a good suggestion to separate the info into coaching and testing units so we don’t overfit.

Let’s evaluate how every language handles this frequent machine learning process:


trainRowCount <- flooring(0.8 * nrow(nba))
prepareIndex <- pattern(1:nrow(nba), trainRowCount)
prepare <- nba[trainIndex,]
take a look at <- nba[-trainIndex,]


prepare = nba.pattern(frac=0.8, random_state=1)
take a look at = nba.loc[~nba.index.isin(train.index)]

Comparing Python vs R, we are able to see that R has extra information evaluation functionality built-in, like flooring, pattern, and set.seed, whereas these in Python these are known as by way of packages (math.flooring, random.pattern, random.seed).

In Python, a latest model of pandas got here with a pattern technique that returns a sure proportion of rows randomly sampled from a supply dataframe — this makes the code far more concise.

In R, there are packages to make sampling easier, however they aren’t far more concise than utilizing the built-in pattern operate. In each instances, we set a random seed to make the outcomes reproducible.

R vs Python: Univariate Linear Regression

Continuing with frequent machine learning duties, let’s say we need to predict variety of assists per participant from discipline targets made per participant:


match <- lm(ast ~ fg, information=prepare)
predictions <- predict(match, take a look at)


from sklearn.linear_model import LinearRegression
lr = LinearRegression()
lr.match(prepare[["fg"]], prepare["ast"])
predictions = lr.predict(take a look at[["fg"]])

Python was a bit extra concise in our earlier step, however now R is extra concise right here!

Python’s Scikit-learn package deal has a linear regression mannequin that we are able to match and generate predictions from. 

R depends on the built-in lm and predict capabilities. predict will behave in a different way relying on the form of fitted mannequin that’s handed into it — it may be used with a wide range of fitted fashions.

Calculating Summary Statistics for the Model

Another frequent machine learning process:


lm(method = ast ~ fg, information = prepare)
Residuals:Min 1Q Median 3Q Max
-228.26 -35.38 -11.45 11.99 559.61
[output truncated]


import statsmodels.method.api as sm
mannequin = sm.ols(method='ast ~ fga', information=prepare)
fitted = mannequin.match()
Dep. Variable: ast
R-squared: 0.568
Model: OLS
Adj. R-squared: 0.567
[output truncated]

As we are able to see above, we’ll must do a bit extra in Python than in R if we need to get abstract statistics concerning the match, like r-squared worth.

With R, we are able to use the built-in abstract operate to get data on the mannequin instantly. With Python, we have to use the statsmodels package deal, which allows many statistical strategies for use in Python.

We get comparable outcomes, though typically it’s a bit more durable to do statistical evaluation in Python, and a few statistical strategies that exist in R don’t exist in Python.

Fit a random forest mannequin

Our linear regression labored effectively within the single variable case, however for example we suspect there could also be nonlinearities within the information. Thus, we need to match a random forest mannequin.

Here’s how we’d do this in every language:


predictorColumns <- c("age", "mp", "fg", "trb", "stl", "blk")
rf <- randomForest(prepare[predictorColumns], prepare$ast, ntree=100)
predictions <- predict(rf, take a look at[predictorColumns])


from sklearn.ensemble import RandomForestRegressor
predictor_columns = ["age", "mp", "fg", "trb", "stl", "blk"]
rf = RandomForestRegressor(n_estimators=100, min_samples_leaf=3)
rf.match(prepare[predictor_columns], prepare["ast"])
predictions = rf.predict(take a look at[predictor_columns])

The most important distinction right here is that we wanted to make use of the randomForest library in R to make use of the algorithm, whereas that is already in-built to scikit-learn in Python.

Scikit-learn has a unified interface for working with many alternative machine learning algorithms in Python. There’s normally just one most important implementation of every algorithm.

With R, there are numerous smaller packages containing particular person algorithms, typically with inconsistent methods to entry them. This leads to a larger range of algorithms (many have a number of implementations, and a few are contemporary out of analysis labs), however with a little bit of a usability hit.

In different phrases, Python could also be simpler to make use of right here, however R could also be extra versatile.

Calculating Error

Now that we’ve match two fashions, let’s calculate error in R and Python. We’ll use MSE.


imply((take a look at["ast"] - predictions)^2)


from sklearn.metrics import mean_squared_error
mean_squared_error(take a look at["ast"], predictions)

In Python, the scikit-learn library has a wide range of error metrics that we are able to use. In R, there are doubtless some smaller libraries that calculate MSE, however doing it manually is fairly simple in both language.

You could discover there’s a small distinction within the outcomes right here — that is virtually definitely attributable to parameter tuning, and isn’t an enormous deal.

(If you run this code by yourself, you may additionally get barely totally different numbers, relying on the variations of every package deal and language you are utilizing).

R vs Python: Web Scraping, Part 1

We have information on NBA gamers from 2013-2014, however let’s web-scrape some extra information to complement it.

We’ll simply take a look at one field rating from the NBA Finals right here to save lots of time.


url <- "http://www.basketball-reference.com/boxscores/201506140GSW.html"
information <- readLines(url)


import requests
url = "http://www.basketball-reference.com/boxscores/201506140GSW.html"
information = requests.get(url).content material

In Python, the requests package deal makes downloading net pages simple, with a constant API for all request varieties. 

In R, RCurl gives a equally easy approach to make requests.

Both obtain the webpage to a personality datatype.

Note: this step is pointless for the subsequent step in R, however is proven for comparability’s sake.

Web Scraping, Part 2

Now that we have now the online web page dowloaded with each Python and R, we’ll must parse it to extract scores for gamers.


web page <- read_html(url)
desk <- html_nodes(web page, ".stats_table")[3]
rows <- html_nodes(desk, "tr")
cells <- html_nodes(rows, "td a")
groups <- html_text(cells)
extractRow <- operate(rows, i){
  if(i == 1){
  row <- rows[i]
  tag <- "td"
  if(i == 2){
    tag <- "th"
    objects <- html_nodes(row, tag)
scrapeData <- operate(workforce){
  workforceData <- html_nodes(web page, paste("#",workforce,"_basic", sep=""))
  rows <- html_nodes(workforceData, "tr")
  lapply(seq_along(rows), extractRow, rows=rows)
information <- lapply(groups, scrapeData)


from bs4 import BeautifulSoup
import re
soup = BeautifulSoup(information, 'html.parser')
box_scores = []
for tag in soup.find_all(id=re.compile("[A-Z]{3,}_basic")):
    rows = []
    for i, row in enumerate(tag.find_all("tr")):
        if i == 0:
    elif i == 1:
        tag = "th"
        tag = "td"
    row_data = [item.get_text() for item in row.find_all(tag)]

In each languages, this code will create an inventory containing two lists

  1. The field rating for CLE
  2. The field rating for GSW

Both lists include the headers, together with every participant and their in-game stats. We received’t flip this into extra coaching information now, however it may simply be reworked right into a format that could possibly be added to our nba dataframe.

The R code is extra advanced than the Python code, as a result of there isn’t a handy approach to make use of common expressions to pick out objects, so we have now to do extra parsing to get the workforce names from the HTML.

R additionally discourages utilizing for loops in favor of making use of capabilities alongside vectors. We use lapply to do that, however since we have to deal with every row in a different way relying on whether or not it’s a header or not, we go the index of the merchandise we would like, and the whole rows listing into the operate.

In R, we use rvest, a widely-used R net scraping package deal to extract the info we want. Note that we are able to go a url straight into rvest, so the earlier step wasn’t really wanted in R.

In Python, we use BeautifulSoup, probably the most generally used net scraping package deal. It allows us to loop by the tags and assemble an inventory of lists in an easy approach.

R vs Python: Which is Better? It Depends!

We’ve now taken a take a look at the way to analyze a knowledge set with each R and Python. And as we are able to see, though they do issues just a little in a different way, each languages are likely to require about the identical quantity of code to attain the identical output.

Of course, there are numerous duties we didn’t dive into, comparable to persisting the outcomes of our evaluation, sharing the outcomes with others, testing and making issues production-ready, and making extra visualizations.

There is much more to debate on this matter, however simply primarily based on what we’ve completed above, we are able to draw some significant conclusions about how the 2 differ.

(As far as which is definitely higher, that is a matter of non-public choice.)

R is extra purposeful, Python is extra object-oriented.

As we noticed from capabilities like lm, predict, and others, R lets capabilities do a lot of the work. Contrast this to the LinearRegression class in Python, and the pattern technique on Dataframes.

In phrases of knowledge evaluation and data science, both method works.

R has extra information evaluation performance built-in, Python depends on packages.

When we checked out abstract statistics, we may use the abstract built-in operate in R, however needed to import the statsmodels package deal in Python. The Dataframe is a built-in assemble in R, however should be imported by way of the pandas package deal in Python.

Python has “main” packages for information evaluation duties, R has a bigger ecosystem of small packages.

With Python, we are able to do linear regression, random forests, and extra with the scikit-learn package deal. It gives a constant API, and is well-maintained.

In R, we have now a larger range of packages, but in addition larger fragmentation and fewer consistency (linear regression is a built-in, lm, randomForest is a separate package deal, and many others).

R has extra statistical help normally.

R was constructed as a statistical language, and it reveals. statsmodels in Python and different packages present first rate protection for statistical strategies, however the R ecosystem is much bigger.

It’s normally extra simple to do non-statistical duties in Python.

With well-maintained libraries like BeautifulSoup and requests, net scraping in Python is extra simple than in R.

This additionally applies to different duties that we didn’t look into intently, like saving to databases, deploying net servers, or working advanced workflows.

Since Python is used throughout a wide range of industries and programming disciplines, it might be the higher alternative in the event you’re combining your information work with other forms of programming duties. 

On the opposite hand, in the event you’re targeted on information and statistics, R gives some benefits attributable to its having been developed with a give attention to statistics.

There are many parallels between the info evaluation workflow in each.

There are clear factors of similarity between each R and Python (pandas Dataframes had been impressed by R dataframes, the rvest package deal was impressed by BeautifulSoup), and each ecosystems proceed to develop stronger.

In reality, it’s outstanding how comparable the syntax and approaches are for many frequent duties in each languages.

R vs Python: Which Should You Learn?

At Dataquest, we’ve been finest recognized for our Python programs, however we have now completely reworked and relaunched our Data Analyst in R path as a result of we really feel R is one other glorious language for data science.

We see each languages as complementary, and every language has its strengths and weaknesses. Either language could possibly be used as your sole information evaluation instrument, as this walkthrough proves. Both languages have numerous similarities in syntax and method, and you’ll’t go incorrect with both one.

Ultimately, you could find yourself desirous to be taught Python and R so to make use of each languages’ strengths, selecting one or the opposite on a per-project foundation relying in your wants.

And after all, understanding each additionally makes you a extra versatile job candidate in the event you’re wanting for a place within the data science world.


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