R vs Python: Which Language Should You Learn in 2026?

R vs Python comes down to focus. The R programming language was built by statisticians for statistics and charts, while Python is a general-purpose language that also happens to be excellent at data science. If you’re picking one, I’d start with Python.

I’m not running R on this machine, so the R snippets are shown as code only. Every Python example is a real run on Python 3.12.5 with pandas, NumPy and scikit-learn.

Here’s how they compare on syntax, data wrangling, statistics, machine learning, charts and careers, with the same tasks written in both languages.

R vs Python at a glance

	R	Python
Built for	Statistics and data analysis	General programming
First released	1993 (Ross Ihaka and Robert Gentleman)	1991 (Guido van Rossum)
Package hub	CRAN, 20,000+ packages	PyPI, hundreds of thousands of projects
Data frames	Built in, plus dplyr and the tidyverse	pandas or Polars
Charts	ggplot2	matplotlib, seaborn, Plotly
Machine learning	caret, tidymodels	scikit-learn, PyTorch, TensorFlow
Typical IDE	RStudio (Posit)	VS Code, PyCharm, Jupyter
Indexing	Starts at 1	Starts at 0

Loading and summarizing data in R and Python

Both languages read a CSV and summarize it in a couple of lines. In R, read.csv() and summary() are built in:

sales <- read.csv("sales.csv")
head(sales, 3)
summary(sales$revenue)

Python needs pandas, which you add with pip install pandas. After that, it’s just as short:

import pandas as pd

df = pd.read_csv("sales.csv")
print(df.head(3))
print()
print(df["revenue"].describe().round(2))

Output:

  region month  units  revenue
0   West   Jan    120   5400.0
1   West   Feb    135   6075.0
2   East   Jan     98   4410.0

count       6.0
mean     5677.5
std       915.8
min      4410.0
25%      5062.5
50%      5737.5
75%      6345.0
max      6795.0
Name: revenue, dtype: float64

Grouping data: dplyr vs pandas

Grouped summaries are where R’s tidyverse shines. The pipe reads almost like English:

library(dplyr)

sales |>
  group_by(region) |>
  summarise(units = sum(units), revenue = sum(revenue)) |>
  arrange(desc(revenue))

pandas does the same job with method chaining. It’s a bit wordier, but the steps line up one to one:

import pandas as pd

df = pd.read_csv("sales.csv")

by_region = (df.groupby("region", as_index=False)
               .agg(units=("units", "sum"), revenue=("revenue", "sum"))
               .sort_values("revenue", ascending=False))
print(by_region.to_string(index=False))
print(f"\nTotal revenue: ${df['revenue'].sum():,.2f}")

Output:

region  units  revenue
 South    294  13230.0
  West    255  11475.0
  East    208   9360.0

Total revenue: $34,065.00

I’ll admit dplyr feels nicer for this kind of work. If you live in data frames all day, that’s a real point for R. Python’s dictionaries of lists are often how this data starts out.

Is R or Python better for statistics and machine learning?

For classical statistics, R still has the edge. A linear model is one built-in call, and the summary prints p-values and confidence intervals straight away:

homes <- data.frame(sq_ft = c(1100, 1400, 1650, 1900, 2300, 2600),
                    price = c(245000, 289000, 330000, 365000, 420000, 468000))
fit <- lm(price ~ sq_ft, data = homes)
summary(fit)

Python splits this between libraries. scikit-learn is built for prediction, while statsmodels gives you R-style statistical summaries:

import numpy as np
from sklearn.linear_model import LinearRegression

sq_ft = np.array([[1100], [1400], [1650], [1900], [2300], [2600]])
price = np.array([245000, 289000, 330000, 365000, 420000, 468000])

model = LinearRegression().fit(sq_ft, price)
print(f"price per extra sq ft: ${model.coef_[0]:,.2f}")
print(f"R squared: {model.score(sq_ft, price):.3f}")
print(f"predicted 2,000 sq ft home: ${model.predict([[2000]])[0]:,.2f}")

Output:

price per extra sq ft: $147.49
R squared: 0.999
predicted 2,000 sq ft home: $378,644.09

For deep learning, Python wins clearly. PyTorch and TensorFlow are Python-first, and most new AI research code is written in Python.

Charts in R vs Python: ggplot2 vs matplotlib

ggplot2 is many people’s favorite charting library in any language. Its grammar-of-graphics style builds a plot layer by layer:

library(ggplot2)
ggplot(sales, aes(x = month, y = revenue, fill = region)) +
  geom_col(position = "dodge") +
  labs(title = "Revenue by region (USD)")

In Python, pandas can plot straight from a data frame using matplotlib underneath:

import pandas as pd
import matplotlib.pyplot as plt

df = pd.read_csv("sales.csv")
pivot = df.pivot(index="month", columns="region", values="revenue").loc[["Jan", "Feb"]]

ax = pivot.plot(kind="bar", figsize=(7, 4), rot=0)
ax.set_title("Revenue by region (USD)")
ax.set_ylabel("Revenue ($)")
plt.tight_layout()
plt.show()

Syntax differences between R and Python

The biggest shock when switching is indexing. R starts counting at 1 and includes both ends of a range; Python starts at 0 and excludes the end:

states <- c("Texas", "Ohio", "Utah")
states[1]        # "Texas"
states[2:3]      # "Ohio" "Utah"
prices * 1.08    # vector math is built in

states = ["Texas", "Ohio", "Utah"]
print(states[0])        # Python counts from 0
print(states[1:3])      # end index is excluded

import numpy as np
prices = np.array([19.99, 5.49, 102.00])
print(prices * 1.08)    # NumPy gives you R-style vector math

Output:

Texas
['Ohio', 'Utah']
[ 21.5892   5.9292 110.16  ]

R also assigns with <- and treats every value as a vector. Python needs NumPy for vector math, but its syntax is closer to other languages. See the Python for loop guide for the basics.

Which should you learn first, R or Python?

Pick Python if you want one language for data work and also scripts, web apps, automation or AI. Most data job listings in the US ask for Python, and the skills carry over to software roles.

Pick R if you’re in academic research, biostatistics, or a team that already uses it. It’s also a great second language once you know Python, since many analysts use both.

Comparing Python with other languages? I’ve also written Python vs C and Python vs C#.

Ready to start on the Python side? These are good first steps:

• Best Python libraries for data science
• Python libraries for machine learning
• How to run a Python script
• Install multiple Python versions

Frequently asked questions