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

import statistics

marks = [72, 85, 64, 90, 78, 85, 69]
print("mean:", round(statistics.mean(marks), 2))
print("median:", statistics.median(marks))
print("mode:", statistics.mode(marks))
print("stdev:", round(statistics.stdev(marks), 2))

passed = [m for m in marks if m >= 70]
print("passed:", len(passed), "of", len(marks))

marks <- c(72, 85, 64, 90, 78, 85, 69)
cat("mean:", round(mean(marks), 2), "\n")
cat("median:", median(marks), "\n")
cat("stdev:", round(sd(marks), 2), "\n")

# R has no built-in mode function; use a frequency table
freq <- table(marks)
cat("mode:", names(freq)[which.max(freq)], "\n")

passed <- marks[marks >= 70]
cat("passed:", length(passed), "of", length(marks), "\n")

import pandas as pd

df = pd.DataFrame({
    "city": ["Pune", "Jaipur", "Pune", "Lucknow", "Jaipur", "Pune"],
    "sales": [1200, 800, 950, 400, 1100, 700],
})

summary = (df.groupby("city")["sales"]
             .agg(["count", "sum", "mean"])
             .sort_values("sum", ascending=False))
print(summary)

# install.packages("dplyr")
library(dplyr)

df <- data.frame(
  city  = c("Pune", "Jaipur", "Pune", "Lucknow", "Jaipur", "Pune"),
  sales = c(1200, 800, 950, 400, 1100, 700)
)

df %>%
  group_by(city) %>%
  summarise(count = n(), total = sum(sales), avg = mean(sales)) %>%
  arrange(desc(total))

import re
from collections import Counter

# Paste text from 10-20 real listings you read (one string per listing)
listings = [
    "Data analyst: SQL, Python, Power BI, Excel",
    "Research analyst: R, statistics, survey data",
    "Junior data scientist: Python, pandas, scikit-learn, SQL",
    "Biostatistician: R, SAS, clinical trials",
]
counts = Counter()
for text in listings:
    words = set(re.findall(r"\b(python|r|sql|sas|excel)\b", text.lower()))
    counts.update(words)
for lang, n in counts.most_common():
    print(f"{lang:7} {n}")

def first_language(goal: str) -> str:
    goal = goal.lower()
    if any(w in goal for w in ["research", "biostat", "academic", "clinical"]):
        return "Start with R, add Python later"
    return "Start with Python (plus SQL), add R if your field needs it"

for g in ["data analyst in retail", "clinical research analyst", "not sure"]:
    print(f"{g:28} -> {first_language(g)}")

My target role: ______________________
Industry: ______________________

Listings read: ___
Mentions of Python: ___
Mentions of R: ___
Mentions of SQL: ___

Decision: Learn ________ first, with SQL.
Review date: after my second project.

Weeks 1-3   Language basics, data types, functions
Weeks 4-6   Data cleaning and summarising (pandas or dplyr)
Weeks 7-8   Charts (Seaborn or ggplot2)
Weeks 9-10  SQL alongside the language
Weeks 11-12 Two projects with clear write-ups

Then decide whether to add the second language.

Project 1: Clean and analyse a public dataset;
           answer 3 business questions with charts.
Project 2: A simple model or statistical test with an
           honest explanation of results and limits.

If you later learn the other language, rebuild
Project 1 in it. It is the fastest way to transfer skills.