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Python vs. R

Explore the differences between Python and R in data science contexts. Understand when to use each language based on programming mindset, statistical needs, and task complexity. Learn the basics required to start using both tools, including programming logic and data visualization techniques.

What is Python?

Python is a free and open-source programming language that can run on operating systems such as Windows, Linux, Unix, and macOS. You can use it for data analysis, visualization, website development, software development, automation, and so on. The most attractive thing is its syntax, which imitates the natural language we humans speak, as shown in the examples below:

Python
# Program to check if 9 is a prime number or not
num = 9
# define a flag variable
flag = False
# prime numbers are greater than 1
if num > 1:
# check for factors
for i in range(2, num):
if (num % i) == 0:
# if factor is found, set flag to True
flag = True
# break out of loop
break
# check if flag is True
if flag:
print(num, "is not a prime number")
else:
print(num, "is a prime number")

Here's another example of Python's simplicity:

Python
import scipy.stats as stats
data = [151,153,158,148,153,155,149,151,152,149,147,154]
# perform one sample t-test
t_statistic, p_value = stats.ttest_1samp(a=data, popmean=150)
print(t_statistic , p_value)

What is R?

R is a free and open-source programming language that can run on operating systems such as Windows, Linux, Unix, and macOS. You can use it for data analysis and visualization. The most attractive thing about R is how easy it is to perform statistical computing, as shown in the examples below:

R
# Program to check if 9 is a prime number or not
num = 9
flag = 0
# prime numbers are greater than 1
if(num > 1) {
# check for factors
flag = 1
for(i in 2:(num-1)) {
if ((num %% i) == 0) {
flag = 0
break
}
}
}
if(num == 2) flag = 1
if(flag == 1) {
print(paste(num,"is a prime number"))
} else {
print(paste(num,"is not a prime number"))
}
...