Why I Chose R for Information Evaluation and Machine Learning
When I first began with data science, like many programmers, I leaned greatly on Python. But as I dived deeper into stats, visualization, and progressed data adjustment, I recognized R had something special. It had not been simply a programming language it was an entire environment built around information, models, and research workflows That’s what drew me towards discovering and dealing with R.
Setting Up R and RStudio for the Very First Time
Before I might do anything, I had to establish the environment. One of the most typical choice is RStudio IDE , which offers a clean and user-friendly interface for coding, plotting, and managing data tasks.
# Set up and load essential collections
install.packages("tidyverse")
install.packages("caret")
install.packages("ggplot 2)
collection(tidyverse)
collection(caret)
library(ggplot 2
The tidyverse immediately felt like Python’s pandas , while ggplot 2 was a game-changer for constructing visualizations.
Data Cleaning Up in R: My Very First Real Obstacle
One of the earliest tasks I had was cleaning up raw datasets. Unlike Python, where I relied on pandas , in R I checked out dplyr features.
# Cleansing and changing data
cleaned_data <%
filter(! is.na(Sales)) %>>%
mutate(ProfitMargin = Earnings/ Sales) %>>%
set up(desc(ProfitMargin))
The pipe operator %>>% offered me a smooth operations that felt all-natural. I could chain numerous transformations without breaking my thought process.
Data Visualization: Why I Fell in Love With ggplot 2
Data visualization is where R absolutely radiates. My first attempt at outlining with ggplot 2 really felt so fluid compared to other collections I had actually made use of.
# Picturing sales vs earnings
ggplot(cleaned_data, aes(x = Sales, y = Earnings, color = Group)) +
geom_point(size = 3, alpha = 0. 7 +
theme_minimal()
This simple code created a magnificent scatter story. I understood R had visualization baked deep right into its DNA.
Artificial Intelligence in R: Structure My First Model
R additionally stunned me with its equipment learning plans. Making use of caret , I built a classification model in just a few lines.
# Dividing dataset
set.seed( 123
trainIndex <