R for Data Science Solutions
R for Data Science Solutions
R for Data Science Solutions
R in data science is used to deal with, store and break down data. It can be used for data analysis and statistical demonstrating. R is an environment for statistical analysis. R has various statistical and graphical capabilities.
Table of Contents
- Functions in R
- Data Extracting, Transforming, and Loading
- Data Pre-Processing and Preparation
- Data Manipulation
- Visualizing Data with ggplot2
- Making Interactive Reports
- Simulation from Probability Distributions
- Statistical Inference in R
- Rule and Pattern Mining with R
- Time Series Mining with R
- Supervised Machine Learning
- Unsupervised Machine Learning
R for Data Science Solutions FAQs
Is R enough for data science?
Assuming you're passionate with regards to the statistical estimation and data visualisation portions of data analysis, R could be ideal for you. If, then again, you're interested in turning into a data scientist and working with large data, artificial insight, and profound learning algorithms, Python would be the better fit.
What is the role of R?
R is a Procedural Programming Language that breaks down a task into a sequence of Stages, Processes, and Subroutines. This allows R to easily transform data into significant Statistics, Graphs, and foster Statistical Learning Models for predictions and inferences.
Is it easy to learn R?
R is a great language for programming beginners to learn, and you needn't bother with any prior experience with code to get it. Nowadays, R is easier to learn than any time in recent memory thanks to the assortment of packages.