What is R programming for data science?

R language is a dynamic, array-based, object-oriented, imperative, functional, procedural, and reflective computer programming language. The language first appeared in 1993 but has become popular in the past few years among data scientists and machine learning developers for its functional and statistical algorithm features.

R language is one of the most popular programming languages among data scientists and statistical engineers. R supports Linux, OS X, and Windows operating systems. There are several R packages available publicly to download on project R website here: https://cran.r-project.org/

The R interface to Tensor-Flow lets you work productively using the high-level Kara’s and Estimator APIs, and when you need more control provides full access to the core Tensor-Flow API: https://tensorflow.rstudio.com/

Now, let’s start with machine learning. In the first part, I will explain some data pre-processing steps and show them implementation code in R.

Data Pre-processing

Whenever you interact with the data, you have to pre-process it or in simple terms, you have to clean the data and make it smooth for analysis. The pre-processing steps we follow here are as follow -

• Handling Missing Data
• Categorize the data
• Split the data
• Feature scaling

In this part, I am going to show you how to handle missing figures in our data and in the remaining pre-processing steps.

We have a data set of 10 employees.

Here, we can easily see the missing age of the employee 1007 and the salary of 1005. To fill the gap of missing data, we have mean, median, and mode strategy but here, we use mean strategy to fill the gap. We simply take the mean of our required column where data is missing then put the value there.

To do this, I wrote a code in R which gives us mean values. The code snippet is given below.

First, import the dataset.

1. dataset=read.csv('dataset.csv')  

Below code can take the mean of a column's Age and Salary.

1.  dataset$Age = ifelse(is.na(dataset$Age),  
2. ave(dataset$Age, FUN = function(x) mean(x, na.rm=TRUE)),  
3. dataset$Age)  
4. dataset$Salary = ifelse(is.na(dataset$Salary),  
5. ave(dataset$Salary, FUN = function(x) mean(x, na.rm = TRUE)), 

Data science is a congregation of machine learning, statistics and visualisation. The figure below was the first one I saw when I encountered the same question you can ask here data science certification

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So, in order to carry out these things easily, a programming language (a statistical package) called R was developed. R is not the first language to serve the propose; SAS was there before and still exists.

R programming in data science means using the language R to carry out your data science tasks like exploring a dataset, learning certain statistical parameters of the dataset and visualizing the data.

R programming is used for making reports. Different Graphs, charts, histograms are made using R code. In Data Science, R programming is used as science involved with data. Authorities have to take decision regularly. To make the accurate decision and remove uncertainty data visualization, statistics and machine learning is involved. So, the decision is supported by facts which are present in data. R programming converts data to information and knowledge is extracted to make tough decision.

R for Data Science

Widely preferred by data miners and statisticians as a top-choice for data analysis and developing statistical software, R is a dynamic programming language available under the GNU GPL v2 license. This means that the statistical programming language is completely free to use.

This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames, and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.

What makes this course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform. Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.

·         The techniques we have seen so far can be executed in excel and even in R.

·         Complex data mining techniques are not possible in excel.

·         R is a very powerful tool

·         Available freely on the web

Learn R Programming and Data Science course,

What you'll learn

·         ExploreR language fundamentals, including basic syntax, variables, and types

·         How to create functions and use control flow.

·         Details on reading and writing data in R

·         Work with data in R

·         Create and customize visualizations using ggplot2

·         Perform predictive analytics using R