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Publisher : O’Reilly Media; 1st edition (October 22, 2013)
Language : English
Paperback : 396 pages
ISBN-10 : 1449357105
ISBN-13 : 978-1449357108
Item Weight : 1.41 pounds
Dimensions : 7 x 0.82 x 9.19 inches
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Customers find the book provides a good introduction to R. It’s easy for them to read and follow, with useful tips and explanations on data structures. The book does a good job as a tutorial that walks through many topics.
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Are you interested in learning R for data analysis but don’t know where to start? Look no further! In this post, we will provide you with a step-by-step function guide to help you on your journey to mastering R.
Step 1: Installing R and RStudio
The first step in learning R is to download and install R and RStudio on your computer. R is a programming language and software environment for statistical computing and graphics, while RStudio is an integrated development environment (IDE) for R. You can download R from the Comprehensive R Archive Network (CRAN) website and RStudio from the RStudio website.
Step 2: Understanding R basics
Once you have installed R and RStudio, it’s time to familiarize yourself with the basics of R. This includes learning about data types, variables, functions, and data structures in R. You can start by using the console in RStudio to practice running simple commands and getting comfortable with the R syntax.
Step 3: Importing data
One of the key tasks in data analysis is importing data into R. You can import data from various sources such as CSV files, Excel files, databases, and APIs. R provides several functions for importing data, including read.csv(), read.table(), read.xlsx(), and readRDS(). You can use these functions to read data into R and start exploring and analyzing it.
Step 4: Data manipulation
Once you have imported your data, you may need to manipulate it to clean and prepare it for analysis. R provides a wide range of functions for data manipulation, including subsetting, merging, filtering, and transforming data. Some popular packages for data manipulation in R include dplyr, tidyr, and data.table.
Step 5: Data visualization
Visualizing data is an essential part of data analysis, as it helps you gain insights and communicate your findings effectively. R offers a variety of packages for data visualization, such as ggplot2, plotly, and ggvis. You can use these packages to create various types of plots, including scatter plots, bar plots, histograms, and heatmaps.
Step 6: Statistical analysis
Finally, once you have cleaned and visualized your data, you can perform statistical analysis to draw conclusions and make predictions. R provides a wide range of functions and packages for statistical analysis, including hypothesis testing, regression analysis, clustering, and machine learning. Some popular packages for statistical analysis in R include stats, MASS, caret, and glmnet.
By following this step-by-step function guide, you can start learning R for data analysis and unlock the power of this versatile programming language. Happy coding!
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