R is a language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering) and graphical techniques, and is highly extensible. One of Rβs strengths is the ease with which well-designed publication-quality plots can be produced, including mathematical symbols and formulae where needed.
R is an integrated suite of software facilities for data manipulation, calculation and graphical display. It includes
- an effective data handling and storage facility,
- a suite of operators for calculations on arrays, in particular matrices,
- a large, coherent, integrated collection of intermediate tools for data analysis,
- graphical facilities for data analysis and display either on-screen or on hardcopy, and
- a well-developed, simple and effective programming language which includes conditionals, loops, user-defined recursive functions and input and output facilities.
This repo contains tutorials about data science with R that talked about in 1 YouTube video.
This video covers the topics below.
00:05:19 What is R?
00:06:40 The advantages of R
00:08:27 R setup
00:09:47 R Studio setup
00:10:58 How to use R Studio?
00:16:17 Working space
00:19:04 Packages and libraries
00:23:37 Basic operators
00:31:30 Vectors
00:37:49 Matrix and array
00:51:44 Factor
00:56:43 List
01:01:06 DataFrame
01:06:37 Working directory
01:08:27 Transform data type
01:14:05 Missing value
01:16:22 Reading data
01:18:02 Rcmdr
01:36:56 Writing data
01:42:50 Date and time
01:46:53 Subset data set
01:53:46 Reshape data set
01:59:06 Split data set
02:09:48 Dana manipulation
02:17:40 Strings
02:28:48 Random data
02:36:12 Loop and control structures
02:44:09 Loop functions
02:53:01 Writing function
02:59:06 Pratical plot
03:10:05 Data visualization with ggplot2
03:22:27 Regression analysis
03:42:51 Logistic regression analysis
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