Livelessons_R_Programming_Video_Training_Fundamentals_to_Advanced

آر (R)، یک زبان برنامه‌نویسی و محیط نرم‌افزاری برای محاسبات آماری و تحلیل داده است که بر اساس زبان‌های اس و اسکیم پیاده‌سازی شده است. این نرم‌افزار متن باز، تحت اجازه‌نامه عمومی همگانی گنو عرضه شده و به رایگان قابل دسترس است. زبان اس بجز R، توسط شرکت Insightful، در نرم‌افزار تجاری اس‌پلاس نیز پیاده‌سازی شده است. اگرچه دستورات اس‌پلاس و R بسیار شبیه است اما این دو نرم‌افزار دارای هسته‌های متمایزی می باشند. R، حاوی محدوده گسترده‌ای از تکنیک‌های آماری (از جمله: مدل‌سازی خطی و غیرخطی، آزمون‌های کلاسیک آماری، تحلیل سری‌های زمانی، رده‌بندی، خوشه‌بندی و غیره) و قابلیت‌های گرافیکی است. در محیط R، کدهای سی، سی++ و فورترن قابلیت اتصال و فراخوانی هنگام اجرای برنامه را دارند و کاربران خبره می‌توانند توسط کدهای سی، مستقیماً اشیا R را تغییر دهند.

به درخواست دوستان دوره آموزش مقدماتی تا پیشرفته  برنامه نویسی به زبان R را برای شما آماده کردیم.

این دوره آموزشی محصول LiveLessons می باشد.

سرفصل های دوره آموزشی:

  • مقدمه ای بر برنامه نویسی R LiveLessons
  • آغاز به کار با R
  • دانلود و نصب R
  • کار در محیط R
  • کار با متغییر ها
  • ایجاد و دسترسی به اطلاعات در data.frames
  • خواندن داده ها  در  R
  • خواندن CSV  در  R
  • ایجاد و دسترسی به اطلاعات در لیست ها
  • ساختمان داده ها در R
  • ذخیره داده ها در بردار
  • شناخت انواع داده های مختلف
  • بارگذاری فایل های باینری R
  • ساخت آماری نمودار ها
  • Make boxplots  با  base graphics
  • ایجاد multiple های کوچک
  • اضافه کردن theme ها به نمودار
  • مبانی برنامه نوسیی
  • استفاده از دستور if برای کنترل جریان برنامه
  • تکرار با یک حلقه for
  • مدل های خطی
  • مدل مقایسه
  • سایر مدل ها
  • درک ACF و PACF
  • تناسب و ارزیابی مدل ARIMA
  • استفاده از VAR برای سری زمانی چند متغیره
  • بررسی و ساخت یک پکیج
  • ارسال بسته به CRAN
  • خلاصه ای از برنامه نویسی LiveLessons R


لیست سرفصل های دوره آموزشی:

Overview
Other Videos in This Category
R Quick Syntax Reference
R Quick Syntax Reference
Margot Tollefson
Practical Data Science with R
Practical Data Science with R
Nina Zumel and John Mount
Building Interactive Graphs with ggplot2 and Shiny
Building Interactive Graphs with ggplot2 and Shiny
Christophe Ladroue
Social Media Mining with R
Social Media Mining with R
Richard Heimann; Nathan Danneman
Foundational and Applied Statistics for Biologists Using R
Foundational and Applied Statistics for Biologists Using R
Ken Aho
R Programming LiveLessons: Fundamentals to Advanced is a tour through the most important parts of R, the statistical programming language, from the very basics to complex modeling. It covers reading data, programming basics, visualization, data munging, regression, classification, clustering, modern machine learning and more.

About the Author:

Data scientist, Columbia University adjunct Professor, author and organizer of the New York Open Statistical Programming meetup Jared P. Lander presents the 20 percent of R functionality to accomplish 80 percent of most statistics needs. This video is based on the material in R for Everyone and is a condensed version of the course Mr. Lander teaches at Columbia. You start with simply installing R and setting up a productive work environment. You then learn the basics of data and programming using these skills to munge and prepare data for analysis. You then learn visualization, modeling and predicting and close with generating reports and websites and building R packages.



Chapter/Selection
Time
Introduction
Introduction to R Programming LiveLessons
Preview
00:04:02
Lesson 1: Getting Started with R
Learning objectives
Preview
00:00:29
1.1 Download and install R
Preview
00:06:23
1.2 Work in The R environment
Preview
00:18:50
1.3 Install and load packages
Preview
00:04:48
Lesson 2: The Basic Building Blocks in R
Learning objectives
Preview
00:00:27
2.1 Use R as a calculator
Preview
00:03:43
2.2 Work with variables
Preview
00:04:11
2.3 Understand the different data types
Preview
00:11:32
2.4 Store data in vectors
Preview
00:16:36
2.5 Call functions
Preview
00:04:02
Lesson 3: Advanced Data Structures in R
Learning objectives
Preview
00:00:25
3.1 Create and access information in data.frames
Preview
00:17:20
3.2 Create and access information in lists
Preview
00:10:57
3.3 Create and access information in matrices
Preview
00:08:01
3.4 Create and access information in arrays
Preview
00:02:00
Lesson 4: Reading Data into R
Learning objectives
Preview
00:00:24
4.1 Read a CSV into R
Preview
00:05:58
4.2 Understand that Excel is not easily readable into R
Preview
00:01:08
4.3 Read from databases
Preview
00:05:58
4.4 Read data files from other statistical tools
Preview
00:01:16
4.5 Load binary R files
Preview
00:04:40
4.6 Load data included with R
Preview
00:01:48
4.7 Scrape data from the web
Preview
00:02:28
Lesson 5: Making Statistical Graphs
Learning objectives
Preview
00:00:29
5.1 Find the diamonds data
Preview
00:01:13
5.2 Make histograms with base graphics
Preview
00:01:29
5.3 Make scatterplots with base graphics
Preview
00:02:01
5.4 Make boxplots with base graphics
Preview
00:01:39
5.5 Get familiar with ggplot2
Preview
00:02:30
5.6 Plot histograms and densities with ggplot2
Preview
00:03:51
5.7 Make scatterplots with ggplot2
Preview
00:05:12
5.8 Make boxplots and violin plots with ggplot2
Preview
00:04:24
5.9 Make line plots
Preview
00:08:21
5.10 Create small multiples
Preview
00:04:01
5.11 Control colors and shapes
Preview
00:01:18
5.12 Add themes to graphs
Preview
00:02:18
Lesson 6: Basics of Programming
Learning objectives
Preview
00:00:28
6.1 Write the classic “Hello, World!” example
Preview
00:02:04
6.2 Understand the basics of function arguments
Preview
00:10:32
6.3 Return a value from a function
Preview
00:02:47
6.4 Gain flexibility with do.call
Preview
00:03:46
6.5 Use if statements to control program flow
Preview
00:02:07
6.6 Stagger if statements with else
Preview
00:05:32
6.7 Check multiple statements with switch
Preview
00:03:51
6.8 Run checks on entire vectors
Preview
00:05:17
6.9 Check compound statements
Preview
00:05:40
6.10 Iterate with a for loop
Preview
00:06:07
6.11 Iterate with a while loop
Preview
00:01:30
6.12 Control loops with break and next
Preview
00:02:05
Lesson 7: Data Munging
Learning objectives
Preview
00:00:31
7.1 Repeat an operation on a matrix using apply
Preview
00:04:45
7.2 Repeat an operation on a list
Preview
00:03:05
7.3 The mapply
Preview
00:04:34
7.4 The aggregate function
Preview
00:05:26
7.5 The plyr package
Preview
00:17:18
7.6 Combine datasets
Preview
00:03:51
7.7 Join datasets
Preview
00:05:56
7.8 Switch storage paradigms
Preview
00:05:11
Lesson 8: Manipulating Strings
Learning objectives
Preview
00:00:20
8.1 Combine strings together
Preview
00:07:28
8.2 Extract text
Preview
00:32:00
Lesson 9: Basic Statistics
Learning objectives
Preview
00:00:19
9.1: Draw numbers from probability distributions
Preview
00:21:09
9.2: Calculate averages, standard deviations and correlations
Preview
00:16:13
9.3: Compare samples with t-tests and analysis of variance
Preview
00:18:58
Lesson 10: Linear Models
Learning objectives
Preview
00:00:26
10.1 Fit simple linear models
Preview
00:10:14
10.2 Explore the data
Preview
00:08:33
10.3 Fit multiple regression models
Preview
00:19:16
10.4 Fit logistic regression
Preview
00:10:06
10.5 Fit Poisson regression
Preview
00:07:05
10.6 Analyze survival data
Preview
00:12:01
10.7 Assess model quality with residuals
Preview
00:05:15
10.8 Compare models
Preview
00:07:18
10.9 Judge accuracy using cross-validation
Preview
00:09:06
10.10 Estimate uncertainty with the bootstrap
Preview
00:06:23
10.11 Choose variables using stepwise selection
Preview
00:02:42
Lesson 11: Other Models
Learning objectives
Preview
00:00:27
11.1 Select variables and improve predictions with the elastic net
Preview
00:14:14
11.2 Decrease uncertainty with weakly informative priors
Preview
00:08:53
11.3 Fit nonlinear least squares
Preview
00:05:16
11.4 Splines
Preview
00:06:48
11.5 GAMs
Preview
00:05:24
11.6 Fit decision trees to make a random forest
Preview
00:06:34
Lesson 12: Time Series
Learning objectives
Preview
00:00:20
12.1 Understand ACF and PACF
Preview
00:07:15
12.2 Fit and assess ARIMA models
Preview
00:05:13
12.3 Use VAR for multivariate time series
Preview
00:08:06
12.4 Use GARCH for better volatility modeling
Preview
00:09:24
Lesson 13: Clustering
Learning objectives
Preview
00:00:19
13.1: Partition data with K-means
Preview
00:12:26
13.2: Robustly cluster, even with categorical data, with PAM
Preview
00:02:13
13.3: Perform hierarchical clustering
Preview
00:05:37
Lesson 14: Reports and Slideshows with knitr
Learning objectives
Preview
00:00:30
14.1: Understand the basics of LaTeX
Preview
00:07:16
14.2: Weave R code into LaTeX using knitr
Preview
00:05:33
14.3: Understand the basics of Markdown
Preview
00:02:45
14.4: Weave R code into Markdown using knitr
Preview
00:02:53
14.5: Use pandoc to convert from Markdown to HTML5 slideshow
Preview
00:07:09
Lesson 15: Package Building
Learning objectives
Preview
00:00:22
15.1: Understand the folder structure and files in a package
Preview
00:05:25
15.2: Write and document functions
Preview
00:07:32
15.3: Check and build a package
Preview
00:02:09
15.4: Submit a package to CRAN
Preview
00:00:46
Summary
Summary of R Programming LiveLessons
Preview
00:01:22

عنوان دوره:Livelessons – R Programming Video Training Fundamentals to Advanced
حجم فایل: 1.49GB