The document discusses using R with interactive design and Ruby on Rails. It provides examples of using RSRuby to integrate R functions and graphics with Ruby code. This allows R output like histograms and graphs to be generated from within Ruby on Rails applications and displayed on web pages. It also describes how R can be used for data analysis and visualization independently or with tools like TIBCO Spotfire and Excel.
This is an analysis of the "Auto" data set from the ISLR (An Introduction to Statistical Learning: with Applications in R) package. The analysis presented here includes the following topics: data manipulation, exploratory data analysis, simple linear regression, correlation matrix, multiple linear regression, model diagnostics, residuals, normality, variance inflation factor (vif) to test for multi collinearity, levearages and modifying the model. Packages used are: ggplot2, xtable and car.
tibbles are an alternative for dataframes. You will learn how tibbles are different from dataframes, why you should use them, how to create and modify them.
Latin America Tour 2019 - 10 great sql featuresConnor McDonald
By expanding our knowledge of SQL facilities, we can let all the boring work be handled via SQL rather than a lot of middle-tier code, and we can get performance benefits as an added bonus. Here are some SQL techniques to solve problems that would otherwise require a lot of complex coding, freeing up your time to focus on the delivery of great applications.
This is an analysis of the "Auto" data set from the ISLR (An Introduction to Statistical Learning: with Applications in R) package. The analysis presented here includes the following topics: data manipulation, exploratory data analysis, simple linear regression, correlation matrix, multiple linear regression, model diagnostics, residuals, normality, variance inflation factor (vif) to test for multi collinearity, levearages and modifying the model. Packages used are: ggplot2, xtable and car.
tibbles are an alternative for dataframes. You will learn how tibbles are different from dataframes, why you should use them, how to create and modify them.
Latin America Tour 2019 - 10 great sql featuresConnor McDonald
By expanding our knowledge of SQL facilities, we can let all the boring work be handled via SQL rather than a lot of middle-tier code, and we can get performance benefits as an added bonus. Here are some SQL techniques to solve problems that would otherwise require a lot of complex coding, freeing up your time to focus on the delivery of great applications.
Interactively querying Google Analytics reports from R using ganalyticsJohann de Boer
This presentation introduces the Google Analytics reporting APIs and how these can be accessed for data analysis using R. Examples are provided that demonstrate using the ganalytics package for R to answer analysis questions with accompanying visualisations.
it is a good file to learn matlab. lots of examples were solved by matlab and were gathered in this file.
better files are on the way
hossein gholizadeh
power engineering student at SBU
R is a fun and versatile language for statistical analysis, visualization, and data exploration. Target audience are software engineers/programmers who can code comfortably in another language. Emphasis in this lesson is on data structures, and light on analysis examples (to be covered at later date) but you are exposed to the basic concepts and commands. Email me for the pptx file which has notes.
29. Ruby on Rails
• Ruby web
• MySQL Sqlite3 DB
rails hoge
cd hoge
ruby script/generate scaffold
hoge id:integer val:integer
rake db:migrate
30. RSRuby
• “RSRuby is a bridge between Ruby and the R
interpreted language.” http://rubyforge.org/projects/rsruby/
• Ruby R
• Tsukuba.R #7 @jj0c_0jjj
• 2009 Feb.
http://open-bio.jp/archive/20070302_OB6/OB6-RSRuby-Gutteridge.pdf