Statistical Process Control

  • https://rpubs.com/anhoej/controlcharts
  • https://link.springer.com/chapter/10.1007/978-1-4614-3652-2_12
  • https://stackoverflow.com/questions/38661660/spc-control-charts-by-group-in-r
  • http://blog.yhat.com/posts/quality-control-in-r.html


  • spc
  • qicharts
  • qcc


  • The documentation on how to use R is straight up crystal clear spotless perfect, easy to understand.
  • You don’t even need to install any libraries to perform powerful statistical and visualization tests.
  • It’s all built-in, and the dataframe structure is completely genius and simple!
  • In other languages you would have to make multi-dimensional arrays, where referring to each object in the array makes you want to cry.
  • The only time that I don’t use R is when my data is an absolute mess.
  • Also, in R you don’t need to use your standard loops very much, getting lost in loop-ception because R has simple wrapper loops that you can use.
  • Even though python is very powerful for a lot of things including web-development, I can’t be bothered to use it because white-space is an error.
  • In R, white-space isn’t necessary if you have newlines so it’s not an error.

Source: Unknown

R User Conference


  • pool connections with shiny proxy
  • dataMaid package: clean() function
  • Opening the Publication Process with Executable Research dlib.org
  • docker-reproducible-research
  • containerit Generating dockerfiles for reproducible research with R
  • drake
  • remake … %>% htmlwidgets::widgetFrame()
  • time series imputation: multivariate, ts imputation: Amelia, mtsdi; in case of high correlation between cross-section variables, use those packages

Graphical User Interface (GUI)

Archlinux version


sudo dnf install boost
sudo dnf install -y python*-Cython*

Geospatial Data

Update Packages

update.packages(ask = FALSE, dependencies = c('Suggests'))
all.packages <- installed.packages()
r.version <- paste(version[['major']], '.', version[['minor']], sep = '')
for (i in 1:nrow(all.packages))
    package.name <- all.packages[i, 1]
    package.version <- all.packages[i, 3]
    if (package.version != r.version)
        print(paste('Installing', package.name))

Code Style

Text Mining

Class System

RStudio IDE

Updating RStudio

  • check https://www.rstudio.com/products/rstudio/download/ for updates

install using rpm in Fedora

cd ~/Downloads
wget https://download1.rstudio.org/rstudio-1.0.153-x86_64.rpm
rpm -e rstudio
rpm -ivh rstudio-1.0.153-x86_64.rpm

Building RStudio from source

C/C++ Tools

  • Qt Creator: Download download Qt Creator 4.2.2 for Linux 64-bit (94 MB), allow executing and double-click run file
  • Boost: navigate to ./dependencies/common and execute bash install-boost

after building CMakeLists.txt using Qt Creator

cd ./src/build-cpp-Desktop-Default

# run RStudio server on [http://localhost:8787](http://localhost:8787)

# run desktop application
export QT_QPA_PLATFORM_PLUGIN_PATH=/home/xps13/Qt5.4.0/5.4/gcc_64/plugins/platforms


monitor java files for changes and recompile upon change

ant devmode

Eclipse (CDT) + StatET

Learning R

Connect to DB

Microsoft SQL Server

con <- dbConnect(RSQLServer::SQLServer(), server="localhost", port=1401, properties=list(useNTLMv2="true", user="SA", password=Sys.getenv("MSSQLPW"))
con <- dbConnect(RSQLServer::SQLServer(), server = "TEST", database = "TestDB")
dbWriteTable(con, "band_members", dplyr::band_members)
dbWriteTable(con, "band_instruments", dplyr::band_instruments)
dbReadTable(con, 'band_members')
dbListFields(con, 'band_instruments')

dplyr usage

library(dplyr, warn.conflicts = FALSE)
members <- tbl(con, "band_members")
instruments <- tbl(con, "band_instruments")
members %>% 
  left_join(instruments) %>% 
  filter(band == "Beatles")

clean up

dbRemoveTable(con, "band_instruments")
dbRemoveTable(con, "band_members")


system requirements

sudo dnf install -y postgresql-devel
Rscript -e 'install.packages("RPostgreSQL")'

connect to database

con <- dbConnect(drv = "PostgreSQL",
                 dbname = "szgdlszd",
                 host = "horton.elephantsql.com",
                 port = 5432,
                 user = "szgdlszd",
                 password = Sys.getenv("ELEPHANTSQLPW"))


  • github: rstats-db: RMySQL
  • requires mariadb-devel (Fedora <= 25), ‘MariaDB-devel’ (Fedora 26) or libmariadb-client-lgpl-dev (Debian)

using MariaDB 10.2.8, need to copy C header files to location specified in RMariaDB/configure

sudo cp /usr/include/mysql/server/mysql_version.h /usr/include/mysql/mysql/mysql_version.h
sudo cp /usr/include/mysql/server/mysql_com.h /usr/include/mysql/mysql/mysql_com.h

Install R on Linux

Debian Jessie

  • add to /etc/apt/sources.list
deb http://cran.univ-paris1.fr/bin/linux/debian jessie-cran3/

Uninstall version installed from source

  • download source code from cran
  • extract and execute sudo make uninstall

RStudio using Docker

running locally
$ sudo docker run -d -p 8787:8787 rocker/hadleyverse will download and install

RStudio in AWS


LinkedIn Quora software topic followers

When/Why R is Better than Excel

Data Manipulation.
R allows you to manipulate (e.g., subset, recode, merge) data quickly. Some R packages have been designed specifically for these purposes, e.g., plyr. Typically, a majority of the time spent on an analysis project is spent before the analysis—preparing the data. R is much more adept and efficient in data preparation than Excel. Fantasy data scraped from websites often require many steps in data processing to be ready for analysis, so R is ideal.
Easier Automation
R uses a scripting language rather than a GUI, so it’s much easier to automate things in R than in Excel. This can save you loads of time, especially when you plan to re-run the same analysis multiple times (e.g., every new fantasy season).
Faster Computation
Because of the automation provided by R scripts, many operations are much faster to perform in R than Excel.
It Reads Any Type of Data
R can basically read any type of data (.txt, .csv, .dat, etc.). There are also R packages specifically designed to read JSON, SPSS, Excel, SAS, and STATA files. You can also scrape data from websites and execute SQL queries. Scraping websites can be useful for downloading fantasy projections from ESPN and other websites for data analysis.
Easier Project Organization
In Excel, projects are often organized in different tabs of the same file. This can make the Excel file slow, clunky, and difficult to navigate. It is easier to keep a project organized when dealing with R scripts because different tasks or sub-projects can be stored in separate files stored in the same folder and linked together in the same project with RStudio. For an example folder structure for R projects, see here.
It Supports Larger Data Sets
Excel has restrictions for how large your data can be. Even if if your data don’t exceed this maximum size, Excel can become slow with large data sets (especially after you add tabs, formulas, and references). R supports larger data, and can support big data with packages such as Hadoop.
R has features that make it much easier to reproduce the findings of your analysis, which is important for detecting errors. First, it’s easy to add comments to your scripts to make it clear what you’re doing. Commenting your code is crucial, and can serve as a translation for someone else looking at your code, or as a reminder of what you did 6 months ago! It is difficult to document steps you’ve done in Excel. Second, data and analysis are separated in R, allowing you to see the logical progression for data analysis in the R code. In Excel, however, data and formulas are together, and it can be difficult to follow the data analyst’s train of logic. Third, you can use version control with git a) to track (and revert) changes you make over time and b) to share your scripts with others to collaborate on projects as a community. Having more people examining your work can help find and fix errors and make other improvemnts. Excel files are binary files, so you can’t track changes to Excel files. The github site hosting the R scripts for this site is located here. Feel free to use the scripts and suggest improvements!
Researchers have shown that Excel and other spreadsheets show important inaccuracies for basic analyses like linear regression. R was specifically designed for statistical analysis, so it is more precise and accurate for data analysis.
Easier to Find and Fix Errors
Because R uses scripting rather than clicking, and allows comments and version control, one can see a history of the actions taken to achieve the result. This makes it easier to find and troubleshoot errors. In Excel, however, errors can be hidden in formulas in cells that can be difficult to find. Spreadsheet errors have led to widely-publicized mistakes, including disastrous financial losses, faulty government policies, and the wrong drugs being given to cancer patients. Humans make mistakes and mistakes in data analysis are inevitable, whether with spreadsheets or with R code. The bottom line is that it’s easier to find and fix these mistakes in R than it is in Excel, making it more likely that you’re getting an accurate result in R.
It’s Free
Enough said.
It’s Open Source
Unlike Excel and other proprietary software used for data analysis, R is not a black box. You can examine the code for any function or computation you perform. You can even modify and improve these functions by changing the code.
Advanced Statistics
R has many more (and more advanced) statistics capabilities than Excel does. They also tend to be faster and more flexible. Part of the advanced capabilities of R owes to the fact that R is open source and many users have contributed packages for performing specialized functions. For example, this fantasy football draft optimizer uses the Rglpk package to find your optimal starting lineup of players that maximizes the team’s projected points while minimizing its downside risk.
State-of-the-Art Graphics
R has advanced graphics capabilities (see here for examples and code for how to create them). You can create beautiful graphics using the base R package, or with the lattice or ggplot packages. People like to digest and understand statistics visually, and R provides a better tool for creating pretty visualizations than Excel does.
It Runs on Many Platforms
You can use R on Windows, Mac, Linux, and Unix.
Anyone (Including You) Can Contribute Packages to the Community to Improve its Functionality
In the chance there isn’t an R package that does what you need to do, you can write a function to perform the task and can contribute it as a package to the community for others to use and improve. The number of R packages contributed to the community is increasing at a rapid rate. Chances are, if there’s an analysis you need to do, an R package exists to do it.


CGE Computable General Equilibrium (and DSGE)

Image Processing

  • vignette with TOC: vector images: layers, frames, other composition etc.

Data Cleaning

Debian Linux

Syntax Highlighter


start help server
options(error = [NULL | recover])

.Rprofile: Rinitfunctions

select CRAN mirror
current_repo["CRAN"] <- "http://cran.us.r-project.org"
print list of mirrors



copy Github repository
$ git clone https://github.com/cran/rscala
navigate to folder containing JARs
$ cd rscala/inst/java
start scala instance with specified classpath
$ scala -cp rscala_2.10-1.0.6.jar
instantiate an R interpreter
scala> val R = org.ddahl.rscala.callback.RClient()
evaluate R expression
scala> val side = R.evalS0("sample(c('heads', 'tails', 1))")
print statement
scala> println(s"Your coin landed $side.")



add rJava JARs to Java CLASSPATH
$ nano ~/.profile add export CLASSPATH="$CLASSPATH:/home/xps13/R/x86_64-redhat-linux-gnu-library/3.2/rJava/jri/JRI.jar"

$ sudo ln -s /home/xps13/R/x86_64-redhat-linux-gnu-library/3.2/rJava/jri/libjri.so /usr/lib64/libjri.so

run examples in rJava/jri/examples
./run rtest, ./run rtest2
add packaged example in subfolder pkg
[... jri]$ javac -cp JRI.jar:. examples/pkg/Temp.java
[... jri]$ ./run pkg.Temp

Web queries




Web applications


SAP Hana integration

Parallel Computation

Map Reduce





  • download Apache Spark
  • set SPARK_HOME environment variable and add $SPARK_HOME/bin to PATH environment variable
from GitHub
devtools::install_github("apache/spark", subdir = file.path("R", "pkg"))


  • gist.github.com: shivaram: dataframe_example.R

  • download Spark 1.4 from http://spark.apache.org/downloads.html
  • download the nyc flights dataset as a CSV from https://s3-us-west-2.amazonaws.com/sparkr-data/nycflights13.csv
  • launch SparkR using ./bin/sparkR --packages com.databricks:spark-csv_2.10:1.0.3

web scraping / harvesting


RStudio Cheat sheets



dplyr, tidyr






$ ./configure && make && sudo make install
$ sudo mkdir /usr/local/lib64/pkgconfig
$ sudo cp ~/Downloads/JAGS-4.2.0/etc/jags.pc /usr/local/lib64/pkgconfig/



-The ggedit gitbook


%} +——————+
| htmlwidgets |
+————+ | | +—————–+ | R function | +—> | JS library | +—> | SVG for website | +————+ | | +—————–+ | custom user data |




Color input

Google Sheets

Mail lists

R devel



  • Ihaka R, Gentleman R (1996). “R: A Language for Data Analysis and Graphics.” Journal of Computational and Graphical Statistics, 5(3), 299–314.
  • R Development Core Team (2010). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria.
  • Gentleman R (2008). R Programming for Bioinformatics. CRC Press.
  • R Development Core Team (2008). R: Regulatory Compliance and Validation Issues A Guidance Document for the Use of R in Regulated Clinical Trial Environments. R Foundation for Statistical Computing, Vienna, Austria.




Machine Learning for Hackers
Author: Drew Conway and John Myles White
Subtitle: Case Studies and Algorithms to Get You Started
Publisher: O’Reilly
Year: 2012


Environment Variables


PATH to select default R version of




# Extra material related but not to be included in the package


# Git related

# Travis scripts

# Extra Vignette materials

# Misc



Travis CI

Unit tests

Each new feature should be accompanied with unit tests, by using the testthat R package.

to set up your package to use testthat, run

For each R-script file named script.R, a correspond test file should be created in tests/testthat directory, using the writing convention test_<script>.R The test_<script>.R should have the following structure:

require(rsdmx, quietly = TRUE) #load the rsdmx package
require(testthat) # load the testthat package
context("script") # create a unit test context for the given script file

#unit test 1

#unit test 2

Build tests

After any modification of the source code (bug fix, enhancement, added feature), a package build should be tested by the developer using the command R CMD check (requires installation of an R instance and RTools). The option –as-cran should be enabled to ensure the updated package will be later accepted by CRAN. Such program will run a set of check operations required for a proper package build, including the unit tests. In order to guarantee a proper R package build, the R CMD check will be performed automatically after each commit, through Travis Continuous Integration (see [travis-ci.org]{https://travis-ci.org/opensdmx/rsdmx}). This second build test is required to ensure users will be able to successfully install the package from Github.

Test coverage

Excel interaction

Open Office

Options - ROOo - Path Settings
R-Home: /usr/lib/R Proxy: /usr/local/lib/R/site-library/rscproxy/libs


13 February 2015