Category Archives: R

Update on Snowdoop, a MapReduce Alternative

Mad (Data) Scientist

In blog posts a few months ago, I proposed an alternative to MapReduce, e.g. to Hadoop, which I called “Snowdoop.” I pointed out that systems like Hadoop and Spark are very difficult to install and configure, are either too primitive (Hadoop)  or too abstract (Spark) to program, and above all, are SLOW. Spark is of course a great improvement on Hadoop, but still suffers from these problems to various extents.

The idea of Snowdoop is to

  • retain the idea of Hadoop/Spark to work on top of distributed file systems (“move the computation to the data rather than vice versa”)
  • work purely in R, using familiar constructs
  • avoid using Java or any other external language for infrastructure
  • sort data only if the application requires it

I originally proposed Snowdoop just as a concept, saying that I would slowly develop it into an actual package. I later put the beginnings of a…

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Installing R in Ubuntu

First of all, It’s possible to install R from the Ubuntu Software Center

Screenshot from 2015-05-24 02:33:20

But it’s so outdated, so some packages won’t work for maintenance issues.

To be able to install the current version you must modify the file sources.list

To do that, go to the terminal and type:

sudo nano /etc/apt/sources.list

And you should add the following line:

deb http://dirichlet.mat.puc.cl//bin/linux/ubuntu trusty/

That adress is because I’m on Chile, thus you have to replace it for the right mirror belonging to your country. To know that, just go to:

http://cran.r-project.org/mirrors.html

After you have modified the sources.list type the following:

sudo apt-get install r-base
sudo apt-get install r-base-dev

And now you have R ready to use it. Though, I recommend to use it along with RStudio.

Data Visualization cheatsheet, plus Spanish translations

RStudio Blog

data visualization cheatsheet

We’ve added a new cheatsheet to our collection. Data Visualization with ggplot2 describes how to build a plot with ggplot2 and the grammar of graphics. You will find helpful reminders of how to use:

  • geoms
  • stats
  • scales
  • coordinate systems
  • facets
  • position adjustments
  • legends, and
  • themes

The cheatsheet also documents tips on zooming.

Download the cheatsheet here.

Bonus – Frans van Dunné of Innovate Online has provided Spanish translations of the Data Wrangling, R Markdown, Shiny, and Package Development cheatsheets. Download them at the bottom of the cheatsheet gallery.

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RStudio v0.99 Preview: Code Completion

Great! This is very useful, it’s something we were waiting for.

RStudio Blog

We’re busy at work on the next version of RStudio (v0.99) and this week will be blogging about some of the noteworthy new features. If you want to try out any of the new features now you can do so by downloading the RStudio Preview Release.

The first feature to highlight is a fully revamped implementation of code completion for R. We’ve always supported a limited form of completion however (a) it only worked on objects in the global environment; and (b) it only worked when expressly requested via the tab key. As a result not nearly enough users discovered or benefitted from code completion. In this release code completion is much more comprehensive.

Smarter Completion Engine

Previously RStudio only completed variables that already existed in the global environment, now completion is done based on source code analysis so is provided even for objects that haven’t been fully evaluated:

document-inferred

Completions are also provided…

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Writing multiple csv files from a xlsx

What I used for this example is an open data about “Recycling places”, you can find it on the web page of Portal de datos Públicos.

The data, is an xlsx file

xlsx_wordpress

The file has 8 columns, one of them is town. So, now, the questions is:

How do I generate multiple files, one for each town?. The answer is simple: R

Why R? Because, you can automatize it. It avoid you to make different filters, and save the new file each time.

Let’s start:

To read the file, you can use the XLConnect package, and to split the data: the plyr package.

You can load the file with the function readWorksheet, but, as you can see, on the first row, six of the eight cells, are merged. So, when you load the file, that will disappear. Thus, we will read the file with headers and we’ll rename the columns with troubles; and then we will remove the row without data.

After that, we will use the d_ply function, which lets us to split the data. On this function, we put the field on which the split should be based. Then, we use the sdf function, which allows us to write the csv files; and as a final step, we extract the names from the field chosen, to paste them on the name of the new files.

library(XLConnect) #Functions to read excel
library(plyr) #Functions to split data

wb = loadWorkbook("Formato_Puntos_de_almacenamiento_Muni_consolidado.xlsx")
df = readWorksheet(wb, sheet = "Hoja1", header = TRUE)
colnames(df)[c(5, 6)] <- c("Este UTM", "Norte UTM")  #change col names
df2 <- df[-1,] #remove first row

d_ply(df2, .(Comuna),
      function(sdf) write.csv(sdf,
                              file=paste(sdf$Comuna[[1]],".csv",sep=""))) #write multiple csv

A DBI for PostgreSQL on R

Between the capabilities of R there is the possibility of querying databases thorough R. The DBMS that I know more it’s PostgreSQL. What I like about it, that it is an open source object-relational DBMS. It’s so simple, an also it has an extension for Spatial and Geographical objects called PostGIS.

Thus, the DBI (Database Interface) package I’ve chosen for querying PostgreSQL is RPostgreSQL. To work with it, just I have to download the package from the Repository and use the following code:

library(RPostgreSQL)

drv <- dbDriver("PostgreSQL")
con <- dbConnect(drv, host = "localhost", user= "***", password="***", dbname="Procesos_UChile")

dbListConnections(drv)
dbGetInfo(drv)
summary(con)

df = dbReadTable(con,'etapas_sept_2013')
summary(df)
head(df, 3)
dbDisconnect(con)

This DBI is a nice product, but it’s limited by the ram, this problem appears when I tried to read a table over 10GB. So, I’m stuck on here. I know, this year was released a library called PivotalR, which allows you to manage big amounts of data with the library madlib.

Pivotal is a software company that provides software and services for the development of custom applications for data and analytics based on cloud computing technology.

And they made a an open-source library for scalable in-database analytics that provides data parallel implementations of mathematical, statistical and machine-learning methods for structured and unstructured data called Madlib.

The next step is trying to installing this library on ubuntu to see how it works. The instructions are on this URL:

https://gist.github.com/thinkerbot/8699369

You can also watch a presentation of PivotalR with a demo on the following video:
https://www.youtube.com/watch?v=6cmyRCMY6j0

My First Post

The motivation of this blog, is because I'm on the process of learning R. I studied Geographical Engineering, we always used softwares, but we never saw R, not even for doing some stats; so I didn't know what it was.
When I had to do the thesis a teacher told me to do everything on R; so, thence I knew how amazing it is. Though, still I'm a beginner.
A couple of months ago, I watched some videos from R-bloggers; there some R gurú told that the best way to learn is doing a blog. So, here I am; and, also it has a double purpose, because it will help me to improve my writing skills.

For posting this, I'm using R, just I followed the excellent code from William K. Morris’s Blog.