Distance Matrix in R

I’ll do an exercise to make a distance matrix, for that we only need the function spDistN1 from the library “sp”. 

The basic use of the function is the following:

library(sp)
data(meuse)
coordinates(meuse) <- c("x", "y")
spDistsN1(meuse, meuse[1,], longlat=TRUE)

The limitation this is it only return the distance of the first element (or given row), because what the function does is to measure the distance of all the elements in relation with only one geometric element (meuse[1, ]) . To solve that we can use the apply function:

apply(meuse@coords, 1, function(x) spDistsN1(meuse@coords, x, longlat = T))

This is a great solution, but the issue is when the data is too big. In that case it will take too much time to process. (Imagine the case I do this with a for loop instead of apply, it would be much slower than now).

To solve the aforementioned problem I must to process with parallelization and as you can guess, the function parApply is just like apply but with parallelization development on it:

library(sp)
library(parallel)
m.coord <- meuse@coords
ncore <- detectCores()
cl <- makeCluster(ncore)
clusterExport(cl, c("m.coord"))
clusterEvalQ(cl = cl, expr = c(library(sp)))
parApply(cl = cl, X = m.coord, MARGIN = 1, 
FUN = function(x) spDistsN1(m.coord, x, longlat = T))
stopCluster(cl)

Then we have a nice and an efficient solution.

Note: I know it can be easily done with a GIS software, but in this way it
can be incorporated in a bigger and automatized process.

Nearest Facility Threshold

In the previous post I created a R function which allows to find the nearest facility, now I want to show a bit update in which we can select only the cases under a threshold distance.

For this we can easily use the dplyr function named as filter, so we add a simple line of code.

DF <- filter(.data = DF, DIST <= threshold)

Then we can add this to the function which the final function will be:

DistMin_Cent <- function(DF, Destination, Distance,threshold){
library(dplyr)
library(tidyr)
DF <- spread_(DF, Destination, Distance)
DF$MIN <- apply(DF[, c(2:ncol(DF))], 1, FUN = min)
c_col <- c(ncol(DF)-1)
DF$CName <- as.numeric(colnames(DF[, c(2:c_col)])[apply(DF[,
c(2:c_col)], 1, which.min)])
DF <- DF[, c(1, ncol(DF), c_col)]
rm(c_col)
names(DF) <- c("PA", "PB", "DIST")
DF <- filter(.data = DF, DIST <= threshold)
DF
}

An example of use for this is:

DistMin_Cent(Dist_mtx, "D", "DIST", 1000)

Installing R in Ubuntu

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

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.

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

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.