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Category: R

Reshaping Data Frames With tidyr

Published 2018-10-23 by Kevin Feasel

Anisa Dhana shows off some of the data reshaping functionality available in the tidyr package:

As it is shown above, the variable agegp has 6 groups (i.e., 25-34, 35-44) which has different alcohol intake and smoking use combinations. I think it would be interesting to transform this dataset from long to wide and to create a column for each age group and show the respective cases. Let see how the dataset will look like.

dt %>% 
  spread(agegp, ncases) %>% 
  slice(1:5)

Click through for a few additional transformations.

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Using cdata To Created Faceted Plots

Published 2018-10-22 by Kevin Feasel

Nina Zumel shows how to use the cdata package to create faceted ggplot2 plots:

First, load the packages and data:

library("ggplot2")
library("cdata")

iris <- data.frame(iris)

Now define the data-shaping transform, or control table. The control table is basically a picture that sketches out the final data shape that I want. I want to specify the x and y columns of the plot (call these the value columns of the data frame) and the column that I am faceting by (call this the key column of the data frame). And I also need to specify how the key and value columns relate to the existing columns of the original data frame.

Read on to see how you can use cdata to tie together different faceted plots.

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Using R To Hit Azure ML From Power BI

Published 2018-10-16 by Kevin Feasel

Leila Etaati shows how you can use R to hit an Azure ML endpoint to populate a data set in Power BI:

You need to create a model in Azure ML Studio and create a web service for it.

The traditional example in Predict a passenger on Titanic ship is going to survived or not?

we have a dataset about passengers like their age, gender, and passenger class, then we are going to predict whether they are going to survive or not

Open Azure ML Studio and follow the steps to create a model for predicting this. Navigate to Azure ML Studio.

Then download the dataset for titanic from here

Click through for the step-by-step instructions.

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Connecting To Elasticsearch With R

Published 2018-10-15 by Kevin Feasel

Jerod Johnson has a sample of connecting to Elasticsearch with R:

You will need the following information to connect to Elasticsearch as a JDBC data source:

  • Driver Class: Set this to cdata.jdbc.elasticsearch.ElasticsearchDriver.
  • Classpath: Set this to the location of the driver JAR. By default, this is the lib subfolder of the installation folder.

The DBI functions, such as dbConnect anddbSendQuery , provide a unified interface for writing data access code in R. Use the following line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for Elasticsearch:

Read on for the full instructions.

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Voice Control For Shiny Apps

Published 2018-10-12 by Kevin Feasel

Over at Jumping Rivers, an example of using a Javascript library to control a page using voice commands:

I have found that performance across all devices and browsers is definitely not equal. By far the best browser I have found for viewing the apps is Google Chrome. I have also tended to find that my Ubuntu machines don’t do as well as Microsoft machines in picking up words correctly. A chat I had with someone recently suggested this might be down to drivers under Ubuntu for the microphones but that is not my area of expertise. Voice recognition was also fine on both of my Blackberry phones (one running BB OS 10, the other running Android 7).

It is worth noting that this does require an internet connection to function, in Chrome the voice to text is performed in the cloud.

The other thing I have noticed is that annyang seems relatively sensitive to background noise. This isn’t so bad for functions called using specific phrases but does sometimes have a large effect on the multi-word splats. This is because the splats are greedy and the background noise makes the recognition engine think that you are still talking long after you finished which gives the appearance of the application hanging.

The solution is by no means perfect, but it does look quite interesting.

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Visualizing In R: 3 Packages

Published 2018-10-11 by Kevin Feasel

Kristian Larsen has a quick demo of three R visualization packages, ggplot2, dygraphs, and plotly:

Another value generating visualisation package in R is dygraphs. This package focuses on creating interactive visualisations with elegant interactive coding modules. Furthermore, the package specialises in creating visualisations for machine learning methods. The below coding generates different visualisation graphs with dygraphs:

Three useful libraries to learn.  Two more which might be useful are ggvis and rbokeh.

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Modifying A ggplot2 Theme

Published 2018-10-11 by Kevin Feasel

Sebastian Sauer gives us an example of modifying a standard ggplot2 theme:

ggplot2 is customizeable. Frankly, one can change a heap of details – not everything probably, but a lot. Of course, one can add a theme to the ggplot call, in order to change the theme. However, a more catch-it-all approach would be to change the standard theme of ggplot itself. In this post, we’ll investigate this option.

To date, I’ve only used themes others have created, but if you need to customize a theme, there’s a lot you can do here.

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Multi-Threaded R With Microsoft R Client

Published 2018-10-10 by Kevin Feasel

David Parr shows us how to get started with Microsoft R Client and performs some quick benchmarking:

This message will pop up, and it’s worth noting as it’s got some information in it that you might need to think about:

  • It’s worth noting that right now Microsoft r Client is lagging behind the current R version, and is based on version 3.4 of R, not 3.5. This will mean your default package libraries will not be shared between the installations if you are running R 3.5.

  • It’s using a snapshot of CRAN called MRAN to source packages by default. 90% of the time it will operate just as you expect, but because it takes a ‘snapshot’ of packages, newer features and changes that have hit CRAN may not be in the version of the package you are grabbing.

    • RevoScaleR and probably the ggplot2 and dplyr packages will likely be installed for you already as default in Microsoft R Client. The other two you will probably have to install yourself.

  • Intel MKL will have scanned your system on install and attempted to work out how many cores your processor has. Here it’s identified 2 on my old Lenovo Yoga. This is where the speed boost will come from.

I had an old two-core Lenovo Yoga too, so this article really spoke to me.

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Image Clustering With Keras And R

Published 2018-10-09 by Kevin Feasel

Shirin Glander shows us how to use R to extract learned features from Keras and cluster those features:

For each of these images, I am running the predict() function of Keras with the VGG16 model. Because I excluded the last layers of the model, this function will not actually return any class predictions as it would normally do; instead we will get the output of the last layer: block5_pool (MaxPooling2D).

These, we can use as learned features (or abstractions) of the images. Running this part of the code takes several minutes, so I save the output to a RData file (because I samples randomly, the classes you see below might not be the same as in the sample_fruits list above).

Read the whole thing.

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