How to add code coverage (codecov) to your R package?

During the development of another R package I wasted a bit of time figuring out how to add code coverage to my package. I had the same problem last time so I decided to write up the procedure step-by-step.

Provided that you’ve already written an R package, the next step is to create tests. Luckily, devtools package makes setting up both testing and code coverage a breeze.

Let’s start with adding an infrastructure for tests with devtools:
library(devtools)
use_testthat()

Then add a test file of your_function() to your tests folder:
use_test("your_function")

Then add the scaffolding for the code coverage (codecov)
use_coverage(pkg = ".", type = c("codecov"))

After running this code you will get a code that can be added to your README file to display a codecov badge. In my case it’s the following:
[![Coverage Status](https://img.shields.io/codecov/c/github/erzk/PostcodesioR/master.svg)](https://codecov.io/github/erzk/PostcodesioR?branch=master)

This will create a codecov.yml file that needs to be edited by adding:
comment: false
language: R
sudo: false
cache: packages
after_success:
- Rscript -e 'covr::codecov()'

Now log in to codecov.io using the GitHub account. Give codecov access to the project where you want to cover the code. This should create a screen where you can see a token which needs to be copied:

Once this is completed, go back to R and run the following commands to use covr:

install.packages("covr")
library(covr)
codecov(token = "YOUR_TOKEN_GOES_HERE")

The last line will connect your package to codecov. If the whole process worked, you should be able to see a percentage of coverage in your badge, like this:

Click on it to see which functions are not fully covered/need more test:

I hope this will be useful and will save a lot of frustrations.

Using ESRI shapefiles to create maps in R

R has a number of libraries that can be used for plotting. They can be combined with open GIS data to create custom maps.
In this post I’ll demonstrate how to create several maps.

First step is getting shapefiles that will be used to create maps. One of the sources could be this site, but any source with open .shp files will do.

Here I’ll focus on country level (administrative) data for Poland.
If you follow the link to diva-gis you should see the following screen:
diva-gis_poland

I’ll plot powiats and voivodeships which are first- and second-level administrative subdivisions in Poland.

After downloading and unzipping POL_adm.zip into your working directory in R you will be able to use the scripts underneath to recreate the maps.

The simplest map is using only the shapefiles without any extra background.
shapefile_map_poland_1
Clearly, it’s not the most attractive map, but it’s still informative.
It was generated with the following code:

Nicer maps can be generated with ggmap package. This package allows adding a shapefile overlay onto Google Maps or OSM. In this example I used get_googlemap function, but if you want other background then you should use get_map with appropriate arguments.
shapefile_map_poland_2_google_maps
Code used to generate the map above:

And last, but not least is my favourite interactive map created with leaflet.

Snippet:


> sessionInfo()
R version 3.2.4 Revised (2016-03-16 r70336)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 7 x64 (build 7601) Service Pack 1

locale:
[1] LC_COLLATE=English_United Kingdom.1252 LC_CTYPE=English_United Kingdom.1252
[3] LC_MONETARY=English_United Kingdom.1252 LC_NUMERIC=C
[5] LC_TIME=English_United Kingdom.1252

attached base packages:
[1] stats graphics grDevices utils datasets methods base

other attached packages:
[1] rgdal_1.1-7 ggmap_2.6.1 ggplot2_2.1.0 leaflet_1.0.1 maptools_0.8-39
[6] sp_1.2-2

loaded via a namespace (and not attached):
[1] Rcpp_0.12.4 magrittr_1.5 maps_3.1.0 munsell_0.4.3
[5] colorspace_1.2-6 geosphere_1.5-1 lattice_0.20-33 rjson_0.2.15
[9] jpeg_0.1-8 stringr_1.0.0 plyr_1.8.3 tools_3.2.4
[13] grid_3.2.4 gtable_0.2.0 png_0.1-7 htmltools_0.3.5
[17] yaml_2.1.13 digest_0.6.9 RJSONIO_1.3-0 reshape2_1.4.1
[21] mapproj_1.2-4 htmlwidgets_0.6 labeling_0.3 stringi_1.0-1
[25] RgoogleMaps_1.2.0.7 scales_0.4.0 jsonlite_0.9.19 foreign_0.8-66
[29] proto_0.3-10

Automatic pitch extraction from speech recordings

I needed to extract mean pitch values from audio recordings of human speech, but I wanted to automate it and easily recreate my analyses so I wrote a couple of scripts that can do it much faster.

Here is a recipe for extracting pitch from voice recordings.

 

  • Cleaning audio files

My audio files were stereo recordings of a participant saying /a/ while hearing (near) real-time pitch shifts in their own productions. The left channel contains the shifted pitch (heard by participants) and the right channel contains the original speech productions.

The first step is to examine the audio recordings for any non-speech sounds. I used Audacity for that. Any grunts or sights can mess up the outcome of scripts used in the analysis. Irrelevant parts of the audio track can be silenced (CTRL+L in Audacity). Once the audio track is cleaned, I split the channels and save them in separate wav files.

audio

Acoustic signal used in the analysis. Highlighted part is showing noise that should be removed.

  • Splitting continuous recordings using SFS

My pitch-extracting scripts expects each utterance to be saved in a separate wav file so I need to split the continuous recordings. It could be done manually but for longer recordings it’s cumbersome. Speech Filing System (SFS) has an option that allows splitting the continuous files on silence.

Manual:

1. Load a sound file

load

2. Create multiple annotations

Tools > Speech > Annotate > Find multiple endpoints

annotations

Specify the values of npoint. More information can be found here. You don’t need to know the exact number of utterances, but a close approximation should work.

 

Visualise the results of automatic annotation:

display

Check if the annotations are correct. If not, then tweak the npoint settings to get the effect you need.

 

3. Chop the files on annotations

Tools > Speech > Export > Chop signal into annotated regions

This will save the files in the sfs format, but PraatR can’t work with these files. They need to be transformed into wav.

 

4. Convert sfs into wav files

Load the files you want to convert, highlight them, and go to:

File > Export > Speech

 

Automatic:

If you don’t want to spend hours doing what I’ve just described then a simpler solution is using a program that runs all the commands described above.

Use the batch script that follows the steps described above (plus some extras).

 

  • Extracting mean pitch using PraatR

Pitch could be extracted manually in Praat by going to

View & Edit > Pitch > Get pitch

but doing this for many files would take a lot of time and would be error-prone.

praat_get_pitch

Luckily, there is a connection between Praat and R (PraatR) which can speed up this task.

I extracted mean pitch and duration of files. The latter can be used to reject any non-speech files. Here’s the script:

Now you should get a nicely formatted csv file.

I hope this will save you a lot of time.

 

Butterworth Filter Demo in Shiny

I am using EEGLab to process my electroencephalografic data (i.e. brain’s electric activity), but I wanted to have an interactive visualisation showing how different filter settings change my data. I prefer using R to Matlab, so I decided to create a Shiny app that would do just that.

I tried to filter brainstem’s activity during several speech conditions using Butterworth band-pass filter to get rid of the artefacts.

I wrote a butterHz function which is based on butter_filtfilt.m from the EEGLab Matlab package and is using butter function from the signal R package.

Here I used a time-domain waveform of speech-evoked Auditory Brainstem Responses to demostrate the use of the Butterworth filter.


The code is available on GitHub.