Taming exam results in pdf with pdftools

You can also check this post, written in #blogdown, here: taming-exam-results-with-pdf.

Introduction

There are several ways to mine tables and other content from a pdf, using R. After a lot of trial & error, here’s how I managed to extract global exam results from an international, massive, yearly examination, the EDAIC.

This is my first use case of “pdf mining” with R, and also a fairly simple one. However, more complex and very fine examples of this can be found elsewhere, using both pdftools and tabulizer packages.

As can be seen from the original pdf, exam results are anonymous. They consist on a numeric, 6-digit code and a binary result: “FAIL / PASS”. I was particularly interested into seeing how many of them passed the exam, as some indirect measure of how “hard” it can be.

Mining the table

In this case I preferred pdftools as it allowed me to extract the whole content from the pdf:

install.packages("pdftools")
library(pdftools) 
txt <- pdf_text("EDAIC.pdf") 
txt[1] 
class(txt[1]) 
  [1] "EDAIC Part I 2017                                                  Overall Results\n                                         Candidate N°       Result\n                                            107131            FAIL\n                                            119233            PASS\n                                            123744            FAIL\n                                            127988            FAIL\n                                            133842            PASS\n                                            135692            PASS\n                                            140341            FAIL\n                                            142595            FAIL\n                                            151479            PASS\n                                            151632            PASS\n                                            152787            PASS\n                                            157691            PASS\n                                            158867            PASS\n                                            160211            PASS\n                                            161970            FAIL\n                                            162536            PASS\n                                            163331            PASS\n                                            164442            FAIL\n                                            164835            PASS\n                                            165734            PASS\n                                            165900            PASS\n                                            166469            PASS\n                                            167241            FAIL\n                                            167740            PASS\n                                            168151            FAIL\n                                            168331            PASS\n                                            168371            FAIL\n                                            168711            FAIL\n                                            169786            PASS\n                                            170721            FAIL\n                                            170734            FAIL\n                                            170754            PASS\n                                            170980            PASS\n                                            171894            PASS\n                                            171911            PASS\n                                            172047            FAIL\n                                            172128            PASS\n                                            172255            FAIL\n                                            172310            PASS\n                                            172706            PASS\n                                            173136            FAIL\n                                            173229            FAIL\n                                            174336            PASS\n                                            174360            PASS\n                                            175177            FAIL\n                                            175180            FAIL\n                                            175184            FAIL\nYour candidate number is indicated on your admission document        Page 1 of 52\n"
  [1] "character"

These commands return a lenghty blob of text. Fortunately, there are some \n symbols that signal the new lines in the original document.

We will use these to split the blob into something more approachable, using tidyversal methods…

  • Split the blob.
  • Transform the resulting list into a character vector with unlist.
  • Trim leading white spaces with stringr::str_trim.
library(tidyverse) 
library(stringr) 
tx2 <- strsplit(txt, "\n") %>% # divide by carriage returns
  unlist() %>% 
  str_trim(side = "both") # trim white spaces
tx2[1:10]
   [1] "EDAIC Part I 2017                                                  Overall Results"
   [2] "Candidate N°       Result"                                                         
   [3] "107131            FAIL"                                                            
   [4] "119233            PASS"                                                            
   [5] "123744            FAIL"                                                            
   [6] "127988            FAIL"                                                            
   [7] "133842            PASS"                                                            
   [8] "135692            PASS"                                                            
   [9] "140341            FAIL"                                                            
  [10] "142595            FAIL"
  • Remove the very first row.
  • Transform into a tibble.
tx3 <- tx2[-1] %>% 
  data_frame() 
tx3
  # A tibble: 2,579 x 1
                             .
                         <chr>
   1 Candidate N°       Result
   2    107131            FAIL
   3    119233            PASS
   4    123744            FAIL
   5    127988            FAIL
   6    133842            PASS
   7    135692            PASS
   8    140341            FAIL
   9    142595            FAIL
  10    151479            PASS
  # ... with 2,569 more rows
  • Use tidyr::separate to split each row into two columns.
  • Remove all spaces.
tx4 <- separate(tx3, ., c("key", "value"), " ", extra = "merge") %>%  
  mutate(key = gsub('\\s+', '', key)) %>%
  mutate(value = gsub('\\s+', '', value)) 
tx4
  # A tibble: 2,579 x 2
           key    value
         <chr>    <chr>
   1 Candidate N°Result
   2    107131     FAIL
   3    119233     PASS
   4    123744     FAIL
   5    127988     FAIL
   6    133842     PASS
   7    135692     PASS
   8    140341     FAIL
   9    142595     FAIL
  10    151479     PASS
  # ... with 2,569 more rows
  • Remove rows that do not represent table elements.
tx5 <- tx4[grep('^[0-9]', tx4[[1]]),] 
tx5
  # A tibble: 2,424 x 2
        key value
      <chr> <chr>
   1 107131  FAIL
   2 119233  PASS
   3 123744  FAIL
   4 127988  FAIL
   5 133842  PASS
   6 135692  PASS
   7 140341  FAIL
   8 142595  FAIL
   9 151479  PASS
  10 151632  PASS
  # ... with 2,414 more rows

Extracting the results

We already have the table! now it’s time to get to the summary:

library(knitr)
tx5 %>%
  group_by(value) %>%
  summarise (count = n()) %>%
  mutate(percent = paste( round( (count / sum(count)*100) , 1), "%" )) %>% 
  kable()
value count percent
FAIL 1017 42 %
PASS 1407 58 %

From these results we see that the EDAIC-Part1 exam doesn’t have a particularly high clearance rate. It is currently done by medical specialists, but its dificulty relies in a very broad list of subjects covered, ranging from topics in applied physics, the entire human physiology, pharmacology, clinical medicine and latest guidelines.

Despite being a hard test to pass -and also the exam fee-, it’s becoming increasingly popular among anesthesiologists and critical care specialists that wish to stay up-to date with the current medical knowledge and practice.

 

 

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Starting a Rmarkdown Blog with Blogdown + Hugo + Github

Finally, -after 24h of failed attempts-, I could get my favourite Hugo theme up and running with R Studio and Blogdown.

All the steps I followed are detailed in my new Blogdown entry, which is also a GitHub repo.

After exploring some alternatives, like Shirin’s (with Jekyll), and Amber Thomas advice (which involved Git skills beyond my basic abilities), I was able to install Yihui’s hugo-lithium-theme in a new repository.

However, I wanted to explore other blog templates, hosted in GiHub, like:

The three first themes are currently linked in the blogdown documentation as being most simple and easy to set up for unexperienced blog programmers, but I hope the list will grow in the following months. For those who are willing to experiment, the complete list is here.

Finally I chose the hugo-tranquilpeak theme, by Thibaud Leprêtre, for which I mostly followed Tyler Clavelle’s entry on the topic. This approach turned out to be easy and good, given some conditions:

  • Contrary to Yihui Xie’s advice, I chose github.io to host my blog, instead of Netlify (I love my desktop integration with GitHub, so it was interesting for me not to move to another service for my static content).
  • In my machine, I installed Blogdown & Hugo using R studio (v 1.1.336).
  • In GiHub, it was easier for me to host the blog directly in my main github pages repository (always named [USERNAME].github.io), in the master branch, following Tyler’s tutorial.
  • I have basic knowledge of html, css and javascript, so I didn’t mind to tinker around with the theme.
  • My custom styles didn’t involve theme rebuilding. At this moment they’re simple cosmetic tricks.

The steps I followed were:

Git & GitHub repos

  • Setting a GitHub repo with the name [USERNAME].github.io (in my case aurora-mareviv.github.io). See this and this.
  • Create a git repo in your machine:
    • Create manually a new directory called [USERNAME].github.io.
    • Run in the terminal (Windows users have to install git first):
    cd /Git/[USERNAME].github.io # your path may be different
    
    git init # initiates repo in the directory
    git remote add origin https://github.com/[USERNAME]/[USERNAME].github.io # connects git local repo to remote Github repo
    
    git pull origin master # in case you have LICENSE and Readme.md files in the GitHub repo, they're downloaded
  • For now, your repo is ready. We will now focus in creating & customising our Blogdown.

RStudio and blogdown

  • We will open RStudio (v 1.1.336, development version as of today).
    • First, you may need to install Blogdown in R:
    install.packages("blogdown")
    • In RStudio, select the Menu > File > New Project following the lower half of these instructions. The wizard for setting up a Hugo Blogdown project may not be yet available in your RStudio version (not for much longer probably).

Creating new Project

Creating new Project

Selecting Hugo Blogdown format

Selecting Hugo Blogdown format

Selecting Hugo Blogdown theme

Selecting Hugo Blogdown theme

A config.toml file appears

 

config.toml file appears


Customising paths and styles

Before we build and serve our site, we need to tweak a couple of things in advance, if we want to smoothly deploy our blog into GitHub pages.

Modify config.toml file

To integrate with GiHub pages, there are the essential modifications at the top of our config.toml file:

  • We need to set up the base URL to the “root” of the web page (https://[USERNAME].github.io/ in this case)
  • By default, the web page is published in the “public” folder. We need it to be published in the root of the repository, to match the structure of the GitHub masterbranch:
baseurl = "/./" 
publishDir = "./"
  • Other useful global settings:
ignoreFiles = ["\\.Rmd$", "\\.Rmarkdown$", "_files$", "_cache$"]
enableEmoji = true

Images & styling paths

We can revisit the config.toml file to make changes to the default settings.

The logo that appears in the corner must be in the root folder. To modify it in the config.toml:

picture = "logo.png" # the path to the logo

The cover (background) image must be located in /themes/hugo-tranquilpeak-theme/static/images . To modify it in the config.toml:

coverImage = "myimage.jpg"

We want some custom css and js. We need to locate it in /static/css and in /static/jsrespectively.

# Custom CSS. Put here your custom CSS files. They are loaded after the theme CSS;
# they have to be referred from static root. Example
customCSS = ["css/my-style.css"]

# Custom JS. Put here your custom JS files. They are loaded after the theme JS;
# they have to be referred from static root. Example
customJS = ["js/myjs.js"]

Custom css

We can add arbitrary classes to our css file (see above).

Since I started writing in Bootstrap, I miss it a lot. Since this theme already has bootstrap classes, I brought some others I didn’t find in the theme (they’re available for .md files, but currently not for .Rmd)

Here is my custom css file to date:

/* @import url('https://maxcdn.bootstrapcdn.com/bootswatch/3.3.7/cosmo/bootstrap.min.css'); may conflict with default theme*/
@import url('https://fonts.googleapis.com/icon?family=Material+Icons'); /*google icons*/
@import url('https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css'); /*font awesome icons*/

.input-lg {
  font-size: 30px;
}
.input {
  font-size: 20px;
}
.font-sm {
    font-size: 0.7em;
}
.texttt {
  font-family: monospace;
}
.alert {
padding: 15px;
margin-bottom: 20px;
border: 1px solid transparent;
border-radius: 4px;
}
.alert-success {
color: #3c763d;
background-color: #dff0d8;
border-color: #d6e9c6;
}
.alert-danger,
.alert-error {
  color: #b94a48;
  background-color: #f2dede;
  border-color: #eed3d7;
}
.alert-info {
  color: #3a87ad;
  background-color: #d9edf7;
  border-color: #bce8f1;
}
.alert-gray {
  background-color: #f2f3f2;
  border-color: #f2f3f2;
}

/*style for printing*/
@media print {
    .noPrint {
       display:none;
   }
    }

/*link formatting*/
a:link {
    color: #478ca7;
    text-decoration: none;
} 
a:visited {
    color: #478ca7;
    text-decoration: none;
}
a:hover {
    color: #82b5c9;
    text-decoration: none;
}

Also, we have font-awesome icons!

Site build with blogdown

Once we have ready our theme, we can add some content, modifying or deleting the various examples we will find in /content/post .

We need to make use of Blogdown & Hugo to compile our .Rmd file and create our html post:

blogdown::build_site()
blogdown::serve_site()

In the viewer, at the right side of the IDE you can examine the resulting html and see if something didn’t go OK.

Deploying the site

Updating the local git repository

This can be done with simple git commands:

cd /Git/[USERNAME].github.io # your path to the repo may be different
git add . # indexes all files that wil be added to the local repo
git commit -m "Starting my Hugo blog" # adds all files to the local repo, with a commit message

Pushing to GitHub

git push origin master # we push the changes from the local git repo to the remote repo (GitHub repo)

Just go to the page https://[USERNAME].github.io and enjoy your blog!


R code

Works just the same as in Rmarkdown. R code is compiled into an html and published as static web content in few steps. Welcome to the era of reproducible blogging!

The figure 1 uses the ggplot2 library:

library(ggplot2)
ggplot(diamonds, aes(x=carat, y=price, color=clarity)) + geom_point()

diamonds plot with ggplot2.

Figure 1: diamonds plot with ggplot2.

Rmd source code

You can download it from here

I, for one, welcome the new era of reproducible blogging!

hexbins

Quick wordclouds from PubMed abstracts – using PMID lists in R

Wordclouds are one of the most visually straightforward, compelling ways of displaying text info in a graph.

Of course, we have a lot of web pages (and even apps) that, given an input text, will plot you some nice tagclouds. However, when you need reproducible results, or getting done complex tasks -like combined wordclouds from several files-, a programming environment may be the best option.

In R, there are (as always), several alternatives to get this done, such as tagcloud and wordcloud.

For this script I used the following packages:

  • RCurl” to retrieve a PMID list, stored in my GitHub account as a .csv file.
  • RefManageR” and plyr to retrieve and arrange PM records. To fetch the info from the inets, we’ll be using the PubMed API (free version, with some limitations). 
  • Finally, tm, SnowballC” to prepare the data and wordcloud” to plot the wordcloud. This part of the script is based on this from Georeferenced.

One of the advantages of using RefManageR is that you can easily change the field which you are importing from, and it usually works flawlessly with the PubMed API.

My biggest problem sources when running this script: download caps, busy hours, and firewalls!.

At the beginning of the gist, there is also a handy function that automagically downloads all needed packages for you.

To source the script, simply type in the R console:

This script creates two directories in your working directory: ‘corpus1‘ for the abstracts file, and ‘wordcloud‘ to store the plot.

library(devtools)
source_url("https://gist.githubusercontent.com/aurora-mareviv/697cbb505189591648224ed640e70fb1/raw/b42ac2e361ede770e118f217494d70c332a64ef8/pmid.tagcloud.R")

And there is the code:

Enjoy!

wordcloud

R/Shiny for clinical trials: simple randomization tables

One of the things I most like from R + Shiny is that it enables me to serve the power and flexibility of R in small “chunks” to cover different needs, allowing people not used to R to benefit from it. However, what I like most is that’s really fun and easy to program those utilities for a person without any specific programming background.

Here’s a small hack done in R/Shiny: it covered an urgent need for a study involving patient randomisation to two branches of treatment, in what is commonly known as a clinical trial. This task posed some challenges:

  • First, this trial was not financed in any way (at least initially). It was a small, independent study comparing two approved techniques for chronic pain, so the sponsor had to avoid expensive software or services.
  • Another reason for software customization is that treatment groups were partially ‘blind’: for people who assessed effectiveness and… also for statistical analysis (treatment administration was open-label). This means that the person in charge of data analysis must know which group is assigned to a patient, but doesn’t know what treatment is assigned to either group.

To tackle the points above, my app should have two main features:

  • The sponsor (here, a medical doctor) must be able to effectively control study blindness and also provide emergency blind disclosure. This control should extend to data analysis to minimize bias favoring either treatment.
  • R has tools to create random samples, but the MD in charge of the study sponsoring doesn’t know how to use R. We needed a friendly interface for random table creation.

Here’s how I got it to work:

  • The very core of this Shiny app is a combination between the set.seed and sample R functions. The PIN number (the set.seed argument) works like a secret passcode that links to a given random table. E.g., every time I enter ‘5432’, the random tables will look the same. This protects from accidental blindness disclosure, as nobody can find the correct random table without the proper PIN, even if they can access the app’s source code.
  • The tables are created column by column, ordered at first. Then we proceed to randomize (via the sample function) both the treatment column (in the random table) and the Group column (in the PIN table).
  • Once the tables are created they can be downloaded as .CSV files, printed, signed and dated to document the randomization procedure. The app’s open source code and the PIN number will provide reproducibility to the procedure for many years.

Unfortunately I wasn’t able to insert iframes to embed the app, so I posted a screenshot:

Random table generator for clinical trials

The app is far from perfect, but it covers the basic needs for the trial. You can test it here:

http://aurora.shinyapps.io/random_gen

And the GitHub repo is available here. Feel free to use/ adapt/ fork it to your needs!

https://github.com/aurora-mareviv/random_gen

Also, you can cite it if it’s been useful for your study methods!

 

 

 

 

 

Happening just now… 6th Conference of the R Spanish User Community

The R-Spain Conferences have been taking place since 2009 as an expression of the growing interest that R elicits in many fileds. The organisers are the Comunidad R Hispano (R-es). The community supports many groups and initiatives aimed to develop R knowledge and widen its use.

To attend the talks by streaming (they are in Spanish) you must registrate.

There is also a scientific programme with the presentations (some in English) here.

Ro Conferences 2014

Install R in Android, via GNURoot -no root required!

Playing with my tablet some time ago, I wondered if installing R could be possible. You know, a small android device “to the power of R”…

After searching on Google from time to time, I came across some interesting possibilities:

  • R Instructor, created “to bridge the gap between authoritative (but expensive) reference textbooks and free but often technical and difficult to understand help files“.
  • R Console Free. provides the necessary C, C++ and Fortran compilers to build and install R packages.
  • There’s always possible to root your device and install a Linux distribution for Android, which will let you install any repository/package, just like in any linux console.
  • Running R from your dedicated R server or from an external one (see R-fiddle), using your own browser. I see this option as particularly useful for those who want maximum performance.
  • Some additional thoughts on this topic are also stored in these Stack Overflow pages.
  • Without needing to root my device, I found GNURoot, an app that “provides a method for you to install and use GNU/Linux distributions and their associated applications/packages alongside Android“.

Finally, my preferred solution came with GNURoot (see this tutorial), and here’s how I managed to install the newest CRAN repositories! (NOTE: It should work “out of the box” but, as problems might appear, some experience with Linux is always advisable).

1. Install the .apk of GNURoot in your Android device. Don’t forget to donate if you like it! 🙂

2. Following the app instructions, download and install a linux distribution to run. In my case, I chose the .apk GNURoot Wheezy (a Debian Wheezy distro without Xterms). EDIT: Just be sure of having enough memory for it in your device

3. Once installed, just follow the steps to launch the Rootfs (Wheezy) as Fake Root. You will see a bash prompt, from which you can access a complete linux directory tree. This is the same as if you were in a computer (however, if you aren’t root you won’t be able to access the directories via your file browser from Android)

GNURoot1

4. Now, we just have to update and upgrade:

apt-get update
apt-get upgrade

5. Then, update the sources.list file. We don’t have any graphical text editor (like gedit or kate)… but we have nano!:

nano /etc/apt/sources.list

GNURoot2

Using the volume up + “W/S/A/D” you can move between the lines. Or alternatively, you can install a convenient keyboard with arrow buttons, like Hacker’s Keyboard! (thanks to JTT!)

Following instructions from CRAN, I added the following line to sources.list:

deb http://<favorite-cran-mirror>/bin/linux/debian wheezy-cran3/

Exit saving changes. But before “update and upgrade” again, don’t forget to add the key for the repository running the following:

apt-key adv --keyserver keys.gnupg.net --recv-key 381BA480

5. Update and upgrade…. voilà!

apt-get update
apt-get upgrade
apt-get install r-base r-base-dev

6. Now, you only have to run R just like in any bash console:

RinAndroid

R GRAPHS

With this method you only have a prompt, without any graphical interface. ¿How do I make and see plots here?. If R runs from “inside” Android one option is to connect your Linux to an X-server app (thanks, J. Liebig). However, due to memory issues, I couldn’t put in practice this idea and see what happens. Try at your own risk! 🙂

Fortunately, there’s always possible to print R graphs in various formats, with the inconvenient that you have to browse to the plot’s location in Android -every time you need to check the output.

setwd("/sdcard")
data(cars)
pdf("cars.pdf")
plot(cars)
dev.off()

Here I leave a small script to begin playing with R on Android. Hope you enjoy it!

…start using R, from scratch!

Some time ago, since I was able to use R by myself, have found some fellows and other people who wanted to learn R as well. Then I pointed them to help pages, to CRAN repositories… but in some cases they said that didn’t know how to start using those resources. Obviously, the main self-perceived limitation for non-programmers is the use of “commands” -ok, many of the 80’s kids will remember the use of some command lines to access games such as PacMan, Frogger… :).

At the same time, they also wanted to refresh some basic statistics, acquiring a general knowledge of their data before asking for a statistician’s help. An idea to quickly help them was to make some scripts to guide them through basic commands, seeing results on real-time, and being able to recycle them for their own data.

If you have just started using R, maybe they can be useful for you. However, I will recommend that you use some open “plain text” file(s) to paste your favorite commands and clone/modify them to suit your needs. Remember to store the files where you can access them later!

  • Tip: you can change the extension of your mytext.txt file into mytext.R file, telling Windows to open it with the Notepad again. It will be also a plain text document, but some text editors will recognize it as an “R script” and will highlight the content according to that.
  • Apart from the Notepad in Windows, you also have a bunch of other text/code editors which are more pleasant to use. See for example R-studio and Notepad ++.

Copy the Gists below into your own text files, and begin playing with R!

Mad (Data) Scientist

Musings, useful code etc. on R and data science

My Blog

A topnotch WordPress.com site

walkandfish

Just another WordPress.com site

Georeferenced

A blog on all things Geo, Data, Technology & the interconnected world. Occasionally off-piste.

Retraction Watch

Tracking retractions as a window into the scientific process

"R" you ready?

My advances in R - a learner's diary

TRinker's R Blog

Experiments & Experiences in R

What You're Doing Is Rather Desperate

Notes from the life of a [data] scientist

On unicorns and genes

Martin Johnsson's blog

vet epi

Denis Haine

FreshBiostats

Young Researchers in Biostatistics

Nicebread

...messing around with free code

TRinker's R Blog

...messing around with free code

Revolutions

...messing around with free code

Learning R

Finding my way around R

R-bloggers

R news and tutorials contributed by (750) R bloggers