Plotting selangkah demi selangkah

Dear friends,

Long time no post LOL. Here's an update.

Finally!

I managed to translate an article by Hadley Wickham "Build a plot layer by layer" to Indonesian language for class purposes. I used R Markdown to write the article. But somehow, I can't get it uploaded to my Rpubs repo, as well as to this blogger site.

I've tried to implement a "(semi)-automating the R markdown to blogger workflow" described here, but did work.

So I rendered the Rmd to pdf and posted to my repos:

1. Academia (pdf format) 
2. ResearchGate (Rmd, R code)

I hope the translation is clear enough for anyone (especially Indonesian), who is starting to learn ggplot2 package.

Here's a plot from the original post.

1st Circular: Indonesia R meet up

Karena ternyata sudah banyak yang "terungkap" sebagai Pengguna R (pada tahap beginners hingga advanced), sudah saatnya merancang acara R meet up. Contohnya seperti ini: http://r-users-group.meetup.com/.

Komunitas Indonesia R User akan menyelenggarakan Indonesia R Meet Up yang pertama, dengan tema R 4 All.

TORnya sebagai berikut:

• Siapa saja yang boleh hadir: semua yang berminat dan join R user group.
  
• Siapa saja yang boleh mengirimkan abstrak: pengguna R (tidak ada batasan kompetensi), dan harus join R user group.
  
• Apa saja yang boleh dipresentasikan: semua topik asal menggunakan R.
  

Topik sementara ini hanya dibagi dua: 

1. Natural sciences (termasuk kedokteran, kesehatan)
   
2. Social sciences (termasuk ekonomi)
   

• Bagaimana format abstrak: 200 kata, ada max 5 kata kunci, menggandung latar belakang, metode, hasil, kesimpulan, rekomendasi. Kode R disampaikan sebagai lampiran.
  
• Dikirim ke mana: di post di Wall R User Group.
  

Masukan-masukan terhadap penyelenggaraan acara agar dapat ditulis di kolom komentar.

Terimakasih.

hydrogeological analysis using open source tools: case Cikapundung River

Dear friends,

The following slides (in Rmd) or pdf format are from my recent talk on Sarasehan Geologi Populer, which was held by Geological Survey of Indonesia. It covers various open source tools, with more focus on R, for geological and hydrogeological analysis. This talk tells some bits of my research on finding out interaction between groundwater and surface water interaction by analysing water quality pattern. I used R in this research. The slides contains some R codes example. The objective of this talk is to raise awareness of open source apps and how its contribution to reproducibility in science.

You can view and download:

• the pdf on my SlideShare repo, 
• the Rmd on my Rpubs, and 
• the raw text file from my Github.

@dasaptaerwin

Tidy data set: an "Orange" data mining try out

Dear friends,

This post will be the first post in 2015. I've got many administrative tasks at the Uni, that consumed my blogging hours. The following post actually a series of my tweets in response to my earlier tweets about using Orange data mining software. It's a free and open source software based on Python. This amazing point and click (pnp) piece is developed and maintained by  Bioinformatics Lab at University of Ljubljana, Slovenia, in collaboration with open source community. I use R most of the time, but knowing this pnp implementation has been interesting since my students are not familiar with programming. So you might want to check it out.

The first three tweets (from my account @dasaptaerwin) were:

1st tweet

Orange data mining app: python-based, free, runs on Linux, Mac, Win. Point-click approach (http://orange.biolab.si/ ) @ubuntu_id

 2nd tweet

Orange data mining app: python-based,free,runs on Linux,Mac,Win. Point-click approach (http://orange.biolab.si/ ) @belajarGEOLOGI @GeologistMY

3rd tweet

And here's the look of Orange data mining app on my Linux desktop @belajarGEOLOGI @GeologistMY 

I am using Ubuntu 14.04, so it will look like this.

Then the tweets were replied by @belajargeologi  

@dasaptaerwin @GeologistMY Thank you for this useful information. We hv downloaded it. Would you like to teach us how to use this program?

So then I decided to blog it. 

Orange offers a visual analytic environment. You could just drag any of the analyses (or Widgets as they are called in Orange language) from the toolbox on the left to the work-space (Orange calls it as Canvas). Then you could connect all of the widgets to one another to produce some outputs on the separate sheet (window). You could save any resulted charts and plots. If you see a "red minus sign", that means Orange can't run your analysis flow and ask you to review it.  

It offers interactivity with its user by showing directions and clues about what inputs are needed and what outputs will be produced for each of the widgets. So you have to pay attention to the directions before connecting the widgets. Each node in the above image is a process that needs inputs from its left and produces outputs to the right. 

Then I tweeted some of the results of my analysis on water quality, as follows:  

Distance matrix

Hierarchy cluster 

Regression tree

Then my tweets grew to the data preparation stage or how to make tidy data set (#tidydataset). 

@belajarGEOLOGI @GeologistMY: start by preparing your #tidydataset, containing only variables in columns & case/observations in rows.

@belajarGEOLOGI @GeologistMY: #tidydataset has to be free from table title, merged columns and/or rows. So it is ready to be analyzed.

@belajarGEOLOGI @GeologistMY: numbers in #tidydataset must not mixed w/ string or character, eg: "151.180" or "151,180" or "1,511.80" R diff

@belajarGEOLOGI @GeologistMY: #tidydataset uses the same coordinate projection sys, strike/dip direction, unit of measurements.

@belajarGEOLOGI @GeologistMY: u can use text (char) for: sampleid, classifier/categories data: rock type, high-med-low in #tidydataset (end)

@belajarGEOLOGI @GeologistMY: left table (not tidy) -> un-necessary formatting, right table #tidydataset

This is an example of the not-tidy data table. You can see how the cells are merged with too many un-necessary formatting. This table is nice for final visualization but not for data analysis. You can also see that the rows are for variables/measurements and the columns are for cases or observations. The standard formats for data table is cases/observations in rows and variables/measurements in columns. However, you might want to consider this format when you have a "long" data formats (read The Wide and Long Data Format for Repeated Measures Data and  Is the wide or long format data more efficient? - Stack Overflow).

).

Not-tidy data table 

Tidy data table

So that's all for now. I might re-visit and add more information. Let me know what you think.

Post arrangements

Dear friends,

It's another 10 degrees morning in Sydney. I know the number is fairly warm for most of you, but as an Indonesian, it needs an effort to keep the finger to hit the right key.

After thinking about the position of my blogs, I will:

1. focus this blog for R and data analysis subject
2. gradually move my posts about hydrology and research/teaching to this blog My Online Water Books
3. gradually move my posts on Linux and other tech-internet to my oldest blog Derwinirawan

I'll post all of my updates on my Twitter (@dasaptaerwin) and Google Plus (+Dasapta Erwin Irawan).

Thank you. All the best for you all.