| title | subtitle | author | tags | date | bibliography | output | fontsize | |||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Galaaz Manual |
How to tightly couple Ruby and R in GraalVM |
Rodrigo Botafogo |
|
2019 |
/home/rbotafogo/Bibliography/stats.bib |
|
11pt |
Galaaz is a system for tightly coupling Ruby and R. Ruby is a powerful language, with a large community, a very large set of libraries and great for web development. However, it lacks libraries for data science, statistics, scientific plotting and machine learning. On the other hand, R is considered one of the most powerful languages for solving all of the above problems. Maybe the strongest competitor to R is Python with libraries such as NumPy, Panda, SciPy, SciKit-Learn and a couple more.
With Galaaz we do not intend to re-implement any of the scientific libraries in R, we allow for very tight coupling between the two languages to the point that the Ruby developer does not need to know that there is an R engine running.
According to Wikipedia "Ruby is a dynamic, interpreted, reflective, object-oriented, general-purpose programming language. It was designed and developed in the mid-1990s by Yukihiro "Matz" Matsumoto in Japan." It reached high popularity with the development of Ruby on Rails (RoR) by David Heinemeier Hansson. RoR is a web application framework first released around 2005. It makes extensive use of Ruby's metaprogramming features. With RoR, Ruby became very popular. According to Ruby's Tiobe index it peeked in popularity around 2008, then declined until 2015 when it started picking up again. At the time of this writing (November 2018), the Tiobe index puts Ruby in 16th position as most popular language.
Python, a language similar to Ruby, ranks 4th in the index. Java, C and C++ take the first three positions. Ruby is often criticized for its focus on web applications. But Ruby can do much more than just web applications. Yet, for scientific computing, Ruby lags way behind Python and R. Python has Django framework for web, NumPy for numerical arrays, Pandas for data analysis. R is a free software environment for statistical computing and graphics with thousands of libraries for data analysis.
Until recently, there was no real perspective for Ruby to bridge this gap. Implementing a complete scientific computing infrastructure would take too long. Enters Oracle's GraalVM:
GraalVM is a universal virtual machine for running applications written in JavaScript, Python 3, Ruby, R, JVM-based languages like Java, Scala, Kotlin, and LLVM-based languages such as C and C++.
GraalVM removes the isolation between programming languages and enables interoperability in a shared runtime. It can run either standalone or in the context of OpenJDK, Node.js, Oracle Database, or MySQL.
GraalVM allows you to write polyglot applications with a seamless way to pass values from one language to another. With GraalVM there is no copying or marshaling necessary as it is with other polyglot systems. This lets you achieve high performance when language boundaries are crossed. Most of the time there is no additional cost for crossing a language boundary at all.
Often developers have to make uncomfortable compromises that require them to rewrite their software in other languages. For example:
- That library is not available in my language. I need to rewrite it.
- That language would be the perfect fit for my problem, but we cannot run it in our environment.
- That problem is already solved in my language, but the language is too slow.
With GraalVM we aim to allow developers to freely choose the right language for the task at hand without making compromises.
As stated above, GraalVM is a universal virtual machine that allows Ruby and R (and other languages) to run on the same environment. GraalVM allows polyglot applications to seamlessly interact with one another and pass values from one language to the other. Although a great idea, GraalVM still requires application writers to know several languages. To eliminate that requirement, we built Galaaz, a gem for Ruby, to tightly couple Ruby and R and allow those languages to interact in a way that the user will be unaware of such interaction. In other words, a Ruby programmer will be able to use all the capabilities of R without knowing the R syntax.
Library wrapping is a usual way of bringing features from one language into another. To improve performance, Python often wraps more efficient C libraries. For the Python developer, the existence of such C libraries is hidden. The problem with library wrapping is that for any new library, there is the need to handcraft a new wrapper.
Galaaz, instead of wrapping a single C or R library, wraps the whole R language in Ruby. Doing so, all thousands of R libraries are available immediately to Ruby developers without any new wrapping effort.
Galaaz is the Portuguese name for "Galahad". From Wikipedia:
Sir Galahad (sometimes referred to as Galeas or Galath),
in Arthurian legend, is a knight of King Arthur's Round Table and one
of the three achievers of the Holy Grail. He is the illegitimate son
of Sir Lancelot and Elaine of Corbenic, and is renowned for his
gallantry and purity as the most perfect of all knights. Emerging quite
late in the medieval Arthurian tradition, Sir Galahad first appears in the
Lancelot–Grail cycle, and his story is taken up in later works such as
the Post-Vulgate Cycle and Sir Thomas Malory's Le Morte d'Arthur.
His name should not be mistaken with Galehaut, a different knight from
Arthurian legend.
- Oracle Linux 7
- Ubuntu 18.04 LTS
- Ubuntu 16.04 LTS
- Fedora 28
- macOS 10.14 (Mojave)
- macOS 10.13 (High Sierra)
- TruffleRuby
- FastR
- Install GrallVM (http://www.graalvm.org/)
- Install Ruby (gu install Ruby)
- Install FastR (gu install R)
- Install rake if you want to run the specs and examples (gem install rake)
-
Interactive shell: use 'gstudio' on the command line
gstudio
vec = R.c(1, 2, 3, 4)
puts vec## [1] 1 2 3 4
-
Run all specs
galaaz specs:all
-
Run graphics slideshow (80+ graphics)
galaaz sthda:all
-
Run labs from Introduction to Statistical Learning with R
galaaz islr:all
-
See all available examples
galaaz -T
Shows a list with all available executalbe tasks. To execute a task, substitute the 'rake' word in the list with 'galaaz'. For instance, the following line shows up after 'galaaz -T'
rake master_list:scatter_plot # scatter_plot from:....
execute
galaaz master_list:scatter_plot
One of the nice aspects of Galaaz on GraalVM, is that variables and functions defined in R, can
be easily accessed from Ruby. For instance, to access the 'mtcars' data frame from R
in Ruby, we use the ':mtcar' symbol preceded by the '' operator, thus ':r_vec' retrieves the
value of the 'mtcars' variable.
puts ~:mtcars## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
To access an R function from Ruby, the R function needs to be preceeded by 'R.' scoping. Bellow we see and example of creating a R::Vector by calling the 'c' R function
puts vec = R.c(1.0, 2.0, 3.0, 4.0)## [1] 1 2 3 4
Note that 'vec' is an object of type R::Vector:
puts vec.class## R::Vector
Every object created by a call to an R function will be of a type that inherits from R::Object. In R, there is also a function 'class'. In order to access that function we can call method 'rclass' in the R::Object:
puts vec.rclass## [1] "numeric"
When working with R::Object(s), it is possible to use the '.' operator to pipe operations. When using '.', the object to which the '.' is applied becomes the first argument of the corresponding R function. For instance, function 'c' in R, can be used to concatenate two vectors or more vectors (in R, there are no scalar values, scalars are converted to vectors of size 1. Within Galaaz, scalar parameter is converted to a size one vector):
puts R.c(vec, 10, 20, 30)## [1] 1 2 3 4 10 20 30
The call above to the 'c' function can also be done using '.' notation:
puts vec.c(10, 20, 30)## [1] 1 2 3 4 10 20 30
We will talk about vector indexing in a latter section. But notice here that indexing an R::Vector will return another R::Vector:
puts vec[1]## [1] 1
Sometimes we want to index an R::Object and get back a Ruby object that is not wrapped in an R::Object, but the native Ruby object. For this, we can index the R object with the '>>' operator:
puts vec >> 0
puts vec >> 2## 1.0
## 3.0
It is also possible to call an R function with named arguments, by creating the function in Galaaz with named parameters. For instance, here is an example of creating a 'list' with named elements:
puts R.list(first_name: "Rodrigo", last_name: "Botafogo")## $first_name
## [1] "Rodrigo"
##
## $last_name
## [1] "Botafogo"
Many R functions receive another function as argument. For instance, method 'map' applies a function to every element of a vector. With Galaaz, it is possible to pass a Proc, Method or Lambda in place of the expected R function. In this next example, we will add 2 to every element of our previously created vector:
puts vec.map { |x| x + 2 }## [1] 3
## [1] 4
## [1] 5
## [1] 6
This manual has been formatted usign gKnit. gKnit uses Knitr and R markdown to knit a document in Ruby or R and output it in any of the available formats for R markdown. gKnit runs atop of GraalVM, and Galaaz. In gKnit, Ruby variables are persisted between chunks, making it an ideal solution for literate programming. Also, since it is based on Galaaz, Ruby chunks can have access to R variables and Polyglot Programming with Ruby and R is quite natural.
The idea of "literate programming" was first introduced by Donald Knuth in the 1980's [@Knuth:literate_programming]. The main intention of this approach was to develop software interspersing macro snippets, traditional source code, and a natural language such as English in a document that could be compiled into executable code and at the same time easily read by a human developer. According to Knuth "The practitioner of literate programming can be regarded as an essayist, whose main concern is with exposition and excellence of style."
The idea of literate programming evolved into the idea of reproducible research, in which all the data, software code, documentation, graphics etc. needed to reproduce the research and its reports could be included in a single document or set of documents that when distributed to peers could be rerun generating the same output and reports.
The R community has put a great deal of effort in reproducible research. In 2002, Sweave was introduced and it allowed mixing R code with Latex generating high quality PDF documents. A Sweave document could include code, the results of executing the code, graphics and text such that it contained the whole narrative to reproduce the research. In 2012, Knitr, developed by Yihui Xie from RStudio was released to replace Sweave and to consolidate in one single package the many extensions and add-on packages that were necessary for Sweave.
With Knitr, R markdown was also developed, an extension to the Markdown format. With R markdown and Knitr it is possible to generate reports in a multitude of formats such as HTML, markdown, Latex, PDF, dvi, etc. R markdown also allows the use of multiple programming languages such as R, Ruby, Python, etc. in the same document.
In R markdown, text is interspersed with code chunks that can be executed and both the code and its results can become part of the final report. Although R markdown allows multiple programming languages in the same document, only R and Python (with the reticulate package) can persist variables between chunks. For other languages, such as Ruby, every chunk will start a new process and thus all data is lost between chunks, unless it is somehow stored in a data file that is read by the next chunk.
Being able to persist data between chunks is critical for literate programming otherwise the flow of the narrative is lost by all the effort of having to save data and then reload it. Although this might, at first, seem like a small nuisance, not being able to persist data between chunks is a major issue. For example, let's take a look at the following simple example in which we want to show how to create a list and the use it. Let's first assume that data cannot be persisted between chunks. In the next chunk we create a list, then we would need to save it to file, but to save it, we need somehow to marshal the data into a binary format:
lst = R.list(a: 1, b: 2, c: 3)
lst.saveRDS("lst.rds")then, on the next chunk, where variable 'lst' is used, we need to read back it's value
lst = R.readRDS("lst.rds")
puts lst## $a
## [1] 1
##
## $b
## [1] 2
##
## $c
## [1] 3
Now, any single code has dozens of variables that we might want to use and reuse between chunks. Clearly, such an approach becomes quickly unmanageable. Probably, because of this problem, it is very rare to see any R markdown document in the Ruby community.
When variables can be used accross chunks, then no overhead is needed:
lst = R.list(a: 1, b: 2, c: 3)
# any other code can be added hereputs lst## $a
## [1] 1
##
## $b
## [1] 2
##
## $c
## [1] 3
In the Python community, the same effort to have code and text in an integrated environment started around the first decade of 2000. In 2006 iPython 0.7.2 was released. In 2014, Fernando Pérez, spun off project Jupyter from iPython creating a web-based interactive computation environment. Jupyter can now be used with many languages, including Ruby with the iruby gem (https://github.com/SciRuby/iruby). In order to have multiple languages in a Jupyter notebook the SoS kernel was developed (https://vatlab.github.io/sos-docs/).
gKnit is based on knitr and R markdown and can knit a document written both in Ruby and/or R and output it in any of the available formats of R markdown. gKnit allows ruby developers to do literate programming and reproducible research by allowing them to have in a single document, text and code.
In gKnit, Ruby variables are persisted between chunks, making it an ideal solution for literate programming in this language. Also, since it is based on Galaaz, Ruby chunks can have access to R variables and Polyglot Programming with Ruby and R is quite natural.
This is not a blog post on R markdown, and the interested user is directed to the following links for detailed information on its capabilities and use.
In this post, we will describe just the main aspects of R markdown, so the user can start gKnitting Ruby and R documents quickly.
An R markdown document should start with a Yaml header and be stored in a file with '.Rmd' extension. This document has the following header for gKitting an HTML document.
---
title: "How to do reproducible research in Ruby with gKnit"
author:
- "Rodrigo Botafogo"
- "Daniel Mossé - University of Pittsburgh"
tags: [Tech, Data Science, Ruby, R, GraalVM]
date: "20/02/2019"
output:
html_document:
self_contained: true
keep_md: true
pdf_document:
includes:
in_header: ["../../sty/galaaz.sty"]
number_sections: yes
---
For more information on the options in the Yaml header, check here.
Document formatting can be done with simple markups such as:
# Header 1
## Header 2
### Header 3
Unordered lists:
* Item 1
* Item 2
+ Item 2a
+ Item 2b
Ordered Lists
1. Item 1
2. Item 2
3. Item 3
+ Item 3a
+ Item 3b
For more R markdown formatting go to https://rmarkdown.rstudio.com/authoring_basics.html.
Running and executing Ruby and R code is actually what really interests us is this blog.
Inserting a code chunk is done by adding code in a block delimited by three back ticks
followed by an open
curly brace ('{') followed with the engine name (r, ruby, rb, include, ...), an
any optional chunk_label and options, as shown bellow:
```{engine_name [chunk_label], [chunk_options]}
```
for instance, let's add an R chunk to the document labeled 'first_r_chunk'. This is a very simple code just to create a variable and print it out, as follows:
```{r first_r_chunk}
vec <- c(1, 2, 3)
print(vec)
```
If this block is added to an R markdown document and gKnitted the result will be:
vec <- c(1, 2, 3)
print(vec)## [1] 1 2 3
Now let's say that we want to do some analysis in the code, but just print the result and not the code itself. For this, we need to add the option 'echo = FALSE'.
```{r second_r_chunk, echo = FALSE}
vec2 <- c(10, 20, 30)
vec3 <- vec * vec2
print(vec3)
```
Here is how this block will show up in the document. Observe that the code is not shown and we only see the execution result in a white box
## [1] 10 40 90
A description of the available chunk options can be found in https://yihui.name/knitr/.
Let's add another R chunk with a function definition. In this example, a vector 'r_vec' is created and a new function 'reduce_sum' is defined. The chunk specification is
```{r data_creation}
r_vec <- c(1, 2, 3, 4, 5)
reduce_sum <- function(...) {
Reduce(sum, as.list(...))
}
```
and this is how it will look like once executed. From now on, to be concise in the presentation we will not show chunk definitions any longer.
r_vec <- c(1, 2, 3, 4, 5)
reduce_sum <- function(...) {
Reduce(sum, as.list(...))
}We can, possibly in another chunk, access the vector and call the function as follows:
print(r_vec)## [1] 1 2 3 4 5
print(reduce_sum(r_vec))## [1] 15
In the following chunk, we create a bubble chart in R using ggplot and include it in this document. Note that there is no directive in the code to include the image, this occurs automatically. The 'mpg' dataframe is natively available to R and to Galaaz as well.
For the reader not knowledgeable of ggplot, ggplot is a graphics library based on "the grammar of graphics" [@Wilkinson:grammar_of_graphics]. The idea of the grammar of graphics is to build a graphics by adding layers to the plot. More information can be found in https://towardsdatascience.com/a-comprehensive-guide-to-the-grammar-of-graphics-for-effective-visualization-of-multi-dimensional-1f92b4ed4149.
In the plot bellow the 'mpg' dataset from base R is used. "The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)
First, the 'mpg' dataset if filtered to extract only cars from the following manumactures: Audi, Ford, Honda, and Hyundai and stored in the 'mpg_select' variable. Then, the selected dataframe is passed to the ggplot function specifying in the aesthetic method (aes) that 'displacement' (disp) should be plotted in the 'x' axis and 'city mileage' should be on the 'y' axis. In the 'labs' layer we pass the 'title' and 'subtitle' for the plot. To the basic plot 'g', geom_jitter is added, that plots cars from the same manufactures with the same color (col=manufactures) and the size of the car point equal its high way consumption (size = hwy). Finally, a last layer is plotter containing a linear regression line (method = "lm") for every manufacturer.
# load package and data
library(ggplot2)## Message:
## Registered S3 methods overwritten by 'ggplot2':
## method from
## [.quosures rlang
## c.quosures rlang
## print.quosures rlang
data(mpg, package="ggplot2")
mpg_select <- mpg[mpg$manufacturer %in% c("audi", "ford", "honda", "hyundai"), ]
# Scatterplot
theme_set(theme_bw()) # pre-set the bw theme.
g <- ggplot(mpg_select, aes(displ, cty)) +
labs(subtitle="mpg: Displacement vs City Mileage",
title="Bubble chart")
g + geom_jitter(aes(col=manufacturer, size=hwy)) +
geom_smooth(aes(col=manufacturer), method="lm", se=F)
Including a Ruby chunk is just as easy as including an R chunk in the document: just change the name of the engine to 'ruby'. It is also possible to pass chunk options to the Ruby engine; however, this version does not accept all the options that are available to R chunks. Future versions will add those options.
```{ruby first_ruby_chunk}
```
In this example, the ruby chunk is called 'first_ruby_chunk'. One important aspect of chunk labels is that they cannot be duplicated. If a chunk label is duplicated, gKnit will stop with an error.
In the following chunk, variable 'a', 'b' and 'c' are standard Ruby variables and 'vec' and 'vec2' are two vectors created by calling the 'c' method on the R module.
In Galaaz, the R module allows us to access R functions transparently. The 'c' function in R, is a function that concatenates its arguments making a vector.
It should be clear that there is no requirement in gknit to call or use any R functions. gKnit will knit standard Ruby code, or even general text without any code.
a = [1, 2, 3]
b = "US$ 250.000"
c = "The 'outputs' function"
vec = R.c(1, 2, 3)
vec2 = R.c(10, 20, 30)In the next block, variables 'a', 'vec' and 'vec2' are used and printed.
puts a
puts vec * vec2## 1
## 2
## 3
## [1] 10 40 90
Note that 'a' is a standard Ruby Array and 'vec' and 'vec2' are vectors that behave accordingly, where multiplication works as expected.
When using a Ruby chunk, the code and the output are formatted in blocks as seen above. This formatting is not always desired. Sometimes, we want to have the results of the Ruby evaluation included in the middle of a phrase. gKnit allows adding inline Ruby code with the 'rb' engine. The following chunk specification will create and inline Ruby text:
This is some text with inline Ruby accessing variable 'b' which has value:
```{rb puts b}
```
and is followed by some other text!
This is some text with inline Ruby accessing variable 'b' which has value: US$ 250.000 and is followed by some other text!
Note that it is important not to add any new line before of after the code block if we want everything to be in only one line, resulting in the following sentence with inline Ruby code.
He have previously used the standard 'puts' method in Ruby chunks in order produce output. The result of a 'puts', as seen in all previous chunks that use it, is formatted inside a white box that follows the code block. Many times however, we would like to do some processing in the Ruby chunk and have the result of this processing generate and output that is "included" in the document as if we had typed it in R markdown document.
For example, suppose we want to create a new heading in our document, but the heading phrase is the result of some code processing: maybe it's the first line of a file we are going to read. Method 'outputs' adds its output as if typed in the R markdown document.
Take now a look at variable 'c' (it was defined in a previous block above) as 'c = "The 'outputs' function". "The 'outputs' function" is actually the name of this section and it was created using the 'outputs' function inside a Ruby chunk.
The ruby chunk to generate this heading is:
```{ruby heading}
outputs "### #{c}"
```
The three '###' is the way we add a Heading 3 in R markdown.
We've just seen the use of method 'outputs' to add text to the the R markdown
document. This technique can also be used to add HTML code to the document. In
R markdown, any html code typed directly in the document will be properly rendered.
Here, for instance, is a table definition in HTML and its output in the document:
<table style="width:100%">
<tr>
<th>Firstname</th>
<th>Lastname</th>
<th>Age</th>
</tr>
<tr>
<td>Jill</td>
<td>Smith</td>
<td>50</td>
</tr>
<tr>
<td>Eve</td>
<td>Jackson</td>
<td>94</td>
</tr>
</table>
| Firstname | Lastname | Age |
|---|---|---|
| Jill | Smith | 50 |
| Eve | Jackson | 94 |
But manually creating HTML output is not always easy or desirable, specially if we intend the document to be rendered in other formats, for example, as Latex. Also, The above table looks ugly. The 'kableExtra' library is a great library for creating beautiful tables. Take a look at https://cran.r-project.org/web/packages/kableExtra/vignettes/awesome_table_in_html.html
In the next chunk, we output the 'mtcars' dataframe from R in a nicely formatted table. Note that we retrieve the mtcars dataframe by using '~:mtcars'.
R.install_and_loads('kableExtra')
outputs (~:mtcars).kable.kable_styling| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
R is a language that was created to be easy and fast for statisticians to use. As far as I know, it was not a language to be used for developing large systems. Of course, there are large systems and libraries in R, but the focus of the language is for developing statistical models and distribute that to peers.
Ruby on the other hand, is a language for large software development. Systems written in Ruby will have dozens, hundreds or even thousands of files. To document a large system with literate programming, we cannot expect the developer to add all the files in a single '.Rmd' file. gKnit provides the 'include' chunk engine to include a Ruby file as if it had being typed in the '.Rmd' file.
To include a file, the following chunk should be created, where is the name of the file to be included and where the extension, if it is '.rb', does not need to be added. If the 'relative' option is not included, then it is treated as TRUE. When 'relative' is true, ruby's 'require_relative' semantics is used to load the file, when false, Ruby's $LOAD_PATH is searched to find the file and it is 'require'd.
```{include <filename>, relative = <TRUE/FALSE>}
```
Bellow we include file 'model.rb', which is in the same directory of this blog.
This code uses R 'caret' package to split a dataset in a train and test sets.
The 'caret' package is a very important a useful package for doing Data Analysis,
it has hundreds of functions for all steps of the Data Analysis workflow. To
use 'caret' just to split a dataset is like using the proverbial cannon to
kill the fly. We use it here only to show that integrating Ruby and R and
using even a very complex package as 'caret' is trivial with Galaaz.
A word of advice: the 'caret' package has lots of dependencies and installing it in a Linux system is a time consuming operation. Method 'R.install_and_loads' will install the package if it is not already installed and can take a while.
```{include model}
```
require 'galaaz'
# Loads the R 'caret' package. If not present, installs it
R.install_and_loads 'caret'
class Model
attr_reader :data
attr_reader :test
attr_reader :train
#==========================================================
#
#==========================================================
def initialize(data, percent_train:, seed: 123)
R.set__seed(seed)
@data = data
@percent_train = percent_train
@seed = seed
end
#==========================================================
#
#==========================================================
def partition(field)
train_index =
R.createDataPartition(@data.send(field), p: @percet_train,
list: false, times: 1)
@train = @data[train_index, :all]
@test = @data[-train_index, :all]
end
end
mtcars = ~:mtcars
model = Model.new(mtcars, percent_train: 0.8)
model.partition(:mpg)
puts model.train.head
puts model.test.head## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
gKnit also allows developers to document and load files that are not in the same directory of the '.Rmd' file.
Here is an example of loading the 'find.rb' file from TruffleRuby. In this example, relative is set to FALSE, so Ruby will look for the file in its $LOAD_PATH, and the user does not need to no it's directory.
```{include find, relative = FALSE}
```
# frozen_string_literal: true
#
# find.rb: the Find module for processing all files under a given directory.
#
#
# The +Find+ module supports the top-down traversal of a set of file paths.
#
# For example, to total the size of all files under your home directory,
# ignoring anything in a "dot" directory (e.g. $HOME/.ssh):
#
# require 'find'
#
# total_size = 0
#
# Find.find(ENV["HOME"]) do |path|
# if FileTest.directory?(path)
# if File.basename(path)[0] == ?.
# Find.prune # Don't look any further into this directory.
# else
# next
# end
# else
# total_size += FileTest.size(path)
# end
# end
#
module Find
#
# Calls the associated block with the name of every file and directory listed
# as arguments, then recursively on their subdirectories, and so on.
#
# Returns an enumerator if no block is given.
#
# See the +Find+ module documentation for an example.
#
def find(*paths, ignore_error: true) # :yield: path
block_given? or return enum_for(__method__, *paths, ignore_error: ignore_error)
fs_encoding = Encoding.find("filesystem")
paths.collect!{|d| raise Errno::ENOENT, d unless File.exist?(d); d.dup}.each do |path|
path = path.to_path if path.respond_to? :to_path
enc = path.encoding == Encoding::US_ASCII ? fs_encoding : path.encoding
ps = [path]
while file = ps.shift
catch(:prune) do
yield file.dup.taint
begin
s = File.lstat(file)
rescue Errno::ENOENT, Errno::EACCES, Errno::ENOTDIR, Errno::ELOOP, Errno::ENAMETOOLONG
raise unless ignore_error
next
end
if s.directory? then
begin
fs = Dir.children(file, encoding: enc)
rescue Errno::ENOENT, Errno::EACCES, Errno::ENOTDIR, Errno::ELOOP, Errno::ENAMETOOLONG
raise unless ignore_error
next
end
fs.sort!
fs.reverse_each {|f|
f = File.join(file, f)
ps.unshift f.untaint
}
end
end
end
end
nil
end
#
# Skips the current file or directory, restarting the loop with the next
# entry. If the current file is a directory, that directory will not be
# recursively entered. Meaningful only within the block associated with
# Find::find.
#
# See the +Find+ module documentation for an example.
#
def prune
throw :prune
end
module_function :find, :prune
end
One of the beauties of knitr is that the same input can be converted to many different outputs. One very useful format, is, of course, PDF. In order to converted an R markdown file to PDF it is necessary to have LaTeX installed on the system. We will not explain here how to install LaTeX as there are plenty of documents on the web showing how to proceed.
gKnit comes with a simple LaTeX style file for gknitting this blog as a PDF document. Here is the Yaml header to generate this blog in PDF format instead of HTML:
---
title: "gKnit - Ruby and R Knitting with Galaaz in GraalVM"
author: "Rodrigo Botafogo"
tags: [Galaaz, Ruby, R, TruffleRuby, FastR, GraalVM, knitr, gknit]
date: "29 October 2018"
output:
pdf\_document:
includes:
in\_header: ["../../sty/galaaz.sty"]
number\_sections: yes
---
When a document is converted to PDF it follows a certain convertion template. We've seen above the use of 'galaaz.sty' as a basic template to generate a PDF document. Using the 'gknit-draft' app that comes with Galaaz, the same .Rmd file can be compiled to different looking PDF documents. Galaaz automatically loads the 'rticles' R package that comes with templates for the following journals with the respective template name:
- ACM articles: acm_article
- ACS articles: acs_article
- AEA journal submissions: aea_article
- AGU journal submissions: ????
- AMS articles: ams_article
- American Statistical Association: asa_article
- Biometrics articles: biometrics_article
- Bulletin de l'AMQ journal submissions: amq_article
- CTeX documents: ctex
- Elsevier journal submissions: elsevier_article
- IEEE Transaction journal submissions: ieee_article
- JSS articles: jss_article
- MDPI journal submissions: mdpi_article
- Monthly Notices of the Royal Astronomical Society articles: mnras_article
- NNRAS journal submissions: nmras_article
- PeerJ articles: peerj_article
- Royal Society Open Science journal submissions: rsos_article
- Royal Statistical Society: rss_article
- Sage journal submissions: sage_article
- Springer journal submissions: springer_article
- Statistics in Medicine journal submissions: sim_article
- Copernicus Publications journal submissions: copernicus_article
- The R Journal articles: rjournal_article
- Frontiers articles: ???
- Taylor & Francis articles: ???
- Bulletin De L'AMQ: amq_article
- PLOS journal: plos_article
- Proceedings of the National Academy of Sciences of the USA: pnas_article
In order to create a document with one of those templates, use the following command:
gknit-draft --filename <my_document> --template <template> --package <package>
--create_dir
So, in order to create a template for writing an R Journal, use:
gknit-draft --filename my_r_article --template rjournal_article --package rticles
--create_dir
Galaaz allows Ruby to access variables created in R. For example, the 'mtcars' data set is available in R and can be accessed from Ruby by using the 'tilda' operator followed by the symbol for the variable, in this case ':mtcar'. In the code bellow method 'outputs' is used to output the 'mtcars' data set nicely formatted in HTML by use of the 'kable' and 'kable_styling' functions. Method 'outputs' is only available when used with 'gknit'.
outputs (~:mtcars).kable.kable_styling| mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.570 | 15.84 | 0 | 0 | 3 | 4 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.190 | 20.00 | 1 | 0 | 4 | 2 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.150 | 22.90 | 1 | 0 | 4 | 2 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.30 | 1 | 0 | 4 | 4 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.440 | 18.90 | 1 | 0 | 4 | 4 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.070 | 17.40 | 0 | 0 | 3 | 3 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.730 | 17.60 | 0 | 0 | 3 | 3 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.780 | 18.00 | 0 | 0 | 3 | 3 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.250 | 17.98 | 0 | 0 | 3 | 4 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.00 | 5.424 | 17.82 | 0 | 0 | 3 | 4 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.200 | 19.47 | 1 | 1 | 4 | 1 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.90 | 1 | 1 | 4 | 1 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.70 | 2.465 | 20.01 | 1 | 0 | 3 | 1 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.520 | 16.87 | 0 | 0 | 3 | 2 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.30 | 0 | 0 | 3 | 2 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.840 | 15.41 | 0 | 0 | 3 | 4 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.90 | 1 | 1 | 4 | 1 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.140 | 16.70 | 0 | 1 | 5 | 2 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.90 | 1 | 1 | 5 | 2 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.50 | 0 | 1 | 5 | 4 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.50 | 0 | 1 | 5 | 6 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.60 | 0 | 1 | 5 | 8 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.60 | 1 | 1 | 4 | 2 |
Vectors can be thought of as contiguous cells containing data. Cells are accessed through indexing operations such as x[5]. Galaaz has six basic (‘atomic’) vector types: logical, integer, real, complex, string (or character) and raw. The modes and storage modes for the different vector types are listed in the following table.
| typeof | mode | storage.mode |
|---|---|---|
| logical | logical | logical |
| integer | numeric | integer |
| double | numeric | double |
| complex | complex | comples |
| character | character | character |
| raw | raw | raw |
Single numbers, such as 4.2, and strings, such as "four point two" are still vectors, of length 1; there are no more basic types. Vectors with length zero are possible (and useful). String vectors have mode and storage mode "character". A single element of a character vector is often referred to as a character string.
To create a vector the 'c' (concatenate) method from the 'R' module should be used:
vec = R.c(1, 2, 3)
puts vec## [1] 1 2 3
Lets take a look at the type, mode and storage.mode of our vector vec. In order to print this out, we are creating a data frame 'df' and printing it out. A data frame, for those not familiar with it, is basically a table. Here we create the data frame and add the column name by passing named parameters for each column, such as 'typeof:', 'mode:' and 'storage__mode?'. You should also note here that the double underscore is converted to a '.'. So, when printed 'storage__mode' will actually print as 'storage.mode'.
Data frames will later be more carefully described. In R, the method used to create a data frame is 'data.frame', in Galaaz we use 'data__frame'.
df = R.data__frame(typeof: vec.typeof, mode: vec.mode, storage__mode: vec.storage__mode)
puts df## typeof mode storage.mode
## 1 integer numeric integer
If you want to create a vector with floating point numbers, then we need at least one of the vector's element to be a float, such as 1.0. R users should be careful, since in R a number like '1' is converted to float and to have an integer the R developer will use '1L'. Galaaz follows normal Ruby rules and the number 1 is an integer and 1.0 is a float.
vec = R.c(1.0, 2, 3)
puts vec## [1] 1 2 3
df = R.data__frame(typeof: vec.typeof, mode: vec.mode, storage__mode: vec.storage__mode)
outputs df.kable.kable_styling| typeof | mode | storage.mode |
|---|---|---|
| double | numeric | double |
In this next example we try to create a vector with a variable 'hello' that has not yet being defined. This will raise an exception that is printed out. We get two return blocks, the first with a message explaining what went wrong and the second with the full backtrace of the error.
vec = R.c(1, hello, 5)## Message:
## undefined local variable or method `hello' for #<RC:0x3d8 @out_list=nil>:RC
## Message:
## /home/rbotafogo/desenv/galaaz/lib/util/exec_ruby.rb:103:in `get_binding'
## /home/rbotafogo/desenv/galaaz/lib/util/exec_ruby.rb:102:in `eval'
## /home/rbotafogo/desenv/galaaz/lib/util/exec_ruby.rb:102:in `exec_ruby'
## /home/rbotafogo/desenv/galaaz/lib/gknit/knitr_engine.rb:650:in `block in initialize'
## /home/rbotafogo/desenv/galaaz/lib/R_interface/ruby_callback.rb:77:in `call'
## /home/rbotafogo/desenv/galaaz/lib/R_interface/ruby_callback.rb:77:in `callback'
## (eval):3:in `function(...) {\n rb_method(...)'
## unknown.r:1:in `in_dir'
## unknown.r:1:in `block_exec'
## /usr/local/lib/graalvm-ce-java11-20.0.0/languages/R/library/knitr/R/block.R:92:in `call_block'
## /usr/local/lib/graalvm-ce-java11-20.0.0/languages/R/library/knitr/R/block.R:6:in `process_group.block'
## /usr/local/lib/graalvm-ce-java11-20.0.0/languages/R/library/knitr/R/block.R:3:in `<no source>'
## unknown.r:1:in `withCallingHandlers'
## unknown.r:1:in `process_file'
## unknown.r:1:in `<no source>'
## unknown.r:1:in `<no source>'
## <REPL>:4:in `<repl wrapper>'
## <REPL>:1
Here is a vector with logical values
vec = R.c(true, true, false, false, true)
puts vec## [1] TRUE TRUE FALSE FALSE TRUE
The 'c' functions used to create vectors can also be used to combine two vectors:
vec1 = R.c(10.0, 20.0, 30.0)
vec2 = R.c(4.0, 5.0, 6.0)
vec = R.c(vec1, vec2)
puts vec## [1] 10 20 30 4 5 6
In galaaz, methods can be chainned (somewhat like the pipe operator in R %>%, but more generic). In this next example, method 'c' is chainned after 'vec1'. This also looks like 'c' is a method of the vector, but in reallity, this is actually closer to the pipe operator. When Galaaz identifies that 'c' is not a method of 'vec' it actually tries to call 'R.c' with 'vec1' as the first argument concatenated with all the other available arguments. The code bellow is automatically converted to the code above.
vec = vec1.c(vec2)
puts vec## [1] 10 20 30 4 5 6
Arithmetic operations on vectors are performed element by element:
puts vec1 + vec2## [1] 14 25 36
puts vec1 * 5## [1] 50 100 150
When vectors have different length, a recycling rule is applied to the shorter vector:
vec3 = R.c(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0)
puts vec4 = vec1 + vec3## [1] 11 22 33 14 25 36 17 28 39
Vectors can be indexed by using the '[]' operator:
puts vec4[3]## [1] 33
We can also index a vector with another vector. For example, in the code bellow, we take elements 1, 3, 5, and 7 from vec3:
puts vec4[R.c(1, 3, 5, 7)]## [1] 11 33 25 17
Repeating an index and having indices out of order is valid code:
puts vec4[R.c(1, 3, 3, 1)]## [1] 11 33 33 11
It is also possible to index a vector with a negative number or negative vector. In these cases the indexed values are not returned:
puts vec4[-3]
puts vec4[-R.c(1, 3, 5, 7)]## [1] 11 22 14 25 36 17 28 39
## [1] 22 14 36 28 39
If an index is out of range, a missing value (NA) will be reported.
puts vec4[30]## [1] NA
It is also possible to index a vector by range:
puts vec4[(2..5)]## [1] 22 33 14 25
Elements in a vector can be named using the 'names' attribute of a vector:
full_name = R.c("Rodrigo", "A", "Botafogo")
full_name.names = R.c("First", "Middle", "Last")
puts full_name## First Middle Last
## "Rodrigo" "A" "Botafogo"
Or it can also be named by using the 'c' function with named paramenters:
full_name = R.c(First: "Rodrigo", Middle: "A", Last: "Botafogo")
puts full_name## First Middle Last
## "Rodrigo" "A" "Botafogo"
Vectors created with 'R.c' are of class R::Vector. You might have noticed that when indexing a vector, a new vector is returned, even if this vector has one single element. In order to use R::Vector with other ruby classes it might be necessary to extract the actual Ruby native type from the vector. In order to do this extraction the '>>' operator is used.
puts vec4
puts vec4 >> 0
puts vec4 >> 4## [1] 11 22 33 14 25 36 17 28 39
## 11.0
## 25.0
Note that indexing with '>>' starts at 0 and not at 1, also, we cannot do negative indexing.
A matrix is a collection of elements organized as a two dimensional table. A matrix can be created by the 'matrix' function:
mat = R.matrix(R.c(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0),
nrow: 3,
ncol: 3)
puts mat## [,1] [,2] [,3]
## [1,] 1 4 7
## [2,] 2 5 8
## [3,] 3 6 9
Note that matrices data is organized by column first. It is possible to organize the matrix memory by row first passing an extra argument to the 'matrix' function:
mat_row = R.matrix(R.c(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0),
nrow: 3,
ncol: 3,
byrow: true)
puts mat_row## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9
A matrix can be indexed by [row, column]:
puts mat_row[1, 1]
puts mat_row[2, 3]## [1] 1
## [1] 6
It is possible to index an entire row or column with the ':all' keyword
puts mat_row[1, :all]
puts mat_row[:all, 2]## [1] 1 2 3
## [1] 2 5 8
Indexing with a vector is also possible for matrices. In the following example we want rows 1 and 3 and columns 2 and 3 building a 2 x 2 matrix.
puts mat_row[R.c(1, 3), R.c(2, 3)]## [,1] [,2]
## [1,] 2 3
## [2,] 8 9
Matrices can be combined with functions 'rbind':
puts mat_row.rbind(mat)## [,1] [,2] [,3]
## [1,] 1 2 3
## [2,] 4 5 6
## [3,] 7 8 9
## [4,] 1 4 7
## [5,] 2 5 8
## [6,] 3 6 9
and 'cbind':
puts mat_row.cbind(mat)## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 1 2 3 1 4 7
## [2,] 4 5 6 2 5 8
## [3,] 7 8 9 3 6 9
A list is a data structure that can contain sublists of different types, while vector and matrix can only hold one type of element.
nums = R.c(1.0, 2.0, 3.0)
strs = R.c("a", "b", "c", "d")
bool = R.c(true, true, false)
lst = R.list(nums: nums, strs: strs, bool: bool)
puts lst## $nums
## [1] 1 2 3
##
## $strs
## [1] "a" "b" "c" "d"
##
## $bool
## [1] TRUE TRUE FALSE
Note that 'lst' elements are named elements.
List indexing, also called slicing, is done using the '[]' operator and the '[[]]' operator. Let's first start with the '[]' operator. The list above has three sublist indexing with '[]' will return one of the sublists.
puts lst[1]## $nums
## [1] 1 2 3
Note that when using '[]' a new list is returned. When using the double square bracket operator the value returned is the actual element of the list in the given position and not a slice of the original list
puts lst[[1]]## [1] 1 2 3
When elements are named, as dones with lst, indexing can be done by name:
puts lst[['bool']][[1]] >> 0## true
In this example, first the 'bool' element of the list was extracted, not as a list, but as a vector, then the first element of the vector was extracted (note that vectors also accept the '[[]]' operator) and then the vector was indexed by its first element, extracting the native Ruby type.
A data frame is a table like structure in which each column has the same number of rows. Data frames are the basic structure for storing data for data analysis. We have already seen a data frame previously when we accessed variable '~:mtcars'. In order to create a data frame, function 'data__frame' is used:
df = R.data__frame(
year: R.c(2010, 2011, 2012),
income: R.c(1000.0, 1500.0, 2000.0))
puts df## year income
## 1 2010 1000
## 2 2011 1500
## 3 2012 2000
A data frame can be indexed the same way as a matrix, by using '[row, column]', where row and column can either be a numeric or the name of the row or column
puts (~:mtcars).head
puts (~:mtcars)[1, 2]
puts (~:mtcars)['Datsun 710', 'mpg']## mpg cyl disp hp drat wt qsec vs am gear carb
## Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
## Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
## Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
## Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
## [1] 6
## [1] 22.8
Extracting a column from a data frame as a vector can be done by using the double square bracket operator:
puts (~:mtcars)[['mpg']]## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
A data frame column can also be accessed as if it were an instance variable of the data frame:
puts (~:mtcars).mpg## [1] 21.0 21.0 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 17.8 16.4 17.3 15.2
## [15] 10.4 10.4 14.7 32.4 30.4 33.9 21.5 15.5 15.2 13.3 19.2 27.3 26.0 30.4
## [29] 15.8 19.7 15.0 21.4
Slicing a data frame can be done by indexing it with a vector (we use 'head' to reduce the output):
puts (~:mtcars)[R.c('mpg', 'hp')].head## mpg hp
## Mazda RX4 21.0 110
## Mazda RX4 Wag 21.0 110
## Datsun 710 22.8 93
## Hornet 4 Drive 21.4 110
## Hornet Sportabout 18.7 175
## Valiant 18.1 105
A row slice can be obtained by indexing by row and using the ':all' keyword for the column:
puts (~:mtcars)[R.c('Datsun 710', 'Camaro Z28'), :all]## mpg cyl disp hp drat wt qsec vs am gear carb
## Datsun 710 22.8 4 108 93 3.85 2.32 18.61 1 1 4 1
## Camaro Z28 13.3 8 350 245 3.73 3.84 15.41 0 0 3 4
Finally, a data frame can also be indexed with a logical vector. In this next example, the 'am' column of :mtcars is compared with 0 (with method 'eq'). When 'am' is equal to 0 the car is automatic. So, by doing '(~:mtcars).am.eq 0' a logical vector is created with 'true' whenever 'am' is 0 and 'false' otherwise.
# obtain a vector with 'true' for cars with automatic transmission
automatic = (~:mtcars).am.eq 0
puts automatic## [1] FALSE FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
## [12] TRUE TRUE TRUE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE
## [23] TRUE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
Using this logical vector, the data frame is indexed, returning a new data frame in which all cars have automatic transmission.
# slice the data frame by using this vector
puts (~:mtcars)[automatic, :all]## mpg cyl disp hp drat wt qsec vs am gear carb
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Galaaz extends Ruby to work with complex expressions, similar to R's expressions build with 'quote' (base R) or 'quo' (tidyverse). Let's take a look at some of those expressions.
The code bellow creates an expression summing two symbols
exp1 = :a + :b
puts exp1## a + b
We can build any complex mathematical expression
exp2 = (:a + :b) * 2.0 + :c ** 2 / :z
puts exp2## (a + b) * 2 + c^2L/z
It is also possible to use inequality operators in building expressions
exp3 = (:a + :b) >= :z
puts exp3## a + b >= z
Galaaz provides both symbolic representations for operators, such as (>, <, !=) as functional notation for those operators such as (.gt, .ge, etc.). So the same expression written above can also be written as
exp4 = (:a + :b).ge :z
puts exp4## a + b >= z
Two type of expression can only be created with the functional representation of the operators, those are expressions involving '==', and '='. In order to write an expression involving '==' we need to use the method '.eq' and for '=' we need the function '.assign'
exp5 = (:a + :b).eq :z
puts exp5## a + b == z
exp6 = :y.assign :a + :b
puts exp6## y <- a + b
In general we think that using the functional notation is preferable to using the symbolic notation as otherwise, we end up writing invalid expressions such as
exp_wrong = (:a + :b) == :z
puts exp_wrongand it might be difficult to understand what is going on here. The problem lies with the fact that when using '==' we are comparing expression (:a + :b) to expression :z with '=='. When the comparison is executed, the system tries to evaluate :a, :b and :z, and those symbols at this time are not bound to anything and we get a "object 'a' not found" message. If we only use functional notation, this type of error will not occur.
It is often necessary to create an expression that uses a method or function. For instance, in
mathematics, it's quite natural to write an expressin such as
exp7 = :y.assign E.sin(:x)
puts exp7## y <- sin(x)
Expressions can also be written using '.' notation:
exp8 = :y.assign :x.sin
puts exp8## y <- sin(x)
When a function has multiple arguments, the first one can be used before the '.':
exp9 = :x.c(:y)
puts exp9## c(x, y)
Expressions can be evaluated by calling function 'eval' with a binding. A binding can be provided with a list:
exp = (:a + :b) * 2.0 + :c ** 2 / :z
puts exp.eval(R.list(a: 10, b: 20, c: 30, z: 40))## [1] 82.5
... with a data frame:
df = R.data__frame(
a: R.c(1, 2, 3),
b: R.c(10, 20, 30),
c: R.c(100, 200, 300),
z: R.c(1000, 2000, 3000))
puts exp.eval(df)## [1] 32 64 96
One of the major benefits of Galaaz is to bring strong data manipulation to Ruby. The following examples were extracted from Hardley's "R for Data Science" (https://r4ds.had.co.nz/). This is a highly recommended book for those not already familiar with the 'tidyverse' style of programming in R. In the sections to follow, we will limit ourselves to convert the R code to Galaaz.
For these examples, we will investigate the nycflights13 data set available on the package by the same name. We use function 'R.install_and_loads' that checks if the library is available locally, and if not, installs it. This data frame contains all 336,776 flights that departed from New York City in 2013. The data comes from the US Bureau of Transportation Statistics.
Dplyr uses 'tibbles' in place of data frames; unfortunately, tibbles do not print yet properly in Galaaz due to a bug in fastR. In order to print a tibble we need to convert it to a data frame using the 'as__data__frame' method.
R.install_and_loads('nycflights13')
R.library('dplyr')flights = ~:flights
puts flights.head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
In this example we filter the flights data set by giving to the filter function two expressions: the first :month.eq 1
puts flights.filter((:month.eq 1), (:day.eq 1)).head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
All flights that departed in November of December
puts flights.filter((:month.eq 11) | (:month.eq 12)).head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 11 1 5 2359 6 352
## 2 2013 11 1 35 2250 105 123
## 3 2013 11 1 455 500 -5 641
## 4 2013 11 1 539 545 -6 856
## 5 2013 11 1 542 545 -3 831
## 6 2013 11 1 549 600 -11 912
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
The same as above, but using the 'in' operator. In R, it is possible to define many operators by doing %%. The %in% operator checks if a value is in a vector. In order to use those operators from Galaaz the '._' method is used, where the first argument is the operator's symbol, in this case ':in' and the second argument is the vector:
puts flights.filter(:month._ :in, R.c(11, 12)).head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 11 1 5 2359 6 352
## 2 2013 11 1 35 2250 105 123
## 3 2013 11 1 455 500 -5 641
## 4 2013 11 1 539 545 -6 856
## 5 2013 11 1 542 545 -3 831
## 6 2013 11 1 549 600 -11 912
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
Let's first create a 'tibble' with a Not Available value (R::NA). Tibbles are a modern version of a data frame and operate very similarly to one. It differs in how it outputs the values and the result of some subsetting operations that are more consistent than what is obtained from data frame.
df = R.tibble(x: R.c(1, R::NA, 3))
puts df## # A tibble: 3 x 1
## x
## <int>
## 1 1
## 2
## 3 3
Now filtering by :x > 1 shows all lines that satisfy this condition, where the row with R:NA does not.
puts df.filter(:x > 1)## # A tibble: 1 x 1
## x
## <int>
## 1 3
To match an NA use method 'is__na'
puts df.filter((:x.is__na) | (:x > 1))## # A tibble: 2 x 1
## x
## <int>
## 1
## 2 3
Arrange reorders the rows of a data frame by the given arguments.
puts flights.arrange(:year, :month, :day).head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
To arrange in descending order, use function 'desc'
puts flights.arrange(:dep_delay.desc).head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 9 641 900 1301 1242
## 2 2013 6 15 1432 1935 1137 1607
## 3 2013 1 10 1121 1635 1126 1239
## 4 2013 9 20 1139 1845 1014 1457
## 5 2013 7 22 845 1600 1005 1044
## 6 2013 4 10 1100 1900 960 1342
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
To select specific columns from a dataset we use function 'select':
puts flights.select(:year, :month, :day).head## # A tibble: 6 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
It is also possible to select column in a given range
puts flights.select(:year.up_to :day).head## # A tibble: 6 x 3
## year month day
## <int> <int> <int>
## 1 2013 1 1
## 2 2013 1 1
## 3 2013 1 1
## 4 2013 1 1
## 5 2013 1 1
## 6 2013 1 1
Select all columns that start with a given name sequence
puts flights.select(E.starts_with('arr')).head## # A tibble: 6 x 2
## arr_time arr_delay
## <int> <dbl>
## 1 830 11
## 2 850 20
## 3 923 33
## 4 1004 -18
## 5 812 -25
## 6 740 12
Other functions that can be used:
-
ends_with("xyz"): matches names that end with “xyz”.
-
contains("ijk"): matches names that contain “ijk”.
-
matches("(.)\1"): selects variables that match a regular expression. This one matches any variables that contain repeated characters.
-
num_range("x", (1..3)): matches x1, x2 and x3
A helper function that comes in handy when we just want to rearrange column order is 'Everything':
puts flights.select(:year, :month, :day, E.everything).head## # A tibble: 6 x 19
## year month day dep_time sched_dep_time dep_delay arr_time
## <int> <int> <int> <int> <int> <dbl> <int>
## 1 2013 1 1 517 515 2 830
## 2 2013 1 1 533 529 4 850
## 3 2013 1 1 542 540 2 923
## 4 2013 1 1 544 545 -1 1004
## 5 2013 1 1 554 600 -6 812
## 6 2013 1 1 554 558 -4 740
## # … with 12 more variables: sched_arr_time <int>, arr_delay <dbl>,
## # carrier <chr>, flight <int>, tailnum <chr>, origin <chr>, dest <chr>,
## # air_time <dbl>, distance <dbl>, hour <dbl>, minute <dbl>,
## # time_hour <dttm>
flights_sm = flights.
select((:year.up_to :day),
E.ends_with('delay'),
:distance,
:air_time)
puts flights_sm.head## # A tibble: 6 x 7
## year month day dep_delay arr_delay distance air_time
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227
## 2 2013 1 1 4 20 1416 227
## 3 2013 1 1 2 33 1089 160
## 4 2013 1 1 -1 -18 1576 183
## 5 2013 1 1 -6 -25 762 116
## 6 2013 1 1 -4 12 719 150
flights_sm = flights_sm.
mutate(gain: :dep_delay - :arr_delay,
speed: :distance / :air_time * 60)
puts flights_sm.head## # A tibble: 6 x 9
## year month day dep_delay arr_delay distance air_time gain speed
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2013 1 1 2 11 1400 227 -9 370.
## 2 2013 1 1 4 20 1416 227 -16 374.
## 3 2013 1 1 2 33 1089 160 -31 408.
## 4 2013 1 1 -1 -18 1576 183 17 517.
## 5 2013 1 1 -6 -25 762 116 19 394.
## 6 2013 1 1 -4 12 719 150 -16 288.
Function 'summarise' calculates summaries for the data frame. When no 'group_by' is used a single value is obtained from the data frame:
puts flights.summarise(delay: E.mean(:dep_delay, na__rm: true))## # A tibble: 1 x 1
## delay
## <dbl>
## 1 12.6
When a data frame is groupe with 'group_by' summaries apply to the given group:
by_day = flights.group_by(:year, :month, :day)
puts by_day.summarise(delay: :dep_delay.mean(na__rm: true)).head## # A tibble: 6 x 4
## # Groups: year, month [1]
## year month day delay
## * <int> <int> <int> <dbl>
## 1 2013 1 1 11.5
## 2 2013 1 2 13.9
## 3 2013 1 3 11.0
## 4 2013 1 4 8.95
## 5 2013 1 5 5.73
## 6 2013 1 6 7.15
Next we put many operations together by pipping them one after the other:
delays = flights.
group_by(:dest).
summarise(
count: E.n,
dist: :distance.mean(na__rm: true),
delay: :arr_delay.mean(na__rm: true)).
filter(:count > 20, :dest != "NHL")
puts delays.head## # A tibble: 6 x 4
## dest count dist delay
## <chr> <int> <dbl> <dbl>
## 1 ABQ 254 1826 4.38
## 2 ACK 265 199 4.85
## 3 ALB 439 143 14.4
## 4 ATL 17215 757. 11.3
## 5 AUS 2439 1514. 6.02
## 6 AVL 275 584. 8.00
R.library('data.table')
R.install_and_loads('curl')
input = "https://raw.githubusercontent.com/Rdatatable/data.table/master/vignettes/flights14.csv"
flights = R.fread(input)
puts flights
puts flights.dim## year month day dep_delay arr_delay carrier origin dest air_time
## 1: 2014 1 1 14 13 AA JFK LAX 359
## 2: 2014 1 1 -3 13 AA JFK LAX 363
## 3: 2014 1 1 2 9 AA JFK LAX 351
## 4: 2014 1 1 -8 -26 AA LGA PBI 157
## 5: 2014 1 1 2 1 AA JFK LAX 350
## ---
## 253312: 2014 10 31 1 -30 UA LGA IAH 201
## 253313: 2014 10 31 -5 -14 UA EWR IAH 189
## 253314: 2014 10 31 -8 16 MQ LGA RDU 83
## 253315: 2014 10 31 -4 15 MQ LGA DTW 75
## 253316: 2014 10 31 -5 1 MQ LGA SDF 110
## distance hour
## 1: 2475 9
## 2: 2475 11
## 3: 2475 19
## 4: 1035 7
## 5: 2475 13
## ---
## 253312: 1416 14
## 253313: 1400 8
## 253314: 431 11
## 253315: 502 11
## 253316: 659 8
## [1] 253316 11
data_table = R.data__table(
ID: R.c("b","b","b","a","a","c"),
a: (1..6),
b: (7..12),
c: (13..18)
)
puts data_table
puts data_table.ID## ID a b c
## 1: b 1 7 13
## 2: b 2 8 14
## 3: b 3 9 15
## 4: a 4 10 16
## 5: a 5 11 17
## 6: c 6 12 18
## [1] "b" "b" "b" "a" "a" "c"
# subset rows in i
ans = flights[(:origin.eq "JFK") & (:month.eq 6)]
puts ans.head
# Get the first two rows from flights.
ans = flights[(1..2)]
puts ans
# Sort flights first by column origin in ascending order, and then by dest in descending order:
# ans = flights[E.order(:origin, -(:dest))]
# puts ans.head## year month day dep_delay arr_delay carrier origin dest air_time
## 1: 2014 6 1 -9 -5 AA JFK LAX 324
## 2: 2014 6 1 -10 -13 AA JFK LAX 329
## 3: 2014 6 1 18 -1 AA JFK LAX 326
## 4: 2014 6 1 -6 -16 AA JFK LAX 320
## 5: 2014 6 1 -4 -45 AA JFK LAX 326
## 6: 2014 6 1 -6 -23 AA JFK LAX 329
## distance hour
## 1: 2475 8
## 2: 2475 12
## 3: 2475 7
## 4: 2475 10
## 5: 2475 18
## 6: 2475 14
## year month day dep_delay arr_delay carrier origin dest air_time
## 1: 2014 1 1 14 13 AA JFK LAX 359
## 2: 2014 1 1 -3 13 AA JFK LAX 363
## distance hour
## 1: 2475 9
## 2: 2475 11
# Select column(s) in j
# select arr_delay column, but return it as a vector.
ans = flights[:all, :arr_delay]
puts ans.head
# Select arr_delay column, but return as a data.table instead.
ans = flights[:all, :arr_delay.list]
puts ans.head
ans = flights[:all, E.list(:arr_delay, :dep_delay)]## [1] 13 13 9 -26 1 0
## arr_delay
## 1: 13
## 2: 13
## 3: 9
## 4: -26
## 5: 1
## 6: 0
Creating graphics in Galaaz is quite easy, as it can use all the power of ggplot2. There are many resources in the web that teaches ggplot, so here we give a quick example of ggplot integration with Ruby. We continue to use the :mtcars dataset and we will plot a diverging bar plot, showing cars that have 'above' or 'below' gas consuption. Let's first prepare the data frame with the necessary data:
# copy the R variable :mtcars to the Ruby mtcars variable
mtcars = ~:mtcars
# create a new column 'car_name' to store the car names so that it can be
# used for plotting. The 'rownames' of the data frame cannot be used as
# data for plotting
mtcars.car_name = R.rownames(:mtcars)
# compute normalized mpg and add it to a new column called mpg_z
# Note that the mean value for mpg can be obtained by calling the 'mean'
# function on the vector 'mtcars.mpg'. The same with the standard
# deviation 'sd'. The vector is then rounded to two digits with 'round 2'
mtcars.mpg_z = ((mtcars.mpg - mtcars.mpg.mean)/mtcars.mpg.sd).round 2
# create a new column 'mpg_type'. Function 'ifelse' is a vectorized function
# that looks at every element of the mpg_z vector and if the value is below
# 0, returns 'below', otherwise returns 'above'
mtcars.mpg_type = (mtcars.mpg_z < 0).ifelse("below", "above")
# order the mtcar data set by the mpg_z vector from smaler to larger values
mtcars = mtcars[mtcars.mpg_z.order, :all]
# convert the car_name column to a factor to retain sorted order in plot
mtcars.car_name = mtcars.car_name.factor levels: mtcars.car_name
# let's look at the final data frame
puts mtcars.head## mpg cyl disp hp drat wt qsec vs am gear carb
## Cadillac Fleetwood 10.4 8 472 205 2.93 5.250 17.98 0 0 3 4
## Lincoln Continental 10.4 8 460 215 3.00 5.424 17.82 0 0 3 4
## Camaro Z28 13.3 8 350 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301 335 3.54 3.570 14.60 0 1 5 8
## car_name mpg_z mpg_type
## Cadillac Fleetwood Cadillac Fleetwood -1.61 below
## Lincoln Continental Lincoln Continental -1.61 below
## Camaro Z28 Camaro Z28 -1.13 below
## Duster 360 Duster 360 -0.96 below
## Chrysler Imperial Chrysler Imperial -0.89 below
## Maserati Bora Maserati Bora -0.84 below
Now, lets plot the diverging bar plot. When using gKnit, there is no need to call 'R.awt' to create a plotting device, since gKnit does take care of it. Galaaz provides integration with ggplot. The interested reader should check online for more information on ggplot, since it is outside the scope of this manual describing how ggplot works. We give here but a brief description on how this plot is generated.
ggplot implements the 'grammar of graphics'. In this approach, plots are build by adding layers to the plot. On the first layer we describe what we want on the 'x' and 'y' axis of the plot. In this case, we have 'car_name' on the 'x' axis and 'mpg_z' on the 'y' axis. Then the type of graph is specified by adding 'geom_bar' (for a bar graph). We specify that our bars should be filled using 'mpg_type', which is either 'above' or 'bellow' giving then two colours for filling. On the next layer we specify the labels for the graph, then we add the title and subtitle. Finally, in a bar chart usually bars go on the vertical direction, but in this graph we want the bars to be horizontally layed so we add 'coord_flip'.
require 'ggplot'
puts mtcars.ggplot(E.aes(x: :car_name, y: :mpg_z, label: :mpg_z)) +
R.geom_bar(E.aes(fill: :mpg_type), stat: 'identity', width: 0.5) +
R.scale_fill_manual(name: 'Mileage',
labels: R.c('Above Average', 'Below Average'),
values: R.c('above': '#00ba38', 'below': '#f8766d')) +
R.labs(subtitle: "Normalised mileage from 'mtcars'",
title: "Diverging Bars") +
R.coord_flip
In R, and when coding with 'tidyverse', arguments to a function are usually not referencially transparent. That is, you can’t replace a value with a seemingly equivalent object that you’ve defined elsewhere. To see the problem, let's first define a data frame:
df = R.data__frame(x: (1..3), y: (3..1))
puts df## x y
## 1 1 3
## 2 2 2
## 3 3 1
and now, let's look at this code:
my_var <- x
filter(df, my_var == 1)It generates the following error: "object 'x' not found.
However, in Galaaz, arguments are referencially transparent as can be seen by the code bellow. Note initally that 'my_var = :x' will not give the error "object 'x' not found" since ':x' is treated as an expression and assigned to my_var. Then when doing (my_var.eq 1), my_var is a variable that resolves to ':x' and it becomes equivalent to (:x.eq 1) which is what we want.
my_var = :x
puts df.filter(my_var.eq 1)## x y
## 1 1 3
As stated by Hardley
dplyr code is ambiguous. Depending on what variables are defined where, filter(df, x == y) could be equivalent to any of:
df[df$x == df$y, ]
df[df$x == y, ]
df[x == df$y, ]
df[x == y, ]
In galaaz this ambiguity does not exist, filter(df, x.eq y) is not a valid expression as expressions are build with symbols. In doing filter(df, :x.eq y) we are looking for elements of the 'x' column that are equal to a previously defined y variable. Finally in filter(df, :x.eq :y) we are looking for elements in which the 'x' column value is equal to the 'y' column value. This can be seen in the following two chunks of code:
y = 1
x = 2
# looking for values where the 'x' column is equal to the 'y' column
puts df.filter(:x.eq :y)## x y
## 1 2 2
# looking for values where the 'x' column is equal to the 'y' variable
# in this case, the number 1
puts df.filter(:x.eq y)## x y
## 1 1 3
Let's suppose that we want to write a function that receives as the first argument a data frame and as second argument an expression that adds a column to the data frame that is equal to the sum of elements in column 'a' plus 'x'.
Here is the intended behaviour using the 'mutate' function of 'dplyr':
mutate(df1, y = a + x)
mutate(df2, y = a + x)
mutate(df3, y = a + x)
mutate(df4, y = a + x)
The naive approach to writing an R function to solve this problem is:
mutate_y <- function(df) {
mutate(df, y = a + x)
}
Unfortunately, in R, this function can fail silently if one of the variables isn’t present in the data frame, but is present in the global environment. We will not go through here how to solve this problem in R.
In Galaaz the method mutate_y bellow will work fine and will never fail silently.
def mutate_y(df)
df.mutate(:y.assign :a + :x)
endHere we create a data frame that has only one column named 'x':
df1 = R.data__frame(x: (1..3))
puts df1## x
## 1 1
## 2 2
## 3 3
Note that method mutate_y will fail independetly from the fact that variable 'a' is defined and in the scope of the method. Variable 'a' has no relationship with the symbol ':a' used in the definition of 'mutate_y' above:
a = 10
mutate_y(df1)## Message:
## Error in mutate_impl(.data, dots) :
## Evaluation error: object 'a' not found.
## In addition: Warning message:
## In mutate_impl(.data, dots) :
## mismatched protect/unprotect (unprotect with empty protect stack) (RError)
## Translated to internal error
Let's move to the next problem as presented by Hardley where trying to write a function in R that will receive two argumens, the first a variable and the second an expression is not trivial. Bellow we create a data frame and we want to write a function that groups data by a variable and summarises it by an expression:
set.seed(123)
df <- data.frame(
g1 = c(1, 1, 2, 2, 2),
g2 = c(1, 2, 1, 2, 1),
a = sample(5),
b = sample(5)
)
as.data.frame(df) ## g1 g2 a b
## 1 1 1 3 3
## 2 1 2 2 1
## 3 2 1 5 2
## 4 2 2 4 5
## 5 2 1 1 4
d2 <- df %>%
group_by(g1) %>%
summarise(a = mean(a))
as.data.frame(d2) ## g1 a
## 1 1 2.500000
## 2 2 3.333333
d2 <- df %>%
group_by(g2) %>%
summarise(a = mean(a))
as.data.frame(d2) ## g2 a
## 1 1 3
## 2 2 3
As shown by Hardley, one might expect this function to do the trick:
my_summarise <- function(df, group_var) {
df %>%
group_by(group_var) %>%
summarise(a = mean(a))
}
# my_summarise(df, g1)
#> Error: Column `group_var` is unknownIn order to solve this problem, coding with dplyr requires the introduction of many new concepts and functions such as 'quo', 'quos', 'enquo', 'enquos', '!!' (bang bang), '!!!' (triple bang). Again, we'll leave to Hardley the explanation on how to use all those functions.
Now, let's try to implement the same function in galaaz. The next code block first prints the 'df' data frame defined previously in R (to access an R variable from Galaaz, we use the tilda operator '~' applied to the R variable name as symbol, i.e., ':df'.
puts ~:df## g1 g2 a b
## 1 1 1 3 3
## 2 1 2 2 1
## 3 2 1 5 2
## 4 2 2 4 5
## 5 2 1 1 4
We then create the 'my_summarize' method and call it passing the R data frame and the group by variable ':g1':
def my_summarize(df, group_var)
df.group_by(group_var).
summarize(a: :a.mean)
end
puts my_summarize(:df, :g1)## # A tibble: 2 x 2
## g1 a
## <dbl> <dbl>
## 1 1 2.5
## 2 2 3.33
It works!!! Well, let's make sure this was not just some coincidence
puts my_summarize(:df, :g2)## # A tibble: 2 x 2
## g2 a
## <dbl> <dbl>
## 1 1 3
## 2 2 3
Great, everything is fine! No magic, no new functions, no complexities, just normal, standard Ruby code. If you've ever done NSE in R, this certainly feels much safer and easy to implement.
In the previous section we've managed to get rid of all NSE formulation for a simple example, but does this remain true for more complex examples, or will the Galaaz way prove inpractical for more complex code?
In the next example Hardley proposes us to write a function that given an expression such as 'a' or 'a * b', calculates three summaries. What we want a function that does the same as these R statements:
summarise(df, mean = mean(a), sum = sum(a), n = n())
#> # A tibble: 1 x 3
#> mean sum n
#> <dbl> <int> <int>
#> 1 3 15 5
summarise(df, mean = mean(a * b), sum = sum(a * b), n = n())
#> # A tibble: 1 x 3
#> mean sum n
#> <dbl> <int> <int>
#> 1 9 45 5
Let's try it in galaaz:
def my_summarise2(df, expr)
df.summarize(
mean: E.mean(expr),
sum: E.sum(expr),
n: E.n
)
end
puts my_summarise2((~:df), :a)
puts "\n"
puts my_summarise2((~:df), :a * :b)## mean sum n
## 1 3 15 5
##
## mean sum n
## 1 9 45 5
Once again, there is no need to use any special theory or functions. The only point to be careful about is the use of 'E' to build expressions from functions 'mean', 'sum' and 'n'.
Now the next challenge presented by Hardley is to vary the name of the output variables based on the received expression. So, if the input expression is 'a', we want our data frame columns to be named 'mean_a' and 'sum_a'. Now, if the input expression is 'b', columns should be named 'mean_b' and 'sum_b'.
mutate(df, mean_a = mean(a), sum_a = sum(a))
#> # A tibble: 5 x 6
#> g1 g2 a b mean_a sum_a
#> <dbl> <dbl> <int> <int> <dbl> <int>
#> 1 1 1 1 3 3 15
#> 2 1 2 4 2 3 15
#> 3 2 1 2 1 3 15
#> 4 2 2 5 4 3 15
#> # … with 1 more row
mutate(df, mean_b = mean(b), sum_b = sum(b))
#> # A tibble: 5 x 6
#> g1 g2 a b mean_b sum_b
#> <dbl> <dbl> <int> <int> <dbl> <int>
#> 1 1 1 1 3 3 15
#> 2 1 2 4 2 3 15
#> 3 2 1 2 1 3 15
#> 4 2 2 5 4 3 15
#> # … with 1 more row
In order to solve this problem in R, Hardley needs to introduce some more new functions and notations: 'quo_name' and the ':=' operator from package 'rlang'
Here is our Ruby code:
def my_mutate(df, expr)
mean_name = "mean_#{expr.to_s}"
sum_name = "sum_#{expr.to_s}"
df.mutate(mean_name => E.mean(expr),
sum_name => E.sum(expr))
end
puts my_mutate((~:df), :a)
puts "\n"
puts my_mutate((~:df), :b)## g1 g2 a b mean_a sum_a
## 1 1 1 3 3 3 15
## 2 1 2 2 1 3 15
## 3 2 1 5 2 3 15
## 4 2 2 4 5 3 15
## 5 2 1 1 4 3 15
##
## g1 g2 a b mean_b sum_b
## 1 1 1 3 3 3 15
## 2 1 2 2 1 3 15
## 3 2 1 5 2 3 15
## 4 2 2 4 5 3 15
## 5 2 1 1 4 3 15
It really seems that "Non Standard Evaluation" is actually quite standard in Galaaz! But, you might have noticed a small change in the way the arguments to the mutate method were called. In a previous example we used df.summarise(mean: E.mean(:a), ...) where the column name was followed by a ':' colom. In this example, we have df.mutate(mean_name => E.mean(expr), ...) and variable mean_name is not followed by ':' but by '=>'. This is standard Ruby notation.
[explain....]
Moving on with new complexities, Hardley proposes us to solve the problem in which the summarise function will receive any number of grouping variables.
This again is quite standard Ruby. In order to receive an undefined number of paramenters the paramenter is preceded by '*':
def my_summarise3(df, *group_vars)
df.group_by(*group_vars).
summarise(a: E.mean(:a))
end
puts my_summarise3((~:df), :g1, :g2)## # A tibble: 4 x 3
## # Groups: g1 [?]
## g1 g2 a
## <dbl> <dbl> <dbl>
## 1 1 1 3
## 2 1 2 2
## 3 2 1 3
## 4 2 2 4
NSE introduces a number of new concepts, such as 'quoting', 'quasiquotation', 'unquoting' and 'unquote-splicing', while in Galaaz none of those concepts are needed. What gives?
R is an extremely flexible language and it has lazy evaluation of parameters. When in R a function is called as 'summarise(df, a = b)', the summarise function receives the litteral 'a = b' parameter and can work with this as if it were a string. In R, it is not clear what a and b are, they can be expressions or they can be variables, it is up to the function to decide what 'a = b' means.
In Ruby, there is no lazy evaluation of parameters and 'a' is always a variable and so is 'b'. Variables assume their value as soon as they are used, so 'x = a' is immediately evaluate and variable 'x' will receive the value of variable 'a' as soon as the Ruby statement is executed. Ruby also provides the notion of a symbol; ':a' is a symbol and does not evaluate to anything. Galaaz uses Ruby symbols to build expressions that are not bound to anything: ':a.eq :b' is clearly an expression and has no relationship whatsoever with the statment 'a = b'. By using symbols, variables and expressions all the possible ambiguities that are found in R are eliminated in Galaaz.
The main problem that remains, is that in R, functions are not clearly documented as what type of input they are expecting, they might be expecting regular variables or they might be expecting expressions and the R function will know how to deal with an input of the form 'a = b', now for the Ruby developer it might not be immediately clear if it should call the function passing the value 'true' if variable 'a' is equal to variable 'b' or if it should call the function passing the expression ':a.eq :b'.
In the blog: Programming with dplyr by using dplyr (https://www.r-bloggers.com/programming-with-dplyr-by-using-dplyr/) Iñaki Úcar shows surprise that some R users are trying to code in dplyr avoiding the use of NSE. For instance he says:
Take the example of seplyr. It stands for standard evaluation dplyr, and enables us to program over dplyr without having “to bring in (or study) any deep-theory or heavy-weight tools such as rlang/tidyeval”.
For me, there isn't really any surprise that users are trying to avoid dplyr deep-theory. R users frequently are not programmers and learning to code is already hard business, on top of that, having to learn how to 'quote' or 'enquo' or 'quos' or 'enquos' is not necessarily a 'piece of cake'. So much so, that 'tidyeval' has some more advanced functions that instead of using quoted expressions, uses strings as arguments.
In the following examples, we show the use of functions 'group_by_at', 'summarise_at' and 'rename_at' that receive strings as argument. The data frame used in 'starwars' that describes features of characters in the Starwars movies:
puts (~:starwars).head## # A tibble: 6 x 13
## name height mass hair_color skin_color eye_color birth_year gender
## <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr>
## 1 Luke… 172 77 blond fair blue 19 male
## 2 C-3PO 167 75 <NA> gold yellow 112 <NA>
## 3 R2-D2 96 32 <NA> white, bl… red 33 <NA>
## 4 Dart… 202 136 none white yellow 41.9 male
## 5 Leia… 150 49 brown light brown 19 female
## 6 Owen… 178 120 brown, gr… light blue 52 male
## # … with 5 more variables: homeworld <chr>, species <chr>, films <list>,
## # vehicles <list>, starships <list>
The grouped_mean function bellow will receive a grouping variable and calculate summaries for the value_variables given:
grouped_mean <- function(data, grouping_variables, value_variables) {
data %>%
group_by_at(grouping_variables) %>%
mutate(count = n()) %>%
summarise_at(c(value_variables, "count"), mean, na.rm = TRUE) %>%
rename_at(value_variables, funs(paste0("mean_", .)))
}
gm = starwars %>%
grouped_mean("eye_color", c("mass", "birth_year"))
as.data.frame(gm) ## eye_color mean_mass mean_birth_year count
## 1 black 76.28571 33.00000 10
## 2 blue 86.51667 67.06923 19
## 3 blue-gray 77.00000 57.00000 1
## 4 brown 66.09231 108.96429 21
## 5 dark NaN NaN 1
## 6 gold NaN NaN 1
## 7 green, yellow 159.00000 NaN 1
## 8 hazel 66.00000 34.50000 3
## 9 orange 282.33333 231.00000 8
## 10 pink NaN NaN 1
## 11 red 81.40000 33.66667 5
## 12 red, blue NaN NaN 1
## 13 unknown 31.50000 NaN 3
## 14 white 48.00000 NaN 1
## 15 yellow 81.11111 76.38000 11
The same code with Galaaz, becomes:
def grouped_mean(data, grouping_variables, value_variables)
data.
group_by_at(grouping_variables).
mutate(count: E.n).
summarise_at(E.c(value_variables, "count"), ~:mean, na__rm: true).
rename_at(value_variables, E.funs(E.paste0("mean_", value_variables)))
end
puts grouped_mean((~:starwars), "eye_color", E.c("mass", "birth_year"))## # A tibble: 15 x 4
## eye_color mean_mass mean_birth_year count
## <chr> <dbl> <dbl> <dbl>
## 1 black 76.3 33 10
## 2 blue 86.5 67.1 19
## 3 blue-gray 77 57 1
## 4 brown 66.1 109. 21
## 5 dark NaN NaN 1
## 6 gold NaN NaN 1
## 7 green, yellow 159 NaN 1
## 8 hazel 66 34.5 3
## 9 orange 282. 231 8
## 10 pink NaN NaN 1
## 11 red 81.4 33.7 5
## 12 red, blue NaN NaN 1
## 13 unknown 31.5 NaN 3
## 14 white 48 NaN 1
## 15 yellow 81.1 76.4 11
[TO BE CONTINUED...]
- Fork it
- Create your feature branch (git checkout -b my-new-feature)
- Write Tests!
- Commit your changes (git commit -am 'Add some feature')
- Push to the branch (git push origin my-new-feature)
- Create new Pull Request