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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.
- 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
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.
gknit was describe in more depth in:
- xxx.xxxx.xxx
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 RubyChunk:Class
## Message:
## (eval):1:in `exec_ruby'
## /home/rbotafogo/desenv/galaaz/lib/util/exec_ruby.rb:141:in `instance_eval'
## /home/rbotafogo/desenv/galaaz/lib/util/exec_ruby.rb:141:in `exec_ruby'
## /home/rbotafogo/desenv/galaaz/lib/gknit/knitr_engine.rb:657: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:BLOCK0'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc15/jre/languages/R/library/knitr/R/block.R:102:in `block_exec'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc15/jre/languages/R/library/knitr/R/block.R:92:in `call_block'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc15/jre/languages/R/library/knitr/R/block.R:6:in `process_group.block'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc15/jre/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>:BLOCK1'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc15/jre/languages/R/library/knitr/R/output.R:129:in `<no source>'
## unknown.r:1:in `<no source>:BLOCK1'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc15/jre/languages/R/library/rmarkdown/R/render.R:162:in `<no source>'
## <REPL>:5: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.
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 |
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' and 'cbind'
puts @mat_row.rbind(@mat)
puts @mat_row.cbind(@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
## [,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. Using this logical vector, the data frame is indexed, returning a new data frame in which all cars have automatic transmission.
# obtain a vector with 'true' for cars with automatic transmission
automatic = (~:mtcars).am.eq 0
puts automatic
# slice the data frame by using this vector
puts (~:mtcars)[automatic, :all]## [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
## 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_wrong## Message:
## Error in function (x, y, num.eq = TRUE, single.NA = TRUE, attrib.as.set = TRUE, :
## object 'a' not found (RError)
## Translated to internal error
and 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)
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.
R.install_and_loads('nycflights13')
R.library('dplyr')@flights = ~:flights
puts @flights.head.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 11 UA 1545 N14228 EWR IAH 227 1400 5
## 2 20 UA 1714 N24211 LGA IAH 227 1416 5
## 3 33 AA 1141 N619AA JFK MIA 160 1089 5
## 4 -18 B6 725 N804JB JFK BQN 183 1576 5
## 5 -25 DL 461 N668DN LGA ATL 116 762 6
## 6 12 UA 1696 N39463 EWR ORD 150 719 5
## minute time_hour
## 1 15 2013-01-01 05:00:00
## 2 29 2013-01-01 05:00:00
## 3 40 2013-01-01 05:00:00
## 4 45 2013-01-01 05:00:00
## 5 0 2013-01-01 06:00:00
## 6 58 2013-01-01 05:00:00
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.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 11 UA 1545 N14228 EWR IAH 227 1400 5
## 2 20 UA 1714 N24211 LGA IAH 227 1416 5
## 3 33 AA 1141 N619AA JFK MIA 160 1089 5
## 4 -18 B6 725 N804JB JFK BQN 183 1576 5
## 5 -25 DL 461 N668DN LGA ATL 116 762 6
## 6 12 UA 1696 N39463 EWR ORD 150 719 5
## minute time_hour
## 1 15 2013-01-01 05:00:00
## 2 29 2013-01-01 05:00:00
## 3 40 2013-01-01 05:00:00
## 4 45 2013-01-01 05:00:00
## 5 0 2013-01-01 06:00:00
## 6 58 2013-01-01 05:00:00
All flights that departed in November of December
puts @flights.filter((:month.eq 11) | (:month.eq 12)).head.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 11 1 5 2359 6 352 345
## 2 2013 11 1 35 2250 105 123 2356
## 3 2013 11 1 455 500 -5 641 651
## 4 2013 11 1 539 545 -6 856 827
## 5 2013 11 1 542 545 -3 831 855
## 6 2013 11 1 549 600 -11 912 923
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 7 B6 745 N568JB JFK PSE 205 1617 23
## 2 87 B6 1816 N353JB JFK SYR 36 209 22
## 3 -10 US 1895 N192UW EWR CLT 88 529 5
## 4 29 UA 1714 N38727 LGA IAH 229 1416 5
## 5 -24 AA 2243 N5CLAA JFK MIA 147 1089 5
## 6 -11 UA 303 N595UA JFK SFO 359 2586 6
## minute time_hour
## 1 59 2013-11-01 23:00:00
## 2 50 2013-11-01 22:00:00
## 3 0 2013-11-01 05:00:00
## 4 45 2013-11-01 05:00:00
## 5 45 2013-11-01 05:00:00
## 6 0 2013-11-01 06:00:00
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.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 11 1 5 2359 6 352 345
## 2 2013 11 1 35 2250 105 123 2356
## 3 2013 11 1 455 500 -5 641 651
## 4 2013 11 1 539 545 -6 856 827
## 5 2013 11 1 542 545 -3 831 855
## 6 2013 11 1 549 600 -11 912 923
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 7 B6 745 N568JB JFK PSE 205 1617 23
## 2 87 B6 1816 N353JB JFK SYR 36 209 22
## 3 -10 US 1895 N192UW EWR CLT 88 529 5
## 4 29 UA 1714 N38727 LGA IAH 229 1416 5
## 5 -24 AA 2243 N5CLAA JFK MIA 147 1089 5
## 6 -11 UA 303 N595UA JFK SFO 359 2586 6
## minute time_hour
## 1 59 2013-11-01 23:00:00
## 2 50 2013-11-01 22:00:00
## 3 0 2013-11-01 05:00:00
## 4 45 2013-11-01 05:00:00
## 5 45 2013-11-01 05:00:00
## 6 0 2013-11-01 06:00:00
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.as__data__frame## x
## 1 1
## 2 NA
## 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).as__data__frame## x
## 1 3
To match an NA use method 'is__na'
puts @df.filter((:x.is__na) | (:x > 1)).as__data__frame## x
## 1 NA
## 2 3
Arrange reorders the rows of a data frame by the given arguments.
puts @flights.arrange(:year, :month, :day).head.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 11 UA 1545 N14228 EWR IAH 227 1400 5
## 2 20 UA 1714 N24211 LGA IAH 227 1416 5
## 3 33 AA 1141 N619AA JFK MIA 160 1089 5
## 4 -18 B6 725 N804JB JFK BQN 183 1576 5
## 5 -25 DL 461 N668DN LGA ATL 116 762 6
## 6 12 UA 1696 N39463 EWR ORD 150 719 5
## minute time_hour
## 1 15 2013-01-01 05:00:00
## 2 29 2013-01-01 05:00:00
## 3 40 2013-01-01 05:00:00
## 4 45 2013-01-01 05:00:00
## 5 0 2013-01-01 06:00:00
## 6 58 2013-01-01 05:00:00
To arrange in descending order, use function 'desc'
puts @flights.arrange(:dep_delay.desc).head.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 1 9 641 900 1301 1242 1530
## 2 2013 6 15 1432 1935 1137 1607 2120
## 3 2013 1 10 1121 1635 1126 1239 1810
## 4 2013 9 20 1139 1845 1014 1457 2210
## 5 2013 7 22 845 1600 1005 1044 1815
## 6 2013 4 10 1100 1900 960 1342 2211
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 1272 HA 51 N384HA JFK HNL 640 4983 9
## 2 1127 MQ 3535 N504MQ JFK CMH 74 483 19
## 3 1109 MQ 3695 N517MQ EWR ORD 111 719 16
## 4 1007 AA 177 N338AA JFK SFO 354 2586 18
## 5 989 MQ 3075 N665MQ JFK CVG 96 589 16
## 6 931 DL 2391 N959DL JFK TPA 139 1005 19
## minute time_hour
## 1 0 2013-01-09 09:00:00
## 2 35 2013-06-15 19:00:00
## 3 35 2013-01-10 16:00:00
## 4 45 2013-09-20 18:00:00
## 5 0 2013-07-22 16:00:00
## 6 0 2013-04-10 19:00:00
To select specific columns from a dataset we use function 'select':
puts @flights.select(:year, :month, :day).head.as__data__frame## year month day
## 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.as__data__frame## year month day
## 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.as__data__frame## arr_time arr_delay
## 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.as__data__frame## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time
## 1 2013 1 1 517 515 2 830 819
## 2 2013 1 1 533 529 4 850 830
## 3 2013 1 1 542 540 2 923 850
## 4 2013 1 1 544 545 -1 1004 1022
## 5 2013 1 1 554 600 -6 812 837
## 6 2013 1 1 554 558 -4 740 728
## arr_delay carrier flight tailnum origin dest air_time distance hour
## 1 11 UA 1545 N14228 EWR IAH 227 1400 5
## 2 20 UA 1714 N24211 LGA IAH 227 1416 5
## 3 33 AA 1141 N619AA JFK MIA 160 1089 5
## 4 -18 B6 725 N804JB JFK BQN 183 1576 5
## 5 -25 DL 461 N668DN LGA ATL 116 762 6
## 6 12 UA 1696 N39463 EWR ORD 150 719 5
## minute time_hour
## 1 15 2013-01-01 05:00:00
## 2 29 2013-01-01 05:00:00
## 3 40 2013-01-01 05:00:00
## 4 45 2013-01-01 05:00:00
## 5 0 2013-01-01 06:00:00
## 6 58 2013-01-01 05:00:00
@flights_sm = @flights.
select((:year.up_to :day),
E.ends_with('delay'),
:distance,
:air_time)
puts @flights_sm.head.as__data__frame## year month day dep_delay arr_delay distance air_time
## 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.as__data__frame## year month day dep_delay arr_delay distance air_time gain speed
## 1 2013 1 1 2 11 1400 227 -9 370.0441
## 2 2013 1 1 4 20 1416 227 -16 374.2731
## 3 2013 1 1 2 33 1089 160 -31 408.3750
## 4 2013 1 1 -1 -18 1576 183 17 516.7213
## 5 2013 1 1 -6 -25 762 116 19 394.1379
## 6 2013 1 1 -4 12 719 150 -16 287.6000
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## mpg cyl disp hp drat wt qsec vs am gear carb
## 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
## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 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 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
## 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
## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
## 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
## Merc 450SLC Merc 450SLC -0.81 below
## AMC Javelin AMC Javelin -0.81 below
## Dodge Challenger Dodge Challenger -0.76 below
## Ford Pantera L Ford Pantera L -0.71 below
## Merc 450SE Merc 450SE -0.61 below
## Merc 450SL Merc 450SL -0.46 below
## Merc 280C Merc 280C -0.38 below
## Valiant Valiant -0.33 below
## Hornet Sportabout Hornet Sportabout -0.23 below
## Merc 280 Merc 280 -0.15 below
## Pontiac Firebird Pontiac Firebird -0.15 below
## Ferrari Dino Ferrari Dino -0.06 below
## Mazda RX4 Mazda RX4 0.15 above
## Mazda RX4 Wag Mazda RX4 Wag 0.15 above
## Hornet 4 Drive Hornet 4 Drive 0.22 above
## Volvo 142E Volvo 142E 0.22 above
## Toyota Corona Toyota Corona 0.23 above
## Datsun 710 Datsun 710 0.45 above
## Merc 230 Merc 230 0.45 above
## Merc 240D Merc 240D 0.72 above
## Porsche 914-2 Porsche 914-2 0.98 above
## Fiat X1-9 Fiat X1-9 1.20 above
## Honda Civic Honda Civic 1.71 above
## Lotus Europa Lotus Europa 1.71 above
## Fiat 128 Fiat 128 2.04 above
## Toyota Corolla Toyota Corolla 2.29 above
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:
[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