Introduction
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.
System Compatibility
- Oracle Linux 7
- Ubuntu 18.04 LTS
- Ubuntu 16.04 LTS
- Fedora 28
- macOS 10.14 (Mojave)
- macOS 10.13 (High Sierra)
Dependencies
- TruffleRuby
- FastR
Installation
- 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)
Usage
- 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
gKnitting a Document
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 is described in more details here
Vector
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:0x2e0 @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:BLOCK0'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc16/jre/languages/R/library/knitr/R/block.R:102:in `block_exec'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc16/jre/languages/R/library/knitr/R/block.R:92:in `call_block'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc16/jre/languages/R/library/knitr/R/block.R:6:in `process_group.block'
## /home/rbotafogo/lib/graalvm-ce-1.0.0-rc16/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-rc16/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-rc16/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
Combining Vectors
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
Vector Arithmetic
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
Vector Indexing
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"
Extracting Native Ruby Types from a Vector
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.
Accessing R variables
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 |
Matrix
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
Indexing a Matrix
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
List
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
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.
Data Frame
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
Data Frame Indexing
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
Writing Expressions in Galaaz
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.
Expressions from operators
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.
Expressions with R methods
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 $y = sin(x)$. In this case, the 'sin' function is part of the expression and should not immediately executed. Now, let's say that 'x' is an angle of 45$^\circ$ and we acttually want our expression to be $y = 0.850...$. When we want the function to be part of the expression, we call the function preceeding it by the letter E, such as 'E.sin(x)'
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)
Evaluating an Expression
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
Manipulating Data
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
Filtering rows with Filter
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
Logical Operators
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 %
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
Filtering with NA (Not Available)
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 Rows with arrange
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
Selecting columns
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
Add variables to a dataframe with 'mutate'
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
Summarising data
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)).as__data__frame
## delay
## 1 12.63907
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.as__data__frame
## year month day delay
## 1 2013 1 1 11.548926
## 2 2013 1 2 13.858824
## 3 2013 1 3 10.987832
## 4 2013 1 4 8.951595
## 5 2013 1 5 5.732218
## 6 2013 1 6 7.148014
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.as__data__frame.head
## dest count dist delay
## 1 ABQ 254 1826.0000 4.381890
## 2 ACK 265 199.0000 4.852273
## 3 ALB 439 143.0000 14.397129
## 4 ATL 17215 757.1082 11.300113
## 5 AUS 2439 1514.2530 6.019909
## 6 AVL 275 583.5818 8.003831
Using Data Table
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
Graphics in Galaaz
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.(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
<!-- -->
Coding with Tidyverse
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
Writing a function that applies to different data sets
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)
end
Here 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
Different expressions
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 2 1
## 2 1 2 4 3
## 3 2 1 5 4
## 4 2 2 3 2
## 5 2 1 1 5
d2 <- df %>%
group_by(g1) %>%
summarise(a = mean(a))
as.data.frame(d2)
## g1 a
## 1 1 3
## 2 2 3
d2 <- df %>%
group_by(g2) %>%
summarise(a = mean(a))
as.data.frame(d2)
## g2 a
## 1 1 2.666667
## 2 2 3.500000
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 unknown
In 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 2 1
## 2 1 2 4 3
## 3 2 1 5 4
## 4 2 2 3 2
## 5 2 1 1 5
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).as__data__frame
## g1 a
## 1 1 3
## 2 2 3
It works!!! Well, let's make sure this was not just some coincidence
puts my_summarize(:df, :g2).as__data__frame
## g2 a
## 1 1 2.666667
## 2 2 3.500000
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.
Different input variables
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'.
Different input and output variable
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 2 1 3 15
## 2 1 2 4 3 3 15
## 3 2 1 5 4 3 15
## 4 2 2 3 2 3 15
## 5 2 1 1 5 3 15
##
## g1 g2 a b mean_b sum_b
## 1 1 1 2 1 3 15
## 2 1 2 4 3 3 15
## 3 2 1 5 4 3 15
## 4 2 2 3 2 3 15
## 5 2 1 1 5 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....]
Capturing multiple variables
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).as__data__frame
## g1 g2 a
## 1 1 1 2
## 2 1 2 4
## 3 2 1 3
## 4 2 2 3
Why does R require NSE and Galaaz does not?
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'.
Advanced dplyr features
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.as__data__frame
## name height mass hair_color skin_color eye_color birth_year
## 1 Luke Skywalker 172 77 blond fair blue 19.0
## 2 C-3PO 167 75 <NA> gold yellow 112.0
## 3 R2-D2 96 32 <NA> white, blue red 33.0
## 4 Darth Vader 202 136 none white yellow 41.9
## 5 Leia Organa 150 49 brown light brown 19.0
## 6 Owen Lars 178 120 brown, grey light blue 52.0
## gender homeworld species
## 1 male Tatooine Human
## 2 <NA> Tatooine Droid
## 3 <NA> Naboo Droid
## 4 male Tatooine Human
## 5 female Alderaan Human
## 6 male Tatooine Human
## films
## 1 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
## 2 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
## 3 Attack of the Clones, The Phantom Menace, Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
## 4 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope
## 5 Revenge of the Sith, Return of the Jedi, The Empire Strikes Back, A New Hope, The Force Awakens
## 6 Attack of the Clones, Revenge of the Sith, A New Hope
## vehicles starships
## 1 Snowspeeder, Imperial Speeder Bike X-wing, Imperial shuttle
## 2
## 3
## 4 TIE Advanced x1
## 5 Imperial Speeder Bike
## 6
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")).as__data__frame
## 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
[TO BE CONTINUED...]
Contributing
- 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