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Getting Started with SAS® Viya for Lua

The SAS Scripting Wrapper for Analytics Transfer (SWAT) package for Lua is a Lua interface to SAS Cloud Analytic Services (CAS) which is the centerpiece of the SAS Viya framework. With this package, you can load data into memory and apply CAS actions to transform, summarize, model and score the data. CAS can be running on a single machine (SMP) or in a distributed server (MPP). You can still retain the ease-of-use of Lua on the client side to further post process CAS result tables.

 

Documentation

Getting Started with SAS Viya for Lua
Learn the concepts and capabilities of the Lua API for SAS Viya.

 


Examples

Select a sample below and copy and paste into your application.

Get Descriptive Statistics

The example below shows how to use the SUMMARY ACTION to get various descriptive statistics (minimum and maximum values, mean, standardDeviation, etc.) about a data table using SAS Cloud Analytics Services. This example requires that you have in-memory data to analyze.

Run the Summary Action
Save the sample program below in a file SummaryExample1.lua and run the program with the lua command from a console:


lua SummaryExample.lua

swat = require 'swat'
swat.setOption('display.width', 120)
s = swat.CAS("cloud.example.com", 5570)

                                                                --1
result = s:upload{"/path/to/iris.csv", casout={name="iris", replace=true}}

irisTbl = {}                                                    --2
irisTbl.name = result.tableName
irisTbl.groupBy = {"species"}

stats = {"min", "max", "n", "nmiss", "mean", "std", "stderr"}

results, info = s:simple_summary{table=irisTbl, subset=stats}   --3

bgi = results.ByGroupInfo                                       --4

print(bgi)
print()

-- so we don't print this again
results.ByGroupInfo = nil

for k,v in pairs(results) do
  print(k, v)
  print()
end
  1. Perform a client-side load of the Iris.csv file from a path that Lua can access. The upload method transfers the CSV file from Lua to the server, and then the server loads the data into memory.
  2. A variable with the name IrisTbl is created to represent the in-memory table. The groupBy value, Species, is set as a key in the variable.
  3. The results from the simple_summary action include the descriptive statistics that are listed in the Stats variable.
  4. When you perform BY-group processing, the results include one table that is named ByGroupInfo and a table for each BY-group. The ByGroupInfo table is printed first and then a loop is used to print the remaining tables.

The sample program prints the summary statisctics to standard output:


           ByGroupInfo
Species     Species_f   _key_
Setosa      Setosa      Setosa
Versicolor  Versicolor  Versicolor
Virginica   Virginica   Virginica

ByGroup1.Summary                                       Descriptive Statistics for IRIS
Species  Analysis Variable      Min      Max   N  Number Missing     Mean  Std Dev.  Std Error
Setosa   SepalLength        43.0000  58.0000  50               0  50.0600    3.5249     0.4985
Setosa   SepalWidth         23.0000  44.0000  50               0  34.2800    3.7906     0.5361
Setosa   PetalLength        10.0000  19.0000  50               0  14.6200    1.7366     0.2456
Setosa   PetalWidth          1.0000   6.0000  50               0   2.4600    1.0539     0.1490

ByGroup2.Summary                                         Descriptive Statistics for IRIS
Species     Analysis Variable      Min      Max   N  Number Missing     Mean  Std Dev.  Std Error
Versicolor  SepalLength        49.0000  70.0000  50               0  59.3600    5.1617     0.7300
Versicolor  SepalWidth         20.0000  34.0000  50               0  27.7000    3.1380     0.4438
Versicolor  PetalLength        30.0000  51.0000  50               0  42.6000    4.6991     0.6646
Versicolor  PetalWidth         10.0000  18.0000  50               0  13.2600    1.9775     0.2797

ByGroup3.Summary                                        Descriptive Statistics for IRIS
Species    Analysis Variable      Min      Max   N  Number Missing     Mean  Std Dev.  Std Error
Virginica  SepalLength        49.0000  79.0000  50               0  65.8800    6.3588     0.8993
Virginica  SepalWidth         22.0000  38.0000  50               0  29.7400    3.2250     0.4561
Virginica  PetalLength        45.0000  69.0000  50               0  55.5200    5.5189     0.7805
Virginica  PetalWidth         14.0000  25.0000  50               0  20.2600    2.7465     0.3884
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