Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
Why async and functional programming in PHP7 suck and how to get overr it?Lucas Witold Adamus
This presentation describes basic issues related to functional programming with PHP and solution for most of problems served by the library called PhpSlang.
These are the slides of the second part of this multi-part series, from Learn Python Den Haag meetup group. It covers List comprehensions, Dictionary comprehensions and functions.
The presentation from SPb Python Interest Group community meetup.
The presentation tells about the dictionaries in Python, reviews the implementation of dictionary in CPython 2.x, dictionary in CPython 3.x, and also recent changes in CPython 3.6. In addition to CPython the dictionaries in alternative Python implementations such as PyPy, IronPython and Jython are reviewed.
Ruby plays to many programming paradigms. It's an object-oriented language that can be used in a functional or an imperative/procedural way. But Ruby does not often get used as a logic programming language. In this talk I'll explore logic programming using Ruby. What is it, and is it a tool you want to add to your toolbox? We'll touch on several libraries, we'll primary look at an implementation of minikanren (http://minikanren.org/) for Ruby.
Regular expressions are used to identify whether a pattern exists in a given sequence of characters (string) or not. They help in manipulating textual data, which is often a pre-requisite for data science projects that involve text mining. You must have come across some application of regular expressions: they are used at the server side to validate the format of email addresses or password during registration, used for parsing text data files to find, replace or delete certain string, etc.
Talk is about simple data structures like queue and Tree and their possible implementation in Scala. It also talks about binary search trees and their traversals.
A high level introduction to R statistical programming language that was presented at the Chicago Data Visualization Group's Graphing in R and ggplot2 workshop on October 8, 2012.
These are the outline slides that I used for the Pune Clojure Course.
The slides may not be much useful standalone, but I have uploaded them for reference.
Learn to manipulate strings in R using the built in R functions. This tutorial is part of the Working With Data module of the R Programming Course offered by r-squared.
Why async and functional programming in PHP7 suck and how to get overr it?Lucas Witold Adamus
This presentation describes basic issues related to functional programming with PHP and solution for most of problems served by the library called PhpSlang.
These are the slides of the second part of this multi-part series, from Learn Python Den Haag meetup group. It covers List comprehensions, Dictionary comprehensions and functions.
The presentation from SPb Python Interest Group community meetup.
The presentation tells about the dictionaries in Python, reviews the implementation of dictionary in CPython 2.x, dictionary in CPython 3.x, and also recent changes in CPython 3.6. In addition to CPython the dictionaries in alternative Python implementations such as PyPy, IronPython and Jython are reviewed.
Ruby plays to many programming paradigms. It's an object-oriented language that can be used in a functional or an imperative/procedural way. But Ruby does not often get used as a logic programming language. In this talk I'll explore logic programming using Ruby. What is it, and is it a tool you want to add to your toolbox? We'll touch on several libraries, we'll primary look at an implementation of minikanren (http://minikanren.org/) for Ruby.
Regular expressions are used to identify whether a pattern exists in a given sequence of characters (string) or not. They help in manipulating textual data, which is often a pre-requisite for data science projects that involve text mining. You must have come across some application of regular expressions: they are used at the server side to validate the format of email addresses or password during registration, used for parsing text data files to find, replace or delete certain string, etc.
Talk is about simple data structures like queue and Tree and their possible implementation in Scala. It also talks about binary search trees and their traversals.
A high level introduction to R statistical programming language that was presented at the Chicago Data Visualization Group's Graphing in R and ggplot2 workshop on October 8, 2012.
These are the outline slides that I used for the Pune Clojure Course.
The slides may not be much useful standalone, but I have uploaded them for reference.
Ejercicios de estilo en la programaciónSoftware Guru
El escritor francés Raymond Queneau escribió a mediados del siglo XX un libro llamado "Ejercicios de Estilo" donde mostraba una misma historia corta, redactada de 99 formas distintas.
En esta plática realizaremos el mismo ejercicio con un programa de software. Abarcaremos distintos estilos y paradigmas: programación monolítica, orientada a objetos, relacional, orientada a aspectos, monadas, map-reduce, y muchos otros, a través de los cuales podremos apreciar la riqueza del pensamiento humano aplicado a la computación.
Esto va mucho más allá de un ejercicio académico; el diseño de sistemas de gran escala se alimenta de esta variedad de estilos. También platicaremos sobre los peligros de quedar atrapado bajo un conjunto reducido de estilos a lo largo de tu carrera, y la necesidad de verdaderamente entender distintos estilos al diseñar arquitecturas de sistemas de software.
Semblanza del conferencista:
Crista Lopez es profesora en la Facultad de Ciencias Computacionales de la Universidad de California en Irvine. Su investigación se enfoca en prácticas de ingeniería de software para sistemas de gran escala. Previamente, fue miembro fundador del equipo en Xerox PARC creador del paradigma de programación orientado a aspectos (AOP). Crista es una de las desarrolladoras principales de OpenSimulator, una plataforma open source para crear mundos virtuales 3D. También es fundadora de Encitra, empresa especializada en la utilización de la realidad virtual para proyectos de desarrollo urbano sustentable. @cristalopes
At the Dublin Fashion Insights Centre, we are exploring methods of categorising the web into a set of known fashion related topics. This raises questions such as: How many fashion related topics are there? How closely are they related to each other, or to other non-fashion topics? Furthermore, what topic hierarchies exist in this landscape? Using Clojure and MLlib to harness the data available from crowd-sourced websites such as DMOZ (a categorisation of millions of websites) and Common Crawl (a monthly crawl of billions of websites), we are answering these questions to understand fashion in a quantitative manner.
The latest generation of big data tools such as Apache Spark routinely handle petabytes of data while also addressing real-world realities like node and network failures. Spark's transformations and operations on data sets are a natural fit with Clojure's everyday use of transformations and reductions. Spark MLlib's excellent implementations of distributed machine learning algorithms puts the power of large-scale analytics in the hands of Clojure developers. At Zalando's Dublin Fashion Insights Centre, we're using the Clojure bindings to Spark and MLlib to answer fashion-related questions that until recently have been nearly impossible to answer quantitatively.
Hunter Kelly @retnuh
tech.zalando.com
Morel, a data-parallel programming languageJulian Hyde
What would the perfect data-parallel programming language look like? It would be as expressive as a general-purpose functional programming language, as powerful and concise as SQL, and run programs just as efficiently on a laptop or a thousand-node cluster.
We present Morel, a functional programming language with relational extensions, working towards that goal. Morel is implemented in the Apache Calcite community on top of Calcite’s relational algebra framework. In this talk, we describe Morel’s evolution, including how we are pushing Calcite’s capabilities with graph and recursive queries.
A talk given by Julian Hyde at ApacheCon, New Orleans, October 4th 2022.
Scala er et Java-relateret, statisk typet programmeringssprog i hastig fremmarch. Sproget kombinerer aspekter fra objekt- og funktionsorienterede sprog og fokuserer på skalerbarhed og effektivitet, både på det kodemæssige og afviklingsmæssige niveau. Syntaksen er elegant og koncis. Samtidig indeholder sproget stærke konstruktioner til understøttelse af parallelle applikationer, der udnytter fremtidens hardwarearkitekturer.
Is it easier to add functional programming features to a query language, or to add query capabilities to a functional language? In Morel, we have done the latter.
Functional and query languages have much in common, and yet much to learn from each other. Functional languages have a rich type system that includes polymorphism and functions-as-values and Turing-complete expressiveness; query languages have optimization techniques that can make programs several orders of magnitude faster, and runtimes that can use thousands of nodes to execute queries over terabytes of data.
Morel is an implementation of Standard ML on the JVM, with language extensions to allow relational expressions. Its compiler can translate programs to relational algebra and, via Apache Calcite’s query optimizer, run those programs on relational backends.
In this talk, we describe the principles that drove Morel’s design, the problems that we had to solve in order to implement a hybrid functional/relational language, and how Morel can be applied to implement data-intensive systems.
(A talk given by Julian Hyde at Strange Loop 2021, St. Louis, MO, on October 1st, 2021.)
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Quantitative Data AnalysisReliability Analysis (Cronbach Alpha) Common Method...2023240532
Quantitative data Analysis
Overview
Reliability Analysis (Cronbach Alpha)
Common Method Bias (Harman Single Factor Test)
Frequency Analysis (Demographic)
Descriptive Analysis
Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...pchutichetpong
M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years.
Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success.
MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies.
According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
2. Contents
● What is R? How to invoke?
● Basic data types, control structures
● Environments, functions
● Classes, packages, graphs
3. What is R
● A free software programming language and
software environment for statistical computing
and graphics
● Dialect of the S programming language with
lexical scoping semantic inspired by Scheme
● Provides linear and nonlinear modeling, classical
statistical tests, time-series analysis,
classification, clustering, and more...
● Cross-platform: Windows, Linux, Mac
4. How to invoke
● Command-line interpreter for interactive
programming
– Type R in a terminal
– Use ?topic or help(topic) for help;
try example(topic) for examples
– Use quit() to exit
● GUIs
– RStudio IDE
– Web-interface: http://10.122.85.41:8787/
5. Basic Data Types (1)
● R is value typed. Everything is an object
x<value # < is the assignment
y<x # deep copying
● Atomic data types
– integer (32 bits) age<20L
– double (binary64) gpa<3.34
– character name<"John"
– logical married<TRUE # or FALSE
– complex, raw
mode(x), typeof(x), class(x), str(x)
● Other types
– closure f<function() {}
– language q<quote(x<1)
● Special constants
NULL, NA, Inf, NaN
6. Basic Data Types (2)
● Vectors
– A set of objects of an atomic type
banknotes<c(1,5,10,20,50,100) # c means combine
banknotes[5], length[banknotes], mode(banknotes)
name<c(given="George", middle="Walker", family="Bush")
name[1], name["given"], names(name)
– Tricks with indexes
x<c(1,2,3) # try x[0], x[c(1,3)], x[1], x[c(1,3)]
x[c(T,F,T)], x>1, x[x>1] # logical indexing
– Cycling through a vector argument
c(1,2,3,4) + c(0,1), 10 * c(1,2,3,4)
7. Basic Data Types (3)
●
Lists
– A set of objects of different types
l<list(age=20L, gpa=3.34, name="John", married=TRUE)
length(l), names(l)
– Use [] to extract a sublist
l[1], l[c("name","married")], l[1], l[c(1,3)]
– Use [[]] or $ to access an object in a list
l[[1]], l[["age"]], l$age
list(c(1, 2, 3), c("a", "b"), function(){})
●
Attributes
attributes(age)<list(units="years")
structure(l, comment="those one guy")
8. Basic Data Types (4)
● Matrices
m<matrix(c(1,2,3,4), c(2,2)) # use dim,
nrow, ncol rbind, and cbind with matrices
– Use t to transpose, %*% for matrix multiplication, diag to
extract diagonal
● Arrays
a<array(rnorm(8), c(2,2,2))
● Factors (enumerated type)
faculty<factor("engineering", c("arts",
"law", "engineering", "finances"))
9. Basic Data Types (5)
● Data frames
– A data frame combines a set of vector of the same length
df<data.frame(age=c(20L, 21L),
gpa=c(3.34, 3.14),
name=c("John", "George"),
married=c(T, F))
– Any data frame can be accessed either as a list or as a matrix
df.1<df[df$gpa>3.2, c("name","married")]
df.2<subset(df, subset=(gpa>3.2),
select=c(name,married))
identical(df.1, df.2) # TRUE
12. Environments (1)
● Every variable or function is defined in an environment
environment() # gives the current evaluation
environment
● Environments form a tree with the root given by emptyenv()
● The root environment emptyenv() cannot be populated
● .GlobalEnv is the user's working environment or workspace. It
can also be assessed by globalenv()
identical(environment(), globalenv()) # TRUE
● baseenv() is the library environment for the basic R functions
ls(baseenv())
14. Environments (3)
● parent.env(env) returns the parent of environment env
identical(parent.env(baseenv()),emptyenv()) # TRUE
● To create a new environment use new.env(parent)
– If the parent parameter is omitted, .GlobalEnv is used by default
●
To change the evaluation environment use
evalq(expr, env)
with(data, expr) # does the same to data frames and lists
●
Example
e.1<new.env() # created a new environment e.1
parent.env(e.1) # should be .GlobalEnv
evalq(environment(), e.1) # should be e.1
e.2<new.env(parent=e.1) # created a new environment e.2
parent.env(e.2) # should be e.1
evalq(environment(), e.2) # should be e.2
15. Environments (4)
● When resolving a variable or function name, R searches the current evaluation environment, then the
parent environments along the path to the root environment emptyenv()
x<0 # set x to 0 in .GlobalEnv
Both evalq(x, e.1) and evalq(x, e.2) should give 0
evalq(x<2, e.2) # set x to 2 in e.2
Now evalq(x, e.1) still gives 0 while evalq(x, e.2) has changed to 2
● To set an object, such as a variable or a function, in a particular environment use
assign(obj.name, value, envir=env) # inherits is FALSE by default
● To get the value of an object in a particular environment use
get(obj.name, envir=env) # inherits is TRUE by default
● To check whether an object exists in a particular environment
exists(obj.name, envir=env) # inherits is TRUE by default
For example,
exists("x", e.1, inherits=FALSE) # FALSE
exists("x", e.2, inherits=FALSE) # TRUE
16. Environments (5)
● Every environment can also be treated as a list. For example,
e.2$x gives access to x in e.2
● The so-called search path starts from .GlobalEnv and ends with
baseenv(). The search() function returns string names of the
environments in the search path
[1] ".GlobalEnv" "tools:rstudio"
[3] "package:stats" "package:graphics"
[5] "package:grDevices" "package:utils"
[7] "package:datasets" "package:methods"
[9] "Autoloads" "package:base"
17. Environments (6)
● To restore an environment from its string name, use
as.environment(name). For example,
as.environment("package:base") # maps the
string to the baseenv() object
● Unless the evaluation environment was specified explicitly, the
interpreter searches the environments along the search path,
starting from .GlobalEnv, until it hits emptyenv()
18. Environments (7)
● To add an environment env to the search path one can use
attach(env, pos, name) which creates a copy of the
environment env with string name name and inserts it at position
pos>1 in the search path
● find(obj.name) returns all environments along the search path
containing objects with a specified name
● Example
e.1$x<1; attach(e.1, 2L, "e.1")
assign("x", 11, e.1) # modified e.1 but not its attached
duplicate
get("x", e.1) # returns 11
x # is still 1
19. Functions (1)
● Functions in R are “first class objects” which means they can be
treated much like any other object
– Can be passed as arguments to other functions
– Can be nested, so that you can define a function inside of another function
– Can be returned by other functions
● The return value of a function is the last expression in the function
body to be evaluated
● Example: factorial
fact<function(x) ifelse(x==1, 1, x*fact(x1)) # ? in C
fact # function(x) ifelse(x==1, 1, x*f(x1))
fact(5) # 120
fact(1000) # Inf
20. Functions (2)
●
A function consists of its formal arguments and a body and it has a reference to the
enclosing environment (closure)
formals(fact) # $x
body(fact) # ifelse(x == 1, 1, x * f(x 1))
environment(fact) # .GlobalEnv
●
By default, enclosing environment references the environment in which the function was
created, but it can be redefined with
environment(fact)<some.other.environment
●
Being called, a function creates its own environment, a child of the enclosing environment
●
Thus we have
– the environment where the function is created: find("fact")
– the environment where the function resides (enclosing environment): environment(fact)
– the environment created when a function is run: environment()
– the environment where a function is called: parent.frame()
21. Functions (3)
● Function arguments are named and may have default values
● You can mix positional matching with matching by name. When an
argument is matched by name it is “taken out” of the argument list
and the remaining unnamed arguments are matched in the order that
they are listed in the function definition
f<function(x, y, z=0) as.list(environment())
f(1, 2) # x:1, y:2, z:0
f(y=1, x=2, z=3) # x:2, y:1, z:3
f(y=1) # x:, y:1, z:0
f(z=3, 2, 1) # x: 2, y: 1, z: 3
f(1, 2, 3, 4) # error: unused argument (4)
22. Functions (4)
● The order of operations when given an argument is
– Check for exact match for a named argument
– Check for partial match
– Check for a positional match
– The … argument indicates a variable number of arguments that are usually passed on to other
functions
● Any argument that appears after … in the argument list must be named explicitly and
cannot be partially matched
g<function(y, z=0) as.list(environment())
f<function(x, ...) g(...)
f(1) # y:, z: 0
f(1, 2, 3) # y: 2, z: 3
f(1, 2, 3, 4) # error: unused argument (4)
f(y=1, 2, 3) # y: 1, z: 3
f(2, 3, x=1) # y: 2, z: 3
23. Functions (5)
● Free variables
f<function() x # x is a free variable
f() # error: object 'x' not found
x<1; f() # 1
● Lexical (static) scoping
f<function() {
x<1
g<function() x
}
x<2; h<f()
h() # 1 or 2?
● Why 1?
environment(g) # created by f() call, not .GlobalEnv
environment(g)$x # 1
24. Functions (6)
● Example: function that returns function
power<function(n) {
function(x) x^n
}
n<5 # ignored
square<power(2)
square(3) # 9
cube<power(3)
cube(2) # 8
25. Classes (1)
● Everything in R is an object
● A class is the definition of an object
● A method is a function that performs specific calculations
on objects of a specific class. A generic function is used
to determine the class of its arguments and select the
appropriate method. A generic function is a function with
a collection of methods
● print, plot, summary...
● See ?Classes and ?Methods for more info
26. Classes (2)
● S3 classes – old style, quick and dirty, informal
● Set an object's attribute to the class name, e.g.
x<c("a", "b", "c") # this is an object
class(x)<"X" # set the class of the object
# Define a method specific to the X class
print.X<function(x, ...) {
cat("X obj:n")
print(unclass(x), ...)
}
print(x) # X obj: a b c
● Inheritance
class(x)<c("X", "Y", "Z")
inherits(x, “Z”) # TRUE
28. Classes (4)
● S4 classes – new style, rigorous and formal
● Classes have formal definitions which describe their fields
and inheritance structures (parent classes)
● Method dispatch can be based on multiple arguments to a
generic function, not just one
● There is a special operator, @, for extracting slots (aka
fields) from an S4 object
● All S4 related code is stored in the methods package
29. Classes (5)
● To create a new S4 class
setClass(Class, representation)
● Use new() to generate a new object from a
class
● To create an S4 method
setMethod(f, signature,
definition)
32. Packages (1)
● Packages extend functionality of R
● http://cran.r-project.org/web/packages
– 5434 available packages as of Apr 14, 2014
● repository → installed → loaded
● library(help="package")
● Datasets
data(mtcars); help(mtcars)
● Example: libsvm
install.packages("e1071")
library(e1071)
detach("package:e1071")
33. Packages (2)
● Library and namespace environments
– Library environments, such as "package:stats", contain external objects resolvable to the
user by their names. Thus library environment has to be attached to the search path
– Conversely, the namespace environments contain internal objects that are opaque to the user
but transparent to the library functions. Usually, namespace environment is a
.BaseNamespace's children
find("svm") # "package:e1071"
environment(svm) # <environment: namespace:e1071>
length(ls(environment(svm))) # 90 objects in the namespace
environment
length(ls("package:e1071")) # 58 objects in the package environment
– Path from the namespace environment to .GlobalEnv
<environment: namespace:e1071> "imports:e1071" .BaseNamespaceEnv → →
.GlobalEnv→
34. Packages (3)
● Example SparkR
sc<sparkR.init(master)
parallelize(sc, col, numSlices)
map(rdd, func)
reduce(rdd, func)
reduceByKey(rdd, combineFunc, numPartitions)
cache(rdd)
collect(rdd)
● Pi example
37. Appendix: funny stuff about R
● Expression which returns itself
(function(x) substitute((x)(x)))
(function(x) substitute((x)(x)))
(function(x) substitute((x)(x)))
(function(x) substitute((x)(x)))
expression < (function(x)
substitute((x)(x)))(function(x)
substitute((x)(x)))
expression == eval(expression) # TRUE
Editor's Notes
R is a dialect of the S language. It is a case-sensitive, interpreted language. You can enter commands one at a time at the command prompt or run a set of commands from a source file.
NULL object
Special values: NA, NaN, Inf,
NA may have a type