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.)
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
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.)
R is a programming language and environment commonly used in statistical computing, data analytics and scientific research.
It is one of the most popular languages used by statisticians, data analysts, researchers and marketers to retrieve, clean, analyze, visualize and present data.
Due to its expressive syntax and easy-to-use interface, it has grown in popularity in recent years.
This is the basic introduction of the pandas library, you can use it for teaching this library for machine learning introduction. This slide will be able to help to understand the basics of pandas to the students with no coding background.
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
Is there a perfect data-parallel programming language? (Experiments with More...Julian Hyde
The perfect data parallel language has not yet been invented. SQL queries can achieve great performance and scale, but there are many general purpose algorithms that it cannot express. In Morel, we build on the functional and relational roots of MapReduce in an elegant and strongly-typed general-purpose programming language. But Morel is, in a real sense, a query language; programs are executed on relational frameworks such as Google BigQuery and Spark.
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.
We also introduce Apache Calcite, the popular open source framework for query planning, and describe how Morel's compiler uses Calcite's relational algebra and rewrite rules to generate efficient plans.
Extending Spark for Qbeast's SQL Data Source with Paola Pardo and Cesare Cug...Qbeast
Slides of the Barcelona Spark meetup of the 24th of October 2019. The recording is available at https://www.youtube.com/watch?v=eCoCcBH4hIU.
Abstract
One of the key strengths of Spark is its flexibility as it integrates with dozens of different storage systems and file formats. However, it is not the same reading from a CSV file, or a SQL database, or an exotic stratified sampled multidimensional database. And finding the right balance between modularity and flexibility is not easy!
In this presentation, we will talk about the evolution of Spark's DataSource API, and how it integrates with the SQL optimizer, highlighting how we can make much faster queries with logical and the physical plans that better integrates with the storage. From theory to practise, we will then discuss how we extended the Spark's internals, and we built a new source integration that allows the push-down of both sampling and multidimensional filtering.
About the speakers:
Paola Pardo is a Computer Engineer from Barcelona. She graduated in Computer engineer this last summer at the Technical University of Catalunya with a thesis focused on Data storage push down optimization based on Apache Spark. She is, and she is currently working at Barcelona Supercomputing Center and in its spin-off Qbeast developing a Qbeast-Spark connector.
Cesare Cugnasco is a PhD in Computer Architecture and a researcher at the Barcelona Supercomputing Center. His research focuses on NoSQL databases, distributed computing and High-performance storage. He invented and patented a new database architecture for Big Data, and he is building a spin-off for its commercialization.
Desk reference for data wrangling, analysis, visualization, and programming in Stata. Co-authored with Tim Essam(@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
This is a brief introduction to how R can be useful in the manufacturing sector to calculate the frequency of faults and then developing the model so that preventive maintenance can be done
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
Efficient spatial queries on vanilla databasesJulian Hyde
A talk given by Julian Hyde at the Apache Calcite online meetup, 2021/01/20.
Spatial and GIS applications have traditionally required specialized databases, or at least specialized data structures like r-trees. Unfortunately this means that hybrid applications such as spatial analytics are not well served, and many people are unaware of the power of spatial queries because their favorite database does not support them.
In this talk, we describe how Apache Calcite enables efficient spatial queries using generic data structures such as HBase’s key-sorted tables, using techniques like Hilbert space-filling curves and materialized views. Calcite implements much of the OpenGIS function set and recognizes query patterns that can be rewritten to use particular spatial indexes. Calcite is bringing spatial query to the masses!
Introduction to Pandas and Time Series Analysis [PyCon DE]Alexander Hendorf
Most data is allocated to a period or to some point in time. We can gain a lot of insight by analyzing what happened when. The better the quality and accuracy of our data, the better our predictions can become.
Unfortunately the data we have to deal with is often aggregated for example on a monthly basis, but not all months are the same, they may have 28 days, 31 days, have four or five weekends,…. It’s made fit to our calendar that was made fit to deal with the earth surrounding the sun, not to please Data Scientists.
Dealing with periodical data can be a challenge. This talk will show to how you can deal with it with Pandas.
Is there a perfect data-parallel programming language? (Experiments with More...Julian Hyde
The perfect data parallel language has not yet been invented. SQL queries can achieve great performance and scale, but there are many general purpose algorithms that it cannot express. In Morel, we build on the functional and relational roots of MapReduce in an elegant and strongly-typed general-purpose programming language. But Morel is, in a real sense, a query language; programs are executed on relational frameworks such as Google BigQuery and Spark.
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.
We also introduce Apache Calcite, the popular open source framework for query planning, and describe how Morel's compiler uses Calcite's relational algebra and rewrite rules to generate efficient plans.
Extending Spark for Qbeast's SQL Data Source with Paola Pardo and Cesare Cug...Qbeast
Slides of the Barcelona Spark meetup of the 24th of October 2019. The recording is available at https://www.youtube.com/watch?v=eCoCcBH4hIU.
Abstract
One of the key strengths of Spark is its flexibility as it integrates with dozens of different storage systems and file formats. However, it is not the same reading from a CSV file, or a SQL database, or an exotic stratified sampled multidimensional database. And finding the right balance between modularity and flexibility is not easy!
In this presentation, we will talk about the evolution of Spark's DataSource API, and how it integrates with the SQL optimizer, highlighting how we can make much faster queries with logical and the physical plans that better integrates with the storage. From theory to practise, we will then discuss how we extended the Spark's internals, and we built a new source integration that allows the push-down of both sampling and multidimensional filtering.
About the speakers:
Paola Pardo is a Computer Engineer from Barcelona. She graduated in Computer engineer this last summer at the Technical University of Catalunya with a thesis focused on Data storage push down optimization based on Apache Spark. She is, and she is currently working at Barcelona Supercomputing Center and in its spin-off Qbeast developing a Qbeast-Spark connector.
Cesare Cugnasco is a PhD in Computer Architecture and a researcher at the Barcelona Supercomputing Center. His research focuses on NoSQL databases, distributed computing and High-performance storage. He invented and patented a new database architecture for Big Data, and he is building a spin-off for its commercialization.
Desk reference for data wrangling, analysis, visualization, and programming in Stata. Co-authored with Tim Essam(@StataRGIS, linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/03
Attached here is a presentation that I made covering some bits and pieces of what I got to discover about Data Science and Machine Learning using R Programming Language.
In this tutorial, we learn to access MySQL database from R using the RMySQL package. The tutorial covers everything from creating tables, appending data to removing tables from the database.
Best corporate-r-programming-training-in-mumbaiUnmesh Baile
Vibrant Technologies is headquarted in Mumbai,India.We are the best Teradata training provider in Navi Mumbai who provides Live Projects to students.We provide Corporate Training also.We are Best Teradata Database classes in Mumbai according to our students and corporates
This is a brief introduction to how R can be useful in the manufacturing sector to calculate the frequency of faults and then developing the model so that preventive maintenance can be done
Stata cheat sheet: programming. Co-authored with Tim Essam (linkedin.com/in/timessam). See all cheat sheets at http://bit.ly/statacheatsheets. Updated 2016/06/04
Efficient spatial queries on vanilla databasesJulian Hyde
A talk given by Julian Hyde at the Apache Calcite online meetup, 2021/01/20.
Spatial and GIS applications have traditionally required specialized databases, or at least specialized data structures like r-trees. Unfortunately this means that hybrid applications such as spatial analytics are not well served, and many people are unaware of the power of spatial queries because their favorite database does not support them.
In this talk, we describe how Apache Calcite enables efficient spatial queries using generic data structures such as HBase’s key-sorted tables, using techniques like Hilbert space-filling curves and materialized views. Calcite implements much of the OpenGIS function set and recognizes query patterns that can be rewritten to use particular spatial indexes. Calcite is bringing spatial query to the masses!
Data Science, Statistical Analysis and R... Learn what those mean, how they can help you find answers to your questions and complement the existing toolsets and processes you are currently using to make sense of data. We will explore R and the RStudio development environment, installing and using R packages, basic and essential data structures and data types, plotting graphics, manipulating data frames and how to connect R and SQL Server.
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2. What is R?
• A language and environment for statistical computing
and graphics
• Wide variety of statistical & graphical techniques built in
• Free and Open Source software
• Compiles and runs on a wide variety of UNIX platforms,
Windows and MacOS
2
www.Sanaitics.com
3. R Environment
• Most functionality through built in functions
• Basic functions available by default
• Other functions contained in packages that can be
attached
• All datasets created in the session are in Memory
• Output can be used as input to other functions
• R commands are Case Sensitive
3
www.Sanaitics.com
4. R-CRAN
• The Comprehensive R Archive Network
• A network of global web servers storing identical, up-to-
date, versions of code and documentation for R
• Use the CRAN mirror nearest to you to download R
setup at a faster speed
• http://cran.r-project.org/
4
www.Sanaitics.com
6. The R-UI
6
The R console
-- Type in the command
-- Press enter
-- See the output
www.Sanaitics.com
7. Create Your Data Set
x<-c(12,23,45)
y<-c(13,21,6) Create vectors x, y, z on R Console
z<-c("a","b","c")
data1<-data.frame(x,y,z) Combine them in a table
data1 Type data name for output
Note that R is Case-Sensitive
7
www.Sanaitics.com
8. Your data in Table mode
edit(data1)
Edit your table, add new values & close the window to save your
updates
8
www.Sanaitics.com
9. Deriving & Removing Variables
Add new variables using mathematical operators
data1$z<-data1$x+data1$y
Delete an unwanted column from your table by using one of the
following methods
data1$x<-NULL
data1<-subset(data1, select = -y)
9
www.Sanaitics.com
10. Export your updated table
write.csv (data1,file.choose())
Select the path, name your file with .csv extension, click open and Yes
to export your table
10
www.Sanaitics.com
11. Data Set types
11
Vector 1 dimension
All elements have the same data
types
Numeric
Character
Logic
Factor
Matrix 2 dimensions
Array
2 or more
dimensions
Data frame 2 dimensions
Table-like data object allowing
different data types for different
columns
List
Collection of data objects, each element of a list is a data
object
www.Sanaitics.com
12. Useful In-Built Functions
min(data1$x) Use $ sign after data set name to use specific columns
max(data1$y) length(data1$x)
mean(data1$y)
levels(data1$z) Gives a list of unique categories in the data
12
www.Sanaitics.com
13. Save Your Script
Open a new script, write commands, execute using F5 key. Save the file at a desired
folder. Helps to save the script for a later use.
13
www.Sanaitics.com
14. R Workspace
• Objects that you create during an R session are hold
in memory, the collection of objects that you
currently have is called the workspace
• This workspace is not saved on disk unless you tell R
to do so
• This means that your objects are lost when you close
R and not save the objects, or worse when R or your
system crashes on you during a session
14
www.Sanaitics.com
15. R Workspace
• If you have saved a workspace image and you start R
the next time, it will restore the workspace
• So all your previously saved objects are available
again
• You can also explicitly load a saved workspace i.e.,
that could be the workspace image of someone else
• Go the `File' menu and select `Load workspace'
15
www.Sanaitics.com
16. R Workspace
Display all previous commands
history()
Display last 25 commands
history(max.show=25)
Save your command history to a file. Default is ".Rhistory"
savehistory(file="myfile")
Recall your command history. Default is ".Rhistory"
loadhistory(file="myfile")
16
www.Sanaitics.com
17. Types of Variables
• Type: Logical, integer, double, complex, character,
factor
• Type conversion functions have the naming
convention
as.xxx
• For example, as.integer(3.2) returns the integer 3,
and as.character(3.2) returns the string "3.2".
17
www.Sanaitics.com
18. Factors
• Tell R that a variable is nominal by making it a
factor
• The factor stores the nominal values as a vector of
integers in the range [ 1... k ] (where k is the number
of unique values in the nominal variable), and an
internal vector of character strings (the original
values) mapped to these integers
• An ordered factor is used to represent an ordinal
variable
18
www.Sanaitics.com
19. Factors
• R will treat factors as nominal variables and ordered
factors as ordinal variables in statistical procedures
and graphical analyses
• You can use options in the factor( ) and ordered( )
functions to control the mapping of integers to
strings (overriding the alphabetical ordering)
19
www.Sanaitics.com
20. Help in R
• If you know the topic but not the exact function
help.search(“topic”)
• If you know the exact function
help(function name) OR
?functionname
20
www.Sanaitics.com
21. In-Memory Computing
• Storage of information in the main RAM of
dedicated servers rather than in complicated
relational databases operating on comparatively
slow disk drives
• Helps business customers, including retailers,
banks and utilities, to quickly detect patterns,
analyze massive data volumes on the fly, and
perform their operations quickly
21
www.Sanaitics.com
22. Import .csv Data File
basic_salary<-read.table(“C:/Users/Documents/R/BASIC
SALARY DATA.csv", header=TRUE, sep=",")
• Command requires the file path separated by a /
• header=TRUE if there are row labels
22
www.Sanaitics.com
24. Importing .txt File
Importing a text file having delimiter = “blank/space”
sample1<-read.table(file.choose(),header=T)
sample1
Importing a comma separated text file
sample2<-read.table(file.choose(), header=T, sep=",")
sample2
Various special characters can act as separators. Check them in the
file before importing.
24
www.Sanaitics.com
25. Data Imported
Use Head function to get an idea about how your data looks like
Note that it displays the first 6 rows
head(basic_salary)
25
www.Sanaitics.com
29. Sub-setting using Indices
• Row level: display some selected rows
• By setting condition on observations
• Column level: display some selected columns
• By setting condition on variable names
• Display selected rows for selected columns
• By setting conditions on variables & observations
29
www.Sanaitics.com
60. Merging Cases (append)
• Appending two datasets using rbind function requires
both the datasets with exactly the same number of
variables with exactly the same names
• If datasets do not have the same number of variables,
variables can be either dropped or created so both match
60
www.Sanaitics.com
61. R-String Functions
Extract or replaces substrings in a character vector
General syntax: substr(column name, start, stop)
basic_salary$Location<-substr(basic_salary$Location,1,1)
head(basic_salary)
61
www.Sanaitics.com
62. Replace Function
Try for yourself
x<-c(“green” , “red”, “yellow”); x
x<-replace(x, 1,”good”); x
x<-replace(x, c(2,3), c(“apple”, “banana”)); x
62
www.Sanaitics.com
63. Extracting Complete Cases
Create a dataset
x<-c(12, NA, 13, 12)
y<-c(25, 48, NA, NA)
complete_case<-data.frame(x, y); complete_case
Extract complete cases using any one of the three commnds
case1<-na.omit(complete_case); case1
case2<-complete_case[complete.cases(complete_case),]
case2
case3<-na.exclude(complete_case); case3
63
www.Sanaitics.com
64. FIRST MILESTONE REACHED!
NOW THAT YOU HAVE MASTERED FIRST STEP, IT’S TIME FOR
R GET STARTED II
64
www.Sanaitics.com
