Presentation by Jacob van Etten.
CCAFS workshop titled "Using Climate Scenarios and Analogues for Designing Adaptation Strategies in Agriculture," 19-23 September in Kathmandu, Nepal.
This document provides an introduction to using R for statistical analysis and visualization. It discusses what R is, why it is useful, and 12 reasons to learn R. These include benefits like rigor in data analysis, reproducibility through scripting, access to cutting-edge statistical methods, powerful and customizable graphics, and that it is free and open-source. The document then provides resources for learning R, including tutorials, packages of interest, and how to download the software. It concludes with exercises walking through basic R concepts like vectors, matrices, data frames, importing data from a CSV file, subsetting data, and simple plotting.
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
This document describes Lab 4, which involves creating and traversing a binary tree to sort student data in C or C++. Students will write functions to insert nodes into the binary tree based on student IDs and print out the sorted tree. The lab instructions specify creating a makefile, testing the code with sample input, running it on a provided test file, and submitting the required files in a tarball.
Mohamed Essam teaches about various Python data structures including strings, lists, tuples, sets, and dictionaries. Some key points:
- Lists are ordered and changeable collections that can contain elements of different data types and allow duplicates. They use indexes to access elements.
- Tuples are ordered and unchangeable collections that can contain duplicates. They have fewer methods than lists.
- Sets are unordered and unindexed collections that do not allow duplicates. Items cannot be accessed by index and sets cannot be changed after creation, but new items can be added.
- Dictionaries store elements in key-value pairs and are ordered, changeable collections that do not allow duplicate keys. Items
The document provides an introduction to C++ programming. It outlines key learning outcomes which include data types, input/output operators, control statements, arrays, functions, structures, and object-oriented programming concepts. It then discusses some of these concepts in more detail, including an overview of C++, its characteristics as both a high-level and low-level language, object-oriented programming principles, and basic data types.
This presentation is here to help you understand about the Object reusability in python. Python is an object-oriented programming language. When working on complex programs in particular, which in turn makes it more maintainable.
stay tuned with Learnbay for topics.
Arrays allow the storage of multiple values of the same type in memory locations next to each other. Arrays must be declared with a size and can then be accessed using indexes. Common array operations include traversal, insertion, deletion, searching and sorting. Two-dimensional arrays, also called matrices, store arrays of data in rows and columns. Strings in C are arrays of characters that end with a null terminator. Structures allow the grouping of different data types under one name. Enumerated types define a list of named integer constants.
This document describes Project 4 for CMPSC 201, which involves writing a C++ program to perform linear regression on a set of (x,y) coordinate data to find the line of best fit. The program must read in the data from a file, calculate statistics like the mean, standard deviation, and correlation coefficient, then use those values to compute the slope and y-intercept of the regression line. It also provides specifications on the required functions and expected inputs/outputs of the program.
This document provides an introduction to using R for statistical analysis and visualization. It discusses what R is, why it is useful, and 12 reasons to learn R. These include benefits like rigor in data analysis, reproducibility through scripting, access to cutting-edge statistical methods, powerful and customizable graphics, and that it is free and open-source. The document then provides resources for learning R, including tutorials, packages of interest, and how to download the software. It concludes with exercises walking through basic R concepts like vectors, matrices, data frames, importing data from a CSV file, subsetting data, and simple plotting.
this presentation is an introduction to R programming language.we will talk about usage, history, data structure and feathers of R programming language.
This document describes Lab 4, which involves creating and traversing a binary tree to sort student data in C or C++. Students will write functions to insert nodes into the binary tree based on student IDs and print out the sorted tree. The lab instructions specify creating a makefile, testing the code with sample input, running it on a provided test file, and submitting the required files in a tarball.
Mohamed Essam teaches about various Python data structures including strings, lists, tuples, sets, and dictionaries. Some key points:
- Lists are ordered and changeable collections that can contain elements of different data types and allow duplicates. They use indexes to access elements.
- Tuples are ordered and unchangeable collections that can contain duplicates. They have fewer methods than lists.
- Sets are unordered and unindexed collections that do not allow duplicates. Items cannot be accessed by index and sets cannot be changed after creation, but new items can be added.
- Dictionaries store elements in key-value pairs and are ordered, changeable collections that do not allow duplicate keys. Items
The document provides an introduction to C++ programming. It outlines key learning outcomes which include data types, input/output operators, control statements, arrays, functions, structures, and object-oriented programming concepts. It then discusses some of these concepts in more detail, including an overview of C++, its characteristics as both a high-level and low-level language, object-oriented programming principles, and basic data types.
This presentation is here to help you understand about the Object reusability in python. Python is an object-oriented programming language. When working on complex programs in particular, which in turn makes it more maintainable.
stay tuned with Learnbay for topics.
Arrays allow the storage of multiple values of the same type in memory locations next to each other. Arrays must be declared with a size and can then be accessed using indexes. Common array operations include traversal, insertion, deletion, searching and sorting. Two-dimensional arrays, also called matrices, store arrays of data in rows and columns. Strings in C are arrays of characters that end with a null terminator. Structures allow the grouping of different data types under one name. Enumerated types define a list of named integer constants.
This document describes Project 4 for CMPSC 201, which involves writing a C++ program to perform linear regression on a set of (x,y) coordinate data to find the line of best fit. The program must read in the data from a file, calculate statistics like the mean, standard deviation, and correlation coefficient, then use those values to compute the slope and y-intercept of the regression line. It also provides specifications on the required functions and expected inputs/outputs of the program.
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
The document discusses principles and best practices for writing clean code, including keeping functions small and focused on a single task, using descriptive names, avoiding duplication, minimizing comments by writing expressive code, and agreeing on consistent formatting styles within a team. It emphasizes writing code that is easy to read and understand through applying these guidelines.
Introductiont To Aray,Tree,Stack, QueueGhaffar Khan
This document provides an introduction to data structures and algorithms. It defines key terminology related to data structures like entities, fields, records, files, and primary keys. It also describes common data structures like arrays, linked lists, stacks, queues, trees, and graphs. Finally, it discusses basic concepts in algorithms like control structures, complexity analysis, and examples of searching algorithms like linear search and binary search.
This document summarizes a seminar presentation on the R programming language. It begins with an introduction to R's history and features. Key points covered include that R is a functional programming language developed for statistical analysis. It has a large number of built-in statistical functions and packages. The document then discusses R packages, graphical user interfaces, getting started with basic objects and functions, and programming features like flow control and functions. Examples are provided. Reasons for using R include its matrix calculation, data visualization and statistical analysis capabilities. Comparisons are made to other languages like Python and Java. The document concludes that R has become a high-quality open-source software for statistical computing and graphics.
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
The document discusses Lisp input and output functions. It describes how Lisp represents objects in printed form for input/output and the read function that accepts this printed input and constructs Lisp objects. It covers parsing of numbers, symbols, and macro characters. It also describes output functions like print, princ, and format for writing to streams, as well as input functions like read, read-line, and querying functions like y-or-n-p.
This document provides an introduction to Python programming. It outlines the key topics that will be covered in the course, including data types, control statements, functions, object-oriented programming, exceptions, and file operations. It then discusses specific Python concepts like reading input from the console, operators, operator precedence, strings, control structures like if/else statements and loops.
The document compares and contrasts arrays and linked lists. It states that arrays provide fast random access but fixed size, while linked lists have flexible size but slower sequential access. It also discusses different types of linked lists and their memory usage.
The document discusses thin templates, which is a technique to reduce code bloat when using templates in C++. Thin templates move code that does not use template arguments into non-template base classes and functions. This allows compilers to generate code for template-independent parts only once, reducing the overall size of generated code. The document provides an example of a thin template implementation using a CListThin base class with non-template functions, and a derived CList template class that calls these functions while implementing template-dependent logic.
Ajay Bhullar is seeking a software development position to apply and expand his technical skills. He has a B.S. in Computer Engineering from UC San Diego and received Provost Honors. His coursework covered programming languages like Python, C++, Java, and technical skills include Eclipse, Git, and MATLAB. He has experience as an online tutor through codehs.com, helping students learn programming concepts. For projects, he created a Huffman compressor/decompressor and a program to convert prime numbers using different search algorithms. His most recent project was a Python web scraper to find colon cancer drug side effects.
Introduction to R - from Rstudio to ggplotOlga Scrivner
This document provides an outline for an introduction to R programming. It discusses the materials needed like R Studio and example datasets. It covers R basics like assigning variables, vectors, indexing, logical operators and data types. It also discusses importing data from files like CSV, installing and loading packages, and basic data visualization. The document is intended to introduce learners to key concepts in R through examples and exercises in R Studio.
This tutorial provides instructions for creating Manhattan plots from genome-wide association study (GWAS) results in circular and rectangular forms using the R package CMplot. It describes how to format and import a GWAS data file, install and load the CMplot package, generate Manhattan plots with different parameters, and find additional tutorials. The key steps are to format the GWAS data as a CSV file with SNPs, chromosomes, positions and p-values, read in the file using R, and run the CMplot function to produce circular, rectangular and QQ plots.
Virtual base class is used to avoid ambiguity and multiple inheritance problems in C++. Some key points about virtual base class:
- A virtual base class is declared using the virtual keyword in a derived class.
- When a class is declared as a virtual base class, only one copy of that base class is shared among all the objects.
- It is used to resolve diamond problem in multiple inheritance.
- A virtual base class pointer can be used to access the single copy of the base class object.
2. What is multiple inheritance? Explain with an example.
Ans: Multiple inheritance is a feature of some object-oriented computer programming languages in which classes can inherit features from multiple base or parent classes.
1) The document discusses data visualization using R and provides an introduction to key concepts. It explains why data visualization is important for understanding large and complex data.
2) Basic concepts for effective visual analytics are covered, including understanding the data, determining what to visualize, knowing the audience, and using simple visuals.
3) Different types of plots in R are described like histograms, bar plots, scatter plots, box plots, and plots for descriptive statistics. Steps to install R and RStudio are also provided.
The document summarizes chapter 4 on linked lists from a textbook. It covers different types of linked lists including singly linked lists, doubly linked lists, and circular lists. It describes how to implement basic linked list operations like insertion, deletion, and traversal. It also discusses using linked lists to implement stacks, queues, and sparse matrices. Dynamic storage management using linked lists and garbage collection techniques are explained.
The C++ Standard Library provides functions for common tasks like math, strings, I/O, and more. It includes header files that replace older C-style headers. Key headers include <iostream> for I/O, <cmath> for math functions, and <cctype> for character functions. Functions cover tasks from calculating sines and cosines to converting cases and checking digit/letter types. The Standard Library makes programming easier by providing these useful and necessary capabilities.
A stack is a linear data structure that follows the last-in, first-out (LIFO) principle. Elements can only be inserted or removed from one end, called the top. Common stack operations include push, which adds an element to the top, and pop, which removes the top element. Stacks have many applications, such as evaluating mathematical expressions, interrupt handling in computing, and temporary storage in stack machines. They can be implemented using either arrays or linked lists.
Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx
The document discusses various algorithms for query processing operations like selection, sorting, and join. It provides cost estimates for each algorithm based on factors like the number of block transfers and seeks. The most efficient algorithms depend on characteristics of the relations and whether indices are available. Nested loop and block nested loop joins have high costs, while merge join and hash join may have lower costs depending on the situation.
This document provides instructions for using R software for agricultural data analysis. It discusses downloading and installing R, loading and exploring data, performing common analyses like ANOVA and correlations, and using specialized packages for tasks like AMMI analysis. The document recommends exploring R's capabilities and using reliable packages suited for agricultural research.
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
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
The document discusses principles and best practices for writing clean code, including keeping functions small and focused on a single task, using descriptive names, avoiding duplication, minimizing comments by writing expressive code, and agreeing on consistent formatting styles within a team. It emphasizes writing code that is easy to read and understand through applying these guidelines.
Introductiont To Aray,Tree,Stack, QueueGhaffar Khan
This document provides an introduction to data structures and algorithms. It defines key terminology related to data structures like entities, fields, records, files, and primary keys. It also describes common data structures like arrays, linked lists, stacks, queues, trees, and graphs. Finally, it discusses basic concepts in algorithms like control structures, complexity analysis, and examples of searching algorithms like linear search and binary search.
This document summarizes a seminar presentation on the R programming language. It begins with an introduction to R's history and features. Key points covered include that R is a functional programming language developed for statistical analysis. It has a large number of built-in statistical functions and packages. The document then discusses R packages, graphical user interfaces, getting started with basic objects and functions, and programming features like flow control and functions. Examples are provided. Reasons for using R include its matrix calculation, data visualization and statistical analysis capabilities. Comparisons are made to other languages like Python and Java. The document concludes that R has become a high-quality open-source software for statistical computing and graphics.
Abstract data types (adt) intro to data structure part 2Self-Employed
Abstract Data type (ADT), Related to DATA STRUCTURE and ALGORITHMS STACK QUEUE ARRAY LINKED LIST ALGORITHMS AND INSERTION DELETION MERGE TRAVERSE MODIFY AND OTHER related operation in the algorithms of stack queue array and linked list as an ADT type
The document discusses Lisp input and output functions. It describes how Lisp represents objects in printed form for input/output and the read function that accepts this printed input and constructs Lisp objects. It covers parsing of numbers, symbols, and macro characters. It also describes output functions like print, princ, and format for writing to streams, as well as input functions like read, read-line, and querying functions like y-or-n-p.
This document provides an introduction to Python programming. It outlines the key topics that will be covered in the course, including data types, control statements, functions, object-oriented programming, exceptions, and file operations. It then discusses specific Python concepts like reading input from the console, operators, operator precedence, strings, control structures like if/else statements and loops.
The document compares and contrasts arrays and linked lists. It states that arrays provide fast random access but fixed size, while linked lists have flexible size but slower sequential access. It also discusses different types of linked lists and their memory usage.
The document discusses thin templates, which is a technique to reduce code bloat when using templates in C++. Thin templates move code that does not use template arguments into non-template base classes and functions. This allows compilers to generate code for template-independent parts only once, reducing the overall size of generated code. The document provides an example of a thin template implementation using a CListThin base class with non-template functions, and a derived CList template class that calls these functions while implementing template-dependent logic.
Ajay Bhullar is seeking a software development position to apply and expand his technical skills. He has a B.S. in Computer Engineering from UC San Diego and received Provost Honors. His coursework covered programming languages like Python, C++, Java, and technical skills include Eclipse, Git, and MATLAB. He has experience as an online tutor through codehs.com, helping students learn programming concepts. For projects, he created a Huffman compressor/decompressor and a program to convert prime numbers using different search algorithms. His most recent project was a Python web scraper to find colon cancer drug side effects.
Introduction to R - from Rstudio to ggplotOlga Scrivner
This document provides an outline for an introduction to R programming. It discusses the materials needed like R Studio and example datasets. It covers R basics like assigning variables, vectors, indexing, logical operators and data types. It also discusses importing data from files like CSV, installing and loading packages, and basic data visualization. The document is intended to introduce learners to key concepts in R through examples and exercises in R Studio.
This tutorial provides instructions for creating Manhattan plots from genome-wide association study (GWAS) results in circular and rectangular forms using the R package CMplot. It describes how to format and import a GWAS data file, install and load the CMplot package, generate Manhattan plots with different parameters, and find additional tutorials. The key steps are to format the GWAS data as a CSV file with SNPs, chromosomes, positions and p-values, read in the file using R, and run the CMplot function to produce circular, rectangular and QQ plots.
Virtual base class is used to avoid ambiguity and multiple inheritance problems in C++. Some key points about virtual base class:
- A virtual base class is declared using the virtual keyword in a derived class.
- When a class is declared as a virtual base class, only one copy of that base class is shared among all the objects.
- It is used to resolve diamond problem in multiple inheritance.
- A virtual base class pointer can be used to access the single copy of the base class object.
2. What is multiple inheritance? Explain with an example.
Ans: Multiple inheritance is a feature of some object-oriented computer programming languages in which classes can inherit features from multiple base or parent classes.
1) The document discusses data visualization using R and provides an introduction to key concepts. It explains why data visualization is important for understanding large and complex data.
2) Basic concepts for effective visual analytics are covered, including understanding the data, determining what to visualize, knowing the audience, and using simple visuals.
3) Different types of plots in R are described like histograms, bar plots, scatter plots, box plots, and plots for descriptive statistics. Steps to install R and RStudio are also provided.
The document summarizes chapter 4 on linked lists from a textbook. It covers different types of linked lists including singly linked lists, doubly linked lists, and circular lists. It describes how to implement basic linked list operations like insertion, deletion, and traversal. It also discusses using linked lists to implement stacks, queues, and sparse matrices. Dynamic storage management using linked lists and garbage collection techniques are explained.
The C++ Standard Library provides functions for common tasks like math, strings, I/O, and more. It includes header files that replace older C-style headers. Key headers include <iostream> for I/O, <cmath> for math functions, and <cctype> for character functions. Functions cover tasks from calculating sines and cosines to converting cases and checking digit/letter types. The Standard Library makes programming easier by providing these useful and necessary capabilities.
A stack is a linear data structure that follows the last-in, first-out (LIFO) principle. Elements can only be inserted or removed from one end, called the top. Common stack operations include push, which adds an element to the top, and pop, which removes the top element. Stacks have many applications, such as evaluating mathematical expressions, interrupt handling in computing, and temporary storage in stack machines. They can be implemented using either arrays or linked lists.
Data Structure is the specific method for sorting out the data in a system with the goal that it could be utilized efficiently. These can implement at least one specific abstract data types (ADT), which indicate the operations that can be performed on the data structure and the computational unpredictability of those operations. Copy the link given below and paste it in new browser window to get more information on Data Structure & Algorithms:- www.transtutors.com/homework-help/computer-science/data-structure-and-algorithms.aspx
The document discusses various algorithms for query processing operations like selection, sorting, and join. It provides cost estimates for each algorithm based on factors like the number of block transfers and seeks. The most efficient algorithms depend on characteristics of the relations and whether indices are available. Nested loop and block nested loop joins have high costs, while merge join and hash join may have lower costs depending on the situation.
This document provides instructions for using R software for agricultural data analysis. It discusses downloading and installing R, loading and exploring data, performing common analyses like ANOVA and correlations, and using specialized packages for tasks like AMMI analysis. The document recommends exploring R's capabilities and using reliable packages suited for agricultural research.
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 document discusses using R for initial data analysis. It covers loading data into R from files or by typing it in, exploring and visualizing the data using basic statistics and graphs, and saving outputs. R allows importing data from various sources, creating and editing data structures, and exporting objects and plots for sharing results. The key is becoming familiar with R's programming environment and functions for summarizing, transforming, and visualizing data.
Data Science - Part II - Working with R & R studioDerek Kane
This tutorial will go through a basic primer for individuals who want to get started with predictive analytics through downloading the open source (FREE) language R. I will go through some tips to get up and started and building predictive models ASAP.
This document provides an overview of the R programming language and environment. It discusses why R is useful, outlines its interface and workspace, describes how to access help and tutorials, install packages, and input/output data. The interactive nature of R is highlighted, where results from one function can be used as input for another.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, build statistical models and visualize results. Key features of R include its extensive library of statistical and graphical methods, machine learning algorithms, and ability to handle large and complex data. R is widely used in academia and industry for data science tasks like data analysis, modeling, and communicating results.
R is a programming language and free software used for statistical analysis and graphics. It allows users to analyze data, create visualizations and build predictive models. Key features of R include its extensive library of statistical and machine learning methods, ability to handle large datasets, and functionality for data wrangling, modeling, visualization and communication of results. The document provides instructions on downloading and installing R and RStudio, loading and installing packages, and introduces basic R programming concepts like vectors, matrices, data frames and factors.
This document discusses ways to make R code more reproducible through cleaner coding practices, functional programming, and collaboration tools. It recommends:
1. Writing cleaner code through descriptive names, spacing, and documentation.
2. Functionalizing code by defining reusable functions to avoid repetition and improve flexibility.
3. Using pipes to chain together functions and solve complex problems through simple pieces.
4. Outsourcing functions to external files and version controlling code with Git and GitHub to enable collaboration.
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This document provides an overview of the basics of R including why R is used, tutorials and links for learning R, an overview of the R interface and workspace, and how to get help in R. It discusses that R is a free and open-source statistical programming language used for statistical analysis and graphics. It has a steep learning curve due to the interactive nature of analyzing data through chained commands rather than single procedures. Help is provided through a built-in system and various online tutorials.
Modeling in R Programming Language for Beginers.pptanshikagoel52
This document provides an overview of the basics of R. It discusses why R is useful, outlines its interface and workspace, describes how to get help and install packages, and explains some key concepts like objects, functions, and the search path. The document is intended to introduce new users to commonly used R functions and features to get started with the programming language.
This was a brief 1-hour introduction to R programming, presented at the 1st Inter-experimental Machine Learning (IML) Working Group Workshop at CERN, 20-22 March 2017.
An introduction to R is a document usefulssuser3c3f88
R is a language and environment for statistical computing and graphics. It provides functions for data manipulation, calculation, and graphical displays. Key features of R include its ability to produce publication-quality plots, perform statistical tests, fit models to data, and develop statistical software. R has an extensive library of additional user-contributed packages that extend its capabilities. The document provides information on downloading and using R, reading data into R, customizing plots, and interactive plotting functions.
R is a powerful but complex statistical programming language. It allows users great flexibility as "wizards" to customize their environment and create their own functions ("spells") without paying for licenses. Learning R involves overcoming an initial steep learning curve. There are over 2000 add-on packages available that provide many statistical techniques without delays, but the large number of packages can make choosing the best one difficult. The document provides tips for getting started with R, including installing RStudio, loading packages, and basic syntax like creating vectors and data frames. It also gives an example of analyzing rainfall data from India with R functions like aggregate, plot, and linear regression.
This document discusses different programming paradigms including procedural, object-oriented, and declarative paradigms. It provides examples of code using these paradigms. Specifically, it shows an assembly language program that adds two numbers, a C++ program that calculates the area of a rectangle, and Prolog queries to retrieve information from a database about people's genders and family relationships. It also discusses how parameters are used to pass values to functions in Visual Basic.
This document discusses effective data visualization using Microsoft R Open. It introduces why visualizing data is important, the benefits of using R and Microsoft R Open for data visualization, and how to get started. The document then provides examples of different types of graphs that can be created in R using the base graphics package as well as several other packages. It discusses choosing an appropriate graphics package and considerations for sizing and saving graphs. The examples focus on direct comparisons, distributions, trends over time, relationships, percentages, and special cases. Code and data are provided in appendices and online for reproducing the graphs.
Data science involves extracting knowledge from data to solve business problems. The data science life cycle includes defining the problem, collecting and preparing data, exploring the data, building models, and communicating results. Data preparation is an essential step that can consume 60% of a project's time. It involves cleaning, transforming, handling outliers, integrating, and reducing data. Models are built using machine learning algorithms like regression for continuous variables and classification for discrete variables. Results are visualized and communicated effectively to clients.
The R language is a project designed to create a free, open source language which can be used as a replacement for the S-PLUS language, originally developed as the S language at AT&T Bell Labs, and currently marketed by Insightful Corporation of Seattle, Washington. R is an open source implementation of S, and differs from S-plus largely in its command-line only format.
Topics Covered:
1.Introduction to R
2.Installing R
3.Why Learn R
4.The R Console
5.Basic Arithmetic and Objects
6.Program Example
7.Programming with Big Data in R
8.Big Data Strategies in R
9.Applications of R Programming
10.Companies Using R
11.What R is not so good at
12.Conclusion
The Accelerating Impact of CGIAR Climate Research for Africa (AICCRA) project works to deliver a climate-smart African future driven by science and innovation in agriculture.
AICCRA does this by enhancing access to climate information services and climate-smart agricultural technology to millions of smallholder farmers in Africa.
With better access to climate technology and advisory services—linked to information about effective response measures—farmers can better anticipate climate-related events and take preventative action that help communities better safeguard their livelihoods and the environment.
AICCRA is supported by a grant from the International Development Association (IDA) of the World Bank, which is used to enhance research and capacity-building activities by the CGIAR centers and initiatives as well as their partners in Africa.
About IDA: IDA helps the world’s poorest countries by providing grants and low to zero-interest loans for projects and programmes that boost economic growth, reduce poverty, and improve poor people’s lives.
IDA is one of the largest sources of assistance for the world’s 76 poorest countries, 39 of which are in Africa.
Annual IDA commitments have averaged about $21 billion over circa 2017-2020, with approximately 61 percent going to Africa.
This presentation was given on 27 October 2021 by Mengpin Ge, Global Climate Program Associate at WRI, during the webinar "Achieving NDC Ambition in Agriculture" organized by CCAFS, FAO and WRI.
Find the recording and more information here: https://bit.ly/AchievingNDCs
This presentation was given on 27 October 2021 by Sabrina Rose, Policy Consultant at CCAFS, during the webinar "Achieving NDC Ambition in Agriculture" organized by CCAFS, FAO and WRI.
Find the recording and more information here: https://bit.ly/AchievingNDCs
This presentation was given on 27 October 2021 by Krystal Crumpler, Climate Change and Agricultural Specialist at FAO, during the webinar "Achieving NDC Ambition in Agriculture" organized by CCAFS, FAO and WRI.
Find the recording and more information here: https://bit.ly/AchievingNDCs
This presentation was meant to be included in the 2021 CLIFF-GRADS Welcome Webinar and presented by Ciniro Costa Jr. (CCAFS).
The webinar recording can be found here: https://youtu.be/UoX6aoC4fhQ
The multilevel CSA monitoring set of standard core uptake and outcome indicators + expanded indicators linked to a rapid and reliable ICT based data collection instrument to systematically
assess and monitor:
- CSA Adoption/ Access to CIS
- CSA effects on food security and livelihoods household level)
- CSA effects on farm performance
The document discusses plant-based proteins as a potential substitute for animal-based proteins. It notes that plant-based proteins are growing in popularity due to environmental and ethical concerns with animal agriculture. However, plant-based meats also present some health and nutritional challenges compared to animal proteins. The document analyzes opportunities and impacts related to plant-based proteins across Asia, including leveraging the region's soy and pea production and tailoring products to Asian diets and cultural preferences.
Presented by Ciniro Costa Jr., CCAFS, on 28 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
Presented by Marion de Vries, Wageningen Livestock Research at Wageningen University, on 28 June 2021 at the Asian Development Bank (ADB) Webinar on Sustainable Protein Case Study: Outputs and Synthesis of Results.
This document assesses the environmental sustainability of plant-based meats and pork in China. It finds that doubling food production while reducing agricultural greenhouse gas emissions by 73% by 2050 will be a major challenge. It compares the life cycle impacts of plant-based meats made from soy, pea, and wheat proteins and oils, as well as pork and beef. The results show that the crop type and source country of the core protein ingredient drives the environmental performance of plant-based meats. The document provides sustainability guidelines for sourcing ingredients from regions with low deforestation risk and irrigation needs, using renewable energy in production, and avoiding coal power.
This document summarizes a case study on the dairy value chain in China. It finds that milk production and consumption have significantly increased in China from 1978 to 2018. Large-scale dairy farms now dominate production. The study evaluates greenhouse gas emissions from different stages and finds feed production is a major contributor. It models options to reduce the carbon footprint, finding improving feed practices and yield have high potential. Land use is also assessed, with soybean meal requiring significant land. Recommendations include changing feeds to lower land and carbon impacts.
This document summarizes information on the impacts of livestock production globally and in Asia. It finds that livestock occupies one third of global cropland and one quarter of ice-free land for pastures. Asia accounts for 32% of global enteric greenhouse gas emissions from livestock, with most emissions coming from India, China, Pakistan, and Bangladesh. Rapid growth of livestock production in Asia is contributing to water and air pollution through nutrient runoff and emissions. The document discusses opportunities for public and private investment in more sustainable and climate-friendly livestock systems through technologies, monitoring, plant-based alternatives, and policies to guide intensification.
Presentation by Han Soethoudt, Jan Broeze, and Heike Axmann of Wageningen University & Resaearch (WUR).
WUR and Olam Rice Nigeria conducted a controlled experiment in Nigeria in which mechanized rice harvesting and threshing were introduced on smallholder farms. The result of the study shows that mechanization considerably reduces losses, has a positive impact on farmers’ income, and the climate.
Learn more: https://www.wur.nl/en/news-wur/show-day/Mechanization-helps-Nigerian-farms-reduce-food-loss-and-increase-income.htm
Presentation on the rapid evidence review findings and key take away messages.
Current evidence for biodiversity and agriculture to achieve and bridging gaps in research and investment to reach multiple global goals.
The document evaluates how climate services provided to farmers in Rwanda through programs like Participatory Integrated Climate Services for Agriculture (PICSA) and Radio Listeners’ Clubs (RLC) have impacted women and men differently, finding that the programs have increased women's climate knowledge and participation in agricultural decision making, leading to perceived benefits like higher incomes, food security, and ability to cope with climate risks for both women and men farmers.
This document provides an introduction to climate-smart agriculture (CSA) in Busia County, Kenya. It defines CSA and its three objectives of sustainably increasing agricultural productivity and income, adapting and building resilience to climate change, and reducing and/or removing greenhouse gas emissions. It discusses CSA at the farm and landscape scales and provides examples of CSA practices and projects in Kenya. It also outlines Kenya's response to CSA through policies and programs. The document describes prioritizing CSA options through identifying the local context, available options, relevant outcomes, evaluating evidence on options' impacts, and choosing best-bet options based on the analysis.
1) The document outlines an action plan to scale research outputs from the EC LEDS project in Vietnam. It identifies key activities to update livestock feed databases and software, improve feeding management practices, develop policies around carbon tracking and subsidies, and raise awareness of stakeholders.
2) The plan's main goals are to strengthen national feed resources, update the PC Dairy software, build greenhouse gas inventory systems, and adopt standards to reduce emissions in agriculture and the livestock industry.
3) Key stakeholders involved in implementing the plan include the Department of Livestock Production, universities, and ministries focused on agriculture and the environment.
2. What is R? “R is a free software environment for statistical computing and graphics.” www.r-project.org
3. Why R? R takes time to learn and use. So why should I bother? There are more user-friendly programmes, right?
4. 12 reasons to learn R 1. Rigour and strategy in data analysis – not “thinking after clicking”. 2. Automatizing repeated calculations can save time in the end. 3. A lot of stuff is simply not feasible in menu driven software.
5. 12 reasons to learn R 4. R is not only about software, it is also an online community of user support and collaboration. 5. Scripts make it easy to communicate about your problem. Important for collaborative research! 6. Research becomes replicable when scripts are published.
6. 12 reasons to learn R 7. R packages represent state-of-the-art in many academic fields. 8. Graphics in R are very good. 9. R stimulates learning – graduation from user to developer.
7. 12 reasons to learn R 10. R is free, which saves you money. Or R may be the only option when budgets are restricted. 11. R encourages to freely explore new methods and learn about them. 12. Knowing to work with R is a valuable and transferable skill.
9. Resources to learn R My two picks for you http://pj.freefaculty.org/R/Rtips.html
10. Some R packages of interest RNCEP – Reanalysis data clim.pact– Downscaling climate data GhcnDaily – dailyweather data weatherData(R-Forge) – Daily weather data and derived bioclimatic variables relevant to plant growth raster – gives access to WorldClim data
11. And a lot more here... http://cran.r-project.org/other-docs.html
13. Choose a mirror nearby and then... Binaries “When downloading, a completely functional program without any installer is also often called program binary, or binaries (as opposed to the source code).” (Wikipedia)
14. And finally,you can download R... When downloading has finished, run the installer
17. Four parts Create a new R script: File – New – R Script Scripts, documentation Workspace/history Files, plots, packages and help Console
18. Our first code... Type 1 + 1 into the script area. Then click “Run”. What happens?
19. Exercises: running code Type a second line with another calculation (use “-”, “/”, or “*”) and click “Run” again. Select only one line with the mouse or Shift + arrows. Then click “Run”. Save your first code to a separate folder “Rexercises”.
20. Following exercises In the next exercises, we will develop a script. Just copy every new line and add it to your script, without erasing the previous part. If you want to make a comment in your script, put a # before that line. Like this: #important to remember: use # to comment
21. If the exercises are a bit silly... ...that’s because you are learning.
22. Vector Type a new line with the expression 1:10 in the script and run this line. A concatenation of values is called a vector.
23. Making a new variable If we send 1:10 into the console it will only print the outcome. To “store” this vector, we need to do the following. a <- 1:10 new variable “a” assign vector values 1 to 10
24. Operations with vectors Try the following and see what happens. a a * 2 a * a b <- a * a b print(b)
25. Other ways of making vectors d <- c(1, 6, 9) d class(d) f <- LETTERS f class(f) What is the difference between d and f?
26. Functions Actually, we have already seen functions! Functions consist of a name followed by one or more arguments. Commas and brackets complete the expression. class(f) c(d,f) name argument
27. Cheat sheet When you use R, you will become familiar with the most common functions. If you need a less common function, there are ways to discover the right one. For now, use the cheat sheet to look up the functions you need.
28. Getting help on functions This will open help pages for the functions in your browser. ?c ?class Especially the examples are often helpful. Just copy and paste the code into the console and see with your own eyes what happens!
29. Matrices We have already met the vector. If we put two or more vector together as columns, we get a matrix. X <- c(1,2,3) Y <- c(8,9,7) Z <- c(4,2,8) M <- cbind(X, Y, Z) How many columns and rows does M have?
30. Data frames Matrices must consist of values of the same class. But often datasets consist of a mix of different types of variables (real numbers and groups). This is the job of data frames. L <- c(“a”, “b”, “c”) Df <- data.frame(X,Y,Z,L) Visualize Df like this: str(Df) What would happen if you tried to make a matrix out of these same vectors instead? Try and see.
31. Getting data into R ?read.csv CSV files are a relatively trouble-free way of getting data into R. It is a fairly common format. You can make a CSV file in any spreadsheet software.
32. Create a CSV file Add your own favorite actor, too. Open the file with Notepad. Make sure the values are separated by commas.
33. Now use R to read it Now read it into R. actors <- read.csv(yourfile.csv) str(actors)
34. Subsetting There are many ways of selecting only part of a data frame. Observe carefully what happens. actors[1:2,] actors[,1:2] actors[“Age”] actors[c(“Name”, “Age”)] subset(actors, Age> 40) Now create a new data frame with the actors younger than 45.
35. Graphics The plot function makes graphs. plot(actors[c(“sex”, “Age”)])
36. Summary You now know about: Variables Functions Vectors Matrices Data frames Getting tabular data into R Subsetting Simple plotting