This document provides an agenda and overview for a class on using R to work with data. The class covers topics like calculating, joining, and grouping data in R; using R to build databases in Google Sheets; and introducing R Markdown for automating reporting. Specific sessions will demonstrate generating fake data from GitHub, data transformations with dplyr, different types of joins, uploading/downloading from Google Sheets, and creating dashboards in DataStudio.
5. working on data using R -Cleaning, filtering ,transformation, Samplingkrishna singh
This document discusses various techniques for working with and preparing data for analysis in R, including loading and exploring data, handling different data formats, cleaning data by dealing with missing values and outliers, transforming data through normalization and discretization, sampling data for modeling, and visualizing data. It provides examples of using functions like read.table(), class(), summary(), str(), names(), dim(), ifelse(), merge(), and graphing techniques like histograms, boxplots, and scatter plots to examine relationships in the data.
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...Dataconomy Media
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
DN 2017 | Reducing pain in data engineering | Martin Loetzsch | Project ADataconomy Media
Making the data of a company accessible to analysts, business users and data scientists can be a quite painful endeavor. In the past 5 years, Project A has supported many of its portfolio companies with building data infrastructures and we experienced many of these pains first-hand. This talk shows how some of these pains can be overcome by applying common sense and standard software engineering best practices.
[M3A3] Data Analysis and Interpretation Specialization Andrea Rubio
- The document describes testing a multiple regression model using data from the NESARC dataset to study factors that influence personal income.
- A linear regression is first run on age and income, showing a positive relationship, but the line does not perfectly fit the data pattern.
- A polynomial regression is then applied, showing a better fit with an initial increase then decrease in income with age.
- Additional variables like sex, education level, and employment status are identified for a multiple regression analysis.
This document provides a tutorial for creating a wheel chart visualization in Quantum4D using a sample spreadsheet dataset. The 3-sentence summary is:
The tutorial explains how to use the Quantum4D Excel add-in to tag the sample spreadsheet data with relations, attributes, and dates, then import this tagged data into a new Quantum4D workspace where a wheel chart lens can be dragged onto the workspace to generate an interactive wheel chart visualization of the sample data relationships over time. Basic interactions like animating over time, displaying values, and changing visualization parameters are described, as well as pointers to more advanced Quantum4D features demonstrated in sample files.
5. working on data using R -Cleaning, filtering ,transformation, Samplingkrishna singh
This document discusses various techniques for working with and preparing data for analysis in R, including loading and exploring data, handling different data formats, cleaning data by dealing with missing values and outliers, transforming data through normalization and discretization, sampling data for modeling, and visualizing data. It provides examples of using functions like read.table(), class(), summary(), str(), names(), dim(), ifelse(), merge(), and graphing techniques like histograms, boxplots, and scatter plots to examine relationships in the data.
DN 2017 | Multi-Paradigm Data Science - On the many dimensions of Knowledge D...Dataconomy Media
Gaining insight from data is not as straightforward as we often wish it would be – as diverse as the questions we’re asking are the quality and the quantity of the data we may have at hand. Any attempt to turn data into knowledge thus strongly depends on it dealing with big or not-so-big data, high- or low-dimensional data, exact or fuzzy data, exact or fuzzy questions, and the goal being accurate prediction or understanding. This presentation emphasizes the need for a multi-paradigm data science to tackle all the challenges we are facing today and may be facing in the future. Luckily, solutions are starting to emerge...
DN 2017 | Reducing pain in data engineering | Martin Loetzsch | Project ADataconomy Media
Making the data of a company accessible to analysts, business users and data scientists can be a quite painful endeavor. In the past 5 years, Project A has supported many of its portfolio companies with building data infrastructures and we experienced many of these pains first-hand. This talk shows how some of these pains can be overcome by applying common sense and standard software engineering best practices.
[M3A3] Data Analysis and Interpretation Specialization Andrea Rubio
- The document describes testing a multiple regression model using data from the NESARC dataset to study factors that influence personal income.
- A linear regression is first run on age and income, showing a positive relationship, but the line does not perfectly fit the data pattern.
- A polynomial regression is then applied, showing a better fit with an initial increase then decrease in income with age.
- Additional variables like sex, education level, and employment status are identified for a multiple regression analysis.
This document provides a tutorial for creating a wheel chart visualization in Quantum4D using a sample spreadsheet dataset. The 3-sentence summary is:
The tutorial explains how to use the Quantum4D Excel add-in to tag the sample spreadsheet data with relations, attributes, and dates, then import this tagged data into a new Quantum4D workspace where a wheel chart lens can be dragged onto the workspace to generate an interactive wheel chart visualization of the sample data relationships over time. Basic interactions like animating over time, displaying values, and changing visualization parameters are described, as well as pointers to more advanced Quantum4D features demonstrated in sample files.
Get up to Speed (Quick Guide to data.table in R and Pentaho PDI)Serban Tanasa
1) The document provides a quick guide to using data.table in R and Pentaho Data Integration (PDI) for fast data loading and manipulation. It discusses benchmarks showing data.table is 2-20x faster than traditional methods for reading, ordering, and transforming large data.
2) The outline discusses how to use basic data.table functions for speed gains and to overcome R's scaling limitations. It also provides a very brief overview of PDI's capabilities for Extract/Transform/Load (ETL) workflows without writing code.
3) The benchmarks section shows data.table is up to 500% faster than traditional R methods for reading large CSV files and orders of magnitude faster for sorting and aggregating
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
I am Walker D. I am a Civil and Environmental Engineering assignment Expert at statisticsassignmenthelp.com. I hold a Ph.D. in Civil and Environmental Engineering. I have been helping students with their homework for the past 8 years. I solve assignments related to Civil and Environmental Engineering Assignment. Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Civil and Environmental Engineering assignments.
This document provides an introduction to MATLAB for people working in marketing. It explains that MATLAB is useful for analyzing large or complex datasets, as it can handle data more efficiently than Excel. The document demonstrates how to use MATLAB through a example of modeling mobile app subscription prices and demand based on survey data. Key functions and operations in MATLAB like vectors, matrices, element referencing, basic math operations, plotting, and linear regression are covered. The example shows how to estimate a linear pricing model that fits the sample data well.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses data preprocessing, which includes data cleaning, integration, reduction, and transformation. Data cleaning deals with handling missing, noisy, and inconsistent data. Data integration combines data from multiple sources. Data reduction reduces data volume for analysis through techniques like dimensionality reduction. Data transformation normalizes and discretizes values.
The document discusses various ways to use @Formula in Lotus Notes and XPages applications. It covers using @Formula for input validation, computed values, view selection formulas, and more. Specific @functions discussed include @Success, @Failure, @If, @Trim, @ProperCase, @LowerCase, @ReplaceSubstring, @Round, @Random, @ThisValue, @ThisName, @SetEnvironment, @Environment, @Adjust, @Text, @Unique, @Transform, @Sort, @Max, @Min, and @Matches. Examples are provided for how to use many of these @functions.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
An Interactive Introduction To R (Programming Language For Statistics)Dataspora
This is an interactive introduction to R.
R is an open source language for statistical computing, data analysis, and graphical visualization.
While most commonly used within academia, in fields such as computational biology and applied statistics, it is gaining currency in industry as well – both Facebook and Google use R within their firms.
This document discusses visualizing data in R using various packages and techniques. It introduces ggplot2, a popular package for data visualization that implements Wilkinson's Grammar of Graphics. Ggplot2 can serve as a replacement for base graphics in R and contains defaults for displaying common scales online and in print. The document then covers basic visualizations like histograms, bar charts, box plots, and scatter plots that can be created in R, as well as more advanced visualizations. It also provides examples of code for creating simple time series charts, bar charts, and histograms in R.
Bridging data analysis and interactive visualizationNacho Caballero
Clickme is an R package that lets you generate interactive visualizations directly from R. I presented the latest iteration at the 2013 IBSB conference in Kyoto
This document provides a cheat sheet for frequently used commands in Stata for data processing, exploration, transformation, and management. It highlights commands for viewing and summarizing data, importing and exporting data, string manipulation, merging datasets, and more. Keyboard shortcuts for navigating Stata are also included.
This document provides an overview and introduction to using the statistical software R. It outlines R's interface, workspace, help system, packages, input/output functions, and how to reuse results. It also discusses downloading and installing R, basic functions and syntax, data manipulation techniques like sorting and merging, creating graphs, and performing statistical analyses such as t-tests, regression, ANOVA, and multiple comparisons. The document recommends several tutorials that provide more in-depth information on using R for statistical modeling, data analysis, and graphics.
Nena Marín presents solutions for analyzing large datasets from internet advertising. She discusses building a recommender system using co-clustering that was trained on over 100 million ratings in under 17 minutes. For attribution reporting, pre-aggregated metrics are deployed to a GUI within 20 minutes for weekly reports. Lessons learned include addressing data quality, performance baselines, schema flexibility, and integration challenges.
Mehar Singh, CEO of ProCogia, and Jason Grahn, Senior Business Analyst at Apptio, co-present on the journey from Excel to R at the second Bellevue chapter useR Group Meetup.
If we’re producing analysis that drives business decision making, that’s production-grade code! This talk will address this question, which in turn shows why R is the way to go – assumptions are built into the code and enables the analyst to automate & reproduce their efforts.
This presentation includes:
- Data importing (opening a CSV or connecting to a SQL in both tools)
- Filtering, grouping, summarizing (pivot tables in Excel vs. tidy code in R)
- Visualizations (charts in excel vs ggplot in R)
The document discusses data mining and the Microsoft SQL Server 2005 Data Mining Add-ins for Excel 2007. It provides an overview of data mining, how the add-in works, its prerequisites, who can use it, and how to use its various tools for data preparation, modeling, validation and connection to SQL Server Analysis Services.
Data mining refers to analyzing data sets to discover hidden patterns and trends. This information can help companies improve strategies for marketing, analyzing customers and markets, increasing revenue, and forecasting sales. Data mining has proven useful in business, computing, biotechnology, and analyzing stock markets. While a relatively new term, data mining has long been used by large corporations to analyze large data sets and draw conclusions. Microsoft has introduced the SQL Server Data Mining Add-ins for Office 2007 to make data mining accessible through a familiar Microsoft Office environment. It connects Excel to the powerful data mining algorithms in SQL Server Analysis Services. The add-in allows users to perform tasks like data preparation, modeling, and validating models with just a few clicks.
PHStat Notes Using the PHStat Stack Data and .docxShiraPrater50
PHStat Notes
Using the PHStat Stack Data and Unstack Data Tools p. 28
One‐ and Two‐Way Tables and Charts p. 63
Normal Probability Tools p. 97
Generating Probabilities in PHStat p. 98
Confi dence Intervals for the Mean p. 136
Confi dence Intervals for Proportions p. 136
Confi dence Intervals for the Population Variance p. 137
Determining Sample Size p. 137
One‐Sample Test for the Mean, Sigma Unknown p. 169
One‐Sample Test for Proportions p. 169
Using Two‐Sample t ‐Test Tools p. 169
Testing for Equality of Variances p. 170
Chi‐Square Test for Independence p. 171
Using Regression Tools p. 209
Stepwise Regression p. 211
Best-Subsets Regression p. 212
Creating x ‐ and R ‐Charts p. 267
Creating p ‐Charts p. 268
Using the Expected Monetary Value Tool p. 375
Excel Notes
Creating Charts in Excel 2010 p. 29
Creating a Frequency Distribution and Histogram p. 61
Using the Descriptive Statistics Tool p. 61
Using the Correlation Tool p. 62
Creating Box Plots p. 63
Creating PivotTables p. 63
Excel‐Based Random Sampling Tools p. 134
Using the VLOOKUP Function p. 135
Sampling from Probability Distributions p. 135
Single‐Factor Analysis of Variance p. 171
Using the Trendline Option p. 209
Using Regression Tools p. 209
Using the Correlation Tool p. 211
Forecasting with Moving Averages p. 243
Forecasting with Exponential Smoothing p. 243
Using CB Predictor p. 244
Creating Data Tables p. 298
Data Table Dialog p. 298
Using the Scenario Manager p. 298
Using Goal Seek p. 299
Net Present Value and the NPV Function p. 299
Using the IRR Function p. 375
Crystal Ball Notes
Customizing Defi ne Assumption p. 338
Sensitivity Charts p. 339
Distribution Fitting with Crystal Ball p. 339
Correlation Matrix Tool p. 341
Tornado Charts p. 341
Bootstrap Tool p. 342
TreePlan Note
Constructing Decision Trees in Excel p. 376
This page intentionally left blank
Useful Statistical Functions in Excel 2010 Description
AVERAGE( data range ) Computes the average value (arithmetic mean) of a set of data.
BINOM.DIST( number_s, trials, probability_s, cumulative ) Returns the individual term binomial distribution.
BINOM.INV( trials, probability_s, alpha)
CHISQ.DIST( x, deg_freedom, cumulative )
CHISQ.DIST.RT( x, deg_freedom, cumulative )
CHISQ.TEST( actual_range, expected_range )
Returns the smallest value for which the cumulative binomial
distribution is greater than or equal to a criterion value.
Returns the left-tailed probability of the chi-square distribution.
Returns the right-tailed probability of the chi-square
distribution.
Returns the test for independence; the value of the chi-square
distribution and the appropriate degrees of freedom.
CONFIDENCE.NORM( alpha, standard_dev, size ) Retu ...
- The document discusses computing correlations between variables in R and interpreting the results.
- It provides an example of calculating the correlation between happiness and other life factors like friends and salary.
- The document uses real data from the World Happiness Report to explore correlations between variables like freedom to make life choices and confidence in national government. It finds a positive correlation between these two variables.
The document discusses recent developments in the R programming environment for data analysis, including packages like magrittr, readr, tidyr, and dplyr that enable data wrangling workflows. It provides an overview of the key functions in these packages that allow users to load, reshape, manipulate, model, visualize, and report on data in a pipeline using the %>% operator.
Get up to Speed (Quick Guide to data.table in R and Pentaho PDI)Serban Tanasa
1) The document provides a quick guide to using data.table in R and Pentaho Data Integration (PDI) for fast data loading and manipulation. It discusses benchmarks showing data.table is 2-20x faster than traditional methods for reading, ordering, and transforming large data.
2) The outline discusses how to use basic data.table functions for speed gains and to overcome R's scaling limitations. It also provides a very brief overview of PDI's capabilities for Extract/Transform/Load (ETL) workflows without writing code.
3) The benchmarks section shows data.table is up to 500% faster than traditional R methods for reading large CSV files and orders of magnitude faster for sorting and aggregating
This document discusses the Excel add-in for data mining. It allows users to mine data with a few clicks using advanced algorithms without needing experience in data mining or SQL server configuration. The add-in contains sections for data preparation, modeling, accuracy validation, and connection. Data can be explored, cleaned, and prepared for modeling. Common modeling algorithms like decision trees, clustering, and association rules are available. Accuracy and validation tools allow testing models on real data. The add-in combines the power of SQL Server Analysis Services with the ease of use of Excel.
I am Walker D. I am a Civil and Environmental Engineering assignment Expert at statisticsassignmenthelp.com. I hold a Ph.D. in Civil and Environmental Engineering. I have been helping students with their homework for the past 8 years. I solve assignments related to Civil and Environmental Engineering Assignment. Visit statisticsassignmenthelp.com or email info@statisticsassignmenthelp.com.
You can also call on +1 678 648 4277 for any assistance with Civil and Environmental Engineering assignments.
This document provides an introduction to MATLAB for people working in marketing. It explains that MATLAB is useful for analyzing large or complex datasets, as it can handle data more efficiently than Excel. The document demonstrates how to use MATLAB through a example of modeling mobile app subscription prices and demand based on survey data. Key functions and operations in MATLAB like vectors, matrices, element referencing, basic math operations, plotting, and linear regression are covered. The example shows how to estimate a linear pricing model that fits the sample data well.
Data preprocessing is important for obtaining quality data mining results. It involves cleaning data by handling missing values, outliers, and inconsistencies. It also includes integrating, transforming, reducing and discretizing data. The document outlines various techniques for each task such as mean imputation, binning, and clustering for cleaning noisy data. Dimensionality reduction techniques like feature selection and data compression algorithms are also discussed.
This document summarizes key concepts from Chapter 3 of the textbook "Data Mining: Concepts and Techniques". It discusses data preprocessing, which includes data cleaning, integration, reduction, and transformation. Data cleaning deals with handling missing, noisy, and inconsistent data. Data integration combines data from multiple sources. Data reduction reduces data volume for analysis through techniques like dimensionality reduction. Data transformation normalizes and discretizes values.
The document discusses various ways to use @Formula in Lotus Notes and XPages applications. It covers using @Formula for input validation, computed values, view selection formulas, and more. Specific @functions discussed include @Success, @Failure, @If, @Trim, @ProperCase, @LowerCase, @ReplaceSubstring, @Round, @Random, @ThisValue, @ThisName, @SetEnvironment, @Environment, @Adjust, @Text, @Unique, @Transform, @Sort, @Max, @Min, and @Matches. Examples are provided for how to use many of these @functions.
Data Mining: Concepts and Techniques (3rd ed.)- Chapter 3 preprocessingSalah Amean
the chapter contains :
Data Preprocessing: An Overview,
Data Quality,
Major Tasks in Data Preprocessing,
Data Cleaning,
Data Integration,
Data Reduction,
Data Transformation and Data Discretization,
Summary.
An Interactive Introduction To R (Programming Language For Statistics)Dataspora
This is an interactive introduction to R.
R is an open source language for statistical computing, data analysis, and graphical visualization.
While most commonly used within academia, in fields such as computational biology and applied statistics, it is gaining currency in industry as well – both Facebook and Google use R within their firms.
This document discusses visualizing data in R using various packages and techniques. It introduces ggplot2, a popular package for data visualization that implements Wilkinson's Grammar of Graphics. Ggplot2 can serve as a replacement for base graphics in R and contains defaults for displaying common scales online and in print. The document then covers basic visualizations like histograms, bar charts, box plots, and scatter plots that can be created in R, as well as more advanced visualizations. It also provides examples of code for creating simple time series charts, bar charts, and histograms in R.
Bridging data analysis and interactive visualizationNacho Caballero
Clickme is an R package that lets you generate interactive visualizations directly from R. I presented the latest iteration at the 2013 IBSB conference in Kyoto
This document provides a cheat sheet for frequently used commands in Stata for data processing, exploration, transformation, and management. It highlights commands for viewing and summarizing data, importing and exporting data, string manipulation, merging datasets, and more. Keyboard shortcuts for navigating Stata are also included.
This document provides an overview and introduction to using the statistical software R. It outlines R's interface, workspace, help system, packages, input/output functions, and how to reuse results. It also discusses downloading and installing R, basic functions and syntax, data manipulation techniques like sorting and merging, creating graphs, and performing statistical analyses such as t-tests, regression, ANOVA, and multiple comparisons. The document recommends several tutorials that provide more in-depth information on using R for statistical modeling, data analysis, and graphics.
Nena Marín presents solutions for analyzing large datasets from internet advertising. She discusses building a recommender system using co-clustering that was trained on over 100 million ratings in under 17 minutes. For attribution reporting, pre-aggregated metrics are deployed to a GUI within 20 minutes for weekly reports. Lessons learned include addressing data quality, performance baselines, schema flexibility, and integration challenges.
Mehar Singh, CEO of ProCogia, and Jason Grahn, Senior Business Analyst at Apptio, co-present on the journey from Excel to R at the second Bellevue chapter useR Group Meetup.
If we’re producing analysis that drives business decision making, that’s production-grade code! This talk will address this question, which in turn shows why R is the way to go – assumptions are built into the code and enables the analyst to automate & reproduce their efforts.
This presentation includes:
- Data importing (opening a CSV or connecting to a SQL in both tools)
- Filtering, grouping, summarizing (pivot tables in Excel vs. tidy code in R)
- Visualizations (charts in excel vs ggplot in R)
The document discusses data mining and the Microsoft SQL Server 2005 Data Mining Add-ins for Excel 2007. It provides an overview of data mining, how the add-in works, its prerequisites, who can use it, and how to use its various tools for data preparation, modeling, validation and connection to SQL Server Analysis Services.
Data mining refers to analyzing data sets to discover hidden patterns and trends. This information can help companies improve strategies for marketing, analyzing customers and markets, increasing revenue, and forecasting sales. Data mining has proven useful in business, computing, biotechnology, and analyzing stock markets. While a relatively new term, data mining has long been used by large corporations to analyze large data sets and draw conclusions. Microsoft has introduced the SQL Server Data Mining Add-ins for Office 2007 to make data mining accessible through a familiar Microsoft Office environment. It connects Excel to the powerful data mining algorithms in SQL Server Analysis Services. The add-in allows users to perform tasks like data preparation, modeling, and validating models with just a few clicks.
PHStat Notes Using the PHStat Stack Data and .docxShiraPrater50
PHStat Notes
Using the PHStat Stack Data and Unstack Data Tools p. 28
One‐ and Two‐Way Tables and Charts p. 63
Normal Probability Tools p. 97
Generating Probabilities in PHStat p. 98
Confi dence Intervals for the Mean p. 136
Confi dence Intervals for Proportions p. 136
Confi dence Intervals for the Population Variance p. 137
Determining Sample Size p. 137
One‐Sample Test for the Mean, Sigma Unknown p. 169
One‐Sample Test for Proportions p. 169
Using Two‐Sample t ‐Test Tools p. 169
Testing for Equality of Variances p. 170
Chi‐Square Test for Independence p. 171
Using Regression Tools p. 209
Stepwise Regression p. 211
Best-Subsets Regression p. 212
Creating x ‐ and R ‐Charts p. 267
Creating p ‐Charts p. 268
Using the Expected Monetary Value Tool p. 375
Excel Notes
Creating Charts in Excel 2010 p. 29
Creating a Frequency Distribution and Histogram p. 61
Using the Descriptive Statistics Tool p. 61
Using the Correlation Tool p. 62
Creating Box Plots p. 63
Creating PivotTables p. 63
Excel‐Based Random Sampling Tools p. 134
Using the VLOOKUP Function p. 135
Sampling from Probability Distributions p. 135
Single‐Factor Analysis of Variance p. 171
Using the Trendline Option p. 209
Using Regression Tools p. 209
Using the Correlation Tool p. 211
Forecasting with Moving Averages p. 243
Forecasting with Exponential Smoothing p. 243
Using CB Predictor p. 244
Creating Data Tables p. 298
Data Table Dialog p. 298
Using the Scenario Manager p. 298
Using Goal Seek p. 299
Net Present Value and the NPV Function p. 299
Using the IRR Function p. 375
Crystal Ball Notes
Customizing Defi ne Assumption p. 338
Sensitivity Charts p. 339
Distribution Fitting with Crystal Ball p. 339
Correlation Matrix Tool p. 341
Tornado Charts p. 341
Bootstrap Tool p. 342
TreePlan Note
Constructing Decision Trees in Excel p. 376
This page intentionally left blank
Useful Statistical Functions in Excel 2010 Description
AVERAGE( data range ) Computes the average value (arithmetic mean) of a set of data.
BINOM.DIST( number_s, trials, probability_s, cumulative ) Returns the individual term binomial distribution.
BINOM.INV( trials, probability_s, alpha)
CHISQ.DIST( x, deg_freedom, cumulative )
CHISQ.DIST.RT( x, deg_freedom, cumulative )
CHISQ.TEST( actual_range, expected_range )
Returns the smallest value for which the cumulative binomial
distribution is greater than or equal to a criterion value.
Returns the left-tailed probability of the chi-square distribution.
Returns the right-tailed probability of the chi-square
distribution.
Returns the test for independence; the value of the chi-square
distribution and the appropriate degrees of freedom.
CONFIDENCE.NORM( alpha, standard_dev, size ) Retu ...
- The document discusses computing correlations between variables in R and interpreting the results.
- It provides an example of calculating the correlation between happiness and other life factors like friends and salary.
- The document uses real data from the World Happiness Report to explore correlations between variables like freedom to make life choices and confidence in national government. It finds a positive correlation between these two variables.
The document discusses recent developments in the R programming environment for data analysis, including packages like magrittr, readr, tidyr, and dplyr that enable data wrangling workflows. It provides an overview of the key functions in these packages that allow users to load, reshape, manipulate, model, visualize, and report on data in a pipeline using the %>% operator.
Video and slides synchronized, mp3 and slide download available at URL http://bit.ly/1ZW7TDL.
Richard Dallaway shows an example of what Scala looks like when using pattern matching over classes, how to encode an idea into types and use advanced features of Scala without complicating the code. Filmed at qconlondon.com.
Richard Dallaway is a partner at Underscore -- a consultancy specializing in Scala, especially the type-driven and functional aspects of Scala. He works on client projects writing software and helping teams deliver software with Scala. His focus is on the web, machine learning, and code review. He's the co-author of "Essential Slick" (Underscore), and author of the "Lift Cookbook" (O'Reilly).
This document provides an overview of statistical concepts and analysis techniques in R, including measures of central tendency, data variability, correlation, regression, and time series analysis. Key points covered include mean, median, mode, variance, standard deviation, z-scores, quartiles, standard deviation vs variance, correlation, ANOVA, and importing/working with different data structures in R like vectors, lists, matrices, and data frames.
This document provides an overview of linear regression and logistic regression concepts. It begins with an introduction to linear regression, discussing finding the best fit line to training data. It then covers the loss function and gradient descent optimization algorithm used to minimize loss and fit the model parameters. Next, it discusses logistic regression for classification problems, covering the sigmoid function for hypothesis representation and interpreting probabilities. It concludes by discussing feature scaling techniques like normalization and standardization to prepare data for modeling.
This document discusses structured programming and arrays. It begins by introducing arrays as a way to store multiple values in a structured manner using indices, rather than individual variables. It then discusses how to input and output values from arrays using loops. It also covers multidimensional arrays and declaring arrays. The document provides examples of using arrays to store and manipulate data, such as finding averages and min/max values. It concludes by introducing bubble sort as a way to sort arrays into order.
This document discusses analyzing relationships between variables in a dataset using statistical tests and data visualization. Specifically, it examines:
1) Comparing education levels and gender using pivot tables and bar charts. A chi-squared test of independence finds no significant relationship.
2) Creating new variables for total square footage and sales price from an housing dataset. Scatter plots show sales price increases with square footage.
3) Outliers are removed and the effect on scatter plots is discussed. The ethical implications of removing outliers are considered.
4) Linear regression is proposed to predict sales price from square footage using the least squares method to minimize differences between predicted and actual values. The regression output is displayed.
Introduction to R Short course Fall 2016Spencer Fox
The document provides instructions for an introductory R session, including downloading materials from a GitHub repository and opening an R project file. It outlines logging in, downloading an R project folder containing intro materials, and opening the project file in RStudio.
Data Science as a Career and Intro to RAnshik Bansal
This document discusses data science as a career option and provides an overview of the roles of data analyst, data scientist, and data engineer. It notes that data analysts solve problems using existing tools and manage data quality, while data scientists are responsible for undirected research and strategic planning. Data engineers compile and install database systems. The document also outlines the typical salaries for each role and discusses the growing demand for data science skills. It provides recommendations for learning tools and resources to pursue a career in data science.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
2. I N T R O D U C T I O N
Who is impact extend, and how do we work with data?
02 W H A T M A K E S R S O A W E S O M E ?
Cons and pros against using R to Extract, Transform and load data
based on usecases.
03 C A L C U L A T I N G , J O I N I N G A N D G R O U P I N G D A T A
Unifying and transforming data, always.
01
AGENDA
C R E A T E , W R I T E A N D R E A D F R O M G O O G L E
S H E E T
Using R to build a free database to be used for reporting, datastorage or
Google Data Studio.
05 I N T R O D U C T I O N T O R M A R K D O W N
Automate your reporting framework by leveraging R Markdown, Shiny
and simple HTML
06 S C H E D U L E R S C R I P T S O N Y O U R M A C H I N E
How can you do as little as possible?
04
3. Who is impact extend, and how do we work with data?
01.
INTRODUCTION
4. • Copenhagen based
• Lead analyst at IMPACT EXTEND
• 2 years in doing R
• 5 years in doing GTM and GA work
• 2 years in doing random SEO and Website stuff
About me
5. • Kickass analyst in terms of understanding humans
• BI specialist within using PowerBI to do crazy dashboards
• Former Google Analytics class educator
• The nerd who is always curious about taking it next step
…. Also he build an entire GA validator by himself which is quite
cool
About Rasmus
6. 100% focus on digital commerce Long customer relations 7 x Gazelle
A A R H U S – C O P E N H A G E N - L I S B O A
1 2 6 E M P L O Y E E S
E S T A B L I S H E D I N 1 9 9 8
Market leader in commerce
Established in 2018 150+ Employees Aarhus - Copenhagen - Lisbon
Part of IMPACT A/S Clients: Largest retailers in the nordics Focus is on datadriven marketing
7. OUR OFFERINGS
ATTRACT
ANDSELL
TRAFFIC &
INSIGHTS
SERVE
ANDGROW
DIALOGUE &
LOYALTY
DATAANDINSIGHTS
DMP & INTELLIGENCE
DIGITAL
MARKETING
STRATEGY
Full-service approach with combined services delivering holistic
solutions to address Marketing’s primary pains and objectives with
digital marketing strategies
9. OUR APPROACH TO WORK WITH DATA
Behavioraldata
User ID
Sessions
Cross-device
CRMDATA
User ID
Purchase
Channels
(web/store)
IMPRESSIONDATA
User ID
Conversions
Store Visits
ENGAGEMENT DATA
User ID
Mails
Open/click
MARKETINGDB
Dataconsolidation
Segmentation
Engagement
LTV
Segmentering
Personalization
Dynamisk content
Triggers
11. Cons and pros against using R to Extract, Transform and load data
based on usecases.
02.
WHAT MAKES R SO AWESOME?
12.
13.
14.
15.
16.
17. Extract
GetDatafromAPI
ScrapeWebdata
Workwithnormal worksheets
Transform
Do all your calculations automatically
Splitdataapartandassembleitwith
other data
Do hugeworkloads fastas thereis nota
traditionGUI likeexcel
Load
Senddatato databases
Create dashboards
Makeautomatedreports
Getthedatathewayyouneedit
Makesurethatitlookslikeyouwantit
Dowhateveryouneedyourdatatodo
19. GENERATE FAKE DATA FROM A GITHUB
RESPORATORY
install.packages("RCurl")
library(RCurl)
#go to https://bit.ly/2PSb6FB and copy paste the URL
url <- "thepasted url"
script <- getURL(url, ssl.verifypeer = FALSE)
eval(parse(text = script))
This should give you 300 rows of data, that we can use to do various calculations and modifications with
22. WITH THE ID’S WE CAN CHECK FOR DUPLICATES
This is to determine if there are one or more
users that goes through the dataset. By
knowing we have the same user more than
once, we can aggregate data by user
duplicated(ID$CustomerID)
23. TO UNDERSTAND HOW THIS DATA LOOKS
AGGREGATED ON A USERLEVEL, IN EXCEL IT
WOULD LOOK LIKE THIS
Here, the Google Analytics cookie ID is
assembled with visit to the sites each day. As
each ID is connected to a GA cookie ID, we
can actually see how many devices each users
are going through within a user journey
24. TO DO THE SAME, DPLYR HAS SOME GREAT
WAYS OF WORKING WITH DATA
P I V O T B Y I D W I L L P R O D U C E T H I S
#group by device
ID %>%
group_by(CustomerID) %>%
summarise(devices = n_distinct(GA))
To find out how many devices people are
using, we cam group them by customer
ID and Google Analytics ID
25. TO DO THE SAME, DPLYR HAS SOME GREAT
WAYS OF WORKING WITH DATA
P I V O T B Y S E S S I O N S W I L L P R O D U C E T H I S
#group by device
ID %>%
group_by(CustomerID) %>%
summarise(devices = n_distinct(GA))
To find out how many session the users
had in total, you can use this
26. JOINS
left_join()
return all rows from x, and all
columns from x and y. Rows in
x with no match in y will have
NA values in the new columns.
If there are multiple matches
between x and y, all
combinations of the matches are
returned.
right_join()
return all rows from y, and all
columns from x and y. Rows in
y with no match in x will have
NA values in the new columns.
If there are multiple matches
between x and y, all
combinations of the matches are
returned.
full_join()
return all rows and all columns
from both x and y. Where there
are not matching values, returns
NA for the one missing.
Note: FULL OUTER JOIN can
potentially return very large
result-sets!
I N N E R J O I N L E F T J O I N R I G H T J O I N F U L L J O I N
inner_join()
return all rows from x where
there are matching values in y,
and all columns from x and y. If
there are multiple matches
between x and y, all
combination of the matches are
returned.
27. JOINS
inner_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
left_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
right_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
full_join(x, y, by = NULL, copy = FALSE, suffix = c(".x", ".y"), ...)
semi_join(x, y, by = NULL, copy = FALSE, ...)
anti_join(x, y, by = NULL, copy = FALSE, ...)
x, y tbls to join
by a character vector of variables to join by. If NULL,
the default, *_join() will do a natural join, using all
variables with common names across the two tables. A
message lists the variables so that you can check
they're right (to suppress the message, simply
explicitly list the variables that you want to join).
To join by different variables on x and y use a named
vector. For example, by = c("a" = "b") will
match x.a to y.b.
copy If x and y are not from the same data source,
and copy is TRUE, then y will be copied into the
same src as x. This allows you to join tables across
srcs, but it is a potentially expensive operation so you
must opt into it.
suffix If there are non-joined duplicate variables in x and y,
these suffixes will be added to the output to
disambiguate them. Should be a character vector of
length 2.
29. INNER JOIN
inner_join()
return all rows from x where there are matching
values in y, and all columns from x and y. If
there are multiple matches between x and y, all
combination of the matches are returned.
What does this mean?
We join the two tables where the UserID is
present.
inner_join(Dataset1, Dataset2, by = "UserID",
copy = FALSE, suffix = c(".x", ".y"))
A1 A1
A2
A3
30. LEFT JOIN
left_join()
return all rows from x, and all columns from x
and y. Rows in x with no match in y will have
NA values in the new columns. If there are
multiple matches between x and y, all
combinations of the matches are returned.
What does this mean?
inner_join(Dataset1, Dataset2, by = "UserID",
copy = FALSE, suffix = c(".x", ".y"))
31. RIGHT JOIN
right_join()
return all rows from y, and all columns from x
and y. Rows in y with no match in x will have
NA values in the new columns. If there are
multiple matches between x and y, all
combinations of the matches are returned.
What does this mean?
32. FULL JOIN
left_join()
return all rows from x, and all columns from x
and y. Rows in x with no match in y will have
NA values in the new columns. If there are
multiple matches between x and y, all
combinations of the matches are returned.
What does this mean?
We take table 1 one, and join it with table 2
33. Using R to build a free database to be used for reporting, datastorage or
Google Data Studio.
04.
CREATE, WRITE AND READ
FROM GOOGLE SHEET
34. • We use the google authr package created by Mark
Edmonson
• This allows us to generate a token which we can
use to work with Googles products
AUTHENTICATION
#install and load google drive
install.packages("googlesheets")
library(googlesheets)
googlesheets::gs_auth()
35. CREATE A GOOGLE SHEET
gs_new(title = "impactextendrclass")
gs <- gs_title("impactextendrclass")
gs_browse(gs, ws = 1)
39. LETS ADD SOME MORE DATA TO IT!
eval(parse(text = script))
n <- paste("A",nrow(ID), sep="")
gs_edit_cells(gs, ws = 1, input = ID, anchor = n, byrow = FALSE,
col_names = FALSE, trim = FALSE, verbose = TRUE)
What happens is that we use the “paste” function to
find out where to add the new data from so we don’t
break the old data
40. DOWNLOAD AND MODIFY GS DATA
E X T R A C T T R A N S F O R M L O A D
#download gs data
download <- gs_read(gs)
upload <-
download %>%
group_by(CustomerID,sessions) %>%
summarise(devices =
n_distinct(GA))
gs %>%
gs_ws_new(ws_title =
"aggregated", input
= upload)
41. WHICH SHOULD GIVE YOU THIS
There are many ways to do similar task, and the
usecases are basically endless. For larger dataset we
recommend that you send the data to BigQuery or
other databases which can handle more information.
With BigQuery it will be the same approach except
that it requires that you link your creditcard to the
account
47. Automate your reporting framework by leveraging R Markdown, Shiny
and simple HTML
05.
Introduction to R markdown
48. • An adoptation to general Markdown which is used to do
documentation etc.
• R Markdown makes it possible to generate different types of
documents such as HTML, Word, PDF, Slides etc.
• R markdown is really easy to write with and keeps formatting clean
and simple
• Use the cheat sheet to play around
What is Rmarkdown?
49. • In terms of making sure that our GTM setups were GDPR complient
we wrote a script that took data down from GTM, and then it ran
trough everything to ensure that it was set with the right compliance
rules.
• Today we have this document generated once every 6 months, and it
will flag if there are any issues we need to take care of
Example - HTML
51. DOING
VISUALIZATIONS
• To be able to visualize anything we need to
have the data physically downloaded on our
machine
• Also it needs to be loaded whenever you run
your document
save(upload, download,
file = "data.RData")
load("data.RData")
52. MAKING TABLES
• To be able to visualize anything we need to
have the data physically downloaded on our
machine
• Also it needs to be loaded whenever you run
your document
save(upload, download,
file = "data.RData")
load("data.RData")
```{r table, echo=TRUE, message=FALSE,
warning=FALSE}
library(ggplot2)
library(kableExtra)
library(kableExtra)
library(dplyr)
library(knitr)
head(upload) %>%
kable() %>%
kable_styling("HTML")
```
53. MAKING TABLES
The cool thing here is that
you can do any html and css
styling to your documents.
This means that you can do
basically anything that is
possible within HTML and
CSS
60. PLAY AROUND WITH R MARKDOWN AND PLOTS –
GOOGLE IS YOUR FRIEND FOR SEEING THE
POSSIBILITIES!
61. How can you do as little as possible?
06.
Schedule tasks
62. SCHEDULA(R) Tools à Addins à Browse Addins
Choose the file that should be executed by the file.
Choose the frequency, startDate, startTime of which
the file shall be executed.
63.
64.
65. • On PC:
• - Task Scheduler
• See and kill the process.
• On Mac:
• - Begin Automator. Click “Applications” on the
Dock of your Mac. ...
HOW TO STOP
IT AGAIN!