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PHP 1560/2560 - Statistical Programming in R
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Descrição do Curso
This is the course page for PHP 1560/2560. This is not the typical Datacamp course but is the online version of PHP 2560 at Brown University School of Public Health. With that being said this represents a video textbook for a class that meets in person to explore Statistical Computing further.
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Basics of R Programming
GrátisIn this Section we will begin to explore R and learn how it handles data. We will focus on major data types that we use frequently in statistics and the basics of how we use them. As we move forward these data types will be key to analyzing statistics.
Getting Start in R25 xpPracticing Basic R100 xpSimple Procedures in R25 xpBasic Procedures in R100 xpGetting Help in R25 xpData Objects in R25 xpPracticing with Data Objects in R25 xpVectors in R25 xpVectors in R Exercise 175 xpVectors in R Exercise 275 xpArrays in R25 xpMatrices in R25 xpMatrices in R Practice75 xpLists in R25 xpLists in R Practice75 xpDataframes in R25 xpDataframes in R Practice75 xp - 2
Data Wrangling in R with plyr and dplyr
GrátisIn this chapter we will begin to explore large data in R. We will learn data wrangling techniques such as split, apply and combine with plyr and using tibbles and dplyr on large data.
Tibbles in R25 xpWorking with tibbles in R100 xpBeginning to work with Large Data in R25 xpSplit, Apply and Combine25 xpSplit, Apply and Combine Exercise 175 xpSplit, Apply and Combine Exercise 275 xpSplit, Apply and Combine with plyr25 xpSplit, Apply and Combine Exercise 375 xpSplit, Apply and Combine Exercise 475 xpBRFSS with plyr25 xpFurther work with Split, Apply and Combine25 xpBeginning with dplyr25 xpdplyr Exercise 1100 xpdplyr Exercise 2100 xpChaining and Pipelining with dplyr in R25 xpChaining and Pipelining Exercise 1100 xpChaining and Pipelining Exercise 2100 xpChaining and Pipelining Exercise 3100 xpArranging and Mutating Data25 xpArranging and Mutating Exercise 1100 xpArranging and Mutating Exercise 2100 xpUsing the summarise verb to summarize Data in R25 xpSummrise verb Exercise 1100 xpSummrise verb Exercise 2100 xpChoosing Columns and Rows with dplyr25 xpAdding new variables with dplyr25 xpJenny Bryan's Cheatsheet for dplyr joins25 xpJoins 1100 xpJoins 2100 xpJoins 3100 xp - 3
Flow Control and Initial Functions in R
GrátisIn this chapter we will work on flow control as well as the start of some basic functions in R.
Conditionals in R25 xpBasic Conditionals in R100 xpPracticing `if` statements100 xpPracticing `if` and `else` statements100 xpPutting all the `if` and `else` statements together.100 xpIterations in R25 xpBasic `for()` loops100 xpLoop Through a matrix100 xpUsing a `for` loop to evaluate BMI.100 xpAvoiding Iterations in R25 xpFunctions in R25 xpBasic function in R100 xpBasic functions in R part 2100 xpExample of a Function in R25 xpWhat should be a function?25 xp - 4
Functions in R
GrátisIn this chapter we will explore creating and debugging functions in R.
Basic Function Design in R25 xpBasic Functions in R Exercise100 xpSlightly More advanced Functions in R Exercise100 xpFunctions as Objects in R Exercise100 xpDebugging Functions in R25 xpDebugging Functions Multiple Choice75 xpSome Debugging Methods in R25 xpDebugging Methods in R Exercise100 xpDesigning Programs for Debugging in R25 xpDesigning Programs for Debugging MC75 xpDesigning Programs for Debugging MC #275 xpDesigning Functions in R25 xpTop Down Function Design in R25 xpTop Down Function Design MC75 xpTop Down Function Design MC #275 xpRefactoring Functions in R25 xpRefactoring MC75 xp - 5
Simulations
GrátisIn this chapter we will cover statistical simulations.
Random Variables25 xpDiscrete Random Variables25 xpGenerating Discrete Distributions100 xpContinuous Random Variables25 xpGenerating Continuous Distributions100 xpBasics of Simulations25 xpBasics of Simulations Continued25 xpUsing the function replicates100 xpFurther Functions for Analysis I100 xpFurther Functions for Analysis II100 xpSimulation of Distributions25 xpSimulation with t-test100 xpSimulation with t-test function I100 xpSimulation with t-test function II100 xpFurther Simulations25 xpSampling by Resampling25 xpResampling I75 xpResampling II75 xpResampling III75 xpMarkov Chains25 xpRandom Walk75 xpMarkov Chain75 xpRejection Sampling25 xpRejection Sampling Question75 xpRejection Sampling Example25 xpMetropolis Hastings25 xpMetropolis Hastings Question I75 xpMetropolis Hastings Question II75 xpMetropolis Hastings Question III75 xpMetropolis Hastings Example25 xp - 6
Working with Databases in R
GrátisOnce we know how to bring data into R it is time we learn how to deal with big data in R.
- 7
Textmining in R
GrátisIn this Section we will focus on working with character and string data in R.
- 8
ggplot
GrátisIn this chapter we will explore how to create data visualizations in ggplot.
Beggining ggplot in R25 xpAesthetic Attributes25 xpFacetting25 xpSmoothing25 xpMore Geom plots Part 125 xpMore Geom Plots part 225 xpWorking with Plot Title and Axes25 xpPlots by Layer: Plot and data25 xpPlots by Layer: Aesthetic Mappings and Geoms25 xpPlots by Layer: Stat Transformations and Position Adjustments25 xpModifying Scales and Axes25 xpLegends25 xpExtended ggplot Example25 xp - 9
Webscraping in R
GrátisWe have spent a lot of time working with data and learning how to use Functions and evern write simulations. Now we will focus on bringing data into R.
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