R packages
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Hadley Wickham provides advice on developing R packages based on his experience creating over 20 packages. He discusses getting started with package development by choosing a name, creating directories, and adding documentation. Wickham also covers iterative development, testing packages with devtools, documenting functions and packages, and releasing packages on CRAN. He recommends learning from the source code of established packages and continuing to improve testing, namespaces, code style, and use of Git.
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R workshop iii -- 3 hours to learn ggplot2 series
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02 Ddply
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01 Intro
01 IntroHadley Wickham The document is an introduction to a course on the plyr package in R. It includes:
- An overview of the instructor, Hadley Wickham, who develops tools to make data analysis easier in R.
- A note that the course will include interactive activities to break up the lecture format.
- Information on resources for the course materials and getting help.
- An outline of topics to be covered: exploring US baby name trends, modeling Texas house sales, and how it all fits together using the split-apply-combine approach.
- A list of code files that will be used in examples during the course.
Analyzing Data With Python
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Reshaping Data in R
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Focuses on Hadley Wickham's reshape2 package and uses state population data from the 2010 U.S. Census. Also demonstrates use of dcast() to replace table(), etc. to generate crosstabs from a sample market research consumer survey.
Presented at the April 2011 meeting of the Greater Boston useR Group.
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http://ipython.org/
http://flask.pocoo.org/
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http://www.babel.it
http://vaunaspada.babel.it/blog
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No one could help you?
Please call the BASH Team!
This presentation starts with some preliminary thoughts about software integration.
Shell scripting means knowing your system and the applications you have available that can do the dirty stuff for you.
BASH is all about gluing... so what are the ways we can use to control resources and integrate software through scripting?
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Learn also with real examples explained, namely:
- fetch music streams from web URL's to disk, from a popular Portuguese radio station
- emulate a user/replicate online banking tasks and extract values automatically, with a real bank example (by means of "reverse engineering" web pages)
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27 development
27 developmentHadley Wickham This document provides tips and strategies for professional development, with a focus on communication skills. It discusses setting up a mailing list, recommended books, journals, and developing skills in written, spoken, visual and electronic communication. Specific tips are provided for managing email effectively, including aiming for inbox zero, using minimal responses, filters and deleting emails, and scheduling times to respond to batches of emails.
27 development
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22 spam
22 spamHadley Wickham This document discusses regular expressions and their use for string pattern matching. It begins with examples of using regular expressions to extract phone numbers, dates, times, and dollar amounts from text. It then covers basic regular expression syntax like character classes, quantifiers, and escaping characters. The document provides exercises for the reader to create and test their own regular expressions.
19 tables
19 tablesHadley Wickham Today's presentation will cover reshaping data using the reshape2 package and creating tables in LaTeX using the xtables package. It will demonstrate melting and casting data to change its format, and how to style tables created with xtables to follow best practices using features from the booktabs package. Advanced techniques for xtables include adding captions, labels, and removing row names.
18 cleaning
18 cleaningHadley Wickham This document discusses data cleaning techniques. It introduces the concept of clean data being complete, correct, concise and compatible. Specific techniques discussed include identifying incorrect values that cannot be fixed, like removing rows, and those that can be fixed, like making data more concise by removing duplicate information. The document demonstrates converting data from wide to long format using melt() to make it more compatible for analysis and visualization. Overall, the document provides an overview of assessing and improving data quality through cleaning.
17 polishing
17 polishingHadley Wickham The document discusses analyzing NBA shooting data from the 2008-2009 season to determine the most accurate shooters. It uses R and packages like plyr and ggplot2 to calculate shooting percentages for players and teams, then creates graphs to visualize the results. The highest percentages belong to players like Eddy Curry, Steven Hill, and Marcus Williams who all shot 100% on 1-2 attempts for the season.
16 critique
16 critiqueHadley Wickham The document discusses exploratory data graphics and principles for critiquing graphics. It introduces the "four C's" framework for critique: content, construction, context, and consumption. It also discusses the grammar of graphics as an abstraction for reasoning about and communicating graphics, focusing on the key components of layers, data mappings, geoms, and statistics. Multiple examples are provided and attendees are prompted to practice converting between textual descriptions and ggplot code.
15 time-space
15 time-spaceHadley Wickham The document discusses visualizing time series data on baby names by state in the US from 1960-2008. It demonstrates plotting trends in a name's popularity over time both nationally and by individual state, and explores techniques like smoothing, centering, and scaling the data to better analyze patterns. Maps showing the geographic distribution of a name's popularity in different states in also presented.
14 case-study
14 case-studyHadley Wickham This document discusses analyzing name usage data from a dataset containing boy and girl name usage over time. It uses the plyr package to summarize a subset of names that are used for both boys and girls, looking at the number of years each name has been used and its average usage. Graphs are created to examine patterns in the log ratio of boy to girl usage for selected names over 50 years that had average usage over 0.0005. The analysis aims to understand how usage of these unisex names has changed over time and to potentially estimate error rates in the data.
13 case-study
13 case-studyHadley Wickham This document discusses a case study analyzing gender trends in baby names:
1. It focuses on analyzing a smaller subset of the most popular names over time.
2. The document computes summary statistics like the proportion of babies given each name that were boys or girls and the number of years each name was in the top 1000 for boys and girls.
3. Names are classified as either having dual or separate gender usage over time based on the ratio of boy to girl name assignments each year.
12 adv-manip
12 adv-manipHadley Wickham This document contains the agenda for a class on advanced data manipulation in R. The topics covered include:
1. An upcoming colloquium on student summer research experiences
2. Basics of string manipulation in R
3. Group-wise transformations of data, using packages like plyr
4. Practice challenges for students to apply these skills
11 adv-manip
11 adv-manipHadley Wickham 1. Split the data frame into pieces by name using ddply() or split().
2. Apply a function like summarise() to each piece to calculate the sum of people with that name.
3. Combine the results into a new data frame with the total for each name.
This allows grouping operations like calculating a summary statistic for each unique value in a variable. The data is split, a function is applied to each group, and then combined into a single data frame with the results.
11 adv-manip
11 adv-manipHadley Wickham This document discusses advanced data manipulation techniques in R using packages like plyr and ggplot2. It covers slicing and dicing data frames using functions like subset, summarise, transform and arrange. It also discusses merging data from multiple sources and performing group-wise operations on data. Brainstorming ideas include exploring trends in baby names data over time by variables like first letter, length, rankings, and merging with additional data sources.
10 simulation
10 simulationHadley Wickham This document discusses simulation and random number generation. It covers using floating figures with captions in homework, including comments in code, calculating probabilities and expected values mathematically for simulations, generating random numbers from different distributions using functions like runif and rnorm, pseudorandom number generation in computers using linear congruential generators, and sources of true randomness like atmospheric noise.
10 simulation
10 simulationHadley Wickham This document discusses simulation and random number generation. It covers using floating figures with captions in homework, including comments in code, calculating probabilities and expected values mathematically for simulations, generating random numbers from different distributions using functions like runif and rnorm, pseudorandom number generation in computers using linear congruential generators, and sources of true randomness like atmospheric noise.
09 bootstrapping
09 bootstrappingHadley Wickham This document discusses using bootstrapping to test whether the payout percentage of slot machines is lower than the casino's claimed 92% rate. It introduces generating random prizes from the empirical data distribution to simulate sets of 345 pulls. Functions are defined to calculate a random prize, generate many random prizes, and calculate the mean payout. Repeatedly simulating many sets of 345 pulls allows estimating the distribution of mean payouts under the null hypothesis to compare to the observed 67% rate.
08 functions
08 functionsHadley Wickham The document discusses functions in R, including function structure, calling conventions, writing functions, and using for loops. It provides examples of basic function structure, including name, arguments, body, and output. It also discusses passing arguments by name or position and using default values. The document demonstrates how to write functions to calculate mean and variance and provides exercises for readers to write functions and use for loops to iterate through data frames.
07 problem-solving
07 problem-solvingHadley Wickham This document discusses problem solving for a Stat405 course. It covers several topics:
1. Projects - Students should email their group name and members by Thursday.
2. Saving data - It compares saving data in .csv versus .rdata formats, and discusses using compression.
3. Slot machine question - It asks students to think through how to calculate slot machine payouts based on different window combinations like all the same, all bars, or certain numbers of cherries and diamonds. Code examples are provided to demonstrate solutions.
4. Basic strategy - Students are advised to break problems into smaller parts and translate their description into R code.
The document provides guidance and examples for students to think
05 subsetting
05 subsettingHadley Wickham This document provides an overview of subsetting and shortcuts in the Stat405 course. It discusses the key tools covered in the first three lectures (basic graphics), next three lectures (basic data handling), and next three lectures (basic functions). The document then covers specific subsetting techniques like character subsetting and integer subsetting. It provides examples of sorting a data frame and using shortcuts like subset, summarise, transform and arrange. It emphasizes telling stories with data analyses and concludes with discussing the command line and commands like pwd, ls, cd for navigation.
04 reports
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R packages
- 1. November 2010 Hadley Wickham Assistant Professor / Dobelman Family Junior Chair Department of Statistics / Rice University Making an R package Tuesday, 9 November 2010
- 2. https://github.com/ hadley/devtools/wiki What I’ve learned from developing 20+ R packages Tuesday, 9 November 2010
- 3. 1. Getting started 2. Development cycle 3. Documentation 4. What to learn next W arning: opinionated advice Tuesday, 9 November 2010
- 4. Use a mac! (or linux) Otherwise, start by downloading and installing http://www.murdoch-sutherland.com/Rtools/ Tuesday, 9 November 2010
- 6. 1. Decide on a name (stringr) 2. Create stringr/ and stringr/R/ directories 3. Copy in your existing code !"" R # !"" file1.r # !"" file2.r Tuesday, 9 November 2010
- 7. 4. Add a description file !"" DESCRIPTION !"" R Tuesday, 9 November 2010
- 8. Package: stringr Type: Package Title: Make it easier to work with strings. Version: 0.5 Author: Hadley Wickham <h.wickham@gmail.com> Maintainer: Hadley Wickham <h.wickham@gmail.com> Description: stringr is a set of simple wrappers that make R's string functions more consistent, simpler and easier to use. It does this by ensuring that: function and argument names (and positions) are consistent, all functions deal with NA's and zero length character appropriately, and the output data structures from each function matches the input data structures of other functions. Imports: plyr Depends: R (>= 2.11.0) Suggests: testthat (>= 0.3) License: GPL-2 https://github.com/hadley/devtools/wiki/Package-basics Tuesday, 9 November 2010
- 9. 4. Document your files 5. Run roxygenize() !"" DESCRIPTION !"" man !"" R Tuesday, 9 November 2010
- 10. #' The length of a string (in characters). #' #' @param string input character vector #' @return numeric vector giving number of characters in #' each element of the character vector. Missing strings have #' missing length. #' @keywords character #' @seealso code{link{nchar}} which this function wraps #' @export #' @examples #' str_length(letters) #' str_length(c("i", "like", "programming", NA)) str_length <- function(string) { string <- check_string(string) nc <- nchar(string, allowNA = TRUE) is.na(nc) <- is.na(string) nc } https://github.com/hadley/devtools/wiki/docs-function Tuesday, 9 November 2010
- 11. 6. Run R CMD check 7. Use R CMD build to create a package file 8. Upload file to CRAN: ftp -u ftp://cran.R- project.org/incoming/ stringr_0.5.tar.gz !"" DESCRIPTION !"" man !"" R https://github.com/hadley/devtools/wiki/Release Tuesday, 9 November 2010
- 12. You’re done! Tuesday, 9 November 2010
- 14. Modify and save code Reload in R Does it work? Identify the task Write an automated testYES NO Exploratory programming Tuesday, 9 November 2010
- 15. Confirmatory programming Modify and save code Reload in R Does it work? Write an automated test YES NO You’re done Tuesday, 9 November 2010
- 16. # These patterns are facilitated by the devtools # package: https://github.com/hadley/devtools # Once installed and set up you can easily: # * Reload code and data load_all("stringr") # * Run automated tests test("stringr") # * Update documentation document("stringr") Tuesday, 9 November 2010
- 17. Documentation Tuesday, 9 November 2010
- 18. Documentation Function-level: gives precise details about individual functions Package-level: gives details about how to use the functions to solve real (complex) problems. Both are essential! Tuesday, 9 November 2010
- 19. Function-level Keep code and documentation close together with roxygen. Writing documentation gets easier with time! Tuesday, 9 November 2010
- 20. Package-level Package help topic NEWS Vignettes + CITATION Demos README https://github.com/hadley/devtools/wiki/docs-package Tuesday, 9 November 2010
- 21. Next... Tuesday, 9 November 2010
- 22. Learning more Testing Namespaces Code style Git + github Tuesday, 9 November 2010
- 23. Learn from others Read the source of other packages! https://github.com/hadley/plyr https://github.com/hadley/stringr https://github.com/hadley/lubridate https://github.com/hadley/evaluate https://github.com/hadley/reshape Tuesday, 9 November 2010
- 24. Tuesday, 9 November 2010
- 25. This work is licensed under the Creative Commons Attribution-Noncommercial 3.0 United States License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/ 3.0/us/ or send a letter to Creative Commons, 171 Second Street, Suite 300, San Francisco, California, 94105, USA. Tuesday, 9 November 2010