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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04 Wrapup
04 WrapupHadley Wickham This document summarizes Hadley Wickham's wrap up of the plyr package. It discusses using plyr functions like rlply and mlply to fit multiple models to data with varying parameters. It also covers reporting progress, handling errors, the overall structure of plyr functions, and plans for future improvements to work with larger data. Wickham invites feedback via a web form.
21 spam
21 spamHadley Wickham The document discusses processing email content data to classify emails as spam or ham (non-spam). It introduces loading an "email.rdata" dataset and exploring the "contents" variable. The tasks of splitting each email into header and body sections are described, as well as splitting the header into individual fields. Functions for string processing like str_locate(), str_sub(), and str_split() are demonstrated.
Model Visualisation (with ggplot2)
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03 Conditional
03 ConditionalHadley Wickham This document discusses foundational concepts in probability and statistics including conditional probability, independence, total probability, and paradoxes. It uses examples like HIV testing, family sizes, and coin tossing to illustrate calculating probabilities and determining if events are independent. The goal is to help students develop a careful mathematical process for analyzing probabilities rather than relying on statistical intuition.
Graphical inference
Graphical inferenceHadley Wickham The document discusses visual inference, which is using human judgment to determine if a visualization of real data differs from visualizations of random null data. It describes protocols for generating null data visualizations and having observers try to pick out the real visualization, allowing for statistical tests without predefined test statistics. Potential applications and future work are also discussed, such as integrating this approach into visualization software and studying how training impacts human judgment of random variations.
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03 Modelling
03 ModellingHadley Wickham The document discusses strategies for analyzing large datasets. It introduces housing data from Texas metropolitan areas from 2000 to 2009, including numbers of houses listed and sold, total value, average sale price, and time on market. As an example, the document focuses on Houston's data, exploring seasonal patterns and fitting linear models to remove seasonality and better view long-term trends. These techniques are then applied to sales data from all Texas cities.
R workshop iii -- 3 hours to learn ggplot2 series
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23 data-structures
23 data-structuresHadley Wickham - The document discusses different data types in R including vectors, matrices, arrays, lists, and data frames.
- It provides examples of how to create, subset, and coerce between each data type.
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02 Ddply
02 DdplyHadley Wickham The document describes US baby name data from 1880-2008 containing over 258,000 records with the variables year, name, sex, and percentage. It demonstrates how to use the plyr package in R to transform and summarize the data in various ways, including grouping by variables like sex and year. This allows calculating statistics separately for each subgroup. Plots are suggested to analyze trends in popular baby names over time and whether these trends differ by sex.
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
Analyzing Data With PythonSarah Guido Sarah Guido gave a presentation on analyzing data with Python. She discussed several Python tools for preprocessing, analysis, and visualization including Pandas for data wrangling, scikit-learn for machine learning, NLTK for natural language processing, MRjob for processing large datasets in parallel, and ggplot for visualization. For each tool, she provided examples and use cases. She emphasized that the best tools depend on the type of data and analysis needs.
Reshaping Data in R
Reshaping Data in RJeffrey Breen Overview of how/why to reshape data in R from "wide" (spreadsheet-like) to "long" (database-like) and back.
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.
pandas - Python Data Analysis
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Please call the BASH Team!
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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?
Some set of "advanced" features of BASH are explained and I'm sure you'll find them very handy to enhance your scripts. The BASH recipes can be used for common daily tasks of end users or administrators.
"Hacking" web sites can also be done using BASH and some command line tools. We'll talk a bit about it.
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.
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27 developmentHadley Wickham This document summarizes Hadley Wickham's presentation on professional development. It discusses setting up for a poster presentation, learning tools like typing and software, joining mailing lists, recommended books, journals, developing communication skills through writing, speaking, and using email effectively. Key aspects of using email effectively included regularly emptying your inbox, providing minimal responses, deleting emails, using filters, and scheduling times to respond to emails in batches.
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
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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
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09 bootstrapping
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08 functions
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07 problem-solving
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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
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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