lubridate
Make working with dates in R just that little bit easier
lubridate is an R package that simplifies working with dates and times. It provides intuitive functions for parsing date-time data, extracting and modifying date-time components, and handling time zones.
The package solves the problem of R’s inconsistent and complex date-time handling. It offers fast parsing functions that work with various date formats, simple getter and setter functions for date components like year, month, and hour, and three specialized classes (durations, periods, and intervals) for performing arithmetic operations with dates while correctly handling complexities like leap years, daylight savings time, and time zones.
Contributors#
Resources featuring lubridate#
Alena Reynolds - bRewing code: Ingredients for successful tribal collaboration
Everyone will have their own recipe for bRewing a great collaboration, but we wanted to share ours. Ingredients: equal parts learner and teacher, 90 kg of supportive management, 1 whole database, complete or incomplete, a dash of creativity, 60 hours of time (recipe included in the main presentation), fun to taste. First, make sure your ingredients are organized, and the prep area is tidy. Sift data into a central database and simmer and stir into separate R scripts. In a large cauldron, combine scripts and narrative into one giant Rmarkdown. Lubridate your pan and knit into the desired format. We want to share the rest of our recipe to make a delicious report that builds confidence in the learner, new and strong friendships, and lifelong skills.
Talk by Alena Reynolds and Angie Reed
Slides: https://drive.google.com/file/d/1B3DbooimgrWqLONui_6sh12tam4mqJYW/view?usp=drive_link Volunteer Form: https://docs.google.com/forms/d/e/1FAIpQLSdHj47P0OAbPunyP6zbIihVeOOthiKsrCXWXoUQym_v9XdUog/viewform?pli=1
posit::conf(2023) Workshop: Tidy time series and forecasting in R
Register now: http://pos.it/conf Instructor: Rob J Hyndman Workshop Duration: 2-Day Workshop
This course is for you if you: • already use the tidyverse packages in R such as dplyr, tidyr, tibble and ggplot2 • need to analyze large collections of related time series • would like to learn how to use some tidy tools for time series analysis including visualization, decomposition and forecasting
It is common for organizations to collect huge amounts of data over time, and existing time series analysis tools are not always suitable to handle the scale, frequency and structure of the data collected. In this workshop, we will look at some packages and methods that have been developed to handle the analysis of large collections of time series.
On day 1, we will look at the tsibble data structure for flexibly managing collections of related time series. We will look at how to do data wrangling, data visualizations and exploratory data analysis. We will explore feature-based methods to explore time series data in high dimensions. A similar feature-based approach can be used to identify anomalous time series within a collection of time series, or to cluster or classify time series. Primary packages for day 1 will be tsibble, lubridate and feasts (along with the tidyverse of course).
Day 2 will be about forecasting. We will look at some classical time series models and how they are automated in the fable package, and we will explore the creation of ensemble forecasts and hybrid forecasts. Best practices for evaluating forecast accuracy will also be covered. Finally, we will look at forecast reconciliation, allowing millions of time series to be forecast in a relatively short time while accounting for constraints on how the series are related
Garrett Grolemund | Reproducibility in Production | RStudio (2019)
https://rstudio.com/resources/webinars/reproducibility-in-production/
In part 1 of this 3 part series, Garrett covers the following:
Computational documents offer limitless opportunities for your business. With them, your consumers can rerun your report with new parameters, apply your analysis to new data, or schedule future, automatic updates to your work—all with the click of a button. This is the first in a three part webinar series that will describe this new form of reproducibility. Here, we begin by showing you how to write executable R Markdown documents for a production environment.
About Garrett: Garrett is the author of Hands-On Programming with R and co-author of R for Data Science and R Markdown: The Definitive Guide. He is a Data Scientist at RStudio and holds a Ph.D. in Statistics, but specializes in teaching. He’s taught people how to use R at over 50 government agencies, small businesses, and multi-billion dollar global companies; and he’s designed RStudio’s training materials for R, Shiny, R Markdown and more. Garrett wrote the popular lubridate package for dates and times in R and creates the RStudio cheat sheets
Garrett Grolemund | R Markdown The bigger picture | RStudio (2019)
Statistics has made science resemble math, so much so that we’ve begun to conflate p-values with mathematical proofs. We need to return to evaluating a scientific discovery by its reproducibility, which will require a change in how we report scientific results. This change will be a windfall to commercial data scientists because reproducible means repeatable, automatable, parameterizable, and schedulable.
VIEW MATERIALS https://github.com/garrettgman/rmarkdown-the-bigger-picture
About the Author Garrett Grolemund Garrett is a data scientist and master instructor for RStudio. He excels at teaching, statistics, and teaching statistics. He wrote the popular lubridate package and is the author of Hands On Programming with R and the upcoming book, Data Science with R, from O’Reilly Media. He holds a PhD in Statistics and specializes in Data Visualization
