Earo Wang | Melt the clock Tidy time series analysis | RStudio (2019)
Time series can be frustrating to work with, particularly when processing raw data into model-ready data. This work presents two new packages that address a gap in existing methodology for time series analysis (raised in rstudio::conf 2018). The tsibble package supports organizing and manipulating modern time series, leveraging tidy data principles along with contextual semantics: index and key. The tsibble data structure seamlessly flows into forecasting routines. The fable package is a tidy renovation of the forecast package. It promotes transparent forecasting practices and concise model representations, to empower analysts tackling a broad domain of forecasting problems. This collection of packages form the tidyverts, which facilitates a fluent and fluid workflow for analyzing time series.
VIEW MATERIALS https://slides.earo.me/rstudioconf19
About the Author
Earo Wang
Iām currently doing my Ph.D. on statistical visualisation of temporal-context data at Monash University, supervised by Professor Di Cook and Professor Rob J Hyndman. I enjoy developing open-source tools with R, and is the (co)author of some widely-used R packages including anomalous, hts, sugrrants, rwalkr and tsibble. My research areas invovle data visualisation, time series analysis, and computational statistics
rstudio
Earo Wang
Time Series
RStudio
Data Science
Machine Learning
Python
Stats
Tidyverse
Data Visualization
Data Viz
Ggplot
Technology
Coding
Connect
Server Pro
Shiny
Rmarkdown
Package Manager
CRAN
Interoperability
Serious Data Science
Dplyr
Forcats
Ggplot2
Tibble
Readr
Stringr
Tidyr
Purrr
Github
Data Wrangling
Tidy Data
Odbc
Rayshader
Plumber
Blogdown
Gt
Lazy Evaluation
Tidymodels
Statistics
Debugging
Programming Education
Rstats
Open Source
Oss