tibble
A modern re-imagining of the data frame
The tibble package provides a modern replacement for R’s data.frame that follows stricter, more predictable behavior. Tibbles are designed to be “lazy and surly” - they don’t automatically change variable names or types, don’t do partial matching, and warn you more explicitly about problems.
This stricter behavior helps catch errors earlier in your data analysis workflow. Tibbles also include an enhanced print method that handles large datasets and complex objects better than standard data frames, making it easier to inspect your data in the console. The package offers functions for creating tibbles from existing data structures (as_tibble()), from column vectors (tibble()), and row-by-row (tribble()).
Contributors#
Resources featuring tibble#
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
Rich Iannone || Making Beautiful Tables with {gt} || RStudio
00:00 Introduction 00:37 Adding a title with tab_header() (using Markdown!) 01:47 Adding a subtitle 02:48 Aligning table headers with opt_align_table_header() 03:48 Using {dplyr} with {gt} 06:03 Create a table stub with rowname_col() 07:35 Customizing column labels with col_label() 09:45 Formatting table numbers with fmt_number() 12:10 Adjusting column width with cols_width() 15:39 Adding source notes with tab_source_note() 16:55 Adding footnotes with tab_footnote() 18:55 Customizing footnote marks with opt_footnote_marks() 19:10 Demo of how easy managing multiple footnotes is with {gt} 23:41 Customizing cell styles with tab_style() 27:07 Adding label text to the stubhead with tab_stubhead() 28:15 Changing table font with opt_table_font() 29:25 Automatically scaling cell color based on value using data_color()
With the gt package, anyone can make wonderful-looking tables using the R programming language. The gt philosophy: we can construct a wide variety of useful tables with a cohesive set of table parts. These include the table header, the stub, the column labels and spanner column labels, the table body, and the table footer.
It all begins with table data (be it a tibble or a data frame). You then decide how to compose your gt table with the elements and formatting you need for the task at hand. Finally, the table is rendered by printing it at the console, including it in an R Markdown document, or exporting to a file using gtsave(). Currently, gt supports the HTML, LaTeX, and RTF output formats.
The gt package is designed to be both straightforward yet powerful. The emphasis is on simple functions for the everyday display table needs.
You can read more about gt here: https://gt.rstudio.com/articles/intro-creating-gt-tables.html And you can learn more about Shiny here: https://shiny.rstudio.com/
Got questions? The RStudio Community site is a great place to get assistance: https://community.rstudio.com/
Content: Rich Iannone (@riannone) Design & editing: Jesse Mostipak (@kierisi)
Data Manipulation Tools: dplyr – Pt 3 Intro to the Grammar of Data Manipulation with R
Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks.
dplyr docs: dplyr.tidyverse.org/reference/
- http://dplyr.tidyverse.org/reference/union.html
- http://dplyr.tidyverse.org/reference/intersect.html
- http://dplyr.tidyverse.org/reference/set_diff.htm
Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw
- /01:44 Intro and what’s covered Ground Rules
- /02:40 What’s a tibble
- /04:50 Use View
- /05:25 The Pipe operator:
- /07:20 What do I mean by data wrangling?
Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM
- /00:48 Goal 1 Making your data suitable for R
- /01:40
tidyr“Tidy” Data introduced and motivated - /08:10
tidyr::gather - /12:30
tidyr::spread - /15:23
tidyr::unite - /15:23
tidyr::separate
Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U
- 00.40 setup
- 02:00
dplyr::select - 03:40
dplyr::filter - 05:05
dplyr::mutate - 07:05
dplyr::summarise - 08:30
dplyr::arrange - 09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
- 11:45
dplyr::group_by
Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together
- /00.42
dplyr::bind_cols - /01:27
dplyr::bind_rows - /01:42 Set operations
dplyr::union,dplyr::intersect,dplyr::set_diff - /02:15 joining data
dplyr::left_join,dplyr::inner_join,dplyr::right_join,dplyr::full_join,
Cheatsheets: https://www.rstudio.com/resources/cheatsheets/
Documentation:
tidyr docs: tidyr.tidyverse.org/reference/
tidyrvignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.htmldplyrdocs: http://dplyr.tidyverse.org/reference/dplyrone-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.htmldplyrtwo-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html
Tidy Data and tidyr – Pt 2 Intro to Data Wrangling with R and the Tidyverse
Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your code clean and clear and reduce the cognitive load required for common but often complex data science tasks.
http://tidyr.tidyverse.org/reference/
- http://tidyr.tidyverse.org/reference/gather
- http://tidyr.tidyverse.org/reference/spread
- http://tidyr.tidyverse.org/reference/unite
- http://tidyr.tidyverse.org/reference/separate
Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw
- /01:44 Intro and what’s covered Ground Rules
- /02:40 What’s a tibble
- /04:50 Use View
- /05:25 The Pipe operator:
- /07:20 What do I mean by data wrangling?
Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM
- 00:48 Goal 1 Making your data suitable for R
- 01:40
tidyr“Tidy” Data introduced and motivated - 08:10
tidyr::gather - 12:30
tidyr::spread - 15:23
tidyr::unite - 15:23
tidyr::separate
Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U
- 00.40 setup
- /02:00
dplyr::select - /03:40
dplyr::filter - /05:05
dplyr::mutate - /07:05
dplyr::summarise - /08:30
dplyr::arrange - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
- /11:45
dplyr::group_by - /15:00
dplyr::group_by
Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together
- /00.42
dplyr::bind_cols - /01:27
dplyr::bind_rows - /01:42 Set operations
dplyr::union,dplyr::intersect,dplyr::set_diff - /02:15 joining data
dplyr::left_join,dplyr::inner_join,dplyr::right_join,dplyr::full_join,
Cheatsheets: https://www.rstudio.com/resources/cheatsheets/
Documentation:
tidyr docs: tidyr.tidyverse.org/reference/
tidyrvignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.htmldplyrdocs: http://dplyr.tidyverse.org/reference/dplyrone-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.htmldplyrtwo-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html
What is data wrangling? Intro, Motivation, Outline, Setup – Pt. 1 Data Wrangling Introduction
Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. These videos introduce you to these tools. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks.
Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw
- 01:44 Intro and what’s covered Ground Rules
- 02:40 What’s a tibble
- 04:50 Use View
- 05:25 The Pipe operator:
- 07:20 What do I mean by data wrangling?
Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM
- /00:48 Goal 1 Making your data suitable for R
- /01:40
tidyr“Tidy” Data introduced and motivated - /08:15
tidyr::gather - /12:38
tidyr::spread - /15:30
tidyr::unite - /15:30
tidyr::separate
Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U
- 00.40 setup
- /02:00
dplyr::select - /03:40
dplyr::filter - /05:05
dplyr::mutate - /07:05
dplyr::summarise - /08:30
dplyr::arrange - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
- /11:45
dplyr::group_by - /15:00
dplyr::group_by
Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together
- /00.42
dplyr::bind_cols - /01:27
dplyr::bind_rows - /01:42 Set operations
dplyr::union,dplyr::intersect,dplyr::set_diff - /02:15 joining data
dplyr::left_join,dplyr::inner_join,dplyr::right_join,dplyr::full_join,
Cheatsheets: https://www.rstudio.com/resources/cheatsheets/
Documentation:
tidyr docs: tidyr.tidyverse.org/reference/
tidyrvignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.htmldplyrdocs: http://dplyr.tidyverse.org/reference/dplyrone-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.htmldplyrtwo-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html
New York Times “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights”, By STEVE LOHRAUG. 17, 2014 https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html
Working with Two Datasets: Binds, Set Operations, and Joins – Pt 4 Intro to Data Manipulation
Data wrangling is too often the most time-consuming part of data science and applied statistics. Two tidyverse packages, tidyr and dplyr, help make data manipulation tasks easier. Keep your R code clean and clear and reduce the cognitive load required for common but often complex data science tasks.
dplyr docs: dplyr.tidyverse.org/reference/
Pt. 1: What is data wrangling? Intro, Motivation, Outline, Setup https://youtu.be/jOd65mR1zfw
- /01:44 Intro and what’s covered Ground Rules:
- /02:40 What’s a tibble
- /04:50 Use View
- /05:25 The Pipe operator:
- /07:20 What do I mean by data wrangling?
Pt. 2: Tidy Data and tidyr https://youtu.be/1ELALQlO-yM
- /00:48 Goal 1 Making your data suitable for R
- /01:40
tidyr“Tidy” Data introduced and motivated - /08:10
tidyr::gather - /12:30
tidyr::spread - /15:23
tidyr::unite - /15:23
tidyr::separate
Pt. 3: Data manipulation tools: dplyr https://youtu.be/Zc_ufg4uW4U
- /00.40 setup
- /02:00
dplyr::select - /03:40
dplyr::filter - /05:05
dplyr::mutate - /07:05
dplyr::summarise - /08:30
dplyr::arrange - /09:55 Combining these tools with the pipe (Setup for the Grammar of Data Manipulation)
- /11:45
dplyr::group_by
Pt. 4: Working with Two Datasets: Binds, Set Operations, and Joins https://youtu.be/AuBgYDCg1Cg Combining two datasets together
- 00.42
dplyr::bind_cols - 01:27
dplyr::bind_rows - 01:42 Set operations
dplyr::union,dplyr::intersect,dplyr::set_diff - 02:15 joining data -
dplyr::left_join,dplyr::inner_join, -dplyr::right_join,dplyr::full_join,
Cheatsheets: https://www.rstudio.com/resources/cheatsheets/
Documentation:
tidyr docs: tidyr.tidyverse.org/reference/
tidyrvignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.htmldplyrdocs: http://dplyr.tidyverse.org/reference/dplyrone-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.htmldplyrtwo-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html
