tibble

A modern re-imagining of the data frame

tidyverse/tibble

tibble.tidyverse.org

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Data Wrangling

Tidyverse

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

dplyr ggplot2 lubridate tibble tidyr tidyverse 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 Reticulate

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)

Rich Iannone

dplyr gt rstudio Shiny tibble 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 Reticulate Rich Iannone

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/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-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/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-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/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-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/ http://dplyr.tidyverse.org/reference/setops.html http://dplyr.tidyverse.org/reference/join.html 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/ tidyr vignette: https://cran.r-project.org/web/packages/tidyr/vignettes/tidy-data.html dplyr docs: http://dplyr.tidyverse.org/reference/ dplyr one-table vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/dplyr.html dplyr two-table (join operations) vignette: https://cran.r-project.org/web/packages/dplyr/vignettes/two-table.html