r reporting tool

• Get started
• Example 1: Listing
• Example 2: Table
• Example 3: Table Stub
• Example 4: Table and Text
• Example 5: Spanning Headers
• Example 6: Page Wrap
• Example 7: Page By
• Example 8: Title Header
• Example 9: RTF, PDF, HTML, and DOCX
• Example 10: Fonts and Borders
• Example 11: Figure
• Example 12: Styles
• Example 13: Superscripts, Subscripts, and Symbols
• Example 14: Page Break
• More Examples
• Frequently Asked Questions

Introduction to reporter

Historically, R has not been very strong on reporting. The reporter package aims to fill that gap.

Using reporter , you can create a report in just a few lines of code. Not only is it easy to create a report, but the reporter package can handle all sorts of situations that other packages struggle with.

For example, unlike other packages, the reporter package creates the entire report : page header and footer, titles, footnotes, tables - everything. The end result of a reporter call is a complete, printable report.

In addition, reporter can handle page breaking, page wrapping, and automatic sizing of column widths. The package offers a choice of output file types. And it supports the inclusion of tables, text and graphics into a report.

What is more, the package does not expect you to know R Markdown, knitr, or pandoc. You do not need to learn Latex, HTML, or any other intermediate language. With reporter , you send your data into a create function, assign titles and footnotes, and write the report. That’s it!

If you are familiar with SAS® software, you may notice some similarity between reporter functions and proc report . This similarity, however, is only on the surface. The implementation of reporter is done entirely in R, and, internally, is modeled in a different way. However, SAS® users will find the reporter functions very convenient and easy to understand compared to the alternatives.

Installation

The reporter package can be installed from the console. Simply run the following command:

Or if you want the latest development version, you can install it directly from github:

Then put the following line at the top of your program or script:

The reporter package will give you access to a number of functions to help create, lay out, and write your report to the file system. For examples and usage information, visit the reporter documentation site here .

Getting Help

If you need help, the first place to turn to is the reporter web site. The web site has full documentation on all reporter functions.

If you want to look at the code for the reporter package, visit the github page here .

If you encounter a bug or have a feature request, please submit an issue here .

The reporter package is part of the sassy meta-package. The sassy meta-package includes several packages that help make R easier for SAS® programmers. You can read more about the sassy package here .

• View on CRAN
• Browse source code
• Report a bug
• Full license
• Citing reporter
• David Bosak Author, maintainer
• More about authors...

How To Create Reports In R: A Step-By-Step Approach

Learn to craft comprehensive reports in R with ease. This article guides you through importing data, creating visuals, and assembling reports, tailored for developers seeking efficient reporting solutions in R.

💡 KEY INSIGHTS

• Direct report creation from R scripts is feasible without converting them to Rmd format, simplifying the process and enabling styling, theme application, and lightweight HTML report generation​​.
• The knitr::spin() and rmarkdown::render() functions facilitate straightforward report generation, each providing a default style for HTML rendering, streamlining the transition from R scripts to reports​​.
• For custom styling , users can embed a CSS file in the R script when using spin(), although this method has limitations and is less robust compared to other approaches​​.
• Enhanced flexibility in report styling and formatting is achievable through two-step processes, such as generating an intermediate .Rmd file and then applying desired options in knit2html(), offering more control over the report's appearance​​.

Creating reports in R can be a straightforward and efficient process, essential for data analysts and developers alike. This article guides you through the essential steps, from data preparation to visualization, ensuring your reports are both informative and visually appealing.

Data Preparation

Visualization techniques, report assembly, frequently asked questions.

Data preparation is a crucial step in creating reports in R. It involves importing, cleaning, and transforming data to ensure accuracy and relevance.

Importing Data

Cleaning data, reshaping data, aggregating data.

Importing Data : R offers various functions to import data from different sources. For instance, read.csv() is commonly used for CSV files. Here's an example:

Cleaning Data : After importing, data often requires cleaning. This includes handling missing values, removing duplicates, and correcting data types. For example:

Reshaping Data : You might need to pivot, merge, or split your data. The dplyr package is particularly useful for these operations. For instance:

Aggregating Data : Summarizing data is often necessary for reporting. The aggregate() function is a straightforward way to do this:

By following these steps, your data will be well-prepared for the next stages of report creation in R. Remember, clean and well-structured data is the foundation of any insightful report.

Effective visualization is key to conveying data insights in reports. R provides various libraries for creating diverse types of visualizations.

Choosing The Right Type

Using ggplot2, adjusting aesthetics, adding labels and titles.

Choosing the Right Type : Selecting the appropriate graph type is crucial. Common types include bar charts, line graphs, and scatter plots. Each type serves different purposes and datasets.

Using ggplot2 : One of the most popular packages for data visualization in R is ggplot2 . It offers flexibility and aesthetic appeal. Here's a basic example:

Adjusting Aesthetics : ggplot2 allows you to easily modify the look of your plots. You can change colors, themes, and more.

Adding Labels and Titles : Clear labels and titles are essential for understanding visualizations.

By employing these visualization techniques, your reports in R will not only present data but also tell a compelling story. Remember, the goal is to make complex data easily understandable at a glance.

Assembling your report in R involves combining data, text, and visuals into a coherent and structured document. R Markdown is a powerful tool for this purpose.

Using R Markdown

Headers and sections, embedding code chunks, writing analysis.

Using R Markdown : R Markdown allows you to integrate R code with narrative text, creating a dynamic report. Start by creating a new R Markdown file in RStudio.

Headers and Sections : Use headers to organize your report into sections. R Markdown uses # for headers.

Embedding Code Chunks : In R Markdown, you can embed R code chunks that will execute when you knit the document.

Writing Analysis : Between code chunks, add your analysis and explanations in plain text.

By following these steps, you create a dynamic and informative report in R. R Markdown documents are versatile, allowing you to mix code, output, and narrative in a single document, making your analysis reproducible and transparent.

Can I collaborate with others on an R Markdown report?

Yes, collaboration is possible. You can use version control systems like Git in conjunction with platforms like GitHub or use RStudio's collaborative features for shared projects.

What should I do if my code works in R but not in R Markdown?

Ensure that all necessary libraries are loaded and variables are defined within the R Markdown document. Sometimes, code that relies on the global environment in RStudio may not work directly in R Markdown.

How do I control the layout and appearance of my R Markdown report?

You can control the layout and appearance using Markdown syntax for text formatting and options in the YAML header for overall document settings. Additionally, CSS and HTML can be used for more advanced customizations in HTML outputs.

Is it possible to include interactive elements in R reports?

Yes, you can include interactive elements using packages like Shiny, plotly, or DT in your R Markdown reports. These elements make your reports more engaging and user-interactive.

Let’s test your knowledge!

Which package is commonly used for creating advanced visualizations in R?

Continue learning with these 'programming' guides.

• How To Debug In R: Effective Strategies For Developers
• How To Use R For Simulation: Effective Strategies And Techniques
• How To Install R Packages: Steps For Efficient Integration
• How To Import Data In R: Essential Steps For Efficient Data Analysis
• How To Clean Data In R: Essential Techniques For Effective Data Management

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“From R to your manuscript”

report ’s primary goal is to bridge the gap between R’s output and the formatted results contained in your manuscript. It automatically produces reports of models and data frames according to best practices guidelines (e.g., APA ’s style), ensuring standardization and quality in results reporting.

Installation

The package is available on CRAN and can be downloaded by running:

If you would instead like to experiment with the development version, you can download it from GitHub :

Load the package every time you start R

Tip Instead of library(report) , use library(easystats) . This will make all features of the easystats-ecosystem available. To stay updated, use easystats::install_latest() .

Documentation

The package documentation can be found here .

Report all the things

General Workflow

The report package works in a two step fashion. First, you create a report object with the report() function. Then, this report object can be displayed either textually (the default output) or as a table, using as.data.frame() . Moreover, you can also access a more digest and compact version of the report using summary() on the report object.

The report() function works on a variety of models, as well as other objects such as dataframes:

These reports nicely work within the tidyverse workflow:

t -tests and correlations

Reports can be used to automatically format tests like t -tests or correlations.

As mentioned, you can also create tables with the as.data.frame() functions, like for example with this correlation test:

This works great with ANOVAs, as it includes effect sizes and their interpretation.

Generalized Linear Models (GLMs)

Reports are also compatible with GLMs, such as this logistic regression :

Mixed Models

Mixed models, whose popularity and usage is exploding, can also be reported:

Bayesian Models

Bayesian models can also be reported using the new SEXIT framework, which combines clarity, precision and usefulness.

Other types of reports

Specific parts.

One can, for complex reports, directly access the pieces of the reports:

Report participants’ details

This can be useful to complete the Participants paragraph of your manuscript.

Report sample

Report can also help you create a sample description table (also referred to as Table 1 ).

Report system and packages

Finally, report includes some functions to help you write the data analysis paragraph about the tools used.

If you like it, you can put a star on this repo, and cite the package as follows:

report is a young package in need of affection . You can easily be a part of the developing community of this open-source software and improve science! Don’t be shy, try to code and submit a pull request (See the contributing guide ). Even if it’s not perfect, we will help you make it great!

Code of Conduct

Please note that the report project is released with a Contributor Code of Conduct . By contributing to this project, you agree to abide by its terms.

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Essential Documentation for the report Package in R

• R Package Documentation

Table of contents

• 1. Basics details of the report package
• 2. Technical details of the report package
• 3. Capabilities of the report package
• 4. Getting help with the report package
• 5. Code snippet for getting report package documentation in R
• 6. Additional R package documentation resources

Basic details of the report package

• Title: Automated Reporting of Results and Statistical Models
• Description: The aim of the 'report' package is to bridge the gap between R’s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.
• Author: Dominique Makowski [aut] (, @Dom_Makowski), Daniel Lüdecke [aut] (, @strengejacke), Indrajeet Patil [aut] (, @patilindrajeets), Rémi Thériault [aut, cre] (, @rempsyc), Mattan S. Ben-Shachar [aut] (, @mattansb), Brenton M. Wiernik [aut] (, @bmwiernik), Rudolf Siegel [ctb] ()
• Maintainer: Rémi Thériault (for contact information, visit our R community directory .)
• License type: MIT + file LICENSE (for license details, visit the Open Source Initiative website .)
• Package website: The report package has a dedicated website. You can visit: https://easystats.github.io/report/.

Technical details of the report package

• Version: In R, a version refers to a specific release of a package and can be used to track changes. To find the version number of the report package in the R console, you can use the packageVersion("report") function.
• Compilation requirements: Some R packages include internal code that must be compiled for them to function correctly. The report package does not have compilation requirements.
• Required dependencies: A required dependency refers to another package that is essential for the functioning of the main package. The report package has the following required dependencies: R (>= 3.6).
• Suggested dependencies: A suggested dependency adds extra features to the main package, but the main package can work without it. The report package has the following suggested dependencies: brms, ivreg, knitr, lavaan, lme4, dplyr, rmarkdown, rstanarm, survival, modelbased, emmeans, testthat.
• External dependencies: External dependencies are other packages that the main package depends on for linking at compile time. The report package does not use any external sources.
• Imported packages: Importing packages allows developers to leverage existing code and functionalities without having to reinvent the wheel. The report package has the following imported packages: bayestestR (>= 0.13.1), effectsize (>= 0.8.6), insight (>= 0.19.7), parameters (>= 0.21.3), performance (>= 0.10.8), datawizard (>= 0.9.0), stats, tools, utils.
• Enhancements: Enhancements help developers expand the capabilities of their packages without starting from scratch. The report package has no enhancements.

Capabilities of the report package

• Functions: Functions play a crucial role in R packages. They allow you to perform specific tasks and computations efficiently. To identify the functions in the report package, you can use the ls("package:report") function.
• Datasets: Many R packages include built-in datasets that you can use to familiarize yourself with their functionalities. To identify any built-in datasets in the report package, you can use the data(package = "report") function.
• Vignettes: R vignettes are documents that include examples for using a package. To view the list of available vignettes, you can use the vignette(package = "report") function.
• Citation information: Citing R packages in your publications is important as it recognizes the contributions of the developers. To find the citation information for the report package in the R console, you can use the citation("report") function.

Getting help with the report package

• The help() function: R’s built-in help system is a handy tool to find documentation. You can use the help("report") function to retrieve detailed information, examples, and usage instructions.
• The ? operator: The ? operator is a shortcut for the help() function. You can simply type ?report to quickly find documentation.
• Discussion forums: Online forums are excellent platforms to ask questions, share knowledge, and troubleshoot issues. The most popular forums for R programmers are StackOverflow and Posit Community .

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Code snippet for getting report package documentation in R

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Summary and Analysis of Extension Program Evaluation in R

Salvatore S. Mangiafico

Search Rcompanion.org

• Purpose of this Book
• Author of this Book
• Statistics Textbooks and Other Resources
• Why Statistics?
• Evaluation Tools and Surveys
• Types of Variables
• Descriptive Statistics
• Confidence Intervals
• Basic Plots
• Hypothesis Testing and p-values

Reporting Results of Data and Analyses

• Choosing a Statistical Test
• Independent and Paired Values
• Introduction to Likert Data
• Descriptive Statistics for Likert Item Data
• Descriptive Statistics with the likert Package
• Confidence Intervals for Medians
• Converting Numeric Data to Categories
• Introduction to Traditional Nonparametric Tests
• One-sample Wilcoxon Signed-rank Test
• Sign Test for One-sample Data
• Two-sample Mann–Whitney U Test
• Mood’s Median Test for Two-sample Data
• Two-sample Paired Signed-rank Test
• Sign Test for Two-sample Paired Data
• Kruskal–Wallis Test
• Mood’s Median Test
• Friedman Test
• Scheirer–Ray–Hare Test
• Aligned Ranks Transformation ANOVA
• Nonparametric Regression and Local Regression
• Nonparametric Regression for Time Series
• Introduction to Permutation Tests
• One-way Permutation Test for Ordinal Data
• One-way Permutation Test for Paired Ordinal Data
• Permutation Tests for Medians and Percentiles
• Association Tests for Ordinal Tables
• Measures of Association for Ordinal Tables
• Introduction to Linear Models
• Using Random Effects in Models
• What are Estimated Marginal Means?
• Estimated Marginal Means for Multiple Comparisons
• Factorial ANOVA: Main Effects, Interaction Effects, and Interaction Plots
• p-values and R-square Values for Models
• Accuracy and Errors for Models
• Introduction to Cumulative Link Models (CLM) for Ordinal Data
• Two-sample Ordinal Test with CLM
• Two-sample Paired Ordinal Test with CLMM
• One-way Ordinal Regression with CLM
• One-way Repeated Ordinal Regression with CLMM
• Two-way Ordinal Regression with CLM
• Two-way Repeated Ordinal Regression with CLMM
• Introduction to Tests for Nominal Variables
• Confidence Intervals for Proportions
• Goodness-of-Fit Tests for Nominal Variables
• Association Tests for Nominal Variables
• Measures of Association for Nominal Variables
• Tests for Paired Nominal Data
• Cochran–Mantel–Haenszel Test for 3-Dimensional Tables
• Cochran’s Q Test for Paired Nominal Data
• Models for Nominal Data
• Introduction to Parametric Tests
• One-sample t-test
• Two-sample t-test
• Paired t-test
• One-way ANOVA
• One-way ANOVA with Blocks
• One-way ANOVA with Random Blocks
• Two-way ANOVA
• Repeated Measures ANOVA
• Correlation and Linear Regression
• Advanced Parametric Methods
• Transforming Data
• Normal Scores Transformation
• Regression for Count Data
• Beta Regression for Percent and Proportion Data
• An R Companion for the Handbook of Biological Statistics

Given the variety of experimental designs, potential types of data, and analytical approaches, it is relatively impossible to develop a cookbook approach to reporting data summaries and analyses.  That being said, it is the intent of this chapter to give some broad and practical advice for this task.

Packages used in this chapter

The packages used in this chapter include:

•  FSA

The following commands will install these packages if they are not already installed:

if(!require(FSA)){install.packages("FSA")}

Reporting analyses, results, and assumptions

The following bullets list some of the information you should include in reporting summaries and analyses.  These can be included in a combination of text, plots, tables, plot captions, and table headings.

A variety of plots are shown in the Basic Plots chapter, as well as in the chapters for specific statistical analyses.  Simple tables for grouped data are shown in the chapters on Descriptive Statistics , Confidence Intervals , Descriptive Statistics for Likert Data , and Confidence Intervals for Medians .

•  A description of the analysis.  Give the test used, indicate the dependent variables and independent variables, and describe the experimental design or model in as much detail as is appropriate.  Mention the experimental units, dates, and location.

•  Model assumptions checks.  Describe what assumptions about the data or residuals are made by the test and how you assessed your data’s conformity with these assumptions.

•  Cite the software and packages you used.

•  Cite a reference for statistical procedures or code.

•  A measure of the central tendency or location of the data, such as the mean or median for groups or populations

•  A measure of the variation for the mean or median for each group, such standard deviation, standard error, first and third quartile, or confidence intervals

•  Size of the effect.  Often this is conveyed by presenting means and medians in a plot or table so that the reader can see the differences.  Sometimes, specific statistics are reported to indicate “effect size”.  See the “Optional technical note on effect sizes” below for more information.

•  Number of observations, either for each group, or for total observations

•  p -value from the analysis

•  Goodness-of-fit statistics, if appropriate, such as r - squared , or pseudo R - squared .

•  Any other relevant statistics, such as the predictive equation from linear regression or the critical x value from a linear plateau model.

•  Results from post-hoc analyses.  Summarize the differences among groups with letters, in a plot or table.  Include the alpha value for group separations

Notes on different data and analyses

Interval/ratio or ordinal data analyzed by group.

The bullet points above should be appropriate for this kind of analysis.  Data can be summarized in tables or with in a variety of plots including “plot of means”, “plot of medians”, “interaction plot”, box plot, histogram, or bar plot.  For some of these, error bars can indicate measures of variation.  Mean separations can be indicated with letters within plots.

Nominal data arranged in contingency tables

Nominal data arranged in contingency tables can be presented as frequencies in contingency tables, or in plots.  Bar plots of frequencies can be used.  Confidence intervals can be added as error bars to plots.  Another alternative is using mosaic plots.

Bivariate data analyses

Bivariate relationships can be shown with a scatterplot of the two variables, often with the best fit model drawn in.  These models include those from linear regression or curvilinear regression.  It is usually important to present the p -value for the model and the r-squared or pseudo R-squared value.

Advice on tables and plots

Some advice for producing plots is given in the “Some advice on producing plots” section of the

Basic Plots chapter.

For additional advice on presenting data in plots, see McDonald (2014a) in the “References” section.

Table style and format will vary widely from publication to publication.  As general advice, the style and format should follow that of the journals or extension publications in your field or institution, or where you hope to get published.

For additional advice on presenting data in tables, see McDonald (2014b) in the “References” section.

Table headings and plot captions

Both table headings and plot captions should give as much information as is practical to make the table or plot understandable if it were separated from the rest of the publication.

Example elements

Example description of statistical analysis and results.

The citation information for the R software can be found with:

Citation information for individual packages can be found with, e.g.:

library(FSA) citation("FSA")

A study of student learning was conducted in 1999–2000 in Watkins Glen, NY.  Students were randomly assigned to one of four curricula, which they studied for one-hour per week under teacher-supervised conditions, and their scores on an assessment exam were recorded at the end of the study.  A one-way analysis of variance was conducted with student score as the dependent variable and curriculum as the independent variable (Mangiafico, 2016).  Treatment means were separated by Tukey-adjusted comparisons.  Model residuals were checked for normality and homoscedasticity by visual inspection of residual plots.  Analysis of variance and post-hoc tests were conducted in R (R Core Team, 2016) with the car and emmeans packages.  Data summary was conducted with the FSA package.

Figure 1.  Mean of reading scores for program curricula for 8 th grade students in Watkins Glen, NY, 1999–2000.  Error bars represent standard error of the mean.  The effect of curricula on mean reading score was significant by one-way ANOVA ( p < 0.0001).  Means sharing a letter not significantly different by Tukey-adjusted mean separations ( alpha = 0.05).  Total observations = 24.

Table 1.  Mean of reading scores for program curricula for 8 th grade students in Watkins Glen, NY, 1999–2000.  The effect of curricula on mean reading score was significant by one-way ANOVA ( p < 0.0001).  Means sharing a letter not significantly different by Tukey-adjusted mean separations ( alpha = 0.05).  n indicates number of observations.   Std. err. indicates the standard error of the mean.

Optional technical note on effect sizes

In this chapter, I use the term “size of the effect” in a general sense of the magnitude of differences among groups or the degree of association of variables.  “Effect size” usually describes specific statistics for a given analysis, such as Cohen's d , eta-squared , or odds ratio. 

For some examples of these statistics, see Sullivan and Feinn (2012) or IDRE (2015) in the “References” section of this chapter.

The pwr package can calculate the effect size for proportions, chi-square goodness-of-fit, and chi-square test of association.  For the effect size for one-way ANOVA (Cohen’s f ), see Mangiafico (2015) or IDRE (2015).  For effect sizes for nonparametric tests, see Tomczak and Tomczak (2014), and King and Rosopa (2010).

“One-way Anova” in Mangiafico, S.S. 2015. An R Companion for the Handbook of Biological Statistics , version 1.09. rcompanion.org/rcompanion/d_05.html .

“Guide to fairly good graphs” in McDonald, J.H. 2014a. Handbook of Biological Statistics . www.biostathandbook.com/graph.html .

“Presenting data in tables” in McDonald, J.H. 2014b. Handbook of Biological Statistics . www.biostathandbook.com/table.html .

Sullivan, G.M. and R. Feinn. 2012. Using Effect Size—or Why the P Value is Not Enough. Journal of Graduate Medical Education 4(3): 279–282. www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/ .

Tomczak, M. and Tomczak, E. 2014. The need to report effect size estimates revisited. An overview of some recommended measures of effect size. Trends in Sports Sciences 1(21):1–25. www.tss.awf.poznan.pl/files/3_Trends_Vol21_2014__no1_20.pdf .

[IDRE] Institute for Digital Research and Education. 2015. How is effect size used in power analysis?  UCLA. stats.oarc.ucla.edu/other/mult-pkg/faq/general/effect-size-power/faqhow-is-effect-size-used-in-power-analysis/ .

King, B.M., P.J. Rosopa, and E.W. Minium. 2018. Some (Almost) Assumption-Free Tests. In Statistical Reasoning in the Behavioral Sciences , 7th ed. Wiley.

©2016 by Salvatore S. Mangiafico. Rutgers Cooperative Extension, New Brunswick, NJ.

Non-commercial reproduction of this content, with attribution, is permitted. For-profit reproduction without permission is prohibited.

If you use the code or information in this site in a published work, please cite it as a source.  Also, if you are an instructor and use this book in your course, please let me know.   My contact information is on the About the Author of this Book page.

Mangiafico, S.S. 2016. Summary and Analysis of Extension Program Evaluation in R, version 1.20.05, revised 2023. rcompanion.org/handbook/ . (Pdf version: rcompanion.org/documents/RHandbookProgramEvaluation.pdf .)

Introduction to interactive documents

Garrett grolemund, july 9, 2014.

Interactive documents are a new way to build Shiny apps. An interactive document is an R Markdown file that contains Shiny widgets and outputs. You write the report in markdown , and then launch it as an app with the click of a button.

The previous article, Introduction to R Markdown , described how to write R Markdown files. R Markdown files are useful because

• They are quick and easy to write.
• You can embed executable R code into your file, which saves manual labor and creates a reproducible report.
• You can convert R Markdown files into HTML, PDF, and Word documents with the click of a button.
• You can convert R Markdown files into ioslides and beamer slideshows with the click of a button.

In fact, R Markdown files are the ultimate R reporting tool.

This article will show you one more thing that R Markdown files can do: you can embed Shiny components in an R Markdown file to create an interactive report or slideshow.

Your report will be a complete Shiny app. In fact, R Markdown provides the easiest way to build light-weight Shiny apps. I will refer to apps that combine Shiny with R Markdown as interactive documents .

Interactive documents

You can make an R Markdown document interactive in two steps:

• add runtime: shiny to the document’s YAML header.
• add Shiny widgets and Shiny render functions to the file’s R code chunks

The rmarkdown package will compile your document into a reactive Shiny app. The document will look just as it would otherwise, but it will include reactive components.

runtime: shiny

Notify rmarkdown that your file contains Shiny components by adding runtime: shiny to the file’s YAML header. RStudio will change its “Knit” icon to a “Run Document” icon when you save this change.

“Run Document” is a cue that rmarkdown will no longer compile your document into a static file. Instead it will “run” the document as a live Shiny app.

Since the document is a Shiny app, you must render it into an HTML format. Do this by selecting either html_document or ioslides_presentation for your final output.

To add a widget to your document, call a Shiny widget function in an R code chunk. R Markdown will add the widget to the code chunk’s output.

For example, the file below creates an HTML document with two widgets.

The document looks like this when rendered. ( This is a static image of the output, the actual widgets are “live”; you can manipulate them ).

Rendered output

To add reactive output to your document, call one of the render* functions below in an R code chunk.

R Markdown will include the rendered output in the result of the code chunk.

This output will behave like rendered output in a standard Shiny app. The output will automatically update whenever you change a widget value or a reactive expression that it depends on.

The file below uses renderPlot to insert a histogram that reacts to the two widgets.

The document creates the app below when you click “Run Document.”

The structure of an interactive document

When you run an interactive document, rmarkdown extracts the code in your code chunks and places them into a pseudo server.R file. R Markdown uses the html output of the markdown file as an index.html file to place the reactive elements into.

As a result, outputs in one code chunk can use widgets and reactive expressions that occur in other code chunks.

Since the R Markdown document provides a layout for the app, you do not need to write a ui.R file.

Sharing interactive documents

Interactive documents are a type of Shiny app, which means that you can share them in the same way that you share other Shiny apps. You can

• Email a .Rmd file to a colleague. He or she can run the file locally by opening the file and clicking “Run Document”
• Host the document with Shiny Server or Shiny Server Pro
• Host the document at ShinyApps.io

Note: If you are familiar with R Markdown, you might expect RStudio to save an HTML version of an interactive document in your working directory. However, this only works with static HTML documents. Each interactive document must be served by a computer that manages the document. As a result, interactive documents cannot be shared as a standalone HTML file.

Interactive documents provide a new and easy way to make Shiny apps.

Interactive documents will not replace standard Shiny apps since they cannot provide the design options that come with a ui.R or index.html file. However, interactive documents do create some easy wins:

The R Markdown workflow makes it easy to build light-weight apps. You do not need to worry about laying out your app or building an HTML user interface for the app.

You can use R Markdown to create interactive slideshows, something that is difficult to do with Shiny alone. To create a slideshow, change output: html_document to output: ioslides_presentation in the YAML front matter of your .Rmd file. R Markdown will divide your document into slides when you click “Run Document.” A new slide will begin whenever a header or horizontal rule ( *** ) appears.

Interactive documents enhance the existing R Markdown workflow. R Markdown makes it easy to write literate programs and reproducible reports. You can make these reports even more effective by adding Shiny to the mix.

To learn more about R Markdown and interactive documents, please visit rmarkdown.rstudio.com .

rreporttools

An R package for reporting

View the Project on GitHub sachsmc/rreporttools

• Download ZIP File
• Download TAR Ball
• Fork On GitHub

Introduction and Installation

The goal of this package is to provide a set of tools to make analysts' lives easier. I've written a set of functions that I found to be helpful in performing common reporting tasks. These tasks include reading data from a database, performing simple analyses on a large number of subsets of the data, writing to/reading from MS Excel, and creating Shiny Applications .

My aim is for this package is to get analysts who are not experienced in R to use it regularly, thereby gaining experience. If you find these tools useful and they make your reporting life easier, I encourages you to learn more about R so that you can improve this package even further.

Using these basic tools requires some introductory knowledge of how to use R . I assume for the moment that you have R and R Studio installed on your computer. If not, go submit a ticket, and tell the IT person that open-source software is nothing to fear. (Aside: calling R “freeware” is offensive to the hundreds of developers and thousands of users. )

To get started, I strongly recommend reading R for Beginners . Even if you have used R before, you should read it, it's short and you might learn something new. For links to more detailed courses and reference material, see the table at the end of this document.

All set? Good, to install the rreporttools package, run the following commands:

If you get an error, the most likely cause is a mismatch of the Java and R architechtures. Chances are, you only have 32-bit Java installed, therefore you must run 32-bit R in order to use Java. To change this, go to Tools > Options and Change the R version , then restart Rstudio. If you have further problems with rJava, see this link help with rJava .

Load the package and check for errors.

Reading data into R

The first step to doing an analysis in R is getting the necessary data loaded. Typically, we need to load data from a database of some kind. rreporttools includes several functions to make that possible. These depend heavily on the RJDBC package .

To connect to an Oracle database using Java, you must find the path to the Oracle Java Database Connection (ojdbc) driver on your computer. On Windows, search for “ojdbc6.jar” from the start menu. Then tell the JDBC function where it is located. Finally, tell R what character to use for quoting. By convention, Oracle uses the single quote "'" . The following works on my computer for creating the driver object.

Once you have the driver set up, connecting to the database requires a particular server string. The following works for creating a connection object:

Test out your connection!

Don't forget to disconnect when you are done!

If you have some existing SQL queries in a sas file or in a .sql file, you can use this function to automatically convert them into R queries and optionally returning the results as data frames. See the help file for more information. This is experimental, so check your results carefully!

In R , a SQL query is simply a character object. Therefore, the variety of string manipulation tools in R are at your disposal to dynamically and programmatically modify SQL queries. Try doing that in SAS! For example, let's say we want to pull the tables that are owned by a specified user. First lets connect to an example database.

Now let's create a skeleton query, that we will then modify to specify the owner:

The %s is a placeholder for a string. We will use the sprintf function to replace that placeholder with the desired user:

Pretty cool! You can imagine creating a simple function that returns tables owned by the specified user:

This is a simple example, but there are many useful applications of this concept. For instance, loop through a long list of names and dynamically create queries to search the table using the like statement. Close matches were automatically returned. It saved me a ton of manual searching and copy-pasting. Think about how you could use this technique in one of your recent reports.

Don't forget to disconnect!

The Split \( \rightarrow \) Apply \( \rightarrow \) Combine model for Reporting

The split/apply/combine approach is nothing new, but it was made useful and easy to do in R by Hadley Wickham with the plyr package. The following links will get you up to speed:

• homepage for plyr
• short presentation

This package extends plyr by additionally computing sub-totals and totals. The same thing can be acheived with the data.table package, but faster and with a different syntax. See the functions ddplyWithTotals and DTplyWithTotals for usage and examples. A brief example is given below.

First examine the mtcars dataset, which is a famous data set that has the results of Motor Trends car road tests. It should be loaded automatically when you start R . If not, run data(mtcars) .

Now suppose you want to compute the average car weight by number of cylinders and number of gears. With plyr it's easy.

We can do the same thing with the data.table package, but note that the by variables must be converted to factors first.

Now, lets say we want to add the total.

Options allow for grand totals.

The nestedOnly option computes totals only for values nested within the outermost values. This is important as an option when there are three or more by variables.

The results here are the same, but for large data sets, you will need to use DT . However, DT requires the by variables to be factors, while ddply can deal with numeric variables.

Writing tables into Excel templates

If you do nothing else with R , I encourage you to use the automatic output and formatting functions that I've provided. This package includes an example template template, and can output a data frame to it with the appropriate formatting. Here is a quick example using the mtcars dataset.

Run it for yourself and check out the results. Additional format types are available, see the formatDataFrame help file for more details.

Writing Shiny Applications

To get started writing your own shiny app, we provide a skeleton app with an intialization function authorShiny . Give the function a folder name, and it will create the skeleton app, run it, and open the files that you will need to edit to customize your app. Try it out:

Details on authoring shiny apps are beyond the scope of this vignette. A good place to get started is with the shiny tutorial . For help, email me, or go to the google groups page for shiny . Interactive graphics can be achieved in various ways, but I have been using d3.js .

Additional Resources

This barely scratches the surface of what you can do with R , but hopefully it demostrates that R is very useful for creating accurate and reproducible reports. Additional reading for learning about R is in the table below. When in doubt, read the help files!

Introduction to Programming with R

Chapter 18 reporting.

In this chapter we will jump into ‘reporting’ using the R packages rmarkdown and knitr . This allows us to easily create (interactive) documents in different formats, such as HTML (like this document), PDF, or even Microsoft Word.

To increase the motivation: what we want to do is to bridge the gap between pure code (source code; .R scripts) and a readable file. This file can be for yourself, your colleague, sister, mum, or boss. It helps to structure and document code and possible solutions. As an illustrative example (Rhine river runoff forecasts): we would like to convert our code (left) into a nice html document (right):

18.1 Getting Started

As the title already indicates, R Markdown combines R and markdown. Markdown is a simple but powerful markup language . In contrast to programming languages, markup languages are used to structure text. Some well known markup languages are:

• markdown (which we will use here)
• HTML (HyperText Markup Language)

Markup languages have specific elements which do not count as text (content) of the document, but allow to e.g., specify some text as title, make text bold face or italic, or set up enumerated lists. One example: if we want to define a section title we use:

• # in markdown,
• <h1>...</h1> in HTML,
• or \section{...} in LaTeX.

Create New Document

While R scripts have the file ending .R , R markdown files have the ending .Rmd (for R Markdown). RStudio supports you creating these files. Let us create our first R Markdown file:

• Open your RStudio
• Create a new document by navigating through: Files > New File > R Markdown .
• A new window opens where you can select which type of document you want to create (Document, Presentation, Shiny, From Template). Select Document . You can also specify the title of the document and in which format it should be rendered. Leave this as it is for now.
• Press OK and you should end up with a new document (see below).
• Next step: save this new document ( File > Save ; or press the save icon). Save the file as test.Rmd (important is the file ending) on your harddisc as if you would save an .R script.

The first things we have to know

You should now see something very similar to the screenshot below. By default, RStudio creates an .Rmd document with some “demo-content”. Let us concentrate on the first few lines of the document. This differs from what we have learned from R scripts - --- or title: "Untitled" are no R commands and would result in an error.

Important : this is no R code. That’s the header (to be precise, a so called yml header). It defines the title of the document and how it should be rendered. In this case we would like to have an html_document at the end.

Knit! When writing an R script we have a “source” button to run the script. Here we now have a button called “Knit”. Knit takes our R markdown file and converts it, in this case it tries to create an html document (via the R package knitr ; thus “Knit”).

Try it. As RStudio gives us some demo content, we could try what’s happening if we knit it. Thus, press the “Knit” button. You will see some output in RStudio which comes from the knitr package. If everything works fine, RStudio opens the final html document and you should see this:

Fails? Well, it should not fail. However, if it does, there is an extra “R Markdown” output tab (bottom of your RStudio window). If errors occur, you should see the output down there.

In the example above we defined that we want to have an HTML file at the end ( output: html_document ). After knitting the document RStudio will automatically open the document. In addition, the HTML file is stored on your harddisc.

By default, an HTML file with the same name as your .Rmd file is stored in the same folder as your .Rmd file. In the example above:

• Our .Rmd file is called test.Rmd .
• In the same folder, you will now have a file test.html .

Markdown Syntax

Before we start mixing markdown and R , let’s introduce the markdown syntax. There is only a handfull of commands we need to know - depending on the software you use this list might be extended (special commands for special applications).

18.2 First Pure Markdown File

If you followed the guide above, you should have a file test.Rmd opened in your Rstudio. Let us adjust the .Rmd file and delete everything (in the code editor: simply mark everything and delete it) such that you end up with an empty document (see below).

We will now create new content for this document (pure markdown).

• Define new yml header with title, author, date, and output format.
• Below the yml header, add some content. What we want to have is a section “Introduction” and “Results” with some text content. Not very meaningful - but we will extend this further in a minute.

Write/copy the following lines of code into your test.Rmd file:

… such that you end up with this:

We can now render (knit) this file by pressing the “Knit” button again. If everything works well, a new window will pop up which looks as follows:

Great! First ( R ) markdown file rendered as HTML document. What have we done?

• Header: we defined a document title , author , and date .
• The first section (“Introduction”; heading 1 denoted by # ) contains some text and two links (to discdown and the definitive R markdown guide).
• The second section (“Results”; # ) itself has two subsections (heading 2, denoted by ## ) which contain an unordered list, and some text with text styling (bold face, italic, strike trough, and code).

So far, this has nothing to do with R markdown except that it is rendered via R and rmarkdown / knitr . But we could also do this without R (e.g., solely using pandoc ). Let’s dive a bit deeper and combine Markdown with R .

18.3 First R Markdown File

To create ‘dynamic’ documents we can combine pure Markdown with R chunks. A chunk is nothing else than a block of R code. R chunks are “parts of R scripts” and have to contain executeable R code and/or comments.

This allows us to execute R commands on the fly. Whenever the document is rendered (‘knitted’) the R commands (scripts) will be executed and embedded in the final document.

When to use?

R markdown is very handy for different tasks. This online learning resource ( discdown.org ) is completely written in R markdown. If you took the course “ Introduction to Programming: Programming in R ” at the Universität Innsbruck , everything - from the PDF slides to the exercises, and even the quizzes - is/was based on R markdown.

For yourself

Rather than solely writing an R script, you may write a small R markdown file for yourself. Instead of having a pure script file, R markdown allows you to add extra comments, thoughts, list current restrictions or problems, and even include plots and tables in a structured way.

For your friends

As for yourself, you may write R markdown documents to help out friends and colleagues, or to send a quick update to your boss or advisor. In my old days I (Reto) typically copy-pasted R code into e-mails or skype. That works OKish for simple things with only a hand full lines of code, however, it is hard to structure more complex code/solutions properly. In addition: if the client of your opponent converted everything into html, some parts of the code may be converted into smilies, beer mugs, or flamingos. Don’t believe me?

Try to send this message via skype:

The result is this:

Instead, we could write a short and simple .Rmd file which contains

• The result of the code.
• Additional explanations.
• And even some results (plots, tables).

For your job

More ‘advanced’; imagine working with live data which are getting updated every few hours, days, or weeks, and you do have to create a report for your boss. Or: you create a report for yourself to monitor the data. E.g.,:

• Biology : measuring some parameters of a process in the lab (temperature, nutrition, and the corresponding biomass in a test tube).
• Meteorology : live-measurements of the smog concentration in your city.
• Economics : monthly reports of credit/debit balances.
• Marketing : check if your spendings (advertisements) led to the desired result.
• Tourism : monitor the number of bookings/tickets sold for a specific event, hotel, or skiing ressort.
• Sales management : weekly reports of products sold, products in stock, and an analysis if you have to re-order new some products not to run out of stock.
• Web development : monitor the number of visits on your website and the user behaviour.

As you can see, this is not restricted to a specific field of research or industry. Nowadays, data are collected everywhere and dynamic reports (e.g., using R markdown) can be used to analyze these data - and that’s the basis of data science.

R Code Chunks

Whenever we create a new R Markdown file in RStudio, RStudio gives us a demo file with some content (shown above) which also contains some R chunks. R chunks are defined as follows:

As in pure markdown we open and close a code block with three backticks (see also Markdown Syntax ). To turn a markdown code block into an R chunk, we additionally have to set the {r} when opening the block. This tells knitr that this is code we want to execute. Within the curly braces we set additional options if needed ( {r, ...} ). By default:

• The input (code) will be shown in the final document.
• The output (prints) will be shown in the final document.
• In case there is a plot, the figure will also be included.

Exercise 18.1 Let’s try it out. Create a new .Rmd file. Specify at least a title and the output format in the yml header, and copy the code chunk shown above into this new .Rmd file.

Feel free to add additional content such as text or titles (headings). Once you’ve done, save the .Rmd file if you have not yet done it and knit the document by pressing the “Knit” button.

If everything works as expected, you should end up with an HTML document which shows the R input (code of the R chunk) and the results - in this case the result of print() and a simple figure created by plot() .

18.3.0.1 SourceCode

The source code/content of the .Rmd file:

18.3.0.2 RStudio

A screenshot of the file in RStudio:

18.3.0.3 Result

Code Chunk Options

Additional knitr-options can be provided to control the chunks. These options can be set globally (check ?knitr::opts_knit ) or individually for specific R chunks.

The options are set as key = value pairs within the curly braces ( {r, key = value, key = value, ...} ). The following list shows some of the most important options:

• include = FALSE : if set, the chunk is executed, but nothing shows up in the final document. Can be used to prepare data without including it int the output document.
• echo = FALSE : do not show the input ( R code). Default is TRUE .
• results = "hide" : do not show results (e.g., from print() ).
• fig.keep = "none" : do not include plots.
• fig.width = X : numeric, width of the resulting plot (in inches).
• fig.height = X : numeric, height of the resulting plot (in inches).

An example:

This should lead to the result shown below. As you can see, the R source code (input; echo = FALSE ) is not shown, neither the result of the print() call shows up (output; results = "hide" ). All we get at the end is the plot (format: \(8 \cdot 3\) inches; landscape).

18.3.1 Inline Code

In addition to R code chunks we can also execute R commands “inline”. Inline means “ in text ” and allows us to dynamically create text. As R code chunks are an extension of markdown code blocks, inline code is an extension of markdown inline code (see Markdown Syntax ).

Instead of having three backticks and {r} inline code is defined as follows:

The “r” tells knitr that this expression (code) must be executed when knitting the document. We can now use this in the text. As an example: the following line:

… will generate the following: Hy there, we wish you a Happy 2020! The code within the ticks is executed ( paste("Happy", 2020) ) and “inserted” into the text at this specific position. This can be used to print some numbers in the text (e.g., the current date, or the maximum of a specific variable), or more complex. With some logic we could adjust the text dynamically and generate data-dependent output.

As an example, let’s imagine we have a weather forecast for tomorrow and, depending on the forecasted temperature and sunshine duration, the text output should be different. Therefore, we could write a small function which we can use in combination with inline code:

We can now use the function inline (calling weather_forecast(-9.3, 0.2) or weather_forecast(8.3, 6.2) , …). Two examples:

Tomorrows weather forecast : Cold and only little sun expected for tomorrow. Suggestion: stay at home!

Tomorrows weather forecast : Warm an sunny! With 8.3 degrees Celsius and up to 6.2 hours of sunshine it will be a nice day.

18.3.2 Tables

knitr comes with a function called kable() to create html tables in the output. The input to kable() is typically a matrix or data frame. Let’s use the old faithful data set to demonstrate this feature:

Instead of printing the data frame as shown above, we can call kable(faithful) within the R chunk.

Typically you suppress the input ( R chunk options; set echo = FALSE ) to only show the table, but not the code. One problem with knitr::kable() : if you have dozends of observations, the resulting table will ge very large. Thus, this should be used for summary tables or results, and not to show large data frames or matrices. Other contributed packages (e.g., DT ) provide more functionality to create tables. Just as a teaser:

reporttools: Generate "LaTeX"" Tables of Descriptive Statistics

These functions are especially helpful when writing reports of data analysis using "Sweave".

Documentation:

Please use the canonical form https://CRAN.R-project.org/package=reporttools to link to this page.

• R Reporting Part 1: Tools

This is the first of a series of article on how to use R , RStudio and TexMaker to prepare presentations and batch jobs for automated reporting on a web server or Microsoft SharePoint server. The series is based upon the presentation that I did at the February 27, 2016 Dallas R User Group Meetup . Because the presentation was primarily a demonstration, there really isn’t a presentation to distribute; this series covers the topics from the presentation/demonstration. The series will eventually include the following articles as I complete them over the next couple of weeks:

• R Reporting Part 2: Choosing the Right Markup for the Task
• R Reporting Part 3: Using Rhtml for Batch Web Reporting
• R Reporting Part 4: Using Markdown for Interactive Presentations
• R Reporting Part 5: Using LaTeX/Beamer for PDF Presentations
• R Reporting Part 6: Using LaTeX for PDF Articles
• R Reporting Part 7: Converting R Documents to E-books
• R Reporting Part 8: Using LaTeX to Create Posters

The series of articles describes the process for daily batch jobs that generate the Daily Econometric Graphs web page which includes links to the same econometric charts in several formats, all generated through the same R code:

• Econometric graphs in PDF form for use as slides on a projector
• Econometric graphs in PDF form for printing
• Econometric graphs in EPUB format
• Econometric graphs in Kindle AZW3 format
• Econometric graphs in A0 poster format

All of the examples are based upon the knitr R package; you should reference the knitr documentation, as this article is not a replacement for the knitr documentation.

Software to Install

To use R for presentations and batch reports, there are a number of software applications that you will need install on your desktop and your web server. The sections that following describe the installation of the various packages that you will need. All of the software applications in this section are available on Linux, Windows and OS X.

R and Related Packages

First you will need to install R from CRAN . Install files and instructions are available on the various CRAN mirrors.

RStudio is a popular integrated development environment (IDE) for R, although is not required for any of the presentations here, it makes a number of things very convenient. Other R IDEs are Emacs Speaks Statistics , which has the advantage of working with other statistical and programming languages. Eclipse users should look at StatET for R . The examples for this series are done in RStudio, but would work in the other environments with minimal modification.

Once you have R and RStudio installed, you will need to install knitr , and sweave for any presentation or reporting use, and you will need the fImport, ggplot2 packages to run the examples in this series of articles. Use the following command to install the packages:

The HTML Editor of Your Choice

Although RStudio is a great IDE for R, it does not do a good job of HTML sytax highlighting and spell checking. Once you have the R portions of your code working well, you will want to use a dedicated HTML editor to do the writing, and HTML formatting in your Rhtml documents. You can use any editor that you want; Bluefish is available on Linux, Windows and OS X.

If you need heavy formating, tables of contents, figure cross references and bibliography management in your presentations and reports, you will want to use LaTeX . It was developed primarily for accedemic writing for math and science and thus does a very good job of handling mathmatical symbols and equations, automatic tables of contents, indexing, cross referencing, bibliography, and citation. It is a tag language like HTML.

In Linux, most package managers will allow the easy install of the TeXLive distribution, although not necessarily the most recent one. On Windows, MikTeX is the preferred way to install LaTeX, but you can also install it via Cygwin. On OS X, MacTeX and MacPorts are perhaps the most convenient ways to install the LaTeX distribution. You should install the Beamer package; it is not part of the default installation.

If will be using LaTeX for presentations and articles, you will want to use embed your R code in LaTeX documents ; although RStudio has great capabilities for the R portion of this workflow, at the point that you start working on the writing tasks, you will want to begin using a LaTeX IDE. Texmaker runs on Linux, Windows and OS X; it allows you to run R using sweave and knitr in the same way that RStudio does, but has spell checking features that make working with the LaTeX document easier.

as shown in Figure 2. The final configuration step is changing the Quick Build (F1) key to run Sweave/Knitr before running pdflatex as shown in Figure 3.

Secure File Transfer Utilities–scp, rsync or Something Else

For batch reporting through cron or some other scheduler, you will almost invariably need some way to transfer files between systems. Secure copy or scp is probably the most universal way to do this. It is installed by default on most Linux and OS X systems. On Windows, scp is available as part of Cygwin . In a corporate Windows environment, you should talk to you IT group about what tools to use on your network; in Windows environments, shared drives are a common way to handle file copies. Another alternative is rsync which routinely available on Linux; for OS X, it can be installed via MacPorts while on Windows it can be installed via Cygwin.

For scp and rsync , you will want to use ssh-keygen to allow secure connections without using passwords and potentially ssh-agent for additional security .

Optipng and Other Image Compression Tools

The PNG and other images that R generates are not compressed as fully as is possible. To speed up web pages, you will want to compress images before uploading them using optipng or some other compression optimization tool. Optipng is available routinely in Linux, Cygwin and MacPorts. To call it in R use

where images/figures/*.png is the path to the image files that your R script created.

ImageMagick Image Resizing and Conversion Tools

For web applications, you will probably want additional image sizes for use in links that are specific for different social media sites. ImageMagick is the most convenient tool for converting and resizing images in a script. It is available for Linux, Windows (Cygwin) and OS X (MacPorts). To use it in R to create an 450 pixel image for use in Facebook or some other social media site in R code, you would use something like

Calibre and latex2html for E-book Tools

To create ebooks in EPUB for most e-readers and AZW3 for Kindle e-readers, you will need latex2html (or some other LaTeX to HTML converter) and Calibre . LaTeX2html is not being actively maintained so it is not a good choice for a production environment, but it is available for all platforms.

Server Side Include Software on Web Server

If you are posting your R document to a webserver running a content management system like Wordpress , Joomla or Drupal , you will need an extension to enable server side includes. This will probably require higher administrative rights than is typically given to normal authors, so check with your CMS administrator before you start on a big project. On Joomla, Sourcerer is one of several extensions that allow server side includes. It uses the syntax

• Open Source
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NHS England R Community

Welcome to the NHS England R community documentation site

This site is open to all to share good practice, basic knowledge around access and using R across NHS England platforms, and creating a place for us to share our collective knowledge, code, resources and content. 

The NHS England R Community is not intended to replace any other R user groups - there’s a fantastic national NHS-R Community and lots of local sharing of R work, we want to use this site to signpost to other resources for those specifically using R within NHS England.

It is a community - and we need your help

• Please share and invite other R users to our FutureNHS page
• Please use the forum , ask questions and engage!
• Please share code, your good practice examples, and your learning journey in our GitHub repository

What is R and why is it used at NHSE?

R is an open-source programming language that is widely used among statisticians and data scientists in the NHS. R has a large number of built-in functions and packages for statistical analysis and data visualisation. Along with tools such as Python 1 , R can be used to develop so-called reproducible analytical pipelines (RAP).

Reproducible analytical pipelines are the gold standard for creating analytical outputs in government 2 . It is a set of standards that promote best practice across the sector 3 . By following RAP we can be much more transparent with how we work, increasing trust and confidence in our publications, and make it easier for others to verify and replicate our analysis.

Example analytical reports

Below is an example of an analytical report that is fully open-source and RAP compliant developed using R , Quarto , and plotly , and open data from the NHS-R Community . The NHS theme used in this report is available on the NHS-R Community GitHub repository .

In development:

• ICB / SICBL
• Trust / Ward etc
• Using {targets} we can define dependencies in the data processing stages and process these reports automatically when new data is available.
• A standard set of reporting charts and tables can be pre-configured using plotly , adjusting the colors, fonts, annotations, and labels to match the NHS style .
• A shared library of unit-tested functions allows complex analysis to be run with every calculation fully validated and documented.
• Quality assurance can also be built into these functions so that logging, data validation, and schema checks can be run to ensure that our data pipelines are functioning correctly, and all the data follows the correct structure and format.

Contributing

Contributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated .

See CONTRIBUTING.md for detailed guidance.

Distributed under the MIT License. See LICENSE.md for more information.

No private or patient data are shared in this repository.

See the NHS Python Community ↩︎

See Professor Ben Goldacre’s Better, Broader, Safer review into how the efficient and safe use of health data for research and analysis can benefit patients and the healthcare sector. ↩︎

See NHS Digital’s RAP Community of Practice guide on the levels of RAP . ↩︎

Watch CBS News

"Mammoth" carbon capture facility launches in Iceland, expanding one tool in the climate change arsenal

Updated on: May 10, 2024 / 10:41 AM EDT / CBS/AFP

Hellisheidi, Iceland —  With Mammoth's 72 industrial fans, Swiss start-up Climeworks intends to suck almost 40,000 tons of CO2 from the air annually to bury underground, vying to prove the technology has a place in the fight against global warming. Mammoth, the largest carbon dioxide capture and storage facility of its kind, launched operations this week situated on a dormant volcano in Iceland.

The facility adds significant capacity to Climework's first project, Orca, which also sucks the primary greenhouse gas that is fueling climate change out of the atmosphere.

How does Climeworks capture CO2?

Just 31 miles from an active volcano, the seemingly risky site was chosen for its proximity to the Hellisheidi geothermal energy plant necessary to power the facility's fans and heat chemical filters to extract CO2 with water vapor.

The CO2 is then separated from the steam and compressed in a hangar where huge pipes crisscross.

Finally, the gas is dissolved in water and pumped underground with a "sort of giant SodaStream," said Bergur Sigfusson, chief system development officer for Carbfix, which developed the process.  

A well, drilled under a futuristic-looking dome, injects the water 2,300 feet down into the volcanic basalt that makes up 90% of Iceland's subsoil, where it reacts with the magnesium, calcium and iron in the rock to form crystals — solid reservoirs of CO2.

There are a number of other CO2 capture technologies being put to use around the world, including in the U.S., where the Biden administration has committed nearly $4 billion to jumpstart the industry.

The methods range from warehouses full of stacked limestone blocks that absorb CO2 like sponges to burying compressed industrial and agricultural waste to lock the gas away for centuries.

Lofty carbon capture ambitions

For the world to achieve "carbon neutrality" by 2050, "we should be removing something like six to 16 billion tonnes [17.6 U.S. tons] of CO2 per year from the air," said Jan Wurzbacher, co-founder and co-chief of Climeworks, at the inauguration of the first 12 container fans at Mammoth.

"I quite strongly believe that a large share of these... need to be covered by technical solutions," he said.

"Not we alone, not as a single company. Others should do that as well," he added, setting his start-up of 520 employees the goal of surpassing millions of tons by 2030 — and approaching a billion by 2050.

Speaking last year with CBS' 60 Minutes, Climeworks' chief technology officer Carolos Haertel said that technically the scaling up process can be done on a global scale — but he also said a single company can't do it, and he hinted that political will must also be behind the initiatives. 

"Whether we are taking the right direction will depend as much on societal things than on technical matters," Haertel told 60 Minutes' Bill Whitaker at the Orca facility. "Am I optimistic as an engineer? I am, absolutely. Am I optimistic as a citizen? Maybe half-half. I haven't made my mind up yet."

Three years after opening Orca, Climeworks will increase its capacity from about 4,409 to 44,000 tons of CO2 captured annually once Mammoth is at full capacity — but that represents just seconds of the world's actual emissions.

One of the companies interviewed by CBS News in 2023 about its plans to ramp up carbon capture operations said it hopes to eventually be locking away 50,000 tons of CO2 per year.

Only part of the solution to address emissions

According to the Intergovernmental Panel on Climate Change (IPCC), the United Nations' climate expert body, carbon removal technologies will be necessary to meet the targets of the 2015 Paris Agreement, but major reductions of emissions are the priority.

The role of direct air capture with carbon storage (DACCS) remains minor in the various climate models due to its high price, and its deployment on a large scale depends on the availability of renewable energy to power it.

Climeworks is a pioneer, with the two first plants in the world to have surpassed the pilot stage at a cost of around $1,000 per ton captured. Wurzbacher expects that cost to decline to just $300 by 2030.

More than 20 new infrastructure projects, developed by various players and combining direct capture and storage, should be operational worldwide by 2030, with a combined capacity of around 11 million of tons.

"We need probably around $10 billion to proceed over the next decade to deploy our assets" in the United States, Canada, Norway, Oman and Kenya, said Christoph Gebald, Climeworks co-founder and co-chief. That's 10 times what the company has already raised.

"When I'm standing now at Orca I think: 'Oh this looks like a little bit like Lego bricks'. It's a tiny thing compared to Mammoth," Wurzbacher said.

Lego bought carbon credits generated by Climeworks for every ton of CO2 stored. The credits are a way of making the solution known to the general public, Gebald said, who has not ruled out selling credits to "big polluters" as well.

Critics of the technology point to the risk of giving them "license to pollute" or diverting billions of dollars that could be better invested in readily available technology such as renewable energy or electric vehicles.

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“From R to your manuscript”

report ’s primary goal is to bridge the gap between R’s output and the formatted results contained in your manuscript. It automatically produces reports of models and data frames according to best practices guidelines (e.g., APA ’s style), ensuring standardization and quality in results reporting.

Installation

The package is available on CRAN and can be downloaded by running:

If you would instead like to experiment with the development version, you can download it from GitHub :

Load the package every time you start R

Tip Instead of library(report) , use library(easystats) . This will make all features of the easystats-ecosystem available. To stay updated, use easystats::install_latest() .

Documentation

The package documentation can be found here .

Report all the things

General workflow.

The report package works in a two step fashion. First, you create a report object with the report() function. Then, this report object can be displayed either textually (the default output) or as a table, using as.data.frame() . Moreover, you can also access a more digest and compact version of the report using summary() on the report object.

The report() function works on a variety of models, as well as other objects such as dataframes:

These reports nicely work within the tidyverse workflow:

t -tests and correlations

Reports can be used to automatically format tests like t -tests or correlations.

As mentioned, you can also create tables with the as.data.frame() functions, like for example with this correlation test:

This works great with ANOVAs, as it includes effect sizes and their interpretation.

Generalized Linear Models (GLMs)

Reports are also compatible with GLMs, such as this logistic regression :

Mixed Models

Mixed models, whose popularity and usage is exploding, can also be reported:

Bayesian Models

Bayesian models can also be reported using the new SEXIT framework, which combines clarity, precision and usefulness.

Other types of reports

Specific parts.

One can, for complex reports, directly access the pieces of the reports:

Report participants’ details

This can be useful to complete the Participants paragraph of your manuscript.

Report sample

Report can also help you create a sample description table (also referred to as Table 1 ).

Report system and packages

Finally, report includes some functions to help you write the data analysis paragraph about the tools used.

If you like it, you can put a star on this repo, and cite the package as follows:

report is a young package in need of affection . You can easily be a part of the developing community of this open-source software and improve science! Don’t be shy, try to code and submit a pull request (See the contributing guide ). Even if it’s not perfect, we will help you make it great!

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From The Upshot: What the Data Says

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10 Years, 100 Stories: Ten years ago, The New York Times introduced the Upshot. Here’s a collection of its most distinctive work  from the last decade.

Rent or Buy? : The choice between buying a home and renting one is among the biggest financial decisions that many adults make. Our calculator can help .

Employment Discrimination: Researchers sent 80,000 fake résumés to some of the largest companies in the United States. They found that some discriminated against Black applicants much more than others .

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Dialect Quiz:  What does the way you speak say about where you’re from? Answer these questions to find out .