Nat  Grady

Nat Grady

1667456340

Papaja: Prepare APA Journal Articles with R Markdown

Papaja: Prepare APA Journal Articleswith R Markdown 

papaja is an award-winning R package that facilitates creating computationally reproducible, submission-ready manuscripts which conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). papaja provides

  • an R Markdown template that can be used with (or without) RStudio to create PDF documents (using the apa6 LaTeX class) or Word documents (using a .docx-reference file).
  • Functions to typeset the results from statistical analyses,
  • functions to create tables, and
  • functions to create figures in accordance with APA guidelines.

For a comprehensive introduction to papaja, see the current draft of the manual. If you have a specific question that is not answered in the manual, feel free to ask a question on Stack Overflow using the papaja tag. If you believe you have found a bug or would like to request a new feature, open an issue on Github and provide a minimal complete verifiable example.

Example

Take a look at the source file of the package vignette and the resulting PDF. The vignette also contains some basic instructions.

Installation

To use papaja you need either a recent version of RStudio or pandoc. If you want to create PDF- in addition to DOCX-documents you additionally need a TeX distribution. We recommend you use TinyTex, which can be installed from within R:

if(!requireNamespace("tinytex", quietly = TRUE)) install.packages("tinytex")

tinytex::install_tinytex()

You may also consider MikTeX for Windows, MacTeX for Mac, or TeX Live for Linux. Please refer to the papaja manual for detailed installation instructions.

papaja is available on CRAN but you can also install it from the GitHub repository:

# Install latest CRAN release
install.packages("papaja")

# Install remotes package if necessary
if(!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")

# Install the stable development version from GitHub
remotes::install_github("crsh/papaja")

# Install the latest development snapshot from GitHub
remotes::install_github("crsh/papaja@devel")

Usage

Once papaja is installed, you can select the APA template when creating a new R Markdown file through the RStudio menus.

APA template selection dialog

To add citations, specify your bibliography-file in the YAML front matter of the document (bibliography: my.bib) and start citing (for details, see pandoc manual on the citeproc extension. You may also be interested in citr, an R Studio addin to swiftly insert Markdown citations and R Studio’s visual editor, which also enables swiftly inserting citations.

Typeset analysis results

The functions apa_print() and apa_table() facilitate reporting results of your analyses. When you pass the an output object of a supported class, such as an htest- or lm-object, to apa_print(), it will return a list of character strings that you can use to report the results of your analysis.

my_lm <- lm(
  Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length
  , data = iris
)
apa_lm <- apa_print(my_lm)

apa_lm$full_result$Sepal_Length
## [1] "$b = 0.61$, 95\\% CI $[0.48, 0.73]$, $t(146) = 9.77$, $p < .001$"

papaja currently provides methods for the following object classes:

A-BD-LL-SS-Z
afex_aovdefaultlsmobjsummary.aovlist
anovaemmGridmanovasummary.glht
anova.lmeglhtmerModsummary.glm
Anova.mlmglmmixedsummary.lm
aovhtestpapaja_wscisummary.manova
aovlistlistsummary_emmsummary.ref.grid
BFBayesFactorlmsummary.Anova.mlm 
BFBayesFactorToplmesummary.aov 

Create tables

apa_table() may be used to produce publication-ready tables in an R Markdown document. For instance, you might want to report some condition means (with standard errors).

library("dplyr")
npk |>
  group_by(N, P) |>
  summarise(mean = mean(yield), se = sd(yield) / sqrt(length(yield)), .groups = "drop") |>
  label_variables(N = "Nitrogen", P = "Phosphate", mean = "*M*", se = "*SE*") |>
  apa_table(caption = "Mean pea yield (with standard errors)")

Table 1. Mean pea yield (with standard errors)

NitrogenPhosphateMSE
0051.721.88
0152.422.65
1059.222.66
1156.152.08

This is a fairly simple example, but apa_table() may be used to generate more complex tables.

apa_table(), of course, plays nicely with the output from apa_print(). Thus, it is possible to conveniently report complete regression tables, ANOVA tables, or the output from mixed-effects models.

lm(Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length, data = iris) |>
  apa_print() |>
  apa_table(caption = "Iris regression table.")

Table 2. Iris regression table.

Predictorb95% CItdfp
Intercept1.04[0.51, 1.58]3.85146< .001
Sepal Length0.61[0.48, 0.73]9.77146< .001
Petal Width0.56[0.32, 0.80]4.55146< .001
Petal Length-0.59[-0.71, -0.46]-9.43146< .001

Create figures

papaja further provides functions to create publication-ready plots. For example, you can use apa_barplot(), apa_lineplot(), and apa_beeplot() (or the general function apa_factorial_plot()) to visualize the results of factorial study designs:

apa_beeplot(
  data = stroop_data
  , dv = "response_time"
  , id = "id"
  , factors = c("congruency", "load")
  , ylim = c(0, 800)
  , dispersion = wsci # within-subjects confidence intervals
  , conf.level = .99
  , las = 1
)

Response times from a simulated Stroop experiment. Large dots represent condition means, small dots represent individual participants’ mean response time. Error bars represent 99% within-subjects confidence intervals.

If you prefer ggplot2, try theme_apa().

library("ggplot2")
library("ggforce")

p <- ggplot(
  stroop_data
  , aes(x = congruency, y = response_time, shape = load, fill = load)
) +
  geom_violin(alpha = 0.2, color = grey(0.6)) +
  geom_sina(color = grey(0.6)) +
  stat_summary(position = position_dodge2(0.95), fun.data = mean_cl_normal) +
  lims(y = c(0, max(stroop_data$response_time))) +
  scale_shape_manual(values = c(21, 22)) +
  scale_fill_grey(start = 0.6, end = 1) +
  labs(
    x = "Congruency"
    , y = "Response time"
    , shape = "Cognitive load"
    , fill = "Cognitive load"
  )

p + theme_apa()

Usage without RStudio

Don’t use RStudio? No problem. Use the rmarkdown::render function to create articles:

# Create new R Markdown file
rmarkdown::draft(
  "mymanuscript.Rmd"
  , "apa6"
  , package = "papaja"
  , create_dir = FALSE
  , edit = FALSE
)

# Render manuscript
rmarkdown::render("mymanuscript.Rmd")

Getting help

For a comprehensive introduction to papaja, check out the current draft of the papaja manual. If you have a specific question that is not answered in the manual, feel free to ask a question on Stack Overflow using the papaja tag. If you believe you have found a bug or you want to request a new feature, open an issue on Github and provide a minimal complete verifiable example.

Citation

Please cite papaja if you use it.

Aust, F. & Barth, M. (2022). papaja: Prepare reproducible APA journal articles with R Markdown. R package version 0.1.0.9999. Retrieved from https://github.com/crsh/papaja

For convenience, you can use cite_r() or copy the reference information returned by citation('papaja') to your BibTeX file:


@Manual{,
  title = {{papaja}: {Prepare} reproducible {APA} journal articles with {R Markdown}},
  author = {Frederik Aust and Marius Barth},
  year = {2022},
  note = {R package version 0.1.0.9999},
  url = {https://github.com/crsh/papaja},
}

papaja in the wild

If you are interested in seeing how others are using papaja, you can find a collection of papers and the corresponding R Markdown files in the manual.

If you have published a paper that was written with papaja, please add the reference to the public Zotero group yourself or send us to me.

Computational reproducibility

To ensure mid- to long-term computational reproducibility we highly recommend conserving the software environment used to write a manuscript (e.g. R and all R packages) either in a software container or a virtual machine. This way you can be sure that your R code does not break because of updates to R or any R package. For a brief primer on containers and virtual machines see the supplementary material by Klein et al. (2018).

Docker is the most widely used containerization approach. It is open source and free to use but requires some disk space. CodeOcean is a commercial service that builds on Docker, facilitates setting up and sharing containers and lets you run computations in the cloud. See the papaja manual on how to get started using papaja with Docker or CodeOcean and our Docker workflow tailored for easy use with papaja.

Contribute 

Like papaja and want to contribute? We highly appreciate any contributions to the R package or its documentation. Take a look at the open issues if you need inspiration. There are many additional analyses that we would like apa_print() to support. Any new S3/S4-methods for this function are always appreciated (e.g., factanal, fa, lavaan). For a primer on adding new apa_print()-methods, see the getting-started-vignette:

vignette("extending_apa_print", package = "papaja")

Before working on a contribution, please review our brief contributing guidelines and code of conduct.

Related R packages

By now, there are a couple of R packages that provide convenience functions to facilitate the reporting of statistics in accordance with APA guidelines.

  • apa: Format output of statistical tests in R according to APA guidelines
  • APAstats: R functions for formatting results in APA style and other stuff
  • apaTables: Create American Psychological Association (APA) Style Tables
  • rempsyc: Convenience functions for psychology
  • sigr: Concise formatting of significances in R

If you are looking for other journal article templates, you may be interested in the rticles package.

Package dependencies

Download Details:

Author: crsh
Source Code: https://github.com/crsh/papaja 
License: View license

#r #research #markdown 

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Papaja: Prepare APA Journal Articles with R Markdown

CSharp REPL: A Command Line C# REPL with Syntax Highlighting

C# REPL

A cross-platform command line REPL for the rapid experimentation and exploration of C#. It supports intellisense, installing NuGet packages, and referencing local .NET projects and assemblies.

C# REPL screenshot 

(click to view animation)

C# REPL provides the following features:

  • Syntax highlighting via ANSI escape sequences
  • Intellisense with fly-out documentation
  • Nuget package installation
  • Reference local assemblies, solutions, and projects
  • Navigate to source via Source Link
  • IL disassembly (both Debug and Release mode)
  • Fast and flicker-free rendering. A "diff" algorithm is used to only render what's changed.

Installation

C# REPL is a .NET 6 global tool, and runs on Windows 10, Mac OS, and Linux. It can be installed via:

dotnet tool install -g csharprepl

If you're running on Mac OS Catalina (10.15) or later, make sure you follow any additional directions printed to the screen. You may need to update your PATH variable in order to use .NET global tools.

After installation is complete, run csharprepl to begin. C# REPL can be updated via dotnet tool update -g csharprepl.

Usage:

Run csharprepl from the command line to begin an interactive session. The default colorscheme uses the color palette defined by your terminal, but these colors can be changed using a theme.json file provided as a command line argument.

Evaluating Code

Type some C# into the prompt and press Enter to run it. The result, if any, will be printed:

> Console.WriteLine("Hello World")
Hello World

> DateTime.Now.AddDays(8)
[6/7/2021 5:13:00 PM]

To evaluate multiple lines of code, use Shift+Enter to insert a newline:

> var x = 5;
  var y = 8;
  x * y
40

Additionally, if the statement is not a "complete statement" a newline will automatically be inserted when Enter is pressed. For example, in the below code, the first line is not a syntactically complete statement, so when we press enter we'll go down to a new line:

> if (x == 5)
  | // caret position, after we press Enter on Line 1

Finally, pressing Ctrl+Enter will show a "detailed view" of the result. For example, for the DateTime.Now expression below, on the first line we pressed Enter, and on the second line we pressed Ctrl+Enter to view more detailed output:

> DateTime.Now // Pressing Enter shows a reasonable representation
[5/30/2021 5:13:00 PM]

> DateTime.Now // Pressing Ctrl+Enter shows a detailed representation
[5/30/2021 5:13:00 PM] {
  Date: [5/30/2021 12:00:00 AM],
  Day: 30,
  DayOfWeek: Sunday,
  DayOfYear: 150,
  Hour: 17,
  InternalKind: 9223372036854775808,
  InternalTicks: 637579915804530992,
  Kind: Local,
  Millisecond: 453,
  Minute: 13,
  Month: 5,
  Second: 0,
  Ticks: 637579915804530992,
  TimeOfDay: [17:13:00.4530992],
  Year: 2021,
  _dateData: 9860951952659306800
}

A note on semicolons: C# expressions do not require semicolons, but statements do. If a statement is missing a required semicolon, a newline will be added instead of trying to run the syntatically incomplete statement; simply type the semicolon to complete the statement.

> var now = DateTime.Now; // assignment statement, semicolon required

> DateTime.Now.AddDays(8) // expression, we don't need a semicolon
[6/7/2021 5:03:05 PM]

Keyboard Shortcuts

  • Basic Usage
    • Ctrl+C - Cancel current line
    • Ctrl+L - Clear screen
    • Enter - Evaluate the current line if it's a syntactically complete statement; otherwise add a newline
    • Control+Enter - Evaluate the current line, and return a more detailed representation of the result
    • Shift+Enter - Insert a new line (this does not currently work on Linux or Mac OS; Hopefully this will work in .NET 7)
    • Ctrl+Shift+C - Copy current line to clipboard
    • Ctrl+V, Shift+Insert, and Ctrl+Shift+V - Paste text to prompt. Automatically trims leading indent
  • Code Actions
    • F1 - Opens the MSDN documentation for the class/method under the caret (example)
    • F9 - Shows the IL (intermediate language) for the current statement in Debug mode.
    • Ctrl+F9 - Shows the IL for the current statement with Release mode optimizations.
    • F12 - Opens the source code in the browser for the class/method under the caret, if the assembly supports Source Link.
  • Autocompletion
    • Ctrl+Space - Open autocomplete menu. If there's a single option, pressing Ctrl+Space again will select the option
    • Enter, Right Arrow, Tab - Select active autocompletion option
    • Escape - closes autocomplete menu
  • Text Navigation
    • Home and End - Navigate to beginning of a single line and end of a single line, respectively
    • Ctrl+Home and Ctrl+End - Navigate to beginning of line and end across multiple lines in a multiline prompt, respectively
    • Arrows - Navigate characters within text
    • Ctrl+Arrows - Navigate words within text
    • Ctrl+Backspace - Delete previous word
    • Ctrl+Delete - Delete next word

Adding References

Use the #r command to add assembly or nuget references.

  • For assembly references, run #r "AssemblyName" or #r "path/to/assembly.dll"
  • For project references, run #r "path/to/project.csproj". Solution files (.sln) can also be referenced.
  • For nuget references, run #r "nuget: PackageName" to install the latest version of a package, or #r "nuget: PackageName, 13.0.5" to install a specific version (13.0.5 in this case).

Installing nuget packages

To run ASP.NET applications inside the REPL, start the csharprepl application with the --framework parameter, specifying the Microsoft.AspNetCore.App shared framework. Then, use the above #r command to reference the application DLL. See the Command Line Configuration section below for more details.

csharprepl --framework  Microsoft.AspNetCore.App

Command Line Configuration

The C# REPL supports multiple configuration flags to control startup, behavior, and appearance:

csharprepl [OPTIONS] [response-file.rsp] [script-file.csx] [-- <additional-arguments>]

Supported options are:

  • OPTIONS:
    • -r <dll> or --reference <dll>: Reference an assembly, project file, or nuget package. Can be specified multiple times. Uses the same syntax as #r statements inside the REPL. For example, csharprepl -r "nuget:Newtonsoft.Json" "path/to/myproj.csproj"
      • When an assembly or project is referenced, assemblies in the containing directory will be added to the assembly search path. This means that you don't need to manually add references to all of your assembly's dependencies (e.g. other references and nuget packages). Referencing the main entry assembly is enough.
    • -u <namespace> or --using <namespace>: Add a using statement. Can be specified multiple times.
    • -f <framework> or --framework <framework>: Reference a shared framework. The available shared frameworks depends on the local .NET installation, and can be useful when running an ASP.NET application from the REPL. Example frameworks are:
      • Microsoft.NETCore.App (default)
      • Microsoft.AspNetCore.All
      • Microsoft.AspNetCore.App
      • Microsoft.WindowsDesktop.App
    • -t <theme.json> or --theme <theme.json>: Read a theme file for syntax highlighting. This theme file associates C# syntax classifications with colors. The color values can be full RGB, or ANSI color names (defined in your terminal's theme). The NO_COLOR standard is supported.
    • --trace: Produce a trace file in the current directory that logs CSharpRepl internals. Useful for CSharpRepl bug reports.
    • -v or --version: Show version number and exit.
    • -h or --help: Show help and exit.
  • response-file.rsp: A filepath of an .rsp file, containing any of the above command line options.
  • script-file.csx: A filepath of a .csx file, containing lines of C# to evaluate before starting the REPL. Arguments to this script can be passed as <additional-arguments>, after a double hyphen (--), and will be available in a global args variable.

If you have dotnet-suggest enabled, all options can be tab-completed, including values provided to --framework and .NET namespaces provided to --using.

Integrating with other software

C# REPL is a standalone software application, but it can be useful to integrate it with other developer tools:

Windows Terminal

To add C# REPL as a menu entry in Windows Terminal, add the following profile to Windows Terminal's settings.json configuration file (under the JSON property profiles.list):

{
    "name": "C# REPL",
    "commandline": "csharprepl"
},

To get the exact colors shown in the screenshots in this README, install the Windows Terminal Dracula theme.

Visual Studio Code

To use the C# REPL with Visual Studio Code, simply run the csharprepl command in the Visual Studio Code terminal. To send commands to the REPL, use the built-in Terminal: Run Selected Text In Active Terminal command from the Command Palette (workbench.action.terminal.runSelectedText).

Visual Studio Code screenshot

Windows OS

To add the C# REPL to the Windows Start Menu for quick access, you can run the following PowerShell command, which will start C# REPL in Windows Terminal:

$shell = New-Object -ComObject WScript.Shell
$shortcut = $shell.CreateShortcut("$env:appdata\Microsoft\Windows\Start Menu\Programs\csharprepl.lnk")
$shortcut.TargetPath = "wt.exe"
$shortcut.Arguments = "-w 0 nt csharprepl.exe"
$shortcut.Save()

You may also wish to add a shorter alias for C# REPL, which can be done by creating a .cmd file somewhere on your path. For example, put the following contents in C:\Users\username\.dotnet\tools\csr.cmd:

wt -w 0 nt csharprepl

This will allow you to launch C# REPL by running csr from anywhere that accepts Windows commands, like the Window Run dialog.

Comparison with other REPLs

This project is far from being the first REPL for C#. Here are some other projects; if this project doesn't suit you, another one might!

Visual Studio's C# Interactive pane is full-featured (it has syntax highlighting and intellisense) and is part of Visual Studio. This deep integration with Visual Studio is both a benefit from a workflow perspective, and a drawback as it's not cross-platform. As far as I know, the C# Interactive pane does not support NuGet packages or navigating to documentation/source code. Subjectively, it does not follow typical command line keybindings, so can feel a bit foreign.

csi.exe ships with C# and is a command line REPL. It's great because it's a cross platform REPL that comes out of the box, but it doesn't support syntax highlighting or autocompletion.

dotnet script allows you to run C# scripts from the command line. It has a REPL built-in, but the predominant focus seems to be as a script runner. It's a great tool, though, and has a strong community following.

dotnet interactive is a tool from Microsoft that creates a Jupyter notebook for C#, runnable through Visual Studio Code. It also provides a general framework useful for running REPLs.

Download Details:
Author: waf
Source Code: https://github.com/waf/CSharpRepl
License: MPL-2.0 License

#dotnet  #aspdotnet  #csharp 

Nat  Grady

Nat Grady

1667456340

Papaja: Prepare APA Journal Articles with R Markdown

Papaja: Prepare APA Journal Articleswith R Markdown 

papaja is an award-winning R package that facilitates creating computationally reproducible, submission-ready manuscripts which conform to the American Psychological Association (APA) manuscript guidelines (6th Edition). papaja provides

  • an R Markdown template that can be used with (or without) RStudio to create PDF documents (using the apa6 LaTeX class) or Word documents (using a .docx-reference file).
  • Functions to typeset the results from statistical analyses,
  • functions to create tables, and
  • functions to create figures in accordance with APA guidelines.

For a comprehensive introduction to papaja, see the current draft of the manual. If you have a specific question that is not answered in the manual, feel free to ask a question on Stack Overflow using the papaja tag. If you believe you have found a bug or would like to request a new feature, open an issue on Github and provide a minimal complete verifiable example.

Example

Take a look at the source file of the package vignette and the resulting PDF. The vignette also contains some basic instructions.

Installation

To use papaja you need either a recent version of RStudio or pandoc. If you want to create PDF- in addition to DOCX-documents you additionally need a TeX distribution. We recommend you use TinyTex, which can be installed from within R:

if(!requireNamespace("tinytex", quietly = TRUE)) install.packages("tinytex")

tinytex::install_tinytex()

You may also consider MikTeX for Windows, MacTeX for Mac, or TeX Live for Linux. Please refer to the papaja manual for detailed installation instructions.

papaja is available on CRAN but you can also install it from the GitHub repository:

# Install latest CRAN release
install.packages("papaja")

# Install remotes package if necessary
if(!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")

# Install the stable development version from GitHub
remotes::install_github("crsh/papaja")

# Install the latest development snapshot from GitHub
remotes::install_github("crsh/papaja@devel")

Usage

Once papaja is installed, you can select the APA template when creating a new R Markdown file through the RStudio menus.

APA template selection dialog

To add citations, specify your bibliography-file in the YAML front matter of the document (bibliography: my.bib) and start citing (for details, see pandoc manual on the citeproc extension. You may also be interested in citr, an R Studio addin to swiftly insert Markdown citations and R Studio’s visual editor, which also enables swiftly inserting citations.

Typeset analysis results

The functions apa_print() and apa_table() facilitate reporting results of your analyses. When you pass the an output object of a supported class, such as an htest- or lm-object, to apa_print(), it will return a list of character strings that you can use to report the results of your analysis.

my_lm <- lm(
  Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length
  , data = iris
)
apa_lm <- apa_print(my_lm)

apa_lm$full_result$Sepal_Length
## [1] "$b = 0.61$, 95\\% CI $[0.48, 0.73]$, $t(146) = 9.77$, $p < .001$"

papaja currently provides methods for the following object classes:

A-BD-LL-SS-Z
afex_aovdefaultlsmobjsummary.aovlist
anovaemmGridmanovasummary.glht
anova.lmeglhtmerModsummary.glm
Anova.mlmglmmixedsummary.lm
aovhtestpapaja_wscisummary.manova
aovlistlistsummary_emmsummary.ref.grid
BFBayesFactorlmsummary.Anova.mlm 
BFBayesFactorToplmesummary.aov 

Create tables

apa_table() may be used to produce publication-ready tables in an R Markdown document. For instance, you might want to report some condition means (with standard errors).

library("dplyr")
npk |>
  group_by(N, P) |>
  summarise(mean = mean(yield), se = sd(yield) / sqrt(length(yield)), .groups = "drop") |>
  label_variables(N = "Nitrogen", P = "Phosphate", mean = "*M*", se = "*SE*") |>
  apa_table(caption = "Mean pea yield (with standard errors)")

Table 1. Mean pea yield (with standard errors)

NitrogenPhosphateMSE
0051.721.88
0152.422.65
1059.222.66
1156.152.08

This is a fairly simple example, but apa_table() may be used to generate more complex tables.

apa_table(), of course, plays nicely with the output from apa_print(). Thus, it is possible to conveniently report complete regression tables, ANOVA tables, or the output from mixed-effects models.

lm(Sepal.Width ~ Sepal.Length + Petal.Width + Petal.Length, data = iris) |>
  apa_print() |>
  apa_table(caption = "Iris regression table.")

Table 2. Iris regression table.

Predictorb95% CItdfp
Intercept1.04[0.51, 1.58]3.85146< .001
Sepal Length0.61[0.48, 0.73]9.77146< .001
Petal Width0.56[0.32, 0.80]4.55146< .001
Petal Length-0.59[-0.71, -0.46]-9.43146< .001

Create figures

papaja further provides functions to create publication-ready plots. For example, you can use apa_barplot(), apa_lineplot(), and apa_beeplot() (or the general function apa_factorial_plot()) to visualize the results of factorial study designs:

apa_beeplot(
  data = stroop_data
  , dv = "response_time"
  , id = "id"
  , factors = c("congruency", "load")
  , ylim = c(0, 800)
  , dispersion = wsci # within-subjects confidence intervals
  , conf.level = .99
  , las = 1
)

Response times from a simulated Stroop experiment. Large dots represent condition means, small dots represent individual participants’ mean response time. Error bars represent 99% within-subjects confidence intervals.

If you prefer ggplot2, try theme_apa().

library("ggplot2")
library("ggforce")

p <- ggplot(
  stroop_data
  , aes(x = congruency, y = response_time, shape = load, fill = load)
) +
  geom_violin(alpha = 0.2, color = grey(0.6)) +
  geom_sina(color = grey(0.6)) +
  stat_summary(position = position_dodge2(0.95), fun.data = mean_cl_normal) +
  lims(y = c(0, max(stroop_data$response_time))) +
  scale_shape_manual(values = c(21, 22)) +
  scale_fill_grey(start = 0.6, end = 1) +
  labs(
    x = "Congruency"
    , y = "Response time"
    , shape = "Cognitive load"
    , fill = "Cognitive load"
  )

p + theme_apa()

Usage without RStudio

Don’t use RStudio? No problem. Use the rmarkdown::render function to create articles:

# Create new R Markdown file
rmarkdown::draft(
  "mymanuscript.Rmd"
  , "apa6"
  , package = "papaja"
  , create_dir = FALSE
  , edit = FALSE
)

# Render manuscript
rmarkdown::render("mymanuscript.Rmd")

Getting help

For a comprehensive introduction to papaja, check out the current draft of the papaja manual. If you have a specific question that is not answered in the manual, feel free to ask a question on Stack Overflow using the papaja tag. If you believe you have found a bug or you want to request a new feature, open an issue on Github and provide a minimal complete verifiable example.

Citation

Please cite papaja if you use it.

Aust, F. & Barth, M. (2022). papaja: Prepare reproducible APA journal articles with R Markdown. R package version 0.1.0.9999. Retrieved from https://github.com/crsh/papaja

For convenience, you can use cite_r() or copy the reference information returned by citation('papaja') to your BibTeX file:


@Manual{,
  title = {{papaja}: {Prepare} reproducible {APA} journal articles with {R Markdown}},
  author = {Frederik Aust and Marius Barth},
  year = {2022},
  note = {R package version 0.1.0.9999},
  url = {https://github.com/crsh/papaja},
}

papaja in the wild

If you are interested in seeing how others are using papaja, you can find a collection of papers and the corresponding R Markdown files in the manual.

If you have published a paper that was written with papaja, please add the reference to the public Zotero group yourself or send us to me.

Computational reproducibility

To ensure mid- to long-term computational reproducibility we highly recommend conserving the software environment used to write a manuscript (e.g. R and all R packages) either in a software container or a virtual machine. This way you can be sure that your R code does not break because of updates to R or any R package. For a brief primer on containers and virtual machines see the supplementary material by Klein et al. (2018).

Docker is the most widely used containerization approach. It is open source and free to use but requires some disk space. CodeOcean is a commercial service that builds on Docker, facilitates setting up and sharing containers and lets you run computations in the cloud. See the papaja manual on how to get started using papaja with Docker or CodeOcean and our Docker workflow tailored for easy use with papaja.

Contribute 

Like papaja and want to contribute? We highly appreciate any contributions to the R package or its documentation. Take a look at the open issues if you need inspiration. There are many additional analyses that we would like apa_print() to support. Any new S3/S4-methods for this function are always appreciated (e.g., factanal, fa, lavaan). For a primer on adding new apa_print()-methods, see the getting-started-vignette:

vignette("extending_apa_print", package = "papaja")

Before working on a contribution, please review our brief contributing guidelines and code of conduct.

Related R packages

By now, there are a couple of R packages that provide convenience functions to facilitate the reporting of statistics in accordance with APA guidelines.

  • apa: Format output of statistical tests in R according to APA guidelines
  • APAstats: R functions for formatting results in APA style and other stuff
  • apaTables: Create American Psychological Association (APA) Style Tables
  • rempsyc: Convenience functions for psychology
  • sigr: Concise formatting of significances in R

If you are looking for other journal article templates, you may be interested in the rticles package.

Package dependencies

Download Details:

Author: crsh
Source Code: https://github.com/crsh/papaja 
License: View license

#r #research #markdown 

Dotnet Script: Run C# Scripts From The .NET CLI

dotnet script

Run C# scripts from the .NET CLI, define NuGet packages inline and edit/debug them in VS Code - all of that with full language services support from OmniSharp.

NuGet Packages

NameVersionFramework(s)
dotnet-script (global tool)Nugetnet6.0, net5.0, netcoreapp3.1
Dotnet.Script (CLI as Nuget)Nugetnet6.0, net5.0, netcoreapp3.1
Dotnet.Script.CoreNugetnetcoreapp3.1 , netstandard2.0
Dotnet.Script.DependencyModelNugetnetstandard2.0
Dotnet.Script.DependencyModel.NugetNugetnetstandard2.0

Installing

Prerequisites

The only thing we need to install is .NET Core 3.1 or .NET 5.0 SDK.

.NET Core Global Tool

.NET Core 2.1 introduced the concept of global tools meaning that you can install dotnet-script using nothing but the .NET CLI.

dotnet tool install -g dotnet-script

You can invoke the tool using the following command: dotnet-script
Tool 'dotnet-script' (version '0.22.0') was successfully installed.

The advantage of this approach is that you can use the same command for installation across all platforms. .NET Core SDK also supports viewing a list of installed tools and their uninstallation.

dotnet tool list -g

Package Id         Version      Commands
---------------------------------------------
dotnet-script      0.22.0       dotnet-script
dotnet tool uninstall dotnet-script -g

Tool 'dotnet-script' (version '0.22.0') was successfully uninstalled.

Windows

choco install dotnet.script

We also provide a PowerShell script for installation.

(new-object Net.WebClient).DownloadString("https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.ps1") | iex

Linux and Mac

curl -s https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.sh | bash

If permission is denied we can try with sudo

curl -s https://raw.githubusercontent.com/filipw/dotnet-script/master/install/install.sh | sudo bash

Docker

A Dockerfile for running dotnet-script in a Linux container is available. Build:

cd build
docker build -t dotnet-script -f Dockerfile ..

And run:

docker run -it dotnet-script --version

Github

You can manually download all the releases in zip format from the GitHub releases page.

Usage

Our typical helloworld.csx might look like this:

Console.WriteLine("Hello world!");

That is all it takes and we can execute the script. Args are accessible via the global Args array.

dotnet script helloworld.csx

Scaffolding

Simply create a folder somewhere on your system and issue the following command.

dotnet script init

This will create main.csx along with the launch configuration needed to debug the script in VS Code.

.
├── .vscode
│   └── launch.json
├── main.csx
└── omnisharp.json

We can also initialize a folder using a custom filename.

dotnet script init custom.csx

Instead of main.csx which is the default, we now have a file named custom.csx.

.
├── .vscode
│   └── launch.json
├── custom.csx
└── omnisharp.json

Note: Executing dotnet script init inside a folder that already contains one or more script files will not create the main.csx file.

Running scripts

Scripts can be executed directly from the shell as if they were executables.

foo.csx arg1 arg2 arg3

OSX/Linux

Just like all scripts, on OSX/Linux you need to have a #! and mark the file as executable via chmod +x foo.csx. If you use dotnet script init to create your csx it will automatically have the #! directive and be marked as executable.

The OSX/Linux shebang directive should be #!/usr/bin/env dotnet-script

#!/usr/bin/env dotnet-script
Console.WriteLine("Hello world");

You can execute your script using dotnet script or dotnet-script, which allows you to pass arguments to control your script execution more.

foo.csx arg1 arg2 arg3
dotnet script foo.csx -- arg1 arg2 arg3
dotnet-script foo.csx -- arg1 arg2 arg3

Passing arguments to scripts

All arguments after -- are passed to the script in the following way:

dotnet script foo.csx -- arg1 arg2 arg3

Then you can access the arguments in the script context using the global Args collection:

foreach (var arg in Args)
{
    Console.WriteLine(arg);
}

All arguments before -- are processed by dotnet script. For example, the following command-line

dotnet script -d foo.csx -- -d

will pass the -d before -- to dotnet script and enable the debug mode whereas the -d after -- is passed to script for its own interpretation of the argument.

NuGet Packages

dotnet script has built-in support for referencing NuGet packages directly from within the script.

#r "nuget: AutoMapper, 6.1.0"

package

Note: Omnisharp needs to be restarted after adding a new package reference

Package Sources

We can define package sources using a NuGet.Config file in the script root folder. In addition to being used during execution of the script, it will also be used by OmniSharp that provides language services for packages resolved from these package sources.

As an alternative to maintaining a local NuGet.Config file we can define these package sources globally either at the user level or at the computer level as described in Configuring NuGet Behaviour

It is also possible to specify packages sources when executing the script.

dotnet script foo.csx -s https://SomePackageSource

Multiple packages sources can be specified like this:

dotnet script foo.csx -s https://SomePackageSource -s https://AnotherPackageSource

Creating DLLs or Exes from a CSX file

Dotnet-Script can create a standalone executable or DLL for your script.

SwitchLong switchdescription
-o--outputDirectory where the published executable should be placed. Defaults to a 'publish' folder in the current directory.
-n--nameThe name for the generated DLL (executable not supported at this time). Defaults to the name of the script.
 --dllPublish to a .dll instead of an executable.
-c--configurationConfiguration to use for publishing the script [Release/Debug]. Default is "Debug"
-d--debugEnables debug output.
-r--runtimeThe runtime used when publishing the self contained executable. Defaults to your current runtime.

The executable you can run directly independent of dotnet install, while the DLL can be run using the dotnet CLI like this:

dotnet script exec {path_to_dll} -- arg1 arg2

Caching

We provide two types of caching, the dependency cache and the execution cache which is explained in detail below. In order for any of these caches to be enabled, it is required that all NuGet package references are specified using an exact version number. The reason for this constraint is that we need to make sure that we don't execute a script with a stale dependency graph.

Dependency Cache

In order to resolve the dependencies for a script, a dotnet restore is executed under the hood to produce a project.assets.json file from which we can figure out all the dependencies we need to add to the compilation. This is an out-of-process operation and represents a significant overhead to the script execution. So this cache works by looking at all the dependencies specified in the script(s) either in the form of NuGet package references or assembly file references. If these dependencies matches the dependencies from the last script execution, we skip the restore and read the dependencies from the already generated project.assets.json file. If any of the dependencies has changed, we must restore again to obtain the new dependency graph.

Execution cache

In order to execute a script it needs to be compiled first and since that is a CPU and time consuming operation, we make sure that we only compile when the source code has changed. This works by creating a SHA256 hash from all the script files involved in the execution. This hash is written to a temporary location along with the DLL that represents the result of the script compilation. When a script is executed the hash is computed and compared with the hash from the previous compilation. If they match there is no need to recompile and we run from the already compiled DLL. If the hashes don't match, the cache is invalidated and we recompile.

You can override this automatic caching by passing --no-cache flag, which will bypass both caches and cause dependency resolution and script compilation to happen every time we execute the script.

Cache Location

The temporary location used for caches is a sub-directory named dotnet-script under (in order of priority):

  1. The path specified for the value of the environment variable named DOTNET_SCRIPT_CACHE_LOCATION, if defined and value is not empty.
  2. Linux distributions only: $XDG_CACHE_HOME if defined otherwise $HOME/.cache
  3. macOS only: ~/Library/Caches
  4. The value returned by Path.GetTempPath for the platform.

 

Debugging

The days of debugging scripts using Console.WriteLine are over. One major feature of dotnet script is the ability to debug scripts directly in VS Code. Just set a breakpoint anywhere in your script file(s) and hit F5(start debugging)

debug

Script Packages

Script packages are a way of organizing reusable scripts into NuGet packages that can be consumed by other scripts. This means that we now can leverage scripting infrastructure without the need for any kind of bootstrapping.

Creating a script package

A script package is just a regular NuGet package that contains script files inside the content or contentFiles folder.

The following example shows how the scripts are laid out inside the NuGet package according to the standard convention .

└── contentFiles
    └── csx
        └── netstandard2.0
            └── main.csx

This example contains just the main.csx file in the root folder, but packages may have multiple script files either in the root folder or in subfolders below the root folder.

When loading a script package we will look for an entry point script to be loaded. This entry point script is identified by one of the following.

  • A script called main.csx in the root folder
  • A single script file in the root folder

If the entry point script cannot be determined, we will simply load all the scripts files in the package.

The advantage with using an entry point script is that we can control loading other scripts from the package.

Consuming a script package

To consume a script package all we need to do specify the NuGet package in the #loaddirective.

The following example loads the simple-targets package that contains script files to be included in our script.

#load "nuget:simple-targets-csx, 6.0.0"

using static SimpleTargets;
var targets = new TargetDictionary();

targets.Add("default", () => Console.WriteLine("Hello, world!"));

Run(Args, targets);

Note: Debugging also works for script packages so that we can easily step into the scripts that are brought in using the #load directive.

Remote Scripts

Scripts don't actually have to exist locally on the machine. We can also execute scripts that are made available on an http(s) endpoint.

This means that we can create a Gist on Github and execute it just by providing the URL to the Gist.

This Gist contains a script that prints out "Hello World"

We can execute the script like this

dotnet script https://gist.githubusercontent.com/seesharper/5d6859509ea8364a1fdf66bbf5b7923d/raw/0a32bac2c3ea807f9379a38e251d93e39c8131cb/HelloWorld.csx

That is a pretty long URL, so why don't make it a TinyURL like this:

dotnet script https://tinyurl.com/y8cda9zt

Script Location

A pretty common scenario is that we have logic that is relative to the script path. We don't want to require the user to be in a certain directory for these paths to resolve correctly so here is how to provide the script path and the script folder regardless of the current working directory.

public static string GetScriptPath([CallerFilePath] string path = null) => path;
public static string GetScriptFolder([CallerFilePath] string path = null) => Path.GetDirectoryName(path);

Tip: Put these methods as top level methods in a separate script file and #load that file wherever access to the script path and/or folder is needed.

REPL

This release contains a C# REPL (Read-Evaluate-Print-Loop). The REPL mode ("interactive mode") is started by executing dotnet-script without any arguments.

The interactive mode allows you to supply individual C# code blocks and have them executed as soon as you press Enter. The REPL is configured with the same default set of assembly references and using statements as regular CSX script execution.

Basic usage

Once dotnet-script starts you will see a prompt for input. You can start typing C# code there.

~$ dotnet script
> var x = 1;
> x+x
2

If you submit an unterminated expression into the REPL (no ; at the end), it will be evaluated and the result will be serialized using a formatter and printed in the output. This is a bit more interesting than just calling ToString() on the object, because it attempts to capture the actual structure of the object. For example:

~$ dotnet script
> var x = new List<string>();
> x.Add("foo");
> x
List<string>(1) { "foo" }
> x.Add("bar");
> x
List<string>(2) { "foo", "bar" }
>

Inline Nuget packages

REPL also supports inline Nuget packages - meaning the Nuget packages can be installed into the REPL from within the REPL. This is done via our #r and #load from Nuget support and uses identical syntax.

~$ dotnet script
> #r "nuget: Automapper, 6.1.1"
> using AutoMapper;
> typeof(MapperConfiguration)
[AutoMapper.MapperConfiguration]
> #load "nuget: simple-targets-csx, 6.0.0";
> using static SimpleTargets;
> typeof(TargetDictionary)
[Submission#0+SimpleTargets+TargetDictionary]

Multiline mode

Using Roslyn syntax parsing, we also support multiline REPL mode. This means that if you have an uncompleted code block and press Enter, we will automatically enter the multiline mode. The mode is indicated by the * character. This is particularly useful for declaring classes and other more complex constructs.

~$ dotnet script
> class Foo {
* public string Bar {get; set;}
* }
> var foo = new Foo();

REPL commands

Aside from the regular C# script code, you can invoke the following commands (directives) from within the REPL:

CommandDescription
#loadLoad a script into the REPL (same as #load usage in CSX)
#rLoad an assembly into the REPL (same as #r usage in CSX)
#resetReset the REPL back to initial state (without restarting it)
#clsClear the console screen without resetting the REPL state
#exitExits the REPL

Seeding REPL with a script

You can execute a CSX script and, at the end of it, drop yourself into the context of the REPL. This way, the REPL becomes "seeded" with your code - all the classes, methods or variables are available in the REPL context. This is achieved by running a script with an -i flag.

For example, given the following CSX script:

var msg = "Hello World";
Console.WriteLine(msg);

When you run this with the -i flag, Hello World is printed, REPL starts and msg variable is available in the REPL context.

~$ dotnet script foo.csx -i
Hello World
>

You can also seed the REPL from inside the REPL - at any point - by invoking a #load directive pointed at a specific file. For example:

~$ dotnet script
> #load "foo.csx"
Hello World
>

Piping

The following example shows how we can pipe data in and out of a script.

The UpperCase.csx script simply converts the standard input to upper case and writes it back out to standard output.

using (var streamReader = new StreamReader(Console.OpenStandardInput()))
{
    Write(streamReader.ReadToEnd().ToUpper());
}

We can now simply pipe the output from one command into our script like this.

echo "This is some text" | dotnet script UpperCase.csx
THIS IS SOME TEXT

Debugging

The first thing we need to do add the following to the launch.config file that allows VS Code to debug a running process.

{
    "name": ".NET Core Attach",
    "type": "coreclr",
    "request": "attach",
    "processId": "${command:pickProcess}"
}

To debug this script we need a way to attach the debugger in VS Code and the simplest thing we can do here is to wait for the debugger to attach by adding this method somewhere.

public static void WaitForDebugger()
{
    Console.WriteLine("Attach Debugger (VS Code)");
    while(!Debugger.IsAttached)
    {
    }
}

To debug the script when executing it from the command line we can do something like

WaitForDebugger();
using (var streamReader = new StreamReader(Console.OpenStandardInput()))
{
    Write(streamReader.ReadToEnd().ToUpper()); // <- SET BREAKPOINT HERE
}

Now when we run the script from the command line we will get

$ echo "This is some text" | dotnet script UpperCase.csx
Attach Debugger (VS Code)

This now gives us a chance to attach the debugger before stepping into the script and from VS Code, select the .NET Core Attach debugger and pick the process that represents the executing script.

Once that is done we should see our breakpoint being hit.

Configuration(Debug/Release)

By default, scripts will be compiled using the debug configuration. This is to ensure that we can debug a script in VS Code as well as attaching a debugger for long running scripts.

There are however situations where we might need to execute a script that is compiled with the release configuration. For instance, running benchmarks using BenchmarkDotNet is not possible unless the script is compiled with the release configuration.

We can specify this when executing the script.

dotnet script foo.csx -c release

 

Nullable reference types

Starting from version 0.50.0, dotnet-script supports .Net Core 3.0 and all the C# 8 features. The way we deal with nullable references types in dotnet-script is that we turn every warning related to nullable reference types into compiler errors. This means every warning between CS8600 and CS8655 are treated as an error when compiling the script.

Nullable references types are turned off by default and the way we enable it is using the #nullable enable compiler directive. This means that existing scripts will continue to work, but we can now opt-in on this new feature.

#!/usr/bin/env dotnet-script

#nullable enable

string name = null;

Trying to execute the script will result in the following error

main.csx(5,15): error CS8625: Cannot convert null literal to non-nullable reference type.

We will also see this when working with scripts in VS Code under the problems panel.

image

Download Details:
Author: filipw
Source Code: https://github.com/filipw/dotnet-script
License: MIT License

#dotnet  #aspdotnet  #csharp 

Nat  Grady

Nat Grady

1666879469

Rticles: LaTeX Journal Article Templates for R Markdown

rticles

The rticles package provides a suite of custom R Markdown LaTeX formats and templates for various formats. Most of the templates are provided and maintained by the community, and anyone can contribute a new template. See How to contribute below.

Installation

You can install and use rticles from CRAN as follows:

install.packages("rticles")

If you wish to install the development version from GitHub (which often contains new article formats), you can do this:

remotes::install_github("rstudio/rticles")

Using rticles

To use rticles from RStudio, you can access the templates through File -> New File -> R Markdown. This will open the dialog box where you can select from one of the available templates:

New R
Markdown

If you are not using RStudio, you’ll also need to install Pandoc following these instructions. Then, use the rmarkdown::draft() function to create articles:

rmarkdown::draft(
    "MyJSSArticle.Rmd", template = "jss", package = "rticles"
)
rmarkdown::draft(
    "MyRJournalArticle", template = "rjournal", package = "rticles"
)

This will create a folder containing a Rmd file using the corresponding output format and all the assets required by this format.

Templates

Currently included templates and their contributors are the following:

JournalContributorsPull requestOutput format
ACM: Association for Computings Machinery@ramnathv#8acm_article()
ACS@yufree#15acs_article()
AEA: American Economic Association@sboysel#86aea_articles()
AGU@eliocamp#199agu_article()
AJS: Austrian Journal of Statistics@matthias-da#437ajs_article()
AMS: American Meteorological Society@yufree#96ams_article()
ASA: American Statistical Association https://www.amstat.org/ #111asa_article()
arXiv pre-prints based on George Kour’s template@alexpghayes#236arxiv_article()
Bioinformatics@ShixiangWang#297bioinformatics_article()
Biometrics@daltonhance#170biometrics_article()
Bulletin de l’AMQ@desautm#145amq_article()
Copernicus Publications@nuest, @RLumSK#172, #342copernicus_article()
CTeX  ctex()
Elsevier@cboettig, @robjhyndman#27, #467elsevier_article()
Frontiers@muschellij2#211frontiers_article()
Glossa@stefanocoretta#361glossa_article()
IEEE Transaction@Emaasit, @espinielli, @nathanweeks, @DunLug#97, #169, #227, #263, #264, #265ieee_article()
IMS: Institute of Mathematical Statistics AoAS: Annals of Applied Statistics@auzaheta#372ims_article()
INFORMS: Institute for Operations Research and the Management Sciences@robjhyndman#460informs_article()
ISBA: International Society for Bayesian Analysis@dmi3nko#461isba_article()
IOP: Institute of Physics (https://iopscience.iop.org)@robjhyndman#462iop_article()
JASA: Journal of the Acoustical Society of America@stefanocoretta#364jasa_article()
Journal of Educational Data Mining journal submissions@jooyoungseo#251jedm_article()
JOSS: Journal of Open Source Software JOSE: Journal of Open Source Education@noamross#229joss_article()
JSS: Journal of Statistical Software  jss_article()
LIPIcs@nuest#288lipics_article()
MDPI@dleutnant#147mdpi_article()
MNRAS: Monthly Notices of the Royal Astronomical Society@oleskiewicz#175mnras_article()
OUP: Oxford University Press@dmkaplan#284oup_articles()
PeerJ: Journal of Life and Environmental Sciences@zkamvar#127peerj_article()
PiHPh: Papers in Historical Phonology@stefanocoretta#362pihph_article()
PLOS@sjmgarnier#12plos_article()
PNAS: Proceedings of the National Academy of Sciences@cboettig#72pnas_article()
RSOS: Royal Society Open Science@ThierryO#135rsos_article()
RSS: Royal Statistical Society@carlganz#110rss_article()
Sage@oguzhanogreden#181sage_article()
Springer@strakaps#164springer_article()
Springer Lecture Notes in Computer Science (LCNS)@eliocamp#445lncs_article()
SIM: Statistics in Medicine@ellessenne#231sim_article()
Taylor & Francis@dleutnant#218tf_article()
The R Journal  rjournal_article()
TRB@gregmacfarlane#427trb_article()
Wellcome Open Research@arnold-c#436wellcomeor_article()

You can also get the list of available journal names with rticles::journals().

rticles::journals()
#>  [1] "acm"            "acs"            "aea"            "agu"           
#>  [5] "ajs"            "amq"            "ams"            "arxiv"         
#>  [9] "asa"            "bioinformatics" "biometrics"     "copernicus"    
#> [13] "ctex"           "elsevier"       "frontiers"      "glossa"        
#> [17] "ieee"           "ims"            "informs"        "iop"           
#> [21] "isba"           "jasa"           "jedm"           "joss"          
#> [25] "jss"            "lipics"         "lncs"           "mdpi"          
#> [29] "mnras"          "oup_v0"         "oup_v1"         "peerj"         
#> [33] "pihph"          "plos"           "pnas"           "rjournal"      
#> [37] "rsos"           "rss"            "sage"           "sim"           
#> [41] "springer"       "tf"             "trb"            "wellcomeor"

Those are the values to use within rmarkdown::draft().

Under the hood, LaTeX templates are used to ensure that documents conform precisely to submission standards. At the same time, composition and formatting can be done using lightweight markdown syntax, and R code and its output can be seamlessly included using knitr.

Getting help

There are two main places to get help:

The RStudio community is a friendly place to ask any questions about rticles. Be sure to use the rticles tag.

Stack Overflow is a great source of answers to common bookdown questions. Use the tags [r][rticles] if you ask a question.

Code of Conduct

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

How to contribute?

Most of the templates are contributed directly by the users in the community. If you want rticles to offer a new journal format, you can contribute by the following way.

Suggest an idea for new format opening an issue.

You may not feel confident enough or may not have time to contribute a new format. By opening a new issue, you can share the idea for this format, and see if someone in the community can help on it.
This is not the best way to quickly get your format included but at least it is a great way to see if others are interested too.

To see the existing suggested formats, just filter issues with the help wanted :heart: label. You can then add a :+1: or help to add the template :wink:.

Contribute a new template format opening a pull request.

To contribute a new format, you need to open a new pull request (PR). When opening the PR, you’ll see the PR template explaining how to proceed and what is important to check. Please follow it.
Even if you are just starting or you are not finished, you share your work by creating a draft PR. It is a great way to let us know that you are still working on it (like these opened ones), and it is also a great way to ask for help from the community.
When you are ready, you can submit the PR for review, and we will iterate until it is merged.

Technical resources helpful to contribute a template

The best way to get started is to look at the previous examples of submitted PR. You’ll find links to them in the table above.

All the rticles format are build similarly by providing a new pandoc tex template to replace the default one. You’ll learn more about pandoc templates in these places:

You can study existing formats to see how all this works.

Book

R Markdown: The Definitive Guide

Download Details:

Author: rstudio
Source Code: https://github.com/rstudio/rticles 

#r #rstudio #article  #markdown 

Marcus  Flatley

Marcus Flatley

1594399440

Getting Started with R Markdown — Guide and Cheatsheet

In this blog post, we’ll look at how to use R Markdown. By the end, you’ll have the skills you need to produce a document or presentation using R Mardown, from scratch!

We’ll show you how to convert the default R Markdown document into a useful reference guide of your own. We encourage you to follow along by building out your own R Markdown guide, but if you prefer to just read along, that works, too!

R Markdown is an open-source tool for producing reproducible reports in R. It enables you to keep all of your code, results, plots, and writing in one place. R Markdown is particularly useful when you are producing a document for an audience that is interested in the results from your analysis, but not your code.

R Markdown is powerful because it can be used for data analysis and data science, collaborating with others, and communicating results to decision makers. With R Markdown, you have the option to export your work to numerous formats including PDF, Microsoft Word, a slideshow, or an HTML document for use in a website.

r markdown tips, tricks, and shortcuts

Turn your data analysis into pretty documents with R Markdown.

We’ll use the RStudio integrated development environment (IDE) to produce our R Markdown reference guide. If you’d like to learn more about RStudio, check out our list of 23 awesome RStudio tips and tricks!

Here at Dataquest, we love using R Markdown for coding in R and authoring content. In fact, we wrote this blog post in R Markdown! Also, learners on the Dataquest platform use R Markdown for completing their R projects.

We included fully-reproducible code examples in this blog post. When you’ve mastered the content in this post, check out our other blog post on R Markdown tips, tricks, and shortcuts.

Okay, let’s get started with building our very own R Markdown reference document!

R Markdown Guide and Cheatsheet: Quick Navigation

1. Install R Markdown

R Markdown is a free, open source tool that is installed like any other R package. Use the following command to install R Markdown:

install.packages("rmarkdown")

Now that R Markdown is installed, open a new R Markdown file in RStudio by navigating to File > New File > R Markdown…. R Markdown files have the file extension “.Rmd”.

2. Default Output Format

When you open a new R Markdown file in RStudio, a pop-up window appears that prompts you to select output format to use for the document.

New Document

The default output format is HTML. With HTML, you can easily view it in a web browser.

We recommend selecting the default HTML setting for now — it can save you time! Why? Because compiling an HTML document is generally faster than generating a PDF or other format. When you near a finished product, you change the output to the format of your choosing and then make the final touches.

One final thing to note is that the title you give your document in the pop-up above is not the file name! Navigate to File > Save As.. to name, and save, the document.

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