https://github.com/caltechlibrary/2018-03-29-r-workshop

Repo for files for 2018-03-29 Data Analysis, Visualization, and Reproducibility with R Notebooks workshop.

https://github.com/caltechlibrary/2018-03-29-r-workshop

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Repo for files for 2018-03-29 Data Analysis, Visualization, and Reproducibility with R Notebooks workshop.

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README 2.md


layout: lesson title: Data carpentry: R for data analysis and visualization of Ecological Data

keywords: ["R", "subset", "data.frame", "read.csv", "dplyr", "ggplot2"]

This is an introduction to R designed for participants with no programming experience. These lessons can be taught in 3/4 of a day (6 hours). They start with some basic information about syntax for the R programming language, the RStudio interface, and move through to specific programming tasks, such as importing CSV files, the structure of data frame objects in R, dealing with categorical variables (i.e. factors), basic data manipulation (adding/removing rows and columns), and finishing with calculating summary statistics and a brief introduction to plotting. There is also a lesson on how to use databases from R that is intended to be taught after the SQL lesson, and ideally at the end of a Data Carpentry workshop.

Prerequisites

  • Having R and RStudio installed (though see the first lesson, Before we start for installation instructions)

Topics

Code handout

There is "code handout" (code-handout.R) that is intended to be distributed to the participants. This file includes some of the examples used during teaching and the titles of the section. It provides a guide that the participants can fill in as the lesson progresses. Participants can also source code from this file to avoid typos in more complex examples.

Contributing

If you would like to contribute to the content and development of these lessons, we encourage you to review our contributing guide.

Questions

If you have any questions or feedback, please open an issue or contact the maintainers:

  • François Michonneau
  • Auriel Fournier

Owner

  • Name: Caltech Library
  • Login: caltechlibrary
  • Kind: organization
  • Email: helpdesk@library.caltech.edu
  • Location: Pasadena, CA 91125

We manage the physical and digital holdings of the California Institute of Technology, provide services and training, and develop open-source software.

Citation (CITATION.Rmd)

# CITATION

```{r, echo=FALSE, results='hide'}
eds <- personList(person(given = "Francois", family = "Michonneau"),
                      person(given = "Auriel", family = "Fournier"))

generate_citation <- function(authors = "AUTHORS",
                              editors = eds,
                              doi = "10.5281/zenodo.569338") {
    aut <- readLines(authors)

    # remove first line
    aut <- aut[-1]

    aut <- as.person(aut)

    bibentry(
        bibtype = "Misc",
        author = personList(aut),
        title = "Data Carpentry: R for data analysis and visualization of Ecological Data",
        editor = editors,
        month = format(Sys.Date(), "%B"),
        year = format(Sys.Date(), "%Y"),
        url = "http://datacarpentry.org/R-ecology-lesson",
        doi = doi
    )

}

generate_zenodo_json <- function(editors) {
    tfile <- tempfile()
    system(paste("git shortlog -n -e -s >", tfile))
    aut <- read.table(file = tfile, sep = "\t")
    aut <- as.person(aut[, 2])
    pp <- lapply(aut,  function(x) {
        res <- gsub("^\\s", "", paste(paste(x$given, collapse = " "),
                                      x$family))
        list(name =  res)
    })
    eds <- paste(editors$given, editors$family)
    res <- list(creators = pp)

    if (!is.null(editors)) {
        ctb <- lapply(paste(editors$given, editors$family),
                      function(x) list(type = "Editor", name = x))
        res <- c(list(contributors = ctb), res)
    }
    cat(jsonlite::toJSON(res, auto_unbox = TRUE), file = ".zenodo.json")
}

system("python3 /usr/local/bin/update-copyright.py")
generate_zenodo_json(editors = eds)
```


## Data

Data is from the paper S. K. Morgan Ernest, Thomas J. Valone, and James
H. Brown. 2009. Long-term monitoring and experimental manipulation of a
Chihuahuan Desert ecosystem near Portal, Arizona, USA. Ecology 90:1708.

http://esapubs.org/archive/ecol/E090/118/

A simplified version of this data, suitable for teaching is available on
[figshare](https://dx.doi.org/10.6084/m9.figshare.1314459.v5).


## Lessons

The first workshop was run at NESCent on May 8-9, 2014 with the development and
instruction of lessons by Karen Cranston, Hilmar Lapp, Tracy Teal and Ethan
White and contributions from Deb Paul and Mike Smorul.

Original materials adapted from SWC Python lessons by Sarah Supp. John Blischak
led the continued development of materials with contributions from Gavin
Simpson, Tracy Teal, Greg Wilson, Diego Barneche, Stephen Turner, and Karthik
Ram. This original material has been modified and expanded by Francois
Michonneau.

The **`dplyr`** lesson was created by Kara Woo, who copied and modified and
modified from Jeff
Hollister's [materials](http://usepa.github.io/introR/2015/01/14/03-Clean/).

The **`ggplot2`** lesson was initially created by Mateusz Kuzak, Diana Marek,
and Hedi Peterson, during a Hackathon in Espoo, Finland on March 16-17, 2015,
sponsored by the [ELIXIR project](http://elixir-europe.org/).

You can cite this Data Carpentry lesson as follow:


```{r, echo=FALSE, results='asis'}
print(generate_citation(), style = "html")
```

or as a BibTeX entry:


```{r, echo=FALSE, comment=''}
print(generate_citation(), style = "bibtex")
```

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