class: center, middle, inverse, title-slide .title[ # Introduction to tidyverse and RMarkdown ] .author[ ### Lars Relund Nielsen ] --- layout: true <div class="my-footer"> <span> <a href="https://bss-osca.github.io/tfa/mod-r-tidyverse-intro" target="_blank">Notes</a> | <a href="11_r-tidyverse-intro-slides.html" target="_blank">Slides</a> | <a href="https://github.com/bss-osca/tfa/blob/master/slides/11_r-tidyverse-intro-slides.Rmd" target="_blank">Source</a> | <a href="https://rstudio.cloud/spaces/426101/content/6689205" target="_blank">Posit Cloud</a> </span> </div> --- ## The tidyverse package The [tidyverse][tidyverse-main-page] is a collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. The main packages are loaded using `library(tidyverse)`. We focus on * [readr][tidyverse-readr] provides a fast and friendly way to read rectangular data (like csv files). * [dplyr][tidyverse-dplyr] provides a grammar of data manipulation, providing a consistent set of verbs that solve the most common data manipulation challenges. * [ggplot2][tidyverse-ggplot2] is a system for declaratively creating graphics. * [tidyr][tidyverse-tidyr] provides a set of functions that help you get to tidy data. Tidy data is data with a consistent form: in brief, every variable goes in a column, and every column is an observation. * [tibble][tidyverse-tibble] is a modern re-imagining of the data frame, keeping what time has proven to be effective, and throwing out what has not. Small introductions (with examples) to the packages are given on their documentation pages. --- ## Writing reproducible reports - Reproducible means that the results can be reproduced given access to the original data, software, and code. - In practice it may be hard to make your project totally reproducible. There are different levels of reproducibility. - [Literate programming][wiki-literate-programming] is a programming paradigm focusing on weaving code together with text as documentation. - Want to write reports containing both R code for importing data, wrangling and analysis with comments about the code, plots, analysis, results, etc. - Focus on [RMarkdown][r-markdown]. In RMarkdown, you can weave R code together with text (written in Markdown) to produce rendered output in html, pdf or Word. - Very useful when you have to write reports in your study! - This may seem complicated at first. However, after setup, it makes life much easier, since don't have to copy/paste anymore. --- ## Example Let us have a look at an example in the RStudio Cloud project. We will use the file `lecture/slides-examples.Rmd` throughout the lecture. First, let us try to knit the document in different formats. 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