class: center, middle, inverse, title-slide # Welcome! ## <br><br> Introduction to Data Science with R and Tidyverse ### based on datasciencebox.org --- layout: true <div class="my-footer"> <span> Introduction to Data Science with R and Tidyverse | Lukas Jürgensmeier, Matteo Fina, Jan Bischoff | based on <a href="https://datasciencebox.org" target="_blank">datasciencebox.org</a> </span> </div> --- class: middle # Hello world! --- ## Data science .pull-left-wide[ - Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. - We're going to learn to do this in a `tidy` way -- more on that later! - This is a course on introduction to data science in `R` and the `tidyverse` packages. ] --- ## Course FAQ .pull-left-wide[ **Q - What data science/programming background does this course assume?** A - None. **Q - Is this a course for neuroscientists only?** A - Yes and no. While this course is exclusively for GRADE Brain and other GRADE Centers, the course does not introduce you to neuroscience applications with `R`. Rather, we will discuss some computational tools that most scientists share and which are applicable beyond your field. **Q - Will we be doing computing?** A - Yes. Do as much as possible by yourself — that's how you learn it. ] --- # Course FAQ (2/2) .pull-left-wide[ **Q - Where do I find the course materials?** A - On this website: [coding-intro.github.io/intro-tidyverse-2023-06/](https://coding-intro.github.io/intro-tidyverse-2023-06/) **Q - Can I code on my own during this course?** A - Yes! You can and you should! Go to [Posit Cloud](https://posit.cloud/) (formerly RStudio Cloud) for your development environment and our application exercises. ] --- class: middle # Software --- <img src="img/excel.png" width="75%" style="display: block; margin: auto auto auto 0;" /> --- <img src="img/r.png" width="50%" style="display: block; margin: auto auto auto 0;" /> --- <img src="img/rstudio.png" width="73%" style="display: block; margin: auto auto auto 0;" /> --- class: middle # Data science life cycle --- ## We'll walk you through the data science lifecycle and introduce the tools for each step <img src="img/data-science-cycle/data-science-cycle.001.png" width="90%" style="display: block; margin: auto auto auto 0;" /> --- class: middle # Let's dive in! --- background-image: url("img/unvotes/unvotes-01.jpeg") --- class: inverse <img src="img/unvotes/unvotes-02.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-03.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-04.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-05.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-06.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-07.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-08.jpeg" width="90%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-09.jpeg" width="90%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-10.jpeg" width="90%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-11.jpeg" width="90%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-12.jpeg" width="90%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-13.jpeg" width="100%" style="display: block; margin: auto;" /> --- class: inverse <img src="img/unvotes/unvotes-14.jpeg" width="100%" style="display: block; margin: auto;" /> --- # Is it possible to learn all that in this course? .pull-left-wide[ Yes — if you're actively coding along and invest some time. We'll go through every step in this course: - Plotting with `ggplot2` - Data wrangling with `dplyr` - Communicating your results with `R Markdown` - Regressions with `tidymodels` ] --- .your-turn[ .light-blue[.hand[Your turn:]] `Application Exercise 01 - UN Votes` - Go to [Posit Cloud](https://posit.cloud/) using the invite link we sent you via e-mail. - Start a "New Project" (upper right border) > "New RStudio Project". - Click and open the R Markdown document `unvotes.Rmd`, then *Knit* it. - Try to familiarize yourself with the "R code chunks". ]