jump_summary.qmd

---
title: "Summary"
---

## What did we learn?

### Ditching point-and-click

- The benefits of using R over point-and-click software for data analysis in biological and biomedical sciences are that: 
  - it is open-source,
  - it has a wide and diverse community with a huge number of resources
  - it is relatively easy to learn, and 
  - it offers very well suited workflows for doing reproducible and responsible data analysis
- The tidyverse offers advantages over base R. It offers an intuitive way of coding with functional names and tidy data handling and coding in mind
- R in the browser offers easy access to R without installing software

### Plotting `mtcars`

-   General R coding and execution of code
-   How to look at data tables: `head`, `tail`, `glimpse`
-   The pipe operator `%>%` or `|>`
-   Making factorial data using `as.factor`
-   the `dplyr` function `select`
-   basic `ggplot` functions using `aes` aesthetics and geoms such as `geom_point`
-   adding `color` and `shape` and using `scale_brewer_manual` and `scale_shape`
-   improving layout; `theme_bw`, `base_size` and `labs`
-   using chatGPT for coding improvements

### Plotting Seahorse data

-   Loading data and working with typical Seahorse data
-   Using `janitor` `clean_names`
-   Using the `dplyr` function `filter`
-   Using the `%in%` operator
-   Changing the layout of ggplots usine `theme` elements and arguments.
-   Adding text to ggplot using `geom_text` and `annotate`
-   Adding lines to ggplot using `geom_vline`
-   Nesting pipes in ggplot function for subsetting data
-   Using `facet_wrap` to make multiple similar plots from one datatable
-   Using the `forcats` `fct_reorder` function
-   Changing data formats to numbers using `as.double`
-   Using the dplyr `summarize` function
-   Using `stat_summary` to compute means or medians in ggplots
-   Using `geom_smooth` to make regression lines

## What we did not learn?

-   Base R functions and how to address data in base R, eg `xf$OCR[xf$Group == "Background]` and `xf$Well[10]`
-   Other important tidyverse functions, like `pivot_wider`, `pivot_longer`,
-   More complicated functions like the `map` function from the `purrr` package
-   Other simple ggplot geoms, like `geom_bar`, `geom_boxplot`, `geom_density`
-   How to save images and plots for using them in other software

## External resources

- Tutorials
  - [R for reproducible scientific analysis](https://swcarpentry.github.io/r-novice-gapminder/01-rstudio-intro.html#introduction-to-r) is a great introductory material. It is free, easy to follow and **quick to learn and apply**. You can skip the very first section on RStudio if you want.
  - [The starter guide for transitioning your Python projects to R](https://towardsdatascience.com/the-starter-guide-for-transitioning-your-python-projects-to-r-8de4122b04ad) can be very useful to get quickstarted in R if you are familiar with Python.
  - [R packaging](https://carpentries-incubator.github.io/lesson-R-packaging/) shows you how to write your own R packages.
- Longer reads
  - [R for data science](https://r4ds.had.co.nz/) is a really good book about R, with a focus on tidyverse. It is available for free.
  - [Advanced R](https://adv-r.hadley.nz/) is another pretty good book for those who want to go even deeper into the language. Also available for free.