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
5.1.2 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
5.1.3 Plotting Seahorse data
Loading data and working with typical Seahorse data
Using janitorclean_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 forcatsfct_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
5.2 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
5.3 External resources
Tutorials
R for reproducible scientific analysis 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.