In AshirBorah/cp_bootcamp_r_tutorials:

knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(gapminder)

Overview

General advice
Useful tips

Google is your friend

Pay attention to error messages
'Googling' error messages will very often prove useful
How do I XX in R

Rstudio

Rstudio Projects

Set up an Rstudio 'project' for each project.
These are just folders that Rstudio adds some extra info to.
Make separate folders for project-specific data, code and results
You can switch between projects using the project tab in the upper-right corner of Rstudio.

Rstudio bells and whistles

Functions

If you're redoing the same thing many times, use a function
help(my_function) or ?my_function to pull up help documentation for a function

help(median)

Loading data {.smaller}

Loading tables

Use read_csv, read_tsv to read common text file formats into tibbles
You can tell which to use by the type of file:
read_csv: for .csv files (comma-separated)
read_tsv: for .tsv files (tab-separated)
For .txt files read_table is a good bet

Loading matrices

You can use the same functions as above to load matrices as tibbles
For matrices, it's helpful to have one column be the 'rownames'.
You can convert a tibble df to a matrix mat like this:

mat <- df %>% 
  column_to_rownames(var = 'row_col') %>% 
  as.matrix()

Check your data after loading!

Always inspect data after loading it to make sure that the data type of each field is correct and the column names look right
For tibbles:

glimpse(gapminder)
head(gapminder)

For matrices, the corner function from the useful package is nice, it just shows the upper-left portion of the matrix

useful::corner(my_mat)

File paths

When you have an Rproject loaded, if you've organized your project folder as above, you can easily load data from that project's data folder without having to worry about file 'paths': read_csv(data/my_data.csv)
To load data from elsewhere you need to specify the full 'path', e.g. read_csv(~/my_stuff/general_data/my_data.csv)
You can read more about file paths, e.g. here
As an easy fall-back, you can always use the Import Dataset tool in Rstudio.

Literate coding

Write analysis like an experiment notebook
Emphasis on clarity and communication
Write text to describe what you're doing and why, embed code and results
Encourages reproducibility, clarity, and robustness
ALSO use in-line comments with your code using #
read more

With Rmarkdown

Rmarkdown naturally facilitates literate coding (more)
Intersperse code chunks with explanatory text, and embed results
Easily output as pdf, html, and other formats
Look for examples you like (e.g. gallery) and use as templates
See the RMarkdown cheatsheet for more tips

Tidyverse tips {.smaller}

tidy data provides a consistent framework that works with tidyverse tools
Making plots: ggplot -- Look for examples online and copy the code!
tidyverse cheat sheet
Many more cheat sheets here

Version control

Use version control git [POINTER TO TUTORIAL]

Additional tips

Making interactive tables with datatable {.smaller}

Uses the DT package

VERY easy to create nice interactive tables in your html reports (use with Rmarkdown only)

library(DT)
datatable(gapminder)

Making plots interactive

ggplotly takes as input a ggplot object and converts it into a cool interactive plot (that can be embedded in your html reports or used in Rstudio).

Note that the text aesthetic includes the country name in the hovertip text (so you can see the country associated with each dot when you hover over it with your mouse)

library(plotly)
g <- ggplot(gapminder %>% filter(year == 1952),
       aes(x = gdpPercap, y = lifeExp, text = country)) + 
  geom_point() + 
  scale_x_log10()
ggplotly(g)