In AshirBorah/cp_bootcamp_r_tutorials:

knitr::opts_chunk$set(echo = TRUE)
library(learnr)
tutorial_options(exercise.timelimit = 10, exercise.blanks = "___+")

Welcome!

Why learn to code as a cancer researcher?

• Reproducible data analysis:

• Document and share exactly how you analyzed your data

• Do more with your analysis, more efficiently:

• More control and flexibility

• Use community-created analysis tools

• Leverage cancer data resources (often large datasets)

• Create awesome visualizations

Imagined learning curve

Our view of the learning curve

Course goals

• Get everyone past 'valley of despair' in R learning curve

• Convince you that R is an accessible and useful tool for you in your research

• Prepare you to tackle BootCamp projects next week

• Get you excited to keep developing these coding skills!

Why R specifically

::: {style="font-size: 80%; text-align: left;"}

Upsides

• Free

• Great for data analysis and visualization

• LOTS of bioinformatics/stats tools available

Downsides?

• It's a hodge-podge

• Not the best for engineering software

:::

Quick demo

[LIVE DEMO]

Strategy of the course

• What this course is

• Coding basics

• Heavy emphasis on practical skills (data wrangling, visualization)

• Flagging areas with technical depth but giving the ‘need-to-know'

• What this course is not

• Intro to computer science

• Intro to stats

Course format

• Presentations: lecture style: present key concepts

• Practice workbooks: Hands-on practice with small groups

• Weekend homework assignments: 1 each weekend

• Resources

• TAs: (James, Ashir) plus: Jason Kwon, Aviad Tsherniak, Phil Montgomery, William Colgan

• Course website: broad.io/cp_r_bootcamp

• Slack #cancer-bootcamp_r

Other helpful R resources

• Hadley Wickham's R for Data Science e-book

• R cheet sheets

• Rafael Irizarry's R e-book

• and much more...

Acknowledgements

• Chester Ismay's DataCamp slides

• HBC Intro to R course

• Sam Meier's 2018 R lectures

• Hadley Wickham's R for Data Science

R Basics

The R console

Where the action happens!

• Provide inputs to R

• See outputs of commands you give it (each on separate line)

• Web-based R consoles (Rstudio coming soon!)

R as calculator

List of key math operations

• *: multiplication
• /: division
• +: addition
• -: subtraction
• ^: 'raise to the power'

Logical operations

• == Check equals
• != Check not equals
• > Greater than, < less than
• >= Greater than or equal to, etc.

2^2 == 3

Objects in R

• Variables:

• Store information/data (the "nouns")

• Come in a number of flavors

• Functions:

• Set of instructions to perform some task (the "verbs")

• We'll come back to these in a bit

Creating variables

• You can create new variables with <-

x <- 3 * 4
x

Variable types in R {.smaller}

Numbers

x <- 1
x <- 1.592E-39

Strings (text)

x <- 'abc'
x <- "abc"

Logical (true/false)

x <- TRUE
x <- FALSE
x <- T

Factors (categorical variables)

• e.g. ('bad', 'OK', 'good', 'great')

• We will mostly try to avoid these, but be aware of them.

Variable naming

• Variable names must start with a letter, and can only contain letters, numbers, '_', and '.'.

Not allowed: 4th, my var, weird?, etc. etc.

• Object naming is important for writing good, readable, code

• Make variable names descriptive

avgClicks

• Make function names verbs

calculate_avg_clicks

NOT: var1 or a

Commonly used R data structures

• Vectors

• Lists

• Matrix

• Dataframes

Vectors {.smaller}

• Ordered collection of values. Like a sequence of 'buckets'

• Can hold numeric data

• Or text (strings)

• Or boolean data (TRUE/FALSE)

• All data in a vector has to be the same type!

Making vectors

Use 'combine' function c()

num_vec <- c(1, 2, 3, 4)

log_vec <- c(TRUE, TRUE, FALSE, F)

str_vec <- c('this', 'is', 'a', 'vector', 'of', 'strings')

print(num_vec)

Shorthand to create a sequence of integers

1:4

Missing values

• Quick notes on missing values in R (will be important)

• NA ('not available') is a special value for missing data that can be included in any type of vector

c(1, 2, NA)
c('a', 'b', NA)

Adding more data on to vectors {.smaller}

c() can also be used to add new elements to a vector

string_vec <- c("TP53", "PLEC", "DSPP", "PIK3CA")
string_vec2 <- c(string_vec, "BRAF")
string_vec2

Combining two vectors

string_vec <- c("TP53", "PLEC", "DSPP", "PIK3CA")
string_vec2 <- c("BRAF", "EGFR", "DUSP4")
c(string_vec, string_vec2)

Lists

Lists are basically like relaxed vectors, where elements don't have to be the same type

z <- list('a', 1, TRUE)
z

---

You can combine lists with the c() function as with vectors

z <- list('a', 1, TRUE)
c(z, 'c')

Matrix

• Like vectors, but arranged in 2d with rows and columns

• Example: gene expression values across genes and samples

Dataframe

• Most common way of interacting with data

• Each column is a vector, and they can hold different kinds of data

• Like an Excel table.

Key concepts recap

• Create variables with <-, naming them is important

• Variables can be numbers, text (strings) or TRUE/FALSE (boolean)

• Data organized as vectors, lists, matrices, and dataframes

• Create/add to vectors (or lists) with c()

• Make lists with list()

AshirBorah/cp_bootcamp_r_tutorials documentation built on May 16, 2024, 3:24 p.m.