R: Level 2 (Intermediate)

1. Picking out exactly the data you want

In R Basics you pulled a column with $. Two more ways to select are everywhere in real analysis: by a name and by a condition.

expr <- c(TP53 = 12.4, BRCA1 = 8.1, EGFR = 15.7, MYC = 3.3)

expr["EGFR"]        # by name
expr[expr > 10]       # by condition: keep values above 10
names(expr[expr > 10])   # which genes are those?

2. Matrices and lists: the other building blocks

A matrix is a grid of one type (think an expression matrix). A list can hold a mix of things (a number here, a vector there), which is how most R results are returned.

m <- matrix(1:6, nrow = 2, byrow = TRUE)
t(m)                 # transpose: swap rows and columns

res <- list(gene = "TP53", scores = c(2.1, 3.4))
res$gene             # pull a named piece
res[["scores"]][2]    # second score: 3.4

3. Do something to every row, column, or item

R prefers you not to write loops. The apply family runs a function across a whole structure at once.

m <- matrix(c(2,4,6, 1,3,5), nrow = 2, byrow = TRUE)

apply(m, 1, sum)        # 1 = each row
apply(m, 2, mean)       # 2 = each column

sapply(c(4, 9, 16), sqrt)  # apply to each item, get a vector back

4. Writing your own function

When you repeat a calculation, wrap it in a function. The last line is what the function returns.

zscore <- function(x) {
  (x - mean(x)) / sd(x)        # standardise a set of numbers
}

zscore(c(2, 4, 6, 8))

5. Reading your own data files

Most data arrives as a CSV or a tab-separated table. Tell R where to look, then read it in one line.

getwd()                       # where am I right now?
setwd("~/my_project")          # move to your project folder

counts <- read.csv("counts.csv")
tsv    <- read.table("data.tsv", header = TRUE, sep = "\t")
head(counts)

6. tidyverse, going deeper: joins and reshaping

Two tidyverse moves you will use constantly: joining two tables on a shared column, and reshaping a wide table into the long form that ggplot likes.

library(tidyverse)

# attach sample information onto a counts table by a shared id
joined <- left_join(counts, samples, by = "sample_id")

# wide (one column per sample) into long (one row per value)
long <- pivot_longer(counts, cols = -gene,
                     names_to = "sample", values_to = "count")

7. Cleaner plots: facets and themes optional on first pass

Once your data is long, a small plot per group is one line away with facet_wrap.

ggplot(long, aes(sample, count)) +
  geom_col() +
  facet_wrap(~ gene) +          # one mini-plot per gene
  theme_minimal()

For a deeper, opinionated take on making honest, readable figures, see Plots that tell the truth.

8. Bioconductor: where the biology packages live optional on first pass

Everyday packages come from CRAN (install.packages). Most bioinformatics packages live in a separate home called Bioconductor, installed through one helper.

install.packages("BiocManager")      # the gateway, installed once
BiocManager::install("DESeq2")        # then any Bioconductor package

Practice exercises

Type each into RStudio and run it. Peek at the solution only after you have tried.

Exercise 1: filter by condition. Using the named vector expr from section 1, keep only the genes with expression above 10.

expr[expr > 10]
# or, to get just the gene names:
names(expr[expr > 10])

Exercise 2: apply to each item. Use sapply to get the number of characters in each of c("ATG", "GG", "ATATAT").

sapply(c("ATG", "GG", "ATATAT"), nchar)
# 3 2 6

Exercise 3: a safe function. Write mean_or_zero(x) that returns the mean of x, or 0 when x is empty.

mean_or_zero <- function(x) {
  if (length(x) == 0) {
    0
  } else {
    mean(x)
  }
}

mean_or_zero(c(2, 4, 6))   # 4
mean_or_zero(c())             # 0

Check your understanding

Level 3 (Advanced): statistics in R, data.table performance, and reproducible reports is coming next.