Basics of R Programming

BDSI R Training I

Emi Tanaka

Biological Data Science Institute

2nd November 2023

Simple algebraic operations

You can use R like a calculator

3 + 2 * (2 - 6 / 3)

[1] 3

Assignments to object:
- You can assign values to objects using <- or =
- The name of the object can be variable so long as it is syntactically valid (no spaces and most special characters, and the name cannot start with a digit)

a <- 3
b = 2
2 * a - b

[1] 4

Vectors

We can combine scalars to form vectors using c():

a <- c(1, 2, 3)
a / 2

[1] 0.5 1.0 1.5

This is a vector of length 3

length(a)

[1] 3

This vector is stored as a double with the class as numeric

typeof(a)

[1] "double"

class(a)

[1] "numeric"

Vectors types

There are four primary types of atomic vectors: logical, integer, double and character.

logical_vec <- c(TRUE, FALSE, T, F)
integer_vec <- c(1L, 2L, 3L, 4L)
double_vec <- c(1, 2, 3, 4)
character_vec <- c("A", "B", 'C', 'D')

The integer and double vectors are collectively called numeric vectors.

A vector can only consist of the same type.
If you attempt to combine mismatched types together, it will try to coerce all values to the same type.

c(TRUE, F, "3", 10, 'X')

[1] "TRUE"  "FALSE" "3"     "10"    "X"

Lists

Lists allow to combine elements of different types.

l <- list(c(1, 2, 3), 
          c(2.5, 3.0),
          c(TRUE, FALSE),
          c("a", "b"))

You can use str() to see the internal structure of an object in R.

str(l)

List of 4
 $ : num [1:3] 1 2 3
 $ : num [1:2] 2.5 3
 $ : logi [1:2] TRUE FALSE
 $ : chr [1:2] "a" "b"

Data frames

data.frame is a special type of a named list where each element of the vector is the same length.

df <- data.frame(grade = c("A", "B", "C"),
                     n = c(10, 14, 30))

df

  grade  n
1     A 10
2     B 14
3     C 30

colnames(df)

[1] "grade" "n"

nrow(df)

[1] 3

ncol(df)

[1] 2

Subsetting vectors Part 1

Positive integers select elements at the specified positions:

x <- c(1.1, 2.2, 3.3, 4.4, 5.5)
x[c(3, 1)]

[1] 3.3 1.1

x[c(2, 2)]

[1] 2.2 2.2

x[c(2.3, 2.8)] # double is silently truncated to integers

[1] 2.2 2.2

Negative integers exclude elements at the specified positions:

x[-c(3, 1)]

[1] 2.2 4.4 5.5

x[c(-3, 1)] # you can't mix positive and negative integers

Error in x[c(-3, 1)]: only 0's may be mixed with negative subscripts

Subsetting vectors Part 2

Logical vectors select elements where logical value is TRUE.

x[c(TRUE, FALSE, TRUE, FALSE, TRUE)]

[1] 1.1 3.3 5.5

x[x > 3]

[1] 3.3 4.4 5.5

If the logical vector used for subsetting a vector is shorter than it then the logical vector is recycled to match the length of the vector.

x[c(TRUE, FALSE)] # the same as the first one

[1] 1.1 3.3 5.5

Subsetting named vectors

Character vectors select elements based on the name of the vector (if any):

y <- c("a" = 1.1, "b" = 2.2, "c" = 3.3, "d" = 4.4, "e" = 5.5)
y

  a   b   c   d   e 
1.1 2.2 3.3 4.4 5.5

y[c("c", "a", "a", "f")]

   c    a    a <NA> 
 3.3  1.1  1.1   NA

Subsetting lists

str(l)

List of 4
 $ : num [1:3] 1 2 3
 $ : num [1:2] 2.5 3
 $ : logi [1:2] TRUE FALSE
 $ : chr [1:2] "a" "b"

l[1]

[[1]]
[1] 1 2 3

l[[1]]

[1] 1 2 3

l[c(1, 2)]

[[1]]
[1] 1 2 3

[[2]]
[1] 2.5 3.0

l[[c(1, 2)]] # what's happened here?!

[1] 2

Subsetting named lists

l2 <- list(A = c(1, 2, 3),
           log = c(TRUE, FALSE),
           who = c("Terry", "Jon"))

l2$A

[1] 1 2 3

l2[c("A", "log")]

$A
[1] 1 2 3

$log
[1]  TRUE FALSE

l2[["A"]]

[1] 1 2 3

Subsetting data frames

df

  grade  n
1     A 10
2     B 14
3     C 30

df[1, ]

  grade  n
1     A 10

df[, 1]

[1] "A" "B" "C"

df[, 1, drop = FALSE]

  grade
1     A
2     B
3     C

df$n

[1] 10 14 30

df[["n"]]

[1] 10 14 30

Communicating your problem

🆘 Asking for help 1 Part 1

What do you think about the question below?

🆘 Asking for help 1 Part 2

What do you think now?

I am looking to adjust the size of two separate ggplots within the same R chunk in Rmarkdown. These plots must be different when outputted as a pdf, so defining the dimensions at the beginning of the chunk doesn’t work. Does anyone have any ideas? My code is below.

```{r, fig.height = 3, fig.width = 3}
ggplot(df, aes(weight, height)) +
  geom_point()

ggplot(df, aes(height, volume)) +
  geom_point()
```

🆘 Asking for help 1 Part 3

Is this better?

```{r, fig.height = 3, fig.width = 3}
library(ggplot2)
ggplot(df, aes(weight, height)) +
  geom_point()

ggplot(df, aes(height, volume)) +
  geom_point()
```

🆘 Asking for help 1 Part 4

Okay better now?

```{r, fig.height = 3, fig.width = 3}
library(ggplot2)
df <- read.csv("mydata.csv")
ggplot(df, aes(weight, height)) +
  geom_point()

ggplot(df, aes(height, volume)) +
  geom_point()
```

🆘 Asking for help 1 Part 5

Are we done now?

```{r, fig.height = 3, fig.width = 3}
library(ggplot2)
ggplot(trees, aes(Girth, Height)) +
  geom_point()

ggplot(trees, aes(Height, Volume)) +
  geom_point()
```

❓ How to ask questions?

Checklist (note: not an exhaustive checklist)

Is the problem clearly and succinctly described? Is the expected solution or behaviour outlined? Are you asking the right people at the right place?

If the question is asked in an public forum or similar:

Can people who can answer your question find your question? E.g. does the post have appropriate tags or keywords to reach the right experts?

If the problem is computer system related…

Can the problem be easily reproduced on other people’s system? Is the minimum reproducible code or steps supplied?

If the problem is based on data …

Is the data supplied? If the data is big, could you cull your data further to communicate or reproduce the problem?

🆘 Asking for help 1 Check

Is the problem clearly and succintly defined? Is the expected solution or behaviour outlined? Is the problem asked at the right place or person?

Can people who can answer your question find your question?