## 2.5 Exercises ### 2.5.1 The ER of R

When discussing the fit between tools and tasks in Chapter 1, we encountered the notion of ecological rationality (ER, in Section 1.2.6). Now that we have learned some basic R concepts and commands, we can ask:

• Where would R be in Figure 1.7?

• Where would we place an individual R function?

• How does the arrangement change when addressing R (the language), RStudio (the software), or some R package?

### 2.5.2 Data types and forms

1. Describe the similarities and differences between scalars and vectors in R.

2. Describe the difference between logical and numeric indexing in your own words.

### 2.5.8 Manipulating matrices

Assuming a matrix mx:

mx <- matrix(letters[1:4], nrow = 2, ncol = 2, byrow = TRUE)
mx
#>      [,1] [,2]
#> [1,] "a"  "b"
#> [2,] "c"  "d"

Write R expressions that either apply functions or use some form of indexing to retrieve and replace individual elements for creating the following variants of the matrix mx:

# (a)
mx_1  # transpose mx:
#>      [,1] [,2]
#> [1,] "a"  "c"
#> [2,] "b"  "d"

# (b)
mx_2  # mirror/swap rows of mx:
#>      [,1] [,2]
#> [1,] "c"  "d"
#> [2,] "a"  "b"

# (c)
mx_3  # mirror/swap columns of mx:
#>      [,1] [,2]
#> [1,] "b"  "a"
#> [2,] "d"  "c"

# (d)
mx_4  # swap only the elements of the 2nd column of mx:
#>      [,1] [,2]
#> [1,] "a"  "d"
#> [2,] "b"  "b"

Hint: This exercise could trivially be solved by creating the matrices mx_1 to mx_4 from scratch. However, the purpose of the exercise is to use indexing for retrieving and replacing matrix elements.

### 2.5.9 Exploring participant data

This concludes our first set of exercises on basic R concepts and commands.