Skip to content

Commit

Permalink
Removed slides not used in week 1 (and added one)
Browse files Browse the repository at this point in the history
  • Loading branch information
@robjhyndman
robjhyndman committed Feb 28, 2024
1 parent 98733d2 commit 681e0fc
Show file tree
Hide file tree
Showing 2 changed files with 55 additions and 129 deletions.
2 changes: 1 addition & 1 deletion renv.lock
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1795,7 +1795,7 @@
"Package": "rstudioapi",
"Version": "0.15.0",
"Source": "Repository",
"Repository": "https://packagemanager.rstudio.com/all/__linux__/focal/latest",
"Repository": "CRAN",
"Hash": "5564500e25cffad9e22244ced1379887"
},
"rversions": {
Expand Down
182 changes: 54 additions & 128 deletions week1/slides.qmd
View file
Original file line number Diff line number Diff line change
Expand Up @@ -337,26 +337,6 @@ knitr::include_graphics("../diagrams/name-value/character.png")
```{r, echo = FALSE, out.width = NULL}
knitr::include_graphics("../diagrams/name-value/character-2.png")
```

## Exercises

4. Sketch out the relationship between the following objects:

```{r}
a <- 1:10
b <- list(a, a)
c <- list(b, a, 1:10)
```
5. What happens when you run this code?
```{r}
x <- list(1:10)
x[[2]] <- x
```
Draw a picture.
## Object size

`lobstr::obj_size()` gives the size of an object in memory.
Expand Down Expand Up @@ -388,28 +368,6 @@ obj_size(c(1:1e6, 10))
obj_size(2 * (1:1e6))
```

## Exercises

6. Predict the output of the following code:

```{r}
#| eval: false
a <- runif(1e6)
obj_size(a)
b <- list(a, a)
obj_size(b)
obj_size(a, b)
b[[1]][[1]] <- 10
obj_size(b)
obj_size(a, b)
b[[2]][[1]] <- 10
obj_size(b)
obj_size(a, b)
```
## For loops

Loops have a reputation for being slow, but often that is caused by iterations creating copies.
Expand Down Expand Up @@ -462,6 +420,42 @@ for (i in 1:3) {
\vspace*{10cm}


## Don't allocate memory in a for loop

:::: {.columns}

::: {.column width="50%"}

```{r}
# Allocating memory within the loop
system.time(
{
x <- NULL
for(i in seq(1e5)) {
x <- c(x, i)
}
}
)
```

:::

::: {.column width="50%"}

```{r}
# Allocating memory before the loop
system.time(
{
x <- numeric(1e5)
for(i in seq(1e5)) {
x[i] <- i
}
}
)
```

:::
::::

## Unbinding and the garbage collector {#gc}

Expand Down Expand Up @@ -625,17 +619,17 @@ as.integer(c("1", "1.5", "a"))

## Exercises

7. Predict the output of the following:
4. Predict the output of the following:

```{r, eval=FALSE}
c(1, FALSE)
c("a", 1)
c(TRUE, 1L)
```
8. Why is `1 == "1"` true? Why is `-1 < FALSE` true? Why is `"one" < 2` false?
5. Why is `1 == "1"` true? Why is `-1 < FALSE` true? Why is `"one" < 2` false?
9. Why is the default missing value, `NA`, a logical vector? What's special
6. Why is the default missing value, `NA`, a logical vector? What's special
about logical vectors? (Hint: think about `c(FALSE, NA_character_)`.)
## Getting and setting attributes
Expand Down Expand Up @@ -726,38 +720,17 @@ dim(z) <- c(3, 2)
z
```

## Dimensions

Many of the functions for working with vectors have generalisations for matrices and arrays:

| Vector | Matrix | Array |
|-------------------|----------------------------|------------------|
| `names()` | `rownames()`, `colnames()` | `dimnames()` |
| `length()` | `nrow()`, `ncol()` | `dim()` |
| `c()` | `rbind()`, `cbind()` | `abind::abind()` |
| --- | `t()` | `aperm()` |
| `is.null(dim(x))` | `is.matrix()` | `is.array()` |

## Dimensions

```{r}
str(1:3) # 1d vector
str(matrix(1:3, ncol = 1)) # column vector
str(matrix(1:3, nrow = 1)) # row vector
str(array(1:3, 3)) # "array" vector
```

## Exercises

10. What does `dim()` return when applied to a 1-dimensional vector?
11. When might you use `NROW()` or `NCOL()`?
12. How would you describe the following three objects? What makes them different from `1:5`?
7. What does `dim()` return when applied to a 1-dimensional vector?
8. When might you use `NROW()` or `NCOL()`?
9. How would you describe the following three objects? What makes them different from `1:5`?

```{r}
x1 <- array(1:5, c(1, 1, 5))
x2 <- array(1:5, c(1, 5, 1))
x3 <- array(1:5, c(5, 1, 1))
```
```{r}
x1 <- array(1:5, c(1, 1, 5))
x2 <- array(1:5, c(1, 5, 1))
x3 <- array(1:5, c(5, 1, 1))
```

## S3 atomic vectors

Expand Down Expand Up @@ -864,18 +837,18 @@ structure(now_ct, tzone = "Australia/Lord_Howe")

## Exercises

13. What sort of object does `table()` return? What is its type? What
10. What sort of object does `table()` return? What is its type? What
attributes does it have? How does the dimensionality change as you
tabulate more variables?

14. What happens to a factor when you modify its levels?
11. What happens to a factor when you modify its levels?

```{r, results = FALSE}
f1 <- factor(letters)
levels(f1) <- rev(levels(f1))
```
15. What does this code do? How do `f2` and `f3` differ from `f1`?
12. What does this code do? How do `f2` and `f3` differ from `f1`?
```{r, results = FALSE}
f2 <- rev(factor(letters))
Expand Down Expand Up @@ -1093,11 +1066,11 @@ str(dfm)

## Exercises

16. Can you have a data frame with zero rows? What about zero columns?
17. What happens if you attempt to set rownames that are not unique?
18. If `df` is a data frame, what can you say about `t(df)`, and `t(t(df))`?
13. Can you have a data frame with zero rows? What about zero columns?
14. What happens if you attempt to set rownames that are not unique?
15. If `df` is a data frame, what can you say about `t(df)`, and `t(t(df))`?
Perform some experiments, making sure to try different column types.
19. What does `as.matrix()` do when applied to a data frame with
16. What does `as.matrix()` do when applied to a data frame with
columns of different types? How does it differ from `data.matrix()`?

## `NULL`
Expand All @@ -1113,50 +1086,3 @@ x <- NULL
x == NULL
is.null(x)
```

# Subsetting

## Exercises

20. What is the result of subsetting a vector with positive integers,
negative integers, a logical vector, or a character vector?
21. What's the difference between `[`, `[[`, and `$` when applied to a list?
22. When should you use `drop = FALSE`?
23. If `x` is a matrix, what does `x[] <- 0` do? How is it different from `x <- 0`?
24. How can you use a named vector to relabel categorical variables?

## Exercises
\fontsize{13}{14}\sf

25. Fix each of the following common data frame subsetting errors:

```{r, eval = FALSE}
mtcars[mtcars$cyl = 4, ]
mtcars[-1:4, ]
mtcars[mtcars$cyl <= 5]
mtcars[mtcars$cyl == 4 | 6, ]
```
26. Why does `mtcars[1:20]` return an error? How does it differ from the
similar `mtcars[1:20, ]`?
27. Implement your own function that extracts the diagonal entries from a
matrix (it should behave like `diag(x)` where `x` is a matrix).
## Exercises
28. Extract the residual degrees of freedom from `mod`
```{r}
#| eval: false
mod <- lm(mpg ~ wt, data = mtcars)
```
29. Extract the R squared from the model summary (`summary(mod)`)
## Exercises
30. How would you randomly permute the columns of a data frame?
31. Can you simultaneously permute the rows and columns in one step?
32. How would you select a random sample of m rows from a data frame? What if the sample had to be contiguous (i.e., with an initial row, a final row, and every row in between)?
33. How could you put the columns in a data frame in alphabetical order?

0 comments on commit 681e0fc

Please sign in to comment.