tibble.Rmd

# Tibbles {#tibbles .r4ds-section}

```{r message=FALSE}
library("tidyverse")
```

## Exercise 10.1 {.unnumbered .exercise data-number="10.1"}

<div class="question">
How can you tell if an object is a tibble? (Hint: try printing `mtcars`, which is a regular data frame).
</div>

<div class="answer">

When we print `mtcars`, it prints all the columns.
```{r}
mtcars
```

But when we first convert `mtcars` to a tibble using `as_tibble()`, it prints on the
first ten observations. There are also some other differences in formatting
of the printed data frame.
```{r}
as_tibble(mtcars)
```

You can use the function `is_tibble()` to check whether a data frame is a tibble or not.
The `mtcars` data frame is not a tibble.
```{r}
is_tibble(mtcars)
```
But the `diamonds` and `flights` data are tibbles.
```{r}
is_tibble(ggplot2::diamonds)
is_tibble(nycflights13::flights)
is_tibble(as_tibble(mtcars))
```

More generally, you can use the `class()` function to find out the class of an
object. Tibbles has the classes `c("tbl_df", "tbl", "data.frame")`, while old
data frames will only have the class `"data.frame"`.
```{r}
class(mtcars)
class(ggplot2::diamonds)
class(nycflights13::flights)
```

If you are interested in reading more on R's classes, read the chapters on
object oriented programming in [Advanced R](http://adv-r.had.co.nz/S3.html).

</div>

## Exercise 10.2 {.unnumbered .exercise data-number="10.2"}

<div class="question">
Compare and contrast the following operations on a `data.frame` and equivalent tibble. What is different? Why might the default data frame behaviors cause you frustration?
</div>

<div class="answer">

```{r}
df <- data.frame(abc = 1, xyz = "a")
df$x
df[, "xyz"]
df[, c("abc", "xyz")]
```

```{r}
tbl <- as_tibble(df)
tbl$x
tbl[, "xyz"]
tbl[, c("abc", "xyz")]
```

Using `$` a data.frame will partially complete the column. So even though we
wrote `df$x` it returned `df$xyz`. This saves a few keystrokes, but can result
in accidentally using a different variable than you thought you were using.

With data.frames, with `[` the type of object that is returned differs on the
number of columns. If it is one column, it won't return a data.frame, but
instead will return a vector. With more than one column, then it will return a
data.frame. This is fine if you know what you are passing in, but suppose you
did `df[ , vars]` where `vars` was a variable. Then what that code does
depends on `length(vars)` and you'd have to write code to account for those
situations or risk bugs.

</div>

## Exercise 10.3 {.unnumbered .exercise data-number="10.3"}

<div class="question">
If you have the name of a variable stored in an object, e.g. `var <- "mpg"`, how can you extract the reference variable from a tibble?
</div>

<div class="answer">

You can use the double bracket, like `df[[var]]`. You cannot use the dollar sign, because `df$var` would look for a column named `var`.

</div>

## Exercise 10.4 {.unnumbered .exercise data-number="10.4"}

<div class="question">

Practice referring to non-syntactic names in the following data frame by:

1.  Extracting the variable called 1.
1.  Plotting a scatterplot of 1 vs 2.
1.  Creating a new column called 3 which is 2 divided by 1.
1.  Renaming the columns to one, two and three.

</div>

<div class="answer">

For this example, I'll create a dataset called annoying with
columns named `1` and `2`.

```{r}
annoying <- tibble(
  `1` = 1:10,
  `2` = `1` * 2 + rnorm(length(`1`))
)
```

1.  To extract the variable named `1`:

    ```{r}
    annoying[["1"]]
    ```

    or

    ```{r}
    annoying$`1`
    ```

1.  To create a scatter plot of `1` vs. `2`:

    ```{r}
    ggplot(annoying, aes(x = `1`, y = `2`)) +
      geom_point()
    ```

1.  To add a new column `3` which is `2` divided by `1`:

    ```{r}
    mutate(annoying, `3` = `2` / `1`)
    ```
    
    or 
    
    ```{r}
    annoying[["3"]] <- annoying$`2` / annoying$`1`
    ```

    or

    ```{r}
    annoying[["3"]] <- annoying[["2"]] / annoying[["1"]]
    ```

1.  To rename the columns to `one`, `two`, and `three`, run

    ```{r}
    annoying <- rename(annoying, one = `1`, two = `2`, three = `3`)
    glimpse(annoying)
    ```

</div>

## Exercise 10.5 {.unnumbered .exercise data-number="10.5"}

<div class="question">
What does `tibble::enframe()` do? When might you use it?
</div>

<div class="answer">

The function `tibble::enframe()` converts named vectors to a data frame with names and values

```{r}
enframe(c(a = 1, b = 2, c = 3))
```

</div>

## Exercise 10.6 {.unnumbered .exercise data-number="10.6"}

<div class="question">
What option controls how many additional column names are printed at the footer of a tibble?
</div>

<div class="answer">

The help page for the `print()` method of tibble objects is discussed in `?print.tbl_df`.
The `n_extra` argument determines the number of extra columns to print information for.

</div>