tibble.Rmd

# Tibbles {#tibbles .r4ds-section}

```{r setup,message=FALSE,cache=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 only the first ten observations. 
There are also some other differences in formatting of the printed data frame.
It prints the number of rows and columns and the date type of each column.
```{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")]
```

The `$` operator will match any column name that starts with the name following it.
Since there is a column named `xyz`, the expression `df$x` will be expanded to `df$xyz`. 
This behavior of the `$` operator saves a few keystrokes, but it can result in accidentally using a different column 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`.
The `n_extra` argument determines the number of extra columns to print information for.

</div>