```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

library(tidyverse)
library(viridis)
library(conflicted)
library(nycflights13)

filter <- dplyr::filter
```

# Practice: `dplyr()` Data Manipulation

We'll use another data table from the nycflights13 library, airports. Don't forget to look at the table before doing anything!

```{r}
airports
```

## `dplyr()` functions

### `filter()`

Filter airports for locations in our time zone, the US Eastern time zone.

```{r}

```

### `select()`

Select all columns BUT latitude and longitude.

```{r}

```

### `mutate()`

Add a column indicating the year the data was collected, 2013

```{r}

```

### `arrange()`

Arrange the table by latitude, then longitude

```{r}

```

### `group_by()` / `summarize()`

Find the average altitude by time zone.

```{r}

```

## Chain functions together with pipes

Filter for airports with altitude above 2000 feet and select their FAA airport codes and names

```{r}

```

Filter for airports with an altitude less than 1000 feet and find their average latitude and longitudes by timezone.

```{r}

```

Filter for latitude between 35 and 50, then plot boxplots of altitude by time zone.

```{r}

```




