intro_to_the_tidyverse_TEMPLATE.Rmd

---
title: '(user template) Intro to the Tidyverse: R-Ladies SB May 2019'
author: "Your Name Here"
date: "15 May 2019"
output: html_document
---

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

## **Don't have the tidyverse yet?**
### Install using the following code:

```{r, eval = FALSE}
install.packages("tidyverse")
```

### Load the tidyverse:

```{r, message = FALSE, warning = FALSE}
 
```

## **Data wrangling cheat sheet:**

Let's first create some completely hypothetical data about the number of pizzas eaten by Sam, An, Allison, Julie, and Jamie over the past 3 years :)
```{r}
pizza_data <- tribble(
  ~name,    ~`2017`,   ~`2018`,   ~`2019`, 
  "Sam",       25,        20,        16,   
  "An",        20,        15,        11,  
  "Allison",   18,        17,        10,
  "Julie",     19,        10,        14,
  "Jamie",     21,        13,        14
  )
```

It's a great habit to always familiarize/explore your data before starting to wrangle it:
```{r}

```

**`gather()`** transforms data from wide to long format
```{r}

```

**`spread()`** transform data from long to wide format
```{r}

```

From here on, we'll be working with our **tidy data** i.e. **`tidy_pizza`** to practice some useful wrangling functions.

### Subsetting data: 

**`select()`** select columns to retain and specify their order
```{r}

```

**`filter()`** select observations within columns 
```{r}

```

### Manipulating/adding variables:

**`arrange()`** order observations as specified (default = alphabetical or ascending)
```{r}

```

**`rename()`** rename a column
```{r}

```

**`mutate()`** a versatile function
```{r}

```

### Summarizing data: 

**`group_by()`** groups observations such that data operations are performed at the level of the group
```{r}

```

**`summarize()`** calculate summary statistics
```{r}

```

**`tally()`** sum values across groups
```{r}

```

## **Now let's practice!**

### Load the tidyverse and any additional required packages:

```{r, message = FALSE, warning = FALSE}

```

### Load the data: 

In celebration of this year's superbloom, we'll be exploring phenometric data of flowering California plants from the [USA -- National Phenology Network](https://www.usanpn.org/home).

```{r, message = FALSE, warning = TRUE}

```

Let's pretend we're trying to plan a getaway to the Joshua Tree National Park and want to time our trip so that we have the greatest chance of seeing fully bloomed plants. 

### Explore: 

We should first familiarize ourselves with the data. 

```{r, eval = FALSE}

```

### Wrangle:

This dataset is *huge*--we'll want to wrangle it so that it only includes the information that we're interested in. We will:

a.   
b. 
c.   
d.   
e.  
f. 
g.

```{r}

```

With this simplified and cleaned data set, we're ready to explore a subset of the desert species we're most interested in. We love **Joshua trees** (*Yucca brevifolia*), **creosote bushes** (*Larrea tridentata*), and **Mojave yucca** (*Yucca schidigera*) and want to know when these plants are blooming. Let's first isolate data for these species by:

a. 
b. 
c. 

```{r}

```

### Plot:

a. 
b. 
c. 

```{r, fig.align = 'center', fig.width = 15, fig.height = 10}

```