Data visualization with ggplot2

# Data visualization with <code>ggplot2</code>
### AECN 396/896-002

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# Before you start

## Learning objectives

The objectives of this chapter is to learn how to use the `ggplot2` package to create figures for effective communication

## Table of contents

1. [`ggplot2` basics](#ggplot2-basics)
2. [Different types of figures](#dif-geoms)
3. [Placing more information in one figure](#more-info)
4. [Faceted figures](#faceting)
5. [Other supplementary `geom_*()`](#other-geoms)
6. [Make your figures presentable to others](#fine-tune)
7. [Tips](#tips)
8. [Gallery of other type of figures](#gallery)
9. [Animated figures](#animated)

<br>

<span style="color:red"> Tips: </span>hitting letter "o" key will give you a panel view of the slides

---

# `ggplot2` package

Install the package if you have not.

```r
install.packages('ggplot2')
```

When you load the `tidyverse` package, it automatically load it.

```r
#--- load ggplot2 along with others in the tidyverse package ---#
library(tidyverse)

#--- or ---#
*library(ggplot2)
```

]

---

# The datasets we use

.panel[.panel-name[Instruction]
Go [here](https://www.dropbox.com/sh/63rlp4ydmyjm1ui/AACYSeN0f_WAgKPQKzgpGVe0a?dl=0) and download **county_yield.rds** and then read the file onto R:
  ]

```r
county_yield <- readRDS("county_yield.rds") %>% 
  dplyr::select(soy_yield, corn_yield, year, county_code, state_name, d0_5_9, d1_5_9, d2_5_9, d3_5_9, d4_5_9) %>% 
    filter(state_name %in% c("Nebraska", "Kansas", "Colorado"))
```
  ]

```
##       soy_yield corn_yield year county_code state_name d0_5_9 d1_5_9  d2_5_9
##    1:        NA         NA 2018         053     Kansas 0.8980 3.8186 13.5279
##    2:        NA         NA 2017         053     Kansas 3.9994 7.0006  0.0000
##    3:        NA         NA 2016         053     Kansas 0.5724 0.0996  0.0000
##    4:        NA         NA 2015         053     Kansas 4.4283 1.6177  0.0000
##    5:        NA         NA 2014         053     Kansas 4.7032 9.9327  3.5824
##   ---                                                                       
## 2960:        53        181 2004         073   Nebraska 0.0000 3.2915 19.7085
## 2961:        57        195 2003         073   Nebraska 0.0000 7.7427 11.8459
## 2962:        51        170 2002         073   Nebraska 0.0000 7.0000  1.2978
## 2963:        56        195 2001         073   Nebraska 5.7915 0.0000  0.0000
## 2964:        54        147 2000         073   Nebraska 0.0000 4.7386 17.6887
##       d3_5_9 d4_5_9
##    1: 0.0000      0
##    2: 0.0000      0
##    3: 0.0000      0
##    4: 0.0000      0
##    5: 4.7817      0
##   ---              
## 2960: 0.0000      0
## 2961: 3.4114      0
## 2962: 4.7022      9
## 2963: 0.0000      0
## 2964: 0.5727      0
```

]

.panel[.panel-name[Variable Definitions]
+ `soy_yield`: soybean yield (bu/acre)
+ `corn_yield`: corn yield (bu/acre)
+ `d0_5_9`: ratio of weeks under drought severity of 0 from May to September
+ `d1_5_9`: ~ drought severity of 1 from May to September   
+ `d2_5_9`: ~ drought severity of 2 from May to September   
+ `d3_5_9`: ~ drought severity of 3 from May to September   
+ `d4_5_9`: ~ drought severity of 4 from May to September   
  ]
]

---

# `ggplot2` basics

---

```r
*county_yield
```
]
 
.panel2-taste-user[