README.Rmd

---
output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-"
)
```

# cropdatape

`cropdatape` provides peruvian agricultural production data from the Agriculture Minestry of Peru (MINAGRI). The first version includes 6 crops: rice, quinoa, potato, sweet potato, tomato and wheat; all of them across 24 departments. Initially,  in excel files which has been transformed and assembled using tidy data principles, i.e. each variable is in a column, each observation is a row and each value is in a cell. The variables variables are sowing and harvest area per crop, yield, production and price per plot, every one year, from 2004 to 2014.


## Installation

You can install `cropdatape` directly from `CRAN`:

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

```

Or, you can install from `GitHub`:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("omarbenites/cropdatape")
```

The `cropdatape` data frame include 9 variables,

| variable    | meaning                  | units    |
|:------------|:-------------------------|----------|
| crop        | crop                     |  -       |
| department  | deparment or region      |  -       |
| year        | year                     |  -       |
| month       | month                    |  -       |
| sowa        | sowing area              |  ha      |
| harva       | harvested area           |  ha      |
| production  | production               |  t       |
| yield       | yield                    |  kg/ha   |
| pricePlot   | price per plot           |  s/kg    |

### Usage


#### Example 1: Filter, grouped and summarize cropdatape data

In this example, we will explore the cropdatape dataset, using three (dplyr) functionlities: `filter`, `group` and `summarize`. 

1. `filter` crop by `sweet potato`.
2. `group_by` department column.
3. `summarise` by mean of the sweetpotato yield. 

 `cropdatape` package:

```{r,  message = FALSE, warning = FALSE} 
#Load cropdatape package
library(cropdatape)
#Load dplyr package to filter and select information
library(dplyr)
cropdatape %>% 
      filter(crop == "sweet potato") %>% 
      group_by(department, year) %>% 
      summarise(yieldMean = mean(yield, na.rm = TRUE))
```


#### Example 2: Plot graphics with ggplot using cropdatape data

This second example we will explore the behaviour of the `yield` varible grouped by `crop`, from 2004 till 2014. The `crop` variable involves 6 crops: potato, quinoa, rice, sweet potato and wheat. 


```{r example, echo=TRUE, warning=FALSE}
library(cropdatape)
library(ggplot2)
ggplot(cropdatape, aes(x = crop, y = yield)) +
  geom_boxplot(outlier.colour = "hotpink") +
  geom_jitter(position = position_jitter(width = 0.1, height = 0), alpha = 1/4)
```

#### Example 3: Animations with gganimate

To begin with, install the following packages from Github:

```{r,eval=FALSE}
#Install first devtools package
#install.packages("devtools")
library(devtools)
install_github("thomasp85/gganimate")
install_github("thomasp85/transformr")
install_github("thomasp85/tweenr")
```

Then, we will filter all the information related to sweetpotato

```{r, message=FALSE, warning=FALSE}
library(cropdatape)
library(dplyr)

sp <- cropdatape %>% 
      filter(crop == "quinoa", department == "Puno") %>% 
      group_by(department, year) %>% 
      summarise(sowaMean = mean(sowa,na.rm = TRUE), 
                harvaMean = mean(harva, na.rm = TRUE),
                yieldMean = mean(yield, na.rm = TRUE))

```

Plotting and animating the scatter graph `years` vs `yieldMean`

```{r, cache=TRUE, warning=FALSE}
library(gganimate)
library(ggplot2)
library(transformr)
sp$year <- as.integer(sp$year)
yearlbl<- sp$year
ggplot(sp, aes(year, yieldMean)) + 
  geom_point(size= 1.5)+
  scale_x_continuous(breaks = yearlbl)+
  labs(title = 'Year: {frame_time}', x = 'Year', y = 'Yield') +
  transition_time(year) +
  ease_aes('linear')
```

Install and `emojifonts` package: 

```{r,  message=FALSE, warning=FALSE}
devtools::install_github("dill/emoGG")
library(emoGG)
```

Let the animation begins,

```{r, cache=TRUE, warning=FALSE}
library(gganimate)
library(ggplot2)
library(transformr)
sp$year <- as.integer(sp$year)
yearlbl<- sp$year
ggplot(sp, aes(year, yieldMean)) + 
  scale_x_continuous(breaks = yearlbl)+
  geom_emoji(emoji="1f360")+
  labs(title = 'Year: {frame_time}', x = 'Year', y = 'Yield') +
  transition_time(year) +
  ease_aes('linear')
```