ForestCoverageVSAgricultureCoverage.Rmd

---
title: "Forest Coverage vs Agriculture Coverage"
author: "pdelboca"
date: "30/01/2015"
output: html_document
---
## Introduction
In my [last analysis](https://rpubs.com/pdelboca/forest-coverage) I went through the Forest Coverage in the main Countries of 
the world. Now I'm practicing 2 Variables analysis so I will add the Agriculture 
Coverage of each country to analyze the relationship between deforestation and 
agriculture in each country.

My main concern: **Is the World deforesting in order to have more land for Agriculture?**

```{r,echo=FALSE, message=FALSE}
library(reshape2) # melt function
library(ggplot2)
library(gridExtra)
library(dplyr) # arrange
setwd("~/Repos//data-analysis-with-r")
```

## Getting and Cleaning Data
Data has been obtained and downloaded from [Gapminder](http://www.gapminder.org/data/).

There are 2 datasets: **Agricultural land (% of land area)** and **Forest coverage (%)**.

**Note:** Since the goal of this course is not Getting and Cleaning data, I've
manually removed years from Agriculture data to make it easier to read and clean. 
*This is not a recomended practice*.


```{r,warning=FALSE}
a.coverage <- read.csv("./data//agriculture land.csv",
                   col.names = c("Country","1990","2000","2005"))
f.coverage <- read.csv("./data//indicator_forest coverage.csv",
                   col.names = c("Country","1990","2000","2005"))

a.coverage <- melt(a.coverage, id.vars = c("Country"))
names(a.coverage) <- c("Country","Year","Agriculture.Coverage")
f.coverage <- melt(f.coverage, id.vars = c("Country"))
names(f.coverage) <- c("Country","Year","Forest.Coverage")

data <- merge(a.coverage,f.coverage, by=c("Country","Year"))

data$Year <- gsub("X","",data$Year)
data$Year <- as.factor(data$Year)
data$Agriculture.Coverage <- gsub(",",".",data$Agriculture.Coverage)
data$Agriculture.Coverage <- as.numeric(data$Agriculture.Coverage,na.action)
data$Forest.Coverage <- gsub(",",".",data$Forest.Coverage)
data$Forest.Coverage <- as.numeric(data$Forest.Coverage,na.action)

rm(a.coverage,f.coverage)

head(data)
```

Wow! I'm already learning something. I didn't know that [Afghanistan main economy
was Agriculture](http://en.wikipedia.org/wiki/Afghanistan#Economy)

## Data Processing

#### First some basic histograms of the Variables:

```{r}
p1 <- ggplot(data, aes(x=Forest.Coverage)) + geom_histogram(binwidth = 3) + 
  facet_wrap(~ Year) +
  ggtitle("Forest Coverage around the World per Year")

p2 <- ggplot(data, aes(x=Agriculture.Coverage)) + geom_histogram(binwidth = 3) + 
  facet_wrap(~ Year) +
  ggtitle("Agriculture Coverage around the World per Year")

grid.arrange(p1,p2,ncol = 1)
```

It look's like the Agriculture Coverage tends to a normal distribution. It's a 
pity I don't have data from the last 10 years!

#### Let's plot a few BoxPlots:

```{r, warning=FALSE}
ggplot(data, aes(x = Year, y = Forest.Coverage, fill = Year)) + 
  geom_boxplot(alpha = 0.5) + 
  scale_y_continuous(breaks = seq(0,100,5)) +
  coord_cartesian(ylim = c(00,100)) + 
  ylab("Average Forest Coverage (%)") + 
  ggtitle("Average Forest Coverage (%) by Year")


ggplot(data, aes(x = Year, y = Agriculture.Coverage, fill = Year)) + 
  geom_boxplot(alpha = 0.5) + 
  scale_y_continuous(breaks = seq(0,100,5)) +
  coord_cartesian(ylim = c(00,100)) + 
  ylab("Average Agriculture Coverage (%)") + 
  ggtitle("Average Agriculture Coverage (%) by Year")

```

Regarding Agriculture seems that 75% of the countries has 20% or more Agriculture 
Coverage and the median is almost 40%.

#### Argentina's Agriculture:

```{r}
argentina <- subset(data, Country == "Argentina")
argentina
```
Agriculture Coverage in Argentina has grown almost a 3% Between 1990 and 2005.

```{r, warning=FALSE}
ggplot(argentina) + 
  geom_line(aes(x=Year,y=Agriculture.Coverage, group = Country), stat= "identity",
            size = 1, color = "red") +
  geom_boxplot(data = data , aes(x=Year,y=Agriculture.Coverage, fill = Year), alpha = 0.25) + 
  ggtitle("Agriculture Coverage (%) Changes in Argentina between 1990 and 2005\n 
          compared with the average of the World") + 
  ylab("Average Agriculture Coverage (%)") 
```

It's not surprising to see Argentina closer to the 75% Quantile than the median, 
[Argentina has a long agriculture tradition](http://en.wikipedia.org/wiki/Agriculture_in_Argentina). Also remind that Argentina is the [8th biggest country in the world](http://simple.wikipedia.org/wiki/List_of_countries_by_area) which gives Argentina a lot of square kilometers dedicated
to the agriculture.

#### Agriculture Coverage vs Forest Coverage

Now let's start plotting some scatterplots:

```{r, warning=FALSE}
ggplot(data, aes(x=Forest.Coverage, y=Agriculture.Coverage)) + geom_point()
```

.... Okay.... No idea of how to read this plot. Let's try plotting only 1 Year, 
lets say, the last one:

```{r, warning=FALSE}
ggplot(subset(data, Year == 2005),
       aes(x=Forest.Coverage, y=Agriculture.Coverage)) + 
  geom_point() +
  ggtitle("Forest Coverage vs Agriculture Coverage (2005)")
```


Still no idea... :_)

But I can see **a few interesting things**:  
* There is a Diagonal tendence with negative slope  
* It seems to be a Vertical line near the 0% of Forest Coverage  
* There are only a few points above the diagonal  

Regarding the third point... There shouldn't be points above the main diagonal,
that will mean that Agriculture Coverage + Forest Coverage is bigger than the 100%
of the land. Two options: **bad data** or some countries uses their forest as
agriculture land... Let's dig a little bit.

```{r, warning=FALSE}
ggplot(subset(data, Year == 2005),
       aes(x=Forest.Coverage, y=Agriculture.Coverage)) + 
  geom_point() +
  ggtitle("Forest Coverage vs Agriculture Coverage (2005)") + 
  geom_abline(intercept = 100, slope = -1, linetype = 2, color = "red") +
  scale_y_continuous(breaks = seq(0,100,5)) +
  scale_x_continuous(breaks = seq(0,100,5)) 

subset(data, Forest.Coverage + Agriculture.Coverage > 100)
```


#### Correlation

It's logical to say that Forest Coverage and Agriculture Coverage has a negative
 correlation, after all, you need to deforest in order to have more land to grow crops.
 Let's see if there is a Correlation between the Forest Coverage and Agriculture Coverage
 for 2005 (removing outliers):
 
 
```{r}
with(subset(data, Year == 2005  & Forest.Coverage + Agriculture.Coverage < 100),
     cor.test(Forest.Coverage,Agriculture.Coverage))

ggplot(subset(data, Year == 2005 & Forest.Coverage + Agriculture.Coverage < 100),
       aes(x=Forest.Coverage, y=Agriculture.Coverage)) + 
  geom_point() +
  ggtitle("Forest Coverage vs Agriculture Coverage (2005)") + 
  geom_abline(intercept = 100, slope = -1, linetype = 2, color = "red") +
  scale_y_continuous(breaks = seq(0,100,5)) +
  scale_x_continuous(breaks = seq(0,100,5)) +
  geom_smooth(method = "lm")
```


#### South America Countries

Let's check South America Countries:

```{r}
south.america <- c("Argentina", "Chile", "Uruguay", "Paraguay", "Brazil",
                   "Peru", "Ecuador", "Bolivia", "Venezuela", "Colombia",
                   "Suriname", "French Guiana", "Guyana")

with(subset(data, Year == 2005 & Country %in% south.america),
     cor.test(Forest.Coverage,Agriculture.Coverage))

ggplot(subset(data, Year == 2005 & Country %in% south.america),
       aes(x=Forest.Coverage, y=Agriculture.Coverage)) + 
  geom_point(aes(color = Country), size = 5, alpha = 1/2) +
  ggtitle("Forest Coverage vs Agriculture Coverage (2005)") +
  geom_abline(intercept = 100, slope = -1, linetype = 2, color = "red") +
  scale_y_continuous(breaks = seq(0,100,5)) +
  scale_x_continuous(breaks = seq(0,100,5)) +
  geom_smooth(method = "lm")
  
```

The correlation in South America countries seems to be stronger than the rest of the world.

Does it mean that countries in South America whit less forest has more agriculture lands?
Does it mean that countries in South America deforest in order to have more land for agriculture?


#### Correlation between changes in Forest Coverage and Changes in Agriculture Coverage

The main question here is: **Is there a correlation between the % of Deforestation and the increment in Agriculture Coverage?**

```{r}
Country <- as.character(unique(data$Country))
Forest.Changes <- subset(data, Year == 2005)$Forest.Coverage - subset(data, Year == 1990)$Forest.Coverage
Agriculture.Changes <- subset(data, Year == 2005)$Agriculture.Coverage - subset(data, Year == 1990)$Agriculture.Coverage

countries.Changes <- data.frame(Country, Forest.Changes, Agriculture.Changes)
countries.Changes$Country <- factor(countries.Changes$Country,
                                      levels = countries.Changes$Country) 

summary(countries.Changes)
```

It seems that there is a lot of missing data. Regarding Forest.Coverage it is no
surprising to see those values, but Is it possible that a countrie has change its Ariculture
Coverage in -40%?

Let's look at the Worst 5%...

```{r}
subset(countries.Changes, 
       Agriculture.Changes < quantile(Agriculture.Changes, probs = 0.05, na.rm = TRUE))
```

I can't found anything about drastic changes on those countries so I'm going to treat them as outliers. What about other outliers?

```{r}
subset(countries.Changes, 
       Agriculture.Changes > quantile(Agriculture.Changes, probs = 0.95, na.rm = TRUE))
```

It seems that Saudi Arabia [had an amazing increase in its agriculture](http://www.saudiembassy.net/about/country-information/agriculture_water/Agricultural_Achievements.aspx) without
even cutting a single tree. Not so sure about other outliers...

#### Forest Changes vs Agriculture Changes
 
```{r, warning=FALSE}
ggplot(subset(countries.Changes, 
       Agriculture.Changes > quantile(Agriculture.Changes, probs = 0.05, na.rm = TRUE)),
       aes(x=Forest.Changes, y=Agriculture.Changes)) +
  geom_point() + 
  geom_smooth(method = "lm") +
  ggtitle("Forest Coverage Changes vs Agriculture Coverage Changes \n between 1990 and 2005")

ggplot(subset(countries.Changes, 
       Agriculture.Changes > quantile(Agriculture.Changes, probs = 0.05, na.rm = TRUE) &
         Country %in% south.america),
       aes(x=Forest.Changes, y=Agriculture.Changes)) +
  geom_point(aes(color = Country), alpha = 1/2, size = 5) + 
  geom_smooth(method = "lm") +
  ggtitle("Forest Coverage Changes vs Agriculture Coverage Changes \n between 1990 and 2005 \n in South America")
```

So it seems that Latin America has a greater negative correlation in Changes than the
rest of the World. That could imply indeed that Deforestation main cause is Agriculture (rather than increment of population, cities, infraestructure, etc...)

#### Conclusion

Although there seems to be a negative correlation between Forest Coverage and Agriculture Coverage, mostly in South America Countries, I feel I cannot rely in the data due to some strange outliers described above and missing data.