Agro Analysis.Rmd

---
title: "Mexican Agriculture"
author: "Galactor"
date: "27/8/2018"
output: html_document
---
## Mexican Agriculture {.tabset .tabset-fade .tabset-pills}

###Intro

```{r}
library(dplyr)
library(ggplot2)
library(magrittr)
library(tidyr)
library(knitr)
library(mxmaps)
```

```{r}
#We load the data imported directly from the Ministry of Agriculture´s website. We will analyse 2017 
mex <- read.csv(file="Agro_2017.csv", header=TRUE, sep=",")
```




```{r}
mex %>% select(Nomcultivo) %>% unique %>% arrange (Nomcultivo) %>% kable("html",row.names = TRUE) 
```

There are more than 300 different kinds of crops grown in Mexico. Maize is a very important crop for Mexicans, 
we would like to know which states have the largest production.

```{r}
mex %>% select(Nomcultivo, Idcultivo, Volumenproduccion) %>%  group_by(Nomcultivo) %>% summarise(total=sum(Volumenproduccion)) %>% arrange(desc(total))
```

Measured in tons, strawberry and sugar cane have the largest production. We excluded tobacco plants since they are measured in plants rather than tons.


```{r}
mex %>% select(Nomcultivo, Idcultivo, Valorproduccion) %>%  group_by(Nomcultivo) %>% summarise(total=sum(Valorproduccion)) %>% arrange(desc(total))
```

Measured in value($), maize and avocado have the most valuable production.

###Maize

```{r}
#Note that in this analysis we include the 3 types of maize available in the data set

maize1 <- mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Idestado) %>% filter(Idcultivo==c("7470000", "7490000", "7510000")) %>% group_by(Nomestado, Idestado) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

maize1
```

Jalisco and Sinaloa are the largest maize producers in Mexico.

```{r}
data("maize1")
maize1$region <- maize1$Idestado
mxstate_choropleth(maize1, title = "Maize production, by state")
```




```{r}


maize2 <- mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Nommunicipio, Idestado, Idmunicipio) %>% filter(Idcultivo==c("7470000", "7490000", "7510000")) %>% group_by(Nomestado, Nommunicipio, Idestado, Idmunicipio) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

maize2



```

We can see that municipalities producing the highest are located at Sinaloa, Jalisco Chihuahua and Coahuila mainly.

### Avocado 

```{r}

avo1 <- mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Idestado) %>% filter(Idcultivo==c("5060000")) %>% group_by(Nomestado, Idestado) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

avo1
```

Michoacan clearly dominates the national avocado production

```{r}
avo1$region <- avo1$Idestado
mxstate_choropleth(avo1, title = "Avocado production, by state")
```


```{r}
mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Nommunicipio) %>% filter(Idcultivo==c("5060000")) %>% group_by(Nomestado, Nommunicipio) %>% summarise(total=sum(Volumenproduccion)) %>% arrange(desc(total))
```

No surprise that the majority of the municipalities included in the list are locates at Michoacan.

###Strawberry

```{r}


str1 <- mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Idestado) %>% filter(Idcultivo==c("6820000")) %>% group_by(Nomestado, Idestado) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

str1
```

```{r}

str1$region <- str1$Idestado
mxstate_choropleth(str1, title = "Strawberry production, by state")

```

Michoacan clearly dominates strawberry production in Mexico

```{r}
mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Nommunicipio) %>% filter(Idcultivo==c("6820000")) %>% group_by(Nomestado, Nommunicipio) %>% summarise(total=sum(Volumenproduccion)) %>% arrange(desc(total))
```


###Peppers

```{r}

ch1 <-mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion,Idestado) %>% filter(Idcultivo==c("6120000")) %>% group_by(Nomestado, Idestado) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

ch1
```

```{r}

ch1$region <- ch1$Idestado
mxstate_choropleth(ch1, title = "Peppers production, by state")
```

Sinaloa, Chihuahua and Sonora clearly dominte the pepper production in Mexico.

```{r}
mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Nommunicipio) %>% filter(Idcultivo==c("6120000")) %>% group_by(Nomestado, Nommunicipio) %>% summarise(total=sum(Volumenproduccion)) %>% arrange(desc(total))
```


###Sugar Cane

```{r}

sug1<- mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Idestado) %>% filter(Idcultivo==c("5820000")) %>% group_by(Nomestado, Idestado) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

sug1
```


```{r}
sug1$region <- sug1$Idestado
mxstate_choropleth(sug1, title = "Sugar Cane production, by state")
```

Veracruz and Jalisco are the largest sugar cane producers.

```{r}
mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Nommunicipio) %>% filter(Idcultivo==c("5820000")) %>% group_by(Nomestado, Nommunicipio) %>% summarise(total=sum(Volumenproduccion)) %>% arrange(desc(total))
```


###Cotton

```{r}

cot1 <- mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Idestado) %>% filter(Idcultivo==c("5190000")) %>% group_by(Nomestado, Idestado) %>% summarise(value=sum(Volumenproduccion)) %>% arrange(desc(value))

cot1
```


```{r}
cot1$region <- cot1$Idestado
mxstate_choropleth(cot1, title = "Cotton production, by state")
```

Chihuahua and Baja California lead the list in cotton production

```{r}
mex %>% select(Nomcultivo, Nomestado, Idcultivo, Volumenproduccion, Nommunicipio) %>% filter(Idcultivo==c("5190000")) %>% group_by(Nomestado, Nommunicipio) %>% summarise(total=sum(Volumenproduccion)) %>% arrange(desc(total))
```