---
title: "Crop Yields - Tidy Tuesday Week 36"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    source_code: embed
    social: menu
---
    
```{r, include=FALSE}
library(flexdashboard)
library(highcharter)
library(tidyverse)
library(mosaicData)
data(Countries)

key_crop_yields <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-01/key_crop_yields.csv')


crops <- key_crop_yields %>%
  select(-c(Code,Year)) %>%
  group_by(Entity) %>%
  summarise_at(vars(-group_cols()), sum,na.rm=TRUE) %>%
  mutate(total = rowSums(.[,2:12]))

crops <- rename(crops, Country = Entity) 

crops$Country[crops$Country == "Tanzania"] <- "United Republic of Tanzania"
crops$Country[crops$Country == "Iran"] <- "Iran (Islamic Republic of)"

crops <- crops %>%
  inner_join(Countries, by = c("Country"="maptools"))

```

Column 
-------------------------------------

### Total Key Crop Yield (Wheat, Rice, Maize, Soybeans, Potatoes, Beans, Peas, Cassava, Barley, Cocoa, and Bananas) Across the World Since 1961

```{r}
crops$Country[crops$Country == "United States"] <- "United States of America"
crops$Country[crops$Country == "Congo"] <- "Democratic Republic of the Congo"
crops$Country[crops$Country == "Iran (Islamic Republic of)"] <- "Iran"
hcmap(
  "custom/world-robinson-lowres", 
  data = crops,
  name = "Total Key Crop Yield (tonnes per hectare)", 
  value = "total",
  borderWidth = 0,
  nullColor = "#d3d3d3",
  joinBy = c("name", "Country")
  )%>%
  hc_add_theme(hc_theme_elementary())
```

    
Column {.tabset .tabset-fade} 
-------------------------------------
   
### Key Crop Yield Growth Over Time

```{r}
top5 <- crops %>%
  arrange(desc(total)) %>%
  head(5)

top5countries <- key_crop_yields %>%
  filter(Entity %in% top5$Country) %>%
  select(-Code)%>%
  mutate(total = rowSums(.[,2:12],na.rm=TRUE)) 

hchart(top5countries, "line", hcaes(x = Year, y = total, group = Entity)) %>%
  hc_add_theme(hc_theme_elementary())
```   

### Top Wheat Producers from 1961 to 2018

```{r}
wheat <- key_crop_yields %>%
  filter(Entity %in% crops$Country)%>%
  pivot_wider(names_from = Year, values_from=`Wheat (tonnes per hectare)`) %>%
  group_by(Entity) %>%
  select(-Code)%>%
  summarise_at(vars(-group_cols()), sum,na.rm=TRUE) %>%
  mutate(total = rowSums(.[,12:69])) %>%
  rename(Country = Entity)
  

hchart(wheat %>% arrange(desc(total)) %>% head(10), "dumbbell", hcaes(x = Country , low = `1961`, high = `2018` )) %>%
  hc_add_theme(hc_theme_elementary()) %>%
 hc_annotations(
    list(
      labelOptions = list(y = 10, x = 200),
      labels = list(
        list(
          point = list(
            x = 50,
            y = 10 ,yAxis = 0
          ),
          text = "Black: wheat production in 1961;
          Blue: wheat production in 2018"
        )
      )
    )
  )

```


### Top Rice Producers

```{r}
hchart(crops %>% arrange(desc(`Rice (tonnes per hectare)`  )) %>% head(10), "bar", hcaes(x = Country, y = `Rice (tonnes per hectare)`   )) 
```