Crop_production.Rmd

---
title: "Crop Production"
author: "Ido Bar"
date: "`r Sys.Date()`"
output: 
    bookdown::html_document2:
#      css: "style/style.css"
      toc: true
      toc_float: true
      toc_depth: 3
      highlight: pygments
      number_sections: false
      code_folding: hide
      keep_md: false
---

```{r setup, include=FALSE}
my_paks <- c("tidyverse", "ggpubr", "paletteer", "fontawesome", "janitor", "pacman",
             "scales", "here", "plotly", "downloadthis", "cowplot", "showtext", "Mikata-Project/ggthemr")
# pak::pak(my_paks)
pacman::p_load(char = basename(my_paks))
# pacman::p_load_gh("Mikata-Project/ggthemr", update = FALSE)
devtools::source_gist("7f63547158ecdbacf31b54a58af0d1cc", filename = "util.R")
knitr::opts_chunk$set(
	echo = FALSE,
	message = FALSE,
	warning = FALSE,
	out.width="100%",
	fig.height = 5,
	fig.width = 8
)
# add custom font
# font_add_google("Ubuntu", "ubuntu")
# showtext_auto()
# default text size in plots
def_text_size <- 14
# default theme in plots
clean_theme <-  ggthemr("flat", layout = "clean", 
                                 text_size = def_text_size, set_theme = FALSE)
# see also https://www.awe.gov.au/abares/research-topics/agricultural-outlook/data#2021

```

### Tropical Fruit Production
This data was collected from the [Australian Horticulture Statistics Handbook 2020/21](https://www.horticulture.com.au/growers/help-your-business-grow/research-reports-publications-fact-sheets-and-more/grower-resources/ha18002-assets/australian-horticulture-statistics-handbook) to support the Papaya breeding activities funded by Hort Innovation (PP18000 -- Papaya Breeding Program, AS19003 -- Genetics of Fruit Sensory Profiles, AS21006 -- Building an advanced genomics platform for Australian horticulture).

```{r tropical-fruits-data, include=FALSE}
# read data
tropical_fruit_data <- readxl::read_excel("data/tropical_fruit_production_value.xlsx", 
                                          sheet = "2020-21") %>% 
  mutate(State=factor(State, levels = c("QLD", "NSW", "VIC", "NT",  "WA", "TAS", "SA")))

# crop_data %>% filter(Crop=="Papaya", !is.na(Yield)) %>% 
#   mutate(yield_prop=Yield/sum(Yield)) 


state_cols <- levels(tropical_fruit_data$State) %>% 
  set_names(c("#741128", "#88CFEC", "#DBEBE7", "#C35E00", "#FFD800", "#0645AD", "#FF0000"), .)

crop_sum <- tropical_fruit_data %>% group_by(Crop) %>% 
  summarise(Yield=sum(Yield, na.rm=TRUE), Value=sum(Value, na.rm=TRUE)) %>%
  arrange(desc(Value)) 

crop_sum %>% 
  adorn_totals() %>% 
  write_xlsx(., "data/crop_production_value.xlsx", sheet = "summary", 
             overwritesheet = TRUE)

```

```{r tropical-fruits-figure, fig.cap="Production of Papaya, Strawberry, Mango and Pineapple (2020-21)<br>Data source: [Australian Horticulture Statistics Handbook 2020/21](https://www.horticulture.com.au/growers/help-your-business-grow/research-reports-publications-fact-sheets-and-more/grower-resources/ha18002-assets/australian-horticulture-statistics-handbook), Hort Innovation"}
# plot data
#pale_theme <- ggthemr::ggthemr(palette = "pale", set_theme = FALSE, text_size = 16)
fruit_plot <- ggplot(tropical_fruit_data %>% mutate(Crop=factor(Crop, levels = rev(crop_sum$Crop))), 
             aes(x = Crop,  y = Yield/1000)) +
  geom_bar(mapping = aes(fill = State), stat = "identity", width=0.4) + 
  geom_point(data = crop_sum, mapping = aes(y=Value/4e6), size=4, shape = 24, fill = "lightskyblue") +
  # scale_shape_manual(values = 21:24) + 
  scale_y_continuous(name = "Yield (kt)", expand = expansion(mult = c(0, .1)),
                     labels = comma, sec.axis = sec_axis( trans=~.*4, name="Value (M$)")) +
  scale_fill_manual(values = state_cols) +
  # scale_fill_paletteer_d("RColorBrewer::Set1", drop=FALSE) + 
 # labs(title = "Production of Papaya, Strawberry, Mango and Pineapple")  +
  clean_theme$theme
fruit_plot + theme_cowplot(def_text_size) +
  background_grid(major = "y", minor = "y") +
  theme(#text = element_text(family = "ubuntu"),
        axis.title.y.left = element_text(margin = unit(c(0, 3, 0, 0), "mm")),
        axis.title.y.right = element_text(margin = unit(c(0, 0, 0, 3), "mm")))
  # theme_cowplot(16)
# ggsave("plots/tropical_fruits_production_value.pdf", width=7, height = 5)

# gg + # scale_fill_paletteer_d("rcartocolor::Bold", drop=FALSE) +
#   scale_x_date(NULL, breaks = breaks_width("2 month"),
#                labels = label_date_short()) +
#   # guides(color = guide_legend(override.aes = list(size = 1, 
#   #                                                 linetype=c(3, rep(1, nlevels(comparative_vacs_data$Country)-1)))) ) +
#   # labs(colour = "Country") + 
#   # theme_bw()
#   theme_cowplot(16) +
#   background_grid(major = "y", minor = "y") 
download_this(fruit_plot,
                output_name = "tropical_fruits_production_value",
              output_extension = ".pdf",
              button_label = "Download image as pdf file",
              button_type = "success",
              has_icon = TRUE,
              icon = "fa fa-save",
              self_contained = FALSE)
```


### Chickpea Production Data
Chickpea production data was obtained from [Agricultural commodities and trade data](https://www.awe.gov.au/abares/research-topics/agricultural-outlook/data#australian-crop-report-data) at the Department of Agriculture, Water and the Environment, Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES).

```{r chickpea-data, include=FALSE}
chickies_prod_file <- recent_file("data", pattern = "Chickpea_production_.+.xlsx")
states <- set_names(c("QLD", "NSW", "VIC", "SA", "WA"), c("Queensland", "New South Wales", "Victoria", "South Australia", "Western Australia"))

st_cols <- paletteer_d("tidyquant::tq_dark", n = length(states)) %>% as.character() %>% 
  setNames(c("WA", "QLD", "VIC", "SA", "NSW")) %>% 
  .[rev(states)]

chickpea_values <- readxl::read_excel(chickies_prod_file, sheet = "Chickpea_prices_yr") 

prod_data <- readxl::read_excel(chickies_prod_file, sheet = "Chickpea_prod_updated") %>% 
  tidyr::pivot_longer(where(is.double), names_to = "Period", values_to = "value") %>% 
  tidyr::pivot_wider(names_from = Measure, values_from = value) %>% 
  left_join(chickpea_values) %>% 
  mutate(Period=sub(" (\\w)$", "(\\1)", Period)) %>% 
  mutate(State_abbr=factor(states[State], levels = rev(states)),
         State=factor(State, levels = names(rev(states)))) 



period <- unique(prod_data$Period)
# make breaks (always show last period
period_breaks <- period[seq_along(period)%%2==!(length(period)%%2==0)]


```

```{r chickpea-prod2, fig.cap="Chickpea production data 1989-2022<br>Data source: [Agricultural commodities and trade data](https://www.awe.gov.au/abares/research-topics/agricultural-outlook/data#australian-crop-report-data), Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)"}
# multiplier for secondary y-axis
# plot stacked bar graph
gg <- ggplot(prod_data, aes(x=Period, y=Production, fill=State_abbr, group=State_abbr)) +
  # geom_line(size=1) +
  geom_bar(stat = "identity", width = 0.5) +
  # geom_line(mapping = aes(y=Price*coeff), size=0.75, color="lightskyblue4") +
  # scale_fill_paletteer_d("tidyquant::tq_dark") + 
  # scale_fill_brewer(palette = "Set1", direction = -1) +
  scale_fill_manual(values = st_cols) +
  scale_x_discrete(breaks=period_breaks,
                   guide = guide_axis(check.overlap = TRUE,
                                      angle = -45)) +
  scale_y_continuous(name = "Production (kt)",
                     expand = expansion(add = c(0, 200)), labels = scales::comma) + # ggsci, category10_d3 ; ggthemes, wsj_colors6 ; "RColorBrewer::Set1"
  # coord_cartesian(ylim = c(-25, 2200)) + 
  labs(x="Reporting Period", y="Production (kt)", 
       fill="State") +
  # coord_flip(expand = FALSE, ylim = c(-25, 2200)) +
  clean_theme$theme
gg + theme_cowplot(def_text_size) +
  background_grid(major = "y", minor = "y") +
  theme(#text = element_text(family = "ubuntu"),
        axis.title.y.left = element_text(margin = unit(c(0, 3, 0, 0), "mm")),
        axis.title.y.right = element_text(margin = unit(c(0, 0, 0, 3), "mm")))
download_this(gg,
                output_name = "chickpea_production",
              output_extension = ".pdf",
              button_label = "Download image as pdf file",
              button_type = "success",
              has_icon = TRUE,
              icon = "fa fa-save",
              self_contained = FALSE)
# interactive graph
# ggplotly(g)
```

With price of chickpea overlaid.

```{r chickpea-prod, fig.cap="Chickpea production and price data 1989-2022<br>Data source: [Agricultural commodities and trade data](https://www.awe.gov.au/abares/research-topics/agricultural-outlook/data#australian-crop-report-data), Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES)"}
# multiplier for secondary y-axis
coeff <- 1
# plot stacked bar graph
g <- ggplot(prod_data, aes(x=Period, y=Production, fill=State_abbr, group=State_abbr)) +
  # geom_line(size=1) +
  geom_bar(stat = "identity", width = 0.5) +
  geom_line(mapping = aes(y=Price*coeff), size=0.75, color="lightskyblue4") +
  # scale_fill_paletteer_d("tidyquant::tq_dark") + 
  # scale_fill_brewer(palette = "Set1", direction = -1) +
  scale_fill_manual(values = st_cols) +
  scale_x_discrete(breaks=period_breaks,
                   guide = guide_axis(check.overlap = TRUE,
                                      angle = -45)) +
  scale_y_continuous(name = "Production (kt)",
                     expand = expansion(add = c(0, 200)), labels = scales::comma,
                     sec.axis = sec_axis(~./coeff, name="Price ($AUD/t)", labels = scales::comma)) + # ggsci, category10_d3 ; ggthemes, wsj_colors6 ; "RColorBrewer::Set1"
  # coord_cartesian(ylim = c(-25, 2200)) + 
  labs(x="Reporting Period", y="Production (kt)", 
       fill="State") +
  # coord_flip(expand = FALSE, ylim = c(-25, 2200)) +
  clean_theme$theme
g + theme_cowplot(def_text_size) +
  background_grid(major = "y", minor = "y") +
  theme(#text = element_text(family = "ubuntu"),
        axis.title.y.left = element_text(margin = unit(c(0, 3, 0, 0), "mm")),
        axis.title.y.right = element_text(margin = unit(c(0, 0, 0, 3), "mm")))
download_this(g,
                output_name = "chickpea_production",
              output_extension = ".pdf",
              button_label = "Download image as pdf file",
              button_type = "success",
              has_icon = TRUE,
              icon = "fa fa-save",
              self_contained = FALSE)
# interactive graph
# ggplotly(g)
```