SDG-HeatMap.Rmd

---
title: "Heatmap"
date: "2023-03-21"
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
# The script produces a dataset of the overlap of each SDG main goal and SDG relevant goal. The overlap is measured in disbursements.


# Load packages and sdg dataset -------------------------------------------

library(readxl)
library(writexl)
library(dplyr)
library(purrr)
library(tibble)
library(ggplot2)
library(forcats)
library(noradplot)
library(grid)
library(png)
df <- read_excel("output/sdg_dataset.xlsx")

# Function to reshape the SDG dataset from wide to long ----------------------------

# Objective: For the chosen SDG main goal, return a dataframe of the overlap in disbursement with each SDG relevant goal
f_sdg <- function(main_goal = NULL) {
  
  # Elements to map over: SDG relevant goals
  cols_rel <- paste0("sdg_", 1:17)
  
  # Mapping procedure and return a dataframe
  table <- map(syms(cols_rel), ~df |>  # rlang syntax
                    filter(type_of_flow == "ODA") |> 
                    filter(type_of_agreement != "Rammeavtale") |> 
                    filter(sdg_main_goal_code == main_goal) |>
                    filter(!!.x == TRUE) |>  # rlang syntax
                    summarise(nok_mill = sum(disbursed_mill_nok))) |> 
    list_rbind()
  
  # Add label columns and arrange factor levels
  table <- table |> 
    add_column(sdg_main = paste0("sdg_", main_goal),
               sdg_relevant = cols_rel, .before = TRUE) |> 
    mutate(sdg_main = factor(sdg_main, levels = paste0("sdg_", 1:17)),
           sdg_relevant = factor(sdg_relevant, levels = paste0("sdg_", 1:17)))
  
  return(table)
}

# Apply function to each main goal and return as a long dataframe-------------------

df_sdg <- map(1:17, f_sdg) |> 
  list_rbind()

# Plot -----------------------------------------------------------------------------

```

```{r Eliminating double cells}
df_sdg <- df_sdg %>% filter(sdg_main != sdg_relevant)
```

```{r Creating a column for percentage distribution}
df_sdg <- df_sdg%>%
  group_by(sdg_main) %>%
  mutate(fill_prop_unit = nok_mill / sum(nok_mill)*100)
```

```{r Standardizing 0 and NA values}
df_sdg <- df_sdg |> 
  mutate(fill_prop_unit = ifelse(fill_prop_unit==0, NA, fill_prop_unit))
```

```{r Adding images}
img_path <- "~/sdg/SDG-PNGs/Color"
img_files <- img_files <- character(17)
for (i in 1:17) {
  img_files[i] <- paste0(img_path, "/", i, ".png")
}
imgs <- lapply(img_files, readPNG)
img_grobs <- lapply(imgs, function(img) rasterGrob(img, interpolate = TRUE, height = unit(0.1, "npc"), width = unit(0.1, "npc")))

```

```{r Heatmap construction for display in R}
# create the ggplot

ggnorad()
SDG_print <- ggplot(df_sdg, aes(x = sdg_main, y = sdg_relevant, fill = fill_prop_unit)) +
 geom_tile() + scale_fill_norad_c(na.value = "black") +
 labs(title = "Prosentvis fordeling av norsk bistand per SDG", x = "Hovedmål", y = "Relevant Mål", fill = "Prosent") +
 scale_y_discrete(expand = c(0.1,0)) +
 scale_x_discrete(expand = c(0.1,0)) +
 coord_fixed(ratio=1) +
 theme(axis.text.x = element_blank(),
  axis.title.x = element_text (size = 10),
  axis.ticks.x = element_blank(),
  axis.text.y = element_blank(),
  axis.title.y = element_text (size = 10),
  axis.ticks.y = element_blank(),
  axis.line.x = element_blank (),
  axis.line.y = element_blank(),
  legend.text = element_text(size=10),
  plot.title = element_text(size = 14),
  legend.title = element_blank())



# add images to the plot
for (i in seq_along(img_files)) {
  img_grob <- img_grobs[[i]] # get the corresponding image
  SDG_print <- SDG_print +
    annotation_custom(img_grob, 
                      xmin = i + -5, xmax = i + 5, 
                      ymin = -5, ymax = 5) # adjust the coordinates to place the image within the plot area
}
for (i in seq_along(img_files)) {
  img_grob <- img_grobs[[i]] # get the corresponding image
  SDG_print <- SDG_print +
    annotation_custom(img_grob, 
                      xmin = -5, xmax = 5, 
                      ymin = i + -5, ymax = i + 5) # adjust the coordinates to place the image within the plot area
}
print(SDG_print)
```

```{r Heatmap construction for PNG download}
# create the ggplot
ggnorad()
SDG_png <- ggplot(df_sdg, aes(x = sdg_main, y = sdg_relevant, fill = fill_prop_unit)) + 
  geom_tile() + scale_fill_norad_c(na.value = "black") + 
  labs(title = "Prosentvis fordeling av norsk bistand per SDG", x = "Hovedmål", y = "Relevant mål", fill = "Prosent") + 
  scale_y_discrete(expand = c(0.093,0)) +
  scale_x_discrete(expand = c(0.093,0)) +
  coord_fixed(ratio=1) +
  theme(axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        axis.line.x = element_blank (),
        axis.line.y = element_blank (),
        axis.title = element_text(size = 140),
        plot.title = element_text(size = 200),
        legend.text = element_text(size = 80),
        legend.title = element_blank())


# add images to the plot
for (i in seq_along(img_files)) {
  img_grob <- img_grobs[[i]] # get the corresponding image
  SDG_png <- SDG_png +
    annotation_custom(img_grob, 
                      xmin = i + -5, xmax = i + 5, 
                      ymin = -5, ymax = 5) # adjust the coordinates to place the image within the plot area
}
for (i in seq_along(img_files)) {
  img_grob <- img_grobs[[i]] # get the corresponding image
  SDG_png <- SDG_png +
    annotation_custom(img_grob, 
                      xmin = -5, xmax = 5, 
                      ymin = i + -5, ymax = i + 5) # adjust the coordinates to place the image within the plot area
}
```

```{r Saving the Heatmap as PNG}
ggsave("Prosentvis fordeling av millioner NOK per SDG.png", plot = SDG_png, dpi = 1000)
```