include vignette

vignettes/milRex.Rmd

@@ -14,6 +14,90 @@ knitr::opts_chunk$set(
 )
 ```
 
-```{r setup}
+# Getting Data 
+
+Querying the Military Expenditure Database by SIPRI can be done through their [website](https://milex.sipri.org/sipri), however, the resulting xlsx is rather tedious to parse because of some formatting choices taken by the team.
+
+Hence this function makes it easy to directly import this database to R and parse not only the different indicators, but also the associated footnotes. 
+
+The following code is all you need to query data from their website. 
+```{r querying,  message=F, warning=F}
 library(milRex)
+milex <- sipri_get_data(indicator = "constantUSD")
+```
+
+The available indicators are: 
+
+```{r labels, echo = F, message=F, warning=F}
+library(dplyr)
+library(knitr)
+dplyr::tribble(
+  ~sheet_name, ~unit,~skip,~indicator,
+  "Constant (2022) US$", 2, 5,'constantUSD',
+  "Current US$", 2, 5,'currentUSD',
+  "Share of GDP", 1, 5,'shareOfGDP',
+  "Share of Govt. spending", 3, 7,'shareGovt',
+  "Regional totals", 2, 13,'regionalTotals',
+  "Local currency financial years", 2, 7,'currencyFY',
+  "Local currency calendar years", 2, 6,'currencyCY',
+  "Per capita", 2, 6,'perCapita',
+  "all of the above", 2, 6,'all'
+  ) |> 
+  dplyr::select(sheet_name, indicator) |> 
+  knitr::kable()
+```
+
+# Aggregating data by regions
+
+The package also comes with a pre-packaged data-frame that allows you to merge 
+SIPRIs regional coding to the data-frame. 
+
+
+```{r, message=F, warning=F}
+library(tidyverse)
+
+milex_regional <- milex |> 
+  left_join(milRex::sipri_regions) |> 
+  summarise(value = sum(value, na.rm = T), .by = c(region, year)) |> 
+  filter(year >= 1988)|> 
+  mutate(value = if_else(year == 1991, 0, value)) |> 
+  mutate(year = paste(year, "01-01") |> ymd()) 
 ```
+
+Now we can replicate a common graph used by SIPRI to show military spending by regions. 
+
+```{r,warning=F, message=F,out.width="100%",dpi=300}
+library(ggplot2)
+
+ggplot(milex_regional) +
+  geom_col(aes(year, value/1000, fill = reorder(region, value)))+
+  labs(y = "Militarxy expenditure\n (constant 2022 USD billion)",
+       caption = "1991 not present because no values for Soviet Union.\n See https://www.sipri.org/sites/default/files/2024-04/2404_fs_milex_2023.pdf", 
+       fill = "Region",
+       x = "") + 
+  theme_bw() +
+  theme(plot.background = element_rect(fill = rgb(215, 229, 221, 
+                                                  maxColorValue = 255)),
+        panel.background = element_rect(fill = rgb(215, 229, 221, 
+                                                  maxColorValue = 255)),
+        legend.background = element_rect(fill = rgb(215, 229, 221, 
+                                                  maxColorValue = 255)),
+        legend.position = "top",
+        legend.text = element_text(size = 4),
+        legend.title = element_text(size = 4,face  = "bold"),
+        panel.border = element_blank(),
+        axis.line.x = element_line(),
+        text = element_text(size = 7),
+        legend.key.size = unit(0.05, 'in'))+
+  scale_fill_manual(values = 
+                      c(
+                        rgb(83,50,82, maxColorValue = 255),
+                        rgb(118,81,116, maxColorValue = 255),
+                        rgb(186,144,177, maxColorValue = 255),
+                        rgb(154,173,164, maxColorValue = 255),
+                        rgb(100,156,136, maxColorValue = 255)
+                      ))+
+  scale_x_date(breaks = "4 years",date_labels = "%Y")
+```
+
+