report-mic.Rmd

---
title: "`r params$center_name_clean`"
output: 
  officedown::rdocx_document:
    reference_docx: custom-reference-doc-rmd-mic.docx
params:
  center: "Ballard-Interbay"
  filename: "Ballard-Interbay"
  center_name_clean: "City of Seattle Ballard-Interbay"
---

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

fx_files <- list.files('functions', full.names = TRUE)
sapply(fx_files, source)

# "`r params$center` Manufacturing/Industrial Center"

```

```{r}
library(tidyverse)
library(flextable)
library(openxlsx)
library(officer)
library(leaflet)
library(sf)
library(officedown)
library(here)
```

```{r gen data}

# tables
df <- read.xlsx("data/all-data-mic.xlsx")|>
  filter(center_name == params$center) |>
  mutate(core_ind_uses = str_replace(core_ind_uses, "\n", "\n\n")) |> 
  mutate(market_targets = str_replace(market_targets, "\n", "\n\n"))

# criteria table will be based on urban or metro ...
crit_df_main <- read.xlsx("criteria-table-ref-mic.xlsx")

# shapes
wgs84 <- 4326

rgc_shp <- readRDS("data/mics.rds")

rgc_ctr <- rgc_shp |>
  st_transform(wgs84) |> 
  filter(name_monitoring == params$center)

```

```{r prep table}

df_filled <- df |> 
  select(center_name, center_type, existing_jobs, planned_jobs, industrial_employment, size, transit_service, core_ind_uses, ind_ret_strategies, market_targets, subarea_plan) 

df_filled <- df_filled |>
  pivot_longer(cols = colnames(df_filled)[!(colnames(df_filled) %in% "center_name")],
               names_to = "join_field",
               values_to = "Status_filled")

crit_df <- crit_df_main |>
  left_join(df_filled , by = "join_field") |>
  mutate(Status = Status_filled)|> 
  select(-Status_filled, -center_name)

```

```{r icons}
# https://stackoverflow.com/questions/77597806/how-to-add-fontawesome-icons-to-a-flextable
# https://ardata-fr.github.io/flextable-book/cell-content.html#images

df_icon_paths <- add_image_paths(data_path = "data/all-data-icons-mic.xlsx", 
                            center = params$center)
  
```

```{r assemble tables}
crit_df2 <- crit_df |> 
  left_join(df_icon_paths, by = "join_field") |> 
  mutate(Icon = icon) |> 
  mutate(Icon = ifelse(is.na(Icon), "./images/status_icons/hyphen-solid.png", Icon)) |>
  select(-icon, -join_field)
   
```

::: {custom-style="Designation Recommendation"}
Designation Recommendation
:::

::: {custom-style="Designation Recommendation Text"}
`r df$desig_rec`
:::

# Background

```{r}
map_alt_text <- paste("A map of", params$center,"with the manufacturing/industrial center outlined in pink")
```

```{r map, fig.width=3, fig.alt=map_alt_text}

m <- create_map(center_shape = rgc_ctr)
m
```

`r df$info`
\

::: {custom-style="Table Caption"}
Evaluation of Manufacturing/Industrial Centers Requirements
:::

<!-- ![](images/status_icons/legend.png) -->
```{r, fig.height= .5, fig.width=6.5, fig.align="left", fig.alt = "A key for the Status Icons. A green checkmark means 'on track'; a yellow triangle with an exclamation mark inside means 'needs improvement'; a red circle with a line across it means 'does not meet criteria'; a grey dash means 'not applicable.'."}
knitr::include_graphics("images/status_icons/legend.png")
```




```{r criteria table}

cft <- create_table(data_table = crit_df2)
cft
```
\

::: {custom-style="Comment Header"}
Jurisdiction Comments / Additional Context
:::