Climate, social, and environmental justice markers for the Pacific Northwest

The YAML header for the app is:

Code 
---
title: "Climate, social, and environmental justice markers for the Pacific Northwest"
output: flexdashboard::flex_dashboard
runtime: shiny
---

global code chunk:

Code 
```{r global}
#| eval: false
# replace 'eval:false' with 'include: false'

library(shiny)
library(sf)
library(tidyverse)
library(tmap)
tmap_mode("view")

cejst <- st_read("/opt/data/data/assignment01/cejst_nw.shp")

# get column codes and meanings
col_choices <- read_csv("/opt/data/data/assignment04/columns.csv") %>%
  # make nicer column names for a display table
  rename("Code" = "shapefile_column", "Description" = "column_name") %>%
  # keep only "percentile" type columns
  filter(str_detect(Code, "PFS"))
```

Create a sidebar column:

Code 
Column {.sidebar}
-----------------------------------------------------------------------

Select the cejst marker:

Add code chunk with inputs under the sidebar column:

Code 
# Box with choices: which cejst column to map
selectInput("column_select", 
            label = "Justice Marker:",
            choices = col_choices$Code, 
            selected = "DF_PFS")

# Two sliders to select the maximum and minimum values to map
sliderInput("min_threshold_adjust", 
            label = "Minimum value:",
            min = 0, 
            max = 1, 
            value = 0.5, 
            step = 0.05)
sliderInput("max_threshold_adjust", label = "Maximum value:",
            min = 0, max = 1, value = 1, step = 0.05)

Add table in sidebar:

Code 
knitr::kable(col_choices[,1:2])

Add another column for map display:

Code 
Column
-----------------------------------------------------------------------

### Climate, Social, and Environmental Justice

Render map with filters based on user inputs:

Code 
# renderTmap is a tmap special case of renderPlot
renderTmap({
  # put reactively filtered data in tm_shape
  tm_shape(subset(cejst[, input$column_select], # subset data to user's column
                  # use the subset in the filtering steps, selecting the column of data with [[1]]
                  cejst[, input$column_select][[1]] <= input$max_threshold_adjust & # data column should be less than or equal to the user's max threshold
                    cejst[, input$column_select][[1]] >= input$min_threshold_adjust)) + #more than or equal to the min threshold
    # add the polygons filled by the user's selected column
    tm_polygons(col = input$column_select)
})

You can test your code by creating the “base” plot first, then adding the input$ reactive elements later.

Code 
# how to test your code
tm_shape(subset(cejst[, "DF_PFS"], 
                  cejst[, "DF_PFS"][[1]] <= 1 &
                    cejst[, "DF_PFS"][[1]] >= 0.5)) +
    tm_polygons(col = "DF_PFS")