Rapid Assessment of the Ottawa NF with 2 example interactive charts, code and data

Most simply land managers need to know what they’ve got, i.e., how much of each ecosystem, the condition of those ecosystems and what the potential for future management might be. Using LANDFIRE data, R and R-Shiny we delivered a quick assessment of the Ottawa National Forest in hopes of priming the conversation pump. This is just the beginning of deciding what to do where.

Randy Swaty

Take home messages

The assessment can be accessed at here.

The task

For this “Rapid Assessment” I aimed to provide basic ecosystem information: which ecosystems were on the Ottawa NF historically, what is there now, what the historical fire regimes were and how historical and current ecosystem structure varies. Additionally, I wanted to try to present the information in an attractive and interactive online document.

General methods

For the assessment I used LANDFIRE datasets, including:

Additionally, to explore climate change resiliency, I used the Resilient and Connected Landscapes data developed by The Nature Conservancy’s Eastern Conservation Science Team.

These datasets were all clipped to the Ottawa NF proclamation boundary using the “extract by mask” tool in ArcMap 10.4. To get at ecosytem condition and canopy cover per ecosystem all datasets were “combined” in ArcMap 10.4 using the combine tool in the spatial analyst toolbox.

For more detailed methods please me at .

The document…

was created in R-Studio as an r-markdown file with the Tufte package (which has excellent documentation that loads when you open a Tufte file!). It has R-Shiny elements (the interactive features) so is hosted on the Shiny Server.

I find that unless you have a real need for R-Shiny it’s best to avoid it. I have had many issues with deployment, and the learning curve to troubleshoot is pretty steep (at least it was for me). You can still have fun interactive charts without it using Plotly, treemap/d3treeR and sunburstR packages to name a few.

Sunburst to explore canopy cover by ecosystems.

# load libraries

# read in data
evt_evc_SB <- read_csv("evt-evc-SB.csv")

# A tibble: 6 x 2
  path                                                Acres
  <chr>                                               <dbl>
1 Alkaline Hardwood Swamp-Tree Cover >= 60 and < 70%  39389
2 Alkaline Hardwood Swamp-Tree Cover >= 70 and < 80%  25140
3 Alkaline Hardwood Swamp-Tree Cover >= 50 and < 60%  17540
4 Alkaline Hardwood Swamp-Shrub Cover >= 10 and < 20% 13022
5 Alkaline Hardwood Swamp-Tree Cover >= 80 and < 90%   5999
6 Alkaline Hardwood Swamp-OTHER                        1516

# make chart
sunBurst <- sunburst(evt_evc_SB, 
              colors = list(range = RColorBrewer::brewer.pal(11, "BrBG")),
              count = TRUE


A few things to note:

Download the data to try yourself here.

Learn about the sunburstR package and find more examples here{target = ‘blank’}

Plotly chart

Here I used Plotly to make an interactive bar chart exploring resiliency categories for the assessment area.

# A tibble: 6 x 3
  VALUE  Acres Resiliency       
  <dbl>  <dbl> <chr>            
1  3500 61.5   Far above average
2  3498  0.222 Far above average
3  3497  0.444 Far above average
4  3494  0.222 Far above average
5  3491  0.222 Far above average
6  3490  0.222 Far above average

About this chart:

Download the data to try yourself here.

ggplot and Plotly are covered extensivily on the web. I suggest simply searching for particular functions such as “panel.grid.major” and “ggplot” to see what you can learn.