https://github.com/tylerlittlefield/treeco
:deciduous_tree: Ecosystem benefits of Trees
https://github.com/tylerlittlefield/treeco
forestry r rstats trees
Last synced: 3 days ago
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:deciduous_tree: Ecosystem benefits of Trees
- Host: GitHub
- URL: https://github.com/tylerlittlefield/treeco
- Owner: tylerlittlefield
- License: mit
- Created: 2018-08-10T20:05:47.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-08T16:28:55.000Z (over 6 years ago)
- Last Synced: 2025-02-25T18:48:23.262Z (4 months ago)
- Topics: forestry, r, rstats, trees
- Language: R
- Homepage: https://treeco.netlify.com/
- Size: 7.71 MB
- Stars: 4
- Watchers: 3
- Forks: 0
- Open Issues: 3
-
Metadata Files:
- Readme: README.Rmd
- License: LICENSE
Awesome Lists containing this project
README
---
output: github_document
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# treeco 
[](https://travis-ci.org/tyluRp/treeco)
[](https://ci.appveyor.com/project/tyluRp/treeco)
[](https://codecov.io/github/tyluRp/treeco?branch=master)
[](https://www.tidyverse.org/lifecycle/#experimental)
The goal of `treeco` is to provide R users a tool for calculating the eco benefits of trees. All data used to calculate benefits is ripped from OpenStreetMaps [`otm-ecoservice`](https://github.com/OpenTreeMap/otm-ecoservice) repository which was (probably) ripped from [i-Tree](https://www.itreetools.org/)'s Eco or Streets software. A single tree is represented by 15 rows and 8 columns as there are 15 benefits calculated for every tree. Since tree inventories can be rather large, `treeco` utilizes the [`data.table`](https://github.com/Rdatatable/data.table) package for speed. All calculations are done on unique species/dbh pairs to avoid redundant computation.
## Installation
`treeco` isn't available on CRAN but you can install it directly from github using [`devtools`](https://github.com/r-lib/devtools):
```r
# install.packages("devtools")
devtools::install_github("tylurp/treeco")
```
## A reproducible example
We can use the [`trees`](https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/trees.html) dataset to demonstrate how `eco_guess` and `eco_run_all` works:
```{r, message=FALSE, warning=FALSE}
library(dplyr)
library(treeco)
df_trees <- trees %>%
mutate(common_name = "Black cherry") %>%
select(common_name, Girth) %>%
mutate(botanical_name = eco_guess(common_name, "botanical"))
eco_run_all(
data = df_trees, # dataset or path to CSV
common_col = "common_name", # common name field
botanical_col = "botanical_name", # botanical name field
dbh_col = "Girth", # dbh field
region = "PiedmtCLT", # region code
n = 0.99, # optional, threshold for species guessing
) %>% as_tibble()
```
## More examples
Use `eco_run` to calculate benefits for a single tree:
```{r}
treeco::eco_run("Common fig", 20, "InlEmpCLM")
```
One issue with eco benefits is that they all rely on i-Tree's `master_species_list` which is a list of 3,000+ species, therefore a users data needs to fit this list in order to extract benefits. For example, "Commn fig" doesn't match i-Tree's "Common fig" because of the typo. So far, there really isn't a great solution to this. For now, `treeco` guesses the species code on the fly by quantifying the "similarity", anything below 90% similar is immediately discarded.
For example, if we misspell "Common fig" as "Commn fig":
```{r}
treeco::eco_run("Commn fig", 20, "InlEmpCLM")
```
If you are missing a field, you can use `eco_guess` to try and find it:
```{r}
x <- c("common fig", "red maple", "fir")
treeco::eco_guess(x, "botanical")
```