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##Need to load the library from previously installed packages or you may need to install some of these packages first.
library(ggplot2)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ lubridate 1.9.4     ✔ tibble    3.3.0
## ✔ purrr     1.1.0     ✔ tidyr     1.3.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(formattable)
library(kableExtra)
## 
## Attaching package: 'kableExtra'
## 
## The following object is masked from 'package:dplyr':
## 
##     group_rows
library(allodb)
library(readxl) 
#allows direct reading of Excel file formats
setwd("~/Desktop/Hopkins/forested landscapes")
#data <- read_excel(file.choose()) #Allows to choose file with mouse
data <- read_excel("dbhtestforAGB.xlsx")
## New names:
## • `` -> `...10`
get_biomass(
  dbh = 50,
  genus = "Quercus",
  species = "alba",
  coords = c(-76.16, 39.68)
)
## [1] 1923.479
data$agb <-
  get_biomass(
    dbh = data$dbh,
    genus = data$genus,
    species = data$species,
    coords = c(-76.16, 39.68)
  )

sum(data$agb) #Add up total biomass
## [1] 23505.41
#Assuming that 50% of wood biomass is carbon, we can also calculate how much carbon is stored in the aboveground portion of the tree, measured in kilograms:
data$Carbon<-round(data$agb/2,1)

#Better to scale to carbon by assuming a 48% carbon content of broadleaved trees in temperate forests (IPCC, 2006).
data$Carbon<-round(data$agb*.48,1)

#For fun let’s calculate carbon dioxide uptake. CO2 (kg) = total carbon (kg) x 3.67
data$CO2e <- round(data$agb*3.67)
sum(data$CO2e) 
## [1] 86265
#Fancy way to make a table to export as a graphic
data_show <- data[, 3:11] %>% 
  head(30) 
data_show %>% 
  kable("html", caption = 'Tree data set with predictions of biomass and carbon in kilograms.') %>%
  # kable_styling(position = "center") %>% 
  kable_styling()
Tree data set with predictions of biomass and carbon in kilograms.
dbh genus species Family long lat Group ID …10 agb
14.7 Pinus strobus Pinaceae -76.60000 39.30000 Wilber NA 70.5406810
82.5 Fagus grandifolia Fagaceae -76.62420 39.32807 ob, cw, lm, tw NA 6158.2985707
32.5 Quercus alba Fagaceae -76.62478 39.32888 ob, cw, lm, tw NA 744.1320791
62.0 Fagus grandifolia Fagaceae -76.62492 39.32919 ob, cw, lm, tw NA 3038.5347634
2.0 Ilex opaca Aquifoliaceae -76.62472 39.32911 ob, cw, lm, tw NA 0.5541136
20.6 Liriodendron tulipifera Magnoliaceae -76.62466 39.32907 ob, cw, lm, tw NA 192.9447592
65.0 Fagus grandifolia Fagaceae -76.62392 39.33009 ES, RA, IN NA 3415.1731825
19.3 Quercus rubra Fagaceae -76.62400 39.33005 ES, RA, IN NA 236.2479569
30.5 Juglans nigra Juglandaceae 76.62397 39.33000 ES, RA, IN NA 527.1669908
9.8 Ilex opaca Aquifoliaceae -76.62390 39.33000 ES, RA, IN NA 28.4374556
9.6 Liriodendron tulipifera Magnoliaceae -76.62406 39.33001 ES, RA, IN NA 31.5801894
49.8 Fagus grandifolia Fagaceae -76.62000 39.33000 SM, MJ, AJ NA 1767.4004581
94.1 Liriodendron tulipifera Magnoliaceae -76.62000 39.33000 SM, MJ, AJ NA 7067.1644075
4.4 Fraxinus americana Oleaceae -76.62000 39.33000 SM, MJ, AJ NA 6.1505114
21.0 Ilex opaca Aquifoliaceae -76.62000 39.33000 SM, MJ, AJ 12.7+8.3 187.9685623
10.7 Magnolia tripetala Magnoliaceae -76.62000 39.33000 SM, MJ, AJ NA 33.1196332
Forest
Forest