Use tidyverse workflows to build stand and stock tables.
Leverage group_by() grouping methods to compute estimates for populations of interest.
Understand complete() and pivot_wider() functions.
Example Stand-Level Estimates
Estimates for number of trees and wood volume per acre by species.
Species
DBH (in)
Trees/ac
Volume (ft3/ac)
Abies balsamea
11.3
8
142.8
Betula papyrifera
14.8
8
269.6
Betula papyrifera
15.4
8
293.7
Pinus strobus
9.8
8
116.3
Pinus strobus
10.7
8
143.6
Pinus strobus
13.1
8
231.9
Stand tables summarize a quantitative discrete variable (e.g., stem count) grouped by one or more categorical (i.e., qualitative) variable (e.g., size class or species).
DBH Classes (in)
Species
[6,10]
(10,14]
(14, 18]
Totals
Abies balsamea
0
8
0
8
Betula papyrifera
0
0
16
16
Pinus strobus
8
16
0
24
Totals
8
24
16
48
Values are numbers of trees per acre.
Stock tables summarize a quantitative continuous variable (e.g., volume, weight, or basal area) grouped by one or more categorical variable.
DBH Classes (in)
Species
[6,10]
(10,14]
(14, 18]
Totals
Abies balsamea
0
142.8
0
142.8
Betula papyrifera
0
0
563.3
563.3
Pinus strobus
116.3
375.5
0
491.8
Totals
116.3
518.3
563.3
1197.9
Values are volume cubic feet per acre.
Each row and column combination in the table defines a unique population.
DBH Classes (in)
Species
[6,10]
(10,14]
(14, 18]
Abies balsamea
Pop 1
Pop 2
Pop 3
Betula papyrifera
Pop 4
Pop 5
Pop 6
Pinus strobus
Pop 7
Pop 8
Pop 9
Our task is to generate an estimate for each population.
Recall this example population
We take a sample
# Read in the stand datastands <-read_csv("two_stands.csv")# Filter only Stand 1 Overstory trees# Select columns needed to build the stand and stock tableso_stand <- stands %>%filter(stand_id ==1, tree_type =="Overstory") %>%select(plot_id, tree_count, scientific_name, DBH_in, vol_cu_ft)o_stand
A plot with no trees is part of the sample, reflects a characteristic of the population, and hence needs to be included as a zero when computing population parameter estimates.
We convert plot_id to a factor (effectively preserving Plot 2 in the dataset as an absent factor level) and remove the row with tree_count == 0.
# Make plot_id a factor then remove plots with zero trees.o_stand <- o_stand %>%mutate(plot_id =as.factor(plot_id)) %>%filter(tree_count !=0)o_stand
We will then add the DBH class column and use the complete() function to add zeros into the dataset where species and DBH class combinations (that define a population) were not observed.
# Add the DBH class column.o_stand <- o_stand %>%mutate(DBH_4in =cut_width(DBH_in, width=4))# Make implicit zeros explicit for species and DBH class # combinations not observed on plots.o_stand <- o_stand %>%complete(plot_id, scientific_name, DBH_4in, fill =list(tree_count =0, vol_cu_ft =0))
