Example growth data frame for small example

Source: R/example_datasets.R

This is an example growth data frame formed from two census data frames. In this case it is made by combining census_1_ex and census_2_ex. The individuals alive in both censuses were linked by their tree ID.

growth_ex

Format

A sf spatial tibble

ID

Tree identification number. This identifies an individual tree and can be used to connect trees between the two censuses.

sp

Species of the individual

codes1

Code for additional information on the stem during the first census: M means the main stem of the individual tree and R means the stem was lost, but the tag was moved to another stem greater than DBH cutoff, this stands for resprout.

dbh1

Diameter at breast height of the tree in cm at the first census

dbh2

Diameter at breast height of the tree in cm at the second census

growth

Average annual growth between the two censuses in cm per year

codes2

Codes at the second census

geometry

Point location of the individual

See also

compute_growth()

Other example data objects: blocks_ex, census_1_ex, census_2008_bw, census_2014_bw, census_2_ex, comp_bayes_lm_ex, focal_vs_comp_ex, growth_spatial_ex, growth_toy, species_bw, study_region_bw, study_region_ex

Examples

library(ggplot2)
library(dplyr)
library(sf)
library(sfheaders)
library(blockCV)

growth_ex %>%
  ggplot() +
  geom_sf()


growth_ex %>%
  group_by(sp) %>%
  summarize(mean(growth))

#> Simple feature collection with 2 features and 2 fields
#> Geometry type: MULTIPOINT
#> Dimension:     XY
#> Bounding box:  xmin: 0.75 ymin: 0.75 xmax: 8.75 ymax: 4.5
#> CRS:           NA
#> # A tibble: 2 × 3
#>   sp             `mean(growth)`                                         geometry
#>   <fct>                   <dbl>                                     <MULTIPOINT>
#> 1 american_beech         NA     ((1.5 2.5), (3 1.5), (5.5 4.5), (8.5 0.75), (8.…
#> 2 sugar_maple             0.920 ((0.75 2.5), (1.75 2.25), (3.25 1.75), (8 1.5),…

# Add buffer
growth_spatial_ex <- growth_ex %>%
  add_buffer_variable(direction = "in", size = 1, region = study_region_ex)

# Add cross-validation folds
fold1 <- rbind(c(0, 0), c(5, 0), c(5, 5), c(0, 5), c(0, 0))
fold2 <- rbind(c(5, 0), c(10, 0), c(10, 5), c(5, 5), c(5, 0))
blocks <- bind_rows(
  sf_polygon(fold1),
  sf_polygon(fold2)
) %>%
  mutate(foldID = c(1, 2))

SpatialBlock_ex <- spatialBlock(
  speciesData = growth_ex,
  verbose = FALSE,
  k = 2,
  selection = "systematic",
  blocks = blocks
)


# Add foldID to data
growth_spatial_ex <- growth_spatial_ex %>%
  mutate(foldID = SpatialBlock_ex$foldID %>% as.factor())