wafR

The goal of wafR is to provide data management and graphing according to the Margules Groome style guide. This package was originally developed for outputs from the New Zealand Wood Availability Forecasts produced by Margules Groome for the NZ Ministry for Primary Industries (MPI) but has now been extended to include other functions. The Margules Groome team may find these functions useful for more general graphing in the house style.

The package has been updated to include growth and yield functions that might be commonly used by the Margules Groome team.

The following is a list of package functions. More functions are being regularly added.

General forest inventory functions
- mth - Calculates mean top height given a set of tree heights
- pdh - Calculates predominant height
- spha - calculates the stems per hectare for an inventory plot
- thpa - Calculates the trees per hectare for an inventory plot
- QMD - Calculates the quadractic mean DBH.
- ba - Calculates the basal area for a plot given a series of DBH values and a plot size (ha)
- RMSE - Calculates the root mean square error (RMSE) given a set of predictions and actual values. Useful for assessing model performance.
- std.err - Calculates the standard error of a set of samples.
- conf.int - Calculates the confidence limit around the mean at the 95% confidence level.
- ple - Calculates the probable limit of error (PLE) for a set of samples. These might be plot level estimates of volume in a forest inventory.
- is_outlier - Identifies outliers in a population.
- mdd - Calculates mean dominant DBH
Graphing helpers
- themeMagGroome - Applies the Margules Groome mapping theme to a ggplot object.
- scale_fill_MagGroome - Applies the Margules Groome colour palette to the fill argulment in a ggplot object when graphing.
- scale_colour_MagGroome - Applies the Margules Groome colour palette to the colour argulment in a ggplot object when graphing.
- shift_legend - Will shift the legend to occupy empty panels in a facetted plot.
- MG_save - saves a ggplot object as a figure suitable for insertion into a Margules Groome report
- MagGroome_cols - Returns the Margules Groome colours
- MagGroome_pal - Returns the Margules Groome colour palette
Volume and Taper Functions
- Schumacher and Hall tree volume functions with coefficients from SA forestry handbook
- Max and Burkhart taper function with coefficients from SA forestry handbook
- Bi taper functions with published coefficients for Australian Eucalypts and P. radiata in NSW.
Growth models
WAF functions

Installation

This package is not on CRAN.You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("jonathandash/wafR")

Example

Example of how to use the Bi taper and volume function

library(wafR)

# View the stored coefficients for the Bi taper function 

bi_tapercoef_aus(species = 'PRAD')
#> [1]  0.82183 -0.10193 -0.00872 -0.37145 -0.00112  0.03741 -0.03672

#Example of how to estimate stem diameter for a subject tree

bi2000(DBH = 21.5, h = 20, H = 28, coefficients = bi_tapercoef_aus(species = 'PRAD'))
#> [1] 10.17623

# Example of how to estimate stem diameter for the same subject tree

bi2000_volume(DBH = 21.5, stump_height = 0.3, h = 20, H = 28, coefficients = bi_tapercoef_aus(species = 'PRAD'))
#> [1] 0.4274929

# Example of how to estimate stem volume a subject tree using South African coefficients from the SA forestry handbook

vol_tree_safh(DBH = 21.5, H = 28, coefficients = vol_tpr_coef_sa(species = 'EGRA'))
#> [1] 0.3817502

# Example of how to estimate stem volume a subject tree using South African coefficients from the SA forestry handbook

tpr_max_burkhart_safh(DBH = 21.5, H = 28, h = 15, coefficients = vol_tpr_coef_sa(species = 'EGRA'))
#> [1] 12.08676

This is a basic example which shows you how to solve a common problem:

library(wafR)
## basic example code

data("WAF_allrep")

volume_graph_bar(data = WAF_allrep,
                 value = value, Year = Year,
                 fillvariable = Ownership,
                 WAF_form = FALSE,
                 Species_select = 'Radiata_pine')


volume_graph_bar(data = WAF_allrep,
                 value = value, Year = Year,
                 fillvariable = Ownership,
                 WAF_form = TRUE,
                 Species_select = 'Radiata_pine')


# Find outliers
x <- rnorm(n = 1000, mean = 100) # Create a normally distributed dataset

is_outlier(x) # Find outliers
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boxplot(x) # Plot x to visualise the outliers

jonathandash / wafR

readme

wafR

Installation

Example