In bmcnellis/RSFIA: FIA Tree Mortality Analysis

library(RSFIA)
library(ClelandEcoregions)
mort_df <- FIA_mortality_with_explanatory

Study area and plot composition

message('Statistics for written results:')
cat('\nOverall mean mortality:', mean(mort_df$mort_rate))
ForestSummary(ftype_min = 2)
message('Plotting Figure 1 (a + b)')
PlotFullMortMap()
od2 <- 'C:/Users/Brandon/Documents/docs/PHD/chapter_1/draft 16'
pw1 <- CalcSectionWilcox(x = 'province')
print(pw1)
pw2 <- CalcPairwiseWilcox('section', 'mort_rate', out_dir = od2, write = T)
# Just reference the section table as a Table

# Supplemental Table S1/S2:
# TODO: combine these tables into one big table
message('Supplemental Table S1:')
tbl1 <- MakePlotCountTable(by = 'section', csv = F)
print(tbl1)

mean(tbl1$`All Mort, Background`)
message('Supplemental Table S2:')
tbl2 <- MakePlotCountTable(by = 'forest_type', csv = F)
print(tbl2)

Tree mortality

# Statistics for written results:
mort_by_province <- aggregate(mort_df$mort_rate, 
                              by = list(mort_df$Cleland_province),
                              FUN = mean)
mort_by_province[, 1] <- KeyClelandCode(mort_by_province[, 1])
mort_by_province[, 2] <- round(mort_by_province[, 2] * 100, 2)
colnames(mort_by_province) <- c('Province', 'Mean 10-Year Mortality')
cat('\nOverall mean mort:', round(mean(mort_df$mort_rate) * 100, 2), '%\n\n')
message('Mean mortality by province, Supplemental Table S3:')
print(mort_by_province)
#write.csv(mort_by_province, 'supplemental_table_s3.csv')

message('Figure 2:')
PlotOutlierMortBoxplot()
message('Table for significance:')
AnalyzeMortByIQR()
# Some stats for the Figure 2 results:
print(outlier_count_table) 
# this table was built by hand, should probably make it into a function

AgentSummary()
PlotPercentAgent()
ComorbidAgentTable(only_multiple = F, only_mort = F)
ComorbidAgentTable(only_multiple = F, only_mort = T)
ComorbidAgentTable(only_multiple = T, only_mort = F)
message('Figures 3/4/5:')
PlotPolarBar(type = 'plots', drop_no_mort = T, split = T, clear_plots = T)
PlotStackedBarChart(
  by = 'section', 
  clear_plots = T, split = T, excl_dom_ftype = T, drop_no_mort = T
  )
#PlotPolarBar(type = 'trees', drop_no_mort = T, split = T, clear_plots = T)
# The tree plot is almost the same plot as the plots plot.
# Update AgentTypeDiffTest() for a statistical test between them
# When split = F, the difference between the plot and tree plots are that
# more trees overall are killed by harvest/fire, and the fraction of
# Other/Vegetation/Weather goes down.

#PlotPolarWithMultipleMort()
# use corplot??? corrplot::cor

message('Figures 6/7:')
PlotStackedUncommForType()
PlotOutlierForestBoxplot()

message('Figures 8/9:')
PlotStackedDomForType()
PlotDomForBoxplot()

bmcnellis/RSFIA documentation built on June 1, 2019, 7:40 a.m.