library(dplyr)
all_di <- read.csv(here::here("analysis", "reports", "submission2", "all_di.csv"), stringsAsFactors = F)
all_ct <- read.csv(here::here("analysis", "reports", "submission2","all_ct.csv"), stringsAsFactors = F)
all_ct <- all_ct %>%
mutate(dat = ifelse(grepl(dat, pattern = "fia"), "fia", dat),
dat = ifelse(dat == "misc_abund_short", "misc_abund", dat))
all_di <- all_di %>%
mutate(log_nparts = log(gmp:::as.double.bigz(nparts)),
log_nsamples = log(nsamples),
log_s0 = log(s0),
log_n0 = log(n0)) %>%
filter(n0 != s0,
s0 != 1,
!singletons,
n0 != (s0 + 1)) %>%
mutate(dat = ifelse(grepl(dat, pattern = "fia"), "fia", dat),
dat = ifelse(dat == "misc_abund_short", "misc_abund", dat)) %>%
mutate(Dataset = dat,
Dataset = ifelse(Dataset == "fia", "FIA", Dataset),
Dataset = ifelse(Dataset == "bbs", "Breeding Bird Survey", Dataset),
Dataset = ifelse(Dataset == "mcdb", "Mammal Communities", Dataset),
Dataset = ifelse(Dataset == "gentry", "Gentry", Dataset),
Dataset = ifelse(Dataset == "misc_abund", "Misc. Abundance", Dataset)) %>%
filter(nparts > 20) %>%
left_join(all_ct)
all_di <- all_di %>%
group_by_all() %>%
mutate(real_po_percentile_mean = mean(real_po_percentile, real_po_percentile_excl),
skew_percentile_mean = mean(skew_percentile, skew_percentile_excl),
simpson_percentile_mean = mean(simpson_percentile,simpson_percentile_excl),
shannon_percentile_mean = mean(shannon_percentile, shannon_percentile_excl),
nsingletons_percentile_mean = mean(nsingletons_percentile, nsingletons_percentile_excl),) %>%
ungroup()
all_di <- all_di %>%
mutate(in_fia = ifelse(Dataset == "FIA", "FIA", "Other datasets"))
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