In BENMMI: Benthic Multi-Metric Index

d_mmi <- d_mmi %>%
    left_join(d_iti, by = "TAXON") %>%
    rename(ITI_GROUP = GROUP)

if (is.null(d_iti)) {
    d <- d_mmi %>% 
        filter(!has_iti(taxon = TAXON)) %>%
        select(TAXON) %>%
        group_by(TAXON) %>%
        summarise(N_RECORDS = n())
} else {
    d <- d_mmi %>% 
        filter(!has_iti(taxon = TAXON, group = ITI_GROUP) | CARNIVORE) %>%
        select(TAXON, CARNIVORE) %>%
        group_by(TAXON, CARNIVORE) %>%
        summarise(N_RECORDS = n())
}

cat(sprintf("\nThe table below gives TAXA without an ITI-class. The total number of taxa without an ITI-class is %i. Among these taxa are %i carnivores which are always excluded from ITI-calculations.\n", nrow(d), sum(d$CARNIVORE, na.rm = TRUE)))
d %>%
    ungroup %>%
    mutate(
        CARNIVORE = as.character(ifelse(is.na(CARNIVORE), FALSE, CARNIVORE)),
        CARNIVORE = ifelse(CARNIVORE, "yes", "no")
    ) %>%
    arrange(CARNIVORE, TAXON) %>%
    xtable %>%
    print(type = "html",  include.rownames = FALSE, sanitize.text = identity)

# make sure that carnivores are excluded from ITI-calculations
d_mmi$ITI_GROUP[which(d_mmi$CARNIVORE)] <- NA_character_

The table below gives for each combination of OBJECTID-HABITAT-YEAR the average total abundance (N) and average percentage of individuals in each ITI class. File r sQuote(basename(settings$files$out_iti)) in the OUTPUT-directory gives these entries per sample.

d <- d_mmi %>%
    group_by(OBJECTID, SAMPLEID, HABITAT, DATE, YEAR, CARNIVORE, ITI_GROUP) %>%
    summarise(n = sum(VALUE)) %>%
    ungroup %>%
    mutate(ITI_GROUP = ifelse(CARNIVORE, "carnivore", ITI_GROUP)) %>%
    select(-CARNIVORE)


d <- d %>% left_join( 
        d %>%
        group_by(OBJECTID, SAMPLEID, HABITAT, DATE, YEAR) %>%
        summarise(N = sum(n)),
        by = c("OBJECTID", "SAMPLEID", "HABITAT", "DATE", "YEAR")) %>%
    mutate(p = as.integer(round(100 * n / N))) %>%
    select(-n) %>%
    spread(key = ITI_GROUP, value = p, fill = 0L) %>%
    write_csv(settings$files$out_iti)

rmean <- function(x){as.integer(round(mean(x)))}
d %>%
    ungroup %>%
    select(-DATE, -SAMPLEID) %>%
    group_by(OBJECTID, HABITAT, YEAR) %>%
    summarise_each(funs(rmean)) %>%
    xtable %>%
    print(type = "html", include.rownames = FALSE)