results %>% filter( ModelType == "aggregate imputed: max ~ Year", !is.na(Parameter) ) %>% mutate( Parameter = as.integer(Parameter) ) -> wintermax results %>% filter( ModelType == "aggregate imputed: sum ~ Year + Month", !is.na(Parameter) ) %>% extract( Parameter, c("type", "winter", "month"), "(.*): ([[:digit:]]*)-([[:alpha:]]*)", convert = TRUE ) %>% mutate( month = factor( month, levels = c("October", "November", "December", "January", "February", "March") ) ) -> mt mt %>% select(id, SpeciesGroup, scientific_name, LocationGroup, type, winter, month, Estimate, LCL, UCL) %>% group_by(id, SpeciesGroup, scientific_name, LocationGroup) %>% nest() %>% left_join( wintermax %>% filter(LocationGroup == "België") %>% arrange(desc(Parameter)) %>% group_by(scientific_name) %>% slice(1) %>% ungroup() %>% select(scientific_name, wintermax = Estimate), by = "scientific_name" ) %>% mutate_at(c("SpeciesGroup", "LocationGroup", "scientific_name"), as.character) %>% arrange(desc(wintermax), SpeciesGroup, LocationGroup) %>% mutate(title = ifelse( SpeciesGroup != lag(SpeciesGroup, default = ""), sprintf( "# _%s_ (%s)\n\n## %s\n\n", scientific_name, SpeciesGroup, LocationGroup ), sprintf("## %s\n\n", LocationGroup) ) ) -> mt results %>% filter(ModelType == "yearly imputed index: Total ~ Year + Month", grepl("total:", Parameter)) %>% transmute(id, winter = as.character(Parameter) %>% str_remove("total: ") %>% as.integer(), Estimate = exp(Estimate), SE ) -> index results %>% filter(ModelType == "imputed average: Total ~ cPeriod", Parameter == "(Intercept)") %>% transmute(id, Estimate = exp(Estimate), SE) -> avg_wintermax results %>% filter( ModelType == "inla nbinomial: Year * (Month + Location)", grepl("location: ", Parameter) ) %>% transmute(id, Parameter = str_remove(Parameter, "location: "), Estimate, LCL, UCL) %>% left_join(location, by = c("Parameter" = "fingerprint")) %>% select(-Parameter) %>% mutate_at(c("Estimate", "LCL", "UCL"), exp) %>% group_by(id) %>% mutate( LCL = LCL / max(Estimate), UCL = UCL / max(Estimate), Estimate = Estimate / max(Estimate) ) %>% ungroup() -> location_effect results %>% filter( ModelType == "inla nbinomial: Year * (Month + Location)", grepl("imputation: ", Parameter) ) %>% select(id, Parameter, Estimate, LCL, UCL, scientific_name, LocationGroup) %>% extract(Parameter, c("fingerprint", "year", "month"), convert = TRUE, "imputation: (.*):(.*):(.*)") %>% inner_join(location, by = "fingerprint") %>% mutate(month = factor( month, levels = c("October", "November", "December", "January", "February", "March")), loc_id = str_remove_all(location, "([[:punct:]]|\\+|=|\\|)") %>% str_replace_all("(è|É|é|ê)", "e") %>% str_replace_all("ç", "c") %>% str_replace_all("î", "i") %>% str_replace_all("ô", "o") %>% str_replace_all("ü", "u") %>% str_replace_all("(à|â)", "a") %>% abbreviate(10) %>% sprintf(fmt = "%2$s-%1$s", id) ) %>% select(-fingerprint) -> imputations
mt %>% distinct(id, scientific_name) %>% arrange(scientific_name) %>% pull(id) %>% sapply( function(id) { knit_expand("_monthly_totals.Rmd", id = id) } ) %>% paste(collapse = "\n\n") -> rmd knit(text = rmd, quiet = TRUE) %>% cat()
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