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662 lines
28 KiB
R
662 lines
28 KiB
R
# ---------------------------------------------------------------------------------
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# Reproduction of the `microorganisms` data set
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# ---------------------------------------------------------------------------------
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# Data retrieved from:
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#
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# [1] Catalogue of Life (CoL) through the Encyclopaedia of Life
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# https://opendata.eol.org/dataset/catalogue-of-life/
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# * Download the resource file with a name like "Catalogue of Life yyyy-mm-dd"
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# * Extract "taxon.tab"
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#
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# [2] Global Biodiversity Information Facility (GBIF)
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# https://doi.org/10.15468/39omei
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# * Extract "Taxon.tsv"
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#
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# [3] Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ)
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# https://www.dsmz.de/support/bacterial-nomenclature-up-to-date-downloads.html
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# * Download the latest "Complete List" as xlsx file (DSMZ_bactnames.xlsx)
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# ---------------------------------------------------------------------------------
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library(dplyr)
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library(AMR)
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data_col <- data.table::fread("Documents/taxon.tab")
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data_gbif <- data.table::fread("Documents/Taxon.tsv")
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# read the xlsx file from DSMZ (only around 2.5 MB):
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data_dsmz <- readxl::read_xlsx("Downloads/DSMZ_bactnames.xlsx")
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# the CoL data is over 3.7M rows:
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data_col %>% freq(kingdom)
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# Item Count Percent Cum. Count Cum. Percent
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# --- ---------- ---------- -------- ----------- -------------
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# 1 Animalia 2,225,627 59.1% 2,225,627 59.1%
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# 2 Plantae 1,177,412 31.3% 3,403,039 90.4%
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# 3 Fungi 290,145 7.7% 3,693,184 98.1%
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# 4 Chromista 47,126 1.3% 3,740,310 99.3%
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# 5 Bacteria 14,478 0.4% 3,754,788 99.7%
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# 6 Protozoa 6,060 0.2% 3,760,848 99.9%
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# 7 Viruses 3,827 0.1% 3,764,675 100.0%
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# 8 Archaea 610 0.0% 3,765,285 100.0%
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# the GBIF data is over 5.8M rows:
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data_gbif %>% freq(kingdom)
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# Item Count Percent Cum. Count Cum. Percent
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# --- --------------- ---------- -------- ----------- -------------
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# 1 Animalia 3,264,138 55.7% 3,264,138 55.7%
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# 2 Plantae 1,814,962 31.0% 5,079,100 86.7%
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# 3 Fungi 538,086 9.2% 5,617,186 95.9%
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# 4 Chromista 181,374 3.1% 5,798,560 99.0%
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# 5 Bacteria 24,048 0.4% 5,822,608 99.4%
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# 6 Protozoa 15,138 0.3% 5,837,746 99.7%
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# 7 incertae sedis 9,995 0.2% 5,847,741 99.8%
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# 8 Viruses 9,630 0.2% 5,857,371 100.0%
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# 9 Archaea 771 0.0% 5,858,142 100.0%
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# Clean up helper function ------------------------------------------------
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clean_new <- function(new) {
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new %>%
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# only the ones that have no new ID to refer to a newer name
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filter(is.na(col_id_new)) %>%
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filter(
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(
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# we only want all MICROorganisms and no viruses
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!kingdom %in% c("Animalia", "Chromista", "Plantae", "Viruses")
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# and not all fungi: Aspergillus, Candida, Trichphyton and Pneumocystis are the most important,
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# so only keep these orders from the fungi:
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& !(kingdom == "Fungi"
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& !order %in% c("Eurotiales", "Saccharomycetales", "Schizosaccharomycetales", "Tremellales", "Onygenales", "Pneumocystales"))
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)
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# or the family has to contain a genus we found in our hospitals last decades (Northern Netherlands, 2002-2018)
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| genus %in% c("Absidia", "Acremonium", "Actinotignum", "Alternaria", "Anaerosalibacter", "Ancylostoma", "Anisakis", "Apophysomyces",
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"Arachnia", "Ascaris", "Aureobacterium", "Aureobasidium", "Balantidum", "Bilophilia", "Branhamella", "Brochontrix",
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"Brugia", "Calymmatobacterium", "Catabacter", "Chilomastix", "Chryseomonas", "Cladophialophora", "Cladosporium",
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"Clonorchis", "Cordylobia", "Curvularia", "Demodex", "Dermatobia", "Diphyllobothrium", "Dracunculus", "Echinococcus",
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"Enterobius", "Euascomycetes", "Exophiala", "Fasciola", "Fusarium", "Hendersonula", "Hymenolepis", "Kloeckera",
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"Koserella", "Larva", "Leishmania", "Lelliottia", "Loa", "Lumbricus", "Malassezia", "Metagonimus", "Molonomonas",
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"Mucor", "Nattrassia", "Necator", "Novospingobium", "Onchocerca", "Opistorchis", "Paragonimus", "Paramyxovirus",
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"Pediculus", "Phoma", "Phthirus", "Pityrosporum", "Pseudallescheria", "Pulex", "Rhizomucor", "Rhizopus", "Rhodotorula",
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"Salinococcus", "Sanguibacteroides", "Schistosoma", "Scopulariopsis", "Scytalidium", "Sporobolomyces", "Stomatococcus",
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"Strongyloides", "Syncephalastraceae", "Taenia", "Torulopsis", "Trichinella", "Trichobilharzia", "Trichomonas",
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"Trichosporon", "Trichuris", "Trypanosoma", "Wuchereria")) %>%
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mutate(
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authors2 = iconv(ref, from = "UTF-8", to = "ASCII//TRANSLIT"),
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# remove leading and trailing brackets
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authors2 = gsub("^[(](.*)[)]$", "\\1", authors2),
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# only take part after brackets if there's a name
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authors2 = ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
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gsub(".*[)] (.*)", "\\1", authors2),
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authors2),
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# get year from last 4 digits
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lastyear = as.integer(gsub(".*([0-9]{4})$", "\\1", authors2)),
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# can never be later than now
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lastyear = ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
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NA,
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lastyear),
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# get authors without last year
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authors = gsub("(.*)[0-9]{4}$", "\\1", authors2),
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# remove nonsense characters from names
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authors = gsub("[^a-zA-Z,'& -]", "", authors),
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# remove trailing and leading spaces
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authors = trimws(authors),
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# only keep first author and replace all others by 'et al'
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authors = gsub("(,| and| et| &| ex| emend\\.?) .*", " et al.", authors),
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# et al. always with ending dot
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authors = gsub(" et al\\.?", " et al.", authors),
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authors = gsub(" ?,$", "", authors),
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# don't start with 'sensu' or 'ehrenb'
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authors = gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE),
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# no initials, only surname
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authors = gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE),
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# combine author and year if year is available
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ref = ifelse(!is.na(lastyear),
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paste0(authors, ", ", lastyear),
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authors),
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# fix beginning and ending
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ref = gsub(", $", "", ref),
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ref = gsub("^, ", "", ref)) %>%
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# remove text if it contains 'Not assigned' like phylum in viruses
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mutate_all(~gsub("Not assigned", "", .)) %>%
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# Remove non-ASCII characters (these are not allowed by CRAN)
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lapply(iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
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as_tibble(stringsAsFactors = FALSE) %>%
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mutate(fullname = trimws(case_when(rank == "family" ~ family,
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rank == "order" ~ order,
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rank == "class" ~ class,
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rank == "phylum" ~ phylum,
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rank == "kingdom" ~ kingdom,
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TRUE ~ paste(genus, species, subspecies))))
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}
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clean_old <- function(old, new) {
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old %>%
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# only the ones that exist in the new data set
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filter(col_id_new %in% new$col_id) %>%
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mutate(
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authors2 = iconv(ref, from = "UTF-8", to = "ASCII//TRANSLIT"),
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# remove leading and trailing brackets
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authors2 = gsub("^[(](.*)[)]$", "\\1", authors2),
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# only take part after brackets if there's a name
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authors2 = ifelse(grepl(".*[)] [a-zA-Z]+.*", authors2),
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gsub(".*[)] (.*)", "\\1", authors2),
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authors2),
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# get year from last 4 digits
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lastyear = as.integer(gsub(".*([0-9]{4})$", "\\1", authors2)),
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# can never be later than now
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lastyear = ifelse(lastyear > as.integer(format(Sys.Date(), "%Y")),
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NA,
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lastyear),
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# get authors without last year
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authors = gsub("(.*)[0-9]{4}$", "\\1", authors2),
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# remove nonsense characters from names
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authors = gsub("[^a-zA-Z,'& -]", "", authors),
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# remove trailing and leading spaces
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authors = trimws(authors),
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# only keep first author and replace all others by 'et al'
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authors = gsub("(,| and| et| &| ex| emend\\.?) .*", " et al.", authors),
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# et al. always with ending dot
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authors = gsub(" et al\\.?", " et al.", authors),
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authors = gsub(" ?,$", "", authors),
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# don't start with 'sensu' or 'ehrenb'
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authors = gsub("^(sensu|Ehrenb.?) ", "", authors, ignore.case = TRUE),
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# no initials, only surname
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authors = gsub("^([A-Z]+ )+", "", authors, ignore.case = FALSE),
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# combine author and year if year is available
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ref = ifelse(!is.na(lastyear),
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paste0(authors, ", ", lastyear),
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authors),
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# fix beginning and ending
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ref = gsub(", $", "", ref),
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ref = gsub("^, ", "", ref)) %>%
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# remove text if it contains 'Not assigned' like phylum in viruses
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mutate_all(~gsub("Not assigned", "", .)) %>%
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# Remove non-ASCII characters (these are not allowed by CRAN)
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lapply(iconv, from = "UTF-8", to = "ASCII//TRANSLIT") %>%
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as_tibble(stringsAsFactors = FALSE) %>%
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select(col_id_new, fullname, ref, authors2) %>%
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left_join(new %>% select(col_id, fullname_new = fullname), by = c(col_id_new = "col_id")) %>%
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mutate(fullname = trimws(
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gsub("(.*)[(].*", "\\1",
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stringr::str_replace(
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string = fullname,
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pattern = stringr::fixed(authors2),
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replacement = "")) %>%
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gsub(" (var|f|subsp)[.]", "", .))) %>%
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select(-c("col_id_new", "authors2")) %>%
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filter(!is.na(fullname), !is.na(fullname_new)) %>%
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filter(fullname != fullname_new, !fullname %like% "^[?]")
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}
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# clean CoL and GBIF ----
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# clean data_col
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data_col <- data_col %>%
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as_tibble() %>%
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select(col_id = taxonID,
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col_id_new = acceptedNameUsageID,
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fullname = scientificName,
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kingdom,
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phylum,
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class,
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order,
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family,
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genus,
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species = specificEpithet,
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subspecies = infraspecificEpithet,
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rank = taxonRank,
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ref = scientificNameAuthorship,
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species_id = furtherInformationURL) %>%
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mutate(source = "CoL")
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# split into old and new
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data_col.new <- data_col %>% clean_new()
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data_col.old <- data_col %>% clean_old(new = data_col.new)
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rm(data_col)
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# clean data_gbif
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data_gbif <- data_gbif %>%
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as_tibble() %>%
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filter(
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# no uncertain taxonomic placements
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taxonRemarks != "doubtful",
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kingdom != "incertae sedis",
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taxonRank != "unranked") %>%
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transmute(col_id = taxonID,
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col_id_new = acceptedNameUsageID,
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fullname = scientificName,
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kingdom,
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phylum,
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class,
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order,
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family,
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genus,
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species = specificEpithet,
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subspecies = infraspecificEpithet,
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rank = taxonRank,
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ref = scientificNameAuthorship,
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species_id = as.character(parentNameUsageID)) %>%
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mutate(source = "GBIF")
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# split into old and new
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data_gbif.new <- data_gbif %>% clean_new()
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data_gbif.old <- data_gbif %>% clean_old(new = data_gbif.new)
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rm(data_gbif)
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# put CoL and GBIF together ----
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MOs.new <- bind_rows(data_col.new,
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data_gbif.new) %>%
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mutate(taxonomic_tree_length = nchar(trimws(paste(kingdom, phylum, class, order, family, genus, species, subspecies)))) %>%
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arrange(desc(taxonomic_tree_length)) %>%
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distinct(fullname, .keep_all = TRUE) %>%
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select(-c("col_id_new", "authors2", "authors", "lastyear", "taxonomic_tree_length")) %>%
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arrange(fullname)
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MOs.old <- bind_rows(data_col.old,
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data_gbif.old) %>%
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distinct(fullname, .keep_all = TRUE) %>%
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arrange(fullname)
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# clean up DSMZ ---
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data_dsmz <- data_dsmz %>%
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as_tibble() %>%
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transmute(col_id = NA_integer_,
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col_id_new = NA_integer_,
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fullname = "",
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# kingdom = "",
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# phylum = "",
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# class = "",
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# order = "",
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# family = "",
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genus = ifelse(is.na(GENUS), "", GENUS),
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species = ifelse(is.na(SPECIES), "", SPECIES),
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subspecies = ifelse(is.na(SUBSPECIES), "", SUBSPECIES),
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rank = ifelse(species == "", "genus", "species"),
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ref = AUTHORS,
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species_id = as.character(RECORD_NO),
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source = "DSMZ")
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# DSMZ only contains genus/(sub)species, try to find taxonomic properties based on genus and data_col
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ref_taxonomy <- MOs.new %>%
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distinct(genus, .keep_all = TRUE) %>%
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filter(family != "") %>%
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filter(genus %in% data_dsmz$genus) %>%
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distinct(genus, .keep_all = TRUE) %>%
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select(kingdom, phylum, class, order, family, genus)
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data_dsmz <- data_dsmz %>%
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left_join(ref_taxonomy, by = "genus") %>%
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mutate(kingdom = "Bacteria")
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data_dsmz.new <- data_dsmz %>%
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clean_new() %>%
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distinct(fullname, .keep_all = TRUE) %>%
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select(colnames(MOs.new)) %>%
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arrange(fullname)
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# combine everything ----
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MOs <- bind_rows(MOs.new,
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data_dsmz.new) %>%
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distinct(fullname, .keep_all = TRUE) %>%
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# not the ones that are old
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filter(!fullname %in% MOs.old$fullname) %>%
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arrange(fullname) %>%
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mutate(col_id = ifelse(source != "CoL", NA_integer_, col_id)) %>%
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filter(fullname != "")
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rm(data_col.new)
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rm(data_col.old)
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rm(data_gbif.new)
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rm(data_gbif.old)
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rm(data_dsmz)
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rm(data_dsmz.new)
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rm(ref_taxonomy)
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rm(MOs.new)
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MOs.bak <- MOs
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# Trichomonas trick ----
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# for species in Trypanosoma and Trichomonas we observe al lot of taxonomic info missing
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MOs %>% filter(genus %in% c("Trypanosoma", "Trichomonas")) %>% View()
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MOs[which(MOs$genus == "Trypanosoma"), "kingdom"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$kingdom
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MOs[which(MOs$genus == "Trypanosoma"), "phylum"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$phylum
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MOs[which(MOs$genus == "Trypanosoma"), "class"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$class
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MOs[which(MOs$genus == "Trypanosoma"), "order"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$order
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MOs[which(MOs$genus == "Trypanosoma"), "family"] <- MOs[which(MOs$fullname == "Trypanosoma"),]$family
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MOs[which(MOs$genus == "Trichomonas"), "kingdom"] <- MOs[which(MOs$fullname == "Trichomonas"),]$kingdom
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MOs[which(MOs$genus == "Trichomonas"), "phylum"] <- MOs[which(MOs$fullname == "Trichomonas"),]$phylum
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MOs[which(MOs$genus == "Trichomonas"), "class"] <- MOs[which(MOs$fullname == "Trichomonas"),]$class
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MOs[which(MOs$genus == "Trichomonas"), "order"] <- MOs[which(MOs$fullname == "Trichomonas"),]$order
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MOs[which(MOs$genus == "Trichomonas"), "family"] <- MOs[which(MOs$fullname == "Trichomonas"),]$family
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# fill taxonomic properties that are missing
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MOs <- MOs %>%
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mutate(phylum = ifelse(phylum %in% c(NA, ""), "(unknown phylum)", phylum),
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class = ifelse(class %in% c(NA, ""), "(unknown class)", class),
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order = ifelse(order %in% c(NA, ""), "(unknown order)", order),
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family = ifelse(family %in% c(NA, ""), "(unknown family)", family))
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# Abbreviations ----
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# Add abbreviations so we can easily know which ones are which ones.
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# These will become valid and unique microbial IDs for the AMR package.
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MOs <- MOs %>%
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arrange(kingdom, fullname) %>%
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group_by(kingdom) %>%
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mutate(abbr_other = case_when(
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rank == "family" ~ paste0("[FAM]_",
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abbreviate(family,
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minlength = 8,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)),
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rank == "order" ~ paste0("[ORD]_",
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abbreviate(order,
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minlength = 8,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)),
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rank == "class" ~ paste0("[CLS]_",
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abbreviate(class,
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minlength = 8,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)),
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rank == "phylum" ~ paste0("[PHL]_",
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abbreviate(phylum,
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minlength = 8,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)),
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rank == "kingdom" ~ paste0("[KNG]_", kingdom),
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TRUE ~ NA_character_
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)) %>%
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# abbreviations determined per kingdom and family
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# becuase they are part of the abbreviation
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mutate(abbr_genus = abbreviate(genus,
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minlength = 7,
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use.classes = TRUE,
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method = "both.sides",
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strict = FALSE)) %>%
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ungroup() %>%
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group_by(genus) %>%
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# species abbreviations may be the same between genera
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# because the genus abbreviation is part of the abbreviation
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mutate(abbr_species = abbreviate(stringr::str_to_title(species),
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minlength = 3,
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use.classes = FALSE,
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method = "both.sides")) %>%
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ungroup() %>%
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group_by(genus, species) %>%
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mutate(abbr_subspecies = abbreviate(stringr::str_to_title(subspecies),
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minlength = 3,
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use.classes = FALSE,
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method = "both.sides")) %>%
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ungroup() %>%
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# remove trailing underscores
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mutate(mo = gsub("_+$", "",
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toupper(paste(
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# first character: kingdom
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ifelse(kingdom %in% c("Animalia", "Plantae"),
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substr(kingdom, 1, 2),
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substr(kingdom, 1, 1)),
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# next: genus, species, subspecies
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ifelse(is.na(abbr_other),
|
|
paste(abbr_genus,
|
|
abbr_species,
|
|
abbr_subspecies,
|
|
sep = "_"),
|
|
abbr_other),
|
|
sep = "_")))) %>%
|
|
mutate(mo = ifelse(duplicated(.$mo),
|
|
# these one or two must be unique too
|
|
paste0(mo, "1"),
|
|
mo),
|
|
fullname = ifelse(fullname == "",
|
|
trimws(paste(genus, species, subspecies)),
|
|
fullname)) %>%
|
|
# put `mo` in front, followed by the rest
|
|
select(mo, everything(), -abbr_other, -abbr_genus, -abbr_species, -abbr_subspecies)
|
|
|
|
# add non-taxonomic entries
|
|
MOs <- MOs %>%
|
|
bind_rows(
|
|
# Unknowns
|
|
data.frame(mo = "UNKNOWN",
|
|
col_id = NA_integer_,
|
|
fullname = "(unknown name)",
|
|
kingdom = "(unknown kingdom)",
|
|
phylum = "(unknown phylum)",
|
|
class = "(unknown class)",
|
|
order = "(unknown order)",
|
|
family = "(unknown family)",
|
|
genus = "(unknown genus)",
|
|
species = "(unknown species)",
|
|
subspecies = "(unknown subspecies)",
|
|
rank = "(unknown rank)",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added",
|
|
stringsAsFactors = FALSE),
|
|
data.frame(mo = "B_GRAMN",
|
|
col_id = NA_integer_,
|
|
fullname = "(unknown Gram-negatives)",
|
|
kingdom = "Bacteria",
|
|
phylum = "(unknown phylum)",
|
|
class = "(unknown class)",
|
|
order = "(unknown order)",
|
|
family = "(unknown family)",
|
|
genus = "(unknown Gram-negatives)",
|
|
species = "(unknown species)",
|
|
subspecies = "(unknown subspecies)",
|
|
rank = "species",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added",
|
|
stringsAsFactors = FALSE),
|
|
data.frame(mo = "B_GRAMP",
|
|
col_id = NA_integer_,
|
|
fullname = "(unknown Gram-positives)",
|
|
kingdom = "Bacteria",
|
|
phylum = "(unknown phylum)",
|
|
class = "(unknown class)",
|
|
order = "(unknown order)",
|
|
family = "(unknown family)",
|
|
genus = "(unknown Gram-positives)",
|
|
species = "(unknown species)",
|
|
subspecies = "(unknown subspecies)",
|
|
rank = "species",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added",
|
|
stringsAsFactors = FALSE),
|
|
# CoNS
|
|
MOs %>%
|
|
filter(genus == "Staphylococcus", species == "") %>% .[1,] %>%
|
|
mutate(mo = paste(mo, "CNS", sep = "_"),
|
|
rank = "species",
|
|
col_id = NA_integer_,
|
|
species = "coagulase-negative",
|
|
fullname = "Coagulase-negative Staphylococcus (CoNS)",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
# CoPS
|
|
MOs %>%
|
|
filter(genus == "Staphylococcus", species == "") %>% .[1,] %>%
|
|
mutate(mo = paste(mo, "CPS", sep = "_"),
|
|
rank = "species",
|
|
col_id = NA_integer_,
|
|
species = "coagulase-positive",
|
|
fullname = "Coagulase-positive Staphylococcus (CoPS)",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
# Streptococci groups A, B, C, F, H, K
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "pyogenes") %>% .[1,] %>%
|
|
# we can keep all other details, since S. pyogenes is the only member of group A
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRA", sep = "_"),
|
|
species = "group A" ,
|
|
fullname = "Streptococcus group A"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
# we can keep all other details, since S. agalactiae is the only member of group B
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRB", sep = "_"),
|
|
species = "group B" ,
|
|
fullname = "Streptococcus group B"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "dysgalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRC", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "group C" ,
|
|
fullname = "Streptococcus group C",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRD", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "group D" ,
|
|
fullname = "Streptococcus group D",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRF", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "group F" ,
|
|
fullname = "Streptococcus group F",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRG", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "group G" ,
|
|
fullname = "Streptococcus group G",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRH", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "group H" ,
|
|
fullname = "Streptococcus group H",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "GRK", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "group K" ,
|
|
fullname = "Streptococcus group K",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added"),
|
|
# Beta-haemolytic Streptococci
|
|
MOs %>%
|
|
filter(genus == "Streptococcus", species == "agalactiae") %>% .[1,] %>%
|
|
mutate(mo = paste(MOs[MOs$fullname == "Streptococcus",]$mo, "HAE", sep = "_"),
|
|
col_id = NA_integer_,
|
|
species = "beta-haemolytic" ,
|
|
fullname = "Beta-haemolytic Streptococcus",
|
|
ref = NA_character_,
|
|
species_id = "",
|
|
source = "manually added")
|
|
)
|
|
|
|
|
|
# everything distinct?
|
|
sum(duplicated(MOs$mo))
|
|
colnames(MOs)
|
|
|
|
# set prevalence per species
|
|
MOs <- MOs %>%
|
|
mutate(prevalence = case_when(
|
|
class == "Gammaproteobacteria"
|
|
| genus %in% c("Enterococcus", "Staphylococcus", "Streptococcus")
|
|
| mo %in% c("UNKNOWN", "B_GRAMN", "B_GRAMP")
|
|
~ 1,
|
|
phylum %in% c("Proteobacteria",
|
|
"Firmicutes",
|
|
"Actinobacteria",
|
|
"Sarcomastigophora")
|
|
| genus %in% c("Aspergillus",
|
|
"Bacteroides",
|
|
"Candida",
|
|
"Capnocytophaga",
|
|
"Chryseobacterium",
|
|
"Cryptococcus",
|
|
"Elisabethkingia",
|
|
"Flavobacterium",
|
|
"Fusobacterium",
|
|
"Giardia",
|
|
"Leptotrichia",
|
|
"Mycoplasma",
|
|
"Prevotella",
|
|
"Rhodotorula",
|
|
"Treponema",
|
|
"Trichophyton",
|
|
"Trichomonas",
|
|
"Ureaplasma")
|
|
| rank %in% c("kingdom", "phylum", "class", "order", "family")
|
|
~ 2,
|
|
TRUE ~ 3
|
|
))
|
|
|
|
# arrange
|
|
MOs <- MOs %>% arrange(fullname)
|
|
|
|
# transform
|
|
MOs <- as.data.frame(MOs, stringsAsFactors = FALSE)
|
|
MOs.old <- as.data.frame(MOs.old, stringsAsFactors = FALSE)
|
|
class(MOs$mo) <- "mo"
|
|
MOs$col_id <- as.integer(MOs$col_id)
|
|
|
|
# get differences in MO codes between this data and the package version
|
|
MO_diff <- AMR::microorganisms %>%
|
|
mutate(pastedtext = paste(mo, fullname)) %>%
|
|
filter(!pastedtext %in% (MOs %>% mutate(pastedtext = paste(mo, fullname)) %>% pull(pastedtext))) %>%
|
|
select(mo_old = mo, fullname, pastedtext) %>%
|
|
left_join(MOs %>%
|
|
transmute(mo_new = mo, fullname_new = fullname, pastedtext = paste(mo, fullname)), "pastedtext") %>%
|
|
select(mo_old, mo_new, fullname_new)
|
|
|
|
mo_diff2 <- AMR::microorganisms %>%
|
|
select(mo, fullname) %>%
|
|
left_join(MOs %>%
|
|
select(mo, fullname),
|
|
by = "fullname",
|
|
suffix = c("_old", "_new")) %>%
|
|
filter(mo_old != mo_new,
|
|
#!mo_new %in% mo_old,
|
|
!mo_old %like% "\\[")
|
|
|
|
mo_diff3 <- tibble(previous_old = names(AMR:::make_trans_tbl()),
|
|
previous_new = AMR:::make_trans_tbl()) %>%
|
|
left_join(AMR::microorganisms %>% select(mo, fullname), by = c(previous_new = "mo")) %>%
|
|
left_join(MOs %>% select(mo_new = mo, fullname), by = "fullname")
|
|
|
|
# what did we win most?
|
|
MOs %>% filter(!fullname %in% AMR::microorganisms$fullname) %>% freq(genus)
|
|
# what did we lose most?
|
|
AMR::microorganisms %>%
|
|
filter(kingdom != "Chromista" & !fullname %in% MOs$fullname & !fullname %in% MOs.old$fullname) %>%
|
|
freq(genus)
|
|
|
|
|
|
# save
|
|
saveRDS(MOs, "microorganisms.rds")
|
|
saveRDS(MOs.old, "microorganisms.old.rds")
|
|
|
|
# on the server, do:
|
|
usethis::use_data(microorganisms, overwrite = TRUE, version = 2)
|
|
usethis::use_data(microorganisms.old, overwrite = TRUE, version = 2)
|
|
rm(microorganisms)
|
|
rm(microorganisms.old)
|
|
|
|
# TO DO AFTER THIS
|
|
# * Update the year and dim()s in R/data.R
|
|
# * Rerun data-raw/reproduction_of_rsi_translation.R
|
|
# * Run unit tests
|