121 lines
2.4 KiB
R
121 lines
2.4 KiB
R
library(tidyverse)
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split_df <- function(df, flt) {
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.in <- df %>%
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filter({{ flt }})
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.out <- df %>%
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filter(! {{ flt }})
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list(i = .in,
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o = .out)
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}
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df <- readr::read_tsv(
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"../../results/wqa/process/all.tsv.gz",
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col_types = cols(
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start = "D",
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species = "f",
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std_value = "d",
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lat = "d",
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long = "d",
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location_name = "c",
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.default = "-"
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)
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) %>%
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# get rid of the deuterium distinction on some pharma species
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mutate(species = str_replace(species, "-(d|D)\\d", ""))
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not_detected <- df %>%
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group_by(species) %>%
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summarize(total = sum(std_value)) %>%
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filter(total == 0) %>%
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pull(species)
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harmless <- c(
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"Sodium",
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"Bicarbonate",
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"Calcium",
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"Magnesium",
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"Potassium",
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"Carbonate",
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"Oxygen",
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"Silica"
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)
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df_detected <- df %>%
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filter(! species %in% not_detected) %>%
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filter(! species %in% harmless)
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df %>%
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filter(lubridate::year(start) > 1990) %>%
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group_by(species) %>%
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summarize(fraction = mean(std_value > 0),
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n = n()) %>%
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mutate(stderr = sqrt(fraction * (1 - fraction) / n)) %>%
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filter(n > 3) %>%
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ggplot(aes(fraction, fct_reorder(species, fraction))) +
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geom_col() +
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geom_errorbarh(aes(xmin = fraction - stderr, xmax = fraction + stderr))
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metals <- c(
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"Lithium",
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"Beryllium",
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"Boron",
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"Aluminum",
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"Vanadium",
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"Chromium",
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"Manganese",
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"Iron",
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"Cobalt",
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"Nickel",
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"Copper",
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"Zinc",
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"Arsenic",
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"Selenium",
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"Strontium",
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"Molybdenum",
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"Silver",
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"Cadmium",
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"Antimony",
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"Barium",
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"Mercury",
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"Thallium",
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"Lead",
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"Uranium"
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)
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halides <- c("Chloride", "Fluoride", "Bromide")
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.nitro <- split_df(df_detected, str_detect(species, "(n|N)itr")
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| str_detect(species, "Ammon"))
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.phospho <- split_df(.nitro$o, str_detect(species, "(P|p)hosph"))
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.metal <- split_df(.phospho$o, species %in% metals)
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.halides <- split_df(.metal$o, species %in% halides)
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.nitro$i %>%
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ggplot(aes(start, std_value, color = species, group = species)) +
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geom_line()
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.halides$i %>%
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ggplot(aes(start, std_value)) +
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geom_point() +
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facet_wrap(scales = "free", c("species"))
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.metal$i %>%
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ggplot(aes(start, std_value)) +
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geom_point() +
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facet_wrap(scales = "free", c("species"))
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.phospho$i %>%
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ggplot(aes(start, std_value)) +
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geom_point() +
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facet_wrap(scales = "free", c("species"))
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.halides$o %>%
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filter(std_value > 1) %>%
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ggplot(aes(std_value, species)) +
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geom_jitter()
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