In WorldFishCenter/peskas.timor.data.pipeline: Functions to Implement the Timor Small Scale Fisheries Data Pipeline

knitr::opts_chunk$set(echo = TRUE)

The following report summarizes the completeness of the forms submitted in Peskas. The data is filtered for boat landings.

library(magrittr)
library(ggplot2)

setwd("../..")
pars <- peskas.timor.data.pipeline::read_config()
metadata <- peskas.timor.data.pipeline::get_preprocessed_metadata(pars)

landings <- peskas.timor.data.pipeline::get_merged_landings(pars, "_weight") %>%
  dplyr::rename(date = date, submitted_by = `_submitted_by`) %>%
  dplyr::select(-c(`_attachments`, species_group, dplyr::starts_with("_"))) %>%
  dplyr::rename_all(~ stringr::str_remove(., "trip_group/")) %>%
  dplyr::filter(!is.na(landing_site_name)) %>%
  dplyr::filter(date >= "2018-01-01")


# Filter by landings with boats and form filled
landings <- 
  landings %>% dplyr::filter(
  has_boat == TRUE,
  Ita_koleta_dadus_husi_atividad == 1 |
    is.na(Ita_koleta_dadus_husi_atividad) & !is.na(total_catch_value)
)

stations <-
  metadata$stations %>%
  dplyr::select(
    landing_site_name = .data$station_code,
    station = .data$station_name
  ) %>%
  dplyr::mutate(landing_site_name = as.character(landing_site_name))

landings <-
  dplyr::left_join(landings, stations, by = "landing_site_name") %>%
  dplyr::select(-landing_site_name) %>%
  dplyr::rename(landing_site_name = station) %>%
  dplyr::mutate(
    landing_site_name = trimws(.data$landing_site_name, which = "both"),
    landing_site_name = dplyr::case_when(
      landing_site_name == "Oenunu/Citrana/Bona/Baoknana" ~ "Oenunu/Citrana",
      landing_site_name == "Beto Tasi/Bebonuk/Fatuhada/Kampung Alor" ~ "Beto Tasi/Bebonuk",
      landing_site_name == "Sentru/Liarafa/Sika/Rau Moko" ~ "Sentru/Liarafa",
      landing_site_name == "Oebone/Sakato/Bausiu/Mahata/Oebau/Oenunu/Kmusa/Bokos/Posto Sika" ~ "Oebone/Sakato",
      landing_site_name == "Dolok Oan/Hera/Metinaro/Manleu" ~ "Dolok Oan/Hera",
      landing_site_name == "Fatu'u/Doru/Iliana/Arlo" ~ "Fatu'u/Doru",
      landing_site_name == "Seiçal/Buruma/Fatuk Bo'ot/Uai'ca" ~ "Seiçal/Buruma",
      landing_site_name == "Beacou/Sulilaran/Palaka" ~ "Beacou/Sulilaran",
      landing_site_name == "Bonuk/Fatumeta/Nunumera" ~ "Bonuk/Fatumeta",
      landing_site_name == "Bidau Santana/Metiaut" ~ "Bidau Santana",
      T ~ landing_site_name
    )
  )

miss_plots <- function(landings = NULL, site = NULL) {
  landings_site <-
    landings %>%
    dplyr::filter(landing_site_name %in% site)

  missing.values <-
    landings_site %>%
    tidyr::gather(key = "key", value = "val") %>%
    dplyr::mutate(isna = is.na(val)) %>%
    dplyr::group_by(key) %>%
    dplyr::mutate(total = dplyr::n()) %>%
    dplyr::group_by(key, total, isna) %>%
    dplyr::summarise(num.isna = dplyr::n()) %>%
    dplyr::mutate(pct = num.isna / total * 100) %>%
    dplyr::ungroup()

  levels1 <-
    missing.values %>%
    dplyr::group_by(key) %>%
    dplyr::tally() %>%
    dplyr::arrange(n) %>%
    dplyr::filter(n == 1) %>%
    magrittr::extract2("key")

  levels2 <-
    (missing.values %>% dplyr::filter(isna == T) %>%
      dplyr::arrange(desc(pct)))$key


  levels3 <- c(levels2, levels1)


  percentage.plot <-
    missing.values %>%
    ggplot() +
    theme_minimal() +
    geom_bar(
      aes(
        x = reorder(key, dplyr::desc(pct)),
        y = pct, fill = isna
      ),
      stat = "identity", alpha = 0.8
    ) +
    scale_x_discrete(limits = levels3) +
    scale_y_continuous(n.breaks = 6) +
    scale_fill_manual(
      name = "",
      values = c("#FDE725FF", "#440154FF"), labels = c("Present", "Missing")
    ) +
    coord_flip() +
    labs(
      title = "Percentage of missing values",
      x = "",
      y = "Missing values (%)"
    ) +
    theme(
      legend.position = "top",
      plot.margin = unit(c(1, 1, 1, 1), "pt")
    ) +
    guides(fill = "none")


  row.plot <-
    landings_site %>%
    dplyr::mutate(id = as.Date(date)) %>%
    tidyr::gather(-id, key = "key", value = "val") %>%
    dplyr::mutate(isna = is.na(val)) %>%
    ggplot(aes(key, id, fill = isna)) +
    theme_minimal() +
    geom_raster(alpha = 0.8) +
    scale_fill_manual(
      name = "",
      values = c("#FDE725FF", "#440154FF"),
      labels = c("Present", "Missing")
    ) +
    scale_x_discrete(limits = levels3) +
    scale_y_date(
      expand = c(0, 0)
    ) +
    labs(
      x = "",
      y = "Date",
      title = "Missing values temporal pattern"
    ) +
    coord_flip() +
    theme(
      legend.position = "right",
      axis.text.y = element_blank(),
      plot.margin = unit(c(1, 1, 1, -7), "pt")
    )

  cowplot::plot_grid(percentage.plot, row.plot, ncol = 2,
                     rel_widths = c(2, 1.25))
}

# plots <-
#  purrr::set_names(unique(landings$landing_site_name)) %>%
#  purrr::map(miss_plots,
#             landings = landings)

missing.values <-
  landings %>%
  tidyr::gather(key = "key", value = "val") %>%
  dplyr::mutate(isna = is.na(val)) %>%
  dplyr::group_by(key) %>%
  dplyr::mutate(total = dplyr::n()) %>%
  dplyr::group_by(key, total, isna) %>%
  dplyr::summarise(num.isna = dplyr::n()) %>%
  dplyr::mutate(pct = num.isna / total * 100) %>%
  dplyr::ungroup()

levels1 <-
  missing.values %>%
  dplyr::group_by(key) %>%
  dplyr::tally() %>%
  dplyr::arrange(n) %>%
  dplyr::filter(n == 1) %>%
  magrittr::extract2("key")

levels2 <-
  (missing.values %>% dplyr::filter(isna == T) %>%
    dplyr::arrange(desc(pct)))$key


levels3 <- c(levels2, levels1)


percentage.plot <-
  missing.values %>%
  ggplot() +
  theme_minimal(13) +
  geom_bar(
    aes(
      x = reorder(key, dplyr::desc(pct)),
      y = pct, fill = isna
    ),
    stat = "identity", alpha = 0.8
  ) +
  scale_x_discrete(limits = levels3) +
  scale_y_continuous(
    n.breaks = 6,
    expand = c(0, 0)
  ) +
  scale_fill_manual(
    name = "",
    values = c("#D2E21BFF", "#472F7DFF"), labels = c("Present", "Missing")
  ) +
  coord_flip() +
  labs(
    title = "Percentage of missing values",
    x = "",
    y = "Missing values (%)"
  ) +
  theme(
    legend.position = "top",
    plot.margin = unit(c(1, 1, 1, 1), "pt")
  ) +
  guides(fill = "none")


row.plot <-
  landings %>%
  dplyr::mutate(id = as.Date(date)) %>%
  tidyr::gather(-id, key = "key", value = "val") %>%
  dplyr::mutate(isna = is.na(val)) %>%
  ggplot(aes(key, id, fill = isna)) +
  theme_minimal(13) +
  geom_raster(alpha = 0.8) +
  scale_fill_manual(
    name = "",
    values = c("#D2E21BFF", "#472F7DFF"),
    labels = c("Present", "Missing")
  ) +
  scale_x_discrete(limits = levels3) +
  scale_y_date(
    expand = c(0, 0)
  ) +
  labs(
    x = "",
    y = "Date",
    title = "Missing values temporal pattern"
  ) +
  coord_flip() +
  theme(
    legend.position = "right",
    axis.text.y = element_blank(),
    plot.margin = unit(c(1, 1, 1, -7), "pt")
  )
cowplot::plot_grid(percentage.plot, row.plot, ncol = 2,
                   rel_widths = c(2, 1))

main_plot_dat <-
  landings %>%
  dplyr::filter(!is.na(landing_site_name)) %>%
  tidyr::gather(-landing_site_name, key = "key", value = "val") %>%
  dplyr::mutate(isna = is.na(val)) %>%
  dplyr::group_by(landing_site_name, key) %>%
  dplyr::mutate(total_obs = dplyr::n()) %>%
  dplyr::group_by(landing_site_name, key, total_obs, isna) %>%
  dplyr::summarise(num.isna = dplyr::n()) %>%
  dplyr::mutate(pct = num.isna / total_obs * 100) %>%
  dplyr::ungroup() %>%
  tidyr::complete(landing_site_name, key, isna) %>%
  dplyr::filter(isna == T) %>%
  dplyr::select(-c(isna, num.isna)) %>%
  dplyr::mutate(pct = ifelse(is.na(pct), 0, pct))

main_plot <-
  main_plot_dat %>%
  ggplot() +
  theme_bw(13) +
  geom_tile(aes(
    y = reorder(key, pct),
    x = reorder(landing_site_name, pct),
    fill = pct,
    color = pct
  )) +
  scale_fill_viridis_c(direction = -1, alpha = 0.75) +
  scale_color_viridis_c(direction = -1) +
  scale_x_discrete(expand = c(0, 0)) +
  scale_y_discrete(expand = c(0, 0)) +
  guides(color = "none") +
  labs(
    x = "",
    y = "",
    fill = "Missing values (%)"
  ) +
  theme(
    panel.grid = element_blank(),
    legend.position = "bottom",
    axis.text.x = element_text(angle = 45, hjust = 1)
  ) +
  guides(color = "none")


obs_plot <-
  main_plot_dat %>%
  ggplot(aes(
    reorder(landing_site_name, pct),
    total_obs
  )) +
  theme_minimal(13) +
  geom_col(position = "identity", alpha = 0.75) +
  theme(
    panel.grid = element_blank(),
    axis.text.x = element_blank(),
    plot.margin = unit(c(-5, 1, -14, 1), "pt")
  ) +
  labs(
    x = "",
    y = "",
    title = ""
  )


cowplot::plot_grid(obs_plot, main_plot,
  ncol = 1,
  rel_heights = c(1, 4),
  align = "hv"
)

WorldFishCenter/peskas.timor.data.pipeline documentation built on April 14, 2025, 1:47 p.m.