In mschmidty/AIMtools: Tools for working with AIM Data.

knitr::opts_chunk$set(echo = TRUE)

DIMA Gaps

library(AIMtools)
data<-read_dima("data/DIMA_5_5/DIMA_2020.mdb")

gap_base <- data$gap_detail %>%
    dplyr::left_join(dplyr::select(data$gap_header, RecKey, LineKey)) %>%
    dplyr::left_join(data$join_table)

  gap_percent <- gap_base %>%
    dplyr::group_by(PlotID, PlotKey) %>%
    dplyr::summarize(total_gap = sum(Gap), total_gap_percent = total_gap/7500) %>%
    dplyr::ungroup()

  gap_by_category <- gap_base %>% 
    dplyr::mutate(gap_cat = dplyr::case_when(Gap < 25 ~ "gap_not_long_enough",
                                             Gap >= 25 & Gap <= 50 ~ "gap_25_to_50cm",
                                             Gap > 50 & Gap <= 100 ~ "gap_51_to_100cm",
                                             Gap > 100 & Gap <= 200 ~ "gap_101_to_200cm",
                                             Gap > 200 ~ "gap_over_200cm")) %>%
    dplyr::group_by(PlotKey, gap_cat) %>%
    dplyr::summarise(perc_gap_by_cat = sum(Gap)/7500) %>%
    dplyr::ungroup() %>% 
    tidyr::pivot_wider(names_from = gap_cat, values_from = perc_gap_by_cat) %>%
    dplyr::select(PlotKey, gap_25_to_50cm, gap_51_to_100cm, gap_101_to_200cm, gap_over_200cm)

  dplyr::left_join(gap_percent, gap_by_category, by = "PlotKey")

test_gap<-dima_gap(data)

test_gap

AGOL

library(magrittr)
library(AIMtools)
library(arcgisbinding)

arc.check_product()
agol<-load_data()
agol_2019<-load_data(year=2019)

names(agol)
agol_2019$gap%>%
  tibble::as_tibble()%>%
  dplyr::filter(stringr::str_detect(PlotKey, "TRFO|COS01000"))%>%
  View()

agol$gap_detail%>%
  tibble::as_tibble()%>%
  dplyr::filter(stringr::str_detect(RecKey, "TRFO|COS01000"))%>%
  dplyr::mutate(gap_cat = dplyr::case_when(Gap < 25 ~ "gap_not_long_enough",
                                             Gap >= 25 & Gap <= 50 ~ "gap_25_to_50cm",
                                             Gap > 50 & Gap <= 100 ~ "gap_51_to_100cm",
                                             Gap > 100 & Gap <= 200 ~ "gap_101_to_200cm",
                                             Gap > 200 ~ "gap_over_200cm")) %>%
  dplyr::group_by(parentglobalid, gap_cat) %>%
  dplyr::summarise(perc_gap_by_cat = sum(Gap, na.rm = T)/2500) %>%
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = gap_cat, values_from = perc_gap_by_cat)%>%
  dplyr::select(parentglobalid, gap_25_to_50cm, gap_51_to_100cm, gap_101_to_200cm, gap_over_200cm) %>%
  dplyr::left_join(
    agol$gap%>%
      dplyr::select(globalid, LineKey, PlotID),
    by=c("parentglobalid"="globalid")
  )%>%
  dplyr::arrange(PlotID)%>%
  View()


agol_2019$gap_detail%>%
  tibble::as_tibble()%>%  
  dplyr::filter(stringr::str_detect(RecKey, "TRFO|COS01000"))%>%
  View()


names(agol$gap)

agol$gap%>%
  tibble::as_tibble()%>%  
  dplyr::filter(stringr::str_detect(LineKey, "TRFO|COS01000"))%>%
  dplyr::group_by(PlotKey, PlotID)%>%
  dplyr::summarize(
    pct_gap_25_to_50cm = mean(as.numeric(pctCanCat1)),
    pct_gap_51_to_100cm = mean(as.numeric(pctCanCat2)),
    pct_gap_101_to_200cm = mean(as.numeric(pctCanCat3)),
    pct_gap_over_200cm = mean(as.numeric(pctCanCat3)),
    )%>%
  dplyr::ungroup()%>%
  dplyr::mutate(pct_total_gap = pct_gap_25_to_50cm+pct_gap_51_to_100cm+pct_gap_101_to_200cm+pct_gap_over_200cm)

test_gap<-gap(agol_2019)

mschmidty/AIMtools documentation built on Sept. 29, 2022, 7:40 p.m.