Checking habitat data exported from TransectMeasure
Claude Spencer & Brooke Gibbons
2023-11-13
check-habitat.RmdThis script documents the necessary steps to take raw data exported from TransectMeasure, check for any errors in the annotation and format it into tidy data. You are required to also have a GlobalArchive format metadata file “*_Metadata.csv”.
R set up
Load the necessary libraries.
library(devtools)
library(CheckEM)
library(tidyverse)
library(ggplot2)
library(ggbeeswarm)
library(leaflet)
library(leaflet.minicharts)
library(RColorBrewer)
library(here)
library(tidyverse)Set the study name.
name <- "example-bruv-workflow"Read in the data
Load and format the metadata.
metadata <- read_metadata(here::here("r-workflows/data/raw/")) %>%
dplyr::select(campaignid, sample, longitude_dd, latitude_dd, date_time, location, site, depth_m, successful_count, successful_length, successful_habitat_forward, successful_habitat_backward) %>%
glimpse()## reading metadata file: /home/runner/work/CheckEM/CheckEM/r-workflows/data/raw//2022-05_PtCloates_stereo-BRUVS_metadata.csv## reading metadata file: /home/runner/work/CheckEM/CheckEM/r-workflows/data/raw//2023-03_SwC_stereo-BRUVs_Metadata.csv## Rows: 94
## Columns: 12
## $ campaignid <chr> "2022-05_PtCloates_stereo-BRUVS", "2022-05…
## $ sample <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9…
## $ longitude_dd <chr> "113.5447", "113.5628", "113.5515", "113.5…
## $ latitude_dd <chr> "-22.7221", "-22.6957", "-22.7379", "-22.7…
## $ date_time <chr> "2022-05-22T10:03:24+08:00", "2022-05-22T1…
## $ location <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ site <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ depth_m <chr> "93.9", "77.3", "78.3", "73.9", "81.9", "7…
## $ successful_count <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", …
## $ successful_length <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", …
## $ successful_habitat_forward <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ successful_habitat_backward <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…Save the metadata as an R data file (this creates a lighter file than saving as a .csv or similar).
Read in the points data exported from Transect Measure, and then filter this into 2 dataframes for relief and habitat annotations.
Filter the data to only include habitat annotations.
habitat <- points %>%
dplyr::filter(relief_annotated %in% "no") %>%
dplyr::select(campaignid, sample, starts_with("level"), scientific) %>%
glimpse()## Rows: 1,241
## Columns: 7
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_stereo-BRUVs", "20…
## $ sample <chr> "10", "10", "10", "10", "10", "10", "10", "10", "10", "10",…
## $ level_2 <chr> "Macroalgae", "Seagrasses", "Substrate", "Macroalgae", "Mac…
## $ level_3 <chr> "Filamentous / filiform", "Strap-like leaves", "Unconsolida…
## $ level_4 <chr> "Red", NA, "Sand / mud (<2mm)", "Red", "Red", NA, NA, "Brow…
## $ level_5 <chr> NA, NA, "Coarse sand (with shell fragments)", NA, NA, NA, N…
## $ scientific <chr> NA, "Thalassodendron spp", NA, NA, NA, "Thalassodendron spp…Filter the data to only include relief annotations.
relief <- points %>%
dplyr::filter(relief_annotated %in% "yes") %>%
dplyr::select(campaignid, sample, starts_with("level"), scientific) %>%
glimpse()## Rows: 1,242
## Columns: 7
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_stereo-BRUVs", "20…
## $ sample <chr> "10", "10", "10", "10", "10", "10", "10", "10", "10", "10",…
## $ level_2 <chr> NA, "Relief", "Relief", "Relief", NA, "Relief", "Relief", "…
## $ level_3 <chr> NA, "High", "High", "High", NA, "High", "Low / moderate", "…
## $ level_4 <chr> NA, "High (>3m)", "High (>3m)", "High (>3m)", NA, "High (>3…
## $ level_5 <chr> NA, "3", "3", "3", NA, "3", "2", "3", NA, "3", "2", "2", NA…
## $ scientific <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…Check the data for a variety of errors
Set the number of expected annotations to check against.
num.points <- 20Check if there are habitat annotations with an unexpected number of annotation points.
wrong.points.habitat <- habitat %>%
group_by(campaignid, sample) %>%
summarise(points.annotated = n()) %>%
left_join(metadata) %>%
dplyr::mutate(expected = case_when(successful_habitat_forward %in% "Yes" & successful_habitat_backward %in% "Yes" ~ num.points * 2, successful_habitat_forward %in% "Yes" & successful_habitat_backward %in% "No" ~ num.points * 1, successful_habitat_forward %in% "No" & successful_habitat_backward %in% "Yes" ~ num.points * 1, successful_habitat_forward %in% "No" & successful_habitat_backward %in% "No" ~ num.points * 0)) %>%
dplyr::filter(!points.annotated == expected) %>%
glimpse()## `summarise()` has grouped output by 'campaignid'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(campaignid, sample)`## Rows: 5
## Columns: 14
## Groups: campaignid [1]
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_s…
## $ sample <chr> "21", "3", "37", "45", "47"
## $ points.annotated <int> 20, 41, 20, 20, 20
## $ longitude_dd <chr> "114.857485", "114.9156417", "114.9235817"…
## $ latitude_dd <chr> "-34.06438333", "-34.10734167", "-34.10853…
## $ date_time <chr> "18/03/2023 2:34", "15/03/2023 1:54", "15/…
## $ location <chr> NA, NA, NA, NA, NA
## $ site <chr> NA, NA, NA, NA, NA
## $ depth_m <chr> "42.3", "46.7", "46.1", "61.9", "73"
## $ successful_count <chr> "Yes", "Yes", "Yes", "Yes", "Yes"
## $ successful_length <chr> "Yes", "Yes", "Yes", "Yes", "Yes"
## $ successful_habitat_forward <chr> "Yes", "Yes", "Yes", "Yes", "Yes"
## $ successful_habitat_backward <chr> "Yes", "Yes", "Yes", "Yes", "Yes"
## $ expected <dbl> 40, 40, 40, 40, 40Check if there are habitat annotations with an unexpected number of annotation points.
wrong.points.relief <- relief %>%
group_by(campaignid, sample) %>%
summarise(points.annotated = n()) %>%
left_join(metadata) %>%
dplyr::mutate(expected = case_when(successful_habitat_forward %in% "Yes" & successful_habitat_backward %in% "Yes" ~ num.points * 2, successful_habitat_forward %in% "Yes" & successful_habitat_backward %in% "No" ~ num.points * 1, successful_habitat_forward %in% "No" & successful_habitat_backward %in% "Yes" ~ num.points * 1, successful_habitat_forward %in% "No" & successful_habitat_backward %in% "No" ~ num.points * 0)) %>%
dplyr::filter(!points.annotated == expected) %>%
glimpse()## `summarise()` has grouped output by 'campaignid'. You can override using the
## `.groups` argument.
## Joining with `by = join_by(campaignid, sample)`## Rows: 4
## Columns: 14
## Groups: campaignid [1]
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_s…
## $ sample <chr> "17", "21", "23", "47"
## $ points.annotated <int> 41, 20, 41, 20
## $ longitude_dd <chr> "114.85758", "114.857485", "114.9189517", …
## $ latitude_dd <chr> "-34.09634667", "-34.06438333", "-34.12832…
## $ date_time <chr> "15/03/2023 3:00", "18/03/2023 2:34", "15/…
## $ location <chr> NA, NA, NA, NA
## $ site <chr> NA, NA, NA, NA
## $ depth_m <chr> "43.3", "42.3", "41", "73"
## $ successful_count <chr> "Yes", "Yes", "Yes", "Yes"
## $ successful_length <chr> "Yes", "Yes", "Yes", "Yes"
## $ successful_habitat_forward <chr> "Yes", "Yes", "Yes", "Yes"
## $ successful_habitat_backward <chr> "Yes", "Yes", "Yes", "Yes"
## $ expected <dbl> 40, 40, 40, 40Check to see if there are any samples in habitat that don’t have a match in the metadata.
habitat.missing.metadata <- anti_join(habitat, metadata, by = c("campaignid", "sample")) %>%
glimpse()## Rows: 40
## Columns: 7
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_stereo-BRUVs", "20…
## $ sample <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ level_2 <chr> "Substrate", "Substrate", "Substrate", "Substrate", "Substr…
## $ level_3 <chr> "Unconsolidated (soft)", "Unconsolidated (soft)", "Unconsol…
## $ level_4 <chr> "Sand / mud (<2mm)", "Sand / mud (<2mm)", "Sand / mud (<2mm…
## $ level_5 <chr> "Coarse sand (with shell fragments)", "Coarse sand (with sh…
## $ scientific <chr> NA, NA, NA, NA, NA, NA, NA, NA, "Thalassodendron spp", NA, …Check to see if there are any samples in the metadata that don’t have matching habitat.
metadata.missing.habitat <- anti_join(metadata, habitat, by = c("campaignid", "sample")) %>%
glimpse()## Rows: 62
## Columns: 12
## $ campaignid <chr> "2022-05_PtCloates_stereo-BRUVS", "2022-05…
## $ sample <chr> "1", "2", "3", "4", "5", "6", "7", "8", "9…
## $ longitude_dd <chr> "113.5447", "113.5628", "113.5515", "113.5…
## $ latitude_dd <chr> "-22.7221", "-22.6957", "-22.7379", "-22.7…
## $ date_time <chr> "2022-05-22T10:03:24+08:00", "2022-05-22T1…
## $ location <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ site <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ depth_m <chr> "93.9", "77.3", "78.3", "73.9", "81.9", "7…
## $ successful_count <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", …
## $ successful_length <chr> "Yes", "Yes", "Yes", "Yes", "Yes", "Yes", …
## $ successful_habitat_forward <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
## $ successful_habitat_backward <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…Format and save the final tidy dataset
Tidy the habitat data by joining with the complete schema file that is loaded with the CheckEM package.
tidy.habitat <- habitat %>%
dplyr::mutate(number = 1) %>%
left_join(catami) %>%
dplyr::select(campaignid, sample, number, starts_with("level"), family, genus, species) %>%
dplyr::filter(!level_2 %in% c("","Unscorable", NA)) %>%
group_by(campaignid, sample, across(starts_with("level")), family, genus, species) %>%
dplyr::tally(number, name = "number") %>%
ungroup() %>%
dplyr::select(campaignid, sample, level_1, everything()) %>%
glimpse()## Joining with `by = join_by(level_2, level_3, level_4, level_5)`## Warning in left_join(., catami): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 23 of `x` matches multiple rows in `y`.
## ℹ Row 193 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.## Rows: 369
## Columns: 14
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_stereo-BRUVs", "20…
## $ sample <chr> "10", "10", "10", "10", "10", "10", "10", "10", "12", "12",…
## $ level_1 <chr> "Biota", "Biota", "Biota", "Biota", "Biota", "Biota", "Biot…
## $ level_2 <chr> "Macroalgae", "Macroalgae", "Macroalgae", "Macroalgae", "Se…
## $ level_3 <chr> "Erect coarse branching", "Erect coarse branching", "Erect …
## $ level_4 <chr> "Brown", "Red", "Red", "Red", NA, "Laminar", "Simple", "San…
## $ level_5 <chr> NA, NA, NA, NA, NA, NA, NA, "Coarse sand (with shell fragme…
## $ level_6 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ level_7 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ level_8 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ family <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Le…
## $ genus <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Ec…
## $ species <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "ra…
## $ number <dbl> 2, 3, 2, 1, 14, 1, 1, 3, 5, 8, 1, 2, 3, 3, 7, 7, 7, 7, 7, 2…Save the tidy habitat data as an R data file.
Tidy the relief data by joining with the complete schema file that is loaded with the CheckEM package.
tidy.relief <- relief %>%
dplyr::select(campaignid, sample, starts_with("level"), scientific) %>%
dplyr::filter(!level_2 %in% c("","Unscorable", NA)) %>%
dplyr::mutate(number = 1) %>%
left_join(catami) %>%
group_by(campaignid, sample, across(starts_with("level"))) %>%
dplyr::tally(number, name = "number") %>%
ungroup() %>%
glimpse() ## Joining with `by = join_by(level_2, level_3, level_4, level_5)`## Rows: 73
## Columns: 11
## $ campaignid <chr> "2023-03_SwC_stereo-BRUVs", "2023-03_SwC_stereo-BRUVs", "20…
## $ sample <chr> "10", "10", "10", "12", "12", "14", "14", "14", "15", "15",…
## $ level_2 <chr> "Relief", "Relief", "Relief", "Relief", "Relief", "Relief",…
## $ level_3 <chr> "High", "High", "Low / moderate", "High", "Low / moderate",…
## $ level_4 <chr> "High (>3m)", "Wall", "Moderate (1-3m)", "High (>3m)", "Mod…
## $ level_5 <chr> "3", "4", "2", "3", "2", "3", "1", "2", "3", "4", "1", "2",…
## $ level_1 <chr> "Physical", "Physical", "Physical", "Physical", "Physical",…
## $ level_6 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ level_7 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ level_8 <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
## $ number <dbl> 8, 10, 5, 17, 4, 14, 3, 8, 16, 1, 4, 6, 20, 2, 13, 6, 14, 1…Save the tidy relief data as an R data file.