Welcome
This document has been created following the generic assessment guidance.
Description
Basic details about the assessment. Update the ‘response’ values as required.
| question | response |
|---|---|
| name_short | RICT |
| name_long | RICT |
| parameter | River Family Invertebrates |
| status | prototype |
| type | metric |
Assessment
If applicable, write a function to assess your input data and return an outcome. For example, a metric, statistic, prediction etc.
assessment_function <- function(data, ...) {
# Calculated some statistic...
# Note, any non-standard base R library must be call using require().
require(rict)
require(macroinvertebrateMetrics)
require(dplyr)
message(unique(data$location_id))
metric_function <- catalogue[catalogue$assessment ==
"Macroinvertebrate Metrics", 3][[1]]
input_data <- data
input_data$location_id <- NULL
output <- metric_function[[1]](input_data)
if(is.null(output)) {
return(NULL)
}
output <- dplyr::filter(output, question %in% c("WHPT_ASPT", "WHPT_NTAXA"))
# Alkalinity ---------
# if(!any(names(data) %in% c("alkalinity"))) {
# alk <- hera:::mean_alkalinity(data)
# data$alkalinity <- NULL
# data <- inner_join(data, alk, by = join_by("sample_id" == "sample_number"))
# }
if (!any(names(data) %in% "alkalinity")) {
predictors <- utils::read.csv(system.file("extdat",
"predictors.csv",
package = "hera"
), check.names = FALSE)
predictors$location_id <- as.character(predictors$location_id)
predict_data <- filter(predictors, location_id %in% unique(data$location_id))
predict_data$date <- as.Date(predict_data$date)
predict_data <- arrange(predict_data, dplyr::desc(date))
output_location <- inner_join(output,
unique(data[, c(
"location_id",
"sample_id",
"date_taken"
)]),
by = "sample_id",
relationship = "many-to-many"
)
whpt_input <- inner_join(output_location, predict_data, by = "location_id", multiple = "first")
} else {
# function to average predictors for each year? See sepaTools package?::
final_data <- data
final_data <- filter(final_data, analysis_repname == "Invert Physical Data")
if (nrow(final_data) < 1) {
return(NULL)
}
summarise_data <- select(
final_data,
"location_id",
"sample_id",
"year",
"question",
"response"
)
summarise_data <- map_df(split(
summarise_data,
summarise_data$sample_id
), function(sample) {
# get a row to add to bottom when mean_depth calculated
row <- sample[1, ]
row$question <- "mean_depth"
if (!any(sample$question %in% "mean_depth")) {
depths <- filter(sample, question %in% c(
"River Depth 1",
"River Depth 2",
"River Depth 3"
))
if (nrow(depths) < 1) {
# some samples don't have Depths or mean_depth...so return NA
row$response <- NA
} else {
mean_depth <- mean(as.numeric(depths$response), na.rm = TRUE)
row$response <- as.character(mean_depth)
}
sample <- bind_rows(sample, row)
return(sample)
} else {
return(sample)
}
})
summarise_data <- summarise_data %>%
filter(question %in% c(
"sand",
"silt_clay",
"boulders_cobbles",
"pebbles_gravel",
"river_width",
"mean_depth"
))
summarise_data <- tidyr::pivot_wider(summarise_data,
names_from = question,
values_from = response
)
summarise_data <- type.convert(summarise_data, as.is = TRUE)
summarise_data <- select(summarise_data, -"sample_id")
summarise_data <- dplyr::group_by(
summarise_data,
location_id
)
# Suppress warning because of missing values
summarise_data <- suppressWarnings(dplyr::summarise_all(
summarise_data,
~ mean(.x, na.rm = TRUE)
))
summarise_data$location_id <- as.character(summarise_data$location_id)
data <- left_join(data, summarise_data, by = join_by(location_id == location_id))
data <- data %>%
group_by(location_id) %>%
mutate("alkalinity" = mean(alkalinity, na.rm = TRUE))
data <- ungroup(data)
data <- select(
data,
"sample_id",
"location_id",
"date_taken",
"grid_reference",
"alkalinity",
"river_width",
"mean_depth",
"boulders_cobbles",
"pebbles_gravel",
"sand",
"silt_clay",
# "northing",
# "easting",
"dist_from_source",
"altitude",
"slope",
"grid_reference",
"discharge_category"
)
whpt_input <- inner_join(output,
unique(data),
by = "sample_id"
)
}
whpt_input$question[whpt_input$question == "WHPT_ASPT"] <- "WHPT ASPT Abund"
whpt_input$question[whpt_input$question == "WHPT_NTAXA"] <- "WHPT NTAXA Abund"
data <- whpt_input
bias <- 1.62
analysis <- "whpt ntaxa abund"
names(data) <- tolower(names(data))
data <- data[!is.na(data$river_width), ]
# if no river width...return NULL
check <- FALSE
if (nrow(data) < 1) {
return(NULL)
}
# Add year columns
data$year <- format.Date(data$date_taken, "%Y")
data$year <- as.integer(data$year)
rict_output <- purrr::map(unique(data$location_id), function(location_id) {
data <- data[data$location_id == location_id, ]
if (!is.null(data$river_width)) {
if (any(!is.na(data$river_width))) {
data$river_width <- as.numeric(data$river_width)
data$mean_depth <- as.numeric(data$mean_depth)
data$boulders_cobbles <- as.numeric(data$boulders_cobbles)
data$pebbles_gravel <- as.numeric(data$pebbles_gravel)
data$silt_clay <- as.numeric(data$silt_clay)
data$sand <- as.numeric(data$sand)
check <- TRUE
} else {
data <- select(
data,
-"river_width",
-"mean_depth",
-"boulders_cobbles",
-"pebbles_gravel",
-"sand",
-"silt_clay"
)
}
}
# NGR columns
data <- tidyr::separate(data,
grid_reference,
into = c(
"NGR",
"NGR_EASTING",
"NGR_NORTHING"
),
sep = " "
)
# needs refactoring - but if no Alk results returned then add blanks/NAs
# data$alkalinity <- 75
data$sample_count <- NA
data$samples_used <- NA
data$min_date <- NA
data$max_date <- NA
data$response <- as.numeric(as.character(data$response))
data <- tidyr::pivot_wider(data,
names_from = question,
values_from = response
)
# Join to template
rict_template <- function() {
template <- data.frame(
"LOCATION" = character(),
"Waterbody" = character(),
"YEAR" = integer(),
"NGR" = character(),
"EASTING" = character(),
"NORTHING" = character(),
"S_ALTITUDE" = numeric(),
"S_SLOPE" = numeric(),
"S_DISCHARGE_CAT" = numeric(),
"S_DIST_FROM_SOURCE" = numeric(),
"River Width (m)" = numeric(),
"Mean Depth (cm)" = numeric(),
"Alkalinity" = numeric(),
"% Boulders/Cobbles" = numeric(),
"% Pebbles/Gravel" = numeric(),
"% Sand" = numeric(),
"% Silt/Clay" = numeric(),
"Spr_Season_ID" = numeric(),
"Spr_TL2_WHPT_NTaxa (AbW,DistFam)" = numeric(),
"Spr_TL2_WHPT_ASPT (AbW,DistFam)" = numeric(),
"Sum_Season_ID" = numeric(),
"Sum_TL2_WHPT_NTaxa (AbW,DistFam)" = numeric(),
"Sum_TL2_WHPT_ASPT (AbW,DistFam)" = numeric(),
"Aut_Season_ID" = numeric(),
"Aut_TL2_WHPT_NTaxa (AbW,DistFam)" = numeric(),
"Aut_TL2_WHPT_ASPT (AbW,DistFam)" = numeric(),
sample_id = character(),
check.names = check
)
}
template_nems <- rict_template()
names(template_nems) <- tolower(names(template_nems))
data$easting <- as.factor(data$NGR_EASTING)
data$northing <- as.factor(data$NGR_EASTING)
names(data) <- tolower(names(data))
data <- dplyr::bind_rows(template_nems, data)
# For each Ecology sample (survey_inv/F_BMWP_SUM) summarise
# data$location <- paste0(data$location_id, ": ", data$location_description)
data$water_body_id <- 3100
names(data) <- tolower(names(data))
data <- data.frame(data, check.names = TRUE)
names(data) <- tolower(names(data))
data$date_taken <- as.Date(data$date_taken)
data$season <- season(data$date_taken)
data <- dplyr::filter(data, season != 4)
# summarise_data <- dplyr::group_by(
# data,
# location_id,
# ngr,
# ngr_easting,
# ngr_northing,
# sample_id,
# season,
# discharge_category,
# water_body_id,
# .name_repair = TRUE
# )
# Suppress warning because of missing values
# summarise_data <- suppressWarnings(dplyr::summarise_all(
# summarise_data,
# ~ mean(.x, na.rm = TRUE)
# ))
# Select
rict_data <- dplyr::select(data,
"SITE" = "location_id",
"Waterbody" = "water_body_id",
"Year" = "year",
"NGR" = "ngr",
"Easting" = "ngr_easting",
"Northing" = "ngr_northing",
"Altitude" = "altitude",
"Slope" = "slope",
"Discharge" = "discharge_category",
"Dist_from_Source" = "dist_from_source",
"Mean_Width" = "river_width",
"Mean_depth" = "mean_depth",
"Alkalinity" = "alkalinity",
"Total_samples" = "sample_count",
"Samples_used" = "samples_used",
"Alk_start" = "min_date",
"Alk_end" = "max_date",
"Boulder_Cobbles" = "boulders_cobbles",
"Pebbles_Gravel" = "pebbles_gravel",
"Sand" = "sand",
"Silt_Clay" = "silt_clay",
"Spr_Season_ID" = "season",
"Spr_TL2_WHPT_NTaxa (AbW,DistFam)" = "whpt.ntaxa.abund",
"Spr_TL2_WHPT_ASPT (AbW,DistFam)" = "whpt.aspt.abund",
"Sum_Season_ID" = "season",
"Sum_TL2_WHPT_NTaxa (AbW,DistFam)" = "whpt.ntaxa.abund",
"Sum_TL2_WHPT_ASPT (AbW,DistFam)" = "whpt.aspt.abund",
"Aut_Season_ID" = "season",
"Aut_TL2_WHPT_NTaxa (AbW,DistFam)" = "whpt.ntaxa.abund",
"Aut_TL2_WHPT_ASPT (AbW,DistFam)" = "whpt.aspt.abund",
"sample_id",
"season" = "season"
)
# Remove season not used
cols <- grep("Sum_|Spr_", names(rict_data), perl = TRUE)
rict_data[rict_data$season == 3, cols] <- NA
cols <- grep("Spr_|Aut_", names(rict_data), perl = TRUE)
rict_data[rict_data$season == 2, cols] <- NA
cols <- grep("Sum_|Aut_", names(rict_data), perl = TRUE)
rict_data[rict_data$season == 1, cols] <- NA
# Add season id when required
rict_data$Spr_Season_ID <- 1
rict_data$Sum_Season_ID <- 2
rict_data$Aut_Season_ID <- 3
# Bias where required
rict_data$SPR_NTAXA_BIAS <- bias
rict_data$SUM_NTAXA_BIAS <- bias
rict_data$AUT_NTAXA_BIAS <- bias
rict_data$VELOCITY <- NA
rict_data$HARDNESS <- NA
rict_data$CALCIUM <- NA
rict_data$CONDUCTIVITY <- NA
# Replace NANs
is.nan.data.frame <- function(x) {
do.call(cbind, lapply(x, is.nan))
}
rict_data[is.nan(rict_data)] <- NA
# Discharge must be numeric to pass validation
rict_data$Discharge <- as.numeric(rict_data$Discharge)
rict_data <- data.frame(rict_data, check.names = FALSE)
# rict_data <- rict_data[rict_data$sample_id != "1582198", ]
# rict_data <- rict_data[rict_data$sample_id != "1017980", ]
if (nrow(rict_data) == 0) {
return(NULL)
}
rict_valid <- rict::rict_validate(rict_data, stop_if_all_fail = FALSE)
if (nrow(rict_valid$data) == 0) {
return(NULL)
}
rict_multi_year <- rict_data %>%
group_by(SITE, Year) %>%
select("SITE", "Year", contains("_WHPT_")) %>%
summarise_all(~ mean(.x, na.rm = TRUE))
predictors <- rict_data %>%
select(-"Year", -"season", -contains("_WHPT_"), -"sample_id") %>%
unique()
multi <- inner_join(rict_multi_year, predictors, by = c("SITE"))
multi <- data.frame(multi, check.names = FALSE)
multi_predict <- rict_predict(multi)
multi_predict <- select(multi_predict, "SITE", "SuitCode", "SuitText")
multi_predict <- unique(multi_predict)
multi_class <- rict::rict(multi, year_type = "multi")
multi_class <- inner_join(multi_class, multi_predict, by = join_by(SITE))
multi_year_ntaxa <- select(
multi_class,
"SITE",
"H" = "H_NTAXA_spr_aut",
"G" = "G_NTAXA_spr_aut",
"M" = "M_NTAXA_spr_aut",
"P" = "P_NTAXA_spr_aut",
"B" = "B_NTAXA_spr_aut",
"Class" = "mostProb_NTAXA_spr_aut",
"EQR" = "NTAXA_aver_spr_aut",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
multi_year_ntaxa$parameter <- "Macroinvertebrates (NTAXA)"
multi_year_aspt <- select(
multi_class,
"SITE",
"H" = "H_ASPT_spr_aut",
"G" = "G_ASPT_spr_aut",
"M" = "M_ASPT_spr_aut",
"P" = "P_ASPT_spr_aut",
"B" = "B_ASPT_spr_aut",
"Class" = "mostProb_ASPT_spr_aut",
"EQR" = "ASPT_aver_spr_aut",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
multi_year_aspt$parameter <- "Macroinvertebrates (ASPT)"
multi_year_output <- bind_rows(
multi_year_aspt,
multi_year_ntaxa
)
multi_year_output <- multi_year_output[complete.cases(multi_year_output), ]
multi_year_output <- mutate_all(multi_year_output, as.character)
multi_year_output <- pivot_longer(multi_year_output,
names_to = "question",
values_to = "response",
cols = c(-parameter, -SITE)
)
# rict_class <- inner_join(rict_class,
# rict_data[, c("SITE","sample_id")],
# by = "sample_id")
multi_year_output <-
rename(multi_year_output, "location_id" = SITE)
predict_single <- rict::rict_predict(rict_data, all_indices = TRUE)
# predict_single$sample_id <- rict_data$sample_id
sample_season <- select(data, "location_id", "sample_id", "season")
sample_season <- unique(sample_season)
sample_season$season <- as.numeric(sample_season$season)
predict_single <- select(predict_single,
"SuitCode", "SuitText",
"SEASON", "SITE",
"TL2_08_Group_ARMI_Score",
"TL2_08_Group_ARMI_NTaxa",
"TL2_WHPT_Score_AbW_DistFam",
"TL2_WHPT_NTAXA_AbW_DistFam",
"TL2_WHPT_ASPT_AbW_DistFam")
predict_single <- unique(predict_single)
predict_single <- inner_join(sample_season,
predict_single,
by = join_by(season == SEASON,
location_id == SITE))
single_predict <- predict_single
# single_predict <- select(predict_single, sample_id, SuitCode, SuitText)
# single_predict <- unique(single_predict)
# ISSUE - rict_output is not in same 'sample' order as rict_data input!
# try running data using sample_id as SITE. Then later join location using
# sample_id
rict_data$location_id <- rict_data$SITE
rict_data$SITE <- rict_data$sample_id
rict_output <- rict::rict(rict_data, year_type = "single")
# Hot fix...try to arrange by year ascending to match output? Works(maybe!).
# rict_data <- arrange(rict_data, Year)
rict_output <- inner_join(rict_output, sample_season, by = join_by(SITE == sample_id))
rict_output$sample_id <- rict_output$SITE
rict_output$SITE <- rict_output$location_id
rict_output <- unique(rict_output)
rict_output <- rict_output[rict_output$sample_id %in% single_predict$sample_id, ]
rict_data <- unique(rict_data)
rict_output <- inner_join(rict_output, single_predict, by = join_by(sample_id))
spr_ntaxa <- select(
rict_output,
"sample_id",
"H" = "H_NTAXA_spr",
"G" = "G_NTAXA_spr",
"M" = "M_NTAXA_spr",
"P" = "P_NTAXA_spr",
"B" = "B_NTAXA_spr",
"Class" = "mostProb_NTAXA_spr",
"EQR" = "NTAXA_eqr_av_spr",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
spr_ntaxa$parameter <- "Macroinvertebrates (NTAXA)"
sum_ntaxa <- select(
rict_output,
"sample_id",
"H" = "H_NTAXA_sum",
"G" = "G_NTAXA_sum",
"M" = "M_NTAXA_sum",
"P" = "P_NTAXA_sum",
"B" = "B_NTAXA_sum",
"Class" = "mostProb_NTAXA_sum",
"EQR" = "NTAXA_eqr_av_sum",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
sum_ntaxa$parameter <- "Macroinvertebrates (NTAXA)"
aut_ntaxa <- select(
rict_output,
"sample_id",
"H" = "H_NTAXA_aut",
"G" = "G_NTAXA_aut",
"M" = "M_NTAXA_aut",
"P" = "P_NTAXA_aut",
"B" = "B_NTAXA_aut",
"Class" = "mostProb_NTAXA_aut",
"EQR" = "NTAXA_eqr_av_aut",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
aut_ntaxa$parameter <- "Macroinvertebrates (NTAXA)"
aut_aspt <- select(
rict_output,
"sample_id",
"H" = "H_ASPT_aut",
"G" = "G_ASPT_aut",
"M" = "M_ASPT_aut",
"P" = "P_ASPT_aut",
"B" = "B_ASPT_aut",
"Class" = "mostProb_ASPT_aut",
"EQR" = "ASPT_eqr_av_aut",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
aut_aspt$parameter <- "Macroinvertebrates (ASPT)"
spr_aspt <- select(
rict_output,
"sample_id",
"H" = "H_ASPT_spr",
"G" = "G_ASPT_spr",
"M" = "M_ASPT_spr",
"P" = "P_ASPT_spr",
"B" = "B_ASPT_spr",
"Class" = "mostProb_ASPT_spr",
"EQR" = "ASPT_eqr_av_spr",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
spr_aspt$parameter <- "Macroinvertebrates (ASPT)"
sum_aspt <- select(
rict_output,
"sample_id",
"H" = "H_ASPT_sum",
"G" = "G_ASPT_sum",
"M" = "M_ASPT_sum",
"P" = "P_ASPT_sum",
"B" = "B_ASPT_sum",
"Class" = "mostProb_ASPT_sum",
"EQR" = "eqr_av_sum_aspt",
"Suit Code" = "SuitCode",
"Suit Text" = "SuitText"
)
sum_aspt$parameter <- "Macroinvertebrates (ASPT)"
rict_class <- bind_rows(
spr_aspt,
sum_aspt,
aut_aspt,
spr_ntaxa,
sum_ntaxa,
aut_ntaxa
)
rict_class <- rict_class[complete.cases(rict_class), ]
rict_class <- mutate_all(rict_class, as.character)
rict_class <- pivot_longer(rict_class,
names_to = "question",
values_to = "response",
cols = c(-sample_id, -parameter)
)
rict_class <- inner_join(rict_class,
rict_data[, c("location_id", "sample_id")],
by = "sample_id",
relationship = "many-to-many"
)
#rict_class <- rename(rict_class, "location_id" = SITE)
rict_aspt <- tibble(
"location_id" = predict_single$location_id,
"sample_id" = predict_single$sample_id,
"question" = "RICT Reference WHPT ASPT",
"response" = as.character(predict_single$TL2_WHPT_ASPT_AbW_DistFam)
)
rict_ntaxa <- tibble(
"location_id" = predict_single$location_id,
"sample_id" = predict_single$sample_id,
"question" = "RICT Rerference WHPT NTAXA",
"response" = as.character(predict_single$TL2_WHPT_NTAXA_AbW_DistFam)
)
rict_river_score <- tibble(
"location_id" = predict_single$location_id,
"sample_id" = predict_single$sample_id,
"question" = "RICT Rerference ARMI Score",
"response" = as.character(predict_single$TL2_08_Group_ARMI_Score)
)
rict_river_ntaxa <- tibble(
"location_id" = predict_single$location_id,
"sample_id" = predict_single$sample_id,
"question" = "RICT Rerference ARMI NTAXA",
"response" = as.character(predict_single$TL2_08_Group_ARMI_NTaxa)
)
predict_single <- bind_rows(rict_aspt, rict_ntaxa, rict_river_score, rict_river_ntaxa)
predict_single$parameter <- "RICT Prediction"
rict_prediction <- bind_rows(
predict_single,
rict_class,
multi_year_output
)
# create row for years included in multi-year
row <- rict_prediction[is.na(rict_prediction$sample_id), ]
row <- row[1:2, ]
row$parameter[1] <- "Macroinvertebrates (ASPT)"
row$parameter[2] <- "Macroinvertebrates (NTAXA)"
row$question <- "Years included"
row$response <- paste(unique(rict_data$Year), collapse = ",")
rict_prediction <- bind_rows(rict_prediction, row)
})
output <- bind_rows(rict_output)
if (nrow(output) < 1) {
return(NULL)
}
output <- unique(output)
output <- mutate(output,
question = ifelse(question == "M",
"CoCM",
question
),
question = ifelse(question == "P",
"CoCP",
question
),
question = ifelse(question == "B",
"CoCB",
question
),
question = ifelse(question == "H",
"CoCH",
question
),
question = ifelse(question == "G",
"CoCG",
question
)
)
output <- mutate(output,
response = ifelse(response == "H",
"High",
response
),
response = ifelse(response == "G",
"Good",
response
),
response = ifelse(response == "M",
"Moderate",
response
),
response = ifelse(response == "P",
"Poor",
response
),
response = ifelse(response == "B",
"Bad",
response
)
)
return(output)
}Outcome
The outcome of your assessment.
| question | response |
|---|---|
| RICT Reference WHPT ASPT | 6.70423763296175 |
| RICT Rerference WHPT NTAXA | 23.3958436732449 |
| RICT Rerference ARMI Score | 13.5271425604722 |
| RICT Rerference ARMI NTAXA | 5.82390315155402 |
| CoCH | 22.83 |
| CoCG | 30.05 |
| CoCM | 31.64 |
| CoCP | 10.48 |
| CoCB | 4.99 |
| Class | Moderate |
| EQR | 0.868839670474072 |
| Suit Code | 1 |
| Suit Text | >5% |
| Years included | 2019 |
Check
Run checks on the assessment.
#> Test passed 🌈
#> Test passed 😀| check | value |
|---|---|
| standard_names | TRUE |
| standard_required | TRUE |
| standard_required_values | TRUE |
Update
Update the catalogue of assessments to make them available.
#> ✔ Setting active project to "/home/runner/work/hera/hera".
#> ✔ Saving "catalogue" to "data/catalogue.rda".
#> ☐ Document your data (see <https://r-pkgs.org/data.html>).After updating the catalogue, rebuild the package, click on Build > Install and Restart menu or ‘Install and Restart’ button in the Build pane.
Test
This section tests if this assessment is usable using
assessment function.
#> Hello from hera, ...work in progress!
#> Hello from hera, ...work in progress!
#> 8175
#> Variables for the 'physical' model detected - applying relevant checks.
#> Grid reference values detected for 'GB' - applying relevant checks.
#> Success, all validation checks passed!
#> Variables for the 'physical' model detected - applying relevant checks.
#> Grid reference values detected for 'GB' - applying relevant checks.
#> Success, all validation checks passed!
#> Variables for the 'physical' model detected - applying relevant checks.
#> Grid reference values detected for 'GB' - applying relevant checks.
#> Success, all validation checks passed!
#> Classifying...
#> Variables for the 'physical' model detected - applying relevant checks.
#> Grid reference values detected for 'GB' - applying relevant checks.
#> Success, all validation checks passed!
#> Warning in data.frame(..., check.names = FALSE): row names were found from a
#> short variable and have been discarded
#> Warning in inner_join(sample_season, predict_single, by = join_by(season == : Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 1 of `x` matches multiple rows in `y`.
#> ℹ Row 5 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.
#> Variables for the 'physical' model detected - applying relevant checks.
#> Grid reference values detected for 'GB' - applying relevant checks.
#> Success, all validation checks passed!
#> Classifying...
#> Warning in inner_join(rict_output, single_predict, by = join_by(sample_id)): Detected an unexpected many-to-many relationship between `x` and `y`.
#> ℹ Row 1 of `x` matches multiple rows in `y`.
#> ℹ Row 3 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.