#' Plot sample records by month
#'
#' @description
#' This function produces a barplot of FORCIS sample records by month.
#'
#' @param data a `data.frame`, i.e. a FORCIS dataset.
#'
#' @return A `ggplot` object.
#'
#' @export
#'
#' @examples
#' # Attach the package ----
#' library("forcis")
#'
#' # Import example dataset ----
#' file_name <- system.file(file.path("extdata", "FORCIS_net_sample.csv"),
#' package = "forcis")
#'
#' net_data <- read.table(file_name, dec = ".", sep = ";")
#'
#' # Add 'data_type' column ----
#' net_data$"data_type" <- "Net"
#'
#' # Plot data by year (example dataset) ----
#' plot_record_by_month(net_data)
plot_record_by_month <- function(data) {
## Check data object ----
check_if_df(data)
check_field_in_data(data, "sample_id")
## Extract year ----
if (get_data_type(data) == "Sediment trap") {
check_field_in_data(data, "sample_date_time_start")
data$"sampling_month" <- as.numeric(sub("^\\d{2}/(\\d{2})/\\d{4}$", "\\1",
data$"sample_date_time_start"))
} else {
check_field_in_data(data, "profile_date_time")
data$"sampling_month" <- as.numeric(sub("^\\d{2}/(\\d{2})/\\d{4}$", "\\1",
data$"profile_date_time"))
}
## Get distinct values & count ----
data <- data %>%
select(.data$sample_id, .data$sampling_month) %>%
group_by(.data$sampling_month) %>%
summarise(count = n_distinct(.data$sample_id))
## Ensure to have all months ----
sampling_month <- data.frame("sampling_month" = 1:12)
data <- merge(data, sampling_month, by = "sampling_month", all = TRUE)
data$"count" <- replace_na(data$"count", 0)
## Trick for ggplot2 ----
data$"sampling_month" <- factor(data$"sampling_month", levels = 1:12)
## Plot ----
ggplot(data, aes(x = .data$sampling_month, y = .data$count)) +
geom_bar(width = 0.7, col = "black", stat = "identity") +
theme_classic() +
xlab("Month") +
ylab("Number of FORCIS samples")
}
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