remove duplicates

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: LightLogR
 Title: Process Data from Wearable Light Loggers and Optical Radiation Dosimeters
-Version: 0.4.1
+Version: 0.4.2
 Authors@R: c(
     person("Johannes", "Zauner", 
       email = "[email protected]", role = c("aut", "cre"),
@@ -32,6 +32,7 @@ Imports:
     ggsci,
     ggtext,
     hms,
+    janitor,
     lubridate,
     magrittr,
     pkgload,

NEWS.md

@@ -1,3 +1,7 @@
+# LightLogR 0.4.2
+
+* `import` functions will now give a warning message about identical observations in the provided data files, stop the import process and return a tibble with the duplicate rows. Through the `remove_duplicates` parameter, the user can decide to automatically remove these duplicates during import. **Note: identical observations refers to identical rows when disregarding the filename.**
+
 # LightLogR 0.4.1
 
 * added support for OcuWEAR devices

R/aaa.r

@@ -1,4 +1,4 @@
-Time <- mEDI <- Time.data <- Datetime <- timestamp <- tz <- Day.data <- `DATE/TIME` <- n <- Datetime.rounded <- id <- sleep.colname.string <- file.name <- Interval <- original.datapoints.fleeting <- MEDI <- State.Brown <- Reference <- Reference.check <- Id <- Start.date.shift <- data <- Shift <- `MELANOPIC EDI` <- State <- group <- End <- Start <- Quant.x <- Quant.y <- is.implicit <- group.indices <- Id2 <- gap.id <- start <- end <- path <- auto.id <- n_max <- manual.id <- silent <- Light <- Day <- N <- is_missing <- Hour <- .change <- dst_start <- .dst <- .dst2 <- dst_adjustment <- auto.plot <- group.1 <- group.2 <- group.indices2 <- cluster_start <- cluster_end <- row_idx <- is_cluster <- cluster_idx <- is_pulse <- pulse_idx <- light <- time <- level <- duration <- mean_duration <- onset <- midpoint <- offset <- mean_onset <- mean_midpoint <- mean_offset <-  Date.data <- print_n <-  NULL
+Time <- mEDI <- Time.data <- Datetime <- timestamp <- tz <- Day.data <- `DATE/TIME` <- n <- Datetime.rounded <- id <- sleep.colname.string <- file.name <- Interval <- original.datapoints.fleeting <- MEDI <- State.Brown <- Reference <- Reference.check <- Id <- Start.date.shift <- data <- Shift <- `MELANOPIC EDI` <- State <- group <- End <- Start <- Quant.x <- Quant.y <- is.implicit <- group.indices <- Id2 <- gap.id <- start <- end <- path <- auto.id <- n_max <- manual.id <- silent <- Light <- Day <- N <- is_missing <- Hour <- .change <- dst_start <- .dst <- .dst2 <- dst_adjustment <- auto.plot <- group.1 <- group.2 <- group.indices2 <- cluster_start <- cluster_end <- row_idx <- is_cluster <- cluster_idx <- is_pulse <- pulse_idx <- light <- time <- level <- duration <- mean_duration <- onset <- midpoint <- offset <- mean_onset <- mean_midpoint <- mean_offset <-  Date.data <- print_n <- remove_duplicates <-  NULL
 
 empty_function <- function() {
   rsconnect::accountInfo()

R/import_LL.R

@@ -44,6 +44,7 @@
 #'   column. If the column is not present it will add this column and fill it
 #'   with the filename of the importfile (see param `auto.id`).
 #' * `print_n` can be used if you want to see more rows from the observation intervals
+#' * `remove_duplicates` can be used if identical observations are present within or across multiple files. The default is `FALSE`. The function keeps only unique observations (=rows) if set to' TRUE'. This is a convenience implementation of [dplyr::distinct()].
 #'
 #' @param ... Parameters that get handed down to the specific import functions
 #' @param device From what device do you want to import? For a few devices,
@@ -282,6 +283,7 @@ imports <- function(device,
       locale = readr::default_locale(),
       silent = FALSE,
       print_n = 10,
+      remove_duplicates = FALSE,
       ... =
     ),
     #function expression
@@ -353,6 +355,22 @@ imports <- function(device,
         )
       }
 
+      #if there are duplicate rows, remove them and print an info message
+      duplicates <- suppressMessages(janitor::get_dupes(data, -file.name) %>% nrow())
+      orig_rows <- data %>% nrow()
+
+      if(duplicates > 0 & remove_duplicates) {
+        data <- data %>% dplyr::distinct(dplyr::pick(-file.name),.keep_all = TRUE)
+        cat(paste0(format(orig_rows - nrow(data), big.mark = "'"), " duplicate rows were removed during import.\n"))
+      }
+
+      #if there are untreated duplicate rows, give a warning
+      if(duplicates > 0 & !remove_duplicates) {
+        messages <- paste0(format(duplicates, big.mark = "'"), " rows in your dataset(s) are identical to at least one other row. This causes problems during analysis. Please set `remove_duplicates = TRUE` during import. Import will be stopped now and a dataframe with the duplicate rows returned \nIf you want to find out which entries are duplicates. Use `{replace_with_data_object} %>% janitor::get_dupes(-file.name) on your imported dataset.\n")
+        warning(messages)
+        return(janitor::get_dupes(data, -file.name))
+      }
+
       #if dst_adjustment is TRUE, adjust the datetime column
       if(dst_adjustment) {
         data <- data %>% dst_change_handler(filename.colname = file.name)