adding timeline function

DESCRIPTION

@@ -1,5 +1,5 @@
 Package: finna
-Title: Title: Access the 'Finna' API
+Title: Access the 'Finna' API
 Version: 0.1.1
 Date: 2025-01-10
 Authors@R: c(
@@ -14,6 +14,8 @@ License: BSD_2_clause + file LICENSE
 Encoding: UTF-8
 Imports: 
     dplyr,
+    tidyr,
+    reshape2,
     glue,
     httr,
     xml2,

NAMESPACE

@@ -16,6 +16,7 @@ export(refine_metadata)
 export(save_for_offline)
 export(search_finna)
 export(search_publisher)
+export(timeline)
 export(top_plot)
 import(dplyr)
 import(progress)
@@ -50,10 +51,12 @@ importFrom(readr,col_character)
 importFrom(readr,cols)
 importFrom(readr,read_csv)
 importFrom(readr,write_csv)
+importFrom(reshape2,melt)
 importFrom(stats,reorder)
 importFrom(stats,setNames)
 importFrom(tibble,as_tibble)
 importFrom(tibble,tibble)
+importFrom(tidyr,pivot_wider)
 importFrom(utils,URLdecode)
 importFrom(utils,menu)
 importFrom(xml2,read_xml)

R/timeline.R

@@ -0,0 +1,75 @@
+#' @title Retrieve Timeline
+#' @description Timeline data for selected variable (possibly across various groups).
+#' @param x data frame
+#' @param field Numeric field to summarize in the timeline. The number of entries (title count) per decade is used by default. If this argument is used, the sum of entries per decade for this field is given.
+#' @param group Optional. Name for a data field that indicates groups to compare.
+#' @param nmin Include only entries with at least nmin absolute frequency
+#' @param mode "absolute" or "relative"
+#' @param time.window Time window for the timeline in years. Default: 10 (publication decade).
+#' @param time.field Specify the field to be used for time. By default: "Year", or if time.window is 10, then "publication_decade"
+#' @return data.frame
+#' @importFrom tidyr pivot_wider
+#' @importFrom reshape2 melt
+#' @export
+#' @author Leo Lahti \email{leo.lahti@@iki.fi}
+#' @references See citation("bibliographica")
+#' @examples \dontrun{timeline(df, "gatherings")}
+#' @keywords utilities
+timeline <- function (x, field = "titlecount", group = NULL, nmin = 0, mode = "absolute", time.window = 10, time.field = "Year") {
+
+  publication_decade <- publication_time <- NULL
+
+  x$publication_time <- x[[time.field]]
+
+  # Set the desired time window (default one decade)
+  x$publication_time <- time.window * floor(x$publication_time / time.window)
+
+
+  if (time.field == "publication_decade" || (time.field == "Year" & time.window == 10 & "publication_decade" %in% names(x))) {
+    x$publication_time <- x$publication_decade
+  }
+
+  if (!is.null(group)) {
+    x <- x[, c("publication_time", group)]
+    x$group <- x[[group]]
+  } else {
+    x$group <- rep(1, nrow(x))
+  }
+
+  if (is.null(field)) {
+    field <- "titlecount"
+  }
+
+  if (field == "titlecount" && !field %in% names(x)) {
+    x[[field]] <- rep(1, nrow(x))
+  }
+
+  x$field <- x[[field]]
+  #print(x)
+
+  df2 <- x %>% filter(!is.na(group)) %>%
+    group_by(publication_time, group) %>%
+    summarise(absolute = sum(field, na.rm = TRUE))
+
+  # Remove entries with too few occurrences
+  df2 <- df2 %>% filter(!is.na(publication_time) &
+                          group %in% setdiff(unique(as.character(unname(unlist(df2[which(df2$absolute >= nmin), "group"])))), "NA"))
+  df2$group <- factor(df2$group)
+  df2$group <- droplevels(df2$group)
+
+  # Add relatives
+  df3 <- pivot_wider(df2, names_from = "publication_time", values_from = "absolute", values_fill = 0)
+  df3 <- as.data.frame(df3)
+  df3[, -1] = 100 * apply(df3[, -1], 2, function (x) {x/sum(x, na.rm = TRUE)})
+  df3 <- melt(as.data.frame(df3), "group")
+  colnames(df3) <- c("group", "publication_time", "relative")
+  df3$publication_time <- as.numeric(as.character(df3$publication_time))
+  df3 <- df3[, c("publication_time", "group", "relative")]
+
+  # Combine counts and relatives
+  dfs <- dplyr::full_join(df2, df3)
+  dfs$mode <- dfs[[mode]]
+
+  return(dfs)
+
+}