# Comparing topics over time

    library(scopusflow)

A common bibliometric question is not how large a literature is, but how its internal emphasis shifts over time. Within deep-learning research, say, is the share of work that also concerns medical imaging growing faster than the share about computer vision? `scopus_compare_topics()` answers exactly this, and `plot_scopus_comparison()` shows the answer. The comparison itself contacts the API, so it is shown but not run. The plotting is reproduced offline from an object of the same shape.

## What the comparison measures

For each year and each comparison term, the function counts the records matching the reference topic and that term, and expresses it as a percentage of the records matching the reference alone. A value of 30% for ‘computer vision’ in 2020 means that 30% of the deep-learning records that year also mention computer vision. The reference is the denominator, so it sits at 100% by construction and is not drawn.

    cmp <- scopus_compare_topics(
      reference_query  = "deep learning",
      comparison_terms = c("computer vision", "natural language processing",
                           "medical imaging", "drug discovery"),
      years            = 2013:2021,
      field            = "TITLE-ABS-KEY"
    )

## The shape of the result

The result is a tidy table with one row per topic and year. A comparison counts whole literatures, so unlike a trend it cannot be derived from the corpus the package bundles for its other examples. The table below is rebuilt in the same shape, with illustrative counts, so the rest of the article runs without a key. The reference set grows over the period, which the uncertainty band will reflect.

    years <- 2013:2021
    ref_n <- round(seq(400, 1600, length.out = length(years)))
    mk <- function(from, to) round(seq(from, to, length.out = length(years)))
    counts <- list(
      "computer vision" = mk(140, 720),
      "natural language processing" = mk(90, 540),
      "medical imaging" = mk(15, 260),
      "drug discovery" = mk(8, 170)
    )
    cmp <- tibble::tibble(
      query = "q",
      query_type = c(rep("reference", length(years)),
                     rep("comparison", length(counts) * length(years))),
      abridged_query = c(rep("deep learning", length(years)),
                         rep(names(counts), each = length(years))),
      year = rep(years, length(counts) + 1),
      n = c(ref_n, unlist(counts, use.names = FALSE)),
      reference_n = rep(ref_n, length(counts) + 1),
      comparison_percentage = 100 * c(ref_n, unlist(counts, use.names = FALSE)) /
        rep(ref_n, length(counts) + 1),
      average_comparison_percentage = c(rep(100, length(years)),
                                        rep(c(40, 33, 15, 9), each = length(years)))
    )
    class(cmp) <- c("scopus_comparison", class(cmp))

    # The whole table is too long to read here, so show its first year across every
    # topic. The `query` column is left out because a real comparison carries the
    # whole query string sent to the API in it, which is too long for a table. The
    # illustrative table built above holds a placeholder there instead.
    cmp[cmp$year == min(cmp$year), setdiff(names(cmp), "query")]

query_type | abridged_query | year | n | reference_n | comparison_percentage | average_comparison_percentage
--- | --- | --- | --- | --- | --- | ---
reference | deep learning | 2013 | 400 | 400 | 100.00 | 100
comparison | computer vision | 2013 | 140 | 400 | 35.00 | 40
comparison | natural language processing | 2013 | 90 | 400 | 22.50 | 33
comparison | medical imaging | 2013 | 15 | 400 | 3.75 | 15
comparison | drug discovery | 2013 | 8 | 400 | 2.00 | 9

Those are the five rows for the first year, one for the reference and one for each comparison term. The whole table has 45 rows on the same pattern. The `query` column left out above is still in the object. A real comparison carries the whole query behind each count there, where this illustrative table carries a placeholder.

The `comparison_percentage` column is the per-year share, and `average_comparison_percentage` is the same ratio computed over the whole period, which is what orders the topics. A year in which the reference has no records has no defined share, so it is recorded as `NA`. A zero there would be read as a real observation.

## A first plot

Drawing the comparison takes one call on the result.

    plot_scopus_comparison(cmp, legend_inside = TRUE)

Here `legend_inside = TRUE` places the topic key inside the panel, in whichever corner has the most free space. Left at its default the chart labels each line at its end, and uses whole-number year breaks and a colour-blind-safe palette, so with only a few topics the reader never has to match colours to a legend at all. Each label carries the topic’s total record count. The shaded band around each line is a Wilson stability range. It is wide in the early years, when the reference set is small and the share would move easily, and narrows as the literature grows. ‘Scopus’ returns exact counts, so nothing here is a sample from which an interval could be estimated. The band is illustrative, a point the `plot_scopus_comparison()` help page sets out.

## When lines converge at the right end

Direct labels are legible only if they do not overlap, and topics sometimes end the period at nearly the same share. `plot_scopus_comparison()` spreads converging labels apart automatically, at the point the figure is actually drawn, so they stay readable at any figure size and never stack into an unreadable pile. Here six sub-areas of materials-science research all end 2013–2021 within three points of one another.

    years <- 2013:2021
    ends <- c(18, 18.6, 19.2, 19.8, 20.4, 21)
    names(ends) <- c(
      "graphene", "perovskites", "MXenes", "COFs", "MOFs", "aerogels"
    )
    ref_n <- round(seq(500, 2000, length.out = length(years)))
    converge <- function(end) round(end *
      (0.5 + 0.5 * (0:(length(years) - 1)) / (length(years) - 1)) * ref_n / 100)
    counts <- lapply(ends, converge)

    cmp_converging <- tibble::tibble(
      query = "q",
      query_type = c(rep("reference", length(years)),
                     rep("comparison", length(counts) * length(years))),
      abridged_query = c(rep("energy materials", length(years)),
                         rep(names(counts), each = length(years))),
      year = rep(years, length(counts) + 1),
      n = c(ref_n, unlist(counts, use.names = FALSE)),
      reference_n = rep(ref_n, length(counts) + 1),
      comparison_percentage = 100 * c(ref_n, unlist(counts, use.names = FALSE)) /
        rep(ref_n, length(counts) + 1),
      average_comparison_percentage = c(rep(100, length(years)),
                                        rep(ends, each = length(years)))
    )
    class(cmp_converging) <- c("scopus_comparison", class(cmp_converging))

    plot_scopus_comparison(cmp_converging)

Without this, six labels ending within three points of each other would print on top of one another. Here every one is still readable, each colour-matched to its own line and spread in the same order as the line ends.

## Drawing the eye to one topic

When one topic is the focus of a figure, `highlight` draws it in an accent colour and greys the rest, which keeps the context visible without letting it compete.

    plot_scopus_comparison(cmp, highlight = "medical imaging")

## Adjusting the labels

The count suffix on each label can be turned off, and the uncertainty band can be removed, when a cleaner look is wanted.

    plot_scopus_comparison(cmp, pub_count_in_legend = FALSE, interval = FALSE)

The return value is an ordinary ggplot2 object, so any further adjustment, a different theme or a saved file, is one `+` or one `ggplot2::ggsave()` away.

## Reading the result as a table

Sometimes the numbers matter more than the picture. Because the output is a tibble, the usual tools apply. Here are the topics ranked by their average share.

    comp <- cmp[cmp$query_type == "comparison", ]
    unique(comp[, c("abridged_query", "average_comparison_percentage")])

abridged_query | average_comparison_percentage
--- | ---
computer vision | 40
natural language processing | 33
medical imaging | 15
drug discovery | 9