This introductory vignette provides a brief, example-driven overview of rtrek.
The rtrek package provides datasets related to the Star Trek fictional universe and functions for working with those datasets. It interfaces with the Star Trek API (STAPI), Memory Alpha and Memory Beta to retrieve data, metadata and other information relating to Star Trek.
The package also contains several local datasets covering a variety of topics such as Star Trek timeline data, universe species data and geopolitical data. Some of these are more information rich, while others are toy examples useful for simple demonstrations. The bulk of Star Trek data is accessed from external sources by API. A future version of rtrek will also include summary datasets resulting from text mining analyses of Star Trek novels.
library(rtrek)
st_datasets()
#> # A tibble: 10 x 2
#> dataset description
#> <chr> <chr>
#> 1 stGeo Map tile set locations of interest.
#> 2 stSpecies Basic intelligent species data.
#> 3 stTiles Available map tile sets.
#> 4 stBooks Star Trek novel metadata.
#> 5 stSeries Names and acronyms of Star Trek series
#> 6 stapiEntities Star Trek API (STAPI) categories
#> 7 stLogos Metadata for various Star Trek logos
#> 8 tlBooks Novel-based timeline dataset
#> 9 tlEvents Event-based timeline dataset
#> 10 tlFootnotes Timeline dataset footnotesAt this time, several of the datasets are very small and are only included in the package in order to demonstrate some very basic examples and they are not particularly useful or interesting beyond this purpose. However, rtek now includes more sizable curated datasets relating the compendium of licensed, published Star Trek literature and multiple versions of Star Trek fictional universe historical timelines.
Package datasets in rtrek are somewhat eclectic and currently limited. They will expand with further package development. To list all available package datasets with a short description, call st_datasets.
A largely comprehensive Star Trek book metadata table is available as stBooks, which is informed and curated from directly parsing Star Trek e-book metadata rather than parsing third party website content.
stBooks
#> # A tibble: 783 x 11
#> title author date publisher identifier series subseries nchap nword nchar
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int> <int>
#> 1 Star ~ Alan ~ 2009~ Simon an~ 1439163391 AV <NA> 18 77035 4.60e5
#> 2 Starf~ Rick ~ 2010~ Simon Sp~ 978144241~ AV Starflee~ 14 40129 2.39e5
#> 3 Starf~ Rudy ~ 2010~ Simon Sp~ 978144241~ AV Starflee~ 31 52547 2.96e5
#> 4 Starf~ Rick ~ 2011~ Simon Sp~ 978144241~ AV Starflee~ 13 39495 2.33e5
#> 5 Starf~ Alan ~ 2012~ Simon Sp~ 978144242~ AV Starflee~ 30 62030 3.50e5
#> 6 Star ~ Alan ~ 2013~ Gallery ~ 978147671~ AV <NA> 17 77438 5.37e5
#> 7 Capta~ James~ 1998~ Pocket B~ 978143910~ CT <NA> 21 95110 5.55e5
#> 8 Capta~ Macke~ 1998~ Pocket B~ 978074345~ CT <NA> 26 76392 4.25e5
#> 9 Capta~ Chris~ 1998~ Pocket B~ 978143910~ CT <NA> 34 78678 4.43e5
#> 10 The C~ John ~ 2000~ Pocket B~ 978074340~ CT <NA> 176 436682 2.47e6
#> # ... with 773 more rows, and 1 more variable: dedication <chr>This dataset is discussed further in the section below on e-book text mining.
Before moving on, it is worth mentioning a helpful table for mapping between series names and their abbreviations used throughout this package (and in the Star Trek community in general).
stSeries
#> # A tibble: 35 x 3
#> id abb type
#> <chr> <chr> <chr>
#> 1 Abramsverse AV series
#> 2 Challenger CHA series
#> 3 Deep Space Nine DS9 series
#> 4 Discovery DSC series
#> 5 Enterprise ENT series
#> 6 Klingon Empire KE series
#> 7 Miscellaneous MISC series
#> 8 New Frontier NF series
#> 9 Prometheus PRO series
#> 10 Seekers SKR series
#> # ... with 25 more rowsThe stTiles data frame shows all available Star Trek-themed map tile sets along with metadata and attribution information. These map tiles can be used with the leaflet and shiny packages to make interactive maps situated in the Star Trek universe.
stTiles
#> # A tibble: 2 x 8
#> id url description width height tile_creator map_creator map_url
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 galax~ https://~ Geopolitic~ 8000 6445 Matthew Leo~ Rob Archer https://ar~
#> 2 galax~ https://~ Geopolitic~ 5000 4000 Matthew Leo~ <NA> http://stt~The list is scant at the moment, but more will come. One thing to keep in mind is these tile sets use a simple/non-geographical coordinate reference system (CRS). Clearly, they are not Earth-based, though they are spatial in more ways than one!
Similar to game maps, there is a sense of space, but it is a simple Cartesian coordinate system and does not use geographic projections like you may be used to working with when analyzing spatial data or making Leaflet maps. This system is much simpler, but simple does not necessarily mean easy!
Inspect stGeo:
stGeo
#> # A tibble: 18 x 4
#> id loc col row
#> <chr> <chr> <dbl> <dbl>
#> 1 galaxy1 Earth 2196 2357
#> 2 galaxy1 Romulus 2615 1742
#> 3 galaxy1 Qo'noS 3310 3361
#> 4 galaxy1 Breen 1004 939
#> 5 galaxy1 Ferenginar 1431 1996
#> 6 galaxy1 Cardassia 1342 2841
#> 7 galaxy1 Tholia 407 3866
#> 8 galaxy1 Tzenketh 1553 2557
#> 9 galaxy1 Talar 1039 3489
#> 10 galaxy2 Earth 2201 1595
#> 11 galaxy2 Romulus 2514 1178
#> 12 galaxy2 Qo'noS 3197 2303
#> 13 galaxy2 Breen 1228 1181
#> 14 galaxy2 Ferenginar 2026 886
#> 15 galaxy2 Cardassia 1543 1903
#> 16 galaxy2 Tholia 713 2971
#> 17 galaxy2 Tzenketh 1734 1721
#> 18 galaxy2 Talar 1338 2368This is another small dataset containing locations of key planets in the Star Trek universe. Notice the coordinates do not appear meaningful. There is no latitude and longitude. Instead there are row and column entries defining cells in a matrix. The matrix dimensions are defined by the pixel dimensions of source map that was used to create each tile set.
The coordinates are also not consistent. Source maps differ significantly. Even if they had identical pixel dimensions, which they do not, each artist’s visual rendering of the fictional universe will place locations differently in space. In this sense, every tile set has a unique coordinate reference system. For each new tile set produced, all locations of interest must be georeferenced again.
This is not ideal, but it gets worse. Once you have locations’ coordinates defined that map onto a particular tile set, the leaflet package does not work in these row and column grids. The (col, row) pairs need to be transformed or projected into Leaflet space. Fortunately, rtrek does this part for you with tile_coords. It takes a data frame like one returned by st_tiles_data with columns named col and row, as well as the name of an available Star Trek map tile set. It returns a data frame with new columns x and y that will map properly in a leaflet map built on that tile set.
id <- "galaxy1"
(d <- st_tiles_data(id))
#> # A tibble: 9 x 8
#> id loc col row body category zone species
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr>
#> 1 galaxy1 Earth 2196 2357 Planet Homewor~ United Federation of ~ Human
#> 2 galaxy1 Romulus 2615 1742 Planet Homewor~ Romulan Star Empire Romulan
#> 3 galaxy1 Qo'noS 3310 3361 Planet Homewor~ Klingon Empire Klingon
#> 4 galaxy1 Breen 1004 939 Planet Homewor~ Breen Confederacy Breen
#> 5 galaxy1 Ferengin~ 1431 1996 Planet Homewor~ Ferengi Alliance Ferengi
#> 6 galaxy1 Cardassia 1342 2841 Planet Homewor~ Cardassian Union Cardassi~
#> 7 galaxy1 Tholia 407 3866 Planet Homewor~ Tholian Assembly Tholian
#> 8 galaxy1 Tzenketh 1553 2557 Planet Homewor~ Tzenkethi Coalition Tzenkethi
#> 9 galaxy1 Talar 1039 3489 Planet Homewor~ Talarian Republic Talarian
(d <- tile_coords(d, id))
#> # A tibble: 9 x 10
#> id loc col row body category zone species x y
#> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl>
#> 1 galax~ Earth 2196 2357 Planet Homewor~ United Feder~ Human 68.6 -73.7
#> 2 galax~ Romulus 2615 1742 Planet Homewor~ Romulan Star~ Romulan 81.7 -54.4
#> 3 galax~ Qo'noS 3310 3361 Planet Homewor~ Klingon Empi~ Klingon 103. -105.
#> 4 galax~ Breen 1004 939 Planet Homewor~ Breen Confed~ Breen 31.4 -29.3
#> 5 galax~ Fereng~ 1431 1996 Planet Homewor~ Ferengi Alli~ Ferengi 44.7 -62.4
#> 6 galax~ Cardas~ 1342 2841 Planet Homewor~ Cardassian U~ Cardass~ 41.9 -88.8
#> 7 galax~ Tholia 407 3866 Planet Homewor~ Tholian Asse~ Tholian 12.7 -121.
#> 8 galax~ Tzenke~ 1553 2557 Planet Homewor~ Tzenkethi Co~ Tzenket~ 48.5 -79.9
#> 9 galax~ Talar 1039 3489 Planet Homewor~ Talarian Rep~ Talarian 32.5 -109.Here is an example using the galaxy1 map with leaflet. The st_tiles function is used to link to the tile provider.
library(leaflet)
tiles <- st_tiles("galaxy1")
leaflet(d, options = leafletOptions(crs = leafletCRS("L.CRS.Simple"))) %>%
addTiles(tiles) %>% setView(108, -75, 2) %>%
addCircleMarkers(lng = ~x, lat = ~y, label = ~loc, color = "white", radius = 20)The stSpecies dataset is just a small table that pairs species named with representative thumbnail avatars, mostly pulled from the Memory Alpha website. There is nothing map-related here, but these are used in this Stellar Cartography example. It is similar to the Leaflet example above, but a bit more interesting, with markers to click on and information displays.
In the course of the above map-related examples, a few functions have also been introduced. st_tiles takes an id argument that is mapped to the available tile sets in stTiles and returns the relevant URL. st_tiles_data takes the same id argument and returns a simple example data frame containing ancillary data related to the available locations from stGeo. The result is always the same except that the grid cells for locations change with respect to the chosen tile set. Finally, tile_coords can be applied to one of these data frames to add x and y columns for a CRS that Leaflet will understand.
Fictional universe historical timeline data is an exciting type of in-universe Star Trek data to have at your fingertips to play around with and explore.
It is also difficult to compile. Many people have labored away intensely over the years compiling various attempts at integrated, internally consistent, accurate timelines of Star Trek universe lore. Some have turned out more successful than others.
As of rtrek v0.2.0 the rudimentary beginnings of what will ideally eventually become an up to date and comprehensive timeline dataset are now underway in the form of two different flavors of timeline datasets.
One is based on published works, mostly consisting of novels, as well as television series and movies, all placed in chronological order.
tlBooks
#> # A tibble: 2,122 x 14
#> year title series anthology format number novelization setting
#> <dbl> <chr> <chr> <chr> <chr> <int> <lgl> <chr>
#> 1 -5 e9 The Q Continuu~ TNG <NA> book 47 FALSE second~
#> 2 -5 e9 Spock's World TOS <NA> book NA FALSE second~
#> 3 -4 e9 Reciprocity TNG SNW story NA FALSE second~
#> 4 -3.5 e9 All Good Thing~ TNG <NA> book NA TRUE second~
#> 5 -2.5 e9 The Q Continuu~ TNG <NA> book 47 FALSE second~
#> 6 -1 e9 The Q Continuu~ TNG <NA> book 48 FALSE second~
#> 7 -4.45e8 The Escape VOY <NA> book 2 FALSE second~
#> 8 -6.40e7 First Frontier TOS <NA> book 75 FALSE second~
#> 9 -5 e5 Spock's World TOS <NA> book NA FALSE second~
#> 10 -3.08e5 The Escape VOY <NA> book 2 FALSE second~
#> # ... with 2,112 more rows, and 6 more variables: stardate_start <dbl>,
#> # stardate_end <dbl>, detailed_date <chr>, section <chr>,
#> # primary_entry_year <int>, footnote <int>The other is an event-driven timeline that consists of textual entries referencing historically significant events, situated chronologically in the timeline.
tlEvents
#> # A tibble: 1,241 x 6
#> year setting series source info footnote
#> <dbl> <chr> <chr> <chr> <chr> <int>
#> 1 -4 e9 seconda~ SNW Reciprocity - SNW 2 ~4 billion years ago.~ NA
#> 2 -4 e9 primary SNW The Beginning - SNW~ ~4 billion years ago.~ 1
#> 3 -6.4 e7 time tr~ TOS First Frontier - TO~ ~64 million years ago~ NA
#> 4 -2.78e4 seconda~ DS9 Horn And Ivory - Ga~ ~27800 BC. Backstory NA
#> 5 -2.7 e3 inferred TOS Yesterday's Son - T~ ~2700 BC. Birth of Sp~ NA
#> 6 -3 e2 seconda~ <NA> Spock's World - Vul~ ~300 BC. Early Vulcan~ NA
#> 7 -7.9 e1 seconda~ <NA> Spock's World - Vul~ Birth of Surak, son o~ NA
#> 8 -7 e1 seconda~ TNG The Devil's Heart -~ ~70 BC. Stef is criti~ 2
#> 9 -3.3 e1 seconda~ <NA> Spock's World - Vul~ Surak discovers his c~ NA
#> 10 -2.2 e1 seconda~ TOS Rihannsu 2: The Rom~ Vulcans make first co~ NA
#> # ... with 1,231 more rowsThe two datasets are quite different in their focus and compliment one another.
One column these two timeline data frames share in common is the footnote column, which you can see only contains ID values for entries which have a footnote. The tlFootnotes dataset can be referenced or joined by footnote to one of the other tables. Footnotes tend to be long strings of text and not associated with most timeline entries, so they are kept in a separate table.
tlFootnotes
#> # A tibble: 605 x 3
#> id id text
#> <chr> <int> <chr>
#> 1 book 1 "This chapter chronicles the period from the formation of the 40~
#> 2 book 2 "The time and manner of Borg origins are entirely speculative. B~
#> 3 book 3 "This chapter chronicles the period from Surak's birth until his~
#> 4 book 4 "As a vision, this may be historically unreliable. The events co~
#> 5 book 5 "This chapter chronicles the period from the Ahkh War (against t~
#> 6 book 6 "The date of the Awakening is around 2000 years prior to \"The S~
#> 7 book 7 "This chapter chronicles the period from S'task's Declaration un~
#> 8 book 8 "This chapter chronicles the journey of the Rihannsu Travelers u~
#> 9 book 9 "These chapters chronicle the period beginning 25.86 real-time y~
#> 10 book 10 "This chapter chronicles the period from the Settlement until th~
#> # ... with 595 more rowstlBooks is novel-driven, meaning that the timeline entries (rows) provide a chronologically ordered list of licensed Star Trek novels. This timeline is helpful for figuring out when stories are set and the relative order in which they occur, but it does not provide any description of events transpiring in the universe.
While this data is very informative, it is many years out of date, being last updated in October of 2006. It is also necessarily speculative. Settings are determined based in part on what is interpreted to be the intention of a given author for a given story.
Nevertheless, it still represents possibly the highest quality representation of the chronological ordering of Star Trek fiction that combines episodes and movies with written works. The concurrent timeline of Star Trek TV episodes and movies are interleaved with the novels and other stories, anthologies and other written fiction. This provides fuller context resulting in a much richer timeline.
tlEvents is event-driven, meaning that the timeline entries (rows) provide chronologically ordered historical events from the Star Trek universe. As with tlBooks, this timeline is quite out of date. In fact it is at least somewhat more out of date than tlBooks, its last content update appearing to be no later than 2005. This timeline is also more problematic than the other, and less relevant moving forward. Its updating essentially ceased as the other began.
However, it is included because unlike tlBooks, which is a timeline of production titles, this timeline dataset is event-driven. While it may now be erroneous in places even independent from being out of date, it is useful for its informative textual entries referencing historically significant events in Star Trek lore.
In summary, these datasets have much value, but they should be used with the awareness that they are necessarily imperfect ans speculative, notably outdated, and tlEvents in particular is less able to stand the test of time as the Star Trek universe moves forward with new publications and productions.
It should also be noted that while it may be tempting to merge these two data frames, this is not advisable if it is important to maintain chronological order. It is generally safe to assume that multiple entries within a single year are listed in a sensible order in cases where it may matter, within-year entries do not have specific, unique within-year dates. They are ordinal only. It is not possible to merge entries from both tables for a specific year and know how the combined set of entries should be ordered- unless you already know everything about Star Trek, in which case please craft the ultimate timeline in a universal file format that can be easily digested by a computer.
Now that you have seen an overview of available rtrek datasets and some associated functions, it is time to turn attention to external datasets. The Star Trek API (STAPI) is a particularly useful data source.
Keep in mind that STAPI focuses more on providing real world data associated with Star Trek (e.g., when did episode X first air on television?) than on fictional universe data, but it contains both and the database holdings will grow with time.
To use the words of the developers, the STAPI is
the first public Star Trek API, accessible via REST and SOAP. It’s an open source project, that anyone can contribute to.
The API is highly functional. Please do not abuse the API with constant requests. Their pages suggest no more than one request per second, but I would suggest ten seconds between successive requests. The default anti-DOS measures in rtrek limit requests to one per second. You can update this global rtrek setting with options, e.g. options(rtrek_antidos = 10) for a minimum ten second wait between API calls to be an even better neighbor. rtrek will not permit faster requests. If set below one second, the option is ignored and a warning thrown when making any API call.
There a many fields, or entities, available in the API. The available IDs can be found in this table:
stapiEntities
#> # A tibble: 40 x 4
#> id class ncol colnames
#> <chr> <chr> <int> <named list>
#> 1 animal tbl_df 7 <chr [7]>
#> 2 astronomicalObject tbl_df 5 <chr [5]>
#> 3 book tbl_df 24 <chr [24]>
#> 4 bookCollection tbl_df 10 <chr [10]>
#> 5 bookSeries tbl_df 11 <chr [11]>
#> 6 character tbl_df 24 <chr [24]>
#> 7 comicCollection tbl_df 14 <chr [14]>
#> 8 comics tbl_df 15 <chr [15]>
#> 9 comicSeries tbl_df 15 <chr [15]>
#> 10 comicStrip tbl_df 12 <chr [12]>
#> # ... with 30 more rowsThese ID values are passed to stapi to perform a search using the API. The other columns provide some information about the object returned from a search. All entity searches return tibble data frames. You can inspect or unnest the column names of each table returned from every available entity search so you can see beforehand what variables are associated with each entity.
Using stapi should be thought of as a three part process:
stapi one more time referencing the specific observation.To determine how many pages of results exist for a given search, set page_count = TRUE. The impact on the API will be equivalent to only searching a single page of results. One page contains metadata including the total number of pages. Nothing is returned in this “safe mode”, but the total number of search results available is printed to the console.
Searching movies only returns one page of results. However, there are a lot of characters in the Star Trek universe. Check the total pages available for character search.
stapi("character", page_count = TRUE)
#> Total pages to retrieve all results: 66And that is with 100 results per page!
The default page = 1 only returns the first page. page can be a vector, e.g. page = 1:62. Results from multi-page searches are automatically combined into a single, constant data frame output. For the second call to stapi, return only page two here, which contains the character, Q (currently, pending future character database updates that may shift the indexing). In case that does change and Q is not always near the top of page two of the search results, the example further below hard-codes his unique/universal ID.
stapi("character", page = 2)
#> # A tibble: 100 x 24
#> uid name gender yearOfBirth monthOfBirth dayOfBirth placeOfBirth
#> <chr> <chr> <chr> <int> <int> <int> <chr>
#> 1 CHMA00001~ Robert Cr~ <NA> NA NA NA <NA>
#> 2 CHMA00001~ Romulus a~ <NA> NA NA NA <NA>
#> 3 CHMA00002~ M. Moreira <NA> NA NA NA <NA>
#> 4 CHMA00000~ Emiraa Kw~ <NA> NA NA NA <NA>
#> 5 CHMA00000~ Tom Purser <NA> NA NA NA <NA>
#> 6 CHMA00000~ Areel Shaw <NA> NA NA NA <NA>
#> 7 CHMA00000~ Lursa <NA> NA NA NA <NA>
#> 8 CHMA00000~ Konsab <NA> NA NA NA <NA>
#> 9 CHMA00000~ Harrison <NA> NA NA NA <NA>
#> 10 CHMA00002~ Tezber <NA> NA NA NA <NA>
#> # ... with 90 more rows, and 17 more variables: yearOfDeath <int>,
#> # monthOfDeath <lgl>, dayOfDeath <lgl>, placeOfDeath <chr>, height <int>,
#> # weight <int>, deceased <lgl>, bloodType <chr>, maritalStatus <chr>,
#> # serialNumber <lgl>, hologramActivationDate <chr>, hologramStatus <chr>,
#> # hologramDateStatus <chr>, hologram <lgl>, fictionalCharacter <lgl>,
#> # mirror <lgl>, alternateReality <lgl>Character tables can be sparse. There are a lot of variables, many of which will contain missing data for rare, esoteric characters. Even for more popular characters about whom much more universe lore has been uncovered, it still takes dedicated nerds to enter all the data in a database.
When a dataset contains a uid column, this can be used subsequently to extract a satellite dataset about that particular observation that was returned in the original search. First you used safe mode, then search mode, and now switch from search mode to extraction mode to obtain data about Q, specifically. All that is required to do this is pass Q’s uid to stapi and call the function one last time. When uid is no longer NULL, stapi knows not to bother with a search and makes a different type of API call requesting information about the uniquely identified entry.
Q <- "CHMA0000025118"
Q <- stapi("character", uid = Q)
library(dplyr)
Q$episodes %>% select(uid, title, stardateFrom, stardateTo)
#> uid title stardateFrom stardateTo
#> 1 EPMA0000001458 All Good Things... 47988.0 47988.0
#> 2 EPMA0000000845 Q-Less 46531.2 46531.2
#> 3 EPMA0000001329 Q Who 42761.3 42761.3
#> 4 EPMA0000000651 Tapestry NA NA
#> 5 EPMA0000001510 The Q and the Grey 50384.2 50392.7
#> 6 EPMA0000000483 Encounter at Farpoint 41153.7 41153.7
#> 7 EPMA0000162588 Death Wish NA NA
#> 8 EPMA0000001413 True Q 46192.3 46192.3
#> 9 EPMA0000001377 Qpid 44741.9 44741.9The data returned on Q is actually a large list, including multiple data frames. For simplicity only a piece of it is shown above. For more examples, see the STAPI vignette.
Some functions in rtrek provide an API-like interface to online Star Trek-related data. Specifically, parsing data from the Memory Alpha and Memory Beta websites. These sites do not provide APIs. Therefore the only option is to read pages into R and parse the html. Behind the scenes this is done using the xml2 and rvest packages, but from the user perspective it is presented as passing an API endpoint string to a function.
memory_alpha and memory_beta, as well as several other related functions, are available in rtrek. These functions access data from Memory Alpha and Memory Beta. For details and examples on these functions, see the Memory Alpha vignette and the Memory Beta vignette.
This section will be continued in a future version of rtrek. For now what is available is a dataset stBooks. This dataset represents metadata parsed, imperfectly but painstakingly and thoroughly, from actual Star Trek books. stBooks contains several different fields, including useful fields for analysts such as the number of words and chapters in a book.
stBooks
#> # A tibble: 783 x 11
#> title author date publisher identifier series subseries nchap nword nchar
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <int> <int> <int>
#> 1 Star ~ Alan ~ 2009~ Simon an~ 1439163391 AV <NA> 18 77035 4.60e5
#> 2 Starf~ Rick ~ 2010~ Simon Sp~ 978144241~ AV Starflee~ 14 40129 2.39e5
#> 3 Starf~ Rudy ~ 2010~ Simon Sp~ 978144241~ AV Starflee~ 31 52547 2.96e5
#> 4 Starf~ Rick ~ 2011~ Simon Sp~ 978144241~ AV Starflee~ 13 39495 2.33e5
#> 5 Starf~ Alan ~ 2012~ Simon Sp~ 978144242~ AV Starflee~ 30 62030 3.50e5
#> 6 Star ~ Alan ~ 2013~ Gallery ~ 978147671~ AV <NA> 17 77438 5.37e5
#> 7 Capta~ James~ 1998~ Pocket B~ 978143910~ CT <NA> 21 95110 5.55e5
#> 8 Capta~ Macke~ 1998~ Pocket B~ 978074345~ CT <NA> 26 76392 4.25e5
#> 9 Capta~ Chris~ 1998~ Pocket B~ 978143910~ CT <NA> 34 78678 4.43e5
#> 10 The C~ John ~ 2000~ Pocket B~ 978074340~ CT <NA> 176 436682 2.47e6
#> # ... with 773 more rows, and 1 more variable: dedication <chr>Obviously, verbatim licensed book content itself cannot be shared, so it is not possible to provide capability in rtrek to enable analysts to perform their own unique text mining analyses on Star Trek novel corpora. However, future versions of rtrek will include more summary datasets that will aim to represent more interesting variables.
A few examples could be:
or any other set of interesting metrics that could be used, for example, to inform suggested reading lists of various titles, or books by particular authors with a favored style or focus.