Updated: – Added Janny Wurts latest, added Dorothy Dunnett If you are curious, here some samples. The numbers are approximate and should omit indexes, appendices and stuff not directly belonging to the text itself. Discrepancies are often due to the fact that Microsoft Word consider “it’s” like one word, instead my data is measured considering it as “it is”, so two words. This can usually add a +5-10k compared to “official” wordcounts. Lord of the Rings – J. R. R. Tolkien (revised to be in line with the rest) The Fellowship of the Ring: 187k The Two Towers: 155k The Return of the King: 131k Total: 473k Wheel of Time – Robert Jordan The Eye of the World: 305k The Great Hunt: 267k The Dragon Reborn: 251k The Shadow Rising: 393k The Fires of Heaven: 354k Lord of Chaos: 389k A Crown of Swords: 295k The Path of Daggers: 226k Winter’s Heart: 238k Crossroads of Twilight: 271k Knife of Dreams: 315k Total: 3 M 304k (official count) New Spring: 121k Brandon Sanderson takeover The Gathering Storm: 303k Towers of Midnight: 325k A Memory of Light: 350k (tentative) Total global: 4 M 403k (official) Stormlight Archives – Brandon Sanderson The Way of Kings: 387k (official count) A Song of Ice And Fire – George R. R. Martin A Game of Thrones: 298k A Clash of kings: 326k A Storm of Swords: 424k A Feast for Crows: 300k A Dance with Dragons: 422k Total: 1 M 770k Malazan Cycle – Steven Erikson @ Andri's solution fails to distinguish between zero and one words. So taking a 'positive' approach to finding words one might sapply(gregexpr( [:alpha:]+, str1 function(x) sum(x > 0) Leading to > sapply(gregexpr( [:alpha:]+, str1 function(x) sum(x > 0) [1] Again the regular expression might be refined for different notions of 'word'. I like the use of gregexpr because it's memory efficient. An alternative using strsplit (like @user813966, but with a regular expression to delimit words) and making use of the original notion of delimiting words is > vapply(strsplit(str1, \ W+ length, integer(1) [1] This needs to.
Total Crack These fics are (as the title suggests) completely crack. Now our definitions of crack may differ, but these are what I would define as so cracky, you’ll need a nutcracker to make it through. NOTE – animal transformations are always crack and I refuse to read tentacle – everyone has their limits, that’s mine. General things like m-pregs, age differences, changes to powers and historical settings are ignored in this category. My Barbaric Darling by baehj2915 Summary: Erik is revivified caveman. Charles is the anthropologist(?) taking care of him. This is as ridiculous as it sounds. Romcom misunderstandings and prehistoric wooing ensues Warnings/ Rating/ Word count: caveman, sexual humour, funny, no powers, bears, just general crack, NC-17, 15,424 (part 1) My Opinion: This series. Whenever I hear ‘crack’ this is my go-to fic. It’s funny and has you questioning what reality even is but it’s adorable still. I love the POV swap and they pretty much nailed how I would interpret Erik if he were a ‘revived caveman’. There’s 5 parts thus far, I think I’ve read them all and it’s just lovely. Check out the fanart too because that is awesome. Summary: Charles is a were-corgi. Six months after Cuba, Erik dognaps him. Warnings/ Rating/ Word count: animal transformation, dog-knapping, canon disabled character, PG, 3679 words. My Opinion: The description of corgi! Charles made me immensely happy. It’s utter crack but it’s cute and leaves me with a smile on my face, what else can you ask for from a story that involves Charles as a were-corgi? Shifter ‘verse ( Rat and Shark) by Macx Summary: Erik Lehnsherr was born different. He was one of the Cursed. He was a shark, a trained killer, a weapon created by Sebastian Shaw. And he was a loner – until Charles Xavier stepped into his life and followed him home. Erik had no idea who and what Charles really was, aside from annyoing and.
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