mammals <- structure(list(species = structure(1:62, .Label = c("Africanelephant", "Africangiantpouchedrat", "ArcticFox", "Arcticgroundsquirrel", "Asianelephant", "Baboon", "Bigbrownbat", "Braziliantapir", "Cat", "Chimpanzee", "Chinchilla", "Cow", "Deserthedgehog", "Donkey", "EasternAmericanmole", "Echidna", "Europeanhedgehog", "Galago", "Genet", "Giantarmadillo", "Giraffe", "Goat", "Goldenhamster", "Gorilla", "Grayseal", "Graywolf", "Groundsquirrel", "Guineapig", "Horse", "Jaguar", "Kangaroo", "Lessershort-tailedshrew", "Littlebrownbat", "Man", "Molerat", "Mountainbeaver", "Mouse", "Muskshrew", "NAmericanopossum", "Nine-bandedarmadillo", "Okapi", "Owlmonkey", "Patasmonkey", "Phanlanger", "Pig", "Rabbit", "Raccoon", "Rat", "Redfox", "Rhesusmonkey", "Rockhyrax(Heterob)", "Rockhyrax(Procaviahab)", "Roedeer", "Sheep", "Slowloris", "Starnosedmole", "Tenrec", "Treehyrax", "Treeshrew", "Vervet", "Wateropossum", "Yellow-belliedmarmot"), class = "factor"), body_wt = c(6654, 1, 3.3849999999999998, 0.92000000000000004, 2547, 10.550000000000001, 0.023, 160, 3.2999999999999998, 52.159999999999997, 0.42499999999999999, 465, 0.55000000000000004, 187.09999999999999, 0.074999999999999997, 3, 0.78500000000000003, 0.20000000000000001, 1.4099999999999999, 60, 529, 27.66, 0.12, 207, 85, 36.329999999999998, 0.10100000000000001, 1.04, 521, 100, 35, 0.0050000000000000001, 0.01, 62, 0.122, 1.3500000000000001, 0.023, 0.048000000000000001, 1.7, 3.5, 250, 0.47999999999999998, 10, 1.6200000000000001, 192, 2.5, 4.2880000000000003, 0.28000000000000003, 4.2350000000000003, 6.7999999999999998, 0.75, 3.6000000000000001, 14.83, 55.5, 1.3999999999999999, 0.059999999999999998, 0.90000000000000002, 2, 0.104, 4.1900000000000004, 3.5, 4.0499999999999998), brain_wt = c(5712, 6.5999999999999996, 44.5, 5.7000000000000002, 4603, 179.5, 0.29999999999999999, 169, 25.600000000000001, 440, 6.4000000000000004, 423, 2.3999999999999999, 419, 1.2, 25, 3.5, 5, 17.5, 81, 680, 115, 1, 406, 325, 119.5, 4, 5.5, 655, 157, 56, 0.14000000000000001, 0.25, 1320, 3, 8.0999999999999996, 0.40000000000000002, 0.33000000000000002, 6.2999999999999998, 10.800000000000001, 490, 15.5, 115, 11.4, 180, 12.1, 39.200000000000003, 1.8999999999999999, 50.399999999999999, 179, 12.300000000000001, 21, 98.200000000000003, 175, 12.5, 1, 2.6000000000000001, 12.300000000000001, 2.5, 58, 3.8999999999999999, 17), non_dreaming = c(NA, 6.2999999999999998, NA, NA, 2.1000000000000001, 9.0999999999999996, 15.800000000000001, 5.2000000000000002, 10.9, 8.3000000000000007, 11, 3.2000000000000002, 7.5999999999999996, NA, 6.2999999999999998, 8.5999999999999996, 6.5999999999999996, 9.5, 4.7999999999999998, 12, NA, 3.2999999999999998, 11, NA, 4.7000000000000002, NA, 10.4, 7.4000000000000004, 2.1000000000000001, NA, NA, 7.7000000000000002, 17.899999999999999, 6.0999999999999996, 8.1999999999999993, 8.4000000000000004, 11.9, 10.800000000000001, 13.800000000000001, 14.300000000000001, NA, 15.199999999999999, 10, 11.9, 6.5, 7.5, NA, 10.6, 7.4000000000000004, 8.4000000000000004, 5.7000000000000002, 4.9000000000000004, NA, 3.2000000000000002, NA, 8.0999999999999996, 11, 4.9000000000000004, 13.199999999999999, 9.6999999999999993, 12.800000000000001, NA), dreaming = c(NA, 2, NA, NA, 1.8, 0.69999999999999996, 3.8999999999999999, 1, 3.6000000000000001, 1.3999999999999999, 1.5, 0.69999999999999996, 2.7000000000000002, NA, 2.1000000000000001, 0, 4.0999999999999996, 1.2, 1.3, 6.0999999999999996, 0.29999999999999999, 0.5, 3.3999999999999999, NA, 1.5, NA, 3.3999999999999999, 0.80000000000000004, 0.80000000000000004, NA, NA, 1.3999999999999999, 2, 1.8999999999999999, 2.3999999999999999, 2.7999999999999998, 1.3, 2, 5.5999999999999996, 3.1000000000000001, 1, 1.8, 0.90000000000000002, 1.8, 1.8999999999999999, 0.90000000000000002, NA, 2.6000000000000001, 2.3999999999999999, 1.2, 0.90000000000000002, 0.5, NA, 0.59999999999999998, NA, 2.2000000000000002, 2.2999999999999998, 0.5, 2.6000000000000001, 0.59999999999999998, 6.5999999999999996, NA), total_sleep = c(3.2999999999999998, 8.3000000000000007, 12.5, 16.5, 3.8999999999999999, 9.8000000000000007, 19.699999999999999, 6.2000000000000002, 14.5, 9.6999999999999993, 12.5, 3.8999999999999999, 10.300000000000001, 3.1000000000000001, 8.4000000000000004, 8.5999999999999996, 10.699999999999999, 10.699999999999999, 6.0999999999999996, 18.100000000000001, NA, 3.7999999999999998, 14.4, 12, 6.2000000000000002, 13, 13.800000000000001, 8.1999999999999993, 2.8999999999999999, 10.800000000000001, NA, 9.0999999999999996, 19.899999999999999, 8, 10.6, 11.199999999999999, 13.199999999999999, 12.800000000000001, 19.399999999999999, 17.399999999999999, NA, 17, 10.9, 13.699999999999999, 8.4000000000000004, 8.4000000000000004, 12.5, 13.199999999999999, 9.8000000000000007, 9.5999999999999996, 6.5999999999999996, 5.4000000000000004, 2.6000000000000001, 3.7999999999999998, 11, 10.300000000000001, 13.300000000000001, 5.4000000000000004, 15.800000000000001, 10.300000000000001, 19.399999999999999, NA), life_span = c(38.600000000000001, 4.5, 14, NA, 69, 27, 19, 30.399999999999999, 28, 50, 7, 30, NA, 40, 3.5, 50, 6, 10.4, 34, 7, 28, 20, 3.8999999999999999, 39.299999999999997, 41, 16.199999999999999, 9, 7.5999999999999996, 46, 22.399999999999999, 16.300000000000001, 2.6000000000000001, 24, 100, NA, NA, 3.2000000000000002, 2, 5, 6.5, 23.600000000000001, 12, 20.199999999999999, 13, 27, 18, 13.699999999999999, 4.7000000000000002, 9.8000000000000007, 29, 7, 6, 17, 20, 12.699999999999999, 3.5, 4.5, 7.5, 2.2999999999999998, 24, 3, 13), gestation = c(645, 42, 60, 25, 624, 180, 35, 392, 63, 230, 112, 281, NA, 365, 42, 28, 42, 120, NA, NA, 400, 148, 16, 252, 310, 63, 28, 68, 336, 100, 33, 21.5, 50, 267, 30, 45, 19, 30, 12, 120, 440, 140, 170, 17, 115, 31, 63, 21, 52, 164, 225, 225, 150, 151, 90, NA, 60, 200, 46, 210, 14, 38), predation = c(3L, 3L, 1L, 5L, 3L, 4L, 1L, 4L, 1L, 1L, 5L, 5L, 2L, 5L, 1L, 2L, 2L, 2L, 1L, 1L, 5L, 5L, 3L, 1L, 1L, 1L, 5L, 5L, 5L, 1L, 3L, 5L, 1L, 1L, 2L, 3L, 4L, 4L, 2L, 2L, 5L, 2L, 4L, 2L, 4L, 5L, 2L, 3L, 1L, 2L, 2L, 3L, 5L, 5L, 2L, 3L, 2L, 3L, 3L, 4L, 2L, 3L), exposure = c(5L, 1L, 1L, 2L, 5L, 4L, 1L, 5L, 2L, 1L, 4L, 5L, 1L, 5L, 1L, 2L, 2L, 2L, 2L, 1L, 5L, 5L, 1L, 4L, 3L, 1L, 1L, 3L, 5L, 1L, 5L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 2L, 4L, 1L, 4L, 5L, 2L, 1L, 1L, 3L, 2L, 2L, 5L, 5L, 2L, 1L, 1L, 1L, 2L, 3L, 1L, 1L), danger = c(3L, 3L, 1L, 3L, 4L, 4L, 1L, 4L, 1L, 1L, 4L, 5L, 2L, 5L, 1L, 2L, 2L, 2L, 1L, 1L, 5L, 5L, 2L, 1L, 1L, 1L, 3L, 4L, 5L, 1L, 4L, 4L, 1L, 1L, 1L, 3L, 3L, 3L, 1L, 1L, 5L, 2L, 4L, 2L, 4L, 5L, 2L, 3L, 1L, 2L, 2L, 3L, 5L, 5L, 2L, 2L, 2L, 3L, 2L, 4L, 1L, 1L)), row.names = c(NA, -62L), class = c("tbl_df", "tbl", "data.frame"))