I ran multiple imputation to impute missing data for 2 variables of a data frame, then I got a new data frame (with 2 columns for 2 imputed variables).
Now, I want to replace the 2 columns in the original data frame with the two newly imputed columns from my new dataframe. What should I do?
Original data frame new data frame for imputed variables
This is the code I used. Only 2 columns in this data frame are missing data, so I only imputed those two. Is that ok? Can you please suggest me a better way?
library("mice")
imi<-mice(subset(data,select=c('ABV','EBC')),m=5,maxit=10)
Data
structure(list(Name = structure(c(58L, 188L, 40L, 155L, 32L,
88L, 92L, 55L, 135L, 31L, 139L, 26L, 126L, 10L, 166L, 104L, 75L,
180L, 35L, 175L, 77L, 99L, 4L, 71L, 141L, 176L, 53L, 39L, 172L,
196L, 123L, 107L, 16L, 96L, 82L, 185L, 30L, 15L, 94L, 129L, 187L,
151L, 33L, 23L, 28L, 44L, 157L, 69L, 132L, 83L, 131L, 11L, 182L,
181L, 54L, 115L, 116L, 183L, 150L, 195L, 45L, 144L, 1L, 110L,
17L, 114L, 9L, 117L, 112L, 70L, 34L, 169L, 27L, 66L, 3L, 73L,
133L, 91L, 154L, 130L, 160L, 105L, 90L, 165L, 67L, 100L, 162L,
98L, 29L, 68L, 189L, 192L, 102L, 190L, 134L, 136L, 52L, 12L,
81L, 59L, 63L, 122L, 93L, 109L, 178L, 138L, 5L, 43L, 140L, 95L,
2L, 174L, 76L, 51L, 156L, 60L, 149L, 128L, 177L, 142L, 103L,
7L, 8L, 14L, 164L, 74L, 145L, 148L, 113L, 86L, 108L, 48L, 163L,
6L, 186L, 89L, 36L, 191L, 125L, 120L, 62L, 65L, 124L, 168L, 147L,
79L, 173L, 84L, 193L, 25L, 146L, 121L, 127L, 153L, 13L, 106L,
119L, 161L, 49L, 97L, 101L, 61L, 137L, 24L, 85L, 194L, 78L, 41L,
170L, 47L, 118L, 184L, 179L, 72L, 42L, 111L, 87L, 57L, 38L, 37L,
171L, 22L, 50L, 80L, 159L, 18L, 152L, 64L, 56L, 158L, 167L, 46L,
19L, 21L, 20L, 143L), .Label = c("#Mashtag 2013", "#Mashtag 2014",
"#Mashtag 2015", "10 Heads High", "5am Saint", "77 Lager", "AB:02",
"AB:03", "AB:04", "AB:06", "AB:08", "AB:10", "AB:11", "AB:13",
"AB:15", "AB:17", "AB:18", "AB:20", "Ace Of Chinook", "Ace Of Citra",
"Ace Of Equinox", "Ace Of Simcoe", "Albino Squid Assasin", "Alice Porter",
"All Day Long - Prototype Challenge", "Alpha Dog", "Alpha Pop",
"Amarillo - IPA Is Dead", "American Ale", "Anarchist Alchemist",
"Arcade Nation", "Avery Brown Dredge", "Baby Dogma", "Baby Saison - B-Sides",
"Bad Pixie", "Barley Wine - Russian Doll", "Barrel Aged Albino Squid Assassin",
"Barrel Aged Hinterland", "Berliner Weisse With Raspberries And Rhubarb - B-Sides",
"Berliner Weisse With Yuzu - B-Sides", "Bitch Please (w/ 3 Floyds)",
"Black Dog", "Black Eye Joe (w/ Stone Brewing Co)", "Black Eyed King Imp",
"Black Eyed King Imp - Vietnamese Coffee Edition", "Black Hammer",
"Black Jacques", "Black Tokyo Horizon (w/Nøgne Ã\230 & Mikkeller)",
"Blitz Berliner Weisse", "Blitz Series", "Born To Die", "Bounty Hunter - Shareholder Brew",
"Bourbon Baby", "Bracken's Porter", "Bramling X", "Brewdog Vs Beavertown",
"Brixton Porter", "Buzz", "Candy Kaiser", "Cap Dog (w/ Cap Brewery)",
"Catherine's Pony (w/ Beavertown)", "Challenger", "Chaos Theory",
"Chili Hammer", "Chinook - IPA Is Dead", "Citra", "Clown King",
"Cocoa Psycho", "Coffee Imperial Stout", "Comet", "Dana - IPA Is Dead",
"Dead Metaphor", "Dead Pony Club", "Deaf Mermaid - B-Sides",
"Devine Rebel (w/ Mikkeller)", "Dog A", "Dog B", "Dog C", "Dog D",
"Dog E", "Dog Fight (w/ Flying Dog)", "Dog Wired (w/8 Wired)",
"Dogma", "Doodlebug", "Double IPA - Russian Doll", "Edge", "El Dorado - IPA Is Dead",
"Electric India", "Ella - IPA Is Dead", "Elvis Juice V2.0 - Prototype Challenge",
"Everday Anarchy", "Fake Lager", "Galaxy", "Goldings - IPA Is Dead",
"Growler", "Hardcore IPA", "Hardkogt IPA", "HBC 366 - IPA Is Dead",
"HBC 369", "Hello My Name Is Beastie", "Hello My Name Is Holy Moose",
"Hello My Name Is Ingrid", "Hello My Name Is Little Ingrid",
"Hello My Name Is Mette-Marit", "Hello My Name Is PaÌ\210ivi",
"Hello My Name is Sonja (w/ Evil Twin)", "Hello My Name is Vladimir",
"Hello My Name Is ZeÌ\201 (w/ 2Cabeças)", "Hinterland", "Hobo Pop",
"Hop Fiction - Prototype Challenge", "Hopped-Up Brown Ale - Prototype Challenge",
"Hoppy Christmas", "Hops Kill Nazis", "Hunter Foundation Pale Ale",
"Hype", "India Session Lager - Prototype Challenge", "International Arms Race (w/ Flying Dog)",
"Interstellar", "Jack Hammer", "Jasmine IPA", "Jet Black Heart",
"Kohatu - IPA Is Dead", "Konnichiwa Kitsune", "Libertine Black Ale",
"Libertine Porter", "Lichtenstein Pale Ale", "Lizard Bride - Prototype Challenge",
"Lost Dog (w/Lost Abbey)", "Lumberjack Stout", "Magic Stone Dog (w/Magic Rock & Stone Brewing Co.)",
"Mandarina Bavaria - IPA Is Dead", "Mango Gose - B-Sides", "Melon And Cucumber IPA - B-Sides",
"Misspent Youth", "Morag's Mojito - B-Sides", "Moshi Moshi 15",
"Motueka", "Movember", "Mr.Miyagi's Wasabi Stout", "Nanny State",
"Nelson Sauvin", "Neon Overlord", "Never Mind The Anabolics",
"No Label", "Nuns With Guns", "Old World India Pale Ale", "Old World Russian Imperial Stout",
"Orange Blossom - B-Sides", "Pale - Russian Doll", "Paradox Islay",
"Paradox Islay 2.0", "Paradox Jura", "Peroxide Punk", "Pilsen Lager",
"Pioneer - IPA Is Dead", "Prototype 27", "Prototype Helles",
"Prototype Pils 2.0", "Pumpkin King", "Punk IPA 2007 - 2010",
"Punk IPA 2010 - Current", "Restorative Beverage For Invalids And Convalescents",
"Rhubarb Saison - B-Sides", "Riptide", "Russian Doll â\200“ India Pale Ale",
"Rye Hammer", "San Diego Scotch Ale (w/Ballast Point)", "Santa Paws",
"Shareholder Black IPA 2011", "Ship Wreck", "Shipwrecker Circus (w/ Oskar Blues)",
"Simcoe", "Sink The Bismarck!", "Skull Candy", "Sorachi Ace",
"Sorachi Bitter - B-Sides", "Spiced Cherry Sour - B-Sides", "Stereo Wolf Stout - Prototype Challenge",
"Storm", "Sub Hop", "Sunk Punk", "Sunmaid Stout", "Sunshine On Rye - B-Sides",
"The Physics", "This. Is. Lager", "TM10", "Trashy Blonde", "Truffle and Chocolate Stout - B-Sides",
"U-Boat (w/ Victory Brewing)", "Vagabond Pale ALe - Prototype Challenge",
"Vagabond Pilsner", "Vic Secret", "Waimea - IPA Is Dead", "Whisky Sour - B-Sides",
"Zephyr"), class = "factor"), ABV = c(4.5, 4.1, 4.2, 6.3, 7.2,
NA, 4.7, 7.5, 7.3, 5.3, 4.5, 4.5, 6.1, 11.2, 6, 8.2, 12.5, 8,
4.7, 3.5, 15, 6.7, 7.8, 6.7, 0.5, 7.5, 5.8, 3.6, 10.5, 12.5,
7.2, 8.2, 10.7, 9.2, 7.1, 5, 16.5, 12.8, 6.7, 10, NA, 10, 4.5,
7.4, 7.2, 9.5, 9.2, 9, 7.2, 7.5, NA, 10.43, 7.1, 8, 5, 5.4, 4.1,
10.2, 4, 7, 12.7, 6.5, 7.5, 4.2, 11.8, 7.6, 15, 4.4, 6.3, 7.2,
NA, 4.5, 4.5, 7.5, 10, 3.8, 6.4, NA, 4, 15.2, 5.4, 8.3, 6.5,
8, 12, 8.2, 5.6, 7.2, 6.3, 10, 5.6, 4.5, 8.2, 8.4, 6, 6.7, 6.5,
11.5, 8.5, 5.2, 7.1, 4.7, 6.7, 9, 6.5, 6.7, 5, 5.8, 7.5, 4.5,
9, 41, 15, 8.5, 7.2, 9, 3.8, 5.7, 6.3, 7.5, 4.4, 18, 10.5, 11.3,
NA, 5.2, 4.5, 9.5, 7.2, 2.7, 6.4, 17.2, 8.5, 4.9, 4.7, 7.2, 10,
4.5, 7.2, 7.2, 6.7, 7.2, 4.4, 9, 7.5, 16.1, 6.7, 2.5, 7.4, 2.8,
4.2, 5.8, 5.2, 10, 12.8, 8.3, 6.5, 6, 3, 7.6, 5.5, 8.8, 5.2,
5.2, 8, 6.7, 15, 11.5, 7.1, NA, 7.5, 7.2, 5.2, 6.8, 5.5, 5.2,
6.7, 5, 9, 9.2, 13.8, 4.5, 3.2, 16.1, 4.7, 14.2, 13, 7.2, 9.2,
4.9, 7.2, 7.2, 4.5, 4.5, 4.5, 7.6), IBU = c(60, 41.5, 8, 55,
59, 38, 40, 75, 30, 60, 50, 42, 45, 150, 70, 70, 100, 60, 45,
33, 90, 67, 70, 70, 55, 75, 35, 8, 85, 125, 70, 70, 100, 125,
65, 47, 20.5, 50, 70, 35, 20, 55, 35, 65, 70, 85, 149, 65, 100,
30, 30, 65, 68, 35, 50, 35, 65, 50, 35, 20, 85, 35, 50, 50, 80,
70, 80, 35, 85, 70, 9, 35, 30, 70, 85, 35, 40, 45, 40, 20, 20,
70, 60, 45, 85, 42, 40, 70, 55, 85, 30, 55, 42, 50, 50, 40, 35,
80, 65, 45, 90, 45, 67, 85, 20, 67, 30, 40, 90, 38, 50, 1085,
90, 85, 100, 80, 20, 35, 130, 75, 35, 70, 14, 50, 25, 65, 25,
80, 70, 36, 50, 75, 100, 30, 37, 100, 80, 55, 50, 250, 67, 100,
70, 70, 80, 85, 70, 35, 70, 30, 25, 40, 50, 55, 70, 70, 55, 60,
8, 175, 35, 40, 45, 55, 85, 70, 90, 50, 80, 45, 0, 130, 55, 30,
60, 40, 70, 50, 85, 65, 60, 40, 8, 100, 25, 20, 100, 250, 50,
18, 250, 250, 40, 40, 40, 70), OG = c(1044, 1041.7, 1040, 1060,
1069, 1045, 1046, 1068, 1079, 1052, 1047, 1046, 1067, 1098, 1058,
1076, 1093, 1082, 1047, 1038, 1120, 1013, 1074, 1066, 1007, 1068,
1049, 1040, 1102, 1087, 1067, 1076, 1105, 1085, 1065, 1048.5,
1112, 1096, 1066, 1080, 1048, 1090, 1048, 1069, 1067, 1095, 1083,
1080, 1064, 1080, 1043, 1095, 1056, 1077, 1049, 1050, 1042, 1026,
1041, 1081, 1113.5, 1050, 1070, 1042, 1096, 1073, 1113, 1040,
1063, 1067, 1032, 1048, 1045, 1068, 1098, 1040, 1057, 1081, 1039,
1110, 1055, 1076, 1060, 1075, 1130, 1078, 1055, 1067, 1060, 1098,
1058, 1046, 1078, 1080, 1050, 1063, 1068, 1096, 1078, 1049, 1067,
1055, 1013, 1094, 1060, 1013, 1050, 1053, 1072, 1042.9, 1084,
1085, 1120, 1072, 1064, 1083, 1039, 1053, 1060, 1068, 1045, 1150,
1093, 1098, 1052, 1048, 1043, 1075, 1067, 1033, 1061, 1156, 1068,
1047, 1043, 1064, 1097, 1045, 1068, 1065, 1064, 1064, 1045, 1090,
1069, 1125, 1063, 1027, 1069, 1032.5, 1044, 1060, 1050, 1128,
1108, 1076, 1059, 1056, 1007, 1072, 1053, 1084, 1048, 1053, 1074,
1066, 1120, 1104, 1067, 1089, 1069, 1065, 1052, 1068, 1062, 1048,
1066, 1053, 1094, 1069, 1088, 1045, 1007, 1015, 1008, 1025, 1015,
1065, 1016, 1010, 1065, 1065, 1045, 1045, 1045, 1067), EBC = c(20,
15, 8, 30, 10, 15, 12, 22, 120, 200, 140, 62, 219, 70, 25, NA,
36, 12, 8, 50, 100, 19, 90, 30, 30, 30, 44, NA, 64, 40, 30, 16,
300, 40, 13, 65, 20, 111, 30, 80, 14, 300, 40, 60, 30, 250, 19.5,
97, 12, 46, 15, 23, 14, 15, 110, 11.5, 17, 197, 45, 12, 250,
23, 40, 30, 115, 59, 400, 12, 24, 30, 2, 44, 25, 30, 130, 25,
10, 15, 18, 158, 30, 30, 25, 240, 24, 90, 15, 30, 30, 30, 54,
25, 70, 200, 8, 15, 250, 115, 31.2, 45, 15, 200, 19, 400, NA,
19, 60, 177.3, 200, 18, 20, 40, 100, 15, 12, 180, 6, 25, 14,
30, 30, 57, NA, 164, 10, 16, 10, 195, 30, 57, 20, 128, 15, 12,
10, 12, 65, 20, 150, 15, 19, 12, 30, 190, 50, 400, 30, 10, 30,
42, 19, 35, 17, 300, 79, 30, 50, 17, 9, 40, 25, 190, 35, 165,
35, 30, 100, 38, 71, 15, 50, 14, 200, 86, 230, 13, 30, 200, 400,
60, 25, 18, 8, 500, 25, 67, 300, 15, 78.8, 13, 17, 104, 18, 18,
18, 20), PH = c(4.4, 4.4, 3.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4,
4.2, 5.2, 4.4, 4.4, 4.4, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4,
4.4, 4.4, 4.4, 4.4, 4.4, 3.2, 4.4, 4.4, 4.4, 4.4, 4.3, 4.4, 4.4,
4.4, 4.4, 4.4, 4.4, 4.4, 4.2, 4.4, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4,
4.4, 4.5, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 3.2, 5.2,
4.4, 4.4, 4.4, 5.2, 4.4, 4, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4, 4.4,
4.4, 4.4, 3.5, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4,
4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2, 4.2, 4.4, 4.4, 4.2,
4.4, 4.4, 4.4, 4.3, 3.2, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4,
4.4, 4.4, 5.2, 5.2, 4.4, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2,
4.2, 4.5, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.2, 4.4, 4.4, 4.2, 4.4,
4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.3, 4.4, 4.2, 4.4, 4.4, 4.4, 4.4,
4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 3.2, 4.4, 4.4, 4.5, 4.4, 5.2, 5.2,
4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 5.2, 5.2, 4.4, 4.4, 4.4, 4.4, 4.4,
4.3, 4.2, 4.4, 4.2, 3.2, 4.4, 4.2, 4, 4.4, 4.4, 4.2, 4.2, 4.4,
4.4, 4.2, 4.2, 4.2, 4.4), AttenuationLevel = c(75, 76, 83, 80,
67, 88.9, 78, 80.9, 74.7, 77, 74.5, 72.8, 70.1, 87, 79.3, 83,
68, 86, 79, 68.4, 98, 79.7, 79.7, 77.3, 28.6, 82.1, 90, 83, 102,
81.2, 82.1, 83, 76.2, 81.2, 85, 79.4, 100, 79.17, 77.3, 85, 89.6,
84.4, 72.9, 82.6, 82.1, 76.8, 83, 76, 84, 70, 81.4, 83.2, 82.1,
79.2, 79, 84, 76.2, 74.5, 75.6, 74, 76.8, 76, 81.4, 76.2, 79.2,
79.5, 84.1, 79.5, 82.6, 82.1, 88, 72.9, 75.6, 82.1, 79.6, 70,
87, 93.8, 76.9, 82, 74.6, 82.9, 83.3, 81.3, 102.3, 83.3, 78,
82.1, 80, 70, 74, 73.9, 83.3, 81.3, 87, 84, 70.6, 79.2, 84.6,
81.6, 80.6, 70, 79.7, 73.4, 87, 79.7, 76, 84.9, 79.2, 81, 82.1,
81.2, 98, 90.3, 84, 83.1, 87, 79.3, 83, 82.1, 73.3, 93.3, 80,
79.6, 87, 79, 79.1, 81.3, 82.1, 70.8, 80.3, 80.8, 95.6, 80.7,
83.7, 84, 79.4, 73.9, 78.6, 84.6, 79.7, 84, 82.9, 80, 82.6, 84,
81, 70.4, 82.6, 63.1, 72.7, 76.7, 80, 89, 81.5, 82.9, 81.4, 82.14,
82.5, 80.6, 79.3, 79.8, 77.1, 75.5, 82.4, 77.3, 98, 85, 79, 94.4,
81.1, 87, 73.1, 76.5, 67.7, 79.2, 77.3, 73.6, 73.4, 82.6, 83,
75.6, 78, 84, 75.6, 75.6, 84.4, 84.6, 81, 78.7, 84.6, 84.6, 75.6,
75.6, 75.6, 82), FermentationTempCelsius = c(19L, 18L, 21L, 9L,
10L, 22L, 10L, 19L, 19L, 19L, 19L, 22L, 18L, 17L, 19L, 19L, 19L,
19L, 19L, 19L, 19L, 19L, 18L, 19L, 19L, 19L, 19L, 21L, 21L, 21L,
19L, 21L, 21L, 21L, 9L, 19L, 20L, 21L, 19L, 19L, 22L, 21L, 19L,
18L, 19L, 18L, 19L, 19L, 19L, 12L, 23L, 21L, 10L, 9L, 19L, 19L,
19L, 21L, 19L, 19L, 18L, 18L, 21L, 19L, 20L, 20L, 21L, 10L, 19L,
19L, 21L, 19L, 19L, 19L, 21L, 19L, 20L, 23L, 19L, 21L, 19L, 21L,
19L, 20L, 21L, 21L, 19L, 19L, 19L, 21L, 19L, 9L, 22L, 14L, 20L,
19L, 19L, 20L, 18L, 14L, 19L, 19L, 19L, 21L, 20L, 19L, 19L, 19L,
21L, 10L, 21L, 21L, 19L, 18L, 19L, 21L, 20L, 17L, 20L, 19L, 19L,
22L, 19L, 20L, 20L, 19L, 15L, 19L, 19L, 19L, 19L, 21L, 21L, 10L,
12L, 19L, 21L, 19L, 19L, 21L, 19L, 19L, 20L, 21L, 22L, 21L, 99L,
19L, 19L, 22L, 16L, 19L, 19L, 21L, 18L, 21L, 19L, 19L, 19L, 21L,
17L, 21L, 19L, 19L, 19L, 19L, 19L, 21L, 19L, 23L, 19L, 20L, 19L,
19L, 19L, 19L, 19L, 19L, 21L, 18L, 21L, 19L, 21L, 21L, 12L, 21L,
21L, 21L, 21L, 12L, 21L, 21L, 19L, 19L, 19L, 21L), Yeast = structure(c(1L,
1L, 1L, 3L, 3L, 4L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 3L, 1L, 2L, 2L, 1L, 2L, 4L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
3L, 4L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
2L, 2L, 3L, 1L, 1L, 4L, 1L, 1L, 1L, 2L, 1L, 1L, 4L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 3L, 2L, 3L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 4L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 3L, 3L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 3L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 1L,
1L, 1L, 2L), .Label = c("Wyeast 1056 - American Ale", "Wyeast 1272 - American Ale II",
"Wyeast 2007 - Pilsen Lager", "Wyeast 3711 - French Saison"), class = "factor")), class = "data.frame", row.names = c(NA,
-196L))
CodePudding user response：
Updated
As @dcarlson recommended, you can run mice on the entire dataframe, then you can use complete to get the whole output dataframe. Then, you can join the new data with your original dataframe.
library(tidyverse)
library(mice)
imi <- mice(data, m=5, maxit=10)
imi_complete <- complete(imi)
res <- data %>%
dplyr::left_join(., imi_complete %>% dplyr::select(Name, ABV, EBC), by = "Name") %>%
dplyr::select(-c(ABV.x, EBC.x)) %>%
dplyr::rename("ABV" = ABV.y, "EBC" = EBC.y)
Output
head(res)
Name IBU OG PH AttenuationLevel FermentationTempCelsius Yeast ABV EBC
1 Buzz 60.0 1044.0 4.4 75.0 19 Wyeast 1056 - American Ale 4.5 20
2 Trashy Blonde 41.5 1041.7 4.4 76.0 18 Wyeast 1056 - American Ale 4.1 15
3 Berliner Weisse With Yuzu - B-Sides 8.0 1040.0 3.2 83.0 21 Wyeast 1056 - American Ale 4.2 8
4 Pilsen Lager 55.0 1060.0 4.4 80.0 9 Wyeast 2007 - Pilsen Lager 6.3 30
5 Avery Brown Dredge 59.0 1069.0 4.4 67.0 10 Wyeast 2007 - Pilsen Lager 7.2 10
6 Electric India 38.0 1045.0 4.4 88.9 22 Wyeast 3711 - French Saison 7.5 15
Old
Since there's no id column in the new dataframe, you can just mutate to replace the columns in the original dataframe with the output from the new dataframe. However, it would be better practice to impute directly into the original dataframe (as suggested by @dcarlson and @r2evans), so that you can ensure that you have the data on the correct rows.
library(tidyverse)
df_orig %>%
dplyr::mutate(ABV = df_new$ABV, EBC = df_new$EBC)
Output
id ABV EBC third
1 1 -61 -58 37.94029
2 2 -80 -67 47.81479
3 3 -62 -66 48.85903
4 4 -69 -78 23.18026
5 5 -51 -77 29.91952
Data
df_orig <-
structure(
list(
id = c(1, 2, 3, 4, 5),
ABV = c(
38.9932923251763,
20.0923723727465,
37.640398349613,
31.4673039061017,
49.192731983494
),
EBC = c(
42.341671793256,
32.936319950968,
33.8184517389163,
21.5938150603324,
22.8182014194317
),
third = c(
37.9402944352478,
47.8147878032178,
48.8590325415134,
23.1802612892352,
29.9195193173364
)
),
class = "data.frame",
row.names = c(NA,-5L)
)
df_new <-
structure(
list(
ABV = c(-61,-80,-62,-69,-51),
EBC = c(-58,-67,-66,-78,-77)
),
class = c("rowwise_df", "tbl_df", "tbl",
"data.frame"),
row.names = c(NA,-5L),
groups = structure(
list(.rows = structure(
list(1L, 2L, 3L, 4L, 5L),
ptype = integer(0),
class = c("vctrs_list_of",
"vctrs_vctr", "list")
)),
row.names = c(NA,-5L),
class = c("tbl_df",
"tbl", "data.frame")
)
)