r - 将许多列乘以和除以每个组的另一列
问题描述
我有一个具有以下结构的数据框:
# A tibble: 95 x 7
# Groups: WallReg_2p5 [19]
CellID_2p5 Y_Coord_2p5Weighting WallReg_2p5 piC_1 piC_2 piC_3 piC_4
<int> <dbl> <chr> <dbl> <dbl> <dbl> <dbl>
1 6561 0.915 African 6.55 6.63 5.84 0.766
2 6278 0.947 African 15.1 5.59 2.15 2.01
3 4394 0.971 African 11.4 3.92 0.774 1.47
4 4840 0.994 African 4.70 0.962 6.21 3.54
5 4105 0.947 African 6.35 2.10 2.25 3.24
6 5228 1.000 Amazonian 8.49 5.00 1.92 2.42
7 5089 1.000 Amazonian 15.6 6.48 2.53 2.89
8 4939 0.998 Amazonian 5.56 2.94 0.389 2.44
9 5088 1.000 Amazonian 12.9 5.16 1.99 3.13
10 4947 0.998 Amazonian 8.05 11.2 2.54 4.61
# ... with 85 more rows
这是dput()
数据帧的一个子集。我的真实数据集由 10,368 行和 255,611 列组成
structure(list(CellID_2p5 = c(6561L, 6278L, 4394L, 4840L, 4105L,
5228L, 5089L, 4939L, 5088L, 4947L, 1710L, 2569L, 1438L, 1175L,
1840L, 6888L, 7185L, 6031L, 7045L, 7044L, 3432L, 3288L, 3143L,
3574L, 3577L, 3260L, 1959L, 2568L, 2986L, 2386L, 5551L, 5407L,
5556L, 4979L, 5694L, 5303L, 4442L, 5587L, 5157L, 4865L, 3294L,
3009L, 2865L, 2722L, 3151L, 6427L, 6571L, 5996L, 6570L, 6139L,
3631L, 3920L, 3342L, 3341L, 4064L, 2617L, 2049L, 3346L, 1599L,
3205L, 7487L, 6612L, 6613L, 7630L, 7916L, 3854L, 3561L, 4290L,
4138L, 3704L, 4211L, 4068L, 4069L, 4357L, 4648L, 5601L, 5600L,
5455L, 5456L, 5458L, 3978L, 3822L, 3532L, 3832L, 3834L, 7105L,
6817L, 6104L, 7963L, 6098L, 3418L, 3424L, 3281L, 3566L, 3273L
), Y_Coord_2p5Weighting = c(0.915311479119447, 0.946930129495106,
0.971342069813261, 0.99405633822232, 0.946930129495106, 0.999762027079909,
0.999762027079909, 0.997858923238603, 0.999762027079909, 0.997858923238603,
0.480988768919388, 0.691513055782269, 0.402746689858737, 0.362438038283702,
0.518773258160522, 0.876726755707508, 0.831469612302545, 0.971342069813261,
0.854911870672947, 0.854911870672947, 0.854911870672947, 0.831469612302545,
0.806444604267483, 0.876726755707508, 0.876726755707508, 0.831469612302545,
0.555570233019602, 0.691513055782269, 0.779884483092882, 0.659345815100069,
0.99405633822232, 0.997858923238603, 0.99405633822232, 0.997858923238603,
0.988361510467761, 0.999762027079909, 0.971342069813261, 0.99405633822232,
0.999762027079909, 0.99405633822232, 0.831469612302545, 0.779884483092882,
0.751839807478977, 0.722363962059756, 0.806444604267483, 0.932007869282799,
0.915311479119447, 0.971342069813261, 0.915311479119447, 0.960049854385929,
0.896872741532688, 0.932007869282799, 0.854911870672947, 0.854911870672947,
0.946930129495106, 0.722363962059756, 0.591309648363582, 0.854911870672947,
0.480988768919388, 0.831469612302545, 0.779884483092882, 0.915311479119447,
0.915311479119447, 0.751839807478977, 0.691513055782269, 0.915311479119447,
0.876726755707508, 0.960049854385929, 0.946930129495106, 0.896872741532688,
0.960049854385929, 0.946930129495106, 0.946930129495106, 0.971342069813261,
0.988361510467761, 0.99405633822232, 0.99405633822232, 0.997858923238603,
0.997858923238603, 0.997858923238603, 0.932007869282799, 0.915311479119447,
0.876726755707508, 0.915311479119447, 0.915311479119447, 0.831469612302545,
0.876726755707508, 0.960049854385929, 0.659345815100069, 0.960049854385929,
0.854911870672947, 0.854911870672947, 0.831469612302545, 0.876726755707508,
0.831469612302545), WallReg_2p5 = c("African", "African", "African",
"African", "African", "Amazonian", "Amazonian", "Amazonian",
"Amazonian", "Amazonian", "Arctico-Siberian", "Arctico-Siberian",
"Arctico-Siberian", "Arctico-Siberian", "Arctico-Siberian", "Australian",
"Australian", "Australian", "Australian", "Australian", "Chinese",
"Chinese", "Chinese", "Chinese", "Chinese", "Eurasian", "Eurasian",
"Eurasian", "Eurasian", "Eurasian", "Guineo-Congolian", "Guineo-Congolian",
"Guineo-Congolian", "Guineo-Congolian", "Guineo-Congolian", "Indo-Malayan",
"Indo-Malayan", "Indo-Malayan", "Indo-Malayan", "Indo-Malayan",
"Japanese", "Japanese", "Japanese", "Japanese", "Japanese", "Madagascan",
"Madagascan", "Madagascan", "Madagascan", "Madagascan", "Mexican",
"Mexican", "Mexican", "Mexican", "Mexican", "North American",
"North American", "North American", "North American", "North American",
"Novozelandic", "Novozelandic", "Novozelandic", "Novozelandic",
"Novozelandic", "Oriental", "Oriental", "Oriental", "Oriental",
"Oriental", "Panamanian", "Panamanian", "Panamanian", "Panamanian",
"Panamanian", "Papua-Melanesian", "Papua-Melanesian", "Papua-Melanesian",
"Papua-Melanesian", "Papua-Melanesian", "Saharo-Arabian", "Saharo-Arabian",
"Saharo-Arabian", "Saharo-Arabian", "Saharo-Arabian", "South American",
"South American", "South American", "South American", "South American",
"Tibetan", "Tibetan", "Tibetan", "Tibetan", "Tibetan"), piC_1 = c(6.54637718200684,
15.1273813247681, 11.4171981811523, 4.70245027542114, 6.35227298736572,
8.48885822296143, 15.5538415908813, 5.56155681610107, 12.9046697616577,
8.04517650604248, 2.95071268081665, 21.6441345214844, 11.2329692840576,
16.1649322509766, 17.2905006408691, 3.43583130836487, 10.0594062805176,
12.3438568115234, 7.94222640991211, 6.89916276931763, 7.45456171035767,
8.77329444885254, 14.3378238677979, 3.86588025093079, 12.4889860153198,
7.18962049484253, 19.2145137786865, 22.0060653686523, 1.86285281181335,
2.09195709228516, 9.87592029571533, 12.2629871368408, 7.31402492523193,
0.601671099662781, 6.9998254776001, 20.6269207000732, 6.21515369415283,
22.039529800415, 8.35955047607422, 9.50113105773926, 7.06818675994873,
4.63532447814941, 5.81412315368652, 0.996474027633667, 8.32744407653809,
5.03945255279541, 0.893457889556885, 2.42736291885376, 10.3842725753784,
3.32475543022156, 8.1105375289917, 6.61336517333984, 4.06754541397095,
3.31069254875183, 8.05746650695801, 1.24714422225952, 6.44647121429443,
2.97141313552856, 13.3264999389648, 4.86157178878784, 6.71903085708618,
20.3318004608154, 20.8287792205811, 10.0042209625244, 12.7859420776367,
13.6358938217163, 15.9491415023804, 11.4823551177979, 18.6053276062012,
16.6047229766846, 16.1496143341064, 2.9492039680481, 13.8130388259888,
18.6300754547119, 14.464674949646, 4.92032289505005, 0.511945068836212,
3.16324853897095, 13.3062620162964, 9.84803581237793, 1.74625515937805,
2.54861640930176, 9.97869968414307, 11.2339553833008, 0.865878522396088,
14.7632684707642, 21.8330593109131, 6.42118740081787, 9.51691722869873,
13.2857227325439, 4.01672554016113, 10.9487056732178, 13.6308097839355,
4.69979858398438, 1.83490359783173), piC_2 = c(6.62732124328613,
5.59194660186768, 3.92186212539673, 0.962285339832306, 2.1002824306488,
4.99801731109619, 6.4822793006897, 2.94481801986694, 5.16082000732422,
11.2070302963257, 0.585842967033386, 4.83236265182495, 1.637331366539,
7.65087461471558, 2.28347945213318, 7.16115474700928, 3.54162955284119,
5.23653078079224, 2.28897953033447, 2.29887819290161, 0.752622723579407,
0.653791189193726, 1.5378258228302, 2.15203213691711, 1.64702248573303,
6.0682373046875, 0.22119003534317, 4.76900386810303, 0.366481363773346,
6.11435651779175, 10.8921070098877, 7.97591733932495, 6.05282688140869,
3.74584698677063, 5.75792741775513, 0.471727430820465, 2.75132250785828,
1.21862363815308, 0.138835281133652, 2.98711204528809, 0.627980709075928,
0.108154557645321, 0.995486855506897, 2.4163064956665, 0.0193456951528788,
5.70003795623779, 5.56746625900269, 2.9861011505127, 0.344279021024704,
0.640789806842804, 9.4457426071167, 7.05727958679199, 3.89853048324585,
0.340702921152115, 1.17963445186615, 8.93050575256348, 14.796028137207,
4.88054323196411, 9.28642845153809, 7.68382120132446, 2.27267980575562,
0.916118919849396, 0.689630210399628, 0.549197673797607, 1.68408465385437,
1.76007652282715, 3.2269868850708, 0.980833470821381, 5.00142002105713,
3.41616177558899, 6.74930334091187, 12.0952653884888, 15.2918863296509,
0.105648428201675, 4.59846162796021, 1.48986113071442, 5.02905178070068,
5.07208204269409, 4.98251914978027, 4.70810985565186, 2.37468719482422,
6.78730487823486, 6.18559217453003, 11.6090707778931, 2.91017484664917,
3.51590204238892, 3.35987615585327, 8.74919319152832, 2.23059439659119,
0.292922139167786, 5.41262531280518, 8.86936473846436, 8.20160961151123,
7.33296489715576, 8.42716407775879), piC_3 = c(5.84101867675781,
2.14856338500977, 0.774434208869934, 6.21446466445923, 2.25056719779968,
1.9200998544693, 2.52935075759888, 0.38894659280777, 1.98762917518616,
2.53701376914978, 6.93642854690552, 0.608367025852203, 4.7472562789917,
1.25435817241669, 4.09390258789062, 5.41882562637329, 0.221905186772346,
3.72868466377258, 0.763698220252991, 0.783569753170013, 8.32380294799805,
4.482017993927, 2.38237118721008, 10.7143220901489, 10.1253957748413,
4.51582384109497, 5.18871164321899, 1.76670265197754, 7.50785446166992,
6.2304630279541, 8.79040622711182, 7.47595691680908, 1.57976567745209,
1.46996772289276, 0.894773840904236, 1.30858862400055, 7.34649181365967,
1.41060519218445, 2.03947067260742, 4.6038031578064, 4.44245910644531,
0.236538723111153, 0.194929093122482, 0.684483885765076, 0.530747056007385,
1.89696133136749, 1.94861626625061, 3.36041831970215, 0.0835498198866844,
2.04665040969849, 7.02379274368286, 2.93551588058472, 5.33355855941772,
1.59516668319702, 2.19099020957947, 2.88170146942139, 7.42911052703857,
4.64155960083008, 2.24829292297363, 3.64715957641602, 0.363596022129059,
1.41882479190826, 0.474381387233734, 2.24125337600708, 4.11492681503296,
3.44695138931274, 3.08158445358276, 0.218709617853165, 2.44625425338745,
1.71628797054291, 1.75634157657623, 4.76044988632202, 0.387977868318558,
1.70636379718781, 1.70855867862701, 3.67641615867615, 0.744896650314331,
1.09648311138153, 1.37377882003784, 0.200171306729317, 1.4753475189209,
6.56762170791626, 7.72892284393311, 2.18395304679871, 0.481256455183029,
0.37385630607605, 4.25140476226807, 6.76727914810181, 4.81376981735229,
3.8882269859314, 2.90145373344421, 7.48540449142456, 9.90997123718262,
4.46362543106079, 5.19004011154175), piC_4 = c(0.765519082546234,
2.01459360122681, 1.4724348783493, 3.53503012657166, 3.23746180534363,
2.42439723014832, 2.89345812797546, 2.43676805496216, 3.13469624519348,
4.61154937744141, 4.51843070983887, 0.767921149730682, 5.01102733612061,
2.94891023635864, 5.20972728729248, 1.1311411857605, 2.22004199028015,
3.79573369026184, 0.551535904407501, 0.574182093143463, 5.87988710403442,
5.06349992752075, 3.72144675254822, 8.49415874481201, 4.27884483337402,
2.48057842254639, 4.45665884017944, 0.667030334472656, 6.93020153045654,
2.26927351951599, 1.5674192905426, 3.63813829421997, 2.73822736740112,
0.674351632595062, 1.89532685279846, 4.79139471054077, 1.34277474880219,
0.564522683620453, 3.33897042274475, 1.42253696918488, 2.7286331653595,
0.960368096828461, 2.00121903419495, 4.58775472640991, 2.11190366744995,
0.29313051700592, 0.0706640183925629, 2.87113666534424, 1.36242246627808,
3.57689785957336, 2.05132532119751, 0.340487778186798, 1.3506361246109,
0.400035679340363, 1.65728294849396, 5.17583227157593, 6.23331356048584,
1.60608506202698, 6.12336874008179, 0.46411395072937, 0.205161795020103,
1.93029391765594, 2.6833176612854, 0.199026927351952, 0.0609574876725674,
1.12770354747772, 1.49503016471863, 0.299944281578064, 0.302427768707275,
0.745285212993622, 2.91650176048279, 4.18865776062012, 2.71514081954956,
1.93356776237488, 1.67894613742828, 1.67655885219574, 3.09425163269043,
2.87126135826111, 2.42724895477295, 5.48751878738403, 3.4703311920166,
3.71456289291382, 4.29666662216187, 3.37810254096985, 3.07785415649414,
1.90873026847839, 3.57397627830505, 0.902793109416962, 3.96058869361877,
0.35958793759346, 2.9896719455719, 1.81924939155579, 4.22445392608643,
2.22684979438782, 4.53710412979126)), row.names = c(NA, -95L), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), .Names = c("CellID_2p5", "Y_Coord_2p5Weighting",
"WallReg_2p5", "piC_1", "piC_2", "piC_3", "piC_4"), vars = "WallReg_2p5", drop = TRUE, indices = list(
0:4, 5:9, 10:14, 15:19, 20:24, 25:29, 30:34, 35:39, 40:44,
45:49, 50:54, 55:59, 60:64, 65:69, 70:74, 75:79, 80:84, 85:89,
90:94), group_sizes = c(5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L), biggest_group_size = 5L, labels = structure(list(
WallReg_2p5 = c("African", "Amazonian", "Arctico-Siberian",
"Australian", "Chinese", "Eurasian", "Guineo-Congolian",
"Indo-Malayan", "Japanese", "Madagascan", "Mexican", "North American",
"Novozelandic", "Oriental", "Panamanian", "Papua-Melanesian",
"Saharo-Arabian", "South American", "Tibetan")), row.names = c(NA,
-19L), class = "data.frame", vars = "WallReg_2p5", drop = TRUE, .Names = "WallReg_2p5"))
我要做的是piC_
为每个区域生成所有列的加权值。每列 ( x
) 的过程涉及 3 个步骤:
- 将列中的每一行乘以中
piC_x
的值Y_Coord_2p5Weighting
piC_x
对每个WallReg_2p5
组内的加权值求和piC_x
将总和值除以Y_Coord_2p5Weighting
每个WallReg_2p5
组的值的总和
经过一番阅读,在大型数据集上似乎data.table
比. 我都尝试过,但是在使用 时得到了不正确的结果,而且我担心将其应用于完整数据帧时的速度。这是我到目前为止尝试过的dplyr
r
data.table
dplyr
dplyr
df <- df %>% tbl_df() %>%
group_by(WallReg_2p5) %>%
summarise(meanS = mean(piC_1), minS = min(piC_1), maxS = max(piC_1))
# A tibble: 19 x 4
WallReg_2p5 meanS minS maxS
<chr> <dbl> <dbl> <dbl>
1 African 8.83 4.70 15.1
2 Amazonian 10.1 5.56 15.6
3 Arctico-Siberian 13.9 2.95 21.6
4 Australian 8.14 3.44 12.3
5 Chinese 9.38 3.87 14.3
6 Eurasian 10.5 1.86 22.0
7 Guineo-Congolian 7.41 0.602 12.3
8 Indo-Malayan 13.3 6.22 22.0
9 Japanese 5.37 0.996 8.33
10 Madagascan 4.41 0.893 10.4
11 Mexican 6.03 3.31 8.11
12 North American 5.77 1.25 13.3
13 Novozelandic 14.1 6.72 20.8
14 Oriental 15.3 11.5 18.6
15 Panamanian 13.2 2.95 18.6
16 Papua-Melanesian 6.35 0.512 13.3
17 Saharo-Arabian 5.27 0.866 11.2
18 South American 13.2 6.42 21.8
19 Tibetan 7.03 1.83 13.6
weighted <- df %>%
mutate_at(.funs = funs(.*Y_Coord_2p5Weighting), .vars = vars(starts_with("piC_"))) %>% ## multiply by lat weight
mutate_at(.funs = funs(sum), .vars = vars(starts_with("piC_"))) %>% ## sum the weighted values
mutate_at(.funs = funs(./sum(Y_Coord_2p5Weighting)), .vars = vars(starts_with("piC_"))) ## divide weighted values by sum of weights
weighted %>% tbl_df %>% group_by(WallReg_2p5) %>% summarise(meanS = mean(piC_1), minS = min(piC_1), maxS = max(piC_1))
# A tibble: 19 x 4
WallReg_2p5 meanS minS maxS
<chr> <dbl> <dbl> <dbl>
1 African 8.82 8.82 8.82
2 Amazonian 10.1 10.1 10.1
3 Arctico-Siberian 14.5 14.5 14.5
4 Australian 8.21 8.21 8.21
5 Chinese 9.32 9.32 9.32
6 Eurasian 9.86 9.86 9.86
7 Guineo-Congolian 7.41 7.41 7.41
8 Indo-Malayan 13.4 13.4 13.4
9 Japanese 5.47 5.47 5.47
10 Madagascan 4.38 4.38 4.38
11 Mexican 6.10 6.10 6.10
12 North American 5.09 5.09 5.09
13 Novozelandic 14.6 14.6 14.6
14 Oriental 15.2 15.2 15.2
15 Panamanian 13.2 13.2 13.2
16 Papua-Melanesian 6.36 6.36 6.36
17 Saharo-Arabian 5.22 5.22 5.22
18 South American 13.2 13.2 13.2
19 Tibetan 7.01 7.01 7.01
使用dplyr
我得到正确的值。但是,当我使用时,data.table
我得到不正确的值。我的代码基于这里的问题,但显然我做错了什么。
数据表
df <- df %>% group_by(WallReg_2p5) %>%
as.data.table(.) %>% setkey(., WallReg_2p5)
is.data.table(df); haskey(df)
[1] TRUE
[1] TRUE
## same as above
df %>% tbl_df %>% group_by(WallReg_2p5) %>%
summarise(meanS = mean(piC_1), minS = min(piC_1), maxS = max(piC_1))
# A tibble: 19 x 4
WallReg_2p5 meanS minS maxS
<chr> <dbl> <dbl> <dbl>
1 African 8.83 4.70 15.1
2 Amazonian 10.1 5.56 15.6
3 Arctico-Siberian 13.9 2.95 21.6
4 Australian 8.14 3.44 12.3
5 Chinese 9.38 3.87 14.3
6 Eurasian 10.5 1.86 22.0
7 Guineo-Congolian 7.41 0.602 12.3
8 Indo-Malayan 13.3 6.22 22.0
9 Japanese 5.37 0.996 8.33
10 Madagascan 4.41 0.893 10.4
11 Mexican 6.03 3.31 8.11
12 North American 5.77 1.25 13.3
13 Novozelandic 14.1 6.72 20.8
14 Oriental 15.3 11.5 18.6
15 Panamanian 13.2 2.95 18.6
16 Papua-Melanesian 6.35 0.512 13.3
17 Saharo-Arabian 5.27 0.866 11.2
18 South American 13.2 6.42 21.8
19 Tibetan 7.03 1.83 13.6
# https://stackoverflow.com/q/28123098/1710632
indx <- grep("piC_", colnames(df))
for (j in indx) {
set(df, i = NULL, j = j, value = df[[j]]*df[["Y_Coord_2p5Weighting"]]) ## multiply by weights
set(df, i = NULL, j = j, value = sum(df[[j]])) ## sum the weighted values
set(df, i = NULL, j = j, value = df[[j]]/sum(df[["Y_Coord_2p5Weighting"]])) ## divide by sum of weights
}
## wrong values
df %>% tbl_df %>% group_by(WallReg_2p5) %>%
summarise(meanS = mean(piC_1), minS = min(piC_1), maxS = max(piC_1))
# A tibble: 19 x 4
WallReg_2p5 meanS minS maxS
<chr> <dbl> <dbl> <dbl>
1 African 9.27 9.27 9.27
2 Amazonian 9.27 9.27 9.27
3 Arctico-Siberian 9.27 9.27 9.27
4 Australian 9.27 9.27 9.27
5 Chinese 9.27 9.27 9.27
6 Eurasian 9.27 9.27 9.27
7 Guineo-Congolian 9.27 9.27 9.27
8 Indo-Malayan 9.27 9.27 9.27
9 Japanese 9.27 9.27 9.27
10 Madagascan 9.27 9.27 9.27
11 Mexican 9.27 9.27 9.27
12 North American 9.27 9.27 9.27
13 Novozelandic 9.27 9.27 9.27
14 Oriental 9.27 9.27 9.27
15 Panamanian 9.27 9.27 9.27
16 Papua-Melanesian 9.27 9.27 9.27
17 Saharo-Arabian 9.27 9.27 9.27
18 South American 9.27 9.27 9.27
19 Tibetan 9.27 9.27 9.27
Reading?set()
声明它不能执行分组操作,但我认为由于我已经定义了我的组,所以这个过程可以工作。我以前从未使用data.table
过,所以任何指导将不胜感激。
解决方案
推荐阅读
- c - 为什么我得到 SIGABRT?
- amazon-web-services - 清理 AWS CodePipeline 中的旧文件
- algorithm - 快速排序代码之间的区别
- javascript - 如何用一个按钮检查特定表格中的所有复选框
- amazon-web-services - 如何增加 AWS-S3 存储中的执行超时
- webrtc - 属性标记的 SDP 值中的“:”或“/”本身是否允许在它们周围有空格?
- sql - 如果有空值,列的聚合函数?
- jquery - $(window).on("load") 在页面加载之前触发
- javascript - 暂停脚本 3M 键盘楔 XML
- angular - Ionic 4 - 找不到 cordova.js 脚本标签。插件加载可能失败