如何使用向量中的兩個條件在r中運行for循環
[英]How to run a for loop in r with two conditions from vectors
問題描述
我有以下數據框（請參見下面的dput() ）：
Date Time Price Volume VolumeSEK Bid.Price Ask.Price BidAskSpread MidPrice Company
1005 11.09.2018 25204.72 98.500 1153 113570.500 98.58 98.58 0.00 98.580 SEB
1071 11.09.2018 25209.89 233.300 158 36861.400 233.30 233.80 0.50 233.550 Alfa Laval
88995 12.09.2018 25220.83 170.500 101 17220.500 170.50 170.60 0.10 170.550 Skanska
1115 11.09.2018 25224.86 233.300 1 233.300 233.30 233.70 0.40 233.500 Alfa Laval
89001 12.09.2018 25229.77 96.960 937 90851.520 96.96 97.04 0.08 97.000 SEB
259224 14.09.2018 25239.65 213.950 126 26957.700 214.00 214.20 0.20 214.100 Swedbank
329555 17.09.2018 25244.28 178.375 19 3389.125 178.35 178.60 0.25 178.475 Skanska
1177 11.09.2018 25248.27 233.400 127 29641.800 233.30 233.60 0.30 233.450 Alfa Laval
1197 11.09.2018 25256.45 286.600 267 76522.200 286.60 287.10 0.50 286.850 Kinnevik
1200 11.09.2018 25258.17 98.520 32 3152.640 98.30 98.38 0.08 98.340 SEB
和兩個向量
Comp<- c("Skanska", "SEB", "Swedbank", "Kinnevik", "Investor", "Alfa Laval")
Day<- c("11.09.2018","12.09.2018", "13.09.2018", "14.09.2018", "15.09.2018", "16.09.2018", "17.09.2018")
我想計算每天每一只股票的平均VolumeSEK，並將結果保存在矩陣中。 我對R很陌生，所以我的第一個猜測是使用for循環。 但是，到目前為止，這對我不起作用。 這是我的方法，但是我有點迷茫，這可能根本不是正確的方法。
mat <- matrix(, nrow = 6, ncol = 7))
for (i in 1:Comp){
for(j in 1:Day){
mat[i,j]= mean(df$VolumeSEK[df$Company==Comp[i]& df$Date==Day[j]])
}#2
}#1
任何反饋和提示都將受到高度贊賞。 提前致謝！
df<-structure(list(X.RIC = structure(c(8L, 2L, 10L, 2L, 8L, 12L,
10L, 2L, 6L, 8L, 12L, 4L, 6L, 8L, 6L, 2L, 6L, 8L, 12L, 4L, 6L,
8L, 12L, 12L, 4L, 4L, 4L, 12L, 4L, 12L, 12L, 4L, 12L, 4L, 4L,
8L, 6L, 12L, 4L, 4L, 6L, 10L, 4L, 10L, 12L, 12L, 8L, 4L, 6L,
8L), .Label = c("ALFA.ST", "ALFAs.BCO", "INVEb.ST", "INVEBs.BCO",
"KINVb.ST", "KINVBs.BCO", "SEBa.ST", "SEBAs.BCO", "SKAb.ST",
"SKABs.BCO", "SWEDa.ST", "SWEDAs.BCO"), class = "factor"), Date = structure(c(1L,
1L, 2L, 1L, 2L, 4L, 5L, 1L, 1L, 1L, 1L, 4L, 2L, 1L, 3L, 2L, 5L,
1L, 1L, 1L, 4L, 2L, 1L, 3L, 2L, 1L, 1L, 5L, 1L, 1L, 5L, 1L, 5L,
1L, 1L, 5L, 5L, 1L, 1L, 1L, 4L, 5L, 1L, 2L, 4L, 1L, 3L, 4L, 5L,
5L), .Label = c("11.09.2018", "12.09.2018", "13.09.2018", "14.09.2018",
"17.09.2018"), class = "factor"), Time = c(25204.724866253, 25209.891063318,
25220.83, 25224.862743496, 25229.77, 25239.65, 25244.28, 25248.266841503,
25256.450392157, 25258.169598025, 25259.431887444, 25265.42,
25267.73, 25282.608168894, 25297.72, 25300.78, 25304.39, 25312.181336031,
25314.992406965, 25334.129581998, 25337.19, 25337.52, 25338.977745285,
25339.14, 25340.48, 25341.34500136, 25346.804459672, 25347.23,
25351.80572164, 25352.089646376, 25354.56, 25356.805147054, 25359.55,
25361.804327741, 25366.804555871, 25370.11, 25372.53, 25378.384314178,
25378.884337058, 25386.788916974, 25388.64, 25389.67, 25392.033315652,
25401.17, 25403.9, 25421.773090991, 25421.98, 25424.19, 25424.21,
25424.85), Type = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Quote",
"Trade"), class = "factor"), Price = c(98.5, 233.3, 170.5, 233.3,
96.96, 213.95, 178.375, 233.4, 286.6, 98.52, 213.1, 409.75, 290.9,
98.42, 291.5, 235, 288.8, 98.4, 213.2, 407.8, 291.3, 96.78, 213,
212.3, 406.48, 407.9, 407.9, 212.8, 407.9, 213, 212.8, 407.9,
212.8, 407.9, 407.9, 96.86, 288.5, 213, 407.7, 407.9, 291.3,
178.7, 407.8, 170.9, 213.9, 212.9, 96.46, 409.7, 288.3, 96.88
), Volume = c(1153L, 158L, 101L, 1L, 937L, 126L, 19L, 127L, 267L,
32L, 64L, 17L, 31L, 733L, 100L, 130L, 51L, 46L, 214L, 21L, 78L,
155L, 55L, 761L, 295L, 121L, 6L, 113L, 5L, 350L, 4L, 5L, 3L,
6L, 5L, 711L, 567L, 350L, 13L, 8L, 4L, 110L, 587L, 607L, 1272L,
363L, 13L, 419L, 63L, 21L), Venue = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "SINT[GV5_TEXT]", class = "factor"),
VolumeSEK = c(113570.5, 36861.4, 17220.5, 233.3, 90851.52,
26957.7, 3389.125, 29641.8, 76522.2, 3152.64, 13638.4, 6965.75,
9017.9, 72141.86, 29150, 30550, 14728.8, 4526.4, 45624.8,
8563.8, 22721.4, 15000.9, 11715, 161560.3, 119911.6, 49355.9,
2447.4, 24046.4, 2039.5, 74550, 851.2, 2039.5, 638.4, 2447.4,
2039.5, 68867.46, 163579.5, 74550, 5300.1, 3263.2, 1165.2,
19657, 239378.6, 103736.3, 272080.8, 77282.7, 1253.98, 171664.3,
18162.9, 2034.48), Bid.Price = c(98.58, 233.3, 170.5, 233.3,
96.96, 214, 178.35, 233.3, 286.6, 98.3, 212.8, 409.6, 290.9,
98.38, 291.5, 235, 288.6, 98.38, 213.1, 407.5, 291.3, 96.76,
213, 212.1, 406.2, 407.5, 407.5, 212.8, 407.5, 213, 212.8,
407.5, 212.8, 407.5, 407.5, 96.88, 288.5, 213.1, 407.3, 407.6,
291.6, 178.6, 407.5, 170.75, 213.8, 213, 96.46, 409.3, 288.2,
96.88), Ask.Price = c(98.58, 233.8, 170.6, 233.7, 97.04,
214.2, 178.6, 233.6, 287.1, 98.38, 213, 410, 291.1, 98.42,
291.7, 235.4, 289, 98.46, 213.3, 407.8, 291.5, 96.78, 213.2,
212.4, 406.5, 407.9, 407.9, 213.1, 407.9, 213.1, 213.1, 407.9,
213.1, 407.9, 407.9, 96.98, 288.8, 213.2, 407.8, 407.9, 291.9,
178.75, 407.8, 170.95, 213.9, 213.1, 96.54, 409.6, 288.6,
96.96), BidAskSpread = c(0, 0.5, 0.1, 0.399999999999977,
0.08, 0.2, 0.25, 0.299999999999983, 0.5, 0.0799999999999983,
0.199999999999989, 0.4, 0.2, 0.0400000000000063, 0.2, 0.4,
0.4, 0.0799999999999983, 0.200000000000017, 0.300000000000011,
0.2, 0.02, 0.199999999999989, 0.3, 0.3, 0.399999999999977,
0.399999999999977, 0.3, 0.399999999999977, 0.0999999999999943,
0.3, 0.399999999999977, 0.3, 0.399999999999977, 0.399999999999977,
0.1, 0.3, 0.0999999999999943, 0.5, 0.299999999999955, 0.3,
0.15, 0.300000000000011, 0.2, 0.1, 0.0999999999999943, 0.08,
0.3, 0.4, 0.08), MidPrice = c(98.58, 233.55, 170.55, 233.5,
97, 214.1, 178.475, 233.45, 286.85, 98.34, 212.9, 409.8,
291, 98.4, 291.6, 235.2, 288.8, 98.42, 213.2, 407.65, 291.4,
96.77, 213.1, 212.25, 406.35, 407.7, 407.7, 212.95, 407.7,
213.05, 212.95, 407.7, 212.95, 407.7, 407.7, 96.93, 288.65,
213.15, 407.55, 407.75, 291.75, 178.675, 407.65, 170.85,
213.85, 213.05, 96.5, 409.45, 288.4, 96.92), Company = structure(c(4L,
1L, 5L, 1L, 4L, 6L, 5L, 1L, 3L, 4L, 6L, 2L, 3L, 4L, 3L, 1L,
3L, 4L, 6L, 2L, 3L, 4L, 6L, 6L, 2L, 2L, 2L, 6L, 2L, 6L, 6L,
2L, 6L, 2L, 2L, 4L, 3L, 6L, 2L, 2L, 3L, 5L, 2L, 5L, 6L, 6L,
4L, 2L, 3L, 4L), .Label = c("Alfa Laval", "Investor", "Kinnevik",
"SEB", "Skanska", "Swedbank"), class = "factor")), .Names = c("X.RIC",
"Date", "Time", "Type", "Price", "Volume", "Venue", "VolumeSEK",
"Bid.Price", "Ask.Price", "BidAskSpread", "MidPrice", "Company"
), row.names = c(1005L, 1071L, 88995L, 1115L, 89001L, 259224L,
329555L, 1177L, 1197L, 1200L, 1201L, 259266L, 89158L, 1253L,
178546L, 89199L, 329638L, 1312L, 1319L, 1369L, 259339L, 89245L,
1383L, 178643L, 89249L, 1385L, 1388L, 329712L, 1401L, 1404L,
329722L, 1412L, 329729L, 1418L, 1421L, 329762L, 329771L, 1437L,
1443L, 1471L, 259393L, 329810L, 1485L, 89373L, 259439L, 1532L,
178820L, 259511L, 329870L, 329871L), class = "data.frame")
3 個解決方案
解決方案1
1 2019-03-06 13:32:44
data.table方法，轉換為寬格式
library(data.table)
dcast( setDT(df)[ Company %in% Comp & Date %in% Day, ][, list( mean = mean(VolumeSEK)), by = .(Company, Date)], Company ~ Date )
# Company 11.09.2018 12.09.2018 13.09.2018 14.09.2018 17.09.2018
# 1: Alfa Laval 22245.50 30550.00 NA NA NA
# 2: Investor 31687.49 119911.60 NA 89315.02 NA
# 3: Kinnevik 76522.20 9017.90 29150.00 11943.30 65490.40
# 4: SEB 48347.85 52926.21 1253.98 NA 35450.97
# 5: Skanska NA 60478.40 NA NA 11523.06
# 6: Swedbank 49560.15 NA 161560.30 149519.25 8512.00
切換Company ~ Date to Date〜Company以另一種方式進行轉換（即，將Date ~ Company轉換為column）。
解決方案2
1 2019-03-06 13:38:19
使用基本R：
aggregate(VolumeSEK ~ Date + Company, data=df, FUN = mean)
Date Company VolumeSEK
1 11.09.2018 Alfa Laval 22245.50
2 12.09.2018 Alfa Laval 30550.00
3 11.09.2018 Investor 31687.49
4 12.09.2018 Investor 119911.60
5 14.09.2018 Investor 89315.02
6 11.09.2018 Kinnevik 76522.20
7 12.09.2018 Kinnevik 9017.90
8 13.09.2018 Kinnevik 29150.00
9 14.09.2018 Kinnevik 11943.30
10 17.09.2018 Kinnevik 65490.40
11 11.09.2018 SEB 48347.85
12 12.09.2018 SEB 52926.21
13 13.09.2018 SEB 1253.98
14 17.09.2018 SEB 35450.97
15 12.09.2018 Skanska 60478.40
16 17.09.2018 Skanska 11523.06
17 11.09.2018 Swedbank 49560.15
18 13.09.2018 Swedbank 161560.30
19 14.09.2018 Swedbank 149519.25
20 17.09.2018 Swedbank 8512.00
這將計算每個公司和日期對的VolumeSEK的平均值。 這是一個長格式，但是如果您需要那么寬，請執行以下操作：
reshape(df2, idvar = "Company", timevar = "Date", direction = "wide")
Company VolumeSEK.11.09.2018 VolumeSEK.12.09.2018 VolumeSEK.14.09.2018 VolumeSEK.13.09.2018 VolumeSEK.17.09.2018
1 Alfa Laval 22245.50 30550.00 NA NA NA
3 Investor 31687.49 119911.60 89315.02 NA NA
6 Kinnevik 76522.20 9017.90 11943.30 29150.00 65490.40
11 SEB 48347.85 52926.21 NA 1253.98 35450.97
15 Skanska NA 60478.40 NA NA 11523.06
17 Swedbank 49560.15 NA 149519.25 161560.30 8512.00
其中df2是上述匯總的結果。
對於最后一步，您還可以使用reshape2庫並執行以下操作：
library(reshape2)
dcast(df2, Company ~ Date, value.var = "VolumeSEK")
Company 11.09.2018 12.09.2018 13.09.2018 14.09.2018 17.09.2018
1 Alfa Laval 22245.50 30550.00 NA NA NA
2 Investor 31687.49 119911.60 NA 89315.02 NA
3 Kinnevik 76522.20 9017.90 29150.00 11943.30 65490.40
4 SEB 48347.85 52926.21 1253.98 NA 35450.97
5 Skanska NA 60478.40 NA NA 11523.06
6 Swedbank 49560.15 NA 161560.30 149519.25 8512.00
這是一個簡單得多的命令，並且提供了更簡潔的結果。
解決方案3
0 2019-03-06 13:33:02
這是tidyverse軟件包的解決方案。 請注意，這里不需要循環：
library(tidyverse)
df %>% as_tibble %>% group_by(Date, Company) %>%
summarise(x = mean(VolumeSEK)) %>%
ungroup() %>%
spread(Company, x)
# A tibble: 5 x 7
Date `Alfa Laval` Investor Kinnevik SEB Skanska Swedbank
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 11.09.2018 22246. 31687. 76522. 48348. NA 49560.
2 12.09.2018 30550 119912. 9017.9 52926. 60478. NA
3 13.09.2018 NA NA 29150 1254.0 NA 161560.
4 14.09.2018 NA 89315. 11943. NA NA 149519.
5 17.09.2018 NA NA 65490. 35451. 11523. 8512
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