Sergi
Reputation: 501
For loops with a dataframe in R
I got a dataframe (merged_df) with 52 columns (I show here only the first 4):
Row.names node_demand Node 1 Node 2
1 Node 1 3 0 87
2 Node 10 6 58 52
3 Node 11 10 43 70
4 Node 12 18 94 8
5 Node 13 3 44 63
6 Node 14 6 21 98
7 Node 15 20 31 64
8 Node 16 4 35 76
9 Node 17 14 58 52
10 Node 18 11 19 71
11 Node 19 19 62 38
12 Node 2 14 87 0
13 Node 20 15 102 19
14 Node 21 15 16 76
15 Node 22 4 54 51
16 Node 23 13 59 75
17 Node 24 13 73 28
18 Node 25 5 82 33
19 Node 26 16 62 72
20 Node 27 3 59 30
21 Node 28 7 73 32
22 Node 29 14 45 48
23 Node 3 1 43 78
24 Node 30 17 69 44
25 Node 31 3 70 43
26 Node 32 3 15 87
27 Node 33 12 38 72
28 Node 34 14 62 81
29 Node 35 20 104 17
30 Node 36 13 18 77
31 Node 37 10 70 22
32 Node 38 9 65 46
33 Node 39 6 24 64
34 Node 4 14 68 23
35 Node 40 18 85 8
36 Node 41 7 20 95
37 Node 42 20 55 82
38 Node 43 9 94 16
39 Node 44 1 10 79
40 Node 45 8 62 63
41 Node 46 5 50 88
42 Node 47 1 70 50
43 Node 48 7 54 73
44 Node 49 9 52 43
45 Node 5 19 57 48
46 Node 50 2 4 86
47 Node 6 2 76 22
48 Node 7 14 79 60
49 Node 8 6 108 25
50 Node 9 7 101 18
The columns Node 1, Node 2 .....Node 45....Node 46 show the distance from the Node indicated on the column respect all the other nodes.
I want to pick the closest nodes, and then to select all the nodes under which cumsum() node_demand is less than 120, starting from the first row. Since the first value is the distance between the main Node and itself I don't consider the first row.
To do that for Node 1 I would do:
test <- merged_df[,c(1,2,3)] # Columns 1 and 2 are fixed
test <- test[(order(test[3])),][2:50,] # to get the closest distances first
test<- test[cumsum(test$node_demand)< 120,]
I then need to create a new variable for each node with the last value of the cumsum()
node_1 <- tail(cumsum(test$`Node 1`), n=1) # 381
The output for node_1 would be 381
To do the same for node_2:
test <- merged_df[,c(1,2,4)] #c(1,2,**4**) 4 instead of 3 as before
test <- test[(order(test[3])),][2:50,]
test<- test[cumsum(test$node_demand)< 120,]
node_2 <- tail(cumsum(test$`Node 2`), n=1)
The output for node_2 is 178
Since this process is very repetitive I guess a loop could do it but I am not sure how to create the different variables I need
for(i in 3:52){
test <- merged_df[,c(1,2,i)]
test <- merged_df[order(test[3]),][2:50]
test<- test[cumsum(test$node_demand)< 120,]
}
node_1 <- tail(cumsum(test$test$`Node 1`), n=1) # should return 381
#I'm not sure how to create the variables node_1, node_2....node_50
The process to follow would be:
- Create a subset of the dataframe using columns 1, 2 and i (representing the number from Node 1 to Node 50.
- Sort the subset by the column
Node iso the smallest distances are placed first. - I need to select rows until cumsum(node_demand) < 120. (which is what I do using
test<- test[cumsum(test$node_demand)< 120,]) - Then I need to calculate cumsum(test$
Node 1). This will give me the cumulative distance from all the nodes under the condition of cumsum(node_demand) < 120
Anybody could give me a hand?
Many thanks!
The output of dput() is :
structure(list(Row.names = structure(c("Node 1", "Node 10", "Node 11",
"Node 12", "Node 13", "Node 14", "Node 15", "Node 16", "Node 17",
"Node 18", "Node 19", "Node 2", "Node 20", "Node 21", "Node 22",
"Node 23", "Node 24", "Node 25", "Node 26", "Node 27", "Node 28",
"Node 29", "Node 3", "Node 30", "Node 31", "Node 32", "Node 33",
"Node 34", "Node 35", "Node 36", "Node 37", "Node 38", "Node 39",
"Node 4", "Node 40", "Node 41", "Node 42", "Node 43", "Node 44",
"Node 45", "Node 46", "Node 47", "Node 48", "Node 49", "Node 5",
"Node 50", "Node 6", "Node 7", "Node 8", "Node 9"), class = "AsIs"),
node_demand = c(3L, 6L, 10L, 18L, 3L, 6L, 20L, 4L, 14L, 11L,
19L, 14L, 15L, 15L, 4L, 13L, 13L, 5L, 16L, 3L, 7L, 14L, 1L,
17L, 3L, 3L, 12L, 14L, 20L, 13L, 10L, 9L, 6L, 14L, 18L, 7L,
20L, 9L, 1L, 8L, 5L, 1L, 7L, 9L, 19L, 2L, 2L, 14L, 6L, 7L
), `Node 1` = c(0, 58, 43, 94, 44, 21, 31, 35, 58, 19, 62,
87, 102, 16, 54, 59, 73, 82, 62, 59, 73, 45, 43, 69, 70,
15, 38, 62, 104, 18, 70, 65, 24, 68, 85, 20, 55, 94, 10,
62, 50, 70, 54, 52, 57, 4, 76, 79, 108, 101), `Node 2` = c(87,
52, 70, 8, 63, 98, 64, 76, 52, 71, 38, 0, 19, 76, 51, 75,
28, 33, 72, 30, 32, 48, 78, 44, 43, 87, 72, 81, 17, 77, 22,
46, 64, 23, 8, 95, 82, 16, 79, 63, 88, 50, 73, 43, 48, 86,
22, 60, 25, 18), `Node 3` = c(43, 28, 11, 84, 15, 35, 52,
68, 30, 45, 73, 78, 97, 43, 72, 20, 78, 57, 91, 58, 80, 58,
0, 42, 83, 29, 69, 94, 91, 51, 70, 36, 41, 70, 79, 33, 22,
78, 34, 25, 13, 86, 84, 35, 73, 46, 60, 43, 101, 94), `Node 4` = c(68,
50, 62, 30, 56, 82, 43, 53, 49, 51, 16, 23, 34, 57, 29, 71,
10, 44, 50, 15, 15, 26, 70, 46, 25, 71, 49, 58, 39, 57, 5,
47, 45, 0, 19, 79, 76, 37, 62, 61, 81, 31, 50, 36, 25, 67,
30, 65, 41, 35), `Node 5` = c(57, 62, 66, 54, 62, 74, 30,
33, 61, 39, 10, 48, 55, 45, 15, 79, 23, 66, 26, 30, 24, 16,
73, 62, 22, 64, 27, 34, 63, 42, 28, 62, 37, 25, 42, 71, 80,
62, 55, 73, 84, 24, 28, 48, 0, 55, 53, 81, 63, 58), `Node 6` = c(76,
34, 54, 27, 46, 84, 60, 73, 36, 63, 45, 22, 40, 68, 53, 58,
38, 16, 77, 25, 41, 46, 60, 29, 50, 73, 71, 85, 34, 71, 28,
29, 55, 30, 27, 82, 67, 20, 67, 44, 70, 56, 75, 28, 53, 76,
0, 44, 41, 35), `Node 7` = c(79, 24, 38, 62, 36, 75, 72,
93, 23, 72, 75, 60, 79, 72, 84, 28, 75, 30, 105, 58, 79,
69, 43, 20, 88, 69, 91, 112, 66, 80, 66, 20, 66, 65, 64,
73, 38, 53, 70, 18, 45, 93, 102, 35, 81, 82, 44, 0, 81, 75
), `Node 8` = c(108, 75, 94, 23, 86, 120, 85, 91, 76, 91,
54, 25, 11, 97, 64, 98, 40, 52, 83, 49, 41, 67, 101, 67,
51, 109, 88, 91, 21, 98, 39, 69, 84, 41, 27, 117, 106, 28,
101, 85, 111, 56, 85, 66, 63, 107, 41, 81, 0, 7), `Node 9` = c(101,
68, 87, 17, 79, 113, 78, 86, 69, 85, 48, 18, 9, 90, 58, 91,
35, 46, 78, 42, 36, 60, 94, 60, 47, 102, 83, 87, 18, 91,
32, 62, 78, 35, 20, 110, 99, 23, 94, 79, 104, 52, 80, 59,
58, 100, 35, 75, 7, 0), `Node 10` = c(58, 0, 23, 57, 16,
58, 51, 70, 8, 50, 58, 52, 71, 51, 63, 24, 60, 29, 85, 40,
62, 48, 28, 16, 69, 49, 69, 91, 64, 59, 50, 8, 43, 50, 55,
56, 34, 50, 48, 12, 37, 73, 80, 14, 62, 60, 34, 24, 75, 68
), `Node 11` = c(43, 23, 0, 76, 10, 37, 45, 65, 22, 41, 65,
70, 89, 39, 67, 17, 71, 52, 85, 51, 73, 51, 11, 34, 77, 31,
64, 89, 83, 47, 63, 30, 36, 62, 71, 35, 18, 72, 34, 21, 19,
81, 79, 27, 66, 46, 54, 38, 94, 87), `Node 12` = c(94, 57,
76, 0, 69, 104, 71, 83, 56, 78, 44, 8, 17, 83, 58, 79, 34,
36, 78, 38, 38, 55, 84, 47, 50, 94, 79, 87, 9, 85, 29, 50,
71, 30, 12, 101, 86, 14, 87, 67, 93, 56, 80, 49, 54, 94,
27, 62, 23, 17), `Node 13` = c(44, 16, 10, 69, 0, 43, 45,
63, 18, 40, 60, 63, 82, 40, 61, 22, 65, 44, 82, 44, 67, 47,
15, 30, 71, 34, 62, 86, 77, 48, 56, 24, 34, 56, 65, 41, 27,
64, 34, 19, 26, 74, 76, 21, 62, 47, 46, 36, 86, 79), `Node 14` = c(21,
58, 37, 104, 43, 0, 46, 56, 58, 35, 78, 98, 114, 30, 73,
51, 88, 86, 82, 71, 89, 60, 35, 70, 88, 12, 58, 82, 113,
36, 84, 66, 39, 82, 97, 3, 44, 103, 22, 58, 37, 89, 74, 57,
74, 25, 84, 75, 120, 113), `Node 15` = c(31, 51, 45, 71,
45, 46, 0, 27, 49, 12, 35, 64, 77, 16, 34, 61, 47, 68, 42,
39, 49, 19, 52, 56, 48, 37, 22, 45, 80, 16, 46, 55, 16, 43,
60, 43, 58, 74, 29, 59, 61, 49, 38, 40, 30, 30, 60, 72, 85,
78), `Node 16` = c(35, 70, 65, 83, 63, 56, 27, 0, 70, 26,
42, 76, 85, 32, 28, 82, 53, 85, 29, 48, 51, 30, 68, 77, 44,
48, 9, 28, 93, 23, 55, 74, 29, 53, 72, 54, 80, 87, 38, 79,
79, 42, 19, 59, 33, 31, 73, 93, 91, 86), `Node 17` = c(58,
8, 22, 56, 18, 58, 49, 70, 0, 49, 56, 52, 71, 50, 63, 23,
59, 31, 84, 41, 63, 47, 30, 12, 70, 49, 68, 90, 63, 57, 50,
10, 43, 49, 54, 55, 31, 51, 48, 14, 37, 74, 80, 14, 61, 60,
36, 23, 76, 69), `Node 18` = c(19, 50, 41, 78, 40, 35, 12,
26, 49, 0, 44, 71, 85, 8, 39, 58, 55, 71, 48, 43, 56, 26,
45, 58, 54, 27, 25, 50, 87, 8, 53, 55, 9, 51, 68, 33, 55,
80, 17, 57, 55, 55, 41, 41, 39, 18, 63, 72, 91, 85), `Node 19` = c(62,
58, 65, 44, 60, 78, 35, 42, 56, 44, 0, 38, 46, 50, 19, 77,
14, 58, 35, 24, 18, 18, 73, 56, 21, 68, 37, 44, 53, 48, 19,
57, 40, 16, 32, 75, 79, 53, 58, 69, 84, 25, 37, 44, 10, 60,
45, 75, 54, 48), `Node 20` = c(102, 71, 89, 17, 82, 114,
77, 85, 71, 85, 46, 19, 0, 90, 58, 94, 33, 51, 75, 44, 35,
60, 97, 63, 46, 103, 81, 84, 18, 91, 32, 66, 78, 34, 19,
111, 101, 28, 95, 82, 107, 51, 78, 62, 55, 101, 40, 79, 11,
9), `Node 21` = c(16, 51, 39, 83, 40, 30, 16, 32, 50, 8,
50, 76, 90, 0, 46, 55, 62, 74, 54, 49, 63, 33, 43, 59, 61,
22, 30, 56, 92, 10, 59, 57, 15, 57, 73, 27, 51, 84, 16, 57,
51, 62, 48, 44, 45, 17, 68, 72, 97, 90), `Node 22` = c(54,
63, 67, 58, 61, 73, 34, 28, 63, 39, 19, 51, 58, 46, 0, 81,
26, 67, 26, 28, 23, 19, 72, 65, 16, 63, 26, 33, 67, 42, 29,
64, 35, 29, 46, 70, 83, 64, 52, 74, 84, 16, 23, 49, 15, 52,
53, 84, 64, 58), `Node 23` = c(59, 24, 17, 79, 22, 51, 61,
82, 23, 58, 77, 75, 94, 55, 81, 0, 81, 50, 100, 62, 84, 65,
20, 33, 90, 46, 80, 105, 85, 64, 73, 29, 53, 71, 77, 49,
13, 73, 51, 14, 19, 94, 95, 35, 79, 63, 58, 28, 98, 91),
`Node 24` = c(73, 60, 71, 34, 65, 88, 47, 53, 59, 55, 14,
28, 33, 62, 26, 81, 0, 53, 44, 22, 7, 29, 78, 56, 18, 77,
49, 53, 43, 60, 10, 57, 50, 10, 22, 85, 85, 43, 68, 71, 90,
24, 47, 46, 23, 71, 38, 75, 40, 35), `Node 25` = c(82, 29,
52, 36, 44, 86, 68, 85, 31, 71, 58, 33, 51, 74, 67, 50, 53,
0, 91, 39, 56, 58, 57, 22, 66, 76, 82, 99, 40, 79, 43, 22,
62, 44, 39, 84, 61, 25, 72, 36, 65, 72, 89, 30, 66, 83, 16,
30, 52, 46), `Node 26` = c(62, 85, 85, 78, 82, 82, 42, 29,
84, 48, 35, 72, 75, 54, 26, 100, 44, 91, 0, 53, 43, 37, 91,
86, 34, 74, 25, 9, 86, 46, 51, 86, 50, 50, 66, 80, 99, 86,
64, 95, 101, 30, 12, 71, 26, 59, 77, 105, 83, 78), `Node 27` = c(59,
40, 51, 38, 44, 71, 39, 48, 41, 43, 24, 30, 44, 49, 28, 62,
22, 39, 53, 0, 23, 23, 58, 40, 30, 60, 46, 60, 48, 50, 14,
39, 35, 15, 29, 69, 67, 39, 51, 52, 69, 34, 50, 27, 30, 58,
25, 58, 49, 42), `Node 28` = c(73, 62, 73, 38, 67, 89, 49,
51, 63, 56, 18, 32, 35, 63, 23, 84, 7, 56, 43, 23, 0, 31,
80, 60, 12, 78, 48, 51, 46, 61, 14, 60, 51, 15, 27, 86, 89,
46, 68, 74, 92, 18, 44, 49, 24, 71, 41, 79, 41, 36), `Node 29` = c(45,
48, 51, 55, 47, 60, 19, 30, 47, 26, 18, 48, 60, 33, 19, 65,
29, 58, 37, 23, 31, 0, 58, 50, 31, 50, 26, 43, 64, 31, 28,
50, 23, 26, 44, 57, 66, 59, 40, 59, 69, 33, 34, 35, 16, 43,
46, 69, 67, 60), `Node 30` = c(69, 16, 34, 47, 30, 70, 56,
77, 12, 58, 56, 44, 63, 59, 65, 33, 56, 22, 86, 40, 60, 50,
42, 0, 69, 61, 74, 93, 53, 66, 47, 9, 51, 46, 46, 67, 41,
41, 59, 21, 49, 74, 84, 19, 62, 70, 29, 20, 67, 60), `Node 31` = c(70,
69, 77, 50, 71, 88, 48, 44, 70, 54, 21, 43, 46, 61, 16, 90,
18, 66, 34, 30, 12, 31, 83, 69, 0, 77, 42, 42, 58, 58, 24,
68, 50, 25, 39, 86, 94, 57, 67, 81, 95, 6, 34, 55, 22, 68,
50, 88, 51, 47), `Node 32` = c(15, 49, 31, 94, 34, 12, 37,
48, 49, 27, 68, 87, 103, 22, 63, 46, 77, 76, 74, 60, 78,
50, 29, 61, 77, 0, 50, 75, 102, 29, 73, 57, 28, 71, 86, 9,
42, 92, 11, 51, 35, 78, 66, 47, 64, 19, 73, 69, 109, 102),
`Node 33` = c(38, 69, 64, 79, 62, 58, 22, 9, 68, 25, 37,
72, 81, 30, 26, 80, 49, 82, 25, 46, 48, 26, 69, 74, 42, 50,
0, 26, 88, 22, 51, 72, 28, 49, 67, 55, 79, 84, 40, 78, 79,
40, 18, 57, 27, 34, 71, 91, 88, 83), `Node 34` = c(62, 91,
89, 87, 86, 82, 45, 28, 90, 50, 44, 81, 84, 56, 33, 105,
53, 99, 9, 60, 51, 43, 94, 93, 42, 75, 26, 0, 96, 47, 60,
93, 53, 58, 75, 80, 103, 95, 65, 101, 104, 37, 12, 77, 34,
58, 85, 112, 91, 87), `Node 35` = c(104, 64, 83, 9, 77, 113,
80, 93, 63, 87, 53, 17, 18, 92, 67, 85, 43, 40, 86, 48, 46,
64, 91, 53, 58, 102, 88, 96, 0, 94, 38, 57, 80, 39, 21, 110,
93, 17, 96, 73, 100, 64, 89, 57, 63, 103, 34, 66, 21, 18),
`Node 36` = c(18, 59, 47, 85, 48, 36, 16, 23, 57, 8, 48,
77, 91, 10, 42, 64, 60, 79, 46, 50, 61, 31, 51, 66, 58, 29,
22, 47, 94, 0, 59, 64, 18, 57, 74, 34, 61, 87, 21, 65, 60,
57, 40, 49, 42, 16, 71, 80, 98, 91), `Node 37` = c(70, 50,
63, 29, 56, 84, 46, 55, 50, 53, 19, 22, 32, 59, 29, 73, 10,
43, 51, 14, 14, 28, 70, 47, 24, 73, 51, 60, 38, 59, 0, 48,
47, 5, 18, 81, 78, 35, 64, 62, 82, 30, 52, 37, 28, 69, 28,
66, 39, 32), `Node 38` = c(65, 8, 30, 50, 24, 66, 55, 74,
10, 55, 57, 46, 66, 57, 64, 29, 57, 22, 86, 39, 60, 50, 36,
9, 68, 57, 72, 93, 57, 64, 48, 0, 48, 47, 50, 63, 39, 44,
55, 16, 44, 73, 83, 16, 62, 66, 29, 20, 69, 62), `Node 39` = c(24,
43, 36, 71, 34, 39, 16, 29, 43, 9, 40, 64, 78, 15, 35, 53,
50, 62, 50, 35, 51, 23, 41, 51, 50, 28, 28, 53, 80, 18, 47,
48, 0, 45, 62, 36, 53, 72, 18, 51, 52, 51, 43, 33, 37, 23,
55, 66, 84, 78), `Node 40` = c(85, 55, 71, 12, 65, 97, 60,
72, 54, 68, 32, 8, 19, 73, 46, 77, 22, 39, 66, 29, 27, 44,
79, 46, 39, 86, 67, 75, 21, 74, 18, 50, 62, 19, 0, 94, 83,
23, 78, 66, 89, 45, 68, 44, 42, 84, 27, 64, 27, 20), `Node 41` = c(20,
56, 35, 101, 41, 3, 43, 54, 55, 33, 75, 95, 111, 27, 70,
49, 85, 84, 80, 69, 86, 57, 33, 67, 86, 9, 55, 80, 110, 34,
81, 63, 36, 79, 94, 0, 42, 100, 20, 56, 36, 86, 72, 55, 71,
24, 82, 73, 117, 110), `Node 42` = c(55, 34, 18, 86, 27,
44, 58, 80, 31, 55, 79, 82, 101, 51, 83, 13, 85, 61, 99,
67, 89, 66, 22, 41, 94, 42, 79, 103, 93, 61, 78, 39, 53,
76, 83, 42, 0, 82, 48, 26, 16, 97, 95, 42, 80, 59, 67, 38,
106, 99), `Node 43` = c(94, 50, 72, 14, 64, 103, 74, 87,
51, 80, 53, 16, 28, 84, 64, 73, 43, 25, 86, 39, 46, 59, 78,
41, 57, 92, 84, 95, 17, 87, 35, 44, 72, 37, 23, 100, 82,
0, 86, 59, 87, 63, 87, 45, 62, 94, 20, 53, 28, 23), `Node 44` = c(10,
48, 34, 87, 34, 22, 29, 38, 48, 17, 58, 79, 95, 16, 52, 51,
68, 72, 64, 51, 68, 40, 34, 59, 67, 11, 40, 65, 96, 21, 64,
55, 18, 62, 78, 20, 48, 86, 0, 52, 43, 67, 55, 43, 55, 13,
67, 70, 101, 94), `Node 45` = c(62, 12, 21, 67, 19, 58, 59,
79, 14, 57, 69, 63, 82, 57, 74, 14, 71, 36, 95, 52, 74, 59,
25, 21, 81, 51, 78, 101, 73, 65, 62, 16, 51, 61, 66, 56,
26, 59, 52, 0, 30, 85, 91, 26, 73, 65, 44, 18, 85, 79), `Node 46` = c(50,
37, 19, 93, 26, 37, 61, 79, 37, 55, 84, 88, 107, 51, 84,
19, 90, 65, 101, 69, 92, 69, 13, 49, 95, 35, 79, 104, 100,
60, 82, 44, 52, 81, 89, 36, 16, 87, 43, 30, 0, 98, 95, 45,
84, 54, 70, 45, 111, 104), `Node 47` = c(70, 73, 81, 56,
74, 89, 49, 42, 74, 55, 25, 50, 51, 62, 16, 94, 24, 72, 30,
34, 18, 33, 86, 74, 6, 78, 40, 37, 64, 57, 30, 73, 51, 31,
45, 86, 97, 63, 67, 85, 98, 0, 30, 60, 24, 67, 56, 93, 56,
52), `Node 48` = c(54, 80, 79, 80, 76, 74, 38, 19, 80, 41,
37, 73, 78, 48, 23, 95, 47, 89, 12, 50, 44, 34, 84, 84, 34,
66, 18, 12, 89, 40, 52, 83, 43, 50, 68, 72, 95, 87, 55, 91,
95, 30, 0, 67, 28, 50, 75, 102, 85, 80), `Node 49` = c(52,
14, 27, 49, 21, 57, 40, 59, 14, 41, 44, 43, 62, 44, 49, 35,
46, 30, 71, 27, 49, 35, 35, 19, 55, 47, 57, 77, 57, 49, 37,
16, 33, 36, 44, 55, 42, 45, 43, 26, 45, 60, 67, 0, 48, 53,
28, 35, 66, 59), `Node 50` = c(4, 60, 46, 94, 47, 25, 30,
31, 60, 18, 60, 86, 101, 17, 52, 63, 71, 83, 59, 58, 71,
43, 46, 70, 68, 19, 34, 58, 103, 16, 69, 66, 23, 67, 84,
24, 59, 94, 13, 65, 54, 67, 50, 53, 55, 0, 76, 82, 107, 100
)), .Names = c("Row.names", "node_demand", "Node 1", "Node 2",
"Node 3", "Node 4", "Node 5", "Node 6", "Node 7", "Node 8", "Node 9",
"Node 10", "Node 11", "Node 12", "Node 13", "Node 14", "Node 15",
"Node 16", "Node 17", "Node 18", "Node 19", "Node 20", "Node 21",
"Node 22", "Node 23", "Node 24", "Node 25", "Node 26", "Node 27",
"Node 28", "Node 29", "Node 30", "Node 31", "Node 32", "Node 33",
"Node 34", "Node 35", "Node 36", "Node 37", "Node 38", "Node 39",
"Node 40", "Node 41", "Node 42", "Node 43", "Node 44", "Node 45",
"Node 46", "Node 47", "Node 48", "Node 49", "Node 50"), class = "data.frame", row.names = c(NA,
-50L))
Upvotes: 0
Views: 79
Answers (1)
Roman
Reputation: 17648
You can try a tidyverse
library(tidyverse)
d %>%
as.tibble() %>%
gather(k,v, -node_demand, -Row.names) %>%
arrange(k, v) %>%
group_by(k) %>%
filter(Row.names != k) %>%
filter(cumsum(node_demand)<120) %>%
summarise(sum(v))
# A tibble: 50 x 2
k `sum(v)`
<chr> <dbl>
1 Node 1 381
2 Node 10 202
3 Node 11 332
4 Node 12 186
5 Node 13 262
6 Node 14 419
7 Node 15 282
8 Node 16 279
9 Node 17 272
10 Node 18 302
# ... with 40 more rows
Prove result for Node 1 and 2:
.Last.value %>%
filter(k %in% c("Node 1", "Node 2"))
# A tibble: 2 x 2
k `sum(v)`
<chr> <dbl>
1 Node 1 381
2 Node 2 178
The idea is to transform the data from long to wide. After arranging, we group by Node (column k) and filter 1) "self-nodes" and 2) cumsum<120. Finally calculate the sum for each Node.
Upvotes: 1
Related Questions
- For loop for dataframes in R
- Basic for loop in R
- How to loop dataframe in R
- looping in a Data frame
- R loop question- looping through a dataframe
- For loop in R for dataframe column
- how to use "for loop'" in dataframe in r
- Creating a for loop for a dataframe
- looping through a dataframe
- Loop for a dataframe