I'd like to focus on the flow highlighted above connecting the blue 'Thermal generation' block to the pink 'Electricity grid' block. You'll notice that the flow is 526 TWh, which is row #62 from Energy$links.
Energy$links
source target value
...
62 26 15 525.531
...
Now let's focus on the source and target values which refer to nodes in Energy$nodes.
Energy$nodes
name
...
15 Heating and cooling - homes
16 Electricity grid
...
26 Gas reserves
27 Thermal generation
...
The source value is '26' when it actually refers to row '27' of the nodes data. The target value is '15' when it actually refers to row '16' of the nodes data. Why do the source and target values in the links data actually refer to row x - 1 instead of x in the nodes data? Is there any way around this other than performing the x - 1 calculation in my head when building these Sankey Diagrams?
Here's the full Energy data:
> Energy
$`nodes`
name
1 Agricultural 'waste'
2 Bio-conversion
3 Liquid
4 Losses
5 Solid
6 Gas
7 Biofuel imports
8 Biomass imports
9 Coal imports
10 Coal
11 Coal reserves
12 District heating
13 Industry
14 Heating and cooling - commercial
15 Heating and cooling - homes
16 Electricity grid
17 Over generation / exports
18 H2 conversion
19 Road transport
20 Agriculture
21 Rail transport
22 Lighting & appliances - commercial
23 Lighting & appliances - homes
24 Gas imports
25 Ngas
26 Gas reserves
27 Thermal generation
28 Geothermal
29 H2
30 Hydro
31 International shipping
32 Domestic aviation
33 International aviation
34 National navigation
35 Marine algae
36 Nuclear
37 Oil imports
38 Oil
39 Oil reserves
40 Other waste
41 Pumped heat
42 Solar PV
43 Solar Thermal
44 Solar
45 Tidal
46 UK land based bioenergy
47 Wave
48 Wind
$links
source target value
1 0 1 124.729
2 1 2 0.597
3 1 3 26.862
4 1 4 280.322
5 1 5 81.144
6 6 2 35.000
7 7 4 35.000
8 8 9 11.606
9 10 9 63.965
10 9 4 75.571
11 11 12 10.639
12 11 13 22.505
13 11 14 46.184
14 15 16 104.453
15 15 14 113.726
16 15 17 27.140
17 15 12 342.165
18 15 18 37.797
19 15 19 4.412
20 15 13 40.858
21 15 3 56.691
22 15 20 7.863
23 15 21 90.008
24 15 22 93.494
25 23 24 40.719
26 25 24 82.233
27 5 13 0.129
28 5 3 1.401
29 5 26 151.891
30 5 19 2.096
31 5 12 48.580
32 27 15 7.013
33 17 28 20.897
34 17 3 6.242
35 28 18 20.897
36 29 15 6.995
37 2 12 121.066
38 2 30 128.690
39 2 18 135.835
40 2 31 14.458
41 2 32 206.267
42 2 19 3.640
43 2 33 33.218
44 2 20 4.413
45 34 1 4.375
46 24 5 122.952
47 35 26 839.978
48 36 37 504.287
49 38 37 107.703
50 37 2 611.990
51 39 4 56.587
52 39 1 77.810
53 40 14 193.026
54 40 13 70.672
55 41 15 59.901
56 42 14 19.263
57 43 42 19.263
58 43 41 59.901
59 4 19 0.882
60 4 26 400.120
61 4 12 46.477
62 26 15 525.531 # the highlighted 'flow'
63 26 3 787.129
64 26 11 79.329
65 44 15 9.452
66 45 1 182.010
67 46 15 19.013
68 47 15 289.366
The reason is that ultimately the data gets sent to JavaScript/D3, which uses 0-based indexing... which means the index of the first element of a vector/array/etc. is 0... unlike in R where the index of the first element of a vector is 1.
as an example of easily converting R-style data...
source <- c("A", "A", "B", "C", "D", "D", "E", "E")
target <- c("D", "E", "E", "D", "H", "I", "I", "H")
nodes <- data.frame(name = unique(c(source, target)))
links <- data.frame(source = match(source, nodes$name) - 1,
target = match(target, nodes$name) - 1,
value = 1)
library(networkD3)
sankeyNetwork(links, nodes, "source", "target", "value", "name")
Related
i am working on Shiny app and want to convert entire data set into numeric form.I have used this code for retrieving file from local PC. what changes can be done that while retrieving i can convert entire data set into numeric form
datami <- reactive({
file1 <- input$file
if(is.null(file1)){return()}
read.csv(file=file1$datapath, sep=input$sep, header = input$header, stringsAsFactors = input$stringAsFactors)})
output$table <- renderPrint({
if(is.null(datami())){return ()}
str(datami())})
tabsetPanel(tabPanel("Data",div(h5("Data",style="color:red")),verbatimTextOutput("table"))```
Depending on how you want to deal with lower/uppercase letters (if you have them in your data) we could do one of the following:
MRE:
letter_variable <- c(letters, LETTERS)
Same numeric value for upper and lower case letters:
letter_variable_as_numeric1 <- as.numeric(factor(toupper(letter_variable), levels = LETTERS))
[1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
[22] 22 23 24 25 26 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
[43] 17 18 19 20 21 22 23 24 25 26
Different numeric value for upper and lower case letters:
letter_variable_as_numeric2 <- as.numeric(factor(letter_variable), levels = c(letters, LETTERS))
[1] 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41
[22] 43 45 47 49 51 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
[43] 34 36 38 40 42 44 46 48 50 52
This shouldn't be too hard, but I always have issues when tying to run calculations on a column in a dataframe that relies on the value of a another column in the data frame. Here is my data.frame
stream reach length.km length.m total.sa pools.sa
1 Stream Reach_Code 109 109 1 1
2 Brooks BRK_001 17 14 108 13
3 Brooks BRK_002 15 12 99 9
4 Brooks BRK_003 24 21 94 95
5 Brooks BRK_004 32 29 97 33
6 Brooks BRK_005 27 24 92 79
7 Brooks BRK_006 26 23 95 6
8 Brooks BRK_007 16 13 77 15
9 Brooks BRK_008 29 26 84 26
10 Brooks BRK_009 18 15 87 46
11 Brooks BRK_010 23 20 88 47
12 Brooks BRK_011 22 19 91 40
13 Brooks BRK_012 30 27 98 37
14 Brooks BRK_013 25 22 93 29
19 Buncombe_Hollow BNH_0001 7 4 75 65
20 Buncombe_Hollow BNH_0002 8 5 66 21
21 Buncombe_Hollow BNH_0003 9 6 68 53
22 Buncombe_Hollow BNH_0004 19 16 81 11
23 Buncombe_Hollow BNH_0005 6 3 65 27
24 Buncombe_Hollow BNH_0006 13 10 63 23
25 Buncombe_Hollow BNH_0007 12 9 71 57
I would like to calculate the mean of a column (lets say length.m) where stream = Brooks and then do the same thing for stream = Buncombe_Hollow. I actually have 17 different stream names, and plan on calculating the mean of some column for each stream. I will then store these means as a vector, and bind them to another vector of the stream names, so the end result is something like this
stream truevalue
1 Brooks 0.9440620
2 Siouxon 0.5858527
3 Speelyai 0.5839844
Thanks!
try using aggregate:
# Generate some data to use
someDf <- data.frame(stream = rep(c("Brooks", "Buncombe_Hollow"), each = 10),
length.m = rpois(20, 4))
# Calculate the means with aggregate
with(someDf, aggregate(list(truevalue = length.m), list(stream = stream), mean))
The reason for the "list" bits is to specifically name the columns in the (data frame) output
Start using the dplyr package. It makes such calculations quick as well as very easy to write
library(dplyr)
result <- data %>% group_by(stream) %>% summarize(truevalue = mean(length.m))
I am using the package trees found here, by #jbaums and explained in this post.
My data are the following:
the tree is composed by
the trunk
Trunk
[1] 13.60415
and the branches
Tree
TreeBranchLength TreeBranchID
1 10.004269 1
2 7.994269 2
3 9.028834 11
4 10.817401 12
5 8.551311 111
6 10.599798 112
7 11.073243 121
8 13.367392 122
9 9.625431 1111
10 10.793569 1112
11 9.896499 11121
12 8.687741 11122
13 7.791180 1211
14 12.506105 1212
15 6.768478 1221
16 10.441796 1222
17 10.751892 1121
18 9.458651 1122
19 10.768509 11221
20 10.150673 11222
21 12.377448 111211
22 12.235136 111212
23 9.074079 11211
24 9.996334 11212
25 9.807019 112221
26 10.895809 112222
27 6.741274 1122211
28 15.841272 1122212
29 5.753920 11222111
30 8.846389 11222112
31 11.925961 112111
32 9.780776 112112
33 8.207965 12221
34 10.079375 12222
the 50 squirrel populations -
Populations
PopulationPositionOnBranch PopulationBranchID ID
1 10.6321655 112111 1
2 1.0644897 1 2
3 3.9315473 1 3
4 1.0310244 0 4
5 9.1768846 0 5
6 13.4267181 0 6
7 7.9461528 0 7
8 6.0533401 121 8
9 2.1227425 121 9
10 1.8256787 121 10
11 4.7332588 11222112 11
12 4.4837432 11222112 12
13 4.6200834 11222112 13
14 2.5622276 1221 14
15 1.2446683 1221 15
16 7.0674052 111 16
17 1.3854674 111 17
18 4.8735635 111 18
19 9.5007998 1222 19
20 6.6373468 1222 20
21 12.6757728 122 21
22 4.2685465 122 22
23 3.9806540 2 23
24 3.1025403 2 24
25 3.9119065 11122 25
26 1.5527653 11122 26
27 1.6687957 11122 27
28 8.0697456 1122 28
29 6.7871391 1122 29
30 9.8050713 111212 30
31 8.5226920 111212 31
32 3.6113379 111212 32
33 7.3184965 111211 33
34 8.6142984 111211 34
35 1.3550870 1211 35
36 8.3650639 12 36
37 4.6411446 112112 37
38 3.2985541 112112 38
39 12.2344148 1212 39
40 9.0290776 1212 40
41 1.3900249 1121 41
42 0.9261425 1122212 42
43 15.2522199 1122212 43
44 4.0253771 12222 44
45 8.7507678 11222 45
46 4.6289841 1122211 46
47 9.1799522 112 47
48 5.1293838 12221 48
49 1.1543080 12221 49
50 10.1014837 112222 50
the code to produce the plot
g <- germinate(list(trunk.height=Trunk,
branches=Tree$TreeBranchID,
lengths=Tree$TreeBranchLength),
left='1', right='2', angle=30))
xy <- squirrels(g, Populations$PopulationBranchID, pos=Populations$PopulationPositionOnBranch,
left='1', right='2', pch=21, bg='white', cex=3, lwd=2)
text(xy$x, xy$y, labels=seq_len(nrow(xy)), font=1)
, which produces
As you can see on the plot bellow population 43 (blue arrow) is out of the tree.. It seems that the length of the branches on the plot do not correspond to the data. For example the branch (left green arrow) on which are populations 38 and 37 is longer than the one where population 43 is (right green arrow), that is not the case in the data. What am I doing wrong? Have I understood correctly how to use trees?
On studying the germinate function it seems to me that the Tree values that you are passing to it needs to be sorted on TreeBranchId field in the ascending order.
The BranchID: 1122212 where you have placed 43 is not the actual 1122212 branch.
Due to the order in which you have fed the values in the Tree, the function is somehow messing the location of branch.
I was curious to see if I increase the length of Branch ID: 1122212, will it change the branch where 43 is placed, and guess what? it didn't. The branch which actually showed an increase in length was the branch where you have placed 37 and 38.
So this hint pointed out that something was wrong with germinate function. On further debugging I was able to make it work using the below code.
Tree<-read.csv("treeBranch.csv")
Tree<-Tree[order(Tree$TreeBranchID),]
g <- germinate(list(trunk.height=15,
branches=Tree$TreeBranchID,
lengths=Tree$TreeBranchLength),
left='1', right='2', angle=30)
xy <- squirrels(g, Populations$PopulationBranchID,pos=Populations$PopulationPositionOnBranch,
left='1', right='2', pch=21, bg='white', cex=3, lwd=2)
text(xy$x, xy$y, labels=seq_len(nrow(xy)), font=1)
I am conducting a network meta-analysis on R with two packages, gemtc and rjags. However, when I type
Model <- mtc.model (network, linearmodel=’fixed’).
R always returns “
Error in [.data.frame(data, sel1 | sel2, columns, drop = FALSE) :
undefined columns selected In addition: Warning messages: 1: In
mtc.model(network, linearModel = "fixed") : Likelihood can not be
inferred. Defaulting to normal. 2: In mtc.model(network, linearModel =
"fixed") : Link can not be inferred. Defaulting to identity “
How to fix this problem? Thanks!
I am attaching my codes and data here:
SAE <- read.csv(file.choose(),head=T, sep=",")
head(SAE)
network <- mtc.network(data.ab=SAE)
summary(network)
plot(network)
model.fe <- mtc.model (network, linearModel="fixed")
plot(model.fe)
summary(model.fe)
cat(model.fe$code)
model.fe$data
# run this model
result.fe <- mtc.run(model.fe, n.adapt=0, n.iter=50)
plot(result.fe)
gelman.diag(result.fe)
result.fe <- mtc.run(model.fe, n.adapt=1000, n.iter=5000)
plot(result.fe)
gelman.diag(result.fe)
following is my data: SAE
study treatment responder sample.size
1 1 3 0 76
2 1 30 2 72
3 2 3 99 1389
4 2 23 132 1383
5 3 1 6 352
6 3 30 2 178
7 4 2 6 106
8 4 30 3 95
9 5 3 49 393
10 5 25 18 198
11 6 1 20 65
12 6 22 10 26
13 7 1 1 76
14 7 30 3 76
15 8 3 7 441
16 8 26 1 220
17 9 2 1 47
18 9 30 0 41
19 10 3 10 156
20 10 30 9 150
21 11 1 4 85
22 11 25 5 85
23 11 30 4 84
24 12 3 6 152
25 12 30 5 160
26 13 18 4 158
27 13 21 8 158
28 14 1 3 110
29 14 30 2 111
30 15 3 3 83
31 15 30 1 92
32 16 1 3 124
33 16 22 6 123
34 16 30 4 125
35 17 3 236 1553
36 17 23 254 1546
37 18 6 5 398
38 18 7 6 403
39 19 1 64 588
40 19 22 73 584
How about reading the manual ?mtc.model. It clearly states the following:
Required columns [responders, sampleSize]
So your responder variable should be responders and your sample.size variable should be sampleSize.
Next, your plot(network) should help you determine that some comparisons can not be made. In your data, there are 2 subgroups of trials that were compared. Treatment 18 and 21 were not compared with any of the others. Therefore you can only do a meta-analysis of 21 and 18 or a network meta-analysis of the rest.
network <- mtc.network(data.ab=SAE[!SAE$treatment %in% c(21, 18), ])
model.fe <- mtc.model(network, linearModel="fixed")
maybe it is a very easy question. This is my data.frame:
> read.table("text.txt")
V1 V2
1 26 22516
2 28 17129
3 30 38470
4 32 12920
5 34 30835
6 36 36244
7 38 24482
8 40 67482
9 42 23121
10 44 51643
11 46 61064
12 48 37678
13 50 98817
14 52 31741
15 54 74672
16 56 85648
17 58 53813
18 60 135534
19 62 46621
20 64 89266
21 66 99818
22 68 60071
23 70 168558
24 72 67059
25 74 194730
26 76 278473
27 78 217860
It means that I have 22516 sequences with length 26, 17129 sequences with length 28, etc. I would like to know the sequence length mean and its standard deviation. I know how to do it, but I know to do it creating a list full of 26 repeated 22516 times and so on... and then compute the mean and SD. However, I thing there is a easier method. Any idea?
Thanks.
For mean: (V1 %*% V2)/sum(V2)
For SD: sqrt(((V1-(V1 %*% V2)/sum(V2))**2 %*% V2)/sum(V2))
I do not find mean(rep(V1,V2)) # 61.902 and sd(rep(V1,V2)) # 14.23891 that complex, but alternatively you might try:
weighted.mean(V1,V2) # 61.902
# recipe from http://www.ltcconline.net/greenl/courses/201/descstat/meansdgrouped.htm
sqrt((sum((V1^2)*V2)-(sum(V1*V2)^2)/sum(V2))/(sum(V2)-1)) # 14.23891
Step1: Set up data:
dat.df <- read.table(text="id V1 V2
1 26 22516
2 28 17129
3 30 38470
4 32 12920
5 34 30835
6 36 36244
7 38 24482
8 40 67482
9 42 23121
10 44 51643
11 46 61064
12 48 37678
13 50 98817
14 52 31741
15 54 74672
16 56 85648
17 58 53813
18 60 135534
19 62 46621
20 64 89266
21 66 99818
22 68 60071
23 70 168558
24 72 67059
25 74 194730
26 76 278473
27 78 217860",header=T)
Step2: Convert to data.table (only for simplicity and laziness in typing)
library(data.table)
dat <- data.table(dat.df)
Step3: Set up new columns with products, and use them to find mean
dat[,pr:=V1*V2]
dat[,v1sq:=as.numeric(V1*V1*V2)]
dat.Mean <- sum(dat$pr)/sum(dat$V2)
dat.SD <- sqrt( (sum(dat$v1sq)/sum(dat$V2)) - dat.Mean^2)
Hope this helps!!
MEAN = (V1*V2)/sum(V2)
SD = sqrt((V1*V1*V2)/sum(V2) - MEAN^2)