I am new to R. I am trying to estimate Moran's I result. I have spatial points data over different locations.
I am following this Q&A (2nd answer). When I am running--- patterns <- as.character(interaction(xp > 0, W%*%yp > 0)), then it is showing this error--- Error in xp %*% W : non-conformable arguments. Maybe I am doing something wrong.
Please I appreciate it if someone could help me :)
W <-
structure(c(0, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0.2, 0.2, 0.2, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.2, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0.2, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0,
0, 0, 0, 0, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0.2, 0.2, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0.2, 0.2, 0.2, 0.2, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0.2, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(77L, 77L), .Dimnames = list(
c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11",
"12", "13", "14", "15", "16", "17", "18", "19", "20", "21",
"22", "23", "24", "25", "26", "27", "28", "29", "30", "31",
"32", "33", "34", "35", "36", "37", "38", "39", "40", "41",
"42", "43", "44", "45", "46", "47", "48", "49", "50", "51",
"52", "53", "54", "55", "56", "57", "58", "59", "60", "61",
"62", "63", "64", "65", "66", "67", "68", "69", "70", "71",
"72", "73", "74", "75", "76", "77"), c("1", "2", "3", "4",
"5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15",
"16", "17", "18", "19", "20", "21", "22", "23", "24", "25",
"26", "27", "28", "29", "30", "31", "32", "33", "34", "35",
"36", "37", "38", "39", "40", "41", "42", "43", "44", "45",
"46", "47", "48", "49", "50", "51", "52", "53", "54", "55",
"56", "57", "58", "59", "60", "61", "62", "63", "64", "65",
"66", "67", "68", "69", "70", "71", "72", "73", "74", "75",
"76", "77")))
yp <-
structure(c(-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
-0.0983101073646552, -0.0983101073646552, -0.0983101073646552,
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-0.77630633947831, -0.77630633947831, -0.77630633947831, -0.77630633947831,
-0.77630633947831, -0.77630633947831, -0.77630633947831, -0.77630633947831,
-0.77630633947831, -0.77630633947831, -0.77630633947831, -0.77630633947831,
0.391921068769459, 0.391921068769459, 0.391921068769459, 0.391921068769459,
0.391921068769459, 0.391921068769459, -1.22666218450325, -1.22666218450325,
-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
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-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
-1.22666218450325, -1.22666218450325, -1.22666218450325, -1.22666218450325,
-1.22666218450325, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.711528912177007, 0.711528912177007, 0.711528912177007, 0.711528912177007,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, 0.838865922044882,
0.838865922044882, 0.838865922044882, 0.838865922044882, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, -2.10875006583459, -2.10875006583459,
-2.10875006583459, -2.10875006583459, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978, 0.00410107504179978, 0.00410107504179978,
0.00410107504179978), .Dim = c(468L, 1L), "`scaled:center`" = -0.0965273999166667, "`scaled:scale`" = 0.906349890889938)
Related
So I noticed that the default layout in igraph sometimes behave weird when there's only one component of the graph, specifically vertices form a cluster that overlap. Consider the following matrix:
mat <- structure(c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), .Dim = c(65L, 65L), .Dimnames = list(
NULL, NULL))
g <- graph_from_adjacency_matrix(mat)
plot(g)
Compared to when I add vertex 29 to the main component
mat[29, 44] <- 1
g <- graph_from_adjacency_matrix(mat)
plot(g)
In which case I get the following figure which is much uglier.
Of course, I can work around this by adding an invisible vertex or by creating my own layout but I would like to know why this happens or what is an appropriate layout, similar to the default layout, in the igraph package that doesn't cause the vertices to overlap when I remove the singleton component.
You can try
plot(add_edges(g, c("29", "44")), layout = layout_nicely(g))
I am trying to reuse some old code which I have used to make two separate plots in past, and would like to pout it together into one now.
However I have few problems
color_var <- vector(mode = "double",length = length(OP_2016$risk))
color_var[color_var== '0']<- NA
color_var[OP_2016$risk>=1 & OP_2016$risk<12] <- "yellow"
color_var[OP_2016$risk>=12] <- "red"
ggplot(OP_2016)+
geom_col(aes(x = short_date, y = risk, color = color_var , group = 1), size= 0.9) +
scale_y_continuous(limits = c(0, 100),name = "Accumulated EBHours")+
scale_color_identity("Risk Level", breaks= levels(as.factor(color_var))[c(1,2)],
labels = c("High >12 EBH","Medium 0-12EBH"),
guide = "legend"
)+
geom_line(aes(x = short_date, y= 12), linetype= "dotted", size = 0.8, colour = "red")+
# scale_color_manual("Varieties", values =c( "British Queen"= "orchid1"))+
geom_line(data = dis_fun_df, aes(x= date, y = rating, colour = "green"))
Problems:
Bars and and boxes in the legend are not filled,
I can not add manual color for geom_line and add it to the legend, that I have added from other plot.
Apologies, data set to reproduce the plot is a bit big.
dis_fun_df <- structure(list(date = structure(c(15534, 15540, 15548, 15555,
15562, 15573, 15580), class = "Date"), rating = c(10.2, 30, 61.6666666666667,
81.6666666666667, 95.8333333333333, 99.1666666666667, 100)), row.names = c(NA,
-7L), class = c("tbl_df", "tbl", "data.frame"))
OP_2016 <- structure(list(date = structure(c(1342224000, 1342227600, 1342231200,
1342234800, 1342238400, 1342242000, 1342245600, 1342249200, 1342252800,
1342256400, 1342260000, 1342263600, 1342267200, 1342270800, 1342274400,
1342278000, 1342281600, 1342285200, 1342288800, 1342292400, 1342296000,
1342299600, 1342303200, 1342306800, 1342310400, 1342314000, 1342317600,
1342321200, 1342324800, 1342328400, 1342332000, 1342335600, 1342339200,
1342342800, 1342346400, 1342350000, 1342353600, 1342357200, 1342360800,
1342364400, 1342368000, 1342371600, 1342375200, 1342378800, 1342382400,
1342386000, 1342389600, 1342393200, 1342396800, 1342400400, 1342404000,
1342407600, 1342411200, 1342414800, 1342418400, 1342422000, 1342425600,
1342429200, 1342432800, 1342436400, 1342440000, 1342443600, 1342447200,
1342450800, 1342454400, 1342458000, 1342461600, 1342465200, 1342468800,
1342472400, 1342476000, 1342479600, 1342483200, 1342486800, 1342490400,
1342494000, 1342497600, 1342501200, 1342504800, 1342508400, 1342512000,
1342515600, 1342519200, 1342522800, 1342526400, 1342530000, 1342533600,
1342537200, 1342540800, 1342544400, 1342548000, 1342551600, 1342555200,
1342558800, 1342562400, 1342566000, 1342569600, 1342573200, 1342576800,
1342580400, 1342584000, 1342587600, 1342591200, 1342594800, 1342598400,
1342602000, 1342605600, 1342609200, 1342612800, 1342616400, 1342620000,
1342623600, 1342627200, 1342630800, 1342634400, 1342638000, 1342641600,
1342645200, 1342648800, 1342652400, 1342656000, 1342659600, 1342663200,
1342666800, 1342670400, 1342674000, 1342677600, 1342681200, 1342684800,
1342688400, 1342692000, 1342695600, 1342699200, 1342702800, 1342706400,
1342710000, 1342713600, 1342717200, 1342720800, 1342724400, 1342728000,
1342731600, 1342735200, 1342738800, 1342742400, 1342746000, 1342749600,
1342753200, 1342756800, 1342760400, 1342764000, 1342767600, 1342771200,
1342774800, 1342778400, 1342782000, 1342785600, 1342789200, 1342792800,
1342796400, 1342800000, 1342803600, 1342807200, 1342810800, 1342814400,
1342818000, 1342821600, 1342825200, 1342828800, 1342832400, 1342836000,
1342839600, 1342843200, 1342846800, 1342850400, 1342854000, 1342857600,
1342861200, 1342864800, 1342868400, 1342872000, 1342875600, 1342879200,
1342882800, 1342886400, 1342890000, 1342893600, 1342897200, 1342900800,
1342904400, 1342908000, 1342911600, 1342915200, 1342918800, 1342922400,
1342926000, 1342929600, 1342933200, 1342936800, 1342940400, 1342944000,
1342947600, 1342951200, 1342954800, 1342958400, 1342962000, 1342965600,
1342969200, 1342972800, 1342976400, 1342980000, 1342983600, 1342987200,
1342990800, 1342994400, 1342998000, 1343001600, 1343005200, 1343008800,
1343012400, 1343016000, 1343019600, 1343023200, 1343026800, 1343030400,
1343034000, 1343037600, 1343041200, 1343044800, 1343048400, 1343052000,
1343055600, 1343059200, 1343062800, 1343066400, 1343070000, 1343073600,
1343077200, 1343080800, 1343084400, 1343088000, 1343091600, 1343095200,
1343098800, 1343102400, 1343106000, 1343109600, 1343113200, 1343116800,
1343120400, 1343124000, 1343127600, 1343131200, 1343134800, 1343138400,
1343142000, 1343145600, 1343149200, 1343152800, 1343156400, 1343160000,
1343163600, 1343167200, 1343170800, 1343174400, 1343178000, 1343181600,
1343185200, 1343188800, 1343192400, 1343196000, 1343199600, 1343203200,
1343206800, 1343210400, 1343214000, 1343217600, 1343221200, 1343224800,
1343228400, 1343232000, 1343235600, 1343239200, 1343242800, 1343246400,
1343250000, 1343253600, 1343257200, 1343260800, 1343264400, 1343268000,
1343271600, 1343275200, 1343278800, 1343282400, 1343286000, 1343289600,
1343293200, 1343296800, 1343300400, 1343304000, 1343307600, 1343311200,
1343314800, 1343318400, 1343322000, 1343325600, 1343329200, 1343332800,
1343336400, 1343340000, 1343343600, 1343347200, 1343350800, 1343354400,
1343358000, 1343361600, 1343365200, 1343368800, 1343372400, 1343376000,
1343379600, 1343383200, 1343386800, 1343390400, 1343394000, 1343397600,
1343401200, 1343404800, 1343408400, 1343412000, 1343415600, 1343419200,
1343422800, 1343426400, 1343430000, 1343433600, 1343437200, 1343440800,
1343444400, 1343448000, 1343451600, 1343455200, 1343458800, 1343462400,
1343466000, 1343469600, 1343473200, 1343476800, 1343480400, 1343484000,
1343487600, 1343491200, 1343494800, 1343498400, 1343502000, 1343505600,
1343509200, 1343512800, 1343516400, 1343520000, 1343523600, 1343527200,
1343530800, 1343534400, 1343538000, 1343541600, 1343545200, 1343548800,
1343552400, 1343556000, 1343559600, 1343563200, 1343566800, 1343570400,
1343574000, 1343577600, 1343581200, 1343584800, 1343588400, 1343592000,
1343595600, 1343599200, 1343602800, 1343606400, 1343610000, 1343613600,
1343617200, 1343620800, 1343624400, 1343628000, 1343631600, 1343635200,
1343638800, 1343642400, 1343646000, 1343649600, 1343653200, 1343656800,
1343660400, 1343664000, 1343667600, 1343671200, 1343674800, 1343678400,
1343682000, 1343685600, 1343689200, 1343692800, 1343696400, 1343700000,
1343703600, 1343707200, 1343710800, 1343714400, 1343718000, 1343721600,
1343725200, 1343728800, 1343732400, 1343736000, 1343739600, 1343743200,
1343746800, 1343750400, 1343754000, 1343757600, 1343761200, 1343764800,
1343768400, 1343772000, 1343775600, 1343779200, 1343782800, 1343786400,
1343790000, 1343793600, 1343797200, 1343800800, 1343804400, 1343808000,
1343811600, 1343815200, 1343818800, 1343822400, 1343826000, 1343829600,
1343833200, 1343836800, 1343840400, 1343844000, 1343847600, 1343851200,
1343854800, 1343858400, 1343862000, 1343865600, 1343869200, 1343872800,
1343876400, 1343880000, 1343883600, 1343887200, 1343890800, 1343894400,
1343898000, 1343901600, 1343905200, 1343908800, 1343912400, 1343916000,
1343919600, 1343923200, 1343926800, 1343930400, 1343934000, 1343937600,
1343941200, 1343944800, 1343948400, 1343952000, 1343955600, 1343959200,
1343962800, 1343966400, 1343970000, 1343973600, 1343977200, 1343980800,
1343984400, 1343988000, 1343991600, 1343995200, 1343998800, 1344002400,
1344006000, 1344009600, 1344013200, 1344016800, 1344020400, 1344024000,
1344027600, 1344031200, 1344034800, 1344038400, 1344042000, 1344045600,
1344049200, 1344052800, 1344056400, 1344060000, 1344063600, 1344067200,
1344070800, 1344074400, 1344078000, 1344081600, 1344085200, 1344088800,
1344092400, 1344096000, 1344099600, 1344103200, 1344106800, 1344110400,
1344114000, 1344117600, 1344121200, 1344124800, 1344128400, 1344132000,
1344135600, 1344139200, 1344142800, 1344146400, 1344150000, 1344153600,
1344157200, 1344160800, 1344164400, 1344168000, 1344171600, 1344175200,
1344178800, 1344182400, 1344186000, 1344189600, 1344193200, 1344196800,
1344200400, 1344204000, 1344207600, 1344211200, 1344214800, 1344218400,
1344222000, 1344225600, 1344229200, 1344232800, 1344236400, 1344240000,
1344243600, 1344247200, 1344250800, 1344254400, 1344258000, 1344261600,
1344265200, 1344268800, 1344272400, 1344276000, 1344279600, 1344283200,
1344286800, 1344290400, 1344294000, 1344297600, 1344301200, 1344304800,
1344308400, 1344312000, 1344315600, 1344319200, 1344322800, 1344326400,
1344330000, 1344333600, 1344337200, 1344340800, 1344344400, 1344348000,
1344351600, 1344355200, 1344358800, 1344362400, 1344366000, 1344369600,
1344373200, 1344376800, 1344380400, 1344384000, 1344387600, 1344391200,
1344394800, 1344398400, 1344402000, 1344405600, 1344409200, 1344412800,
1344416400, 1344420000, 1344423600, 1344427200, 1344430800, 1344434400,
1344438000, 1344441600, 1344445200, 1344448800, 1344452400, 1344456000,
1344459600, 1344463200, 1344466800, 1344470400, 1344474000, 1344477600,
1344481200, 1344484800, 1344488400, 1344492000, 1344495600, 1344499200,
1344502800, 1344506400, 1344510000, 1344513600, 1344517200, 1344520800,
1344524400, 1344528000, 1344531600, 1344535200, 1344538800, 1344542400,
1344546000, 1344549600, 1344553200, 1344556800, 1344560400, 1344564000,
1344567600, 1344571200, 1344574800, 1344578400, 1344582000, 1344585600,
1344589200, 1344592800, 1344596400, 1344600000, 1344603600, 1344607200,
1344610800, 1344614400, 1344618000, 1344621600, 1344625200, 1344628800,
1344632400, 1344636000, 1344639600, 1344643200, 1344646800, 1344650400,
1344654000, 1344657600, 1344661200, 1344664800, 1344668400, 1344672000,
1344675600, 1344679200, 1344682800, 1344686400, 1344690000, 1344693600,
1344697200, 1344700800, 1344704400, 1344708000, 1344711600, 1344715200,
1344718800, 1344722400, 1344726000, 1344729600, 1344733200, 1344736800,
1344740400, 1344744000, 1344747600, 1344751200, 1344754800, 1344758400,
1344762000, 1344765600, 1344769200, 1344772800, 1344776400, 1344780000,
1344783600, 1344787200, 1344790800, 1344794400, 1344798000, 1344801600,
1344805200, 1344808800, 1344812400, 1344816000, 1344819600, 1344823200,
1344826800, 1344830400, 1344834000, 1344837600, 1344841200, 1344844800,
1344848400, 1344852000, 1344855600, 1344859200, 1344862800, 1344866400,
1344870000, 1344873600, 1344877200, 1344880800, 1344884400, 1344888000,
1344891600, 1344895200, 1344898800, 1344902400, 1344906000, 1344909600,
1344913200, 1344916800, 1344920400, 1344924000, 1344927600, 1344931200,
1344934800, 1344938400, 1344942000, 1344945600, 1344949200, 1344952800,
1344956400, 1344960000, 1344963600, 1344967200, 1344970800, 1344974400,
1344978000, 1344981600, 1344985200, 1344988800, 1344992400, 1344996000,
1344999600, 1345003200, 1345006800, 1345010400, 1345014000, 1345017600,
1345021200, 1345024800, 1345028400, 1345032000, 1345035600, 1345039200,
1345042800, 1345046400, 1345050000, 1345053600, 1345057200, 1345060800,
1345064400, 1345068000, 1345071600, 1345075200, 1345078800, 1345082400,
1345086000, 1345089600, 1345093200, 1345096800, 1345100400, 1345104000,
1345107600, 1345111200, 1345114800, 1345118400, 1345122000, 1345125600,
1345129200, 1345132800, 1345136400, 1345140000, 1345143600, 1345147200,
1345150800, 1345154400, 1345158000, 1345161600, 1345165200, 1345168800,
1345172400, 1345176000, 1345179600, 1345183200, 1345186800, 1345190400,
1345194000, 1345197600, 1345201200, 1345204800, 1345208400, 1345212000,
1345215600, 1345219200, 1345222800, 1345226400, 1345230000, 1345233600,
1345237200, 1345240800, 1345244400, 1345248000, 1345251600, 1345255200,
1345258800, 1345262400, 1345266000, 1345269600, 1345273200, 1345276800,
1345280400, 1345284000, 1345287600, 1345291200, 1345294800, 1345298400,
1345302000, 1345305600, 1345309200, 1345312800, 1345316400, 1345320000,
1345323600, 1345327200, 1345330800, 1345334400, 1345338000, 1345341600,
1345345200, 1345348800, 1345352400, 1345356000, 1345359600, 1345363200,
1345366800, 1345370400, 1345374000, 1345377600, 1345381200, 1345384800,
1345388400, 1345392000, 1345395600, 1345399200, 1345402800, 1345406400,
1345410000, 1345413600, 1345417200, 1345420800, 1345424400, 1345428000,
1345431600, 1345435200, 1345438800, 1345442400, 1345446000, 1345449600,
1345453200, 1345456800, 1345460400, 1345464000, 1345467600, 1345471200,
1345474800, 1345478400, 1345482000, 1345485600, 1345489200, 1345492800,
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1345518000, 1345521600, 1345525200, 1345528800, 1345532400, 1345536000,
1345539600, 1345543200, 1345546800, 1345550400, 1345554000, 1345557600,
1345561200, 1345564800, 1345568400, 1345572000, 1345575600, 1345579200,
1345582800, 1345586400, 1345590000, 1345593600, 1345597200, 1345600800,
1345604400, 1345608000, 1345611600, 1345615200, 1345618800, 1345622400,
1345626000, 1345629600, 1345633200, 1345636800, 1345640400, 1345644000,
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1345669200, 1345672800, 1345676400, 1345680000, 1345683600, 1345687200,
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1345712400, 1345716000, 1345719600, 1345723200, 1345726800, 1345730400,
1345734000, 1345737600, 1345741200, 1345744800, 1345748400, 1345752000,
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1345820400, 1345824000, 1345827600, 1345831200, 1345834800, 1345838400,
1345842000, 1345845600, 1345849200, 1345852800, 1345856400, 1345860000,
1345863600, 1345867200, 1345870800, 1345874400, 1345878000, 1345881600,
1345885200, 1345888800, 1345892400, 1345896000, 1345899600, 1345903200,
1345906800, 1345910400, 1345914000, 1345917600, 1345921200, 1345924800,
1345928400, 1345932000, 1345935600, 1345939200, 1345942800, 1345946400,
1345950000, 1345953600, 1345957200, 1345960800, 1345964400, 1345968000,
1345971600, 1345975200, 1345978800, 1345982400, 1345986000, 1345989600,
1345993200, 1345996800, 1346000400, 1346004000, 1346007600, 1346011200,
1346014800, 1346018400, 1346022000, 1346025600, 1346029200, 1346032800,
1346036400, 1346040000, 1346043600, 1346047200, 1346050800, 1346054400,
1346058000, 1346061600, 1346065200, 1346068800, 1346072400, 1346076000,
1346079600, 1346083200, 1346086800, 1346090400, 1346094000, 1346097600,
1346101200, 1346104800, 1346108400), class = c("POSIXct", "POSIXt"
), tzone = "UTC"), risk = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
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4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 1, 2, 3, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0)), row.names = c(NA, -1080L), class = c("tbl_df",
"tbl", "data.frame"))
I think this might do the trick, using fill instead of colour
OP_2016$date <- as.Date(OP_2016$date)
color_var <- vector(mode = "double",length = length(OP_2016$risk))
color_var[color_var== '0']<- NA
color_var[OP_2016$risk>=1 & OP_2016$risk<12] <- "yellow"
color_var[OP_2016$risk>=12] <- "red"
ggplot(OP_2016)+
geom_col(aes(x = date, y = risk, group = 1,fill=color_var), size= 0.9) +
scale_y_continuous(limits = c(0, 100),name = "Accumulated EBHours")+
scale_fill_identity("Risk Level", breaks= levels(as.factor(color_var))[c(1,2)],
labels = c("High >12 EBH","Medium 0-12EBH"),
guide = "legend"
)+
geom_line(aes(x = date, y= 12), linetype= "dotted", size = 0.8, colour = "red")+
geom_line(data = dis_fun_df, aes(x= date, y = rating),colour = "green")
To my knowledge ggplot does not support multiple scales of the same type, but others would know better than I.
UPDATE:
For anyone looking to actually use multiple scales for the same type of geom the {ggnewscale} package should provide the functionality that you are looking for:
https://github.com/eliocamp/ggnewscale
I have coefficient matrix f as 55 by 55 matrix which det(f) is not zero :
f = structure(c(0.2, 1, 4, 12, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.25,
1, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.333333333333333, 1, 2,
2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.2, 1, 4, 12, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6,
6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, -2, 0, 0.333333333333333, 1, 2, 2, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, -1, 0, 0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2,
1, 4, 12, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -2, 0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, -1, 0, 0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4,
12, 24, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, -2, 0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-1, 0, 0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-2, 0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0,
0, 0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0,
0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0,
0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0,
0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0,
0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 1, 4, 12, 24, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, -6, 0.25, 1, 3, 6, 6, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2,
0, 0.333333333333333, 1, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0,
0.5, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0, 0, 0, 24, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, -6, 0.25, 0, 0, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -2, 0,
0.333333333333333, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0.5,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, -1, 0, 0, 0, 1, 0, 0, 0, 0), .Dim = c(55L,
55L))
and a vector p like :
p=c(0.02, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0.02,
0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.03, 0, 0, 0,
0, 0.03, 0, 0, 0, 0, 0.03, 0, 0, 0, 0, 0.04, 0, 0, 0, 0, 0.04,
0, 0, 0, 0)
To solve f by using solve(f,p) I'm getting the error :
Error in solve.default(f, p) :
system is computationally singular: reciprocal condition number = 1.31432e-16
Since det(f) is not zero and is inverse-able, why I'm getting this error and how could I resolve that ?
If your matrix is near singular you can perform regularization with a truncated SVD or truncated QR.
tsvd(x, nu, tol = 0.1, maxit = 20, tx, v, pca = FALSE)
the solution then is simply the inverse of this multiplied by your vector p.
The tsvd method uses Baglama and Reichel's efficient agumented
implicitly restarted Lanczos method for computing a truncated SVD of
large dense or sparse matrices. The method works best for the hard
case of close to square matrices. For matrices where the row and
column dimensions are very different (tall, skinny or short, fat
matrices), consider the computationally cheaper eigenvalue
decomposition of the small matrix cross product instead.
https://www.rdocumentation.org/packages/scidb/versions/1.2-0/topics/tsvd
I'm getting error Error in xy.coords(x, y) : 'x' and 'y' lengths differ
when plotting xts object RETURNS. The plotting function used is charts.PerformanceSummary, from the package PerformanceAnalytics.
Would anyone know how to fix this? It plots regardless of the error, but the presence of an error makes conversion to HTML with knitr package fail. Interestingly running the same plotting function on a similar xts object works fine. I'm baffled.
library(PerformanceAnalytics)
# THIS PROVIDES THE ERROR:
RETURNS <- structure(c(NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.0176761850129417, -0.00459544547441126, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), class = c("xts", "zoo"), index = structure(c(1388448000, 1403474400, 1403475300, 1403476200, 1403477100, 1403478000, 1403478900, 1403479800, 1403480700, 1403481600, 1403482500, 1403483400, 1403484300, 1403485200, 1403486100, 1403487000, 1403487900, 1403488800, 1403489700, 1403490600, 1403491500, 1403492400, 1403493300, 1403494200, 1403495100, 1403496000, 1403496900, 1403497800, 1403498700, 1403499600, 1403500500, 1403501400, 1403502300, 1403503200, 1403504100, 1403505000, 1403505900, 1403506800, 1403507700, 1403508600, 1403509500, 1403510400, 1403511300, 1403512200, 1403513100, 1403514000, 1403514900, 1403515800, 1403516700, 1403517600, 1403518500, 1403519400, 1403520300, 1403521200, 1403522100, 1403523000, 1403523900, 1403524800, 1403525700, 1403526600, 1403527500,
1403528400, 1403529300, 1403530200, 1403531100, 1403532000, 1403532900, 1403533800, 1403534700, 1403535600, 1403536500, 1403537400, 1403538300, 1403539200, 1403540100, 1403541000, 1403541900, 1403542800, 1403543700, 1403544600, 1403545500, 1403546400, 1403547300, 1403548200, 1403549100, 1403550000, 1403550900, 1403551800, 1403552700, 1403553600, 1403554500, 1403555400, 1403556300, 1403557200, 1403560800, 1403561700, 1403562600, 1403563500, 1403564400, 1403565300, 1403566200, 1403567100, 1403568000, 1403568900, 1403569800, 1403570700, 1403571600, 1403572500, 1403573400, 1403574300, 1403575200, 1403576100, 1403577000, 1403577900, 1403578800, 1403579700, 1403580600, 1403581500, 1403582400, 1403583300, 1403584200, 1403585100, 1403586000, 1403586900, 1403587800, 1403588700, 1403589600, 1403590500, 1403591400, 1403592300, 1403593200, 1403594100, 1403595000, 1403595900, 1403596800, 1403597700, 1403598600, 1403599500, 1403600400, 1403601300, 1403602200, 1403603100, 1403604000, 1403604900, 1403605800, 1403606700, 1403607600, 1403608500, 1403609400, 1403610300, 1403611200, 1403612100, 1403613000, 1403613900, 1403614800, 1403615700, 1403616600, 1403617500, 1403618400, 1403619300, 1403620200, 1403621100, 1403622000, 1403622900, 1403623800, 1403624700, 1403625600, 1403626500, 1403627400, 1403628300, 1403629200, 1403630100, 1403631000, 1403631900, 1403632800, 1403633700, 1403634600, 1403635500, 1403636400, 1403637300, 1403638200, 1403639100, 1403640000, 1403640900, 1403641800, 1403642700, 1403643600, 1403647200, 1403648100, 1403649000, 1403649900, 1403650800, 1403651700, 1403652600, 1403653500, 1403654400, 1403655300, 1403656200, 1403657100, 1403658000, 1403658900, 1403659800, 1403660700, 1403661600, 1403662500, 1403663400, 1403664300, 1403665200, 1403666100, 1403667000, 1403667900, 1403668800, 1403669700, 1403670600, 1403671500, 1403672400, 1403673300, 1403674200, 1403675100, 1403676000, 1403676900, 1403677800, 1403678700, 1403679600, 1403680500, 1403681400, 1403682300, 1403683200, 1403684100, 1403685000, 1403685900, 1403686800, 1403687700, 1403688600, 1403689500, 1403690400, 1403691300, 1403692200, 1403693100, 1403694000, 1403694900, 1403695800, 1403696700, 1403697600, 1403698500, 1403699400, 1403700300, 1403701200, 1403702100, 1403703000, 1403703900, 1403704800, 1403705700, 1403706600, 1403707500, 1403708400, 1403709300, 1403710200, 1403711100, 1403712000, 1403712900, 1403713800, 1403714700, 1403715600, 1403716500, 1403717400, 1403718300, 1403719200, 1403720100, 1403721000, 1403721900, 1403722800, 1403723700, 1403724600, 1403725500, 1403726400, 1403727300, 1403728200, 1403729100, 1403730000, 1403733600, 1403734500, 1403735400, 1403736300, 1403737200, 1403738100, 1403739000, 1403739900, 1403740800, 1403741700, 1403742600, 1403743500, 1403744400, 1403745300, 1403746200, 1403747100, 1403748000, 1403748900, 1403749800, 1403750700, 1403751600, 1403752500, 1403753400, 1403754300, 1403755200, 1403756100, 1403757000, 1403757900, 1403758800, 1403759700, 1403760600, 1403761500, 1403762400, 1403763300, 1403764200, 1403765100, 1403766000, 1403766900, 1403767800, 1403768700, 1403769600, 1403770500, 1403771400, 1403772300, 1403773200, 1403774100, 1403775000, 1403775900, 1403776800, 1403777700, 1403778600, 1403779500, 1403780400, 1403781300, 1403782200, 1403783100, 1403784000,
1403784900, 1403785800, 1403786700, 1403787600, 1403788500, 1403789400, 1403790300, 1403791200, 1403792100, 1403793000, 1403793900, 1403794800, 1403795700, 1403796600, 1403797500, 1403798400, 1403799300, 1403800200, 1403801100, 1403802000, 1403802900, 1403803800, 1403804700, 1403805600, 1403806500, 1403807400, 1403808300, 1403809200, 1403810100, 1403811000, 1403811900, 1403812800, 1403813700, 1403814600, 1403815500, 1403816400, 1403820000, 1403820900, 1403821800, 1403822700, 1403823600, 1403824500, 1403825400, 1403826300, 1403827200, 1403828100, 1403829000, 1403829900, 1403830800, 1403831700, 1403832600, 1403833500, 1403834400, 1403835300, 1403836200, 1403837100, 1403838000, 1403838900, 1403839800, 1403840700, 1403841600, 1403842500, 1403843400, 1403844300, 1403845200, 1403846100, 1403847000, 1403847900, 1403848800, 1403849700, 1403850600, 1403851500, 1403852400, 1403853300, 1403854200, 1403855100, 1403856000, 1403856900, 1403857800, 1403858700, 1403859600, 1403860500, 1403861400, 1403862300, 1403863200, 1403864100, 1403865000, 1403865900, 1403866800, 1403867700,
1403868600, 1403869500, 1403870400, 1403871300, 1403872200, 1403873100, 1403874000, 1403874900, 1403875800, 1403876700, 1403877600, 1403878500, 1403879400, 1403880300, 1403881200, 1403882100, 1403883000, 1403883900, 1403884800, 1403885700, 1403886600, 1403887500, 1403888400, 1403889300, 1403890200, 1403891100, 1403892000, 1403892900, 1403893800, 1403894700, 1403895600, 1403896500, 1403897400, 1403898300, 1403899200, 1403900100, 1403901000, 1403901900, 1403902800, 1404079200, 1404080100, 1404081000, 1404081900, 1404082800, 1404083700, 1404084600, 1404085500, 1404086400, 1404087300, 1404088200, 1404089100, 1404090000, 1404090900, 1404091800, 1404092700, 1404093600, 1404094500, 1404095400, 1404096300, 1404097200, 1404098100, 1404099000, 1404099900, 1404100800, 1404101700, 1404102600, 1404103500, 1404104400, 1404105300, 1404106200, 1404107100, 1404108000, 1404108900, 1404109800, 1404110700, 1404111600, 1404112500, 1404113400, 1404114300, 1404115200, 1404116100, 1404117000, 1404117900, 1404118800, 1404119700, 1404120600, 1404121500, 1404122400, 1404123300, 1404124200, 1404125100, 1404126000, 1404126900, 1404127800, 1404128700, 1404129600, 1404130500, 1404131400, 1404132300, 1404133200, 1404134100, 1404135000, 1404135900, 1404136800, 1404137700, 1404138600, 1404139500, 1404140400, 1404141300, 1404142200, 1404143100, 1404144000, 1404144900, 1404145800, 1404146700, 1404147600, 1404148500, 1404149400, 1404150300, 1404151200, 1404152100, 1404153000, 1404153900, 1404154800, 1404155700, 1404156600, 1404157500, 1404158400, 1404159300, 1404160200, 1404161100, 1404162000, 1404165600, 1404166500, 1404167400, 1404168300, 1404169200, 1404170100, 1404171000, 1404171900, 1404172800, 1404173700, 1404174600, 1404175500, 1404176400, 1404177300, 1404178200, 1404179100, 1404180000, 1404180900, 1404181800, 1404182700, 1404183600, 1404184500, 1404185400, 1404186300, 1404187200, 1404188100, 1404189000, 1404189900, 1404190800, 1404191700, 1404192600, 1404193500, 1404194400, 1404195300, 1404196200, 1404197100, 1404198000, 1404198900, 1404199800, 1404200700, 1404201600, 1404202500, 1404203400, 1404204300, 1404205200, 1404206100, 1404207000, 1404207900, 1404208800, 1404209700, 1404210600, 1404211500, 1404212400, 1404213300, 1404214200, 1404215100, 1404216000, 1404216900, 1404217800, 1404218700, 1404219600, 1404220500, 1404221400, 1404222300, 1404223200, 1404224100, 1404225000, 1404225900, 1404226800, 1404227700, 1404228600, 1404229500, 1404230400, 1404231300, 1404232200, 1404233100, 1404234000, 1404234900, 1404235800, 1404236700, 1404237600, 1404238500, 1404239400, 1404240300, 1404241200, 1404242100, 1404243000, 1404243900, 1404244800, 1404245700, 1404246600, 1404247500, 1404248400, 1404252000, 1404252900, 1404253800, 1404254700, 1404255600, 1404256500, 1404257400, 1404258300, 1404259200, 1404260100, 1404261000, 1404261900, 1404262800, 1404263700, 1404264600, 1404265500, 1404266400, 1404267300, 1404268200, 1404269100, 1404270000, 1404270900, 1404271800, 1404272700, 1404273600, 1404274500, 1404275400, 1404276300, 1404277200, 1404278100, 1404279000, 1404279900, 1404280800, 1404281700, 1404282600, 1404283500, 1404284400, 1404285300, 1404286200, 1404287100, 1404288000, 1404288900, 1404289800, 1404290700, 1404291600, 1404292500, 1404293400, 1404294300, 1404295200, 1404296100, 1404297000, 1404297900, 1404298800, 1404299700, 1404300600, 1404301500, 1404302400, 1404303300, 1404304200, 1404305100, 1404306000, 1404306900, 1404307800, 1404308700, 1404309600, 1404310500, 1404311400, 1404312300, 1404313200, 1404314100, 1404315000, 1404315900, 1404316800, 1404317700, 1404318600, 1404319500, 1404320400, 1404321300, 1404322200, 1404323100, 1404324000,
1404324900, 1404325800, 1404326700, 1404327600, 1404328500, 1404329400, 1404330300, 1404331200, 1404332100, 1404333000, 1404333900, 1404334800, 1404338400, 1404339300, 1404340200, 1404341100, 1404342000, 1404342900, 1404343800, 1404344700, 1404345600, 1404346500, 1404347400, 1404348300, 1404349200, 1404350100, 1404351000, 1404351900, 1404352800, 1404353700, 1404354600, 1404355500, 1404356400, 1404357300, 1404358200, 1404359100, 1404360000, 1404360900, 1404361800, 1404362700, 1404363600, 1404364500, 1404365400, 1404366300, 1404367200, 1404368100, 1404369000, 1404369900, 1404370800, 1404371700, 1404372600, 1404373500, 1404374400, 1404375300, 1404376200, 1404377100, 1404378000, 1404378900, 1404379800, 1404380700, 1404381600, 1404382500, 1404383400, 1404384300, 1404385200, 1404386100, 1404387000, 1404387900, 1404388800, 1404389700, 1404390600, 1404391500, 1404392400, 1404393300, 1404394200, 1404395100, 1404396000, 1404396900, 1404397800, 1404398700, 1404399600, 1404400500, 1404401400, 1404402300, 1404403200, 1404404100, 1404405000, 1404405900, 1404406800, 1404407700, 1404408600, 1404409500, 1404410400, 1404411300, 1404412200, 1404413100, 1404414000, 1404414900, 1404415800, 1404416700, 1404417600, 1404418500, 1404419400, 1404420300, 1404421200, 1404424800, 1404425700, 1404426600, 1404427500, 1404428400, 1404429300, 1404430200, 1404431100, 1404432000, 1404432900, 1404433800, 1404434700, 1404435600, 1404436500, 1404437400, 1404438300, 1404439200, 1404440100, 1404441000, 1404441900, 1404442800, 1404443700, 1404444600, 1404445500, 1404446400, 1404447300, 1404448200, 1404449100, 1404450000, 1404450900, 1404451800, 1404452700, 1404453600, 1404454500, 1404455400, 1404456300, 1404457200, 1404458100, 1404459000, 1404459900, 1404460800, 1404461700, 1404462600, 1404463500, 1404464400, 1404465300, 1404466200, 1404467100, 1404468000, 1404468900, 1404469800, 1404470700, 1404471600, 1404472500, 1404473400, 1404474300, 1404475200, 1404476100, 1404477000, 1404477900, 1404478800, 1404479700, 1404480600, 1404481500, 1404482400, 1404483300, 1404484200, 1404485100, 1404486000, 1404486900, 1404487800, 1404488700, 1404489600, 1404490500, 1404491400, 1404492300, 1404684000, 1404684900, 1404685800, 1404686700, 1404687600, 1404688500, 1404689400, 1404690300, 1404691200, 1404692100, 1404693000, 1404693900, 1404694800, 1404695700, 1404696600, 1404697500, 1404698400, 1404699300, 1404700200, 1404701100, 1404702000, 1404702900, 1404703800, 1404704700, 1404705600, 1404706500, 1404707400, 1404708300, 1404709200, 1404710100, 1404711000, 1404711900, 1404712800, 1404713700, 1404714600, 1404715500, 1404716400, 1404717300, 1404718200, 1404719100, 1404720000, 1404720900, 1404721800, 1404722700, 1404723600, 1404724500, 1404725400, 1404726300, 1404727200, 1404728100, 1404729000, 1404729900, 1404730800, 1404731700, 1404732600, 1404733500, 1404734400, 1404735300, 1404736200, 1404737100, 1404738000, 1404738900, 1404739800, 1404740700, 1404741600, 1404742500, 1404743400, 1404744300, 1404745200, 1404746100, 1404747000, 1404747900, 1404748800, 1404749700, 1404750600, 1404751500, 1404752400, 1404753300, 1404754200, 1404755100, 1404756000, 1404756900, 1404757800, 1404758700, 1404759600, 1404760500), tzone = "", tclass = c("POSIXct", "POSIXt")), .indexCLASS = c("POSIXct", "POSIXt"), .indexTZ = "", tclass = c("POSIXct", "POSIXt"), tzone = "", .Dim = c(1000L, 1L), .Dimnames = list(NULL, "End.Eq"))
charts.PerformanceSummary(RETURNS, na.rm=T)
# THIS PROVIDES NO ERROR:
RETSBYDAY <- structure(c(0, 0, 0.0131666666666667, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), index = structure(c(1403395200, 1403481600, 1403568000, 1403654400, 1403740800, 1403827200, 1.404e+09, 1404086400, 1404172800, 1404259200, 1404345600, 1404432000, 1404604800, 1404691200), tclass = "Date", tzone = "UTC"), .indexCLASS = "Date", .indexTZ = "", tclass = c("POSIXct", "POSIXt"), tzone = "", .Dim = c(14L, 1L), .Dimnames = list(NULL,"DTT3.DailyEndEq"), class = c("xts", "zoo"))
charts.PerformanceSummary(RETSBYDAY)
#RETURNS and RETSBYDAY are 1xN xts objects.
#RETURNS has an NA value and RETSBYDAY has none; including na.rm=T makes no difference.
I don't get an error using performanceanalytics version 1.4.3541. Perhaps update your version?
I do get warnings though, that "na.rm is not a graphical parameter" when calling charts.PerformanceSummary with na.rm = TRUE. This warning arises because na.rm = T is passed into the function as an extra parameter (because the function has the special parameter ...), which is then passed on to the charting functions inside charts.PerformanceSummary, which include chart.CumReturns, chart.BarVaR, and chart.Drawdown, all of which accept passthrough parameters via ... = ... in their function calls inside charts.PerformanceSummary.
You can't assume every function in R will use na.rm = TRUE to remove NAs. Just mostly base functions like mean, sum, etc... It would be safer to remove the NAs before passing data to a function, if you get errors arising from the NAs in the function call. In case you are not aware, this can usually be easily done in a one liner, such as RETURNS <- RETURNS[!is.na(RETURNS),]
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I would like to put one line plot on each page of pdf file. All the data for plotting is stored in single data frame. Each row should be plotted.
That's how the data looks like:
structure(list(`10` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `34` = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 370500, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1091361.9, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1512409.6,
0, 0, 0, 0, 0, 0), `59` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 4231358.2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 5995680.4, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 2266775, 0, 0, 0, 0, 0, 0, 6864490.1, 0, 0,
0, 0, 0, 0), `84` = c(0, 0, 0, 0, 1783350, 0, 0, 0, 1177650,
0, 0, 0, 0, 0, 0, 0, 0, 4316664.7, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 9262556.7, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 2831286.1, 0, 0, 0, 0, 0, 0, 10643218.2,
0, 0, 0, 0, 0, 0), `110` = c(0, 0, 0, 0, 1778743.3, 0, 0, 0,
1465966.7, 0, 0, 0, 0, 0, 0, 0, 0, 3111700, 0, 0, 1955337.5,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5584784.4, 5584784.4,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3092525, 0,
0, 0, 0, 0, 0, 7847143.8, 0, 0, 0, 0, 0, 0), `134` = c(0, 0,
0, 0, 1121869.4, 0, 0, 0, 1439430.6, 0, 0, 0, 0, 0, 0, 0, 0,
2854250, 0, 0, 0, 0, 0, 0, 914890, 0, 0, 847880, 0, 0, 0, 0,
0, 0, 0, 8191800, 0, 0, 0, 0, 0, 0, 1830904.5, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1650150, 0, 0, 837130, 0, 0, 0, 4925095.1, 0,
0, 0, 0, 0, 0), `165` = c(0, 0, 0, 0, 1432775, 0, 0, 0, 1394186.1,
0, 1120183.3, 0, 0, 0, 0, 0, 0, 2262421.7, 0, 0, 0, 615660, 0,
0, 1292795.8, 0, 0, 712622.5, 0, 0, 0, 0, 0, 0, 0, 2683469.4,
0, 0, 0, 0, 0, 0, 2318485.5, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1561800,
0, 0, 0, 0, 0, 0, 4382993.7, 0, 0, 763460, 0, 0, 0), `199` = c(0,
0, 0, 0, 1314220, 0, 0, 0, 1439718.8, 0, 1929266.7, 0, 0, 0,
1101800, 0, 0, 2759366.7, 0, 0, 0, 1291728.6, 0, 0, 2489775.6,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2858345.8, 0, 0, 0, 1819542.1,
0, 0, 1497640.3, 0, 0, 0, 1300250, 0, 0, 0, 0, 0, 0, 1566875,
0, 0, 0, 0, 0, 0, 4625895.6, 0, 0, 1308158.3, 0, 0, 0), `234` = c(1257250,
0, 0, 0, 0, 0, 0, 0, 1276080, 0, 1848500, 0, 0, 0, 1529350, 0,
0, 2155275, 0, 0, 0, 2023041.9, 0, 0, 1966447.7, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 1184200, 1184200, 0, 0, 1652350, 0, 0, 2018581.7,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1835225, 0, 0, 0, 0, 0, 0, 4639414.7,
0, 0, 720715, 0, 0, 0), `257` = c(0, 0, 0, 0, 0, 669442.5, 0,
0, 1253026.7, 0, 960410, 960410, 0, 0, 1258267.5, 0, 0, 1707392.5,
0, 0, 0, 563280, 0, 0, 2403237.9, 0, 0, 0, 1044100, 0, 2075700,
0, 0, 0, 0, 0, 5718450, 0, 0, 1704550, 0, 0, 1350286.9, 0, 0,
0, 0, 2011700, 0, 0, 0, 0, 0, 1739500, 0, 0, 0, 0, 0, 0, 4612520.8,
4612520.8, 0, 0, 0, 0, 0), `362` = c(0, 1593500, 0, 0, 0, 1610625.3,
0, 0, 1234902.5, 0, 0, 1481036.8, 0, 0, 1583647.5, 0, 0, 1752089.2,
0, 0, 0, 0, 0, 0, 2410809.2, 0, 0, 0, 654940, 0, 0, 0, 0, 0,
0, 0, 7014905.6, 0, 0, 0, 0, 0, 1165672.1, 0, 0, 0, 0, 0, 0,
0, 1029910, 0, 0, 2153087.5, 0, 0, 0, 422920, 0, 0, 0, 7495855.9,
0, 0, 0, 0, 0), `433` = c(0, 0, 0, 0, 0, 1340283.9, 0, 0, 1268996.9,
0, 0, 1416683.3, 0, 0, 1047862.5, 0, 0, 1819653.8, 0, 0, 0, 0,
0, 0, 2227565.7, 0, 0, 0, 763765, 0, 0, 1595430, 0, 0, 0, 0,
4894549, 0, 0, 0, 0, 0, 1061375.4, 0, 0, 0, 0, 0, 2251950, 0,
1042130, 0, 0, 2055300, 0, 0, 0, 696278.3, 0, 0, 0, 5353797.8,
0, 0, 0, 0, 0), `506` = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2020300,
2020300, 0, 0, 0, 0, 0, 0, 7681526, 0, 0, 0, 0, 0), `581` = c(0,
0, 1749237.5, 0, 0, 0, 2421665.8, 0, 0, 1773262.5, 0, 0, 2251004.3,
0, 0, 2570175, 0, 0, 3379756.9, 0, 0, 0, 2054455.6, 0, 0, 2518270.8,
0, 0, 0, 0, 0, 0, 2917968.2, 0, 0, 0, 0, 7004350, 0, 0, 1451600,
0, 0, 1394411, 0, 0, 0, 0, 0, 2507858.3, 0, 2377012.5, 0, 0,
3719165.4, 0, 0, 0, 1472870.3, 0, 0, 9666916.1, 0, 0, 1730300,
0, 0), `652` = c(0, 0, 476910, 476910, 0, 0, 1149078.8, 1149078.8,
0, 1082468.7, 0, 0, 882769.7, 0, 0, 1370449.4, 1370449.4, 0,
1529049, 1529049, 0, 0, 943632.2, 0, 0, 916587.8, 0, 0, 0, 988261.1,
0, 0, 1778007.1, 1778007.1, 0, 0, 0, 3087304.8, 3087304.8, 0,
782860, 782860, 0, 510158.5, 510158.5, 0, 0, 0, 0, 1503750, 0,
1100677.5, 1100677.5, 0, 1669260, 1669260, 0, 0, 770733.2, 0,
0, 4939242.8, 4939242.8, 0, 643564.4, 643564.4, 0), `733` = c(0,
0, 0, 1095060, 0, 0, 0, 1674089.3, 0, 1252101.3, 0, 0, 1259111,
0, 0, 0, 2429293.3, 0, 0, 2326928.3, 0, 0, 1259216.5, 0, 0, 1238837.5,
0, 0, 0, 1224858.3, 0, 0, 0, 2952529.9, 0, 0, 0, 0, 4626414.7,
0, 0, 1121440, 0, 0, 1025386.2, 0, 0, 0, 0, 1917900, 0, 0, 2197533.3,
0, 0, 2840155.5, 0, 0, 1054285.7, 0, 0, 0, 7516814.2, 0, 0, 1329434.4,
0), `818` = c(0, 0, 0, 720551.1, 0, 0, 0, 714662.7, 0, 617012.9,
0, 0, 549850.8, 0, 0, 0, 1197460, 0, 0, 771979.2, 0, 0, 585847.5,
585847.5, 0, 875475.4, 0, 0, 0, 576774, 0, 0, 0, 1147389.8, 0,
0, 0, 0, 2292421.7, 0, 0, 755258.3, 0, 0, 0, 0, 0, 0, 0, 858930,
0, 0, 1242668.3, 0, 0, 1580088.3, 0, 0, 641938.6, 641938.6, 0,
0, 3838660.4, 0, 0, 733140.8, 733140.8), `896` = c(0, 0, 0, 590480,
0, 0, 0, 817087.6, 0, 569869.5, 0, 0, 650822.5, 650822.5, 0,
0, 1624052.5, 0, 0, 682570.8, 0, 0, 0, 1538800, 0, 690488.6,
690488.6, 0, 0, 797923.9, 0, 0, 0, 1204889.3, 0, 0, 0, 0, 2184432.2,
0, 0, 676654.7, 0, 0, 0, 210680, 0, 0, 0, 791152.5, 0, 0, 1599855.8,
0, 0, 1358543.8, 0, 0, 0, 931288, 0, 0, 4683895.2, 0, 0, 0, 1202806
), `972` = c(0, 0, 0, 799116.4, 0, 0, 0, 759169.9, 0, 408845,
0, 0, 0, 948980, 0, 0, 968766.7, 0, 0, 675349.7, 0, 0, 0, 0,
0, 0, 1811117.6, 0, 0, 609098.5, 0, 0, 0, 1073749.1, 0, 0, 0,
0, 2392258.9, 0, 0, 743580, 0, 0, 0, 1020485, 0, 0, 0, 446596.7,
0, 0, 1178583, 0, 0, 1438261.7, 0, 0, 0, 1133057.9, 0, 0, 4445814.7,
0, 0, 0, 1057776.9), `1039` = c(0, 0, 0, 447255.3, 0, 0, 0, 609409.1,
0, 304340, 0, 0, 0, 0, 0, 0, 694232.8, 0, 0, 473015.3, 0, 0,
0, 0, 0, 0, 419524.9, 0, 0, 447760.6, 0, 0, 0, 932513.5, 0, 0,
0, 0, 1251960.5, 0, 0, 276560, 0, 0, 0, 259640, 0, 0, 0, 354995,
0, 0, 1570222.5, 0, 0, 1021822, 0, 0, 0, 811614, 0, 0, 2941698.2,
0, 0, 0, 1199942.5), Gene = 1:67), .Names = c("10", "34", "59",
"84", "110", "134", "165", "199", "234", "257", "362", "433",
"506", "581", "652", "733", "818", "896", "972", "1039", "Gene"
), row.names = c(NA, 67L), class = "data.frame")
I have tried something like that so far...:
for(i in 1:nrow(Tra_decon)){
Tra_decon_melt <- melt(Tra_decon[i,], id = "Gene")
pdf("Test_plot.pdf", onefile = TRUE)
ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
dev.off()
}
A solution without a for loop (and so faster) is this one:
plots <- lapply(1:nrow(Tra_decon), function(i){
Tra_decon_melt <- melt(Tra_decon[i,], id = "Gene")
ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
})
pdf("Test_plot.pdf", onefile = TRUE)
plots
dev.off()
This works for me:
pdf("Test_plot.pdf", onefile = TRUE)
for(i in 1:nrow(Tra_decon)){
Tra_decon_melt <- melt(Tra_decon[i,], id.vars = "Gene")
plot<-list()
plot[[i]]<-ggplot(Tra_decon_melt, aes(variable, log10(value), group=factor(Gene))) +
theme(legend.title=element_blank()) +
ylab("XXX") +
xlab("XXX") +
geom_line(aes(color=factor(Gene)), size = 1.2) +
ggtitle("XXXX") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
print(plot[[i]])
}
dev.off()