Display ID in an Heatmap representation of Distance (Dissimilarity) [R] - r

I have computed the distance (Bray-Curtis dissimilarity) of a dataset, and I am trying to visualize the result in a heatmap with package pheatmap. I succeed to have the heatmap. Nevertheless, the ID of the rows and columns, despite being present in the distance data, does not appear in the heat map which complexify the interpretation. I have tried to use annotation = & annotation_row = on a vector file of the extracted species, but that does not work. Giving that message:
Error in annotation_row[rownames(mat), , drop = F] :
incorrect number of dimensions
Here is my code :
pheatmap(SpeBioDiss, annotation_row = Species, display_numbers = TRUE, cutree_rows = 5)
Here is my distance result:
SpeBioDiss <- structure(c(0.827283062404109, 0.999999999787744, 0.9976722025141,
0.993374122995813, 0.998684926724875, 0.996197694728471, 0.989724955190119,
0.43469497244591, 0.998418051673928, 0.990491388512666, 0.988338518981963,
0.989979469784687, 0.879991761967327, 0.737830539150324, 0.999999999543069,
0.96686014238935, 0.999999999537163, 0.977779088460779, 0.971844000006402,
0.999999999910839, 0.998794230920093, 0.999999999860431, 0.954480706731319,
0.986383026735704, 0.999999999929866, 0.912568317170688, 0.989047686370974,
0.989834812440211, 0.995315319366, 0.989609248520695, 0.928510389713861,
0.998057874156962, 0.987854549530747, 0.992809949172826, 0.997407926107517,
0.909702450081414, 0.899798177145296, 0.99999999991479, 0.95554497428788,
0.999999999914587, 0.981235337313036, 0.967860370501579, 0.999999999951837,
0.971870341370678, 0.999999999940163, 0.980228806939195, 0.928365081882989,
0.999999999936412, 0.989205204788684, 0.984802956273953, 0.990965089341073,
0.990997808073696, 0.998899563434765, 0.868730864585977, 0.97270276778642,
0.796523461858723, 0.893406508131487, 0.859623131053019, 0.97751172307757,
0.992352942466323, 0.99270915413726, 0.999999999607947, 0.9508032302371,
0.960255464055028, 0.647809134982572, 0.965799875755295, 0.906196625100659,
0.881340898467892, 0.986324697364517, 0.984173284697104, 0.961678344499017,
0.97986620519704, 0.98690337782425, 0.999999999932508, 0.989623321891195,
0.944514676798707, 0.958806930494846, 0.98866095752338, 0.95156505020099,
0.996130071685143, 0.999247798044378, 0.965322769139301, 0.999999999924102,
0.978266121630812, 0.92761939748347, 0.999999999955018, 0.944624365903456,
0.999999999944994, 0.999041093040236, 0.927062571232258, 0.486062169541527,
0.593141761296058, 0.516292483313821, 0.99118348278939, 0.996783394765032,
0.619795864489088, 0.982232130897591, 0.992734309081685, 0.924598423484611,
0.974445210104049, 0.99982611778834, 0.69835963101293, 0.999999999996886,
0.709436124371844, 0.78995367441768, 0.971643515325166, 0.837308356943407,
0.980872135495413, 0.924572400917035, 0.653481146200731, 0.591810902482557,
0.672035195833418, 0.996330790132122, 0.997137152915808, 0.635984964998261,
0.981345155269224, 0.994376102065022, 0.930677171351289, 0.983201791967876,
0.999832370468777, 0.764184967748081, 0.999999999996999, 0.7045992031759,
0.812823313491083, 0.949771727368739, 0.834752963315194, 0.970400438275127,
0.925270479615979, 0.558014389501429, 0.672194628479746, 0.995449343202001,
0.998108902874361, 0.622403790473656, 0.987148325574506, 0.995901444878825,
0.954611105456894, 0.980411108141993, 0.999900646342368, 0.82052818896192,
0.999999999998221, 0.781202000345684, 0.847354085250656, 0.96829041721517,
0.853866125723307, 0.982350607718347, 0.938086774654263, 0.719471914702451,
0.992095819587618, 0.997430216274891, 0.809335312202303, 0.978762423998145,
0.994742394681612, 0.938296212728146, 0.97033221807679, 0.999864946331165,
0.639941090476378, 0.999999999997582, 0.719176255540318, 0.894340516954927,
0.975714604352471, 0.915544830974035, 0.988696122154319, 0.94535527415508,
0.649943501040855, 0.949697309110096, 0.990413748324289, 0.997139520382115,
0.984287631185666, 0.979522989853084, 0.590405670464466, 0.996419977905822,
0.972889572022567, 0.999999999390479, 0.968075724489297, 0.967233155902039,
0.999690652287721, 0.999835684664338, 0.998603120221952, 0.940022477459311,
0.989643067553156, 0.995045900529729, 0.882175397694128, 0.892273666456609,
0.937617479767168, 0.931552968844194, 0.918863522176468, 0.991128211890621,
0.999999998120205, 0.989542335192327, 0.993984904250088, 0.972031567351805,
0.991313450355957, 0.994205794688569, 0.977396556368122, 0.996629984503012,
0.967580146059101, 0.989476190216704, 0.890942743652863, 0.983152745974521,
0.999697074109512, 0.847897990098422, 0.999999999994575, 0.705634125835866,
0.605802301755415, 0.906351449109105, 0.695673878775299, 0.959063591120359,
0.921666363807104, 0.820618619072126, 0.865695201384915, 0.693116070958152,
0.971577127295237, 0.988542799582319, 0.93825601292248, 0.99999999978303,
0.912834299792486, 0.935523776828192, 0.892721796075054, 0.930289058045599,
0.897969222994677, 0.913359438526875, 0.973302516149733, 0.857882783195953,
0.990098891491555, 0.993017292621689, 0.982833891711216, 0.999999999145984,
0.977110625992913, 0.970718120565762, 0.965561655724669, 0.972927823056258,
0.973267020957655, 0.975663721930446, 0.99374623773649, 0.909536833391596,
0.99612122887274, 0.824680942705843, 0.999999999930411, 0.77252723021654,
0.811959026265691, 0.874889109841753, 0.81246377408837, 0.935785119858506,
0.786391028551874, 0.926360731578736, 0.997149871048469, 0.900874359222991,
0.999999999839384, 0.885658873115255, 0.965819298805294, 0.997589524147627,
0.99782220913215, 0.996929289000309, 0.822818168192554, 0.961257329845712,
0.999532349794741, 0.999999964187906, 0.999447988081021, 0.999240832367375,
0.997829075841445, 0.999171094979177, 0.997808550561867, 0.998284898285629,
0.999822823307865, 0.999999999991625, 0.462533091236181, 0.827340176688411,
0.990451540594486, 0.875073699833685, 0.981300451114506, 0.876691869899626,
0.635503894157547, 0.999999999990113, 0.999999999983617, 0.999999999889566,
0.999999999985152, 0.999999999800174, 0.99999999995788, 0.999999999996827,
0.743669325337782, 0.879399730516722, 0.717970246714169, 0.918754919809119,
0.79311340763433, 0.632769318134201, 0.935167760442773, 0.643795000909155,
0.974723602282508, 0.936924401954657, 0.874128264084042, 0.885248041356563,
0.879319154156419, 0.871935806863086, 0.963919209341847, 0.91254396820454,
0.882664703372587, 0.869095793241582, 0.938704426640601, 0.978376838321269,
0.87876180016034), maxdist = 1, Size = 25L, Labels = c("Acartia longiremis",
"Acartia spp.", "Bradyidius similis", "Calanoida indet.", "Calanus finmarchicus",
"Calanus glacialis", "Calanus hyperboreus", "Calanus spp.", "Centropages spp.",
"Copepoda", "Metridia longa", "Microcalanus pusillus", "Microcalanus pygmaeus",
"Microcalanus spp.", "Microsetella norvegica", "Oithona atlantica",
"Oithona similis", "Oithona spp.", "Oncaea borealis", "Pareuchaeta glacialis",
"Pareuchaeta norvegica", "Pareuchaeta spp.", "Pseudocalanus acuspes",
"Pseudocalanus minutus", "Pseudocalanus spp."), Diag = FALSE, Upper = FALSE, method = "bray", call = vegdist(x = Zoodata_Biom_DisSpecies,
method = "bray"), class = "dist")
The vector file, containing the extraction of the species from the dataset :
Species <- c("Acartia longiremis", "Acartia spp.", "Bradyidius similis",
"Calanoida indet.", "Calanus finmarchicus", "Calanus glacialis",
"Calanus hyperboreus", "Calanus spp.", "Centropages spp.", "Copepoda",
"Metridia longa", "Microcalanus pusillus", "Microcalanus pygmaeus",
"Microcalanus spp.", "Microsetella norvegica", "Oithona atlantica",
"Oithona similis", "Oithona spp.", "Oncaea borealis", "Pareuchaeta glacialis",
"Pareuchaeta norvegica", "Pareuchaeta spp.", "Pseudocalanus acuspes",
"Pseudocalanus minutus", "Pseudocalanus spp.")

Related

chart.TimeSeries can't add labels or change line type

I am having some trouble with visualizing cumulative returns in R. For some reason I can't change the axis labels and line type in chart.TimeSeries or chart.CumReturns from the PerformanceAnalytics package. Changes in the line type do appear in the legend.
My code:
ret_compare <- cbind(conventional.scaled.returns.monthly, conditional.scaled.returns.monthly, unscaled.returns.monthly)
cum_ret_compare <- cumprod(ret_compare+1)
chart.TimeSeries(cum_ret_compare, ylab = "Cumulatief rendement", colorset= c(2,3,1),lty = 2, legend.loc = "topleft")
The plot I get:
A screenshot of the data I used (below is the full dput() output):
The data I used (dput(ret_compare)):
structure(c(0.0565939990771869, -0.0347482427707391, -0.0277900457059636,
0.0429234080870222, -0.0718787783241953, -0.016863029966348,
0.059355733239068, 0.037562234119064, -0.0677988560026864, 0.0600705787852096,
-0.0517080807079698, 0.00180517117961099, -0.0209223756892264,
0.0272845085630986, 0.0837601002893609, 0.0359655556488165, 0.0289223567394459,
0.00786503568014307, 0.0613306520140755, -0.0691778150018606,
0.0326380403566631, -0.0133417659847783, 0.0124121161628492,
0.0642708816666793, 0.00507849967404161, 0.0261052878209909,
0.0151493267776062, 0.00676515233437147, 0.00653446817356351,
0.0150714363479285, -0.050143190382553, 0.0345058557122753, 0.0441645265494663,
0.0330199447063566, 0.080567937081057, 0.0316571988327838, 0.000198181362019767,
0.0208690458801075, 0.0504761702266239, -0.00550425517164699,
0.0399051303505815, 0.0599723999392408, 0.0568078556781997, -0.047301231431996,
0.0883885279858534, -0.0511316915459871, 0.00804493275737861,
0.0299441033050929, 0.0334788931639478, 0.0582014365192993, 0.0678197532602445,
0.0159992998095304, 0.0125443303556689, 0.00819301763612046,
0.00991755318616083, -0.120605712811205, -0.0262409920857444,
0.040946793757588, 0.0164392980283827, 0.0306621368418329, -0.0238640140220965,
-0.0156901113780832, 0.0101061236361657, 0.0280945470081029,
-0.0493498768067187, 0.0158476544516308, -0.0130536804845046,
0.00928507222402164, -0.0174652889946849, 0.0415549564151227,
0.00501328628644071, 0.141989003051248, -0.0684962605913843,
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-0.0158427614092369, -0.0659110431281645, -0.00361214051470649,
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-0.0433097428906102, -0.0363928303567407, 0.0106002084863943,
-0.027020033384645, -0.0291214507902441, 0.013711263904689, 0.0191434464401923,
0.0249660315555751, -0.0360122263475078, 0.00687569978857283,
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-0.0127341035075788, 0.0669385878663209, 0.0465027267386504,
0.00704423441903423, 0.0217546431041822, -0.00582565417712289,
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-0.00216600237045717, 0.0190268040149375, 0.067817072016372,
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0.0601557176652443, 0.0568172510761795, -0.0485426969753849,
0.00175302828494006, 0.00984938190559648, 0.0240502738702311,
-0.00118579262163876, 0.0561485994364315, 0.0685148593157117,
0.04683955790033, -0.0574638736286489, -0.0138207306792366, 0.0355299071029838,
0.0531266681464893, 0.0117742011764304, -0.00525222583312346,
-0.0293812846037232, -0.0131594446545449, 0.0289964530305211,
0.0360588122490606, -0.0445991604911897, -0.0133107604099818,
-0.038145829508777, -0.000486060215306838, 0.00104676175588558,
0.0208886233797061, -0.00263583111403876, -0.12338493810393,
-0.0166803342153315, -0.0297568741980336, -0.112338323364713,
-0.0693713119660443, -0.0180597800314281, 0.0134278508097088,
-0.0330598180720567, -0.0366483220489359, 0.0339417580345724,
0.0509050678267557, 0.0528878066096272, -0.018266616997294, 0.0677991301131817,
0.0413170767588944, 0.0179821345844948, -0.0120530476718976,
0.0506564243990657, 0.0108111883471529, -0.0607609892616301,
-0.0245384465619248, 0.0513624569791613, -0.0375362970369394,
-0.108672749280212, -0.00337217907778453, 0.087093380777213,
-0.0338856992001196, 0.0840213297383585, 0.0404836319184165,
-0.0844650405081253, 0.0619874075245053, 0.0504278836336267,
0.0415781344576631, -0.0167132483670377, 0.076801882730787, -0.0369574751565279,
-0.0206697388445634, -0.0328788588005816, -0.083383558090487,
-0.0323162584368404, 0.051610493839749, -0.0220809995437766,
-0.00870937103673219, 0.0225163266689465, 0.0397241319702022,
-0.0102895797305198, -0.0281284547507554, -0.0991696936611708,
0.0636541757418483, 0.0179711424415336, 0.0333173103972026, 0.0225887202551132,
0.00443860666159801, 0.014806760557537, 0.0310237163003237, 0.144376590041557,
-0.0448055417930532, -0.00513324649717639, 0.0537257308063575,
-0.00999221359281199, -0.0819189957530423, 0.0476018428802933,
-0.0214364641397353, 0.0918599937028983, 0.068148748800128, 0.00803440955796253,
0.0397444044913302, -0.0552739603600437, 0.10420213043066, -0.0215270370205893,
0.0249963705701839, -0.0093007099097957, -0.00944183950206712,
-0.0971704450184838, 0.00264470427302532, -0.0723146624220592,
-0.0604005470589978, 0.0267660599545763, -0.0809302399859423,
-0.0155073183470853, 0.0664580522537737, -0.0536551996227884,
0.0468841220908309, -0.0208428453992652, -0.0416561940283499,
0.0333869121623922, -0.0710574857344434, -0.0493355477841083,
0.0574454161378841, -0.0413939958360005, -0.0361717353766919,
-0.0730611841593626, -0.0306524518642073, 0.0448704347638376,
0.0189138798885842, -0.019112690070504, -0.0607784992411501,
0.0200647095833417, 4.64025545323654e-05, 0.0167228957527705,
-0.0399352463740604, -0.0533583100515406, 0.0922371375755624,
0.0469543874397995, 0.0130431580117298, 0.0904898600303281, 0.0657425429880596,
0.0499463961351239, -0.0261922592092307, 0.065751610214466, -0.00621555863424983,
0.0832799329468406, 0.00866941466260029, -0.017688747365096,
0.0449778523037858, 0.0954877576723518, -0.118831287262529, -0.0142308642932637,
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-0.16121577788262, 0.0851533099681305, -0.0386176257065057, -0.0712227636357597,
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0.0151856742327374, 0.0150795243191497, 0.0256024944818467, 0.0319573321526792,
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0.0582468900635822, 0.0619978730333017, 0.077263516690542, -0.0171388878765958,
0.0199810159760507, 0.0158023347139837, -0.103334180665634, 0.0549659271604657,
-0.0893066705987667, 0.101606771360827, 0.0492865186589087, -0.0238877039788714,
-0.0277900457059636, 0.0404617408226444, -0.0718787783241953,
-0.0123575474841724, 0.059355733239068, 0.029908291943469, -0.0677988560026864,
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-0.0209223756892264, 0.0272845085630986, 0.0837601002893609,
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0.0613306520140755, -0.0691778150018606, 0.0246049232880761,
-0.0133417659847783, 0.0124121161628492, 0.0642708816666793,
0.00507849967404161, 0.0261052878209909, 0.0151493267776062,
0.00676515233437147, 0.00653446817356351, 0.0150714363479285,
-0.050143190382553, 0.0277659888972834, 0.0441645265494663, 0.0330199447063566,
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0.048456257520995, 0.0430035722547741, -0.047301231431996, 0.087565977773425,
-0.050604038742966, 0.00804493275737861, 0.0299441033050929,
0.0357934879374062, 0.0582014365192993, 0.0698786365111124, 0.0171741022871303,
0.0125443303556689, 0.00819301763612046, 0.00991755318616083,
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1490918400, 1493337600, 1496188800, 1498780800, 1501459200, 1504137600,
1506643200, 1509408000, 1.512e+09, 1514505600, 1517356800, 1519776000,
1522281600, 1525046400, 1527724800, 1530230400, 1532995200, 1535673600,
1538092800, 1540944000, 1543536000, 1546214400, 1548892800, 1551312000,
1553817600, 1556582400, 1559260800, 1561680000, 1564531200, 1567123200,
1569801600, 1572480000, 1574985600, 1577750400, 1580428800, 1582848000,
1585612800, 1588204800, 1590710400, 1593475200, 1596153600, 1598832000,
1601424000, 1604016000, 1606694400, 1609372800, 1611878400, 1614297600,
1617148800, 1619740800, 1622160000, 1625011200, 1627603200, 1630368000,
1632960000, 1635465600, 1638230400, 1640908800), tzone = "UTC", tclass = c("POSIXct",
"POSIXt")), class = c("xts", "zoo"))

Similar to you, I could only affect the legend by changing lty or lwd parameters. The package authors state that
"This function is intended to be used inside other charting functions."
— ?chart.TimeSeries
Indeed, you can select a plot.engine by passing it a function argument: plot.engine = "ggplot2" will return and plot a ggplot graph, "plotly" an interactive plotly-plot and so on. Then again you'll need the corresponding libraries installed and some basic aquaintance with each package (so I wonder why to use chart.TimeSeries as a wrapper anyway).
Edit - two alternatives to plot your data with library(ggplot2)
Fast and simple:
cum_ret_compare %>% autoplot.zoo
with basic dplyr and ggplot operations:
my_plot <-
cum_ret_compare %>%
as.data.frame %>%
mutate(Date = as.Date(time(cum_ret_compare))) %>% ## extract date to column Date
## stack variables instead of side-by-side
## to easier apply variable-wise color further down:
pivot_longer(cols = -Date,
names_to = 'strategy_type',
values_to = 'value'
) %>%
## construct ggplot
ggplot(aes(Date, value)) +
## add lines per strategy_type
geom_line(aes(col = strategy_type),
lty = 1, ## linetype
lwd = .1 ## linewidth
) +
labs(x = 'Date', y = 'cum. return')
## plot it:
my_plot
## make it look impressive ;-)
library(ggthemes)
my_plot + theme_economist()

extract item from list in R using tidyverse

I have a list in R from a Structural VAR Model I've run. Here is the dataset I've used
structure(c(-2.46773175636719, -7.72826473957142, 11.7280053716074,
-1.90017613519231, 2.46070753200911, -10.9055849254775, 12.5325444143789,
1.3401777880631, 1.11706357603545, -11.9683333308867, 13.8014223673123,
3.00567366940339, 1.93467425287253, -12.4811258520841, 12.5337510851622,
6.17970387794458, -0.311524449552714, -4.43067381564184, 6.00580266302142,
0.733215859597713, -0.0920826461465296, -10.4968848655989, 15.1592141225619,
-0.0142294828419764, 2.81612947228318, -7.95484787976672, 11.3258100085652,
-4.26536944046738, 1.71777230300645, 3.05071906367438, 6.86888493397788,
-2.04121390648186, -5.45632423113958, 4.32701496742318, -1.77039007913358,
-3.38387552001187, -3.6948032720689, 9.71137617961375, 7.70239243534423,
-7.34908250395865, 1.66529724744393, 4.12699586248877, 1.46757481960265,
0.371903496562886, -3.1265641490501, 10.1504446414976, -6.97113134346488,
10.6335568680931, -7.74296970668011, -0.31563625249369, 4.86848083529221,
0.0394209612387897, 4.1060141554107, 4.40828962229318, -3.65631265465627,
-0.804371886780864, 5.300399767623, 3.33547294572138, 2.19966779280814,
-2.96620054964727, 2.01147630916658, 3.00808531323007, -1.78858517036105,
-0.262917554632125, -2.63540419004151, 3.42060573075127, -0.379197105963414,
-2.24861964821645, -1.33132752566709, 1.67672890048411, 2.42834777679839,
-1.28230298674303, 2.33228006495114, -1.79196678955762, -3.25533945043563,
-2.49506277883942, 3.87275419034108, -0.237088301198796, 3.15158720918571,
-8.16796902280537, -5.40342750457601, -0.684253285161596, 2.76776077124627,
3.03762540403962, 1.59655060303945, 2.81108799665279, 2.12619249528281,
1.25976266808472, 1.75072385830202, 3.35523567403357, 3.66056933213099,
2.81157744760496, 2.8694753969682, 1.22389693906415, 1.23949528512668,
0.655297291930701, -0.0811557166513488, 2.49469766016768, 1.04750905161959,
0.553154355370644, 0.0394037933455493, 0.495095459931427, 0.263970407073533,
1.46876270708987, 1.02690822562028, 1.21249150827216, 0.744350196120713,
2.28082505446663, 1.40375659950536, 1.59077468759525, 1.74048892256584,
1.81791859189397, 0.640927792895951, 2.05320969806211, 3.31708856718933,
3.94172305193239, 1.29907384513261, 2.18844272980503, 2.02564945461834,
2.89662683480758, 0.531971587307556, 1.69103059033393, 0.195326999327161,
1.20287931837835, -0.0417495586606087, 1.89055607207678, 1.8793164132429,
1.19212718508228, 1.43097285231573, 2.4079363839828, 1.02287700043888,
1.41700117422383, 1.9324642470476, 1.42192313279805, 1.00916357593048,
1.26198292983659, 0.929701659038162, 1.77329986790085, 1.88793046797189,
0.685737107435092, 0.400744609924519, 0.766302538581343, 1.09780031685389,
1.00049427220021, 0.535284014354875, 3.18688973234602, 1.75770144393539,
1.19538307882463, 0.904511107610428, 3.52708500618766, 0.526934426385495,
0.361429093322574, 0.624469028588326, 1.95600784906818, 0.781933189336748,
1.09121935535335, 1.30962848646536, 1.24173607356797, 0.453213216209214,
0.468243562258674, 0.489076348335971, 0.848706378173603, 0.125371629239357,
0.817153292286932), .Dim = c(82L, 2L), .Dimnames = list(NULL,
c("GDP_NAM", "CPI_NAM")), index = structure(c(962323200,
970272000, 978220800, 985996800, 993859200, 1001808000, 1009756800,
1017532800, 1025395200, 1033344000, 1041292800, 1049068800, 1056931200,
1064880000, 1072828800, 1080691200, 1088553600, 1096502400, 1104451200,
1112227200, 1120089600, 1128038400, 1135987200, 1143763200, 1151625600,
1159574400, 1167523200, 1175299200, 1183161600, 1191110400, 1199059200,
1206921600, 1214784000, 1222732800, 1230681600, 1238457600, 1246320000,
1254268800, 1262217600, 1269993600, 1277856000, 1285804800, 1293753600,
1301529600, 1309392000, 1317340800, 1325289600, 1333152000, 1341014400,
1348963200, 1356912000, 1364688000, 1372550400, 1380499200, 1388448000,
1396224000, 1404086400, 1412035200, 1419984000, 1427760000, 1435622400,
1443571200, 1451520000, 1459382400, 1467244800, 1475193600, 1483142400,
1490918400, 1498780800, 1506729600, 1514678400, 1522454400, 1530316800,
1538265600, 1546214400, 1553990400, 1561852800, 1569801600, 1577750400,
1585612800, 1593475200, 1601424000), tzone = "UTC", tclass = "Date"), class = c("xts",
"zoo"))
and the code I've used to create the model
library(tidyverse)
library(vars)
var.namibia <- namibia %>% VAR(.,p=1,type = 'both',season=NULL)
SVAR.namibia <- BQ(var.namibia)
from this, I want to extract residuals from the variables in my model. I can do it by indexing (see below), but I'm interested in finding a dplyr solution to achieve the same. Any suggestions?
res <- SVAR.namibia$var$varresult$CPI_NAM$residuals

You can use purrr's pluck function.
SVAR.namibia %>%
purrr::pluck('var', 'varresult', 'CPI_NAM', 'residuals')

Change range of "fill" values in choropleth map R

Any help would be greatly appreciated!!
I'm trying to create a choropleth map in R that shows the counties of texas, color-coded by their population ranges.
My problem is that the range of populations is too large. The highest population is over 4 million, but most of the counties have a population under 50,000. The criteria for the fill is: (0-1mil), (1-2mil), (2-3mil), (3-4mil), (4-5mil) but almost all fall under 0-1mil.
How can I change the legend to account for different ranges of numbers? For example, maybe:
(0-1,000), (1,000-10,000), (10,000-100,000), (100,000-1mil), (1mil-5mil)
Here's the code I wrote to plot the data:
txplot <- ggplot(txczpop, aes(fill=pop2014)) + geom_map(txmap)
tm_shape(txmap) +
tm_fill("pop2014", title="TX County Population", palette = "PRGn") +
tm_borders(alpha=.5) +
tm_style_beaver()
Here's the result:
[![enter image description here][1]][1]
I'm using a census county shapefile and population also retrieved from a census file.
Here's the output of my population data:
txczpop <- structure(list(county_fips = c(48001L, 48003L, 48005L, 48007L,
48009L, 48011L, 48013L, 48015L, 48017L, 48019L, 48021L, 48023L,
48025L, 48027L, 48029L, 48031L, 48033L, 48035L, 48037L, 48039L,
48041L, 48043L, 48045L, 48047L, 48049L, 48051L, 48053L, 48055L,
48057L, 48059L, 48061L, 48063L, 48065L, 48067L, 48069L, 48071L,
48073L, 48075L, 48077L, 48079L, 48081L, 48083L, 48085L, 48087L,
48089L, 48091L, 48093L, 48095L, 48097L, 48099L, 48101L, 48103L,
48105L, 48107L, 48109L, 48111L, 48113L, 48115L, 48117L, 48119L,
48121L, 48123L, 48125L, 48127L, 48129L, 48131L, 48133L, 48135L,
48137L, 48141L, 48139L, 48143L, 48145L, 48147L, 48149L, 48151L,
48153L, 48155L, 48157L, 48159L, 48161L, 48163L, 48165L, 48167L,
48169L, 48171L, 48173L, 48175L, 48177L, 48179L, 48181L, 48183L,
48185L, 48187L, 48189L, 48191L, 48193L, 48195L, 48197L, 48199L,
48201L, 48203L, 48205L, 48207L, 48209L, 48211L, 48213L, 48215L,
48217L, 48219L, 48221L, 48223L, 48225L, 48227L, 48229L, 48231L,
48233L, 48235L, 48237L, 48239L, 48241L, 48243L, 48245L, 48247L,
48249L, 48251L, 48253L, 48255L, 48257L, 48259L, 48261L, 48263L,
48265L, 48267L, 48269L, 48271L, 48273L, 48275L, 48283L, 48277L,
48279L, 48281L, 48285L, 48287L, 48289L, 48291L, 48293L, 48295L,
48297L, 48299L, 48301L, 48303L, 48305L, 48313L, 48315L, 48317L,
48319L, 48321L, 48323L, 48307L, 48309L, 48311L, 48325L, 48327L,
48329L, 48331L, 48333L, 48335L, 48337L, 48339L, 48341L, 48343L,
48345L, 48347L, 48349L, 48351L, 48353L, 48355L, 48357L, 48359L,
48361L, 48363L, 48365L, 48367L, 48369L, 48371L, 48373L, 48375L,
48377L, 48379L, 48381L, 48383L, 48385L, 48387L, 48389L, 48391L,
48393L, 48395L, 48397L, 48399L, 48401L, 48403L, 48405L, 48407L,
48409L, 48411L, 48413L, 48415L, 48417L, 48419L, 48421L, 48423L,
48425L, 48427L, 48429L, 48431L, 48433L, 48435L, 48437L, 48439L,
48441L, 48443L, 48445L, 48447L, 48449L, 48451L, 48453L, 48455L,
48457L, 48459L, 48461L, 48463L, 48465L, 48467L, 48469L, 48471L,
48473L, 48475L, 48477L, 48479L, 48481L, 48483L, 48485L, 48487L,
48489L, 48491L, 48493L, 48495L, 48497L, 48499L, 48501L, 48503L,
48505L, 48507L), county_name = c("Anderson", "Andrews", "Angelina",
"Aransas", "Archer", "Armstrong", "Atascosa", "Austin", "Bailey",
"Bandera", "Bastrop", "Baylor", "Bee", "Bell", "Bexar", "Blanco",
"Borden", "Bosque", "Bowie", "Brazoria", "Brazos", "Brewster",
"Briscoe", "Brooks", "Brown", "Burleson", "Burnet", "Caldwell",
"Calhoun", "Callahan", "Cameron", "Camp", "Carson", "Cass", "Castro",
"Chambers", "Cherokee", "Childress", "Clay", "Cochran", "Coke",
"Coleman", "Collin", "Collingsworth", "Colorado", "Comal", "Comanche",
"Concho", "Cooke", "Coryell", "Cottle", "Crane", "Crockett",
"Crosby", "Culberson", "Dallam", "Dallas", "Dawson", "Deaf Smith",
"Delta", "Denton", "DeWitt", "Dickens", "Dimmit", "Donley", "Duval",
"Eastland", "Ector", "Edwards", "El Paso", "Ellis", "Erath",
"Falls", "Fannin", "Fayette", "Fisher", "Floyd", "Foard", "Fort Bend",
"Franklin", "Freestone", "Frio", "Gaines", "Galveston", "Garza",
"Gillespie", "Glasscock", "Goliad", "Gonzales", "Gray", "Grayson",
"Gregg", "Grimes", "Guadalupe", "Hale", "Hall", "Hamilton", "Hansford",
"Hardeman", "Hardin", "Harris", "Harrison", "Hartley", "Haskell",
"Hays", "Hemphill", "Henderson", "Hidalgo", "Hill", "Hockley",
"Hood", "Hopkins", "Houston", "Howard", "Hudspeth", "Hunt", "Hutchinson",
"Irion", "Jack", "Jackson", "Jasper", "Jeff Davis", "Jefferson",
"Jim Hogg", "Jim Wells", "Johnson", "Jones", "Karnes", "Kaufman",
"Kendall", "Kenedy", "Kent", "Kerr", "Kimble", "King", "Kinney",
"Kleberg", "Knox", "La Salle", "Lamar", "Lamb", "Lampasas", "Lavaca",
"Lee", "Leon", "Liberty", "Limestone", "Lipscomb", "Live Oak",
"Llano", "Loving", "Lubbock", "Lynn", "Madison", "Marion", "Martin",
"Mason", "Matagorda", "Maverick", "McCulloch", "McLennan", "McMullen",
"Medina", "Menard", "Midland", "Milam", "Mills", "Mitchell",
"Montague", "Montgomery", "Moore", "Morris", "Motley", "Nacogdoches",
"Navarro", "Newton", "Nolan", "Nueces", "Ochiltree", "Oldham",
"Orange", "Palo Pinto", "Panola", "Parker", "Parmer", "Pecos",
"Polk", "Potter", "Presidio", "Rains", "Randall", "Reagan", "Real",
"Red River", "Reeves", "Refugio", "Roberts", "Robertson", "Rockwall",
"Runnels", "Rusk", "Sabine", "San Augustine", "San Jacinto",
"San Patricio", "San Saba", "Schleicher", "Scurry", "Shackelford",
"Shelby", "Sherman", "Smith", "Somervell", "Starr", "Stephens",
"Sterling", "Stonewall", "Sutton", "Swisher", "Tarrant", "Taylor",
"Terrell", "Terry", "Throckmorton", "Titus", "Tom Green", "Travis",
"Trinity", "Tyler", "Upshur", "Upton", "Uvalde", "Val Verde",
"Van Zandt", "Victoria", "Walker", "Waller", "Ward", "Washington",
"Webb", "Wharton", "Wheeler", "Wichita", "Wilbarger", "Willacy",
"Williamson", "Wilson", "Winkler", "Wise", "Wood", "Yoakum",
"Young", "Zapata", "Zavala"), pop2014 = c(57627L, 17477L, 87750L,
24972L, 8811L, 1955L, 47774L, 29114L, 6910L, 20892L, 78069L,
3592L, 32863L, 329140L, 1855866L, 10812L, 652L, 17780L, 93275L,
338124L, 209152L, 9173L, 1536L, 7194L, 37653L, 17253L, 44943L,
39810L, 21797L, 13513L, 420392L, 12621L, 6013L, 30261L, 7781L,
38145L, 50902L, 7089L, 10370L, 2935L, 3254L, 8430L, 885241L,
3017L, 20719L, 123694L, 13550L, 4050L, 38761L, 75562L, 1415L,
4950L, 3812L, 5899L, 2266L, 7135L, 2518638L, 13372L, 19195L,
5238L, 753363L, 20684L, 2218L, 11089L, 3543L, 11533L, 18176L,
153904L, 1879L, 833487L, 159317L, 40147L, 16989L, 33752L, 24833L,
3831L, 5949L, 1275L, 685345L, 10600L, 19762L, 18531L, 19425L,
314198L, 6435L, 25520L, 1291L, 7549L, 20462L, 23044L, 123534L,
123204L, 27172L, 147250L, 34720L, 3147L, 8199L, 5509L, 3928L,
55621L, 4441370L, 67336L, 6089L, 5769L, 185025L, 4180L, 79290L,
831073L, 34848L, 23577L, 53921L, 35921L, 22741L, 36651L, 3211L,
88493L, 21773L, 1574L, 8855L, 14739L, 35552L, 2204L, 252235L,
5255L, 41353L, 157456L, 19936L, 14906L, 111236L, 38880L, 400L,
785L, 50562L, 4438L, 262L, 3526L, 32190L, 3858L, 7474L, 49523L,
13574L, 20156L, 19721L, 16742L, 16861L, 78117L, 23524L, 3553L,
12091L, 19510L, 86L, 293974L, 5771L, 13861L, 10149L, 5460L, 4071L,
36519L, 57023L, 8199L, 243441L, 805L, 47894L, 2147L, 155830L,
24256L, 4870L, 9076L, 19416L, 518947L, 22148L, 12743L, 1153L,
65301L, 48195L, 14138L, 15093L, 356221L, 10758L, 2070L, 83433L,
28096L, 23769L, 123164L, 9908L, 15893L, 46079L, 121627L, 6976L,
11032L, 128220L, 3755L, 3371L, 12446L, 14349L, 7302L, 928L, 16500L,
87809L, 10416L, 53923L, 10350L, 8610L, 27099L, 66915L, 5622L,
3162L, 17328L, 3343L, 25515L, 3084L, 218842L, 8694L, 62955L,
9405L, 1339L, 1403L, 3972L, 7581L, 1945360L, 135143L, 927L, 12739L,
1608L, 32506L, 116608L, 1151145L, 14224L, 21418L, 40354L, 3454L,
27117L, 48974L, 52910L, 91081L, 69789L, 46820L, 11625L, 34438L,
266673L, 41168L, 5714L, 132355L, 12973L, 21903L, 489250L, 46402L,
7821L, 61638L, 42852L, 8286L, 18350L, 14319L, 12267L)), .Names = c("county_fips",
"county_name", "pop2014"), row.names = c(5100L, 5101L, 5103L,
5106L, 5107L, 5109L, 5112L, 5114L, 5116L, 5118L, 5120L, 5121L,
5124L, 5126L, 5128L, 5129L, 5131L, 5133L, 5136L, 5137L, 5140L,
5141L, 5143L, 5146L, 5147L, 5150L, 5152L, 5153L, 5156L, 5158L,
5159L, 5161L, 5163L, 5166L, 5168L, 5170L, 5171L, 5174L, 5176L,
5178L, 5179L, 5182L, 5183L, 5185L, 5188L, 5190L, 5192L, 5194L,
5195L, 5198L, 5200L, 5201L, 5203L, 5205L, 5208L, 5209L, 5212L,
5214L, 5215L, 5218L, 5219L, 5221L, 5224L, 5226L, 5228L, 5230L,
5232L, 5233L, 5235L, 5239L, 5237L, 5242L, 5244L, 5245L, 5248L,
5249L, 5251L, 5254L, 5256L, 5257L, 5260L, 5261L, 5264L, 5265L,
5268L, 5270L, 5272L, 5274L, 5276L, 5278L, 5280L, 5281L, 5284L,
5286L, 5288L, 5290L, 5292L, 5293L, 5296L, 5298L, 5300L, 5301L,
5303L, 5306L, 5308L, 5309L, 5312L, 5314L, 5316L, 5317L, 5319L,
5321L, 5323L, 5326L, 5327L, 5330L, 5332L, 5334L, 5335L, 5337L,
5339L, 5341L, 5343L, 5346L, 5348L, 5349L, 5352L, 5354L, 5356L,
5357L, 5360L, 5362L, 5364L, 5365L, 5368L, 5369L, 5372L, 5374L,
5382L, 5376L, 5378L, 5379L, 5383L, 5385L, 5388L, 5390L, 5392L,
5394L, 5396L, 5398L, 5400L, 5401L, 5404L, 5412L, 5413L, 5416L,
5418L, 5419L, 5421L, 5406L, 5407L, 5409L, 5423L, 5425L, 5427L,
5429L, 5432L, 5434L, 5435L, 5438L, 5440L, 5442L, 5443L, 5446L,
5448L, 5449L, 5451L, 5453L, 5456L, 5457L, 5460L, 5461L, 5464L,
5465L, 5468L, 5470L, 5472L, 5474L, 5476L, 5477L, 5480L, 5482L,
5484L, 5486L, 5488L, 5489L, 5491L, 5494L, 5496L, 5498L, 5499L,
5501L, 5504L, 5505L, 5508L, 5510L, 5511L, 5514L, 5516L, 5518L,
5520L, 5522L, 5524L, 5526L, 5527L, 5530L, 5531L, 5533L, 5536L,
5537L, 5540L, 5542L, 5544L, 5546L, 5547L, 5550L, 5552L, 5554L,
5555L, 5558L, 5559L, 5562L, 5563L, 5566L, 5568L, 5569L, 5571L,
5574L, 5575L, 5578L, 5579L, 5582L, 5584L, 5585L, 5587L, 5590L,
5592L, 5594L, 5595L, 5598L, 5600L, 5602L, 5604L, 5606L), class = "data.frame")

I just created a new column in the population dataframe that summarizes the population based on the ranges that I want to use, and then use that as the criteria for the fill:
txczpop$poprange[txczpop$pop2014 >= 0 & txczpop < 1000] <- "0-1,000"
txczpop$poprange[txczpop$pop2014 >= 1000 & txczpop < 10000] <- "1-10,000"
txczpop$poprange[txczpop$pop2014 >= 10000 & txczpop$pop2014 < 100000] <- "10,000-100,000"
txczpop$poprange[txczpop$pop2014 >= 100000 & txczpop$pop2014 < 1000000] <- "100,000 - 1,000,000"
txczpop$poprange[txczpop$pop2014 >= 1000000 & txczpop$pop2014 <= 5000000] <- "1,000,000 - 5,000,000"

R - Color or shade area between lines

I'm trying to replicate with R a chart I made on Excel, which should represent a 95% Confidence Interval (CI) around a time series forecast. The Excel chart looks like this:
So, basically, the original historical time series and from a certain point in time the forecast of what it could be with its respective CI.
They way it's done on Excel is a bit inefficient:
I have four time series which overlap much of the time;
The actual/historical time series (blue line above) simply stops when the forecast begins;
The forecast (dotted red above) is simply hidden below the blue one until the forecast period begins;
Then I have a time series representing the difference between the the upper bound and the lower bound of the CI, which playing around with Excel Stacked Areas charts, becomes the shaded area in the chart above.
Obviously, the computation to generate the forecast and the CIs is much faster and easier to generalize and use with R, and while I could complete the task on R and then simply copy the output on Excel to draw the chart, doing everything in R would be much nicer.
At the end of the question I provided the raw data with dput() as suggested by #MLavoie.
Here the packages I loaded (not sure you need them all here, but they are the ones I usually work with):
require(zoo)
require(xts)
require(lattice)
require(latticeExtra)
My data looks like this for the first 100 rows:
> head(data)
fifth_percentile Median nintyfifth_percentile
2017-06-18 1.146267 1.146267 1.146267
2017-06-19 1.134643 1.134643 1.134643
2017-06-20 1.125664 1.125664 1.125664
2017-06-21 1.129037 1.129037 1.129037
2017-06-22 1.147542 1.147542 1.147542
2017-06-23 1.159989 1.159989 1.159989
Then after the 100 data point, the time series start to diverge and at the end they look like this:
> tail(data)
fifth_percentile Median nintyfifth_percentile
2017-12-30 0.9430930 1.125844 1.341603
2017-12-31 0.9435227 1.127391 1.354928
2018-01-01 0.9417235 1.124625 1.355527
2018-01-02 0.9470077 1.124088 1.361420
2018-01-03 0.9571596 1.127299 1.364005
2018-01-04 0.9515535 1.127978 1.369536
Solution provided by DaveTurek
Thanks to DaveTurek I've found the answer. However, only difference is that for my xts dataframe, apparently, I need first to convert each column to numbers (with as.numeric()). No idea if that stems from me doing something wrong with xts and lattice, or it is the only way to achieve it using DaveTurek suggestion. Will try to investigate it further.
Here is the code to generate the chart:
x = index(data[1:100,2])
y = as.numeric(data[1:100,2])
ex.x = index(data[101:200,2])
ex.y = as.numeric(data[101:200,2])
ex.lo = as.numeric(data[101:200,1])
ex.hi = as.numeric(data[101:200,3])
xyplot(y~x, ylim = c(0.9,1.4),
panel=function(x,y,...) {
panel.lines(x,y,lwd=2,col=4)
panel.polygon(c(ex.x,rev(ex.x)),c(ex.lo,rev(ex.hi)),border=NA,col=5)
panel.lines(ex.x,ex.y,lwd=2,col=2)
})
And here the final result:
Here is the final dataset, from dput(), that I'm trying to plot:
> dput(data)
structure(c(1.14626724930899, 1.13464279067717, 1.12566420479952,
1.12903662366847, 1.14754211999921, 1.15998855701439, 1.15274364578958,
1.16226441955745, 1.16169992687419, 1.16520028734587, 1.16823402018407,
1.19832130049664, 1.18411773220697, 1.18531274215286, 1.16421444455115,
1.17108139956539, 1.18392357740377, 1.20103911352579, 1.17791736605905,
1.18277944964829, 1.20162550199013, 1.19665058179752, 1.19411188122108,
1.19367558590966, 1.19803272562951, 1.20600155861871, 1.22189449901607,
1.22072774140118, 1.22312376195254, 1.25355505518571, 1.25895911759195,
1.2613354420716, 1.24440525381363, 1.24444079462029, 1.24168652168112,
1.24154936710117, 1.23440527301777, 1.22592718438811, 1.21709102449773,
1.21448030929365, 1.23109601090898, 1.24401127451953, 1.23953314346685,
1.21863565024168, 1.20834325548551, 1.20281193695583, 1.20405850724191,
1.19608032796923, 1.22008184095742, 1.21675995421116, 1.20198916403093,
1.20029121301547, 1.18822375424598, 1.19007923345344, 1.19285965857709,
1.1971013197471, 1.1776860331227, 1.18028531916998, 1.18394951589397,
1.16712430930941, 1.17827461393349, 1.18751430033172, 1.21482260909863,
1.2167262724184, 1.21729489152574, 1.21847062594996, 1.21932070698031,
1.19678189566773, 1.17678214957629, 1.17586968485613, 1.16903708967946,
1.16967697995898, 1.14498266161799, 1.12782282645368, 1.11540004479973,
1.12639853863918, 1.11402516325222, 1.10511837662567, 1.10600107687395,
1.10243149863659, 1.10404564773364, 1.12949458422398, 1.11679224666313,
1.11338078540871, 1.10762728498848, 1.12437898939299, 1.11572706259347,
1.1148111967932, 1.12358625045939, 1.11169207274881, 1.13009253108247,
1.13772927166761, 1.12550770863279, 1.13062401691547, 1.12821231512428,
1.13174620070443, 1.13072790983063, 1.1428325334377, 1.12739171867048,
1.1214997813059, 1.11870510839984, 1.096148222775, 1.08805136310032,
1.08701594286129, 1.08047984136855, 1.07939438148434, 1.0684082570972,
1.06497159411023, 1.05820047926833, 1.06322519359802, 1.06234781015662,
1.05431808916504, 1.054405104791, 1.05330182895869, 1.04787681441803,
1.041698698458, 1.03870702538097, 1.03300007904201, 1.02741553353049,
1.03525701392318, 1.0339774223954, 1.0328464056954, 1.03100871401712,
1.03348765946373, 1.03473218333386, 1.02942612874379, 1.02109481188296,
1.02301597272716, 1.01553904377803, 1.0031650628692, 1.00779708136199,
1.01322764666693, 1.01964272925677, 1.02125480865504, 1.02300342204156,
1.02563993245866, 1.02972111884963, 1.02048756192688, 1.00481457379443,
1.00512607721887, 1.01094340128446, 1.01377432300649, 1.01170553705668,
1.00551128145228, 1.00612634442438, 1.00735643866839, 1.0080606590012,
0.985706701720841, 0.982234200010558, 0.975314534071082, 0.973611418201841,
0.968118612511537, 0.973092829667201, 0.975599110408158, 0.967214930243667,
0.968569928969912, 0.963572085616274, 0.964901787179726, 0.957782708788541,
0.951868416101986, 0.956694066411684, 0.956937537219092, 0.956303331651844,
0.947880835881923, 0.956308493824626, 0.948146077843001, 0.945939091828748,
0.945082701640947, 0.937222489932819, 0.937989843132858, 0.948712728941467,
0.939050882255992, 0.946264846068344, 0.944926693194716, 0.946825914432391,
0.939070104432721, 0.950666108330947, 0.949365988007735, 0.943616625744159,
0.946600795357699, 0.941276090147603, 0.939957902451166, 0.941523527816784,
0.946611480333791, 0.959236316317354, 0.96165367272139, 0.957508302724503,
0.954774123925477, 0.960811125123549, 0.956525507301749, 0.948237690612711,
0.951299123137395, 0.945212566792479, 0.94507842203255, 0.942735006048921,
0.943093032220433, 0.943522672031737, 0.941723495992432, 0.947007713852018,
0.95715960245335, 0.951553478810637, 1.14626724930899, 1.13464279067717,
1.12566420479952, 1.12903662366847, 1.14754211999921, 1.15998855701439,
1.15274364578958, 1.16226441955745, 1.16169992687419, 1.16520028734587,
1.16823402018407, 1.19832130049664, 1.18411773220697, 1.18531274215286,
1.16421444455115, 1.17108139956539, 1.18392357740377, 1.20103911352579,
1.17791736605905, 1.18277944964829, 1.20162550199013, 1.19665058179752,
1.19411188122108, 1.19367558590966, 1.19803272562951, 1.20600155861871,
1.22189449901607, 1.22072774140118, 1.22312376195254, 1.25355505518571,
1.25895911759195, 1.2613354420716, 1.24440525381363, 1.24444079462029,
1.24168652168112, 1.24154936710117, 1.23440527301777, 1.22592718438811,
1.21709102449773, 1.21448030929365, 1.23109601090898, 1.24401127451953,
1.23953314346685, 1.21863565024168, 1.20834325548551, 1.20281193695583,
1.20405850724191, 1.19608032796923, 1.22008184095742, 1.21675995421116,
1.20198916403093, 1.20029121301547, 1.18822375424598, 1.19007923345344,
1.19285965857709, 1.1971013197471, 1.1776860331227, 1.18028531916998,
1.18394951589397, 1.16712430930941, 1.17827461393349, 1.18751430033172,
1.21482260909863, 1.2167262724184, 1.21729489152574, 1.21847062594996,
1.21932070698031, 1.19678189566773, 1.17678214957629, 1.17586968485613,
1.16903708967946, 1.16967697995898, 1.14498266161799, 1.12782282645368,
1.11540004479973, 1.12639853863918, 1.11402516325222, 1.10511837662567,
1.10600107687395, 1.10243149863659, 1.10404564773364, 1.12949458422398,
1.11679224666313, 1.11338078540871, 1.10762728498848, 1.12437898939299,
1.11572706259347, 1.1148111967932, 1.12358625045939, 1.11169207274881,
1.13009253108247, 1.13772927166761, 1.12550770863279, 1.13062401691547,
1.12821231512428, 1.13174620070443, 1.13072790983063, 1.1428325334377,
1.12739171867048, 1.1214997813059, 1.11870510839984, 1.11811303551412,
1.11855383782522, 1.11981261957516, 1.12096887905804, 1.12162710713999,
1.12015553029278, 1.12189306008921, 1.1236834173899, 1.12204149206779,
1.12075809542535, 1.12116672935174, 1.12216772364685, 1.11821915571021,
1.12117719223463, 1.11896003906963, 1.11563621625852, 1.1183625095638,
1.12053072892388, 1.1216348268255, 1.12317377733957, 1.11873136428952,
1.12267083202989, 1.12642930089215, 1.13027646770951, 1.13129632891931,
1.12700346009603, 1.12060488827701, 1.12390899402613, 1.13129350591169,
1.12786650327192, 1.1274201121913, 1.13101906643359, 1.12727135093377,
1.12458327192256, 1.12259738972645, 1.12097982776572, 1.12073621452193,
1.12364872830763, 1.12644326299714, 1.12556263098661, 1.12797963752343,
1.12734519199847, 1.1261793072762, 1.12911407446825, 1.12754878937943,
1.12777579027467, 1.12554965831588, 1.12324469267853, 1.12231558194992,
1.12135908710208, 1.11923353817423, 1.12345300992675, 1.12186883237389,
1.12173652640663, 1.12488148969114, 1.12664301925369, 1.12294230775256,
1.12393650688095, 1.13038044949978, 1.12822226676967, 1.12934384230215,
1.1217648908055, 1.12218158739803, 1.12302651609468, 1.12682187689922,
1.13537701046932, 1.13172108462183, 1.1374053505525, 1.13498257452656,
1.12692005654471, 1.13210629725645, 1.12868775509168, 1.13073909215368,
1.13098804355869, 1.13353301668386, 1.13336476594698, 1.13233873705211,
1.12667020676157, 1.12133152301322, 1.12418759586717, 1.12048022460741,
1.12798162212357, 1.13053093896994, 1.12019367019997, 1.12422483586498,
1.11303086301782, 1.11986711815552, 1.12504718249418, 1.11341517044014,
1.12495096618792, 1.12995127061511, 1.13538401552385, 1.13145536081928,
1.1264465959783, 1.12584386458867, 1.1273908895838, 1.12462482614994,
1.1240880626286, 1.12729907535003, 1.12797751377714, 1.14626724930899,
1.13464279067717, 1.12566420479952, 1.12903662366847, 1.14754211999921,
1.15998855701439, 1.15274364578958, 1.16226441955745, 1.16169992687419,
1.16520028734587, 1.16823402018407, 1.19832130049664, 1.18411773220697,
1.18531274215286, 1.16421444455115, 1.17108139956539, 1.18392357740377,
1.20103911352579, 1.17791736605905, 1.18277944964829, 1.20162550199013,
1.19665058179752, 1.19411188122108, 1.19367558590966, 1.19803272562951,
1.20600155861871, 1.22189449901607, 1.22072774140118, 1.22312376195254,
1.25355505518571, 1.25895911759195, 1.2613354420716, 1.24440525381363,
1.24444079462029, 1.24168652168112, 1.24154936710117, 1.23440527301777,
1.22592718438811, 1.21709102449773, 1.21448030929365, 1.23109601090898,
1.24401127451953, 1.23953314346685, 1.21863565024168, 1.20834325548551,
1.20281193695583, 1.20405850724191, 1.19608032796923, 1.22008184095742,
1.21675995421116, 1.20198916403093, 1.20029121301547, 1.18822375424598,
1.19007923345344, 1.19285965857709, 1.1971013197471, 1.1776860331227,
1.18028531916998, 1.18394951589397, 1.16712430930941, 1.17827461393349,
1.18751430033172, 1.21482260909863, 1.2167262724184, 1.21729489152574,
1.21847062594996, 1.21932070698031, 1.19678189566773, 1.17678214957629,
1.17586968485613, 1.16903708967946, 1.16967697995898, 1.14498266161799,
1.12782282645368, 1.11540004479973, 1.12639853863918, 1.11402516325222,
1.10511837662567, 1.10600107687395, 1.10243149863659, 1.10404564773364,
1.12949458422398, 1.11679224666313, 1.11338078540871, 1.10762728498848,
1.12437898939299, 1.11572706259347, 1.1148111967932, 1.12358625045939,
1.11169207274881, 1.13009253108247, 1.13772927166761, 1.12550770863279,
1.13062401691547, 1.12821231512428, 1.13174620070443, 1.13072790983063,
1.1428325334377, 1.12739171867048, 1.1214997813059, 1.11870510839984,
1.14162401974592, 1.15630966411729, 1.15992199767135, 1.16683144867851,
1.16928280999155, 1.17287782220285, 1.18184525262982, 1.17555305757354,
1.18031492211593, 1.18142628277888, 1.18307577052783, 1.18257404220722,
1.19421117710041, 1.19403330560815, 1.19510080390052, 1.2058940348108,
1.19848571699109, 1.20138771250604, 1.20660682710938, 1.20790011589089,
1.20963951875753, 1.21572259411602, 1.21379678812156, 1.220302087399,
1.22062959185172, 1.22743877731977, 1.23135277550334, 1.24075667733246,
1.24169498945046, 1.23529301399753, 1.2399941777708, 1.24823732280171,
1.23861121958778, 1.24816319854615, 1.25252933549084, 1.25133386983018,
1.24512546001264, 1.2617641352045, 1.25486018976211, 1.25424601859098,
1.25820538036104, 1.25968528498312, 1.26939611029084, 1.27883933177157,
1.27926882841012, 1.27951234203094, 1.28997494816278, 1.29391898267335,
1.2971442938215, 1.29733541086814, 1.30376525837809, 1.31025722802128,
1.29718190520268, 1.27919305871102, 1.28685138548374, 1.28594279969497,
1.28695233433419, 1.30277136510213, 1.29178316107299, 1.29586799884087,
1.30076586308517, 1.30881154838964, 1.32171887794143, 1.3197588324899,
1.3121332301804, 1.31744410759858, 1.31402945919721, 1.30926303329755,
1.32019231597949, 1.31449633135152, 1.31730801686101, 1.31834557852015,
1.3175761022299, 1.33430488507454, 1.34091614601639, 1.33606628597812,
1.33180446732765, 1.33630738683041, 1.33449101077219, 1.32521028784732,
1.32241490851887, 1.31488015995544, 1.31913131799656, 1.32901121011698,
1.33177659436063, 1.32577077582349, 1.31960627618725, 1.31307169067904,
1.32148403094167, 1.33104893196281, 1.33491831741272, 1.3386091981919,
1.35730874062825, 1.3460340606746, 1.34160318929376, 1.35492848895938,
1.35552729646417, 1.36141957863605, 1.36400538435282, 1.369536167295),
.indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC",
class = c("xts", "zoo"), index = structure(c(1497744000, 1497830400, 1497916800,
1498003200, 1498089600, 1498176000, 1498262400, 1498348800, 1498435200,
1498521600, 1498608000, 1498694400, 1498780800, 1498867200, 1498953600,
1499040000, 1499126400, 1499212800, 1499299200, 1499385600, 1499472000,
1499558400, 1499644800, 1499731200, 1499817600, 1499904000, 1499990400,
1500076800, 1500163200, 1500249600, 1500336000, 1500422400, 1500508800,
1500595200, 1500681600, 1500768000, 1500854400, 1500940800, 1501027200,
1501113600, 1501200000, 1501286400, 1501372800, 1501459200, 1501545600,
1501632000, 1501718400, 1501804800, 1501891200, 1501977600, 1502064000,
1502150400, 1502236800, 1502323200, 1502409600, 1502496000, 1502582400,
1502668800, 1502755200, 1502841600, 1502928000, 1503014400, 1503100800,
1503187200, 1503273600, 1503360000, 1503446400, 1503532800, 1503619200,
1503705600, 1503792000, 1503878400, 1503964800, 1504051200, 1504137600,
1504224000, 1504310400, 1504396800, 1504483200, 1504569600, 1504656000,
1504742400, 1504828800, 1504915200, 1505001600, 1505088000, 1505174400,
1505260800, 1505347200, 1505433600, 1505520000, 1505606400, 1505692800,
1505779200, 1505865600, 1505952000, 1506038400, 1506124800, 1506211200,
1506297600, 1506384000, 1506470400, 1506556800, 1506643200, 1506729600,
1506816000, 1506902400, 1506988800, 1507075200, 1507161600, 1507248000,
1507334400, 1507420800, 1507507200, 1507593600, 1507680000, 1507766400,
1507852800, 1507939200, 1508025600, 1508112000, 1508198400, 1508284800,
1508371200, 1508457600, 1508544000, 1508630400, 1508716800, 1508803200,
1508889600, 1508976000, 1509062400, 1509148800, 1509235200, 1509321600,
1509408000, 1509494400, 1509580800, 1509667200, 1509753600, 1509840000,
1509926400, 1510012800, 1510099200, 1510185600, 1510272000, 1510358400,
1510444800, 1510531200, 1510617600, 1510704000, 1510790400, 1510876800,
1510963200, 1511049600, 1511136000, 1511222400, 1511308800, 1511395200,
1511481600, 1511568000, 1511654400, 1511740800, 1511827200, 1511913600,
1.512e+09, 1512086400, 1512172800, 1512259200, 1512345600, 1512432000,
1512518400, 1512604800, 1512691200, 1512777600, 1512864000, 1512950400,
1513036800, 1513123200, 1513209600, 1513296000, 1513382400, 1513468800,
1513555200, 1513641600, 1513728000, 1513814400, 1513900800, 1513987200,
1514073600, 1514160000, 1514246400, 1514332800, 1514419200, 1514505600,
1514592000, 1514678400, 1514764800, 1514851200, 1514937600, 1515024000
), tzone = "UTC", tclass = "Date"), .Dim = c(201L, 3L), .Dimnames = list(
NULL, c("fifth_percentile", "Median", "nintyfifth_percentile"
)))

I haven't tried with your data, but if the question is how to shade the forecast area, maybe this simple example will help.
library(lattice)
x = 1:12 # base data
y = x
ex.x = 12:16 # extrapolated data
ex.y = 12:16
ex.lo = 12+0:4*.3 # lower bound
ex.hi = 12+0:4*1.6 # upper bound
xyplot(y~x,xlim=c(0:18),ylim=c(0:20),
panel=function(x,y,...) {
panel.lines(x,y,lwd=2,col=4)
panel.polygon(c(ex.x,rev(ex.x)),c(ex.lo,rev(ex.hi)),border=NA,col=5)
panel.lines(ex.x,ex.y,lwd=2,col=2)
})
You can add the shaded polygon to the lattice plot in a panel function. I used c(ex.x,rev(ex.x)) and c(ex.lo,rev(ex.hi)) to construct the polygon boundary.

Calculating Time Weighted Rate of Return in R

Is there an R function or library that will give me the monthly (or any other specified timeframe) time weighted rate of return (twrr) for my portfolio?
I am including a dput dump of sample data below of the date and portfolio ending balance below. Not sure why the dates were dput'ed the way they were, but the first date 12053 is '2003-01-01' and the last date 12195 is '2003-05-23'.
portfolio.df <- structure(
list(
Date = structure(c(12053, 12054, 12055, 12058,
12059, 12060, 12061, 12062, 12065, 12066, 12067, 12068, 12069,
12073, 12074, 12075, 12076, 12079, 12080, 12081, 12082, 12083,
12086, 12087, 12088, 12089, 12090, 12093, 12094, 12095, 12096,
12097, 12101, 12102, 12103, 12104, 12107, 12108, 12109, 12110,
12111, 12114, 12115, 12116, 12117, 12118, 12121, 12122, 12123,
12124, 12125, 12128, 12129, 12130, 12131, 12132, 12135, 12136,
12137, 12138, 12139, 12142, 12143, 12144, 12145, 12146, 12149,
12150, 12151, 12152, 12153, 12156, 12157, 12158, 12159, 12163,
12164, 12165, 12166, 12167, 12170, 12171, 12172, 12173, 12174,
12177, 12178, 12179, 12180, 12181, 12184, 12185, 12186, 12187,
12188, 12191, 12192, 12193, 12194, 12195),
class = "Date"),
Ending_Balance = c(56250000L,
56852500L, 57080000L, 57355000L, 57477500L, 56817500L, 57885000L,
57810000L, 57732500L, 57670000L, 57520000L, 57285000L, 57270000L,
56655000L, 55802500L, 56337500L, 55642500L, 54510000L, 54987500L,
55802500L, 56065000L, 56865000L, 56635000L, 56497500L, 56640000L,
56155000L, 55757500L, 55972500L, 55865000L, 55535000L, 55885000L,
56840000L, 56902500L, 56945000L, 56622500L, 57012500L, 57200000L,
58072500L, 57612500L, 57447500L, 57157500L, 57032500L, 57405000L,
57502500L, 56785000L, 57007500L, 56342500L, 55697500L, 56655000L,
56900000L, 57002500L, 57465000L, 57467500L, 57382500L, 57982500L,
56562500L, 58065000L, 58935000L, 58502500L, 58200000L, 57767500L,
57757500L, 58055000L, 58305000L, 58277500L, 58295000L, 59047500L,
58907500L, 59125000L, 59072500L, 59107500L, 59315000L, 59690000L,
58957500L, 59407500L, 59385000L, 59965000L, 60297500L, 59890000L,
59822500L, 60367500L, 60407500L, 60380000L, 60815000L, 61155000L,
61080000L, 61132500L, 61265000L, 60912500L, 61107500L, 61445000L,
61345000L, 61137500L, 61035000L, 60707500L, 61340000L, 61365000L,
61402500L, 61640000L, 61675000L)),
.Names = c("Date", "Ending_Balance"),
row.names = c(NA, 100L),
class = "data.frame")

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