extract item from list in R using tidyverse - r

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')

Related

group by year in yearqrt format R

I would like to group values of all other columns by the year in column yearqtr the following data
dput(narepurchasement)
structure(list(Date = structure(c(844128000, 852076800, 859852800,
867715200, 875664000, 883612800, 891388800, 899251200, 907200000,
915148800, 922924800, 930787200, 938736000, 946684800, 954547200,
962409600, 970358400, 978307200, 986083200, 993945600, 1001894400,
1009843200, 1017619200, 1025481600, 1033430400, 1041379200, 1049155200,
1057017600, 1064966400, 1072915200, 1080777600, 1088640000, 1096588800,
1104537600, 1112313600, 1120176000, 1128124800, 1136073600, 1143849600,
1151712000, 1159660800, 1167609600, 1175385600, 1183248000, 1191196800,
1199145600, 1207008000, 1214870400, 1222819200, 1230768000, 1238544000,
1246406400, 1254355200, 1262304000, 1270080000, 1277942400, 1285891200,
1293840000, 1301616000, 1309478400, 1317427200, 1325376000, 1333238400,
1341100800, 1349049600, 1356998400, 1364774400, 1372636800, 1380585600,
1388534400, 1396310400, 1404172800, 1412121600, 1420070400, 1427846400,
1435708800, 1443657600, 1451606400, 1459468800, 1467331200, 1475280000,
1483228800, 1491004800, 1498867200, 1506816000, 1514764800, 1522540800,
1530403200, 1538352000, 1546300800, 1554076800, 1561939200, 1569888000,
1577836800, 1585699200, 1593561600, 1601510400, 1609459200, 1617235200,
1625097600, 1633046400, 1640995200, 1648771200), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), NetIssuance = c("-7450", "-13950",
"-14675", "-22875", "-25875", "-21675", "-17808", "-64840", "-111214",
"-6920", "-76700", "-26188", "-1", "27044", "-50630", "-10731",
"-83887", "-4850", "-14775", "-27350", "-1150", "-2644", "6357",
"-20316", "2098", "-10173", "-3438", "0", "-2055", "-0.802",
"-16823", "-32200", "-70730", "-43031", "-58722", "-90630", "-83784",
"-110795", "-116977", "-107859", "-137542", "-109583", "-149516",
"-162019", "-226618", "-84099", "-38612", "-73533", "-93475",
"-37950", "39311", "20920", "-62302", "-35987", "-35433", "-71238",
"-58295", "-59766", "-101392", "-133088", "-88329", "-49568",
"-99135", "-73428", "-77876", "-38256", "-73497", "-60269", "-105274",
"-101911", "-48493", "-80452", "-71090", "-116963", "-102404",
"-129399", "-104711", "-127487", "-136914", "-150658", "-80792",
"-89438", "-55464", "-119607", "-61042", "-122438", "-225035",
"-79778", "-190075", "-174006", "-46583", "-111504", "-124927",
"-95947", "-14946", "7398", "-67450", "-30403", "-133211", "-218291",
"-237670", "-227868", "-135084"), GrossIssuance = c(35393, 34426,
39963, 36586, 40630, 36993, 57637, 31110, 52737, 52487, 78711,
65846, 95574, 113349, 86067, 75480, 71906, 54552, 64094, 39824,
55322, 43624, 50257, 29329, 35664, 32098, 36084, 42285, 48634,
57955, 47497, 43892, 55599, 48385, 52197, 63692, 63159, 68401,
69557, 63825, 94723, 88627, 97967, 102944, 108022, 86316, 96002,
93730, 75885, 64674, 77307, 62616, 66705, 54873, 57173, 48392,
68703, 64334, 69966, 43637, 55198, 66678, 70380, 68331, 72198,
73702, 83784, 103945, 94138, 89471, 100239, 100418, 111302, 129933,
124281, 116589, 97678, 106734, 118234, 106262, 107965, 122679,
115625, 107485, 112226, 106358, 99560, 101952, 91526, 95447,
118912, 108570, 100615, 107853, 154908, 134115, 154227, 163567,
126579, 112180, 132474, 92327, 80342), GrossRetirement = c(42843,
48376, 54638, 59461, 66505, 58668, 75445, 95950, 163951, 59407,
155411, 92034, 96134, 86305, 136697, 86211, 155793, 59402, 78869,
67174, 56472, 46268, 43900, 49645, 33566, 42271, 39522, 42226,
50689, 58757, 64320, 76092, 126329, 91416, 110919, 154322, 146943,
179196, 186534, 171684, 232265, 198210, 247483, 264963, 334640,
170415, 134614, 167263, 169360, 102624, 37996, 41696, 129007,
90860, 92606, 119630, 126998, 124100, 171358, 176725, 143527,
116246, 169515, 141759, 150074, 111958, 157281, 164214, 199412,
191382, 148732, 180870, 182392, 246896, 226685, 245988, 202389,
234221, 255148, 256920, 188757, 212117, 171089, 227092, 173268,
228796, 324595, 181730, 281601, 269453, 165495, 220074, 225542,
203800, 169854, 126717, 221677, 193970, 259790, 330471, 370144,
320195, 215426), Repurchases = c(22263, 22638, 23514, 25005,
34369, 26643, 29082, 41095, 27253, 31805, 30779, 29350, 35972,
38084, 22859, 24761, 30152, 25245, 26623, 27689, 24038, 20954,
27243, 27314, 18885, 20208, 22000, 25993, 34329, 31567, 34011,
42358, 46643, 52980, 63201, 66599, 90778, 76295, 97243, 91990,
96248, 92541, 121025, 121251, 98213, 94359, 75799, 80943, 45745,
26459, 17862, 24888, 33600, 40277, 59624, 57199, 62624, 66172,
73022, 96186, 74495, 64511, 83483, 65770, 86040, 77135, 100169,
97375, 105120, 124551, 99652, 108215, 106062, 113685, 100343,
122057, 107005, 123418, 99546, 75010, 89025, 93073, 81638, 84879,
87762, 143170, 138764, 134874, 148169, 137193, 107400, 108922,
119371, 143785, 79929, 88312, 110984, 128796, 141252, 154680,
195502, 220050, 120000), MA = c(20579, 25738, 31124, 34456, 32136,
32025, 46364, 54855, 136698, 27602, 124632, 62684, 60162, 48221,
113837, 61450, 125641, 34157, 52246, 39486, 32434, 25314, 16657,
22331, 14681, 22063, 17522, 16233, 16360, 27191, 30309, 33735,
79686, 38436, 47718, 87723, 56166, 102901, 89291, 79694, 136016,
105669, 126458, 143711, 236427, 76055, 58816, 86320, 123615,
76166, 20134, 16809, 95407, 50583, 32982, 62430, 64373, 57928,
98336, 80539, 69032, 51735, 86032, 75988, 64033, 34823, 57112,
66838, 94292, 66831, 49079, 72655, 76330, 133211, 126342, 123931,
95384, 110803, 155602, 181911, 99732, 119044, 89451, 142213,
85506, 85626, 185832, 46856, 133432, 132260, 58095, 111152, 106172,
60015, 89925, 38404, 110693, 65174, 118539, 175791, 174642, 100146,
95426), GDP = c(8259.771, 8362.655, 8518.825, 8662.823, 8765.907,
8866.48, 8969.699, 9121.097, 9293.991, 9411.682, 9526.21, 9686.626,
9900.169, 10002.179, 10247.72, 10318.165, 10435.744, 10470.231,
10599, 10598.02, 10660.465, 10783.5, 10887.46, 10984.04, 11061.433,
11174.129, 11312.766, 11566.669, 11772.234, 11923.447, 12112.815,
12305.307, 12527.214, 12767.286, 12922.656, 13142.642, 13324.204,
13599.16, 13753.424, 13870.188, 14039.56, 14215.651, 14402.082,
14564.117, 14715.058, 14706.538, 14865.701, 14898.999, 14608.208,
14430.901, 14381.236, 14448.882, 14651.248, 14764.611, 14980.193,
15141.605, 15309.471, 15351.444, 15557.535, 15647.681, 15842.267,
16068.824, 16207.13, 16319.54, 16420.386, 16629.05, 16699.551,
16911.068, 17133.114, 17144.281, 17462.703, 17743.227, 17852.54,
17991.348, 18193.707, 18306.96, 18332.079, 18425.306, 18611.617,
18775.459, 18968.041, 19148.194, 19304.506, 19561.896, 19894.75,
20155.486, 20470.197, 20687.278, 20819.269, 21013.085, 21272.448,
21531.839, 21706.532, 21538.032, 19636.731, 21362.428, 21704.706,
22313.85, 23046.934, 23550.42, 24349.121, 24740.48, 25248.476
)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-103L))
I don't know how exactly...

We may use
library(dplyr)
library(zoo)
library(lubridate)
narepurchasement %>%
mutate(yearqtr = as.yearqtr(Date)) %>%
group_by(year = year(yearqtr))

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,
0.0476352610219926, 0.0161558138743416, -0.0288967250807524,
-0.0136524963030579, 0.013609536698864, -0.0101217287483828,
-0.0158427614092369, -0.0659110431281645, -0.00361214051470649,
-0.0339148683510243, 0.0549197973662541, 0.00351817414237154,
-0.0836247487330098, -0.0660975163205073, 0.0304100958405675,
-0.0433097428906102, -0.0363928303567407, 0.0106002084863943,
-0.027020033384645, -0.0291214507902441, 0.013711263904689, 0.0191434464401923,
0.0249660315555751, -0.0360122263475078, 0.00687569978857283,
0.0574596331980592, -0.0114262145986058, -0.00694415263166959,
-0.0519113175679171, -0.092809259472171, -0.000240961580031951,
-0.0573097859137003, 0.0476330153045359, 0.0158596707003653,
-0.0261396384934428, -0.0286505239756832, -0.0366692368775817,
-0.0127341035075788, 0.0669385878663209, 0.0465027267386504,
0.00704423441903423, 0.0217546431041822, -0.00582565417712289,
0.0130023382378637, 0.10511251526633, 0.0518522334205838, 0.107127520905078,
0.0178719315683786, 0.0278267973953032, -0.0485503100189258,
-0.0108659147438031, 0.0109666518413147, 0.0154645672337712,
-0.0338505700478167, -0.00436219527802972, 0.05104724595336,
0.0541549024268144, 0.0873384487014046, 0.0628186392725139, -0.0288161017299512,
0.0950492127308722, -0.0491296716777756, -0.0444532289596897,
-0.00216600237045717, 0.0190268040149375, 0.067817072016372,
0.0146918057312835, 0.0295514071747491, -0.0435830986730233,
0.0215714644357461, 0.0494583026657589, 0.0913329109870025, -0.0053448319045083,
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,
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0.0126255977120013, -0.0535302063156535, 0.0438272536940851,
-0.0576089770159162, 0.0647885069687177), .Dim = c(336L, 3L), .Dimnames = list(
NULL, c("Conventionele.strategie", "Conditionele.strategie",
"Niet.geschaald")), index = structure(c(759974400, 762393600,
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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()

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,
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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,
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1.512e+09, 1512086400, 1512172800, 1512259200, 1512345600, 1512432000,
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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.

R: merge.xts and specific fill function

After merging two unequal time series I want to fill the blanks with a custom function. Lets say my Series1 is daily data and Series2 is monthly data. So now Series1 has for example 30 data points for one month and Series2 only one. If I make a left join Series 2 has 29 NAs which I don't like. Ideally I would like a fill function so that Series 2 takes always the previous value to fill these 29 days.
So for example if the 31. of January has a value of 10 and the 28th of February a value of 15, February 1-27. should have a value of 10 as well. Of course in the beginning this doesn't work (since the first row is probably also a NA), so the first row should take the value of the first row containing a value at all.
At the moment I have this, but still, all NAs are present:
Test<-merge.xts(Series1, Series2, join="left", fill=function(x) x[index(x)-1,])
Series1:
structure(c(1.51762156049755, 1.52103159497526, 1.51401262063846,
1.5226927459172, 1.52933295052158, 1.52409353403389, 1.52292452830189,
1.5268928035982, 1.53555449785816, 1.54004946727549, 1.54031650339111,
1.53987556561086, 1.53733857383492, 1.52781969068276, 1.5303624813154,
1.53149347601615, 1.53200449185851, 1.53034081463009, 1.52689961175818,
1.52616010353115, 1.52004035586536, 1.52604263206673, 1.53170366207736,
1.53332707472775, 1.5400318381871, 1.53717071341521, 1.53998696583186,
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1.53600669892073, 1.53977166385926, 1.53468288606184, 1.53986928104575,
1.54024911693623, 1.5402127262549, 1.54151119402985, 1.53934776549289,
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1.53853103261361, 1.53776866137519, 1.54085533920156, 1.5410640956972,
1.54313041923661, 1.54222657292872, 1.54302034987504, 1.54211182336182,
1.54181785998761, 1.5424089337942, 1.53578353604795, 1.53286652078775,
1.53120629370629, 1.53219713608012, 1.53192052980132, 1.53522245762712,
1.53543098889476, 1.53283647523016, 1.5296408481177, 1.52531916716648,
1.52295699845811, 1.52777060191165, 1.52890571231934, 1.5233980665583,
1.52386256533288, 1.51978021978022, 1.52140011865412, 1.51797040169133,
1.51707941929974, 1.52089868588385, 1.52408100748809, 1.52491920394625,
1.52068065032432, 1.52637418914305, 1.52848101265823, 1.52656088306313,
1.52858618908214, 1.53068778514246, 1.52826643894108, 1.52470085470085,
1.51927185710623, 1.52041166380789, 1.51975945017182, 1.52318452637941,
1.51831155433287, 1.51966908661151, 1.52143645470753, 1.52183128444256,
1.52286417239331, 1.52149627623561, 1.52065908330545, 1.51957958976098,
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1.51222222222222, 1.5101414692347, 1.5068328319725, 1.50393081761006,
1.50417972831766, 1.50391986062718, 1.50638741635526, 1.50589880276151,
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1.50718251520226, 1.50750460809269, 1.50457827082233, 1.50718301061836,
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1.51964269437608, 1.52107244513819, 1.51828822238478, 1.51868515287852,
1.52112289685443, 1.52031478770132, 1.5218941402322, 1.51964269437608,
1.51789300712069, 1.51745137247773, 1.51548186148772, 1.51610254538819,
1.51619929213177, 1.51333333333333, 1.51241134751773, 1.51200286635614,
1.51837734821672, 1.5163433908046, 1.49981738495252, 1.50498640072529,
1.5011387446479, 1.49350888500138, 1.4836323284631, 1.48080845540515,
1.47762023908813, 1.47091566935708, 1.44464775846295, 1.46478356566398,
1.46516563624619, 1.47632234837995, 1.48080808080808, 1.47685016405396,
1.48288833837967, 1.48791693466875, 1.48385916780979, 1.48779368575624,
1.4842056932966, 1.48020986745214, 1.48406538215688, 1.48219003370684,
1.4840747090138, 1.48181569592562, 1.47840712792072, 1.48482921511628,
1.48070841239722, 1.47882236069719, 1.47693552738063, 1.47952903398448,
1.47818343722173, 1.48081910042028, 1.47554444841128, 1.47437042328987,
1.47387958352196, 1.46947082767978, 1.47113912651959, 1.47202166064982,
1.47102365047843, 1.47226211849192, 1.47248814529838, 1.46853962839961,
1.46421559878636, 1.46491463305623, 1.46394424090787, 1.47141221037794,
1.46876654314452, 1.46473285134897, 1.46621860629643, 1.45898901098901,
1.45649677590319, 1.4541381128097, 1.45816872969889, 1.46286215978929,
1.46461267605634, 1.46386925795053, 1.46151797603196, 1.46911608093717,
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1.47583108715184, 1.47547136091502, 1.47256621169665, 1.47307171853857,
1.47527795353882, 1.47582605564059, 1.46818468184682, 1.46878890272097,
1.48522318688065, 1.48453427065026, 1.48568912373404, 1.4814585908529,
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1.47985739750446, 1.48092011412268, 1.47403176869534, 1.48108736475007,
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1.47194032439934, 1.47180647406892, 1.47518534670737, 1.47624474053296,
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1.46710702490404, 1.46860547847741, 1.46663705019991, 1.4664345652562,
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1.45595482546201, 1.45030384179627, 1.45351356929109, 1.45500778412039,
1.44706984490476, 1.45556604763404, 1.45198866617693), .indexTZ = "UTC", .indexCLASS = "Date", tclass = "Date", tzone = "UTC", class = c("xts",
"zoo"), index = structure(c(978307200, 978393600, 978480000,
978566400, 978652800, 978912000, 978998400, 979084800, 979171200,
979257600, 979516800, 979603200, 979689600, 979776000, 979862400,
980121600, 980208000, 980294400, 980380800, 980467200, 980726400,
980812800, 980899200, 980985600, 981072000, 981331200, 981417600,
981504000, 981590400, 981676800, 981936000, 982022400, 982108800,
982195200, 982281600, 982540800, 982627200, 982713600, 982800000,
982886400, 983145600, 983232000, 983318400, 983404800, 983491200,
983750400, 983836800, 983923200, 984009600, 984096000, 984355200,
984441600, 984528000, 984614400, 984700800, 984960000, 985046400,
985132800, 985219200, 985305600, 985564800, 985651200, 985737600,
985824000, 985910400, 986169600, 986256000, 986342400, 986428800,
986515200, 986774400, 986860800, 986947200, 987033600, 987120000,
987379200, 987465600, 987552000, 987638400, 987724800, 987984000,
988070400, 988156800, 988243200, 988329600, 988588800, 988675200,
988761600, 988848000, 988934400, 989193600, 989280000, 989366400,
989452800, 989539200, 989798400, 989884800, 989971200, 990057600,
990144000, 990403200, 990489600, 990576000, 990662400, 990748800,
991008000, 991094400, 991180800, 991267200, 991353600, 991612800,
991699200, 991785600, 991872000, 991958400, 992217600, 992304000,
992390400, 992476800, 992563200, 992822400, 992908800, 992995200,
993081600, 993168000, 993427200, 993513600, 993600000, 993686400,
993772800, 994032000, 994118400, 994204800, 994291200, 994377600,
994636800, 994723200, 994809600, 994896000, 994982400, 995241600,
995328000, 995414400, 995500800, 995587200, 995846400, 995932800,
996019200, 996105600, 996192000, 996451200, 996537600, 996624000,
996710400, 996796800, 997056000, 997142400, 997228800, 997315200,
997401600, 997660800, 997747200, 997833600, 997920000, 998006400,
998265600, 998352000, 998438400, 998524800, 998611200, 998870400,
998956800, 999043200, 999129600, 999216000, 999475200, 999561600,
999648000, 999734400, 999820800, 1000080000, 1000166400, 1000252800,
1000339200, 1000425600, 1000684800, 1000771200, 1000857600, 1000944000,
1001030400, 1001289600, 1001376000, 1001462400, 1001548800, 1001635200,
1001894400, 1001980800, 1002067200, 1002153600, 1002240000, 1002499200,
1002585600, 1002672000, 1002758400, 1002844800, 1003104000, 1003190400,
1003276800, 1003363200, 1003449600, 1003708800, 1003795200, 1003881600,
1003968000, 1004054400, 1004313600, 1004400000, 1004486400, 1004572800,
1004659200, 1004918400, 1005004800, 1005091200, 1005177600, 1005264000,
1005523200, 1005609600, 1005696000, 1005782400, 1005868800, 1006128000,
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1006905600, 1006992000, 1007078400, 1007337600, 1007424000, 1007510400,
1007596800, 1007683200, 1007942400, 1008028800, 1008115200, 1008201600,
1008288000, 1008547200, 1008633600, 1008720000, 1008806400, 1008892800,
1009152000, 1009238400, 1009324800, 1009411200, 1009497600, 1009756800,
1009843200, 1009929600, 1010016000, 1010102400, 1010361600, 1010448000,
1010534400, 1010620800, 1010707200, 1010966400, 1011052800, 1011139200,
1011225600, 1011312000, 1011571200, 1011657600, 1011744000, 1011830400,
1011916800, 1012176000, 1012262400, 1012348800, 1012435200, 1012521600,
1012780800, 1012867200, 1012953600, 1013040000, 1013126400, 1013385600,
1013472000, 1013558400, 1013644800, 1013731200, 1013990400, 1014076800,
1014163200, 1014249600, 1014336000, 1014595200, 1014681600, 1014768000,
1014854400, 1014940800, 1015200000, 1015286400, 1015372800, 1015459200,
1015545600, 1015804800, 1015891200, 1015977600, 1016064000, 1016150400,
1016409600, 1016496000, 1016582400, 1016668800, 1016755200, 1017014400,
1017100800, 1017187200, 1017273600, 1017360000, 1017619200, 1017705600,
1017792000, 1017878400, 1017964800, 1018224000, 1018310400, 1018396800,
1018483200, 1018569600, 1018828800, 1018915200, 1019001600, 1019088000,
1019174400, 1019433600, 1019520000, 1019606400, 1019692800, 1019779200,
1020038400, 1020124800, 1020211200, 1020297600, 1020384000, 1020643200,
1020729600, 1020816000, 1020902400, 1020988800, 1021248000, 1021334400,
1021420800, 1021507200, 1021593600, 1021852800, 1021939200, 1022025600,
1022112000, 1022198400, 1022457600, 1022544000, 1022630400, 1022716800,
1022803200, 1023062400, 1023148800, 1023235200, 1023321600, 1023408000,
1023667200, 1023753600, 1023840000, 1023926400, 1024012800, 1024272000,
1024358400, 1024444800, 1024531200, 1024617600, 1024876800, 1024963200,
1025049600, 1025136000, 1025222400, 1025481600, 1025568000, 1025654400,
1025740800, 1025827200, 1026086400, 1026172800, 1026259200, 1026345600,
1026432000, 1026691200, 1026777600, 1026864000, 1026950400, 1027036800,
1027296000, 1027382400, 1027468800, 1027555200, 1027641600, 1027900800,
1027987200, 1028073600, 1028160000, 1028246400, 1028505600, 1028592000,
1028678400, 1028764800, 1028851200, 1029110400, 1029196800, 1029283200,
1029369600, 1029456000, 1029715200, 1029801600, 1029888000, 1029974400,
1030060800, 1030320000, 1030406400, 1030492800, 1030579200, 1030665600,
1030924800, 1031011200, 1031097600, 1031184000, 1031270400, 1031529600,
1031616000, 1031702400, 1031788800, 1031875200, 1032134400, 1032220800,
1032307200, 1032393600, 1032480000, 1032739200, 1032825600, 1032912000,
1032998400, 1033084800, 1033344000, 1033430400, 1033516800, 1033603200,
1033689600, 1033948800, 1034035200, 1034121600, 1034208000, 1034294400,
1034553600, 1034640000, 1034726400, 1034812800, 1034899200, 1035158400,
1035244800, 1035331200, 1035417600, 1035504000, 1035763200, 1035849600,
1035936000, 1036022400, 1036108800, 1036368000, 1036454400, 1036540800,
1036627200, 1036713600, 1036972800, 1037059200, 1037145600, 1037232000,
1037318400, 1037577600, 1037664000, 1037750400, 1037836800, 1037923200,
1038182400, 1038268800, 1038355200, 1038441600, 1038528000, 1038787200,
1038873600, 1038960000, 1039046400, 1039132800, 1039392000, 1039478400,
1039564800, 1039651200, 1039737600, 1039996800, 1040083200, 1040169600,
1040256000, 1040342400, 1040601600, 1040688000, 1040774400, 1040860800,
1040947200, 1041206400, 1041292800), tzone = "UTC", tclass = "Date"), .Dim = c(522L,
1L), .Dimnames = list(NULL, "Series1"))
Series2:
structure(c(100, 100.32, 100.57, 100.82, 100.98, 101.01, 101.16,
101.3, 101.75, 102.07, 102.12, 102.3, 102.44, 102.59, 102.62,
102.74, 102.84, 103.09, 103.25, 103.31, 103.35, 103.48, 103.6,
103.72, 103.84, 103.96, 104.1, 104.35, 104.52, 104.69, 104.82,
104.96, 104.9, 105.03, 105.08, 105.27, 105.46, 105.55, 105.78,
105.94, 106.11, 106.36, 106.52, 106.6, 106.7, 106.92, 107.1,
107.27, 107.39, 107.41, 107.54, 107.72, 107.96, 108.13, 108.3,
108.43, 108.56, 108.68, 108.77), .indexCLASS = "Date", tclass = "Date", .indexTZ = "UTC", tzone = "UTC", class = c("xts",
"zoo"), index = structure(c(1010102400, 1010707200, 1011312000,
1011916800, 1012435200, 1012521600, 1013126400, 1013731200, 1014336000,
1014854400, 1014940800, 1015545600, 1016150400, 1016755200, 1017360000,
1017964800, 1018569600, 1019174400, 1019779200, 1020124800, 1020384000,
1020988800, 1021593600, 1022198400, 1022803200, 1023408000, 1024012800,
1024617600, 1025222400, 1025827200, 1026432000, 1027036800, 1027641600,
1028073600, 1028246400, 1028851200, 1029456000, 1030060800, 1030665600,
1031270400, 1031875200, 1032480000, 1033084800, 1033344000, 1033689600,
1034294400, 1034899200, 1035504000, 1036022400, 1036108800, 1036713600,
1037318400, 1037923200, 1038528000, 1039132800, 1039737600, 1040342400,
1040947200, 1041292800), tzone = "UTC", tclass = "Date"), .Dim = c(59L,
1L), .Dimnames = list(NULL, "Series2"))

na.locf() will do the job:
Test<-merge.xts(Series1, Series2, join="left", fill=na.locf())
This function "Last Observation Carried Forward" fills the NA with the last knowne value.
Hope this helps people landing here, years after you asked the question.

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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