Legend for plot.zoo - r
The code below uses plot.zoo function to plot two variables (in xts format) on the same panel with different colors. I need to generate a legend for variables, showing variable name and associated color (to distinguish one from the other). I could not find any examples. How can this be achieved?
library(xts)
XTS1 <- structure(c(0.228369333217119, 0.228289904084397, 0.228447230658951, 0.228791576808238, 0.230747528582736, 0.231930951391005, 0.23218095279688, 0.232655671733178, 0.232780332790686, 0.232737419368931, 0.232662430253128, 0.232742568778743, 0.233149068581523, 0.233801848401534, 0.233772492034511, 0.234624948258082, 0.23483556890299, 0.234796321794256, 0.23604671397228, 0.236133475815726, 0.236165665606426, 0.236028593613328, 0.23653404809854, 0.236405363926972, 0.236289501951192, 0.23595748354442, 0.235856148573546, 0.236430492268466, 0.236509873541663, 0.236320952959145, 0.236324663117125, 0.236230849130264, 0.236256142711785, 0.236443262637705, 0.236474635240435, 0.236478152698594, 0.236446010557742, 0.236433585738065, 0.236318823709326, 0.236343709733496, 0.236353484497234, 0.236293047785645, 0.236299398621113, 0.23632602130195, 0.236415592036867, 0.23644183016732, 0.236410860513062, 0.236407738860613, 0.236488620207848, 0.236521885178186, 0.236524525976254, 0.236543261190294, 0.236545852954505, 0.236554043549182, 0.236558510962661, 0.236500857781485, 0.236456811183667, 0.236429475415132, 0.23640238691203, 0.236406136067832, 0.236417470442709, 0.236338677460503, 0.236460106937516, 0.236499259900878, 0.236586474063617, 0.236543271644404, 0.236513672740977, 0.236369639034221, 0.236362697687712, 0.236797843111073, 0.237345016578261, 0.23783668813706, 0.237923216897962, 0.238248672738757, 0.23827277432708, 0.238353796658431, 0.238432929684165, 0.238426329912625, 0.238345183139418, 0.238458037645863, 0.238479196815153, 0.238658887053061, 0.238465939669501, 0.238523649852224, 0.238510811967291, 0.238618072611544, 0.238704138338676, 0.238707447338575, 0.23996516914318, 0.239424589962875, 0.239561518553478, 0.239421256904757, 0.239816852104079, 0.239445276547969, 0.239609107802299, 0.240012364436436, 0.24002458654273, 0.240130590848021, 0.239736081751086, 0.239857898122929, 0.249230991607954, 0.239805445609213, 0.220333558462471, 0.210410600760776, 0.210378078591145, 0.240399004222477, 0.239804814201628, 0.240033060931268, 0.240089397482534, 0.240197041264942, 0.239940687229403, 0.239983219836939, 0.240022917769706, 0.240337756345468, 0.240638638953238, 0.240145924499555, 0.240402820873626, 0.240634154733532, 0.240611140050359, 0.240534865617682, 0.240951950137048, 0.241127845406939, 0.241125700147753, 0.241126235785769, 0.24107570794763, 0.241088551839332, 0.241092988182315, 0.241086472173767, 0.241083390403282, 0.241091888348645, 0.241083158087253, 0.241064751836135, 0.241064210485496, 0.241091022867438, 0.240953810187424, 0.240481188224338, 0.240621074487305, 0.240541023392083, 0.240396295416436, 0.240434646218793, 0.240610526667198, 0.240808207542551, 0.240631718256586, 0.24064699063015, 0.24068954416015, 0.240711128194114, 0.240723728654195, 0.240775499747258, 0.24076580240029, 0.240772143433637, 0.240793451595961, 0.240796153528682, 0.240819395590463, 0.240807928484687, 0.241070198676456, 0.241015031342511, 0.240992277437459, 0.241067975400449, 0.241065024303712, 0.241163875314065, 0.241210015023487, 0.24111104460957, 0.241143389170502, 0.241126406233165, 0.241236385470386, 0.241216017627354, 0.241242224901225, 0.241204855537627, 0.241161113202215, 0.241188009018793, 0.241155655393916, 0.241104796660299, 0.241229168370518, 0.241187912977691, 0.241205571324508, 0.241275488693839, 0.241201890756208, 0.241062150030789, 0.241056667454678, 0.241020382598737, 0.241011174472952, 0.241075153997628, 0.240860609085096, 0.241671867834864, 0.242222875244792, 0.242105277990476, 0.242789197437053, 0.24262651942461, 0.242835209836078, 0.242065722773181, 0.242128191776504, 0.241866750527435, 0.242222755781399, 0.242405920724485, 0.242704642796114, 0.243681731065406, 0.243823985845211, 0.244075416453679, 0.244168166839201), .indexCLASS = c("POSIXct", "POSIXt"), .indexTZ = "", tclass = c("POSIXct", "POSIXt"), tzone = "", class = c("xts", "zoo"), .CLASS = structure("double", class = "CLASS"), formattable = structure(list(
formatter = "formatC", format = structure(list(format = "f", digits = 2), .Names = c("format", "digits")), preproc = "percent_preproc", postproc = "percent_postproc"), .Names = c("formatter", "format", "preproc", "postproc")), index = structure(c(1413981900, 1413982800, 1413983700, 1413984600, 1413985500, 1413986400, 1413987300, 1413988200, 1413989100, 1413990000, 1413990900, 1413991800, 1413992700, 1413993600, 1413994500, 1413995400, 1413996300, 1413997200, 1413998100, 1413999000, 1413999900, 1414000800, 1414001700, 1414002600, 1414003500, 1414004400, 1414005300, 1414006200, 1414007100, 1414008000, 1414009800, 1414010700, 1414011600, 1414015200, 1414016100, 1414017000, 1414017900, 1414018800, 1414019700, 1414020600, 1414021500, 1414022400, 1414023300, 1414024200, 1414025100, 1414026000, 1414026900, 1414027800, 1414028700, 1414029600, 1414030500, 1414031400, 1414032300, 1414033200, 1414034100, 1414035000, 1414035900, 1414036800, 1414037700, 1414038600, 1414039500, 1414040400, 1414041300, 1414042200, 1414043100, 1414044000, 1414044900, 1414045800, 1414046700, 1414047600, 1414048500, 1414049400, 1414050300, 1414051200, 1414052100, 1414053000, 1414053900, 1414054800, 1414055700, 1414056600, 1414057500, 1414058400, 1414059300, 1414060200, 1414061100, 1414062000, 1414062900, 1414063800, 1414064700, 1414065600, 1414066500, 1414067400, 1414068300, 1414069200, 1414070100, 1414071000, 1414071900, 1414072800, 1414073700, 1414074600, 1414075500, 1414076400, 1414077300, 1414078200, 1414079100, 1414080000, 1414080900, 1414081800, 1414082700, 1414083600, 1414084500, 1414085400, 1414086300, 1414087200, 1414088100, 1414089000, 1414089900, 1414090800, 1414091700, 1414092600, 1414093500, 1414094400, 1414096200, 1414097100, 1414098000, 1414101600, 1414102500, 1414103400, 1414104300, 1414105200, 1414106100, 1414107000, 1414107900, 1414108800, 1414109700, 1414110600, 1414111500, 1414112400, 1414113300, 1414114200, 1414115100, 1414116000, 1414116900, 1414117800, 1414118700, 1414119600, 1414120500, 1414121400, 1414122300, 1414123200, 1414124100, 1414125000, 1414125900, 1414126800, 1414127700, 1414128600, 1414129500, 1414130400, 1414131300, 1414132200, 1414133100, 1414134000, 1414134900, 1414135800, 1414136700, 1414137600, 1414138500, 1414139400, 1414140300, 1414141200, 1414142100, 1414143000, 1414143900, 1414144800, 1414145700, 1414146600, 1414147500, 1414148400, 1414149300, 1414150200, 1414151100, 1414152000, 1414152900, 1414153800, 1414154700, 1414155600, 1414156500, 1414157400, 1414158300, 1414159200, 1414160100, 1414161000, 1414161900, 1414162800, 1414163700, 1414164600, 1414165500, 1414166400, 1414167300), tzone = "", tclass = c("POSIXct", "POSIXt")), .Dim = c(199L, 1L))
XTS2 <- structure(c(0.238369333217119, 0.238289904084397, 0.238447230658951, 0.238791576808238, 0.230747528582736, 0.231930951391005, 0.23218095279688, 0.232655671733178, 0.232780332790686, 0.232737419368931, 0.232662430253128, 0.232742568778743, 0.233149068581523, 0.233801848401534, 0.233772492034511, 0.234624948258082, 0.23483556890299, 0.234796321794256, 0.23604671397238, 0.236133475815726, 0.236165665606426, 0.236028593613328, 0.23653404809854, 0.236405363926972, 0.236289501951192, 0.23595748354442, 0.235856148573546, 0.236430492368466, 0.236509873541663, 0.236320952959145, 0.236324663117125, 0.236230849130264, 0.236256142711785, 0.236443262637705, 0.236474635240435, 0.236478152698594, 0.236446010557742, 0.236433585738065, 0.236318823709326, 0.236343709733496, 0.236353484497234, 0.236293047785645, 0.236299398621113, 0.23632602130195, 0.236415592036867, 0.23644183016732, 0.236410860513062, 0.236407738860613, 0.236488620207848, 0.236521885178186, 0.236524525976254, 0.236543261190294, 0.236545852954505, 0.236554043549182, 0.236558510962661, 0.236500857781485, 0.236456811183667, 0.236429475415132, 0.23640238691203, 0.236406136067832, 0.236417470442709, 0.236338677460503, 0.236460106937516, 0.236499259900878, 0.236586474063617, 0.236543271644404, 0.216513672740977, 0.216369639034221, 0.216362697687712, 0.216797843111073, 0.217345016578261, 0.21783668813706, 0.217921216897962, 0.218248672738757, 0.21827277432708, 0.218353796658431, 0.218432929684165, 0.218426329912625, 0.218345183139418, 0.218458037645863, 0.218479196815153, 0.238658887053061, 0.238465939669501, 0.238523649852224, 0.238510811967291, 0.238618072611544, 0.238704138338676, 0.238707447338575, 0.23996516914318, 0.239424589962875, 0.239561518553478, 0.239421256904757, 0.239816852104079, 0.239445276547969, 0.239609107802299, 0.240012364436436, 0.24002458654273, 0.240130590848021, 0.239736081751086, 0.239857898122929, 0.239230991607954, 0.239805445609213, 0.240333558462471, 0.240410600760776, 0.240378078591145, 0.240399004222477, 0.239804814201628, 0.240033060931268, 0.240089397482534, 0.240197041264942, 0.239940687229403, 0.239983219836939, 0.240022917769706, 0.240337756345468, 0.240638638953238, 0.240145924499555, 0.240402820873626, 0.240634154733532, 0.240611140050359, 0.240534865617682, 0.240951950137048, 0.241127845406939, 0.241125700147753, 0.241126235785769, 0.24107570794763, 0.241088551839332, 0.241092988182315, 0.241086472173767, 0.241083390403282, 0.241091888348645, 0.241083158087253, 0.241064751836135, 0.241064210485496, 0.241091022867438, 0.240953810187424, 0.240481188224338, 0.240621074487305, 0.240541023392083, 0.240396295416436, 0.240434646218793, 0.240610526667198, 0.240808207542551, 0.240631718256586, 0.24064699063015, 0.24068954416015, 0.240711128194114, 0.240723728654195, 0.240775499747258, 0.24076580240029, 0.240772143433637, 0.240793451595961, 0.240796153528682, 0.240819395590463, 0.240807928484687, 0.241070198676456, 0.241015031342511, 0.240992277437459, 0.241067975400449, 0.241065024303712, 0.241163875314065, 0.241210015023487, 0.24111104460957, 0.241143389170502, 0.241126406233165, 0.241236385470386, 0.241216017627354, 0.241242224901225, 0.241204855537627, 0.241161113202215, 0.241188009018793, 0.241155655393916, 0.241104796660299, 0.241229168370518, 0.241187912977691, 0.241205571324508, 0.241275488693839, 0.241201890756208, 0.241062150030789, 0.241056667454678, 0.241020382598737, 0.241011174472952, 0.241075153997628, 0.240860609085096, 0.241671867834864, 0.242222875244792, 0.242105277990476, 0.242789197437053, 0.24262651942461, 0.242835209836078, 0.242065722773181, 0.242128191776504, 0.241866750527435, 0.242222755781399, 0.242405920724485, 0.242704642796114, 0.243681731065406, 0.243823985845211, 0.244075416453679, 0.244168166839201), .indexCLASS = c("POSIXct", "POSIXt"), .indexTZ = "", tclass = c("POSIXct", "POSIXt"), tzone = "", class = c("xts", "zoo"), .CLASS = structure("double", class = "CLASS"), formattable = structure(list(
formatter = "formatC", format = structure(list(format = "f", digits = 2), .Names = c("format", "digits")), preproc = "percent_preproc", postproc = "percent_postproc"), .Names = c("formatter", "format", "preproc", "postproc")), index = structure(c(1413981900, 1413982800, 1413983700, 1413984600, 1413985500, 1413986400, 1413987300, 1413988200, 1413989100, 1413990000, 1413990900, 1413991800, 1413992700, 1413993600, 1413994500, 1413995400, 1413996300, 1413997200, 1413998100, 1413999000, 1413999900, 1414000800, 1414001700, 1414002600, 1414003500, 1414004400, 1414005300, 1414006200, 1414007100, 1414008000, 1414009800, 1414010700, 1414011600, 1414015200, 1414016100, 1414017000, 1414017900, 1414018800, 1414019700, 1414020600, 1414021500, 1414022400, 1414023300, 1414024200, 1414025100, 1414026000, 1414026900, 1414027800, 1414028700, 1414029600, 1414030500, 1414031400, 1414032300, 1414033200, 1414034100, 1414035000, 1414035900, 1414036800, 1414037700, 1414038600, 1414039500, 1414040400, 1414041300, 1414042200, 1414043100, 1414044000, 1414044900, 1414045800, 1414046700, 1414047600, 1414048500, 1414049400, 1414050300, 1414051200, 1414052100, 1414053000, 1414053900, 1414054800, 1414055700, 1414056600, 1414057500, 1414058400, 1414059300, 1414060200, 1414061100, 1414062000, 1414062900, 1414063800, 1414064700, 1414065600, 1414066500, 1414067400, 1414068300, 1414069200, 1414070100, 1414071000, 1414071900, 1414072800, 1414073700, 1414074600, 1414075500, 1414076400, 1414077300, 1414078200, 1414079100, 1414080000, 1414080900, 1414081800, 1414082700, 1414083600, 1414084500, 1414085400, 1414086300, 1414087200, 1414088100, 1414089000, 1414089900, 1414090800, 1414091700, 1414092600, 1414093500, 1414094400, 1414096200, 1414097100, 1414098000, 1414101600, 1414102500, 1414103400, 1414104300, 1414105200, 1414106100, 1414107000, 1414107900, 1414108800, 1414109700, 1414110600, 1414111500, 1414112400, 1414113300, 1414114200, 1414115100, 1414116000, 1414116900, 1414117800, 1414118700, 1414119600, 1414120500, 1414121400, 1414122300, 1414123200, 1414124100, 1414125000, 1414125900, 1414126800, 1414127700, 1414128600, 1414129500, 1414130400, 1414131300, 1414132200, 1414133100, 1414134000, 1414134900, 1414135800, 1414136700, 1414137600, 1414138500, 1414139400, 1414140300, 1414141200, 1414142100, 1414143000, 1414143900, 1414144800, 1414145700, 1414146600, 1414147500, 1414148400, 1414149300, 1414150200, 1414151100, 1414152000, 1414152900, 1414153800, 1414154700, 1414155600, 1414156500, 1414157400, 1414158300, 1414159200, 1414160100, 1414161000, 1414161900, 1414162800, 1414163700, 1414164600, 1414165500, 1414166400, 1414167300), tzone = "", tclass = c("POSIXct", "POSIXt")), .Dim = c(199L, 1L))
z <- cbind(XTS1, XTS2)
colnames(z) <- c("XTS1", "XTS2")
plot.zoo(z, plot.type = "single", col = 1:2, xy.labels = "text")
Please note I need to use plot.zoo rather than plot.
Solution was provided in one of the comments. Here's full solution plus some explanatory notes:
z <- cbind(XTS1, XTS2)
colnames(z) <- c("XTS1", "XTS2")
plot.zoo(z, plot.type = "single", col = 1:2)
legend("topright", inset=c(0,0), y.intersp = 1, legend = c("XTS1", "XTS2"), lty = 1, bty = "n", col = c(1,2), cex = .5)
#cex: font size
#col: color
#bty: box around legends
#pch: symbol for legends (eg pch = c(1,2) => circle, triangle)
#lty: lines for legends (eg: lty = c(1,2) => solid, dash)
#y.intersp: vert interspacing (wout it legends may be far apart from each other)
#inset: for adjusting pos relative to eg "topright" (useful for xts as x coord in posixct format)
Related
How to NOT write_csv if data frame is empty
I have a dataframe that is gathered everyday via a sql query. Sometimes it'll have rows in it, sometimes it wont. I then write_csv it into a onedrive location which triggers an automated email.
df and code like this if relevant:
df<-structure(list(PROTOCOL_ID = numeric(0), PROTOCOL_NO = character(0),
STATUS = character(0), STATUS_DATE = structure(numeric(0), tzone = "", class = c("POSIXct",
"POSIXt")), PROCESSED_FLAG = character(0), INITIATOR_CODE = numeric(0),
CHANGE_REASON_CODE = numeric(0), PR_STATUS_ID = numeric(0),
COMMENTS = character(0), CREATED_DATE = structure(numeric(0), tzone = "", class = c("POSIXct",
"POSIXt")), CREATED_USER = character(0), MODIFIED_DATE = structure(numeric(0), tzone = "", class = c("POSIXct",
"POSIXt")), MODIFIED_USER = character(0), OUTCOME_ID = numeric(0),
IRB_NO = character(0), NCT_NUMBER = character(0), PI_NAMES = character(0)), row.names = integer(0), class = "data.frame")
write_csv(df, "df.csv")
If the dataframe has zero rows that day, I'd rather it DIDN'T write the csv. I'm sure I could figure out a step that deletes the data frame if empty and then the write_csv line would error, but I'd rather not do that. Is there an easy way to 'turn off' the write?
We could have a condition to only write to csv when the number of rows is greater than 0 if(nrow(df) > 0) readr::write_csv(df, "df.csv")
Merge multiple xts objects with matching or nearest dates
I have two xts files with daily data (The data is only one date for a month).
The first file is this: - The dates in this xts are typically end of the month trading dates in a given month.
structure(c(-0.0329199999999997, 0.0874901766141374, 0.0545883292605231,
0.0687945180777207, 0.0550784545301166, 0.074678777314922, -0.0866534235058661,
0.161206236457536, 0.0704023794825748, 0.074691325661258), class = c("xts",
"zoo"), ret_type = "discrete", coredata_content = "discreteReturn", index = structure(c(1114732800,
1117497600, 1120089600, 1122595200, 1125446400, 1128038400, 1130716800,
1133308800, 1135900800, 1138665600), tzone = "UTC", tclass = "Date"), dim = c(10L,
1L), dimnames = list(NULL, "xts_left"))
The second xts file is:
structure(c(0.0052512320343876, 0.00540733325225928, 0.00580017750416384,
0.005701283061746, 0.00556285472234541, 0.00561113650865441,
0.00580424365658105, 0.005816988308881, 0.00571552920344676,
0.00574088497469671, 0.00574737930337577, 0.00589584054618375,
0.00592325487612455), class = c("xts", "zoo"), .CLASS = "double", index = structure(c(1107216000,
1109635200, 1112313600, 1114905600, 1117584000, 1120176000, 1122854400,
1125532800, 1128124800, 1130803200, 1133395200, 1136073600, 1138752000
), tzone = "UTC", tclass = "Date"), dim = c(13L, 1L))
This is how I want the output of the merge: To pick the value from right xts that corresponds to the closest date value in the left xts. For example, the value on 29-04-2005 be matched with the nearest, i.e. 01-05-2005 (dd-mm-yyyy format).
I have seen a possible way to do this using data.table with rolling joins, but I wanted to know if there is a way to do this within the xts (or similar) framework.
Using x1 and x2 in the Note at the end define near which given a date, tt, finds the nearest date in x2 and returns the corresponding data value. Then apply that to each date in x1.
near <- function(tt) x2[which.min(abs(time(x2) - tt))]
x12 <- transform(x1, xts_right = sapply(time(x1), near)); x12
giving:
xts_left xts_right
2005-04-29 -0.03292000 0.005701283
2005-05-31 0.08749018 0.005562855
2005-06-30 0.05458833 0.005611137
2005-07-29 0.06879452 0.005804244
2005-08-31 0.05507845 0.005816988
2005-09-30 0.07467878 0.005715529
2005-10-31 -0.08665342 0.005740885
2005-11-30 0.16120624 0.005747379
2005-12-30 0.07040238 0.005895841
2006-01-31 0.07469133 0.005923255
In the example shown in the question the nearest x2 is always at a strictly later date than x1 and x2 starts before x1. If those are general features of the problem it could alternately be expressed as:
transform(x1, xts_right = coredata(x2)[findInterval(time(x1), time(x2)) + 1])
Note
x1 <-
structure(c(-0.0329199999999997, 0.0874901766141374, 0.0545883292605231,
0.0687945180777207, 0.0550784545301166, 0.074678777314922, -0.0866534235058661,
0.161206236457536, 0.0704023794825748, 0.074691325661258), .Dim = c(10L,
1L), class = c("xts", "zoo"), ret_type = "discrete",
coredata_content = "discreteReturn", index = structure(c(1114732800,
1117497600, 1120089600, 1122595200, 1125446400, 1128038400, 1130716800,
1133308800, 1135900800, 1138665600), tzone = "UTC", tclass = "Date"),
.Dimnames = list(NULL, "xts_left"))
x2 <-
structure(c(0.0052512320343876, 0.00540733325225928, 0.00580017750416384,
0.005701283061746, 0.00556285472234541, 0.00561113650865441,
0.00580424365658105, 0.005816988308881, 0.00571552920344676,
0.00574088497469671, 0.00574737930337577, 0.00589584054618375,
0.00592325487612455), .Dim = c(13L, 1L), class = c("xts", "zoo"
), .CLASS = "double", index = structure(c(1107216000, 1109635200,
1112313600, 1114905600, 1117584000, 1120176000, 1122854400, 1125532800,
1128124800, 1130803200, 1133395200, 1136073600, 1138752000),
tzone = "UTC", tclass = "Date"))
time average for specific time range in r
I am trying to extract average values of all variables between 0 to 40 minutes every hour.
dput(head(df))
structure(list(DateTime = structure(c(1563467460, 1563468060,
1563468660, 1563469260, 1563469860, 1563470460), class = c("POSIXct",
"POSIXt"), tzone = "GMT"), date = structure(c(1563467460, 1563468060,
1563468660, 1563469260, 1563469860, 1563470460), class = c("POSIXct",
"POSIXt"), tzone = "GMT"), Date = structure(c(18095, 18095, 18095,
18095, 18095, 18095), class = "Date"), TimeCtr = structure(c(1563467460,
1563468060, 1563468660, 1563469260, 1563469860, 1563470460), class = c("POSIXct",
"POSIXt"), tzone = "GMT"), MassConc = c(0.397627, 0.539531, 0.571902,
0.608715, 0.670382, 0.835773), VolConc = c(175.038, 160.534,
174.386, 183.004, 191.074, 174.468), NumbConc = c(234.456, 326.186,
335.653, 348.996, 376.018, 488.279), MassD = c(101.426, 102.462,
101.645, 102.145, 101.255, 101.433)), .Names = c("DateTime",
"date", "Date", "TimeCtr", "MassConc", "VolConc", "NumbConc",
"MassD"), row.names = c(NA, 6L), class = "data.frame")
What I've tried so far..
hourly_mean<-mydata %>%
filter(between(as.numeric(format(DateTime, "%M")), 0, 40)) %>%
group_by(DateTime=format(DateTime, "%Y-%m-%d %H")) %>%
summarise(variable1_mean=mean(variable1))
But it gives me a single average value for the whole period. Any help is very much welcomed.
We can convert DateTime , use ceiling_date with hourly unit to round Datetime, extract minutes from DateTime and filter to keep minutes which are less than 40, group_by hour and take mean of values.
library(lubridate)
library(dplyr)
df %>%
dplyr::mutate(DateTime = ymd_hm(DateTime),
hour = ceiling_date(DateTime, "hour"),
minutes = minute(DateTime)) %>%
filter(minutes <= 40) %>%
group_by(hour) %>%
summarise_at(vars(ends_with("Conc")), mean)
data
df <- structure(list(DateTime = structure(1:7, .Label = c("2019-08-0810:07",
"2019-08-0810:17", "2019-08-0810:27", "2019-08-0810:37", "2019-08-0810:47",
"2019-08-0810:57", "2019-08-0811:07"), class = "factor"), MassConc = c(0.556398,
1.06868, 0.777654, 0.87289, 0.789704, 0.51948, 0.416676), NumbConc = c(588.069,
984.018, 964.634, 997.678, 1013.52, 924.271, 916.357), VolConc = c(582.887,
979.685, 963.3, 994.178, 1009.52, 922.104, 916.856), Conc = c(281.665,
486.176, 420.058, 422.101, 429.841, 346.539, 330.282)), class =
"data.frame", row.names = c(NA, -7L))
Change data to numeric type to determine which distribution fits better
I am trying to figure out which distribution fits best logarithmic stock returns. Here is my code:
library(TTR)
sign="^GSPC"
start=19900101
end=20160101
x <- getYahooData(sign, start = start, end = end, freq = "daily")
x$logret <- log(x$Close) - lag(log(x$Close))
x=x[,6]
I want to use the function descdist(x, discrete = FALSE) which I got from this amazing post https://stats.stackexchange.com/questions/132652/how-to-determine-which-distribution-fits-my-data-best Nonetheless r gives me this error: Error in descdist(x, discrete = FALSE) : data must be a numeric vector How do I transform my data to numeric vector??
The output from dput(head(x)) is:
structure(c(NA, -0.00258888580664607, -0.00865029791190164, -0.00980414107803274,
0.00450431207515223, -0.011856706127011), class = c("xts", "zoo"
), .indexCLASS = "Date", .indexTZ = "UTC", tclass = "Date", tzone = "UTC", index = structure(c(631238400,
631324800, 631411200, 631497600, 631756800, 631843200), tzone = "UTC", tclass = "Date"), .Dim = c(6L,
1L), .Dimnames = list(NULL, "logret"))
Pre-process x using as.numeric(na.omit(x)), or simply run descdist(as.numeric(na.omit(x)), discrete = FALSE)
Access First Graph
So I am using the PerformanceAnalytics package to plot performance summary of a simple PnL series so
library(xts)
library(PerformanceAnalytics)
dates <- structure(c(14008, 14011, 14012, 14013, 14014, 14015, 14018, 14019, 14020, 14021),
class = "Date")
PnL.xts = structure(c(0, -0.00510803851321091, -0.0102109843849305, -0.00138369232677364,
-0.00255257489213331, -0.00200279255353461, 0.0104232666033935,
0.00181846800788812, 4.72633257030091e-05, 0.0138334493571853),
.Dim = c(10L, 1L),
index = structure(c(1210291200, 1210550400, 1210636800, 1210723200,
1210809600, 1210896000, 1211155200, 1211241600,
1211328000, 1211414400),
tzone = "UTC", tclass = "Date"),
.indexCLASS = "Date", tclass = "Date",
.indexTZ = "UTC", tzone = "UTC", .Dimnames = list(NULL, "PnL"),
class = c("xts", "zoo"))
PnL.cum = cumsum(PnL.xts)
ret.ann = Return.annualized(PnL.xts, geometric = FALSE)
ret.cum = Return.cumulative(PnL.xts, geometric = FALSE)
ret.min = min(PnL.cum)
stdev = StdDev.annualized(PnL.xts)
sharpe = SharpeRatio.annualized(PnL.xts, geometric = FALSE)
stats = paste(paste("Annualized Return:", percent(round(ret.ann, 5))),
paste("Cumulative Return:", percent(round(ret.cum, 5))),
paste("Standard Deviation:", round(stdev, 5)),
paste("Sharpe Ratio:", round(sharpe, 5)), sep = '\n' )
lag = 1
descr = paste("Following fitted Granger model - ", lag, " day lag", sep = "")
charts.PerformanceSummary(R = PnL.xts, geometric = FALSE)
text(midrange(dates),ret.min, labels = stats, cex = 1)
mtext(descr, side = 3, line = 31)
However, I want to add some descriptive text into the cumulative PnL chart such as annualized return, cumulative return, standard deviation, and sharpe. How can I paste this into the whitespace in the first graph?
If I plotted the graph by itself, I could just do it with the above code. However, since the charts.PerformanceSummary function automatically plots 3 graphs, I can only access the 3rd graph it seems. Is there any way to access the first of 3 graphs printed by a function, so that I can write text on it relative to its own coordinates?
Here is an example of what I want: http://i.imgur.com/QXUb2Aq.png. But in this case, I had to manually, test values of the y coordinate until I found somthing that worked. Thanks!