From 201906141930 to "2019-16-14 19:30:00"

From 201906141930 to "2019-16-14 19:30:00" - r

I've a data frame with a column dateHourMinute that I need it as POSIXct to make a plot.
For example this dateHourMinute 201906141930, I'd like to get: 2019-06-14 19:30:00 as a POSIXct element.
data:
structure(list(dateHourMinute = c("201906141930", "201906141931",
"201906141932", "201906141933", "201906141934", "201906141935",
"201906141936", "201906141937", "201906141938", "201906141939",
"201906141940", "201906141941", "201906141942", "201906141943",
"201906141944", "201906141945", "201906141946", "201906141947",
"201906141948", "201906141949", "201906141950", "201906141951",
"201906141952", "201906141953", "201906141954", "201906141955",
"201906141956", "201906141957", "201906141958", "201906141959",
"201906142000", "201906142001", "201906142002", "201906142003",
"201906142004", "201906142005", "201906142006", "201906142007",
"201906142008", "201906142009", "201906142010", "201906142011",
"201906142012", "201906142013", "201906142014", "201906142015",
"201906142016", "201906142017", "201906142018", "201906142019",
"201906142020", "201906142021", "201906142022", "201906142023",
"201906142024", "201906142025", "201906142026", "201906142027",
"201906142028", "201906142029", "201906142030", "201906142031",
"201906142032", "201906142033", "201906142034", "201906142035",
"201906142036", "201906142037", "201906142038", "201906142039",
"201906142040", "201906142041", "201906142042", "201906142043",
"201906142044", "201906142045", "201906142046", "201906142047",
"201906142048", "201906142049", "201906142050", "201906142051",
"201906142052", "201906142053", "201906142054", "201906142055",
"201906142056", "201906142057", "201906142058", "201906142059",
"201906142100", "201906142101", "201906142102", "201906142103",
"201906142104", "201906142105", "201906142106", "201906142107",
"201906142108", "201906142109", "201906142110", "201906142111",
"201906142112", "201906142113", "201906142114", "201906142115",
"201906142116", "201906142117", "201906142118", "201906142119",
"201906142120", "201906142121", "201906142122", "201906142123",
"201906142124", "201906142125", "201906142126", "201906142127",
"201906142128", "201906142129", "201906142130", "201906142131",
"201906142132", "201906142133", "201906142134", "201906142135",
"201906142136", "201906142137", "201906142138", "201906142139",
"201906142140", "201906142141", "201906142142", "201906142143",
"201906142144", "201906142145", "201906142146", "201906142147",
"201906142148", "201906142149", "201906142150", "201906142151",
"201906142152", "201906142153", "201906142154", "201906142155"
), users = c(2894, 2969, 3031, 2912, 2845, 2837, 2832, 2731,
2784, 2681, 2682, 2614, 2569, 2551, 2580, 2588, 2574, 2458, 2419,
2504, 2430, 2401, 2322, 2252, 2329, 2374, 2201, 2142, 2163, 2133,
2087, 2078, 2053, 2206, 2093, 2091, 2045, 2059, 1945, 1943, 1951,
1972, 1899, 1822, 1841, 1906, 1778, 2148, 3297, 2098, 1801, 1650,
1630, 1626, 1674, 1647, 1633, 1671, 1757, 1862, 1968, 2045, 2119,
2396, 2513, 2394, 2375, 2492, 2488, 2381, 2417, 2337, 2243, 2211,
1999, 2021, 2037, 2418, 2254, 2050, 2004, 1944, 1802, 1718, 1726,
1725, 1641, 1657, 1592, 1604, 1551, 1553, 1486, 1481, 1518, 1479,
1310, 1317, 1329, 1259, 1255, 1259, 1407, 1352, 1250, 1250, 1223,
1149, 1103, 1108, 1025, 1165, 1870, 1452, 1418, 1469, 1522, 1303,
1147, 1060, 1004, 1001, 1003, 983, 894, 870, 882, 863, 832, 790,
819, 732, 751, 752, 694, 692, 926, 862, 755, 736, 796, 803, 771,
869, 745, 709)), row.names = c(NA, -146L), totals = list(list(
users = "2016665")), minimums = list(list(users = "1")), maximums = list(
list(users = "11863")), isDataGolden = TRUE, rowCount = 2875L, class = "data.frame")

You could use :
df$dateHourMinute <- as.POSIXct(df$dateHourMinute,format = "%Y%m%d%H%M", tz = "UTC")
#Or with `strptime`
#df$dateHourMinute <- strptime(df$dateHourMinute, format = "%Y%m%d%H%M", tz = "UTC")
head(df)
# dateHourMinute users
#1 2019-06-14 19:30:00 2894
#2 2019-06-14 19:31:00 2969
#3 2019-06-14 19:32:00 3031
#4 2019-06-14 19:33:00 2912
#5 2019-06-14 19:34:00 2845
#6 2019-06-14 19:35:00 2837
Or with lubridate
df$dateHourMinute <- lubridate::ymd_hm(df$dateHourMinute))

Related

how to extract fitted values in a forecast model after multiple model simulations

This is my original df and fitted model
library(tsibble)
library(tibble)
library(ISOweek)
library(fable)
library(forecast)
library(fpp3)
library(dplyr)
library(tidyverse)
Original.df <- structure(list(YearWeek = c("201901", "201902", "201903", "201904",
"201905", "201906", "201907", "201908", "201909", "201910", "201911",
"201912", "201913", "201914", "201915", "201916", "201917", "201918",
"201919", "201920", "201921", "201922", "201923", "201924", "201925",
"201926", "201927", "201928", "201929", "201930", "201931", "201932",
"201933", "201934", "201935", "201936", "201937", "201938", "201939",
"201940", "201941", "201942", "201943", "201944", "201945", "201946",
"201947", "201948", "201949", "201950", "201951", "201952", "202001",
"202002", "202003", "202004", "202005", "202006", "202007", "202008",
"202009", "202010", "202011", "202012", "202013", "202014", "202015",
"202016", "202017", "202018", "202019", "202020", "202021", "202022",
"202023", "202024", "202025", "202026", "202027", "202028", "202029",
"202030", "202031", "202032", "202033", "202034", "202035", "202036",
"202037", "202038", "202039", "202040", "202041", "202042", "202043",
"202044", "202045", "202046", "202047", "202048", "202049", "202050",
"202051", "202052", "202053", "202101", "202102", "202103", "202104",
"202105", "202106", "202107", "202108", "202109", "202110", "202111",
"202112", "202113", "202114", "202115", "202116", "202117", "202118",
"202119", "202120", "202121", "202122", "202123", "202124", "202125",
"202126", "202127", "202128", "202129", "202130", "202131", "202132",
"202133", "202134", "202135", "202136", "202137", "202138", "202139",
"202140", "202141", "202142", "202143"), Shipment = c(418, 1442,
1115, 1203, 1192, 1353, 1191, 1411, 933, 1384, 1362, 1353, 1739,
1751, 1595, 1380, 1711, 2058, 1843, 1602, 2195, 2159, 2009, 1812,
2195, 1763, 821, 1892, 1781, 2071, 1789, 1789, 1732, 1384, 1435,
1247, 1839, 2034, 1963, 1599, 1596, 1548, 1084, 1350, 1856, 1882,
1979, 1021, 1311, 2031, 1547, 591, 724, 1535, 1268, 1021, 1269,
1763, 1275, 1411, 1847, 1379, 1606, 1473, 1180, 926, 800, 840,
1375, 1755, 1902, 1921, 1743, 1275, 1425, 1088, 1416, 1168, 842,
1185, 1570, 1435, 1209, 1470, 1368, 1926, 1233, 1189, 1245, 1465,
1226, 887, 1489, 1369, 1358, 1179, 1200, 1226, 1066, 823, 1913,
2308, 1842, 910, 794, 1098, 1557, 1417, 1851, 1876, 1010, 160,
1803, 1607, 1185, 1347, 1700, 981, 1191, 1058, 1464, 1513, 1333,
1169, 1294, 978, 962, 1254, 987, 1290, 758, 436, 579, 636, 614,
906, 982, 649, 564, 502, 274, 473, 506, 902, 639, 810, 398, 488
), Production = c(0, 198, 1436, 1055, 1396, 1330, 1460, 1628,
1513, 1673, 1737, 1274, 1726, 1591, 2094, 1411, 2009, 1909, 1759,
1693, 1748, 1455, 2078, 1717, 1737, 1886, 862, 1382, 1779, 1423,
1460, 1454, 1347, 1409, 1203, 1235, 1397, 1563, 1411, 1455, 1706,
688, 1446, 1336, 1618, 1404, 1759, 746, 1560, 1665, 1317, 0,
441, 1390, 1392, 1180, 1477, 1265, 1485, 1495, 1543, 1584, 1575,
1609, 1233, 1420, 908, 1008, 1586, 1392, 1385, 1259, 1010, 973,
1053, 905, 1101, 1196, 891, 1033, 925, 889, 1136, 1058, 1179,
1047, 967, 900, 904, 986, 1014, 945, 1030, 1066, 1191, 1143,
1292, 574, 1174, 515, 1296, 1315, 1241, 0, 0, 1182, 1052, 1107,
1207, 1254, 1055, 258, 1471, 1344, 1353, 1265, 1444, 791, 1397,
1186, 1264, 1032, 949, 1059, 954, 798, 956, 1074, 1136, 1209,
975, 833, 994, 1127, 1153, 1202, 1234, 1336, 1484, 1515, 1151,
1175, 976, 1135, 1272, 869, 1900, 1173), Net.Production.Qty = c(22,
188, 1428, 1031, 1382, 1368, 1456, 1578, 1463, 1583, 1699, 1318,
1582, 1537, 2118, 1567, 1961, 1897, 1767, 1603, 1666, 1419, 2186,
1621, 1677, 1840, 698, 1290, 1411, 927, 1754, 1222, 1411, 1549,
1491, 1359, 1179, 1945, 1463, 1465, 1764, 764, 810, 1308, 1830,
1542, 1695, 544, 1482, 1673, 1659, 0, 445, 1358, 1364, 1224,
1417, 1239, 1387, 1595, 1469, 1624, 1643, 1763, 1217, 1456, 568,
1290, 1666, 1428, 1327, 773, 1118, 1231, 1143, 921, 1083, 1124,
935, 903, 937, 849, 1132, 1032, 1143, 1081, 891, 886, 880, 1002,
1072, 969, 1000, 996, 1243, 1183, 1306, 650, 1226, 553, 1306,
1379, 1359, 0, 0, 1182, 988, 1099, 1173, 1244, 1039, 254, 1425,
1318, 1385, 1221, 1364, 739, 1397, 1112, 1160, 924, 971, 1015,
978, 828, 868, 994, 1090, 1165, 783, 887, 934, 1023, 1045, 1114,
1052, 1186, 1456, 1401, 1249, 779, 430, 1625, 1498, 883, 1860,
1101), isoweek = c("2019-W01-1", "2019-W02-1", "2019-W03-1",
"2019-W04-1", "2019-W05-1", "2019-W06-1", "2019-W07-1", "2019-W08-1",
"2019-W09-1", "2019-W10-1", "2019-W11-1", "2019-W12-1", "2019-W13-1",
"2019-W14-1", "2019-W15-1", "2019-W16-1", "2019-W17-1", "2019-W18-1",
"2019-W19-1", "2019-W20-1", "2019-W21-1", "2019-W22-1", "2019-W23-1",
"2019-W24-1", "2019-W25-1", "2019-W26-1", "2019-W27-1", "2019-W28-1",
"2019-W29-1", "2019-W30-1", "2019-W31-1", "2019-W32-1", "2019-W33-1",
"2019-W34-1", "2019-W35-1", "2019-W36-1", "2019-W37-1", "2019-W38-1",
"2019-W39-1", "2019-W40-1", "2019-W41-1", "2019-W42-1", "2019-W43-1",
"2019-W44-1", "2019-W45-1", "2019-W46-1", "2019-W47-1", "2019-W48-1",
"2019-W49-1", "2019-W50-1", "2019-W51-1", "2019-W52-1", "2020-W01-1",
"2020-W02-1", "2020-W03-1", "2020-W04-1", "2020-W05-1", "2020-W06-1",
"2020-W07-1", "2020-W08-1", "2020-W09-1", "2020-W10-1", "2020-W11-1",
"2020-W12-1", "2020-W13-1", "2020-W14-1", "2020-W15-1", "2020-W16-1",
"2020-W17-1", "2020-W18-1", "2020-W19-1", "2020-W20-1", "2020-W21-1",
"2020-W22-1", "2020-W23-1", "2020-W24-1", "2020-W25-1", "2020-W26-1",
"2020-W27-1", "2020-W28-1", "2020-W29-1", "2020-W30-1", "2020-W31-1",
"2020-W32-1", "2020-W33-1", "2020-W34-1", "2020-W35-1", "2020-W36-1",
"2020-W37-1", "2020-W38-1", "2020-W39-1", "2020-W40-1", "2020-W41-1",
"2020-W42-1", "2020-W43-1", "2020-W44-1", "2020-W45-1", "2020-W46-1",
"2020-W47-1", "2020-W48-1", "2020-W49-1", "2020-W50-1", "2020-W51-1",
"2020-W52-1", "2020-W53-1", "2021-W01-1", "2021-W02-1", "2021-W03-1",
"2021-W04-1", "2021-W05-1", "2021-W06-1", "2021-W07-1", "2021-W08-1",
"2021-W09-1", "2021-W10-1", "2021-W11-1", "2021-W12-1", "2021-W13-1",
"2021-W14-1", "2021-W15-1", "2021-W16-1", "2021-W17-1", "2021-W18-1",
"2021-W19-1", "2021-W20-1", "2021-W21-1", "2021-W22-1", "2021-W23-1",
"2021-W24-1", "2021-W25-1", "2021-W26-1", "2021-W27-1", "2021-W28-1",
"2021-W29-1", "2021-W30-1", "2021-W31-1", "2021-W32-1", "2021-W33-1",
"2021-W34-1", "2021-W35-1", "2021-W36-1", "2021-W37-1", "2021-W38-1",
"2021-W39-1", "2021-W40-1", "2021-W41-1", "2021-W42-1", "2021-W43-1"
), date = structure(c(17896, 17903, 17910, 17917, 17924, 17931,
17938, 17945, 17952, 17959, 17966, 17973, 17980, 17987, 17994,
18001, 18008, 18015, 18022, 18029, 18036, 18043, 18050, 18057,
18064, 18071, 18078, 18085, 18092, 18099, 18106, 18113, 18120,
18127, 18134, 18141, 18148, 18155, 18162, 18169, 18176, 18183,
18190, 18197, 18204, 18211, 18218, 18225, 18232, 18239, 18246,
18253, 18260, 18267, 18274, 18281, 18288, 18295, 18302, 18309,
18316, 18323, 18330, 18337, 18344, 18351, 18358, 18365, 18372,
18379, 18386, 18393, 18400, 18407, 18414, 18421, 18428, 18435,
18442, 18449, 18456, 18463, 18470, 18477, 18484, 18491, 18498,
18505, 18512, 18519, 18526, 18533, 18540, 18547, 18554, 18561,
18568, 18575, 18582, 18589, 18596, 18603, 18610, 18617, 18624,
18631, 18638, 18645, 18652, 18659, 18666, 18673, 18680, 18687,
18694, 18701, 18708, 18715, 18722, 18729, 18736, 18743, 18750,
18757, 18764, 18771, 18778, 18785, 18792, 18799, 18806, 18813,
18820, 18827, 18834, 18841, 18848, 18855, 18862, 18869, 18876,
18883, 18890, 18897, 18904, 18911, 18918, 18925), class = "Date")), row.names = c(NA,
148L), class = "data.frame")
# Converting the df to accomodate leap year for weekly observations
Original.df <- Original.df %>%
mutate(
isoweek =stringr::str_replace(YearWeek, "^(\\d{4})(\\d{2})$", "\\1-W\\2-1"),
date = ISOweek::ISOweek2date(isoweek)
)
# creating test and train data
Original.train.df <- Original.df %>%
filter(date >= "2018-12-31", date <= "2021-03-29")
Original.test.df <- Original.df %>%
filter(date >= "2021-04-05", date <= "2021-10-25")
# splitting the original train data to contain only Week, Dependent and Independent variables
Total.train.df<-Original.train.df %>%
mutate(Week.1 = yearweek(ISOweek::ISOweek(date))) %>%
select(-YearWeek, -Production, -date,-isoweek) %>%
as_tsibble(index = Week.1)
#Fitting forecast model(Arima with Fourier terms) to Net.Production.qty
fit_all_models.Prod.1 <- list()
for(K in seq(25)){
fit.Prod.1 <- Total.train.df %>%
model(ARIMA(Net.Production.Qty ~ fourier(K = K),stepwise = FALSE, approximation = FALSE))
names(fit.Prod.1) <- paste0("arima_", K)
fit_all_models.Prod.1 <- bind_cols(fit_all_models.Prod.1, fit.Prod.1)
}
glance(fit_all_models.Prod.1) %>% arrange(AICc) %>% select(.model:BIC)
best_model.Prod.1 <- glance(fit_all_models.Prod.1) %>%
filter(AICc == min(AICc)) %>%
select(.model) %>%
as.character
#Forecasting Net.Production.Qty for 30 steps using the fitted model above-Model.1
Forecast.Net.Prod.1<-fit_all_models.Prod.1 %>%
select(all_of(best_model.Prod.1)) %>%
forecast(h = 30)
#To extract fitted values from the model which has min AICc
fitted.Prod.1<-fit.Prod.1 %>%
filter(AICc == min(AICc)) %>% fitted()
As you see from the last step above I'm trying to extract the fitted values from the model which has minimum AICc- which is not working though
If anyone could help me getting the fitted values from the model above which has min.AICc would be really helpful
Thank you

You are almost there:
# your code .....
# get the fitted based on the selection in best_model.Prod.1
fitted.Prod.1 <- fit_all_models.Prod.1 %>%
select(all_of(best_model.Prod.1)) %>%
fitted()
fitted.Prod.1
# A tsibble: 118 x 3 [1W]
# Key: .model [1]
.model Week.1 .fitted
<chr> <week> <dbl>
1 arima_13 2019 W01 21.0
2 arima_13 2019 W02 486.
3 arima_13 2019 W03 1007.
4 arima_13 2019 W04 965.
5 arima_13 2019 W05 1012.
6 arima_13 2019 W06 1088.
7 arima_13 2019 W07 1175.
8 arima_13 2019 W08 1166.
9 arima_13 2019 W09 1305.
10 arima_13 2019 W10 1613.
# ... with 108 more rows

How do I make a single graph of multiple columns from a dataset?

So I have a large dataset with the daily price of gold in various currencies. The columns in the dataset are Date, USD, GBP, EURO. I want to represent each currency in a single graph. I have this so far:
p1 <- ggplot(data = GOLD) +
geom_line(aes(x = Date, y = USD, GBP, EURO), col ="red") +
labs(title = "Daily price of Gold",
x = "Date", y = "Price in Various Currencies")
But, having y represent each of the currencies is erroring out.

If this is the structure of your data frame, called gold:
str(gold)
'data.frame': 602 obs. of 5 variables:
$ Date: Date, format: "1969-12-31" "1970-01-30" "1970-02-27" "1970-03-31" ...
$ USD : num 35.2 35 35 35.3 35.9 ...
$ EUR : num 22.2 23.2 23.3 23.4 23.8 ...
$ GBP : num 14.7 14.5 14.6 14.7 14.9 ...
$ JPY : num 12592 12496 12530 12628 12858 ...
Then the following commands will produce the desired graph:
library(tidyr) # Needed to reshape the data to longer format
library(ggplot2) # Needed to plot the data
library(scales) # Needed to modify the y-axis tick labels
gold %>%
pivot_longer(cols=USD:JPY, names_to="Currency", values_to="Price") %>%
ggplot(aes(x = Date, y = Price, color=Currency)) +
geom_line() +
labs(title = "Daily price of gold",
x = "Date", y = "Price in various currencies") +
scale_y_log10(labels=comma) # Otherwise you get scientific notation
Data: (available from https://www.gold.org/goldhub/data/gold-prices)
gold <- structure(list(Date = structure(c(-1, 29, 57, 89, 119, 148, 180,
211, 242, 272, 302, 333, 364, 393, 421, 454, 484, 515, 545, 575,
607, 637, 666, 698, 729, 760, 789, 820, 848, 881, 911, 942, 973,
1002, 1034, 1064, 1093, 1126, 1154, 1184, 1215, 1246, 1275, 1307,
1338, 1366, 1399, 1429, 1460, 1491, 1519, 1548, 1580, 1611, 1639,
1672, 1702, 1733, 1764, 1793, 1825, 1856, 1884, 1915, 1945, 1975,
2006, 2037, 2066, 2098, 2129, 2157, 2190, 2220, 2248, 2281, 2311,
2342, 2372, 2402, 2434, 2464, 2493, 2525, 2556, 2587, 2615, 2646,
2675, 2707, 2737, 2766, 2799, 2829, 2860, 2890, 2920, 2952, 2980,
3011, 3039, 3072, 3102, 3133, 3164, 3193, 3225, 3255, 3284, 3317,
3345, 3375, 3406, 3437, 3466, 3498, 3529, 3557, 3590, 3620, 3651,
3682, 3711, 3742, 3772, 3802, 3833, 3864, 3893, 3925, 3956, 3984,
4017, 4047, 4075, 4107, 4137, 4166, 4198, 4229, 4260, 4290, 4320,
4351, 4382, 4411, 4439, 4472, 4502, 4533, 4563, 4593, 4625, 4655,
4684, 4716, 4747, 4778, 4806, 4837, 4866, 4898, 4928, 4957, 4990,
5020, 5051, 5081, 5111, 5143, 5172, 5202, 5233, 5264, 5293, 5325,
5356, 5384, 5417, 5447, 5478, 5509, 5537, 5566, 5598, 5629, 5657,
5690, 5720, 5751, 5782, 5811, 5843, 5874, 5902, 5933, 5963, 5993,
6024, 6055, 6084, 6116, 6147, 6175, 6208, 6238, 6266, 6298, 6328,
6357, 6389, 6420, 6451, 6481, 6511, 6542, 6573, 6602, 6633, 6664,
6693, 6725, 6755, 6784, 6817, 6847, 6878, 6908, 6938, 6970, 6998,
7029, 7057, 7090, 7120, 7151, 7182, 7211, 7243, 7273, 7302, 7335,
7363, 7393, 7424, 7455, 7484, 7516, 7547, 7575, 7608, 7638, 7669,
7700, 7728, 7757, 7789, 7820, 7848, 7881, 7911, 7942, 7973, 8002,
8034, 8065, 8093, 8125, 8155, 8184, 8216, 8247, 8278, 8308, 8338,
8369, 8400, 8429, 8457, 8490, 8520, 8551, 8581, 8611, 8643, 8673,
8702, 8734, 8765, 8796, 8824, 8855, 8884, 8916, 8946, 8975, 9008,
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276.5, 282.3, 296.85, 301.4, 308.2, 326.6, 318.5, 304.65, 312.8,
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201.89, 200.16, 204.89, 217.99, 195.22, 198.43, 200.1, 186.26,
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205.14, 207.54, 188.75, 197.82, 200.97, 196.89, 189.55, 184.53,
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219.8, 212.03, 209.63, 211.25, 201.02, 199.3, 200.61, 205.58,
185.7, 176.16, 176.38, 186.45, 180.63, 179.75, 185.83, 180.05,
177.58, 176.56, 163.27, 172.91, 174.54, 178.54, 172.98, 173.73,
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Yearly seasonal sums for DJF

I want to create sums for the meteorological nomenclature of DJF, that means December values are from the year x-1.
There is already a suggestion, using the packages seas and zoo for my kind of problem: Link to the reference. Can I use a loop regarding the time index of my zoo-object, to get the winter sums for each year and different columns? There are already only the winter months in my sample data:
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1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095, 1096, 1097, 1098,
1099, 1100, 1101, 1102, 1103, 1104, 1105, 1106, 1107, 1108, 1109,
1110, 1111, 1112, 1113, 1114, 1115, 1116, 1117, 1118, 1119, 1120,
1121, 1122, 1123, 1124, 1125, 1126, 1127, 1128, 1129, 1130, 1131,
1132, 1133, 1134, 1135, 1136, 1137, 1138, 1139, 1140, 1141, 1142,
1143, 1144, 1145, 1146, 1147, 1148, 1149, 1150, 1151, 1152, 1153,
1154), class = "Date"), class = "zoo")

library(hydroTSM)
dm2seasonal(df, FUN=sum, season="DJF")
I've used the package hydroTSM. The package can also be used for other seasons (MAM, JJA, SON) and with other functions (e.g. mean). You can compute the yearly seasonal sums for every column in your matrix (df). seas does the same for every column, but you have to write your own loop to get yearly seasonal sums I guess. mkseas() from seas will compute the sum over all winter months in your timeseries.

R - How to perform cross-year date operations?

I am working with daily measurements of temperature. In total I have about 40 years of observations. How can I perform date operations covering a time interval that crosses years?
For example, I want to sum the values from every october-to-february period. However, the sum should be taken only on the contiguous period of oct-nov-dec-jan-feb.
"Isolated" months should not be taken into account, like for example jan and feb of the first year, and oct-nov-dec of the last year. The sum has to run over the contiguous period only (from oct-nov-dec-jan-fev).
For example, this is what I am looking for:
1st year 2nd year 3rd year
J-F-M-A-M-J-J-A-S-**O-N-D J-F**-M-A-M-J-J-A-S-**O-N-D J-F**-M-A-M-J-J-A-S-O-N-D
But this is not OK:
1st year 2nd year 3rd year
**J-F**-M-A-M-J-J-A-S-**O-N-D J-F**-M-A-M-J-J-A-S-**O-N-D J-F**-M-A-M-J-J-A-S-**O-N-D**
This is a sample data frame to work on:
df <- structure(list(date = structure(c(-3653, -3622, -3593, -3562,
-3532, -3501, -3471, -3440, -3409, -3379, -3348, -3318, -3287,
-3256, -3228, -3197, -3167, -3136, -3106, -3075, -3044, -3014,
-2983, -2953, -2922, -2891, -2863, -2832, -2802, -2771, -2741,
-2710, -2679, -2649, -2618, -2588, -2557, -2526, -2498, -2467,
-2437, -2406, -2376, -2345, -2314, -2284, -2253, -2223, -2192,
-2161, -2132, -2101, -2071, -2040, -2010, -1979, -1948, -1918,
-1887, -1857, -1826, -1795, -1767, -1736, -1706, -1675, -1645,
-1614, -1583, -1553, -1522, -1492, -1461, -1430, -1402, -1371,
-1341, -1310, -1280, -1249, -1218, -1188, -1157, -1127, -1096,
-1065, -1037, -1006, -976, -945, -915, -884, -853, -823, -792,
-762, -731, -700, -671, -640, -610, -579, -549, -518, -487, -457,
-426, -396, -365, -334, -306, -275, -245, -214, -184, -153, -122,
-92, -61, -31, 0, 31, 59, 90, 120, 151, 181, 212, 243, 273, 304,
334, 365, 396, 424, 455, 485, 516, 546, 577, 608, 638, 669, 699,
730, 761, 790, 821, 851, 882, 912, 943, 974, 1004, 1035, 1065,
1096, 1127, 1155, 1186, 1216, 1247, 1277, 1308, 1339, 1369, 1400,
1430, 1461, 1492, 1520, 1551, 1581, 1612, 1642, 1673, 1704, 1734,
1765, 1795, 1826, 1857, 1885, 1916, 1946, 1977, 2007, 2038, 2069,
2099, 2130, 2160, 2191, 2222, 2251, 2282, 2312, 2343, 2373, 2404,
2435, 2465, 2496, 2526, 2557, 2588, 2616, 2647, 2677, 2708, 2738,
2769, 2800, 2830, 2861, 2891, 2922, 2953, 2981, 3012, 3042, 3073,
3103, 3134, 3165, 3195, 3226, 3256, 3287, 3318, 3346, 3377, 3407,
3438, 3468, 3499, 3530, 3560, 3591, 3621, 3652, 3683, 3712, 3743,
3773, 3804, 3834, 3865, 3896, 3926, 3957, 3987, 4018, 4049, 4077,
4108, 4138, 4169, 4199, 4230, 4261, 4291, 4322, 4352, 4383, 4414,
4442, 4473, 4503, 4534, 4564, 4595, 4626, 4656, 4687, 4717, 4748,
4779, 4807, 4838, 4868, 4899, 4929, 4960, 4991, 5021, 5052, 5082,
5113, 5144, 5173, 5204, 5234, 5265, 5295, 5326, 5357, 5387, 5418,
5448, 5479, 5510, 5538, 5569, 5599, 5630, 5660, 5691, 5722, 5752,
5783, 5813, 5844, 5875, 5903, 5934, 5964, 5995, 6025, 6056, 6087,
6117, 6148, 6178, 6209, 6240, 6268, 6299, 6329, 6360, 6390, 6421,
6452, 6482, 6513, 6543, 6574, 6605, 6634, 6665, 6695, 6726, 6756,
6787, 6818, 6848, 6879, 6909, 6940, 6971, 6999, 7030, 7060, 7091,
7121, 7152, 7183, 7213, 7244, 7274), class = "Date"), temp = c(22.9223529411765,
23.0705882352941, 23.1094117647059, 20.7835294117647, 17.4517647058824,
17.3176470588235, 18.0494117647059, 19.6188235294118, 21.3023529411765,
23.1105882352941, 22.2364705882353, 22.7482352941176, 23.5870588235294,
24.0023529411765, 23.0094117647059, 22.0176470588235, 19.4917647058824,
18.1011764705882, 18.3164705882353, 20.0623529411765, 22.8717647058824,
23.2576470588235, 23.68, 22.3694117647059, 22.9517647058824,
23.6976470588235, 23.3294117647059, 20.8564705882353, 18.16,
15.8988235294118, 15.7988235294118, 18.4176470588235, 20.8423529411765,
20.3247058823529, 22.3070588235294, 22.2035294117647, 24.2235294117647,
23.6976470588235, 24.4082352941176, 21.1752941176471, 18.1023529411765,
16.1211764705882, 18.3164705882353, 19.7635294117647, 23.1294117647059,
22.9964705882353, 23.6552941176471, 22.6964705882353, 23.6011764705882,
23.6517647058824, 23.7035294117647, 22.4352941176471, 18.5835294117647,
16.5976470588235, 15.7741176470588, 19.2541176470588, 20.8776470588235,
20.5729411764706, 21.1729411764706, 21.5870588235294, 22.4576470588235,
23.6058823529412, 21.84, 21.6694117647059, 19.2458823529412,
18.7517647058824, 17.7811764705882, 19.4764705882353, 21.9270588235294,
21.5470588235294, 22.88, 23.2458823529412, 24.2776470588235,
25.2470588235294, 23.4694117647059, 21.4435294117647, 19.3941176470588,
18.5447058823529, 17.6, 18.3764705882353, 19.8529411764706, 22.0823529411765,
22.7294117647059, 23.4011764705882, 23.3611764705882, 24.2505882352941,
23.2870588235294, 21.9482352941176, 20.5552941176471, 18.0788235294118,
18.5929411764706, 20.8752941176471, 21.9023529411765, 23.6105882352941,
22.4070588235294, 21.5635294117647, 23.3129411764706, 22.9741176470588,
23.3670588235294, 19.6105882352941, 16.9941176470588, 17.7670588235294,
17.4858823529412, 17.8517647058824, 20.26, 22.1576470588235,
23.8364705882353, 23.4447058823529, 24.8129411764706, 25.1764705882353,
24.2694117647059, 21.5035294117647, 20.0458823529412, 18.4694117647059,
18.4541176470588, 19.5388235294118, 22.02, 20.5364705882353,
22.9858823529412, 21.9752941176471, 23.7729411764706, 24.0576470588235,
24.0941176470588, 22.1552941176471, 21.2329411764706, 19.5611764705882,
17.8788235294118, 18.6823529411765, 20.1541176470588, 21.6258823529412,
21.5211764705882, 23.9811764705882, 24.8352941176471, 24.5882352941176,
24.1729411764706, 21.1035294117647, 19.0435294117647, 17.08,
17.4529411764706, 19.1458823529412, 20.4447058823529, 20.7129411764706,
21.5047058823529, 22.6952941176471, 23.4364705882353, 23.1, 24.1847058823529,
19.8105882352941, 19.9847058823529, 20.5188235294118, 17.7658823529412,
19.4435294117647, 20.7588235294118, 21.7835294117647, 22.7788235294118,
23.2388235294118, 24.9129411764706, 25.6, 23.5647058823529, 24.0058823529412,
19.7823529411765, 19.3152941176471, 18.7741176470588, 19.0305882352941,
20.5576470588235, 21.3611764705882, 21.4247058823529, 23.4811764705882,
23.6505882352941, 25.1870588235294, 23.3541176470588, 21.4823529411765,
18.7364705882353, 17.7235294117647, 18.3976470588235, 19.7235294117647,
21.0741176470588, 21.6094117647059, 22.9635294117647, 22.4011764705882,
23.4152941176471, 24.7741176470588, 24.3270588235294, 20.7976470588235,
18.8764705882353, 17.7788235294118, 16.4129411764706, 21.4117647058824,
22.3317647058824, 21.66, 22.3694117647059, 23.0917647058824,
24.4541176470588, 23.2847058823529, 23.3164705882353, 21.2529411764706,
19.1258823529412, 17.3882352941176, 17.3823529411765, 19.0529411764706,
19.6576470588235, 20.2976470588235, 21.9023529411765, 23.3094117647059,
24.0117647058824, 25.5611764705882, 24.9129411764706, 21.3964705882353,
19.9870588235294, 18.3929411764706, 20.9917647058824, 20.3058823529412,
21.4435294117647, 23.1941176470588, 22.8388235294118, 22.5176470588235,
24.6317647058824, 24.6541176470588, 24.2, 20.84, 18.4576470588235,
17.5011764705882, 19.16, 20.54, 20.1517647058824, 22.6776470588235,
22.7470588235294, 22.7882352941176, 22.0811764705882, 24.2152941176471,
22.9235294117647, 20.8411764705882, 19.6188235294118, 17.16,
16.0529411764706, 20.3223529411765, 19.9752941176471, 22.5152941176471,
22.2705882352941, 23.1541176470588, 23.1047058823529, 23.9517647058824,
24.8176470588235, 22.18, 20.5023529411765, 17.3505882352941,
19.1917647058824, 19.9894117647059, 19.0235294117647, 22.8235294117647,
22.7094117647059, 23.8741176470588, 24.0517647058824, 25.1764705882353,
23.9235294117647, 21.2929411764706, 20.6117647058824, 17.1305882352941,
16.3470588235294, 19.6470588235294, 21.3341176470588, 20.2176470588235,
23.7435294117647, 22.6741176470588, 22.9070588235294, 24.7152941176471,
23.2905882352941, 20.5776470588235, 18.9635294117647, 19.0658823529412,
18.8423529411765, 20.0729411764706, 21.3047058823529, 22.1588235294118,
24.0388235294118, 22.1917647058824, 24.0517647058824, 24.8729411764706,
23.0117647058824, 23, 21.3094117647059, 19.4105882352941, 20.3470588235294,
19.4482352941176, 20.0670588235294, 21.6364705882353, 23.4211764705882,
23.16, 25.4788235294118, 26.4741176470588, 24.0482352941176,
21.4176470588235, 21.7164705882353, 19.0905882352941, 19.6752941176471,
18.1611764705882, 20.0482352941176, 23.4917647058824, 23.4894117647059,
22.5482352941176, 23.1376470588235, 24.9811764705882, 24.1552941176471,
22.8423529411765, 19.7435294117647, 16.4, 17.3105882352941, 20.5235294117647,
21.0494117647059, 23.1352941176471, 23.9435294117647, 23.9058823529412,
24.9835294117647, 24.6952941176471, 24.0047058823529, 23.3164705882353,
21.5823529411765, 18.3447058823529, 18.1964705882353, 20.0035294117647,
20.7152941176471, 22.5705882352941, 24.6541176470588, 23.2329411764706,
25.0517647058824, 24.3329411764706, 23.5811764705882, 22.9988235294118,
19.4976470588235, 17.3188235294118, 19.5635294117647, 19.0211764705882,
19.7223529411765, 22.6858823529412, 23.9423529411765, 23.6905882352941,
25.7129411764706, 23.9505882352941, 24.4376470588235, 22.6070588235294,
19.8882352941176, 17.2058823529412, 16.4211764705882, 20.02,
21.9458823529412, 21.9341176470588, 22.74, 23.8, 23.9611764705882,
24.4564705882353, 24, 23.2129411764706, 19.4729411764706, 17.7105882352941,
16.9682352941176, 19.0341176470588, 20.2917647058824, 20.7776470588235,
22.9364705882353, 22.7894117647059)), .Names = c("date", "temp"
), row.names = c(NA, -360L), class = "data.frame")
Any input appreciated.

Hopefully this helps:
df$date = as.POSIXct(df$date,format="%Y-%m-%d")
df$year = as.numeric(format(df$date,format="%Y"))
df$month = as.numeric(format(df$date,format="%m"))
years = unique(df$year)
# initialize a new data frame to store in your summed values
newdf=NULL
# run through a loop starting at your second year and ending at second last
for(i in 2:(length(years)-1)){
#data from year1
start = df[df$year==years[i] & df$month %in% c(10,11,12),]
end = df[df$year==years[i+1] & df$month %in% c(1,2),]
data1 = rbind(start,end)
# in case you have NAs in your data you can add ra.rm = T
sum.data = sum(data1$temp,na.rm = T)
df1 = as.data.frame(list(Year = years[i],
sum.data = sum.data))
# or paste year 1 and year 2 together
#df1 = as.data.frame(list(Year = paste(years[i],years[i+1],sep="-"),
# sum.data = sum.data))
newdf = rbind(newdf,df1)
}
head(newdf)

Time Series based Forecasting for Daily Data but Seasonality is Quarterly - in R

I have demand for a product on daily bases for last 4 years. This demand has quarterly seasonal patterns, as shown in following image
I would like to do time series based forecasting on this data. Following is my code
myts = ts(forecastsku1$Value,frequency=90)
fit <- stl(myts, s.window="period")
plot(fit)
fit <- decompose(myts)
plot(fit)
Here instead of 4 seasonal factor ts is creating 90 seasonal factor, which is not what I want. I want to apply same seasonality on 3 month duration and then do forecasting.
Data for reference
dput(head(forecastsku1,100))
structure(list(date = structure(c(14625, 14626, 14627, 14628, 14629, 14630, 14631, 14632, 14633, 14634, 14635, 14636, 14637,
14638, 14639, 14640, 14641, 14642, 14643, 14644, 14645, 14646, 14647, 14648, 14649, 14650, 14651, 14652, 14653, 14654, 14655,
14656, 14657, 14658, 14659, 14660, 14661, 14662, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 14670, 14671, 14672, 14673,
14674, 14675, 14676, 14677, 14678, 14679, 14680, 14681, 14682, 14683, 14684, 14685, 14686, 14687, 14688, 14689, 14690, 14691,
14692, 14693, 14694, 14695, 14696, 14697, 14698, 14699, 14700, 14701, 14702, 14703, 14704, 14705, 14706, 14707, 14708, 14709,
14710, 14711, 14712, 14713, 14714, 14715, 14716, 14717, 14718, 14719, 14720, 14721, 14722, 14723, 14724), class = "Date"),
Value = c(1407, 1413, 1407, 1406, 1401, 1410, 1411, 1416, 1404, 1409, 1414, 1414, 1400, 1421, 1398, 1404, 1397, 1404, 1407, 1409, 1406, 1395, 1397,
1403, 1412, 1399, 1409, 1393, 1405, 1403, 1406, 1402, 1405, 1386, 1393, 1405, 1397, 1393, 1402, 1402, 1393, 1391, 1410, 1402, 1408,
1394, 1404, 1398, 1406, 1389, 1401, 1391, 1394, 1384, 1377, 1390, 1395, 1399, 1384, 1397, 1398, 1384, 1377, 1394, 1398, 1394, 1391,
1403, 1382, 1390, 1385, 1403, 1390, 1388, 1391, 1384, 1392, 1390, 1381, 1387, 1395, 1390, 1388, 1384, 1387, 1395, 1380, 1378, 1383,
1384, 1232, 1247, 1232, 1248, 1236, 1236, 1231, 1237, 1224, 1236)),
.Names = c("date", "Value"), row.names = 13150:13249, class = "data.frame")
Can anyone help me in this case? Please let me know if more data required.
myts = ts(forecastsku1$Value,frequency=4)
fit <- decompose(myts)
plot(fit)
Result would be:

It is creating a 90 seasonal factor because your frequency is 90 in the ts definition. What you need to do is to specify a start and end in the ts and the period=4 so that the observations can be segregated the way you want them to be.. if you can successfully create a 4 seasonal factor, you can obviousy predict quarterly (4*3=12) . So instead of these dates I think it is more clear to have like start=c(2005,1) .Hopefully this is useful

this is an old question, but still, maybe my answer is of some value.
You can seasonally adjust daily data using the dsa package (disclaimer: I'm the author).
I tried to replicate your time series (or something similar) to give you an idea of how to seasonally adjust them (the setting of the seasonal adjustment try to help modelling the jumping behaviour of the time series appropriately):
# loading packages
library(dsa); library(xts)
# Replication of the data
set.seed(23)
data <- seq(1250, 1000, , length.out=365.25*4) + rnorm(365.25*4, 0, 5)
time <- seq(as.Date("2008-01-01"), by="days", length.out=365.25*4)
x <- xts(data, time)
ind <- as.numeric(format(zoo::index(x), "%m")) # Indicator of day of year
x[ind==1 | ind==2 | ind==3 | ind==7 | ind==8 | ind==9] <-
x[ind==1 | ind==2 | ind==3 | ind==7 | ind==8 | ind==9] + 200
# Seasonally adjusting the data
result <- dsa(x, fourier_number=40, reiterate3=4, reg.create=NULL, cval=30)
sa <- result$output[,1]
xtsplot(result$output[,c(2,1)], names=c("original", "seasonally adjusted"))
output(result) # creates a html in your working directory.