Subsetting xts objects - index functions not working - r
In an effort to subset an xts object, I am using the .index* family of functions (http://www.inside-r.org/packages/cran/xts/docs/indexClass) as shown in one of the answers to this question: Return data subset time frames within another timeframes?.
The sample xts object used in this example has 1000 hourly data points from 2015-12-29 to 2016-02-29. The code below attempts to create subsets for various time selection criteria. Some of the functions are not working: indexyear, indexweek, indexday and indexmin fail to produce any output (please note the rest of the functions work as intended). Would anyone know why? Thanks very much!
library(xts)
DATSxts <- structure(c(1069.4, 1070.7, 1070.4, 1070.3, 1068.7, 1068.7, 1069.4, 1069.7, 1069.5, 1068.1, 1069.3, 1069.2, 1069.3, 1070.7, 1070.9, 1070.7, 1070.8, 1070.5, 1070.5, 1069.7, 1068.9, 1070.2, 1067.3, 1067.8, 1067.9, 1061.7, 1060.8, 1061.7, 1060.9, 1060.2, 1060.9, 1061, 1060.3, 1061.2, 1061.9, 1062, 1061.9, 1062.5, 1062.3, 1062.3, 1062, 1062.7, 1063, 1062, 1062.6, 1062.4, 1061.8, 1058.9, 1061.5, 1062.4, 1061.4, 1060.9, 1062, 1060.6, 1059.4, 1060.5, 1061.3, 1063.1, 1064.6, 1063.5, 1064.3, 1063.7, 1065.3, 1068.7, 1069.8, 1071.3, 1072.8, 1073.8, 1072.9, 1072.7, 1072.6, 1078.8, 1080.6, 1078.2, 1073.7, 1076.4, 1075.2, 1075.4, 1075.4, 1074.7, 1073.2, 1076, 1076.5, 1076.2, 1077.1, 1077.6, 1078.5, 1079.1, 1077, 1077, 1078.1, 1077.7, 1080, 1079.7, 1075.6, 1077.2, 1077.2, 1080, 1080, 1078.8, 1079, 1078.9, 1077.8, 1078, 1077.7, 1076.1, 1077, 1078.4, 1078.6, 1079.5, 1080.5, 1082.1, 1083.3, 1083.5, 1085.6, 1085.2, 1088.8, 1085.1, 1090.5, 1088, 1091.1, 1091.4, 1094.3, 1094.4, 1094.2, 1094.1, 1093.1, 1093.1, 1097.6, 1098.7, 1098.2, 1100.1,
1099.4, 1100.1, 1097.6, 1096.1, 1097, 1096.4, 1098.5, 1100.8, 1101.8, 1106.4, 1103.5, 1106.4, 1109.5, 1108.7, 1110.4, 1109.8, 1109.5, 1109.9, 1109.1, 1104.3, 1105.2, 1104.1, 1103.5, 1104, 1102.9, 1101.7, 1099.8, 1096.8, 1098.6, 1100, 1100.1, 1100, 1101.9, 1103.7, 1103.8, 1101.2, 1098.9, 1103.1, 1104.6, 1104.9, 1107.2, 1107.1, 1106.1, 1105.2, 1104.5, 1104.4, 1105.6, 1107.4, 1106.7, 1100.8, 1100, 1104.4, 1103.4, 1104, 1101.4, 1102, 1100, 1098.9, 1099.6, 1097.3, 1096.8, 1095.4, 1094.5, 1096.3, 1095.4, 1095.8, 1096, 1097.4, 1098.4, 1096.3, 1095, 1095.4, 1096.9, 1095.4, 1092.3, 1091.6, 1087.8, 1089, 1085.8, 1088.8, 1086.8, 1086.8, 1087.5, 1090.3, 1090, 1086.8, 1087.1, 1086.6, 1086.2, 1085, 1084.4, 1083.9, 1085.7, 1085.9, 1082.9, 1082.7, 1081.7, 1082.7, 1083.3, 1083.2, 1082.2, 1088.7, 1089.5, 1092.4, 1091.6, 1093, 1094.4, 1095.2, 1094.1, 1095.8, 1094.5, 1093.1, 1093.7, 1093.1, 1095.4, 1092.3, 1091.5, 1089.1, 1092.5, 1092.4, 1091.4, 1092.3, 1086.1, 1084.8, 1088.7, 1082.9, 1082.6, 1078.4, 1073.6, 1076.9, 1077.6, 1079.1, 1077.7, 1078.6, 1078.8, 1080.4, 1081.2, 1081.5, 1081.1, 1083.6, 1084.9, 1084.2, 1083.9, 1081.3, 1082.4, 1084.6, 1094.6, 1094.1, 1091.1, 1090.7, 1090.8, 1090.3, 1089.3, 1088.8, 1089.4, 1091, 1091.5, 1091.9, 1093.3, 1092.4, 1093.1, 1090.9, 1090.6, 1091, 1089.3, 1091.8, 1091.2, 1090.5, 1091, 1090.6, 1089.8, 1089.7, 1090.1, 1089.7, 1089.3, 1089.1, 1086.4, 1090.6, 1090.3, 1089, 1089.3, 1089.5, 1090.8, 1093.3, 1089.5, 1087.8, 1084.7, 1085.3, 1086.7, 1086.8, 1086.9, 1087.7, 1090.1, 1090.8, 1088.3, 1087.9, 1088.1, 1088.6, 1090.1, 1092.2, 1091.6, 1093.3, 1093.3, 1091.9, 1092.7, 1094.8, 1096.3, 1094.1, 1095.5, 1095.9, 1102.5, 1100.5, 1101.9, 1102.8, 1104.3, 1106.8, 1105.6, 1104.5, 1102.7, 1101.8, 1100.4, 1099.7, 1100.7, 1100.3, 1100.9, 1103, 1104.3, 1103.8, 1104.1, 1101.4, 1099.7, 1098.3, 1100.2, 1100.6, 1098.6, 1097.2, 1095.7, 1095.4, 1096, 1101.8, 1101.6, 1103.5, 1102.2, 1101.7, 1100.3, 1100.4, 1100.1, 1102.1, 1100.6, 1099.8, 1099.5, 1097.8, 1096.4, 1098.1, 1098.7, 1099.1, 1098.9, 1096.2, 1097.3, 1101.3, 1100, 1099, 1097, 1097.8, 1098.8, 1099, 1099.1, 1099.3, 1101.3, 1102.1, 1102.9, 1103.1, 1101.9, 1102.3, 1105.2, 1104.3, 1105.7, 1105.1, 1107.5, 1105.4, 1107.9, 1107.4, 1107.5, 1108.8, 1107, 1106.4, 1106.5, 1109.6, 1108.9, 1109.7, 1109.4, 1110.9, 1112.2, 1114.1, 1113.5, 1113.5, 1115.9, 1117.3, 1115.2, 1114.5, 1114.9, 1112.7, 1113.2, 1114.2, 1114.9, 1119.6, 1118.8, 1119.6, 1122.1, 1123.1, 1122.6, 1120.7, 1120.5, 1121.4, 1121.3, 1122, 1122.3, 1121, 1121.5, 1120.4, 1119, 1118.4, 1119.7, 1117.9, 1118.9, 1119.8, 1119.4, 1117.6, 1117.2, 1117.5, 1118.2, 1117.3, 1128.3, 1126.4, 1125.7, 1125.3, 1122.8, 1120.6, 1121, 1122.3, 1121.1, 1120.3, 1118, 1118.5, 1119.3, 1118.2, 1118.9, 1122.1, 1120.3, 1120.8, 1114, 1115.9, 1116.5, 1116.1, 1115.8, 1115.9, 1114.3, 1115.6, 1114.6, 1114.8, 1116.3, 1114.9, 1112.7, 1115.7, 1114.6, 1115.5, 1113.4, 1111.7, 1113.4, 1112.8, 1112.1, 1115.4, 1113.5, 1112.4, 1118, 1117, 1117.1, 1116.6, 1116.5, 1117.9, 1118.4, 1117.8, 1118.4, 1120.4, 1121.4, 1122.5, 1122.3, 1122.3, 1121.8, 1122.9, 1122, 1122, 1123, 1121.8, 1123.9, 1122.9, 1126.4, 1126.3, 1126.7, 1127.2, 1127.6, 1128.8, 1129.7, 1128.6, 1128.6, 1129.5, 1127.6, 1127.4, 1126.1, 1125.8, 1125.8, 1125.9, 1126.1, 1126.4, 1125.8, 1125.2, 1124.8, 1125.4, 1127.4, 1128.9, 1128.3, 1125, 1126.3, 1128, 1128.3, 1130.1, 1129.6, 1127.9, 1127.6, 1128.4, 1128.7, 1127.8, 1127.1, 1128.5, 1128.5, 1124.9, 1126.8, 1128.2, 1130.4, 1128.6, 1130.2, 1126.7, 1132.5, 1138.2, 1138.6, 1140, 1144.2, 1138.9, 1142.8, 1143, 1142.6, 1143.5, 1141.8, 1141.3, 1141.2, 1141.8, 1143.5, 1143.9, 1142.4, 1145.4, 1146.4, 1147.6, 1145.8, 1148.2, 1153.5, 1155.3, 1152.1, 1154.6, 1155.9, 1155.5, 1156.4, 1156.4, 1156, 1156, 1156.1, 1154.8, 1156.4, 1156.1, 1155.7, 1155.3, 1154.8, 1154.8, 1156.3, 1157.9, 1159.4, 1159.4, 1159, 1151.5, 1149.9, 1154, 1155.3, 1157.6, 1157.9, 1162.8, 1174.5, 1174.1, 1174.1, 1165.3, 1168.5, 1165.4, 1165.5, 1166.2, 1166.7, 1166.2, 1166.4, 1165.8, 1168.5, 1173.8, 1176.4, 1175.9, 1182.5, 1182.1, 1192.1, 1193.6, 1194.4, 1195.6, 1201, 1195.5, 1190.7, 1189.6, 1191.5,
1193.2, 1193.9, 1194, 1193.2, 1193.1, 1191.8, 1193.4, 1186.7, 1188.7, 1189.5, 1189.3, 1191.7, 1196.6, 1198.5, 1189.7, 1195.5, 1195.6, 1197.3, 1195.2, 1190.8, 1187.9, 1189.4, 1189.6, 1192.1, 1193.4, 1191.5, 1191.3, 1191.8, 1191.8, 1191.2, 1188, 1187, 1182.3, 1182.6, 1182.8, 1183.7, 1187.9, 1191, 1192.6, 1193.3, 1192.1, 1192.5, 1194, 1196.9, 1197.4, 1197.4, 1199, 1208.7, 1208.4, 1207, 1206.1, 1209.3, 1206.7, 1208.2, 1207.6, 1217.6, 1217.6, 1224.8, 1237.3, 1235.9, 1236, 1233, 1247.1, 1254.5, 1248.4, 1249.4, 1245, 1245.1, 1247, 1240.7, 1238.6, 1236.3, 1238.2, 1233.9, 1233.5, 1239.3, 1245, 1242.6, 1239.5, 1238.4, 1242.6, 1238, 1238.7, 1236, 1239.5, 1238.4, 1237.8, 1236.9, 1239.3, 1238.7, 1238.5, 1238.5, 1233.8, 1228.1, 1223.7, 1224.7, 1221, 1220.6, 1219.8, 1219.5, 1212.4, 1210.1, 1209.5, 1210.2, 1209.9, 1209.6, 1211.7, 1210, 1205.7, 1207.4, 1209.4, 1208.4, 1208.4, 1204.7, 1198.4, 1193.4, 1194.2, 1197.9, 1201.3, 1196.2, 1200, 1211, 1212, 1215.4, 1213.9, 1215.3, 1211.2, 1216.4, 1213, 1209.1, 1204.2, 1204.2, 1200.4, 1200.9, 1203.2, 1198.2, 1202.6, 1203.7, 1209.3, 1209.1, 1206.8, 1207.3, 1211.5, 1204.6, 1202.9, 1203, 1202.3, 1207, 1209.4, 1210.9, 1210.6, 1212.8, 1210.2, 1210.3, 1208.9, 1208.3, 1209, 1207.4, 1209.3, 1209.1, 1210.5, 1210.1, 1209.5, 1209.9, 1208.1, 1207.5, 1207.6, 1206.8, 1203.4, 1204.3, 1205.8, 1206.9, 1210.8, 1215.8, 1218.8, 1227.8, 1232.9, 1234.7, 1237.5, 1231.3, 1231.6, 1232.8, 1227.2, 1227.5, 1228.4, 1228, 1226.7, 1226.5, 1224.4, 1224.2, 1221.5, 1222.9, 1230.8, 1231.7, 1230.7, 1229.3, 1231.6, 1233, 1231.3, 1231.9, 1232.7, 1229.6, 1226.6, 1226.1, 1225.9, 1223.5, 1223.3, 1217.1, 1216.3, 1217, 1217.7, 1211, 1209.5, 1206.6, 1205.2, 1204, 1207.6, 1209.1, 1212.6, 1212.2, 1210.3, 1211.1, 1209.2, 1208.2, 1207.9, 1208.9, 1209.3, 1211.2, 1215.8, 1215.7, 1221.1, 1220.5, 1216.6, 1216.7, 1217.8, 1216.9, 1219.2, 1218.8, 1217.4, 1219.3, 1218.6, 1221.6, 1225.6, 1224.6, 1223.8, 1222.8, 1223.6, 1226.1, 1226, 1231.1, 1230, 1225.9, 1228, 1228.6, 1228.7, 1228.1, 1226, 1224.2, 1226.2, 1230.6, 1237.1, 1238, 1236.1, 1244.4, 1250.2, 1248.6, 1245.6, 1238.4, 1239.7, 1230.5, 1230.3, 1229.4, 1225, 1228.5, 1236.3, 1233.3, 1234.4, 1233.4, 1234.5, 1237.2, 1241, 1239.5, 1237.4, 1236, 1233.3, 1236.5, 1234.8, 1236.4, 1239.2, 1239.8, 1242.1, 1235.1, 1239.1, 1233.6, 1233.7, 1234.5, 1237.5, 1239, 1237.6, 1236.5, 1238.1, 1240, 1239.6, 1237, 1231.4, 1233.1, 1233.4, 1236.6, 1237.1, 1229.3, 1227.6, 1217.5, 1219.2, 1220.4, 1222.3, 1226.2, 1224.9, 1222.8, 1221.9, 1221.8, 1226.4, 1226.4, 1225.2, 1226.8, 1229, 1227.3, 1230.8, 1234.1, 1235.1, 1234.9, 1229.9, 1231.3, 1229.2, 1234.6, 1232.4, 1233.8, 1232.6, 1234.6, 1239.6, 1240.9, 1239.3, 1241.1, 1241.4, 1247.2, 1244.8, 1244.4, 1245.7),
.Dim = c(1000L, 1L), index = structure(c(1451386800, 1451390400, 1451394000, 1451397600, 1451401200, 1451404800, 1451408400, 1451412000, 1451415600, 1451419200, 1451422800, 1451430000, 1451433600, 1451437200, 1451440800, 1451444400, 1451448000, 1451451600, 1451455200, 1451458800, 1451462400, 1451466000, 1451469600, 1451473200, 1451476800, 1451480400, 1451484000, 1451487600, 1451491200, 1451494800, 1451498400, 1451502000, 1451505600, 1451509200, 1451516400, 1451520000, 1451523600, 1451527200, 1451530800, 1451534400, 1451538000, 1451541600, 1451545200, 1451548800, 1451552400, 1451556000, 1451559600, 1451563200, 1451566800, 1451570400, 1451574000, 1451577600, 1451581200, 1451584800, 1451588400, 1451592000, 1451595600, 1451862000, 1451865600, 1451869200, 1451872800, 1451876400, 1451880000, 1451883600, 1451887200, 1451890800, 1451894400, 1451898000, 1451901600, 1451905200, 1451908800, 1451912400, 1451916000, 1451919600, 1451923200, 1451926800, 1451930400, 1451934000, 1451937600, 1451941200, 1451948400, 1451952000, 1451955600, 1451959200, 1451962800, 1451966400, 1451970000, 1451973600, 1451977200, 1451980800, 1451984400, 1451988000, 1451991600, 1451995200, 1451998800, 1452002400, 1452006000, 1452009600, 1452013200, 1452016800, 1452020400, 1452024000, 1452027600, 1452034800, 1452038400, 1452042000, 1452045600, 1452049200, 1452052800, 1452056400, 1452060000, 1452063600, 1452067200, 1452070800, 1452074400, 1452078000, 1452081600, 1452085200, 1452088800, 1452092400, 1452096000, 1452099600, 1452103200, 1452106800, 1452110400, 1452114000, 1452121200, 1452124800, 1452128400, 1452132000, 1452135600, 1452139200, 1452142800, 1452146400, 1452150000, 1452153600, 1452157200, 1452160800, 1452164400, 1452168000, 1452171600, 1452175200, 1452178800, 1452182400, 1452186000, 1452189600, 1452193200, 1452196800, 1452200400, 1452207600, 1452211200, 1452214800, 1452218400, 1452222000, 1452225600, 1452229200, 1452232800, 1452236400, 1452240000, 1452243600, 1452247200, 1452250800, 1452254400, 1452258000, 1452261600, 1452265200, 1452268800, 1452272400, 1452276000, 1452279600, 1452283200, 1452286800, 1452466800, 1452470400, 1452474000, 1452477600, 1452481200, 1452484800, 1452488400, 1452492000, 1452495600, 1452499200, 1452502800, 1452506400, 1452510000, 1452513600, 1452517200, 1452520800, 1452524400, 1452528000, 1452531600, 1452535200, 1452538800, 1452542400, 1452546000, 1452553200, 1452556800, 1452560400, 1452564000, 1452567600, 1452571200, 1452574800, 1452578400, 1452582000, 1452585600, 1452589200, 1452592800, 1452596400, 1452600000, 1452603600, 1452607200, 1452610800, 1452614400, 1452618000, 1452621600, 1452625200, 1452628800, 1452632400, 1452639600, 1452643200, 1452646800, 1452650400, 1452654000, 1452657600, 1452661200, 1452664800, 1452668400, 1452672000, 1452675600, 1452679200, 1452682800, 1452686400, 1452690000, 1452693600, 1452697200, 1452700800, 1452704400, 1452708000, 1452711600, 1452715200, 1452718800, 1452726000, 1452729600, 1452733200, 1452736800, 1452740400, 1452744000, 1452747600, 1452751200, 1452754800, 1452758400, 1452762000, 1452765600, 1452769200, 1452772800, 1452776400, 1452780000, 1452783600, 1452787200, 1452790800, 1452794400, 1452798000, 1452801600, 1452805200, 1452812400, 1452816000, 1452819600, 1452823200, 1452826800, 1452830400, 1452834000, 1452837600, 1452841200, 1452844800, 1452848400, 1452852000, 1452855600, 1452859200, 1452862800, 1452866400, 1452870000, 1452873600, 1452877200, 1452880800, 1452884400, 1452888000, 1452891600, 1453071600, 1453075200, 1453078800, 1453082400, 1453086000, 1453089600, 1453093200, 1453096800, 1453100400, 1453104000, 1453107600, 1453111200, 1453114800, 1453118400, 1453122000, 1453125600, 1453129200, 1453132800, 1453136400, 1453158000, 1453161600, 1453165200, 1453168800, 1453172400, 1453176000, 1453179600, 1453183200, 1453186800, 1453190400, 1453194000, 1453197600, 1453201200, 1453204800, 1453208400, 1453212000, 1453215600, 1453219200, 1453222800,
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1454655600, 1454659200, 1454662800, 1454666400, 1454670000, 1454673600, 1454677200, 1454680800, 1454684400, 1454688000, 1454691600, 1454695200, 1454698800, 1454702400, 1454706000, 1454709600, 1454886000, 1454889600, 1454893200, 1454896800, 1454900400, 1454904000, 1454907600, 1454911200, 1454914800, 1454918400, 1454922000, 1454925600, 1454929200, 1454932800, 1454936400, 1454940000, 1454943600, 1454947200, 1454950800, 1454954400, 1454958000, 1454961600, 1454965200, 1454972400, 1454976000, 1454979600, 1454983200, 1454986800, 1454990400, 1454994000, 1454997600, 1455001200, 1455004800, 1455008400, 1455012000, 1455015600, 1455019200, 1455022800, 1455026400, 1455030000, 1455033600, 1455037200, 1455040800, 1455044400, 1455048000, 1455051600, 1455058800, 1455062400, 1455066000, 1455069600, 1455073200, 1455076800, 1455080400, 1455084000, 1455087600, 1455091200, 1455094800, 1455098400, 1455102000, 1455105600, 1455109200, 1455112800, 1455116400, 1455120000, 1455123600, 1455127200, 1455130800, 1455134400, 1455138000, 1455141600, 1455145200, 1455148800, 1455152400, 1455156000,
1455159600, 1455163200, 1455166800, 1455170400, 1455174000, 1455177600, 1455181200, 1455184800, 1455188400, 1455192000, 1455195600, 1455199200, 1455202800, 1455206400, 1455210000, 1455213600, 1455217200, 1455220800, 1455224400, 1455231600, 1455235200, 1455238800, 1455242400, 1455246000, 1455249600, 1455253200, 1455256800, 1455260400, 1455264000, 1455267600, 1455271200, 1455274800, 1455278400, 1455282000, 1455285600, 1455289200, 1455292800, 1455296400, 1455300000, 1455303600, 1455307200, 1455310800, 1455490800, 1455494400, 1455498000, 1455501600, 1455505200, 1455508800, 1455512400, 1455516000, 1455519600, 1455523200, 1455526800, 1455530400, 1455534000, 1455537600, 1455541200, 1455544800, 1455548400, 1455552000, 1455555600, 1455577200, 1455580800, 1455584400, 1455588000, 1455591600, 1455595200, 1455598800, 1455602400, 1455606000, 1455609600, 1455613200, 1455616800, 1455620400, 1455624000, 1455627600, 1455631200, 1455634800, 1455638400, 1455642000, 1455645600, 1455649200, 1455652800, 1455656400, 1455663600, 1455667200, 1455670800, 1455674400, 1455678000, 1455681600, 1455685200, 1455688800, 1455692400, 1455696000, 1455699600, 1455703200, 1455706800, 1455710400, 1455714000, 1455717600, 1455721200, 1455724800, 1455728400, 1455732000, 1455735600, 1455739200, 1455742800, 1455750000, 1455753600, 1455757200, 1455760800, 1455764400, 1455768000, 1455771600, 1455775200, 1455778800, 1455782400, 1455786000, 1455789600, 1455793200, 1455796800, 1455800400, 1455804000, 1455807600, 1455811200, 1455814800, 1455818400, 1455822000, 1455825600, 1455829200, 1455836400, 1455840000, 1455843600, 1455847200, 1455850800, 1455854400, 1455858000, 1455861600, 1455865200, 1455868800, 1455872400, 1455876000, 1455879600, 1455883200, 1455886800, 1455890400, 1455894000, 1455897600, 1455901200, 1455904800, 1455908400, 1455912000, 1455915600, 1456095600, 1456099200, 1456102800, 1456106400, 1456110000, 1456113600, 1456117200, 1456120800, 1456124400, 1456128000, 1456131600, 1456135200, 1456138800, 1456142400, 1456146000, 1456149600, 1456153200, 1456156800, 1456160400, 1456164000, 1456167600, 1456171200, 1456174800, 1456182000, 1456185600, 1456189200, 1456192800, 1456196400, 1456200000, 1456203600, 1456207200, 1456210800, 1456214400, 1456218000, 1456221600, 1456225200, 1456228800, 1456232400, 1456236000, 1456239600, 1456243200, 1456246800, 1456250400, 1456254000, 1456257600, 1456261200, 1456268400, 1456272000, 1456275600, 1456279200, 1456282800, 1456286400, 1456290000, 1456293600, 1456297200, 1456300800, 1456304400, 1456308000, 1456311600, 1456315200, 1456318800, 1456322400, 1456326000, 1456329600, 1456333200, 1456336800, 1456340400, 1456344000, 1456347600, 1456354800, 1456358400, 1456362000, 1456365600, 1456369200, 1456372800, 1456376400, 1456380000, 1456383600, 1456387200, 1456390800, 1456394400, 1456398000, 1456401600, 1456405200, 1456408800, 1456412400, 1456416000, 1456419600, 1456423200, 1456426800, 1456430400, 1456434000, 1456441200, 1456444800, 1456448400, 1456452000, 1456455600, 1456459200, 1456462800, 1456466400, 1456470000, 1456473600, 1456477200, 1456480800, 1456484400, 1456488000, 1456491600, 1456495200, 1456498800, 1456502400, 1456506000, 1456509600, 1456513200, 1456516800, 1456520400, 1456700400, 1456704000, 1456707600, 1456711200, 1456714800, 1456718400, 1456722000, 1456725600, 1456729200, 1456732800, 1456736400, 1456740000, 1456743600, 1456747200, 1456750800, 1456754400, 1456758000, 1456761600, 1456765200, 1456768800, 1456772400, 1456776000, 1456779600, 1456786800, 1456790400, 1456794000, 1456797600, 1456801200, 1456804800),
tzone = "", tclass = c("POSIXlt","POSIXt")), class = c("xts", "zoo"), .indexCLASS = c("POSIXlt", "POSIXt"), tclass = c("POSIXlt", "POSIXt"), .indexTZ = "", tzone = "", .CLASS = "double")
#THESE WORK AS INTENDED
SelectedDates <- DATSxts['2015-12-30::2015-12-31'] #get data between those dates
SelectedMonth <- DATSxts[.indexmon(DATSxts)==1] #get data for months February
SelectedDayYr <- DATSxts[.indexyday(DATSxts)==5] #get data for 5th trading days of the year
SelectedDayMon <- DATSxts[.indexmday(DATSxts)==10] #get data for 10th day of every month
SelectedDayWk <- DATSxts[.indexwday(DATSxts)==1] #get data for mondays
SelectedHour <- DATSxts[.indexhour(DATSxts)==08] #get data for 8AM
#THESE DO NOT WORK
SelectedYear <- DATSxts[.indexyear(DATSxts)==2016] #get data for year 2016
SelectedWeek <- DATSxts[.indexweek(DATSxts)==1] #get data for weeks 1
SelectedDay <- DATSxts[.indexday(DATSxts)==10] #get data for days 10
SelectedMin <- DATSxts[.indexmin(DATSxts)==30] #get data for every minute 30
****(Note: for some odd reason if I put all data/xts code in less than 8 lines loading the data gives an error message)****
You need to look at what .indexyear actually returns:
> .indexyear(DATSxts[1,])
[1] 115
> length( DATSxts[.indexyear(DATSxts)==116] )
[1] 943
> head( DATSxts[.indexyear(DATSxts)==116] )
[,1]
2016-01-03 15:00:00 1063.1
2016-01-03 16:00:00 1064.6
2016-01-03 17:00:00 1063.5
2016-01-03 18:00:00 1064.3
2016-01-03 19:00:00 1063.7
2016-01-03 20:00:00 1065.3
The 1900 basis is probably a holdover from an effort to emulate Excel on Windows. The weeks and days values are also different than you apparently expected:
> table( .indexweek(DATSxts) )
2400 2401 2402 2403 2404 2405 2406 2407 2408 2409
58 115 115 111 115 116 116 111 115 28
> table( .indexday(DATSxts) )
16798 16799 16800 16803 16804 16805 16806 16807 16808 16810 16811 16812 16813 16814
12 23 22 1 23 23 23 23 22 1 23 23 23 23
16815 16817 16818 16819 16820 16821 16822 16824 16825 16826 16827 16828 16829 16831
22 1 19 23 23 23 22 1 23 23 23 23 22 1
16832 16833 16834 16835 16836 16838 16839 16840 16841 16842 16843 16845 16846 16847
23 23 23 23 23 1 23 23 24 23 22 1 19 23
16848 16849 16850 16852 16853 16854 16855 16856 16857 16859 16860 16861
23 23 22 1 23 23 23 23 22 1 23 5
Granted, these aren't well-documented, but there are .index* functions that do what you want. A quick look at the source of a couple of the functions could have pointed you in the right direction. Take .indexyear for example, and note that ?POSIXlt says year is years since 1900.
R> .indexyear
function (x)
{
as.POSIXlt(.POSIXct(.index(x)))$year
}
<environment: namespace:xts>
SelectedYear <- DATSxts[(1900+.indexyear(DATSxts))==2016] #data for year 2016
Week of the year is not simple, and looking at the source would not have likely helped... There are several different ways to count the weeks in a year. See the "%U", "%V", and "%W" formats in ?strftime.
SelectedWeek <- DATSxts[strftime(index(DATSxts),"%W")=="00"] #get data for weeks 1
.indexday returns the day/date of the underlying index. Use .indexmday if you want the day of the month.
SelectedDay <- DATSxts[.indexmday(DATSxts)==10] #get data for days 10
I'm not sure what you expect .indexmin to do, since you do not have any data that occur on the 30th minute of any hour. The function below seems to be working as expected.
SelectedMin <- DATSxts[.indexmin(DATSxts)==30] #get data for every minute 30
Related
The best way to filter timestamps with hours [duplicate]
This question already has answers here:
Subsetting data.table set by date range in R
(3 answers)
Subset a dataframe between 2 dates
(8 answers)
Closed 4 years ago.
structure(list(id = c(14735, 11589, 1165, 7864, 9151, 6662, 26, 6638, 7635, 10204, 10588, 11923, 2119, 2487, 11571, 6759, 9591,
1592, 12725, 5086, 3039, 10576, 2526, 1127, 583, 12879, 5686, 13405, 1375, 7547, 11479, 9220, 8040, 13848, 14996, 4256, 1879,
2653, 15220, 1896, 4547, 2505, 1105, 3625, 10896, 9806, 1154, 2626, 2215, 5957, 3522, 8531, 8867, 1501, 2415, 14009, 13056,
13740, 1751, 540, 8896, 4771, 8457, 5383, 2176, 8611, 5072, 1828, 3884, 5364, 12617, 11887, 14267, 12735, 2261, 8962, 12501, 9586,
7129, 3925, 373, 4987, 3410, 13304, 10276, 7975, 8456, 3752, 111, 14384, 10901, 4234, 11273, 13196, 5764, 10902, 3631, 9814,
14781, 5726), full_date = structure(c(1522781128, 1522108662, 1519981076, 1521121363, 1521457099, 1520859176, 1519631141, 1520856439,
1521056830, 1521753388, 1521853223, 1522173544, 1520160750, 1520238731, 1522105027, 1520873428, 1521581248, 1520074992, 1522326287, 1520600253,
1520300281, 1521850210, 1520242830, 1519972727, 1519776890, 1522350676, 1520695451, 1522446321, 1520025013, 1521042071, 1522085537, 1521481296,
1521154393, 1522521269, 1522939333, 1520464381, 1520117285, 1520254658, 1523208654, 1520119807, 1520509952, 1520241232, 1519954606, 1520375033,
1521937117, 1521645125, 1519978700, 1520252273, 1520176769, 1520750012, 1520362171, 1521290786, 1521377710, 1520061816, 1520221068, 1522546134,
1522389100, 1522506558, 1520098043, 1519764349, 1521384566, 1520541898, 1521271618, 1520642366, 1520169730, 1521307430, 1520598946, 1520109560,
1520416134, 1520639385, 1522308872, 1522167432, 1522609927, 1522327583, 1520186995, 1521399556, 1522283646, 1521580399, 1520947600, 1520421124, 1519712304, 1520583404, 1520349813, 1522426307, 1521776496, 1521143409,
1521271618, 1520395530, 1519645678, 1522639423, 1521939121, 1520461036, 1522043512, 1522409242, 1520707332, 1521939139, 1520376024, 1521647387,
1522804886, 1520700876), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
real_time = c("18:45:28", "23:57:42", "8:57:56", "13:42:43",
"10:58:19", "12:52:56", "7:45:41", "12:7:19", "19:47:10",
"21:16:28", "1:0:23", "17:59:4", "10:52:30", "8:32:11", "22:57:7",
"16:50:28", "21:27:28", "11:3:12", "12:24:47", "12:57:33",
"1:38:1", "0:10:10", "9:40:30", "6:38:47", "0:14:50", "19:11:16",
"15:24:11", "21:45:21", "21:10:13", "15:41:11", "17:32:17",
"17:41:36", "22:53:13", "18:34:29", "14:42:13", "23:13:1",
"22:48:5", "12:57:38", "17:30:54", "23:30:7", "11:52:32",
"9:13:52", "1:36:46", "22:23:53", "0:18:37", "15:12:5", "8:18:20",
"12:17:53", "15:19:29", "6:33:32", "18:49:31", "12:46:26",
"12:55:10", "7:23:36", "3:37:48", "1:28:54", "5:51:40", "14:29:18",
"17:27:23", "20:45:49", "14:49:26", "20:44:58", "7:26:58",
"0:39:26", "13:22:10", "17:23:50", "12:35:46", "20:39:20",
"9:48:54", "23:49:45", "7:34:32", "16:17:12", "19:12:7",
"12:46:23", "18:9:55", "18:59:16", "0:34:6", "21:13:19",
"13:26:40", "11:12:4", "6:18:24", "8:16:44", "15:23:33",
"16:11:47", "3:41:36", "19:50:9", "7:26:58", "4:5:30", "11:47:58",
"3:23:43", "0:52:1", "22:17:16", "5:51:52", "11:27:22", "18:42:12",
"0:52:19", "22:40:24", "15:49:47", "1:21:26", "16:54:36")), row.names = c(NA, -100L), class = c("tbl_df", "tbl", "data.frame"))
I want to filter above data and find observations that happened between two hours, let's say 09:30:00 and 16:15:30. What's the best way to do that, maybe with lubridate or hms packages?
As I said in comments this can be solved such as start_data = "18:45:28" end_date = "19:45:28" # df - your data.frame df[df$real_time > start_data & df$real_time < end_date , ] Output: id full_date real_time 26 12879 2018-03-29 19:11:16 19:11:16 51 3522 2018-03-06 18:49:31 18:49:31 73 14267 2018-04-01 19:12:07 19:12:7 75 2261 2018-03-04 18:09:55 18:9:55 76 8962 2018-03-18 18:59:16 18:59:16
plotting a distribution in R using percentiles and corresponding values
I would like to plot the SAT dataset found here https://blog.prepscholar.com/sat-percentiles-high-precision-2016 My issue with doing this is that I am unsure if there is a more elegant way to do this than I am doing. Currently, I am converting those percentiles to Z scores and then using that associated probability to create a distribution. The problem that I have is The SAT scores do not seem to be normally distributed. They approximate a normal distribution, but they do not seem to be normally distributed. This makes using the tried and true rnorm (X, mean = 1000, sd = 200) unusable. I am not sure if there is a better way than converting the percentiles to Z scores since the SAT scores do not seem to be normally distributed. Any help would be appreciated. My long term plan is to create a shiny ap where a person can use a slider to move along the distribution to see where their score puts them in what percentile. But the first step is to actually figure out a way to produce an accurate distribution. Here is the code in total: scores <- as.numeric( c( 1600, 1593, 1587, 1580, 1573, 1567, 1560, 1553, 1547, 1540, 1533, 1527, 1520, 1513, 1507, 1500, 1493, 1487, 1480, 1473, 1467, 1460, 1453, 1447, 1440, 1433, 1427, 1420, 1413, 1407, 1400, 1393, 1387, 1380, 1373, 1367, 1360, 1353, 1347, 1340, 1333, 1327, 1320, 1313, 1307, 1300, 1293, 1287, 1280, 1273, 1267, 1260, 1253, 1247, 1240, 1233, 1227, 1220, 1213, 1207, 1200, 1193, 1187, 1180, 1173, 1167, 1160, 1153, 1147, 1140, 1133, 1127, 1120, 1113, 1107, 1100, 1093, 1087, 1080, 1073, 1067, 1060, 1053, 1047, 1040, 1033, 1027, 1020, 1013, 1007, 1000, 0993, 0987, 0980, 0973, 0967, 0960, 0953, 0947, 0940, 0933, 0927, 0920, 0913, 0907, 0900, 0893, 0887, 0880, 0873, 0867, 0860, 0853, 0847, 0840, 0833, 0827, 0820, 0813, 0807, 0800, 0793, 0787, 0780, 0773, 0767, 0760, 0753, 0747, 0740, 0733, 0727, 0720, 0713, 0707, 0700, 0693, 0687, 0680, 0673, 0667, 0660, 0653, 0647, 0640, 0633, 0627, 0620, 0613, 0607, 0600, 0593, 0587, 0580, 0573, 0567, 0560, 0553, 0547, 0540, 0533, 0527, 0520, 0513, 0507, 0500, 0493, 0487, 0480, 0473, 0467, 0460, 0453, 0447, 0440, 0433, 0427, 0420, 0413, 0407, 0400 ) ) probs <- as.numeric( c( 0.000665335, 0.001508711, 0.002067932, 0.002878073, 0.003976493, 0.005137125, 0.006483906, 0.008169371, 0.010066473, 0.012051019, 0.014095858, 0.016307247, 0.01851226, 0.020808986, 0.023364025, 0.02601564, 0.028721866, 0.031560739, 0.034551519, 0.037733663, 0.041036965, 0.044427406, 0.047928757, 0.051538726, 0.055292787, 0.059145645, 0.063187376, 0.067367682, 0.071691308, 0.076255174, 0.080943422, 0.085690142, 0.090464868, 0.095379534, 0.100511283, 0.105758358, 0.111083521, 0.116571178, 0.122285078, 0.128123463, 0.13397113, 0.139830118, 0.145699225, 0.151666124, 0.157793404, 0.164023115, 0.170358704, 0.17676173, 0.183278673, 0.189955902, 0.196651135, 0.203363202, 0.210071134, 0.216801722, 0.223562248, 0.230347853, 0.237183732, 0.243934459, 0.250657665, 0.257436698, 0.264227682, 0.270968969, 0.277599004, 0.284165141, 0.290648753, 0.297040829, 0.303401865, 0.30964291, 0.315714288, 0.321696268, 0.3275473, 0.33324613, 0.338771004, 0.344104024, 0.34924206, 0.354185931, 0.358956451, 0.363504804, 0.36782396, 0.371962147, 0.375851065, 0.37943538, 0.382754048, 0.38580143, 0.388568593, 0.391025026, 0.393142417, 0.394947023, 0.39643261, 0.397582763, 0.398388967, 0.398835641, 0.398933788, 0.398676885, 0.398065535, 0.397089484, 0.395745285, 0.394041326, 0.391951596, 0.389478503, 0.386655445, 0.383491062, 0.379943329, 0.376014793, 0.371758542, 0.367167906, 0.362242796, 0.356970877, 0.351398549, 0.345499578, 0.33929925, 0.332851353, 0.326122779, 0.31911459, 0.311858166, 0.304408822, 0.296767961, 0.288895933, 0.280893084, 0.272865458, 0.264642129, 0.256259902, 0.247931807, 0.239607, 0.231228792, 0.222875786, 0.214544714, 0.206273084, 0.198003801, 0.189731661, 0.181671447, 0.173826196, 0.16610093, 0.158499433, 0.151067478, 0.143751108, 0.136558209, 0.129683746, 0.123040703, 0.11660098, 0.110455988, 0.104559737, 0.098857524, 0.093254931, 0.087923914, 0.08286171, 0.077907056, 0.073124914, 0.068583012, 0.064242767, 0.059980362, 0.055906234, 0.052062062, 0.048298984, 0.044678669, 0.041258641, 0.03796372, 0.034841037, 0.03185105, 0.028992313, 0.026330747, 0.023870486, 0.0215867, 0.019418127, 0.017379035, 0.015436224, 0.01358768, 0.011905384, 0.01032104, 0.008865584, 0.007564083, 0.006380472, 0.005299104, 0.004317991, 0.003512008, 0.00284036, 0.002268168, 0.001724469, 0.001327348, 0.001030367, 0.000112 ) ) data <- as.data.frame (cbind (scores, probs)) data2 <- sample (data$scores, 10000000, replace = T, prob = data$probs) den <- density (data2, adjust = 2) plot (den , xlim = c(400,1600))
How to specify explicitly the month section while plotting a seasonal plot?
I am trying to learn forecast using fpp2 package on bitcoin data. The bitcoin_ts excel just has two columns, one the date column another the closing price for each date.
library(fpp2)
bitcoin_ts <- read_excel("bitcoin_ts.xlsx")
head(bitcoin_ts)
myts <- ts(bitcoin_ts[,2], start = c(2013, 2), frequency = 366)
##autoplot(myts, facets = FALSE)
ggseasonplot(myts)
As beginning of the data is 2013 and from the month of April, which is the second quarter, I specified start as 2013 and 2 and frequency I gave as number of days in a year.
I get a graph as attached the lower scale (season), my requirement is to show months but I guess due to the all the dates being included in the data it is getting superimposed. How can I fix this to achieve only months over there?
I would like to see something similar to this chart (of course the data upon which this chart was plotted had only year and month (ex: Mar-81)
Unable to attach the entire 1655 observations as the this textbox is set to a limit of 30K characters.
However I attaching the link to the dataset as well.
Complete Dataset
Below find the sample of first 500,
> dput(head(bitcoin_ts,500))
structure(list(Date = structure(c(1367107200, 1367193600, 1367280000,
1367366400, 1367452800, 1367539200, 1367625600, 1367712000, 1367798400,
1367884800, 1367971200, 1368057600, 1368144000, 1368230400, 1368316800,
1368403200, 1368489600, 1368576000, 1368662400, 1368748800, 1368835200,
1368921600, 1369008000, 1369094400, 1369180800, 1369267200, 1369353600,
1369440000, 1369526400, 1369612800, 1369699200, 1369785600, 1369872000,
1369958400, 1370044800, 1370131200, 1370217600, 1370304000, 1370390400,
1370476800, 1370563200, 1370649600, 1370736000, 1370822400, 1370908800,
1370995200, 1371081600, 1371168000, 1371254400, 1371340800, 1371427200,
1371513600, 1371600000, 1371686400, 1371772800, 1371859200, 1371945600,
1372032000, 1372118400, 1372204800, 1372291200, 1372377600, 1372464000,
1372550400, 1372636800, 1372723200, 1372809600, 1372896000, 1372982400,
1373068800, 1373155200, 1373241600, 1373328000, 1373414400, 1373500800,
1373587200, 1373673600, 1373760000, 1373846400, 1373932800, 1374019200,
1374105600, 1374192000, 1374278400, 1374364800, 1374451200, 1374537600,
1374624000, 1374710400, 1374796800, 1374883200, 1374969600, 1375056000,
1375142400, 1375228800, 1375315200, 1375401600, 1375488000, 1375574400,
1375660800, 1375747200, 1375833600, 1375920000, 1376006400, 1376092800,
1376179200, 1376265600, 1376352000, 1376438400, 1376524800, 1376611200,
1376697600, 1376784000, 1376870400, 1376956800, 1377043200, 1377129600,
1377216000, 1377302400, 1377388800, 1377475200, 1377561600, 1377648000,
1377734400, 1377820800, 1377907200, 1377993600, 1378080000, 1378166400,
1378252800, 1378339200, 1378425600, 1378512000, 1378598400, 1378684800,
1378771200, 1378857600, 1378944000, 1379030400, 1379116800, 1379203200,
1379289600, 1379376000, 1379462400, 1379548800, 1379635200, 1379721600,
1379808000, 1379894400, 1379980800, 1380067200, 1380153600, 1380240000,
1380326400, 1380412800, 1380499200, 1380585600, 1380672000, 1380758400,
1380844800, 1380931200, 1381017600, 1381104000, 1381190400, 1381276800,
1381363200, 1381449600, 1381536000, 1381622400, 1381708800, 1381795200,
1381881600, 1381968000, 1382054400, 1382140800, 1382227200, 1382313600,
1382400000, 1382486400, 1382572800, 1382659200, 1382745600, 1382832000,
1382918400, 1383004800, 1383091200, 1383177600, 1383264000, 1383350400,
1383436800, 1383523200, 1383609600, 1383696000, 1383782400, 1383868800,
1383955200, 1384041600, 1384128000, 1384214400, 1384300800, 1384387200,
1384473600, 1384560000, 1384646400, 1384732800, 1384819200, 1384905600,
1384992000, 1385078400, 1385164800, 1385251200, 1385337600, 1385424000,
1385510400, 1385596800, 1385683200, 1385769600, 1385856000, 1385942400,
1386028800, 1386115200, 1386201600, 1386288000, 1386374400, 1386460800,
1386547200, 1386633600, 1386720000, 1386806400, 1386892800, 1386979200,
1387065600, 1387152000, 1387238400, 1387324800, 1387411200, 1387497600,
1387584000, 1387670400, 1387756800, 1387843200, 1387929600, 1388016000,
1388102400, 1388188800, 1388275200, 1388361600, 1388448000, 1388534400,
1388620800, 1388707200, 1388793600, 1388880000, 1388966400, 1389052800,
1389139200, 1389225600, 1389312000, 1389398400, 1389484800, 1389571200,
1389657600, 1389744000, 1389830400, 1389916800, 1390003200, 1390089600,
1390176000, 1390262400, 1390348800, 1390435200, 1390521600, 1390608000,
1390694400, 1390780800, 1390867200, 1390953600, 1391040000, 1391126400,
1391212800, 1391299200, 1391385600, 1391472000, 1391558400, 1391644800,
1391731200, 1391817600, 1391904000, 1391990400, 1392076800, 1392163200,
1392249600, 1392336000, 1392422400, 1392508800, 1392595200, 1392681600,
1392768000, 1392854400, 1392940800, 1393027200, 1393113600, 1393200000,
1393286400, 1393372800, 1393459200, 1393545600, 1393632000, 1393718400,
1393804800, 1393891200, 1393977600, 1394064000, 1394150400, 1394236800,
1394323200, 1394409600, 1394496000, 1394582400, 1394668800, 1394755200,
1394841600, 1394928000, 1395014400, 1395100800, 1395187200, 1395273600,
1395360000, 1395446400, 1395532800, 1395619200, 1395705600, 1395792000,
1395878400, 1395964800, 1396051200, 1396137600, 1396224000, 1396310400,
1396396800, 1396483200, 1396569600, 1396656000, 1396742400, 1396828800,
1396915200, 1397001600, 1397088000, 1397174400, 1397260800, 1397347200,
1397433600, 1397520000, 1397606400, 1397692800, 1397779200, 1397865600,
1397952000, 1398038400, 1398124800, 1398211200, 1398297600, 1398384000,
1398470400, 1398556800, 1398643200, 1398729600, 1398816000, 1398902400,
1398988800, 1399075200, 1399161600, 1399248000, 1399334400, 1399420800,
1399507200, 1399593600, 1399680000, 1399766400, 1399852800, 1399939200,
1400025600, 1400112000, 1400198400, 1400284800, 1400371200, 1400457600,
1400544000, 1400630400, 1400716800, 1400803200, 1400889600, 1400976000,
1401062400, 1401148800, 1401235200, 1401321600, 1401408000, 1401494400,
1401580800, 1401667200, 1401753600, 1401840000, 1401926400, 1402012800,
1402099200, 1402185600, 1402272000, 1402358400, 1402444800, 1402531200,
1402617600, 1402704000, 1402790400, 1402876800, 1402963200, 1403049600,
1403136000, 1403222400, 1403308800, 1403395200, 1403481600, 1403568000,
1403654400, 1403740800, 1403827200, 1403913600, 1.404e+09, 1404086400,
1404172800, 1404259200, 1404345600, 1404432000, 1404518400, 1404604800,
1404691200, 1404777600, 1404864000, 1404950400, 1405036800, 1405123200,
1405209600, 1405296000, 1405382400, 1405468800, 1405555200, 1405641600,
1405728000, 1405814400, 1405900800, 1405987200, 1406073600, 1406160000,
1406246400, 1406332800, 1406419200, 1406505600, 1406592000, 1406678400,
1406764800, 1406851200, 1406937600, 1407024000, 1407110400, 1407196800,
1407283200, 1407369600, 1407456000, 1407542400, 1407628800, 1407715200,
1407801600, 1407888000, 1407974400, 1408060800, 1408147200, 1408233600,
1408320000, 1408406400, 1408492800, 1408579200, 1408665600, 1408752000,
1408838400, 1408924800, 1409011200, 1409097600, 1409184000, 1409270400,
1409356800, 1409443200, 1409529600, 1409616000, 1409702400, 1409788800,
1409875200, 1409961600, 1410048000, 1410134400, 1410220800), class = c("POSIXct",
"POSIXt"), tzone = "UTC"), Close = c(134.21, 144.54, 139, 116.99,
105.21, 97.75, 112.5, 115.91, 112.3, 111.5, 113.57, 112.67, 117.2,
115.24, 115, 117.98, 111.5, 114.22, 118.76, 123.02, 123.5, 121.99,
122, 122.88, 123.89, 126.7, 133.2, 131.98, 133.48, 129.75, 129,
132.3, 128.8, 129, 129.3, 122.29, 122.22, 121.42, 121.65, 118,
111.5, 108.3, 100, 106.35, 108.9, 108.15, 104, 99.98, 99.99,
99.51, 101.7, 107.4, 108.25, 110.15, 109.5, 108.3, 107.6, 102.74,
103.95, 104, 101.44, 94.65, 94.99, 96.61, 88.05, 90.13, 77.53,
80.53, 68.43, 70.28, 74.56, 76.52, 76.69, 86.76, 88.98, 93.59,
98.13, 94.69, 98.4, 97.45, 98.5, 90.58, 92.17, 89.39, 90.76,
91.61, 95.56, 94.51, 96.9, 96.02, 94.12, 99.76, 101.2, 107.99,
106.09, 104, 104.5, 104, 105.14, 106.22, 106.75, 106.75, 103,
102.8, 103, 105, 106.64, 109, 112.56, 109.99, 108.99, 113.5,
113.5, 119, 121.2, 123.3, 121.15, 118.5, 120.05, 122.11, 120.06,
126.5, 122.62, 122.39, 133.49, 135.35, 138.34, 135.85, 136.77,
126.74, 126.43, 119.15, 124.15, 121.66, 127.11, 125.91, 135.25,
133.13, 134.98, 129.22, 130.37, 131.72, 131.66, 131.47, 129.65,
127.04, 127.43, 129.12, 125.95, 127.25, 128.22, 128.38, 133.78,
134.78, 137.34, 133, 132.18, 114.13, 123.63, 129.01, 128.55,
129, 126.94, 126, 130.69, 130.59, 130.9, 135.19, 138.13, 140.52,
145.24, 142.55, 146.25, 155.96, 172.42, 174.61, 182.21, 193.76,
213.62, 198.23, 186.69, 177.32, 196.44, 198.55, 204.39, 199.97,
204, 206.18, 206.22, 215.05, 229.1, 245.24, 262.5, 296.41, 338.11,
339.11, 326.62, 342.44, 360.33, 407.37, 420.2, 417.95, 440.22,
492.11, 703.56, 584.61, 590.83, 722.43, 771.44, 797.82, 774.25,
799.11, 928.1, 1001.96, 1031.95, 1131.97, 1129.43, 955.85, 1043.33,
1078.28, 1151.17, 1045.11, 829.45, 698.23, 795.87, 893.19, 988.51,
878.48, 873.26, 892.58, 872.6, 876.12, 705.97, 682.12, 522.7,
691.96, 625.32, 605.66, 617.18, 673.41, 665.58, 682.21, 761.98,
735.07, 727.83, 745.05, 756.13, 754.01, 771.4, 802.39, 818.72,
859.51, 933.53, 953.29, 802, 842.72, 846.86, 868.48, 913.95,
863.22, 841.2, 833.27, 860.9, 835.63, 814.64, 840, 870.96, 870.2,
863.91, 845.59, 822.04, 797.07, 853.61, 885.28, 771.39, 812.51,
826, 819.03, 829.92, 832.58, 825.37, 823.83, 827.96, 811.91,
781.55, 712.4, 673.92, 682.9, 681.03, 672.17, 651.72, 605.24,
661.99, 650.92, 616.63, 626.27, 626.6, 623.03, 556.14, 574.16,
605.42, 605.82, 546.32, 538.71, 582.69, 578.77, 549.26, 565.61,
559.79, 667.76, 666.78, 665.51, 663.86, 629.15, 617.45, 636.96,
627.79, 634.11, 632.1, 638.14, 628.8, 636.12, 631.11, 622.37,
614.83, 609.89, 588.77, 571.49, 565.04, 561.27, 583.41, 583.92,
580.83, 471.24, 495.67, 491.17, 460.27, 457, 478.38, 437.14,
444.72, 447.53, 461.91, 460.5, 449.42, 453.09, 442.73, 365.18,
420.95, 421.12, 414.06, 458.79, 515.59, 527.39, 495.96, 479.64,
501.56, 498.17, 495.77, 487.92, 491.3, 500.46, 461.45, 458.6,
436.39, 440.29, 447.21, 447.64, 457.76, 449.38, 437.76, 436.4,
433.48, 428.96, 438.82, 440.17, 449.46, 454.43, 438.89, 441.46,
440.67, 443.97, 447.25, 448.06, 448.9, 446.26, 446.18, 485.72,
491.77, 524.58, 520.22, 525.14, 571.59, 583.42, 571.24, 577.06,
568.18, 615.33, 623.68, 630.23, 660.62, 667.61, 641.61, 659.26,
653.7, 654.97, 656.14, 649.16, 653.15, 633.02, 586.95, 600.16,
577.36, 592.94, 592.19, 610.86, 607.96, 598.07, 594.15, 594.99,
602.27, 593.98, 582.36, 566.34, 581.14, 597.26, 596.55, 602.72,
639.8, 640.81, 650.88, 645.16, 630.69, 631.46, 635.81, 624.09,
624.82, 624.51, 616.76, 632, 633.71, 626.5, 619.32, 621.59, 616.8,
623.09, 628.78, 628.51, 623.9, 622.21, 621.55, 619.41, 601.73,
601.09, 595.81, 593.85, 585.69, 584.73, 567.29, 586.24, 594.92,
589.33, 586.67, 588.78, 585.44, 584.65, 588.87, 592.58, 589.37,
591.06, 576.37, 569.64, 546.66, 505.97, 497.01, 519.71, 491.8,
461.46, 485.25, 511.98, 517.24, 514.04, 498.07, 508.29, 502.5,
511.57, 511.15, 507.81, 508.52, 504.25, 477.76, 474.88, 477.43,
477.59, 489.66, 483.34, 484.83, 482.28, 474.6, 475.26)), .Names = c("Date",
"Close"), row.names = c(NA, -500L), class = c("tbl_df", "tbl",
"data.frame"))
Merge data [Gregorian calendar] with data with a 360 day calendar
I have the following problem. Basically I have data, that follows the Gregorian calendar and data, which follows a 360 day calendar and a 365 day calendar (no leap years). Its a feature of the Model, which provides the data. I only could create a timeindex for data with a 360 or 365 day calendar with the help of library(PCICt), therefore the timeindex is of class PCICt and not class date for those data sets with 360d or 365d calendars. I managed to change the class of the timeindex of data with a 365 day calendar easily, because it always missed the 29th February. Overall it has less days than data with a Gregorian calendar, thats why the following code worked: index(Modellwind.IPSL.1971_2100.mat.5.zoo) <- as.Date(paste(index(Modellwind.IPSL.1971_2100.mat.5.zoo), "%Y-%m-%d")) class(index(Modellwind.IPSL.1971_2100.mat.5.zoo)) any(is.na(Modellwind.IPSL.1971_2100.mat.5.zoo)) I could merge data with a Gregorian calendar with data with a 365 day calendar (Modellwind.IPSL.1971_2100.mat.5.zoo has a 360d calendar) with the following code: library(zoo) library(PCICt) Mergedata.Gregorian_cal.365d_cal <- merge(Modellwind.1971_2100.mat.5.zoo[,1],Modellwind.IPSL.1971_2100.mat.5.zoo[,1], fill = NA) sum(is.na(Mergedata.Gregorian_cal.365d_cal)) #Gives you the number of leap years between the starting date and end date. Between 1971 and 2100, you have 32 leap years. The problem is, that the 360 day calendar has 30 days per month. Because the 30th February doesnt exist, I cant use the method above. Is there the possibility to use the index of the data set with a Gregorian calendar, to mask the days in the data with a 360 days calendar, which arent present in the data with a Gregorian calendar? I would have NA for the 31th for each month in the data with a 360 day calendar, which has 31 days and the 30th of February would always be excluded/"deleted". Example data for the year 1972 for all these 2 calendar types. 1972 is a leap year. Data with Gregorian calendar: structure(c(18.0824119718086, 22.0984972927228, 15.6444417189609, 5.89666444009656, 18.1725165479582, 21.9512885928156, 4.88193965506205, 13.933395151291, 13.0150555148858, 12.2412020084762, 15.1707058527428, 11.9102181846508, 11.8373087451369, 4.88134153024087, 3.9195944541078, 8.15191977152853, 16.4878972832708, 22.3388856060094, 24.9339938808775, 9.40486557731387, 3.28778357417694, 5.4431969404311, 20.9010729191345, 21.576769300227, 23.5252763396514, 18.7725442737342, 19.7400816746119, 16.9286422645386, 11.5214494652195, 12.6356049638603, 7.8885628659944, 7.81687922456664, 4.76970848125104, 4.71443717941174, 4.3332862268629, 5.39949220500925, 17.2259740635503, 8.94893163333614, 10.5413637024979, 7.17270151460124, 13.1168987701018, 7.80375818376942, 9.19842495123789, 6.50186468363422, 2.72654917702192, 4.76044540462425, 9.33231780065151, 2.94546305078892, 4.67014980066904, 23.8062593679947, 20.0513022569902, 17.0747102447771, 22.1857749192152, 16.3213003571625, 5.59820337769238, 11.7719699596263, 2.80575446040357, 6.01800547567089, 8.40297112483879, 11.8088715968982, 4.98554106381165, 4.57889903991417, 5.46723000525948, 2.2046248257294, 6.50476062233784, 5.93160587570127, 2.66548923257402, 6.18402932922427, 6.90886137443288, 3.85090651354255, 8.24126904737149, 10.9654562975344, 26.2730566456532, 20.7006666233841, 6.82182724533744, 12.9025799024923, 9.59995956664874, 18.3086576464973, 8.53427988021107, 13.5057006787102, 2.58426562914531, 4.77444858313551, 4.01236032427182, 11.972201385, 19.541507043929, 12.7122726666622, 6.58520942950129, 10.0492820845097, 5.32566668447011, 2.57782664078923, 7.15092309537708, 13.5172698318347, 14.9398233030208, 14.5222484425227, 8.00143290635926, 11.5258287774697, 6.38313299466099, 1.79298307790425, 4.16626735598483, 10.4754961358211, 7.29533307327725, 6.14846988554932, 12.0495274648323, 5.53397133979113, 8.37782693478431, 7.52399322022154, 13.8764650137859, 10.9413294317165, 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10.0359820698446, 11.6007096598598, 9.40090116780089, 9.33769236702315, 9.95757914421451, 8.55990604010644, 10.4960731180582, 15.1722236227608, 11.1686532067003, 12.5359489386538, 9.08166190238287, 3.05025798629833, 8.2121249493627, 13.5877493909831, 17.366136605839, 10.1228300566323, 8.27402110802059, 9.36245413432149, 8.46450110123141, 1.84281651654745, 3.30160338329847, 6.3138195121321, 11.728173138611, 10.6622626897554, 16.4094777741508, 20.0381278953011, 19.9861861731441, 14.5139852521008, 5.48642303877599, 7.72640073447677, 6.40813436142624, 1.68384368141763, 4.77881376279741, 5.18058883352236, 2.98929028921609, 4.63220103226812, 4.16738796400037, 13.0532771268428, 7.3104783906477, 2.22615400974864, 2.64542408728002, 7.21256528138117, 14.5616168774743, 6.49359376934273, 1.80947777511883, 1.05548742272005, 10.0100349138233, 12.9534450881407, 7.3887732493981, 2.06346550336316, 8.31717299234876, 7.78924597525132, 2.52953776616571, 8.82704926761683, 17.6640298084431, 13.3475985590147, 17.628200973858, 21.7018534179796, 18.5008930385254, 10.1186544078028, 20.3816253684839, 24.926808778224, 21.9880227371171, 20.1845589773564, 16.8169067228104, 10.2309882726068, 11.1248083859065, 20.5125621219226, 16.2733638505053, 18.8559651435026, 9.34653256821625, 16.1736806222654, 15.2975717551524, 14.5146239904197, 5.49088884606638, 10.6410636803798, 23.2390025461356, 17.9439644921709, 30.2719783889658, 17.8897458886807, 21.8089336705463, 21.181790238746, 7.74189585366475, 15.5669977526734, 22.2278845648006, 24.8537097755812, 23.4403701940983, 21.6831582162925, 2.51132630151019, 5.92677645017391, 10.9441543623409, 2.36029949616021, 1.16606557900293, 3.50793234808807, 9.61440088811323, 14.137409810834, 18.381771312848, 12.6572683827955, 18.797705699369, 20.8717521227799, 20.2278258689499, 12.9121727366459, 12.4660577833913, 19.7783723336818, 21.467091272057, 19.825201185869, 21.5289275033657, 25.2513848648435, 19.3231430907612, 14.4548913656037, 18.1364925154159, 25.6372229193355, 10.7132780942731, 3.39420618551996, 18.5659343284766, 21.7217416658684, 18.5067855518246, 17.6834051522095, 15.6325986696431, 11.6100239010514, 15.0764596715006, 8.46048294087618, 19.6747151949836, 10.0466139442861, 7.41836369302161, 19.4712502377319, 15.3111265245001, 6.54184803095167, 3.95906471757932, 4.6122997606339, 7.13881563394315, 2.48477412801708, 6.19131168025857, 7.82629557777152, 8.93410147891196, 6.27319459383102, 4.05195951965034, 8.77535610150876, 8.45968682510812, 6.27599562509301, 5.11571448525756, 1.68865280924914, 14.4333064594824, 20.5250630694977, 24.0600954370164, 13.4110946493079, 2.92078462265695, 9.06322147728374), index = structure(c(730, 731, 732, 733, 734, 735, 736, 737, 738, 739, 740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755, 756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771, 772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787, 788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803, 804, 805, 806, 807, 808, 809, 810, 811, 812, 813, 814, 815, 816, 817, 818, 819, 820, 821, 822, 823, 824, 825, 826, 827, 828, 829, 830, 831, 832, 833, 834, 835, 836, 837, 838, 839, 840, 841, 842, 843, 844, 845, 846, 847, 848, 849, 850, 851, 852, 853, 854, 855, 856, 857, 858, 859, 860, 861, 862, 863, 864, 865, 866, 867, 868, 869, 870, 871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883, 884, 885, 886, 887, 888, 889, 890, 891, 892, 893, 894, 895, 896, 897, 898, 899, 900, 901, 902, 903, 904, 905, 906, 907, 908, 909, 910, 911, 912, 913, 914, 915, 916, 917, 918, 919, 920, 921, 922, 923, 924, 925, 926, 927, 928, 929, 930, 931, 932, 933, 934, 935, 936, 937, 938, 939, 940, 941, 942, 943, 944, 945, 946, 947, 948, 949, 950, 951, 952, 953, 954, 955, 956, 957, 958, 959, 960, 961, 962, 963, 964, 965, 966, 967, 968, 969, 970, 971, 972, 973, 974, 975, 976, 977, 978, 979, 980, 981, 982, 983, 984, 985, 986, 987, 988, 989, 990, 991, 992, 993, 994, 995, 996, 997, 998, 999, 1000, 1001, 1002, 1003, 1004, 1005, 1006, 1007, 1008, 1009, 1010, 1011, 1012, 1013, 1014, 1015, 1016, 1017, 1018, 1019, 1020, 1021, 1022, 1023, 1024, 1025, 1026, 1027, 1028, 1029, 1030, 1031, 1032, 1033, 1034, 1035, 1036, 1037, 1038, 1039, 1040, 1041, 1042, 1043, 1044, 1045, 1046, 1047, 1048, 1049, 1050, 1051, 1052, 1053, 1054, 1055, 1056, 1057, 1058, 1059, 1060, 1061, 1062, 1063, 1064, 1065, 1066, 1067, 1068, 1069, 1070, 1071, 1072, 1073, 1074, 1075, 1076, 1077, 1078, 1079, 1080, 1081, 1082, 1083, 1084, 1085, 1086, 1087, 1088, 1089, 1090, 1091, 1092, 1093, 1094, 1095), class = "Date"), class = "zoo") Data with 360 day calendar: structure(c(16.2042726556084, 5.54411632675392, 8.39523962307452, 12.9056155820263, 2.61097842984656, 5.36226881021485, 6.11436671091439, 7.80097325957491, 2.6959250785196, 1.21734133687523, 9.83094824076473, 14.6123034310063, 22.5841804523832, 18.878943978969, 21.5918638597738, 21.6111797366467, 12.163190670239, 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18.6696075241301, 8.33988152577507, 7.42672203446524, 1.28062769400986, 8.37801485190093, 10.8397879874016, 8.72028550374256, 0.580257801954223, 4.39712796914426, 7.84989207801702), index = structure(c(62208000, 62294400, 62380800, 62467200, 62553600, 62640000, 62726400, 62812800, 62899200, 62985600, 63072000, 63158400, 63244800, 63331200, 63417600, 63504000, 63590400, 63676800, 63763200, 63849600, 63936000, 64022400, 64108800, 64195200, 64281600, 64368000, 64454400, 64540800, 64627200, 64713600, 64800000, 64886400, 64972800, 65059200, 65145600, 65232000, 65318400, 65404800, 65491200, 65577600, 65664000, 65750400, 65836800, 65923200, 66009600, 66096000, 66182400, 66268800, 66355200, 66441600, 66528000, 66614400, 66700800, 66787200, 66873600, 66960000, 67046400, 67132800, 67219200, 67305600, 67392000, 67478400, 67564800, 67651200, 67737600, 67824000, 67910400, 67996800, 68083200, 68169600, 68256000, 68342400, 68428800, 68515200, 68601600, 68688000, 68774400, 68860800, 68947200, 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I am not familiar with 360 day calendar, but the 360-day calender's index seems an epoch-second with the origin of 1970-01-01 in Gregorian Calendar. If this is the case this code will work. library(data.table) library(dplyr) date <- attr(vec360, "index") %>% as.numeric() %>% as.POSIXct(origin="1970-01-01") %>% as.Date() dt360 <- data.table(vec360, date) date <- attr(vec365, "index") dt365 <- data.table(vec365, date) merged <- merge(dt360, dt365, all = T) print(merged) # date vec360 vec365 # 1: 1971-12-22 16.204273 NA # 2: 1971-12-23 5.544116 NA # 3: 1971-12-24 8.395240 NA # 4: 1971-12-25 12.905616 NA # 5: 1971-12-26 2.610978 NA # --- # 372: 1972-12-27 NA 20.525063 # 373: 1972-12-28 NA 24.060095 # 374: 1972-12-29 NA 13.411095 # 375: 1972-12-30 NA 2.920785 # 376: 1972-12-31 NA 9.063221 # get a zoo object back merged_zoo <- zoo(merged) Please make sure to check that the epoch second in the index of 360-day vector has the the origin of 1970-01-01.
R forecast function not picking up seasonality
I am having trouble picking up the seasonality the seems to be implied in the data. I think (though its just a guess that its using additive and not multiplicative seasonality). I am using the forecast function and thought it would automatically pick what I need based on a lecture from Dr. Hyndman. The following snipet of code plots the chart and I would have expected the forecast to be higher then it is. Am I missing a model parameter or something? Any help would be appreciated. sw<-c(2280, 1754, 1667, 1359, 1285, 1379, 2166, 1053, 1076, 1149, 1277, 1577, 1639, 1719, 1592, 2306, 3075, 2897, 1875, 1966, 2927, 3528, 2948, 2890, 3947, 3913, 3885, 4148, 5293, 5752, 6001, 7719, 5512, 6782, 6320, 6425, 6406, 7237, 8655, 9269, 12447, 13470, 13469, 13949, 17753, 17653, 14531, 14496, 13643, 12652, 12665, 10629, 8962, 8198, 6833, 5027, 4407, 4449, 4399, 5896, 6589, 3786, 4386, 4847, 5597, 5407, 4800, 7803, 9255, 10423, 5523, 8121, 6944, 8434, 9847, 9292, 9794, 10195, 10124, 11310, 12245, 12798, 14611, 15402, 13532, 16154, 15101, 14755, 17139, 16475, 19935, 19980, 25173, 28568, 27839, 28991, 27073, 29615, 25849, 27910, 27067, 21303, 20544, 15188, 13706, 9277, 10815, 7228, 4608, 4409, 9866, 8471, 8223, 6445, 6641, 6833, 11421, 8945, 8127, 10380, 12005, 13272, 9431, 12144, 14934, 14052, 11712, 14888, 15824, 17275, 18067, 19839, 21192, 22763, 22976, 23721, 22681, 20131, 19965, 20539, 19517, 22022, 23076, 30574, 40247, 43111, 39577, 40724, 44982, 44388, 46372, 43153, 36821, 32258, 31256, 27153, 23180, 18252, 16381, 13220, 12500, 10727, 9636, 8892, 8644, 9482, 9170, 10937, 12299, 15781, 11477, 16524, 16752, 18072, 14776, 13388, 18056, 19815, 21263, 22046, 26415, 24247, 25403, 30058, 26331, 32533, 31891, 35973, 27558, 24554, 25692, 25955, 24284, 24930, 28354, 34840, 40055, 42099, 42768, 48279, 50086, 56466, 42244, 51451, 44583, 39091, 33391, 29452, 25533) swts <- ts(sw, frequency=52, start=c(2006,30)) swfc <- forecast(swts,h=52) plot(swfc)
Did you data have multiple seasonal periods? If so you could check the tbats function. Anyway, your seasonal period is greater than 12, so forecast is using a stl decomposition to adjust your seasonal data. Maybe you wanna check ?stlf for more info on what parameters you can change, or try a BoxCox transformation: lambda <- BoxCox.lambda(sw) swfc <- forecast(swts,h=52, lambda = lambda, robust = TRUE) plot(swfc)