R-润滑:将持续时间分成“持续时间"和“持续时间". [英] R - lubridate: split durations into "sub-durations"

问题描述

我有一个整洁的数据集my_durations，其中数据框中的每种情况对应于一段时间内采集的样本，如下所示:

I have a R tidy dataset my_durations where each case in the data frame corresponds to a sample taken over a duration of time like so:

> glimpse(my_durations)
Observations: 300
Variables: 5
$ sample_id  <int> 2, 8, 25, 41, 59, 70, 98, 100, 105, 106, 108, 114, 119, 126,...
$ site_id    <int> 2, 13, 12, 23, 47, 23, 66, 72, 72, 50, 50, 54, 45, 73, 48, 7...
$ start_date <dttm> 2015-04-12, 2015-06-10, 2015-07-02, 2015-07-22, 2015-07-29,...
$ end_date   <dttm> 2015-05-14, 2015-06-18, 2015-07-08, 2015-07-24, 2015-07-30,...
$ duration   <time> 32 days, 8 days, 6 days, 2 days, 1 days, 4 days, 12 days, 2...

其中sample_id是该样本的唯一ID，site_id只是用于跟踪采样位置的ID，start_date和end_date是采样开始和结束的时间，而只是start_date和end_date之间的时间差.

Where sample_id is the unique ID for that sample, site_id is just an ID for keeping track of where the sample was taken, start_date and end_date are when the sampling began and ended, and duration is simply the difference in time between start_date and end_date.

这是数据集的完整dput():

structure(list(sample_id = c(2L, 8L, 25L, 41L, 59L, 70L, 98L, 
100L, 105L, 106L, 108L, 114L, 119L, 126L, 128L, 146L, 151L, 164L, 
167L, 169L, 175L, 190L, 198L, 200L, 222L, 237L, 254L, 273L, 276L, 
280L, 281L, 290L, 300L, 305L, 314L, 345L, 354L, 371L, 376L, 379L, 
380L, 382L, 383L, 389L, 401L, 410L, 413L, 424L, 439L, 466L, 469L, 
476L, 482L, 484L, 499L, 505L, 517L, 538L, 580L, 582L, 583L, 584L, 
635L, 650L, 655L, 658L, 662L, 671L, 674L, 702L, 709L, 710L, 712L, 
715L, 716L, 724L, 734L, 735L, 738L, 785L, 789L, 793L, 794L, 803L, 
833L, 856L, 859L, 865L, 866L, 888L, 895L, 898L, 900L, 907L, 938L, 
979L, 980L, 988L, 991L, 1009L, 1026L, 1031L, 1034L, 1050L, 1058L, 
1061L, 1063L, 1066L, 1069L, 1077L, 1081L, 1091L, 1092L, 1099L, 
1100L, 1108L, 1115L, 1119L, 1143L, 1149L, 1158L, 1180L, 1190L, 
1195L, 1198L, 1207L, 1231L, 1234L, 1236L, 1242L, 1249L, 1250L, 
1271L, 1288L, 1294L, 1311L, 1312L, 1313L, 1319L, 1337L, 1341L, 
1345L, 1349L, 1360L, 1374L, 1379L, 1389L, 1393L, 1396L, 1401L, 
1404L, 1407L, 1422L, 1434L, 1438L, 1448L, 1454L, 1463L, 1473L, 
1489L, 1508L, 1514L, 1518L, 1531L, 1551L, 1564L, 1565L, 1571L, 
1572L, 1597L, 1602L, 1619L, 1624L, 1629L, 1630L, 1659L, 1661L, 
1666L, 1669L, 1672L, 1678L, 1690L, 1697L, 1700L, 1703L, 1707L, 
1715L, 1719L, 1725L, 1732L, 1737L, 1739L, 1754L, 1771L, 1788L, 
1790L, 1796L, 1799L, 1802L, 1805L, 1813L, 1814L, 1832L, 1839L, 
1844L, 1848L, 1873L, 1887L, 1893L, 1900L, 1901L, 1917L, 1920L, 
1939L, 1948L, 1954L, 1956L, 1968L, 1971L, 1975L, 1979L, 2008L, 
2015L, 2019L, 2021L, 2028L, 2035L, 2048L, 2062L, 2071L, 2072L, 
2075L, 2085L, 2090L, 2091L, 2100L, 2106L, 2115L, 2172L, 2178L, 
2181L, 2221L, 2225L, 2228L, 2230L, 2231L, 2237L, 2241L, 2265L, 
2266L, 2271L, 2282L, 2284L, 2311L, 2319L, 2337L, 2377L, 2405L, 
2409L, 2412L, 2429L, 2434L, 2460L, 2483L, 2485L, 2488L, 2490L, 
2500L, 2513L, 2520L, 2521L, 2527L, 2539L, 2555L, 2569L, 2599L, 
2605L, 2610L, 2635L, 2640L, 2641L, 2656L, 2667L, 2689L, 2705L, 
2720L, 2747L, 2753L, 2756L, 2761L, 2769L, 2772L, 2809L, 2816L, 
2818L, 2821L, 2823L, 2828L, 2837L, 2838L), site_id = c(2L, 13L, 
12L, 23L, 47L, 23L, 66L, 72L, 72L, 50L, 50L, 54L, 45L, 73L, 48L, 
73L, 84L, 85L, 85L, 52L, 66L, 73L, 76L, 95L, 61L, 73L, 106L, 
72L, 108L, 90L, 91L, 44L, 103L, 90L, 108L, 105L, 122L, 131L, 
133L, 133L, 133L, 133L, 133L, 52L, 138L, 136L, 113L, 146L, 55L, 
147L, 113L, 151L, 147L, 117L, 74L, 160L, 55L, 73L, 74L, 73L, 
151L, 73L, 169L, 168L, 73L, 73L, 44L, 73L, 182L, 74L, 73L, 105L, 
160L, 184L, 184L, 74L, 74L, 73L, 113L, 199L, 73L, 202L, 198L, 
73L, 199L, 74L, 73L, 74L, 74L, 198L, 213L, 212L, 213L, 44L, 160L, 
221L, 218L, 230L, 226L, 201L, 74L, 73L, 230L, 184L, 161L, 74L, 
74L, 73L, 214L, 74L, 73L, 73L, 74L, 73L, 74L, 73L, 74L, 74L, 
226L, 73L, 74L, 74L, 201L, 201L, 73L, 74L, 242L, 226L, 74L, 74L, 
113L, 73L, 249L, 73L, 249L, 74L, 247L, 240L, 73L, 74L, 44L, 73L, 
201L, 74L, 74L, 191L, 73L, 254L, 201L, 248L, 237L, 260L, 73L, 
226L, 74L, 191L, 226L, 259L, 73L, 226L, 74L, 237L, 74L, 248L, 
275L, 276L, 276L, 277L, 277L, 260L, 280L, 280L, 160L, 244L, 262L, 
74L, 44L, 74L, 44L, 73L, 74L, 73L, 73L, 74L, 74L, 73L, 74L, 73L, 
244L, 74L, 73L, 105L, 74L, 74L, 294L, 73L, 223L, 223L, 248L, 
295L, 73L, 74L, 74L, 295L, 73L, 269L, 73L, 201L, 199L, 74L, 74L, 
74L, 271L, 292L, 105L, 292L, 199L, 267L, 292L, 305L, 74L, 74L, 
295L, 309L, 74L, 310L, 310L, 271L, 316L, 74L, 73L, 305L, 73L, 
113L, 74L, 74L, 73L, 191L, 74L, 245L, 226L, 321L, 241L, 320L, 
113L, 323L, 73L, 320L, 73L, 74L, 74L, 73L, 73L, 191L, 74L, 73L, 
74L, 74L, 74L, 73L, 245L, 113L, 73L, 16L, 73L, 348L, 350L, 245L, 
306L, 191L, 245L, 350L, 244L, 348L, 113L, 191L, 306L, 73L, 73L, 
306L, 350L, 73L, 361L, 245L, 73L, 114L, 191L, 73L, 357L, 361L, 
376L, 364L, 360L, 378L, 357L, 73L, 380L, 73L, 350L, 364L), start_date = structure(c(1428796800, 
1433894400, 1435795200, 1437523200, 1438128000, 1438300800, 1437609600, 
1438905600, 1438905600, 1438041600, 1438041600, 1438646400, 1438560000, 
1439424000, 1438819200, 1440115200, 1439856000, 1440115200, 1440115200, 
1438041600, 1440460800, 1441497600, 1440547200, 1441238400, 1438992000, 
1442707200, 1443225600, 1439337600, 1440633600, 1442707200, 1442707200, 
1444089600, 1442534400, 1444348800, 1443225600, 1444694400, 1445817600, 
1445472000, 1446854400, 1446854400, 1446854400, 1446854400, 1446854400, 
1441584000, 1447459200, 1447372800, 1444348800, 1447977600, 1448064000, 
1448064000, 1446940800, 1449014400, 1448064000, 1445904000, 1449878400, 
1449792000, 1449878400, 1451001600, 1452729600, 1452902400, 1452470400, 
1452988800, 1453075200, 1454889600, 1455235200, 1455408000, 1454976000, 
1455753600, 1453766400, 1456963200, 1457308800, 1456876800, 1456876800, 
1456790400, 1457395200, 1457827200, 1458086400, 1458172800, 1455580800, 
1460073600, 1460419200, 1456617600, 1460073600, 1460851200, 1460073600, 
1462233600, 1462320000, 1462492800, 1462665600, 1460073600, 1462579200, 
1462492800, 1462579200, 1462838400, 1463443200, 1463702400, 1463616000, 
1464912000, 1464825600, 1465171200, 1466035200, 1466121600, 1464912000, 
1461888000, 1464652800, 1466467200, 1466553600, 1466640000, 1462579200, 
1466726400, 1466985600, 1467331200, 1467331200, 1467590400, 1467590400, 
1467936000, 1468108800, 1468281600, 1468368000, 1469145600, 1469404800, 
1470009600, 1470009600, 1470009600, 1470441600, 1470787200, 1471219200, 
1470096000, 1470268800, 1470873600, 1467590400, 1471564800, 1471478400, 
1472256000, 1471478400, 1472515200, 1471219200, 1472256000, 1472601600, 
1472860800, 1472688000, 1473292800, 1472947200, 1473638400, 1474243200, 
1474156800, 1475193600, 1475193600, 1474761600, 1475193600, 1471046400, 
1475193600, 1476316800, 1473724800, 1476748800, 1476403200, 1476748800, 
1477785600, 1478044800, 1477958400, 1479168000, 1478304000, 1479254400, 
1473811200, 1477699200, 1476576000, 1476576000, 1477872000, 1478476800, 
1475193600, 1477094400, 1477094400, 1479859200, 1479340800, 1475884800, 
1480896000, 1480464000, 1480982400, 1480982400, 1481241600, 1481846400, 
1482192000, 1482451200, 1482537600, 1482624000, 1482969600, 1483228800, 
1483401600, 1481846400, 1483747200, 1483920000, 1483488000, 1484438400, 
1484956800, 1485216000, 1485388800, 1473292800, 1478995200, 1485216000, 
1485216000, 1485907200, 1485907200, 1486339200, 1485216000, 1486512000, 
1485216000, 1487116800, 1487030400, 1485388800, 1487721600, 1487808000, 
1488153600, 1487289600, 1485129600, 1488240000, 1485129600, 1485388800, 
1480896000, 1485129600, 1488412800, 1489104000, 1490054400, 1485216000, 
1490054400, 1490400000, 1490572800, 1490659200, 1489622400, 1489881600, 
1491436800, 1491523200, 1488412800, 1491782400, 1488758400, 1491868800, 
1492473600, 1492646400, 1492387200, 1494633600, 1494288000, 1494288000, 
1495152000, 1494201600, 1494979200, 1491868800, 1495065600, 1496102400, 
1494979200, 1497052800, 1497052800, 1497225600, 1497657600, 1497744000, 
1498435200, 1499212800, 1499904000, 1501372800, 1502409600, 1502582400, 
1502668800, 1502236800, 1501718400, 1504569600, 1502841600, 1505174400, 
1503878400, 1503964800, 1505260800, 1503964800, 1505606400, 1505865600, 
1503964800, 1504656000, 1503878400, 1505520000, 1508716800, 1503964800, 
1509580800, 1510704000, 1503964800, 1503964800, 1511481600, 1508889600, 
1512518400, 1513987200, 1513555200, 1514764800, 1516665600, 1515456000, 
1508889600, 1517097600, 1511654400, 1510012800, 1518393600, 1515456000, 
1519257600, 1518825600, 1519344000, 1503964800, 1511654400), class = c("POSIXct", 
"POSIXt"), tzone = "UTC"), end_date = structure(c(1431561600, 
1434585600, 1436313600, 1437696000, 1438214400, 1438646400, 1438646400, 
1439078400, 1439078400, 1438128000, 1438128000, 1439164800, 1438905600, 
1439942400, 1438992000, 1440288000, 1440460800, 1440720000, 1440720000, 
1438128000, 1440547200, 1441584000, 1441238400, 1441670400, 1439769600, 
1443052800, 1444003200, 1439769600, 1441324800, 1444348800, 1444348800, 
1444694400, 1444521600, 1444867200, 1445126400, 1445212800, 1446336000, 
1445558400, 1447113600, 1447286400, 1447372800, 1447545600, 1447632000, 
1442707200, 1447545600, 1448236800, 1444608000, 1448236800, 1449014400, 
1449273600, 1449273600, 1449532800, 1449446400, 1445990400, 1450051200, 
1450137600, 1450396800, 1451174400, 1452902400, 1452988800, 1453075200, 
1453075200, 1454716800, 1455148800, 1455321600, 1455494400, 1455580800, 
1455840000, 1454976000, 1457049600, 1457395200, 1457395200, 1457395200, 
1457395200, 1457481600, 1457913600, 1458172800, 1458345600, 1458518400, 
1460419200, 1460592000, 1457222400, 1460592000, 1460937600, 1461801600, 
1462320000, 1462406400, 1462665600, 1462752000, 1462924800, 1462924800, 
1463097600, 1463270400, 1463443200, 1464134400, 1464912000, 1464912000, 
1465084800, 1465257600, 1465776000, 1466121600, 1466208000, 1466208000, 
1463702400, 1466553600, 1466553600, 1466640000, 1466726400, 1466726400, 
1466812800, 1467072000, 1467417600, 1467417600, 1467676800, 1467676800, 
1468022400, 1468195200, 1468368000, 1468972800, 1469232000, 1469491200, 
1470096000, 1470614400, 1470614400, 1470528000, 1470873600, 1471305600, 
1471305600, 1470355200, 1471219200, 1471564800, 1471651200, 1471910400, 
1472342400, 1472083200, 1472601600, 1472601600, 1472601600, 1472688000, 
1473120000, 1473206400, 1473379200, 1473638400, 1473811200, 1474502400, 
1474761600, 1475280000, 1475366400, 1475452800, 1475712000, 1472515200, 
1475884800, 1476403200, 1475625600, 1476835200, 1477180800, 1477440000, 
1477872000, 1478131200, 1478649600, 1479254400, 1479513600, 1479600000, 
1476316800, 1478476800, 1477526400, 1477699200, 1478304000, 1478649600, 
1476576000, 1477699200, 1480204800, 1480464000, 1480636800, 1476403200, 
1480982400, 1480982400, 1481241600, 1481587200, 1481673600, 1481932800, 
1482278400, 1482537600, 1482624000, 1482796800, 1483056000, 1483315200, 
1483488000, 1483660800, 1483833600, 1484006400, 1484006400, 1484524800, 
1485043200, 1485388800, 1485475200, 1474761600, 1480809600, 1485734400, 
1485734400, 1485993600, 1485993600, 1486425600, 1486425600, 1486598400, 
1486684800, 1487203200, 1487462400, 1487635200, 1487808000, 1487894400, 
1488240000, 1488240000, 1488672000, 1488844800, 1488931200, 1488931200, 
1485648000, 1489449600, 1489449600, 1489536000, 1490140800, 1490313600, 
1490400000, 1490572800, 1490659200, 1490745600, 1491004800, 1489968000, 
1491523200, 1491609600, 1491609600, 1491868800, 1491868800, 1492128000, 
1492560000, 1492732800, 1492992000, 1494720000, 1494892800, 1494979200, 
1495670400, 1495843200, 1495670400, 1495929600, 1495324800, 1496188800, 
1496188800, 1497139200, 1497139200, 1497312000, 1497744000, 1497830400, 
1498953600, 1499299200, 1499990400, 1501459200, 1502496000, 1502668800, 
1502755200, 1503446400, 1503446400, 1504656000, 1503100800, 1505260800, 
1505260800, 1505260800, 1505865600, 1506211200, 1506297600, 1506470400, 
1506470400, 1507075200, 1507248000, 1508025600, 1509235200, 1509321600, 
1509753600, 1510790400, 1510790400, 1510790400, 1511568000, 1511913600, 
1513123200, 1514160000, 1514764800, 1516320000, 1516752000, 1516838400, 
1516838400, 1517184000, 1517270400, 1518480000, 1518998400, 1519171200, 
1519344000, 1519344000, 1519430400, 1519689600, 1519689600), class = c("POSIXct", 
"POSIXt"), tzone = "UTC"), duration = structure(c(32, 8, 6, 2, 
1, 4, 12, 2, 2, 1, 1, 6, 4, 6, 2, 2, 7, 7, 7, 1, 1, 1, 8, 5, 
9, 4, 9, 5, 8, 19, 19, 7, 23, 6, 22, 6, 6, 1, 3, 5, 6, 8, 9, 
13, 1, 10, 3, 3, 11, 14, 27, 6, 16, 1, 2, 4, 6, 2, 2, 1, 7, 1, 
19, 3, 1, 1, 7, 1, 14, 1, 1, 6, 6, 7, 1, 1, 1, 2, 34, 4, 2, 7, 
6, 1, 20, 1, 1, 2, 1, 33, 4, 7, 8, 7, 8, 14, 15, 2, 5, 7, 1, 
1, 15, 21, 22, 1, 1, 1, 48, 1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 
1, 1, 7, 7, 1, 1, 1, 14, 1, 4, 46, 1, 5, 1, 7, 1, 16, 4, 1, 3, 
6, 1, 8, 2, 3, 7, 1, 2, 8, 6, 17, 8, 1, 22, 1, 9, 8, 1, 1, 8, 
1, 14, 4, 29, 9, 11, 13, 5, 2, 16, 7, 36, 7, 15, 6, 1, 6, 3, 
7, 5, 1, 1, 1, 1, 2, 1, 1, 1, 21, 1, 1, 6, 1, 1, 2, 1, 17, 21, 
6, 6, 1, 1, 1, 14, 1, 17, 1, 5, 26, 1, 1, 1, 11, 41, 7, 44, 41, 
55, 50, 12, 5, 1, 59, 4, 2, 1, 1, 16, 1, 1, 1, 37, 1, 36, 3, 
1, 1, 7, 1, 7, 8, 6, 19, 8, 47, 3, 1, 14, 1, 1, 1, 1, 1, 6, 1, 
1, 1, 1, 1, 1, 14, 20, 1, 3, 1, 16, 15, 7, 26, 8, 7, 29, 28, 
39, 29, 6, 62, 2, 1, 79, 79, 1, 35, 7, 2, 14, 18, 1, 16, 92, 
1, 65, 98, 7, 43, 1, 6, 1, 182, 93), class = "difftime", units = "days")), class = c("tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -300L))

我现在面临的挑战是，对于任何超过n天的duration(现在让我们使用n == 7)，我想将持续时间拆分"为n天的长时间段.例如，对于sample_id == 2，duration是32天，因此我想将其分为四个7天的细分，再加上一个4天的细分.最后，sample_id == 2的原始行将变成 5 行，每行分别具有start_date，end_date和duration，分别与五个段中的每个段相对应.我想要一个名为segment_id的新列，以标识每个新创建的段，同时保留所有原始列.顺便说一句，对于原始duration短于n的sample_id，我想保持原样，并且得到segment_id == 1.

The challenge I am facing now is that for any duration that's more than n days (let's go with n == 7 for now), I want to "split" that duration into n-day long segments. For example, for sample_id == 2, the duration is 32 days, so I want to split that into four 7-day segments, plus one 4-day segment. In the end, the original row for sample_id == 2 would turn into five rows each with start_date, end_date, and duration that correspond to each of the five segments. I would like to have a new column called segment_id to identify each of the newly-created segments while keeping all of the original columns. BTW, for the sample_ids that have original durations shorter than n, I want to keep them as is, and they would get segment_id == 1.

我很困惑.是否有一种相当整洁"的方式来实现这一目标?提前非常感谢您.

I am stumped. Is there a fairly "tidy" way to achieve this? Thank you very much in advance.

推荐答案

我们可以创建列表的列并对其进行unnest:

We can create columns of lists and unnest them:

library(tidyverse)
library(lubridate)
df %>% group_by(sample_id,site_id) %>% 
  mutate(duration_new = (as.numeric(duration)-1) %>% seq(0,.,by=7) %>% c(duration) %>% diff %>% list,
         start_date_new = list(start_date + days(c(0,cumsum(head(duration_new[[1]],-1))))),
         end_date_new = list(start_date + days(cumsum(duration_new[[1]]))),
         segment_id = list(seq_along(duration_new[[1]]))) %>% 
  unnest %>%
  ungroup
# # A tibble: 619 x 9
#    sample_id site_id start_date          end_date            duration duration_new start_date_new      end_date_new        segment_id
#        <int>   <int> <dttm>              <dttm>              <time>          <dbl> <dttm>              <dttm>                   <int>
#  1         2       2 2015-04-12 00:00:00 2015-05-14 00:00:00 32                  7 2015-04-12 00:00:00 2015-04-19 00:00:00          1
#  2         2       2 2015-04-12 00:00:00 2015-05-14 00:00:00 32                  7 2015-04-19 00:00:00 2015-04-26 00:00:00          2
#  3         2       2 2015-04-12 00:00:00 2015-05-14 00:00:00 32                  7 2015-04-26 00:00:00 2015-05-03 00:00:00          3
#  4         2       2 2015-04-12 00:00:00 2015-05-14 00:00:00 32                  7 2015-05-03 00:00:00 2015-05-10 00:00:00          4
#  5         2       2 2015-04-12 00:00:00 2015-05-14 00:00:00 32                  4 2015-05-10 00:00:00 2015-05-14 00:00:00          5
#  6         8      13 2015-06-10 00:00:00 2015-06-18 00:00:00 8                   7 2015-06-10 00:00:00 2015-06-17 00:00:00          1
#  7         8      13 2015-06-10 00:00:00 2015-06-18 00:00:00 8                   1 2015-06-17 00:00:00 2015-06-18 00:00:00          2
#  8        25      12 2015-07-02 00:00:00 2015-07-08 00:00:00 6                   6 2015-07-02 00:00:00 2015-07-08 00:00:00          1
#  9        41      23 2015-07-22 00:00:00 2015-07-24 00:00:00 2                   2 2015-07-22 00:00:00 2015-07-24 00:00:00          1
# 10        59      47 2015-07-29 00:00:00 2015-07-30 00:00:00 1                   1 2015-07-29 00:00:00 2015-07-30 00:00:00          1
# # ... with 609 more rows

嵌套的工作方式

在mutate调用之后，data.frame的行数与以前一样，但是有新的列.这些新列都包含列表元素(即列表元素)，我们称它们为列表列.

After the mutate call, the data.frame has just the same amount of rows as before, but it has new columns. These new columns all contain list elements (elements that are lists), we call them list columns.

然后unnest垂直开发这些列表，我们得到了更多的行，只是重复了不是列表列的列中的元素.

Then unnest develop these lists vertically and we get our additional rows, elements from columns that were not list columns are just repeated.

from ?unnest:如果您有一个列表列，这会使列表的每个元素成为其自己的行.

from ?unnest : If you have a list-column, this makes each element of the list its own row.

duration_new的构建方式

how duration_new is built

我们使用seq进行了一系列简单的中断(请参阅?seq)，但是seq没有给出最后一个元素(这是完整的持续时间)，因此我们将其与c相加. diff休息时间给出了单独的持续时间.

We a simple sequence of breaks using seq (see ?seq), seq doesn't give the last element however (which is the complete duration), so we add it with c. The diff of breaks gives the individual durations.

您可以逐步执行此操作(选择直到在管道之前输入ctrl + enter):

You can execute this step by step (select until before a pipe, ctrl+enter):

duration <- 32
(as.numeric(duration)-1) %>% seq(0,.,by=7) %>% c(duration) %>% diff %>% list

start_date_new和end_date_new的构建方式

how start_date_new and end_date_new are built

我们从start_date开始并添加累积的持续时间，对于start_date_new，我们将0添加到第一个元素，因此我们将其保持偏移.

We start from start_date and add the cumulated durations, for start_date_new we add 0 to the first element, so we keep them offset.

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