How can I find the indices of a continuous string of numbers?

Question

I have a dataframe where I want to find the start and end indices of every combination of 5 or more zero values.

Basically, if the string is

1 2 0 0 0 0 0 0 4 2 22 41 0 0 0 0 0 5 6 0 0 0 4

I want to have:

startindiex endindex
   3           8
   13          17

Using rle

library(bio3d)
runs <- rle(t$precip == 0)
runs2 <- rle2(t$precip == 0)

myruns = which(runs2$values == TRUE & runs2$lengths >= 5)

But I am not sure how to get those indices. Actual data -

    structure(list(time = structure(c(1147579200, 1147579500, 1147579800, 
1147580100, 1147580400, 1147580700, 1147581000, 1147581300, 1147581600, 
1147581900, 1147582200, 1147582500, 1147582800, 1147583100, 1147583400, 
1147583700, 1147584000, 1147584300, 1147584600, 1147584900, 1147585200, 
1147585500, 1147585800, 1147586100, 1147586400, 1147586700, 1147587000, 
1147587300, 1147587600, 1147587900, 1147588200, 1147588500, 1147588800, 
1147589100, 1147589400, 1147589700, 1147590000, 1147590300, 1147590600, 
1147590900, 1147591200, 1147591500, 1147591800, 1147592100, 1147592400, 
1147592700, 1147593000, 1147593300, 1147593600, 1147593900, 1147594200, 
1147594500, 1147594800, 1147595100, 1147595400, 1147595700, 1147596000, 
1147596300, 1147596600, 1147596900, 1147597200, 1147597500, 1147597800, 
1147598100, 1147598400, 1147598700, 1147599000, 1147599300, 1147599600, 
1147599900, 1147600200, 1147600500, 1147600800, 1147601100, 1147601400, 
1147601700, 1147602000, 1147602300, 1147602600, 1147602900, 1147603200, 
1147603500, 1147603800, 1147604100, 1147604400, 1147604700, 1147605000, 
1147605300, 1147605600, 1147605900, 1147606200, 1147606500, 1147606800, 
1147607100, 1147607400, 1147607700, 1147608000, 1147608300, 1147608600, 
1147608900, 1147609200, 1147609500, 1147609800, 1147610100, 1147610400, 
1147610700, 1147611000, 1147611300, 1147611600, 1147611900, 1147612200, 
1147612500, 1147612800, 1147613100, 1147613400, 1147613700, 1147614000, 
1147614300, 1147614600, 1147614900, 1147615200, 1147615500, 1147615800, 
1147616100, 1147616400, 1147616700, 1147617000, 1147617300, 1147617600, 
1147617900, 1147618200, 1147618500, 1147618800, 1147619100, 1147619400, 
1147619700, 1147620000, 1147620300, 1147620600, 1147620900, 1147621200, 
1147621500, 1147621800, 1147622100, 1147622400, 1147622700, 1147623000, 
1147623300, 1147623600, 1147623900, 1147624200, 1147624500, 1147624800, 
1147625100, 1147625400, 1147625700, 1147626000, 1147626300, 1147626600, 
1147626900, 1147627200, 1147627500, 1147627800, 1147628100, 1147628400, 
1147628700, 1147629000, 1147629300, 1147629600, 1147629900, 1147630200, 
1147630500, 1147630800, 1147631100, 1147631400, 1147631700, 1147632000, 
1147632300, 1147632600, 1147632900, 1147633200, 1147633500, 1147633800, 
1147634100, 1147634400, 1147634700, 1147635000, 1147635300, 1147635600, 
1147635900, 1147636200, 1147636500, 1147636800, 1147637100, 1147637400, 
1147637700, 1147638000, 1147638300, 1147638600, 1147638900, 1147639200, 
1147639500, 1147639800, 1147640100, 1147640400, 1147640700, 1147641000, 
1147641300, 1147641600, 1147641900, 1147642200, 1147642500, 1147642800, 
1147643100, 1147643400, 1147643700, 1147644000, 1147644300, 1147644600, 
1147644900, 1147645200, 1147645500, 1147645800, 1147646100, 1147646400, 
1147646700, 1147647000, 1147647300, 1147647600, 1147647900, 1147648200, 
1147648500, 1147648800, 1147649100, 1147649400, 1147649700, 1147650000, 
1147650300, 1147650600, 1147650900, 1147651200, 1147651500, 1147651800, 
1147652100, 1147652400, 1147652700, 1147653000, 1147653300, 1147653600, 
1147653900, 1147654200, 1147654500, 1147654800, 1147655100, 1147655400, 
1147655700, 1147656000, 1147656300, 1147656600, 1147656900, 1147657200, 
1147657500, 1147657800, 1147658100, 1147658400, 1147658700, 1147659000, 
1147659300, 1147659600, 1147659900, 1147660200, 1147660500, 1147660800, 
1147661100, 1147661400, 1147661700, 1147662000, 1147662300, 1147662600, 
1147662900, 1147663200, 1147663500, 1147663800, 1147664100, 1147664400, 
1147664700, 1147665000, 1147665300, 1147665600, 1147665900, 1147666200, 
1147666500, 1147666800, 1147667100, 1147667400, 1147667700, 1147668000, 
1147668300, 1147668600, 1147668900, 1147669200, 1147669500, 1147669800, 
1147670100, 1147670400, 1147670700, 1147671000, 1147671300, 1147671600, 
1147671900, 1147672200, 1147672500, 1147672800, 1147673100, 1147673400, 
1147673700, 1147674000, 1147674300, 1147674600, 1147674900, 1147675200, 
1147675500, 1147675800, 1147676100, 1147676400, 1147676700, 1147677000, 
1147677300, 1147677600, 1147677900, 1147678200, 1147678500, 1147678800, 
1147679100, 1147679400, 1147679700, 1147680000, 1147680300, 1147680600, 
1147680900, 1147681200, 1147681500, 1147681800, 1147682100, 1147682400, 
1147682700, 1147683000, 1147683300, 1147683600, 1147683900, 1147684200, 
1147684500, 1147684800, 1147685100, 1147685400, 1147685700, 1147686000, 
1147686300, 1147686600, 1147686900, 1147687200, 1147687500, 1147687800, 
1147688100, 1147688400, 1147688700, 1147689000, 1147689300, 1147689600, 
1147689900, 1147690200, 1147690500, 1147690800, 1147691100, 1147691400, 
1147691700, 1147692000, 1147692300, 1147692600, 1147692900, 1147693200, 
1147693500, 1147693800, 1147694100, 1147694400, 1147694700, 1147695000, 
1147695300, 1147695600, 1147695900, 1147696200, 1147696500, 1147696800, 
1147697100, 1147697400, 1147697700, 1147698000, 1147698300, 1147698600, 
1147698900, 1147699200, 1147699500, 1147699800, 1147700100, 1147700400, 
1147700700, 1147701000, 1147701300, 1147701600, 1147701900, 1147702200, 
1147702500, 1147702800, 1147703100, 1147703400, 1147703700, 1147704000, 
1147704300, 1147704600, 1147704900, 1147705200, 1147705500, 1147705800, 
1147706100, 1147706400, 1147706700, 1147707000, 1147707300, 1147707600, 
1147707900, 1147708200, 1147708500, 1147708800, 1147709100, 1147709400, 
1147709700, 1147710000, 1147710300, 1147710600, 1147710900, 1147711200, 
1147711500, 1147711800, 1147712100, 1147712400, 1147712700, 1147713000, 
1147713300, 1147713600, 1147713900, 1147714200, 1147714500, 1147714800, 
1147715100, 1147715400, 1147715700, 1147716000, 1147716300, 1147716600, 
1147716900, 1147717200, 1147717500, 1147717800, 1147718100, 1147718400, 
1147718700, 1147719000, 1147719300, 1147719600, 1147719900, 1147720200, 
1147720500, 1147720800, 1147721100, 1147721400, 1147721700, 1147722000, 
1147722300, 1147722600, 1147722900, 1147723200, 1147723500, 1147723800, 
1147724100, 1147724400, 1147724700, 1147725000, 1147725300, 1147725600, 
1147725900, 1147726200, 1147726500, 1147726800, 1147727100, 1147727400, 
1147727700, 1147728000, 1147728300, 1147728600, 1147728900, 1147729200, 
1147729500, 1147729800, 1147730100, 1147730400, 1147730700, 1147731000, 
1147731300, 1147731600, 1147731900, 1147732200, 1147732500, 1147732800, 
1147733100, 1147733400, 1147733700, 1147734000, 1147734300, 1147734600, 
1147734900, 1147735200, 1147735500, 1147735800, 1147736100, 1147736400, 
1147736700, 1147737000, 1147737300, 1147737600, 1147737900, 1147738200, 
1147738500, 1147738800, 1147739100, 1147739400, 1147739700, 1147740000, 
1147740300, 1147740600, 1147740900, 1147741200, 1147741500, 1147741800, 
1147742100, 1147742400, 1147742700, 1147743000, 1147743300, 1147743600, 
1147743900, 1147744200, 1147744500, 1147744800, 1147745100, 1147745400, 
1147745700, 1147746000, 1147746300, 1147746600, 1147746900, 1147747200, 
1147747500, 1147747800, 1147748100, 1147748400, 1147748700, 1147749000, 
1147749300, 1147749600, 1147749900, 1147750200, 1147750500, 1147750800, 
1147751100, 1147751400, 1147751700, 1147752000, 1147752300, 1147752600, 
1147752900, 1147753200, 1147753500, 1147753800, 1147754100, 1147754400, 
1147754700, 1147755000, 1147755300, 1147755600, 1147755900, 1147756200, 
1147756500, 1147756800, 1147757100, 1147757400, 1147757700, 1147758000, 
1147758300, 1147758600, 1147758900, 1147759200, 1147759500, 1147759800, 
1147760100, 1147760400, 1147760700, 1147761000, 1147761300, 1147761600, 
1147761900, 1147762200, 1147762500, 1147762800, 1147763100, 1147763400, 
1147763700, 1147764000, 1147764300, 1147764600, 1147764900, 1147765200, 
1147765500, 1147765800, 1147766100, 1147766400, 1147766700, 1147767000, 
1147767300, 1147767600, 1147767900, 1147768200, 1147768500, 1147768800, 
1147769100, 1147769400, 1147769700, 1147770000, 1147770300, 1147770600, 
1147770900, 1147771200, 1147771500, 1147771800, 1147772100, 1147772400, 
1147772700, 1147773000, 1147773300, 1147773600, 1147773900, 1147774200, 
1147774500, 1147774800, 1147775100, 1147775400, 1147775700, 1147776000, 
1147776300, 1147776600, 1147776900, 1147777200, 1147777500, 1147777800, 
1147778100, 1147778400, 1147778700, 1147779000, 1147779300, 1147779600, 
1147779900, 1147780200, 1147780500, 1147780800, 1147781100, 1147781400, 
1147781700, 1147782000, 1147782300, 1147782600, 1147782900, 1147783200, 
1147783500, 1147783800, 1147784100, 1147784400, 1147784700, 1147785000, 
1147785300, 1147785600, 1147785900, 1147786200, 1147786500, 1147786800, 
1147787100, 1147787400, 1147787700, 1147788000, 1147788300, 1147788600, 
1147788900, 1147789200, 1147789500, 1147789800, 1147790100, 1147790400, 
1147790700, 1147791000, 1147791300, 1147791600, 1147791900, 1147792200, 
1147792500, 1147792800, 1147793100, 1147793400, 1147793700, 1147794000, 
1147794300, 1147794600, 1147794900, 1147795200, 1147795500, 1147795800, 
1147796100, 1147796400, 1147796700, 1147797000, 1147797300, 1147797600, 
1147797900, 1147798200, 1147798500, 1147798800, 1147799100, 1147799400, 
1147799700, 1147800000, 1147800300, 1147800600, 1147800900, 1147801200, 
1147801500), class = c("POSIXct", "POSIXt"), tzone = "UTC"), 
    precip = c(2.02, 2.18, 1.09, 0.29, 0.28, 0.03, 0.04, 0.07, 
    0.23, 0.43, 0.65, 0.69, 0.68, 0.73, 1.2, 1.5, 1.89, 1.5, 
    1.12, 1.12, 0.52, 0.69, 1.16, 1.36, 1.22, 1.59, 1.58, 1.46, 
    1.42, 0.84, 0.28, 0.18, 0.28, 0.5, 0.5, 0.43, 0.28, 0.1, 
    0.01, 0, 0.06, 0.06, 0.02, 0.02, 0.08, 0.07, 0.07, 0.04, 
    0.04, 0.12, 0.31, 0.35, 0.41, 0.3, 0.3, 0.38, 0.53, 0.41, 
    0.2, 0.26, 0.26, 0.27, 0.27, 0.37, 0.35, 0.55, 0.32, 0.31, 
    0.67, 0.82, 1.08, 1.35, 1.78, 2.35, 2.31, 2.09, 2.1, 1.92, 
    1.89, 1.57, 1.3, 0.99, 1.04, 1.69, 1.69, 2.67, 3.4, 4.8, 
    4.93, 5.7, 6.66, 6.67, 6.83, 6.18, 5.62, 5.05, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 1.64, 1.69, 1.83, 1.96, 2.04, 2.05, 
    2.06, 1.88, 1.94, 2.07, 2.35, 2.64, 2.68, 3.09, 3.26, 2.32, 
    1.9, 1.72, 1.76, 3.45, 3.46, 2.65, 3.36, 2.75, 2.38, 1.67, 
    1.4, 1.38, 1.04, 0.81, 0.64, 0.79, 0.62, 0.42, 0.43, 0.38, 
    0.31, 0.24, 0.09, 0.07, 0.07, 0.1, 0.16, 0.17, 0.17, 0.14, 
    0.06, 0.06, 0.13, 0.32, 0.45, 0.49, 0.37, 0.28, 0.21, 0.21, 
    0.12, 0.11, 0.06, 0.03, 0.06, 0.06, 0.03, 0.05, 0, 0.09, 
    0.05, 0.03, 0.04, 0.04, 0.24, 0.33, 0.54, 0.74, 1.69, 2.21, 
    2.36, 2.38, 3.23, 2.05, 2.1, 1.88, 2.77, 2.47, 2.45, 2.72, 
    2.72, 2.79, 3.08, 3.19, 2.91, 2.94, 1.63, 1.55, 2.22, 1.43, 
    1.58, 1.13, 0.83, 0.81, 1.07, 1.63, 2.26, 2.32, 1.45, 0.84, 
    0.81, 0.8, 0.93, 0.73, 1.09, 1.13, 0.7, 0.69, 0.65, 0.61, 
    0.56, 0.4, 0.23, 0.05, 0.01, 0.01, 0.04, 0.09, 0.12, 0.18, 
    0.48, 0.58, 0.61, 0.78, 0.56, 0.32, 0.1, 0.1, 0.09, 0.18, 
    0.44, 0.8, 0.94, 0.73, 0.51, 0.5, 0.2, 0.25, 0.28, 0.23, 
    0.13, 0.13, 0.21, 0.22, 0.25, 0.25, 0.15, 0.01, 0.01, 0.02, 
    0.02, 0.04, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0.01, 
    0.01, 0.02, 0.02, 0.03, 0.03, 0.05, 0.03, 0.01, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03, 
    0.08, 0.17, 0.15, 0.07, 0.03, 0.03, 0.03, 0.04, 0.14, 0.29, 
    0.42, 0.62, 0.51, 0.38, 0.35, 0.26, 0.14, 0.14, 0.18, 0.19, 
    0.26, 0.18, 0.2, 0.21, 0.11, 0.14, 0.24, 0.26, 0.39, 0.44, 
    0.43, 0.48, 0.51, 0.53, 0.51, 0.94, 1.25, 1.1, 1.05, 0.56, 
    0.13, 0.02, 0.03, 0.13, 0.36, 0.36, 0.4, 0.46, 0.57, 0.31, 
    0.09, 0.09, 0.05, 0.05, 0.19, 0.09, 0.05, 0.23, 0.25, 0.16, 
    0.18, 0.17, 0.31, 0.57, 0.64, 0.77, 1.03, 1.05, 1.18, 0.98, 
    0.75, 0.36, 0.2, 0.12, 0.15, 0.15, 0.16, 0.15, 0.08, 0.03, 
    0.03, 0.02, 0.02, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0.01, 0.03, 0.03, 0.01, 0.01, 0, 0.01, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.01, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0.03, 0.07, 0.14, 0.21, 0.31, 
    0.39, 0.6, 0.6, 0.56, 0.48, 0.5, 0.51, 0.52, 0.74, 0.55, 
    0.47, 0.38, 0.39, 0.54, 0.58, 0.65, 0.85, 0.98, 0.85, 0.65, 
    0.62, 0.48, 0.39, 0.33, 0.31, 0.58, 0.59, 0.77, 1.07, 2.57, 
    3.06, 2.94, 1.32, 1.24, 1.13, 0.91, 1.11, 0.4, 0.05, 0.02, 
    0.01, 0, 0, 0, 0.07, 0.09, 0.01, 0, 0, 0, 0, 0, 0.01, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
    0, 0.01, 0, 0, 0, 0, 0, 0, 0, 0.3, 0.44, 0.41, 0.2, 0.19, 
    0.08, 0.03, 0.05, 0.05, 0.02, 0.04, 0.01, 0.02, 0, 0.01, 
    0.02, 0.01, 0.04, 0.06, 0.06, 0.13, 0.12, 0.15, 0.23, 0.18, 
    0.24, 0.36, 0.41, 0.4, 0.21, 0.19, 0.23, 0.05, 0.35, 0.35, 
    0.78, 0.88, 0.77, 0.88, 0.9, 0.89, 0.79, 0.55, 0.41, 0.37, 
    0.25, 0.06, 0.06, 0, 0.03, 0.03, 0.14, 0.16, 0.34, 0.43, 
    0.54, 0.61, 0.57, 0.63, 0.59, 0.37, 0.38, 0.41, 0.55, 1.05, 
    2.38, 2.33, 2.46, 3, 2.99, 3.54, 4, 4.23, 4.25, 4.51, 4.44, 
    4.39, 3.93, 3.5, 3.2, 3.1, 2.66, 1.93, 0.83, 0.83, 0.77, 
    1.07, 1.89, 1.77, 1.04, 0.87, 0.86, 0.52, 0.44, 0.35, 0.3, 
    0.29, 0.23, 0.23, 0.15, 0.11, 0.15, 0.18, 0.22, 0.2, 0.21, 
    0.22, 0.34, 0.55, 0.69, 1.01, 1.3, 1.47, 0.63, 1.48, 2.05, 
    2.96, 3.77, 3.7, 3.18, 3.12, 1.67, 1.29, 1.22, 1.33, 1.55, 
    1.55, 1.88, 1.89, 1.98, 2.28, 2.53, 2.38, 2.78, 2.79, 3.11, 
    3.56, 3.2, 3.36, 2.84, 2.44, 2.48, 2.37, 2.37, 1.76, 2.36, 
    2.69, 2.53, 2.51, 1.86, 1.26, 1.27, 1.65, 1.62)), .Names = c("time", 
"precip"), row.names = 459121:459862, class = "data.frame")

score 7 · Accepted Answer · edited May 23 '17 at 12:07

With data.table. Equality testing for floats is messy, so I'd just use a threshold:

library(data.table)
setDT(DF)

res = DF[, if (precip[1] < .001 & .N >= 5) 
  .(
    start = .I[1], 
    end = .I[.N]
  )
, by=rleid(precip < .001)]


   rleid start end
1:     4    97 108
2:     8   268 295
3:    12   307 326
4:    14   411 429
5:    18   437 450
6:    20   452 463
7:    24   516 520
8:    26   522 580
9:    28   582 588

How it works. rleid from the data.table package is doing the heavy lifting; see ?rleid. .I are row numbers; and .N is the number of rows in a by= group. See the package vignettes for general data.table intro materials.

Dplyr analogue The conventional way to use rleid in dplyr is to simply borrow it:

# need data.table installed
library(dplyr)
DF %>% mutate(rn = row_number()) %>% 
  group_by(g = data.table::rleid(precip < .001)) %>% 
  filter(precip[1] < .001, n() >= 5) %>% 
  summarise(start = first(rn), end = last(rn))


# A tibble: 9 x 3
      g start   end
  <int> <int> <int>
1     4    97   108
2     8   268   295
3    12   307   326
4    14   411   429
5    18   437   450
6    20   452   463
7    24   516   520
8    26   522   580
9    28   582   588

Thank you. This works! I am keeping the question open for now to see if someone posts a dplyr solution too. — maximusdooku, Nov 01 '16 at 21:18
@maximusdooku maybe it should have been mentioned that dplyr solution is preferred. — zx8754, Nov 01 '16 at 21:23
@zx8754 No preference. This works great and I am using it now. Just interested in a dplyr solution for learning. — maximusdooku, Nov 01 '16 at 21:25

alistaire · Answer 2 · 2016-11-02T01:04:54.683

With dplyr,

library(dplyr)

# calculate run lengths for whether precip is 0; use a tolerance if you like
rle(df$precip == 0) %>% 
    unclass() %>%    # convert rle object to normal list so...
    data.frame() %>%    # ...it can become a data.frame for dplyr
    mutate(stop = cumsum(lengths),    # stopping indices are just a cumulative sum
           start = stop - lengths + 1L) %>%    # subtract run length to get starting indices (h/t @Jota)
    filter(values, lengths >= 5)    # filter to rows which are TRUE (0) and are 5 or longer

##   lengths values stop start
## 1      12   TRUE  108    97
## 2      28   TRUE  295   268
## 3      20   TRUE  326   307
## 4      19   TRUE  429   411
## 5      14   TRUE  450   437
## 6      12   TRUE  463   452
## 7       5   TRUE  520   516
## 8      59   TRUE  580   522
## 9       7   TRUE  588   582

Since it happens to be simpler to calculate stop before start, the columns will be ordered as such. If you want them in order, just tack on %>% select(start, stop).

`select(-stop, stop)` would be another way to move `stop` from next-to-last to last, though not sure if that's abuse of `select`. — Jota, Nov 02 '16 at 01:00
@Jota Ha, that's clever! I suspect `lengths` and `values` are superfluous once `start` and `stop` are calculated anyway, but I might steal that trick someday. Also useful: alphabetizing: `select_(.dots = sort(names(.)))` and the super lazy: `select(1,2,4,3)` — alistaire, Nov 02 '16 at 01:13

score 0 · Answer 3 · answered Nov 01 '16 at 21:21

This isn't a dplyr solution and is much less elegant than @Frank but here's one using base and regex:

#round to 0's and "not 0's" and collapse to string
workingstr <- paste0(ifelse(dat$precip == 0, 0, 1), collapse = "")
#find starts
starts <- gregexpr("(^|[^0])0", workingstr)[[1]] + 1
#ends
ends <- gregexpr("0([^0]|$)", workingstr)[[1]]
#output
ans <- matrix(c(rbind(starts,ends)), ncol = 2, byrow = T)

score 0 · Answer 4 · edited Nov 02 '16 at 08:18

0

In base R (where d is your data):

rl <- rle(d$precip)$lengths
x <- which(rl>=5)
r <- data.frame(start=cumsum(rl)[x-1]+1, end=cumsum(rl)[x])

# start end
# 1    97 108
# 2   268 295
# 3   307 326
# 4   411 429
# 5   437 450
# 6   452 463
# 7   516 520
# 8   522 580
# 9   582 588

edited Nov 02 '16 at 08:18

Flexo

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answered Nov 01 '16 at 21:36

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Huh. you are not using the actual values of precip at all? I guess that if we had 10 obs in a row at precip of 20 cm, that would also be picked up, then? That may seem unlikely, but if the threshold were dropped from 5 to 2, say, this sort of weird behavior might be seen. – Frank Nov 01 '16 at 21:52
You may end up indexing by 0 with this solution. `cumsum(rl)[x-1]` will cause a problem if `x` contains 1. I was working on addressing that issue when you beat me to posting a `cumsum` solution ;) – Jota Nov 01 '16 at 21:53
Well, there might be many situations for which each answer can be generalized. The point is that the answer covers the OP's expectation given the data. – 989 Nov 01 '16 at 21:54
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OP would expect the correct solution if the first sequence of five 0s were to be start at index 1, and your solution would provide an incorrect answer. Maybe that scenario would never occur, but it's worth acknowledging, especially since it's a pretty easy thing to fix if you're willing... To use OP's original variables, something like `if (myruns[1L] == 1L) {c(0, cumsum(runs$length)[myruns[-1L] - 1L]) + 1L}` would do the trick... – Jota Nov 01 '16 at 22:04
There is not such a case in OP's data as actual data is posted. If it was, I would of course have considered. – 989 Nov 01 '16 at 22:09

How can I find the indices of a continuous string of numbers?

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