Jocelyn
Reputation: 15
Why is complete function in R filling NA where there should be data?
I am attempting to group my data by Date and TagId. Then I want R to insert rows for every timestamp (by 1 second intervals) within the limits of the data since the technology I have used to collect data did not record every second. I have been attempting to use complete() to do this but when I run my code, there are NA where there should be data. Realisrically, I would only have rows with NA where the tech did not record for that timestamp. Why is R not recognizing the original data?
Subset of data:
test.tl.df<-structure(list(TimeStamp = structure(c(1620827781.702, 1620827781.855,
1620827781.97, 1620827782.109, 1620827782.289, 1620827782.543,
1620827782.691, 1620827782.835, 1620827783.017, 1620827783.155,
1620827783.289, 1620827783.451, 1620827783.562, 1620827783.703,
1620827783.891, 1620827784.039, 1620827784.178, 1620827784.35,
1620827784.574, 1620827784.7, 1620827785.049, 1620827785.217,
1620827785.378, 1620827785.531, 1620827785.701, 1620827687.161,
1620827687.336, 1620827687.45, 1620827687.618, 1620827687.72,
1620827687.899, 1620827688.03, 1620827688.819, 1620827689.004,
1620827689.2, 1620827689.694, 1620827689.815, 1620827689.98,
1620827690.115, 1620827690.3, 1620827696.533, 1620827696.638,
1620827696.741, 1620827696.901, 1620827697.157, 1620827697.279,
1620827697.498, 1620827697.659, 1620827697.779, 1620827697.957,
1620827627.151, 1620827627.309, 1620827627.509, 1620827627.73,
1620827627.835, 1620827628.052, 1620827628.171, 1620827628.319,
1620827628.47, 1620827628.576, 1620827628.753, 1620827628.934,
1620827629.09, 1620827629.231, 1620827629.408, 1620827629.518,
1620827629.734, 1620827629.934, 1620827630.092, 1620827630.233,
1620827630.513, 1620827630.631, 1620827631.154, 1620827631.293,
1620827631.432), class = c("POSIXct", "POSIXt"), tzone = ""),
X = c(5.89598826154092, 5.93791271699873, 5.95745747913937,
5.97531673131645, 5.98786056787007, 6.01280433401741, 6.02369080342943,
6.04817366094946, 6.04086924540685, 6.03367357942426, 6.01667755062048,
5.96883360045214, 5.94710260032311, 5.92496326341453, 5.90268626699764,
5.88799316977224, 5.87565620604427, 5.87362664771825, 5.86905500816018,
5.86745834148706, 5.86951914761855, 5.87262950674978, 5.88326620021375,
5.90040833677955, 5.92448840244043, 1.24185943739894, 1.24597573701794,
1.24539804643766, 1.24715100753861, 1.24847594415012, 1.30183097393758,
1.37183019995288, 1.4615760213942, 1.55815413991442, 1.64513860862261,
1.69681188407639, 1.71337459850817, 1.71609057320665, 1.69895410055044,
1.67590031808273, 1.66045021087941, 1.63882386713164, 1.62924888620807,
1.63270621075178, 1.64942435040534, 1.6531270720543, 1.65888472741661,
1.64389882426681, 1.61354696892943, 1.56152546676422, 0.728111843095455,
0.730162725463753, 0.744387941317623, 0.738731735273356,
0.721741721644766, 0.670014886147995, 0.666275229016122,
0.672065863737642, 0.685546403693867, 0.682300753537701,
0.671154297743983, 0.653082501973309, 0.637480718319724,
0.621574289840462, 0.615345992530349, 0.600854359258857,
0.554768843658357, 0.530938553945528, 0.516277055791205,
0.51940892254905, 0.591310138826507, 0.649213991710782, 0.746869513530296,
0.766495679155129, 0.738197864557209), Y = c(2.30724215120166,
2.31883126259496, 2.33222074725113, 2.35302807958263, 2.38472696955849,
2.44714063728847, 2.47064139980452, 2.49481071775822, 2.49256160226708,
2.48806814222051, 2.48844520742674, 2.49893172371777, 2.52027390810229,
2.52862483625626, 2.53039621120783, 2.5246497238482, 2.50629056165106,
2.51418612609813, 2.56721743651182, 2.59204870432762, 2.61260712991711,
2.6264934058931, 2.6306770989127, 2.62053021716598, 2.61239279017464,
2.23113288533871, 2.23432054791702, 2.2117872492948, 2.20389594470134,
2.20773974027402, 2.20871918698254, 2.18952600823194, 2.15764309251788,
2.12054128994731, 2.08556653103235, 2.06726113738676, 2.06718817529792,
2.06117571700761, 2.04376847185883, 2.03232860383726, 2.05100596425561,
2.02161715899392, 1.9693492997952, 1.91920071812734, 1.87069711296396,
1.83411945899655, 1.78516749480308, 1.7939678539998, 1.77098566923588,
1.81066185464076, 2.28703982490007, 2.26723466051699, 2.22439039816399,
2.15960792965659, 2.12080970081487, 2.03567109219226, 2.0171323726191,
2.00794390020906, 1.99716484089622, 1.99129995901906, 1.99099009677074,
1.99773650944296, 2.00857502498479, 2.03691749802669, 2.04653755325499,
2.0562919419392, 2.09175843456784, 2.11384056474162, 2.13824156543216,
2.16150542778873, 2.19639006520483, 2.18802207972054, 2.12418420410169,
2.0583187159211, 2.04396471839008), TagId = c("eba9a4b",
"eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b",
"eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b",
"eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b",
"eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b", "eba9a4b",
"ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e",
"ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e",
"ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e",
"ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e", "ebbb91e",
"ebbb91e", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea",
"ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea",
"ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea",
"ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea",
"ebcf0ea", "ebcf0ea"), Date = structure(c(18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759
), class = "Date")), row.names = c(1L, 2L, 3L, 4L, 5L, 6L,
7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 25L, 1135L, 1136L, 1137L, 1138L, 1139L,
1140L, 1141L, 1142L, 1143L, 1144L, 1145L, 1146L, 1147L, 1148L,
1149L, 1150L, 1151L, 1152L, 1153L, 1154L, 1155L, 1156L, 1157L,
1158L, 1159L, 2087L, 2088L, 2089L, 2090L, 2091L, 2092L, 2093L,
2094L, 2095L, 2096L, 2097L, 2098L, 2099L, 2100L, 2101L, 2102L,
2103L, 2104L, 2105L, 2106L, 2107L, 2108L, 2109L, 2110L, 2111L
), class = "data.frame")
complete code:
complete.test<-test.tl.df %>% group_by(TagId, Date)
complete.tl.df<-tidyr::complete(complete.test, TimeStamp=seq(min(TimeStamp), max(TimeStamp), by = 1))
Output of complete code:
structure(list(TimeStamp = structure(c(1620827627.151, 1620827628.151,
1620827629.151, 1620827630.151, 1620827631.151, 1620827632.151,
1620827633.151, 1620827634.151, 1620827635.151, 1620827636.151,
1620827637.151, 1620827638.151, 1620827639.151, 1620827640.151,
1620827641.151, 1620827642.151, 1620827643.151, 1620827644.151,
1620827645.151, 1620827646.151, 1620827647.151, 1620827648.151,
1620827649.151, 1620827650.151, 1620827651.151, 1620827652.151,
1620827653.151, 1620827654.151, 1620827655.151, 1620827656.151,
1620827657.151, 1620827658.151, 1620827659.151, 1620827660.151,
1620827661.151, 1620827662.151, 1620827663.151, 1620827664.151,
1620827665.151, 1620827666.151, 1620827667.151, 1620827668.151,
1620827669.151, 1620827670.151, 1620827671.151, 1620827672.151,
1620827673.151, 1620827674.151, 1620827675.151, 1620827676.151,
1620827677.151, 1620827678.151, 1620827679.151, 1620827680.151,
1620827681.151, 1620827682.151, 1620827683.151, 1620827684.151,
1620827685.151, 1620827686.151, 1620827687.151, 1620827688.151,
1620827689.151, 1620827690.151, 1620827691.151, 1620827692.151,
1620827693.151, 1620827694.151, 1620827695.151, 1620827696.151,
1620827697.151, 1620827698.151, 1620827699.151, 1620827700.151,
1620827701.151, 1620827702.151, 1620827703.151, 1620827704.151,
1620827705.151, 1620827706.151, 1620827707.151, 1620827708.151,
1620827709.151, 1620827710.151, 1620827711.151, 1620827712.151,
1620827713.151, 1620827714.151, 1620827715.151, 1620827716.151,
1620827717.151, 1620827718.151, 1620827719.151, 1620827720.151,
1620827721.151, 1620827722.151, 1620827723.151, 1620827724.151,
1620827725.151, 1620827726.151, 1620827727.151, 1620827728.151,
1620827729.151, 1620827730.151, 1620827731.151, 1620827732.151,
1620827733.151, 1620827734.151, 1620827735.151, 1620827736.151,
1620827737.151, 1620827738.151, 1620827739.151, 1620827740.151,
1620827741.151, 1620827742.151, 1620827743.151, 1620827744.151,
1620827745.151, 1620827746.151, 1620827747.151, 1620827748.151,
1620827749.151, 1620827750.151, 1620827751.151, 1620827752.151,
1620827753.151, 1620827754.151, 1620827755.151, 1620827756.151,
1620827757.151, 1620827758.151, 1620827759.151, 1620827760.151,
1620827761.151, 1620827762.151, 1620827763.151, 1620827764.151,
1620827765.151, 1620827766.151, 1620827767.151, 1620827768.151,
1620827769.151, 1620827770.151, 1620827771.151, 1620827772.151,
1620827773.151, 1620827774.151, 1620827775.151, 1620827776.151,
1620827777.151, 1620827778.151, 1620827779.151, 1620827780.151,
1620827781.151, 1620827782.151, 1620827783.151, 1620827784.151,
1620827785.151, 1620827781.702, 1620827781.855, 1620827781.97,
1620827782.109, 1620827782.289, 1620827782.543, 1620827782.691,
1620827782.835, 1620827783.017, 1620827783.155, 1620827783.289,
1620827783.451, 1620827783.562, 1620827783.703, 1620827783.891,
1620827784.039, 1620827784.178, 1620827784.35, 1620827784.574,
1620827784.7, 1620827785.049, 1620827785.217, 1620827785.378,
1620827785.531, 1620827785.701, 1620827687.161, 1620827687.336,
1620827687.45, 1620827687.618, 1620827687.72, 1620827687.899,
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1620827690.3, 1620827696.533, 1620827696.638, 1620827696.741,
1620827696.901, 1620827697.157, 1620827697.279, 1620827697.498,
1620827697.659, 1620827697.779, 1620827697.957, 1620827627.309,
1620827627.509, 1620827627.73, 1620827627.835, 1620827628.052,
1620827628.171, 1620827628.319, 1620827628.47, 1620827628.576,
1620827628.753, 1620827628.934, 1620827629.09, 1620827629.231,
1620827629.408, 1620827629.518, 1620827629.734, 1620827629.934,
1620827630.092, 1620827630.233, 1620827630.513, 1620827630.631,
1620827631.154, 1620827631.293, 1620827631.432), tzone = "", class = c("POSIXct",
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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2.12418420410169, 2.0583187159211, 2.04396471839008), TagId = c("ebcf0ea",
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "eba9a4b",
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"ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea", "ebcf0ea",
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
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NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
NA, NA, NA, NA, NA, NA, NA, NA, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759, 18759,
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18759, 18759, 18759, 18759, 18759, 18759), class = "Date")), row.names = c(NA,
-233L), class = c("tbl_df", "tbl", "data.frame"))
Upvotes: 1
Views: 59
Answers (1)
akrun
Reputation: 887118
There is a chance that the timestamps have milliseconds. We may need to use ceiling_date/round_date with unit specified as sec before doing the complete
library(tidyr)
library(dplyr)
library(lubridate)
out <- complete.test %>%
mutate(TimeStamp = ceiling_date(TimeStamp, unit = "sec")) %>%
complete(TimeStamp=seq(min(TimeStamp), max(TimeStamp), by = 1))
-output
as.data.frame(out)
TagId Date TimeStamp X Y
1 eba9a4b 2021-05-12 2021-05-12 09:56:22 5.8959883 2.307242
2 eba9a4b 2021-05-12 2021-05-12 09:56:22 5.9379127 2.318831
3 eba9a4b 2021-05-12 2021-05-12 09:56:22 5.9574575 2.332221
4 eba9a4b 2021-05-12 2021-05-12 09:56:23 5.9753167 2.353028
5 eba9a4b 2021-05-12 2021-05-12 09:56:23 5.9878606 2.384727
6 eba9a4b 2021-05-12 2021-05-12 09:56:23 6.0128043 2.447141
7 eba9a4b 2021-05-12 2021-05-12 09:56:23 6.0236908 2.470641
8 eba9a4b 2021-05-12 2021-05-12 09:56:23 6.0481737 2.494811
9 eba9a4b 2021-05-12 2021-05-12 09:56:24 6.0408692 2.492562
10 eba9a4b 2021-05-12 2021-05-12 09:56:24 6.0336736 2.488068
11 eba9a4b 2021-05-12 2021-05-12 09:56:24 6.0166776 2.488445
12 eba9a4b 2021-05-12 2021-05-12 09:56:24 5.9688336 2.498932
13 eba9a4b 2021-05-12 2021-05-12 09:56:24 5.9471026 2.520274
14 eba9a4b 2021-05-12 2021-05-12 09:56:24 5.9249633 2.528625
15 eba9a4b 2021-05-12 2021-05-12 09:56:24 5.9026863 2.530396
16 eba9a4b 2021-05-12 2021-05-12 09:56:25 5.8879932 2.524650
17 eba9a4b 2021-05-12 2021-05-12 09:56:25 5.8756562 2.506291
18 eba9a4b 2021-05-12 2021-05-12 09:56:25 5.8736266 2.514186
19 eba9a4b 2021-05-12 2021-05-12 09:56:25 5.8690550 2.567217
20 eba9a4b 2021-05-12 2021-05-12 09:56:25 5.8674583 2.592049
21 eba9a4b 2021-05-12 2021-05-12 09:56:26 5.8695191 2.612607
22 eba9a4b 2021-05-12 2021-05-12 09:56:26 5.8726295 2.626493
23 eba9a4b 2021-05-12 2021-05-12 09:56:26 5.8832662 2.630677
24 eba9a4b 2021-05-12 2021-05-12 09:56:26 5.9004083 2.620530
25 eba9a4b 2021-05-12 2021-05-12 09:56:26 5.9244884 2.612393
26 ebbb91e 2021-05-12 2021-05-12 09:54:48 1.2418594 2.231133
27 ebbb91e 2021-05-12 2021-05-12 09:54:48 1.2459757 2.234321
28 ebbb91e 2021-05-12 2021-05-12 09:54:48 1.2453980 2.211787
29 ebbb91e 2021-05-12 2021-05-12 09:54:48 1.2471510 2.203896
30 ebbb91e 2021-05-12 2021-05-12 09:54:48 1.2484759 2.207740
31 ebbb91e 2021-05-12 2021-05-12 09:54:48 1.3018310 2.208719
32 ebbb91e 2021-05-12 2021-05-12 09:54:49 1.3718302 2.189526
33 ebbb91e 2021-05-12 2021-05-12 09:54:49 1.4615760 2.157643
34 ebbb91e 2021-05-12 2021-05-12 09:54:50 1.5581541 2.120541
35 ebbb91e 2021-05-12 2021-05-12 09:54:50 1.6451386 2.085567
36 ebbb91e 2021-05-12 2021-05-12 09:54:50 1.6968119 2.067261
37 ebbb91e 2021-05-12 2021-05-12 09:54:50 1.7133746 2.067188
38 ebbb91e 2021-05-12 2021-05-12 09:54:50 1.7160906 2.061176
39 ebbb91e 2021-05-12 2021-05-12 09:54:51 1.6989541 2.043768
40 ebbb91e 2021-05-12 2021-05-12 09:54:51 1.6759003 2.032329
41 ebbb91e 2021-05-12 2021-05-12 09:54:52 NA NA
42 ebbb91e 2021-05-12 2021-05-12 09:54:53 NA NA
43 ebbb91e 2021-05-12 2021-05-12 09:54:54 NA NA
44 ebbb91e 2021-05-12 2021-05-12 09:54:55 NA NA
45 ebbb91e 2021-05-12 2021-05-12 09:54:56 NA NA
46 ebbb91e 2021-05-12 2021-05-12 09:54:57 1.6604502 2.051006
47 ebbb91e 2021-05-12 2021-05-12 09:54:57 1.6388239 2.021617
48 ebbb91e 2021-05-12 2021-05-12 09:54:57 1.6292489 1.969349
49 ebbb91e 2021-05-12 2021-05-12 09:54:57 1.6327062 1.919201
50 ebbb91e 2021-05-12 2021-05-12 09:54:58 1.6494244 1.870697
51 ebbb91e 2021-05-12 2021-05-12 09:54:58 1.6531271 1.834119
52 ebbb91e 2021-05-12 2021-05-12 09:54:58 1.6588847 1.785167
53 ebbb91e 2021-05-12 2021-05-12 09:54:58 1.6438988 1.793968
54 ebbb91e 2021-05-12 2021-05-12 09:54:58 1.6135470 1.770986
55 ebbb91e 2021-05-12 2021-05-12 09:54:58 1.5615255 1.810662
56 ebcf0ea 2021-05-12 2021-05-12 09:53:48 0.7281118 2.287040
57 ebcf0ea 2021-05-12 2021-05-12 09:53:48 0.7301627 2.267235
58 ebcf0ea 2021-05-12 2021-05-12 09:53:48 0.7443879 2.224390
59 ebcf0ea 2021-05-12 2021-05-12 09:53:48 0.7387317 2.159608
60 ebcf0ea 2021-05-12 2021-05-12 09:53:48 0.7217417 2.120810
61 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6700149 2.035671
62 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6662752 2.017132
63 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6720659 2.007944
64 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6855464 1.997165
65 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6823008 1.991300
66 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6711543 1.990990
67 ebcf0ea 2021-05-12 2021-05-12 09:53:49 0.6530825 1.997737
68 ebcf0ea 2021-05-12 2021-05-12 09:53:50 0.6374807 2.008575
69 ebcf0ea 2021-05-12 2021-05-12 09:53:50 0.6215743 2.036917
70 ebcf0ea 2021-05-12 2021-05-12 09:53:50 0.6153460 2.046538
71 ebcf0ea 2021-05-12 2021-05-12 09:53:50 0.6008544 2.056292
72 ebcf0ea 2021-05-12 2021-05-12 09:53:50 0.5547688 2.091758
73 ebcf0ea 2021-05-12 2021-05-12 09:53:50 0.5309386 2.113841
74 ebcf0ea 2021-05-12 2021-05-12 09:53:51 0.5162771 2.138242
75 ebcf0ea 2021-05-12 2021-05-12 09:53:51 0.5194089 2.161505
76 ebcf0ea 2021-05-12 2021-05-12 09:53:51 0.5913101 2.196390
77 ebcf0ea 2021-05-12 2021-05-12 09:53:51 0.6492140 2.188022
78 ebcf0ea 2021-05-12 2021-05-12 09:53:52 0.7468695 2.124184
79 ebcf0ea 2021-05-12 2021-05-12 09:53:52 0.7664957 2.058319
80 ebcf0ea 2021-05-12 2021-05-12 09:53:52 0.7381979 2.043965
-checking the original data for gaps
complete.test %>%
mutate(TimeStamp = ceiling_date(TimeStamp, unit = "sec")) %>%
ungroup %>%
slice(40:41)
# A tibble: 2 x 5
TimeStamp X Y TagId Date
<dttm> <dbl> <dbl> <chr> <date>
1 2021-05-12 09:54:51 1.68 2.03 ebbb91e 2021-05-12
2 2021-05-12 09:54:57 1.66 2.05 ebbb91e 2021-05-12
Upvotes: 1
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