Here is an example of how to merge data in R with a longitudinal study. In this example, we initially asked for participants ethnicities in the data set dataTest. Given that ethnicity does not change over time, we do not want the participants to insert their ethnicity every time, so we only ask them once and match their unique id and ethnicity with their unique id in the data that we collected from their responses over time (i.e. dataTest1). This process is relatively simple using the merge, and within merge, by function. We merge the two data sets on the unique id variable by the id variables and every time a unique id is present, the participant’s id filled in. In this example, every person is measured four times, therefore, each of the four occasions an id is present in the later data set (i.e. dataTest1 in this example), the ethnicity associated with that id from the initial data set (i.e. dataTest) is inputted. For example, person 20 is Asian American (AA) therefore, in the later data set, dataTest1, each of the four measurements for person 20 is Asian American, while each of the four measurements for person 21 is White, because in the initial data set (i.e. dataTest) person 21 is White.
dataTest = data.frame(id = 1:100, ethnicity = c(rep("AA", 20), rep("WHITE", 30), rep("AI", 20), rep("BLACK", 30)))
head(dataTest)## id ethnicity
## 1 1 AA
## 2 2 AA
## 3 3 AA
## 4 4 AA
## 5 5 AA
## 6 6 AAdataTest1 = data.frame(id = rep(1:100, each = 4), x1 = rnorm(400), x2 = rnorm(400))
head(dataTest1)## id x1 x2
## 1 1 0.7211053 1.5022603
## 2 1 0.4243986 1.4797650
## 3 1 -0.3886065 -0.2450818
## 4 1 0.1090372 0.7141220
## 5 2 -0.4956306 0.9931484
## 6 2 -0.6356422 0.6716928dataTest2 = merge(dataTest, dataTest1, by = c("id"))
head(dataTest2)## id ethnicity x1 x2
## 1 1 AA 0.7211053 1.5022603
## 2 1 AA 0.4243986 1.4797650
## 3 1 AA -0.3886065 -0.2450818
## 4 1 AA 0.1090372 0.7141220
## 5 2 AA -0.4956306 0.9931484
## 6 2 AA -0.6356422 0.6716928dataTest2## id ethnicity x1 x2
## 1 1 AA 0.721105273 1.5022603333
## 2 1 AA 0.424398594 1.4797649971
## 3 1 AA -0.388606533 -0.2450818419
## 4 1 AA 0.109037157 0.7141219777
## 5 2 AA -0.495630596 0.9931483642
## 6 2 AA -0.635642178 0.6716928456
## 7 2 AA 0.164058644 -2.2248544682
## 8 2 AA -0.574536761 1.0744319103
## 9 3 AA 1.011783788 -0.3737145514
## 10 3 AA -1.746421405 0.0781593992
## 11 3 AA 1.149778414 -0.8640947707
## 12 3 AA 1.148782734 -2.1590707175
## 13 4 AA -1.021565498 0.3186510954
## 14 4 AA -0.301270409 0.1923515127
## 15 4 AA -0.407084528 -0.2481305799
## 16 4 AA -0.113581961 0.7161929880
## 17 5 AA -0.433959395 -0.2154132178
## 18 5 AA 0.079502462 1.4843418474
## 19 5 AA -0.135656659 -0.9230903781
## 20 5 AA -1.107433155 -1.6354862419
## 21 6 AA 0.850565377 -0.8903130041
## 22 6 AA -0.061027743 -1.5595705795
## 23 6 AA -0.333954962 0.6925745236
## 24 6 AA -0.344716710 0.1858361638
## 25 7 AA -1.125187850 -0.3053065518
## 26 7 AA 0.337007789 1.8590558973
## 27 7 AA 1.004080454 0.0090751762
## 28 7 AA -1.105713317 1.2171889705
## 29 8 AA -1.021217953 -1.0922350836
## 30 8 AA 1.019661573 -0.4836956856
## 31 8 AA 0.592488014 -0.9131463056
## 32 8 AA -0.549370635 0.8194911180
## 33 9 AA -0.213248210 -1.6547207674
## 34 9 AA 2.393722429 0.4249560672
## 35 9 AA -0.251780439 0.6873670592
## 36 9 AA -1.069289474 0.7031297530
## 37 10 AA 0.715576578 1.3666486938
## 38 10 AA 0.616906917 -1.8920835641
## 39 10 AA 0.953953505 1.0961288423
## 40 10 AA 0.048845131 0.1226401968
## 41 11 AA 0.743909373 0.3495895863
## 42 11 AA -0.936363120 1.4549436387
## 43 11 AA -0.102216384 1.3170651728
## 44 11 AA 0.816278492 -0.6756680050
## 45 12 AA 0.352170037 -0.2255050410
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## 47 12 AA -0.351465560 -0.5041671760
## 48 12 AA -0.482292647 0.6942264903
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## 50 13 AA 0.604906689 -0.6803985165
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## 52 13 AA -0.819605501 0.6841974691
## 53 14 AA 0.825696030 -0.6971608457
## 54 14 AA -1.447494518 1.1067855122
## 55 14 AA 1.074046972 0.5510446626
## 56 14 AA -0.352521961 -1.9384525202
## 57 15 AA 0.404954121 1.1419505300
## 58 15 AA 0.171443518 0.1871549909
## 59 15 AA -2.211615285 -1.1179855767
## 60 15 AA -0.144177243 -0.5682125127
## 61 16 AA 1.977085739 -1.4018228101
## 62 16 AA -0.974510918 -1.1187255769
## 63 16 AA 0.427244651 0.0490979098
## 64 16 AA -1.396910588 0.0126945713
## 65 17 AA -1.634600115 -1.8468934235
## 66 17 AA 0.394172024 -0.2281265695
## 67 17 AA 0.019231601 -1.3560200570
## 68 17 AA -0.218670508 -0.4543705127
## 69 18 AA 0.376776806 -1.7863240053
## 70 18 AA 1.914555561 -0.3080273182
## 71 18 AA -1.681990332 0.3328286098
## 72 18 AA 0.540331710 1.3656664960
## 73 19 AA -0.131025210 -0.1286373510
## 74 19 AA -0.724275740 -1.4326880115
## 75 19 AA 0.037084712 -1.3718351745
## 76 19 AA 1.108136967 -0.6699598805
## 77 20 AA -2.546368476 -0.5649851776
## 78 20 AA -0.872514243 0.8334441832
## 79 20 AA -1.646291523 -0.8019908259
## 80 20 AA 1.060532587 -1.5020535552
## 81 21 WHITE -0.255049454 0.9829529568
## 82 21 WHITE 0.345498565 0.7830022529
## 83 21 WHITE -0.812846640 0.0060248786
## 84 21 WHITE 0.463019823 0.0358043409
## 85 22 WHITE 1.307304152 -0.7529490809
## 86 22 WHITE -1.322916833 -0.1305413127
## 87 22 WHITE -0.236501377 -0.8929770778
## 88 22 WHITE 0.104967982 1.0567621664
## 89 23 WHITE 1.064296884 0.6960754195
## 90 23 WHITE 0.232959670 0.6291387347
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## 92 23 WHITE -0.769240390 -1.0450199755
## 93 24 WHITE 1.612242143 0.0721082412
## 94 24 WHITE 0.196311246 1.6824007534
## 95 24 WHITE 1.282552447 0.0039496936
## 96 24 WHITE 0.526407458 1.0922611057
## 97 25 WHITE -0.468472973 2.5357138058
## 98 25 WHITE 0.329176952 0.6073004752
## 99 25 WHITE 1.486878578 0.3024654193
## 100 25 WHITE 0.017913720 -2.7468739478
## 101 26 WHITE -0.217839069 1.6405496252
## 102 26 WHITE -0.887008023 0.7259086401
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## 117 30 WHITE 0.460917933 1.7008976464
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## 121 31 WHITE 1.008402525 -0.8992728943
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## 129 33 WHITE 0.093924383 -0.7940544750
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## 145 37 WHITE 1.225967952 1.5234114875
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## 200 50 WHITE 0.180732569 -0.6004974165
## 201 51 AI 1.587995366 0.1132850406
## 202 51 AI -1.311570383 -0.5159362268
## 203 51 AI 1.113175413 0.2302760206
## 204 51 AI -0.579740493 -0.6157636646
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## 228 57 AI -1.250687124 -0.1589810591
## 229 58 AI 0.584313161 -1.1152516892
## 230 58 AI -1.472760449 -1.6998638748
## 231 58 AI 1.623420593 -1.5552832062
## 232 58 AI 0.072942383 1.0977820650
## 233 59 AI 1.435879169 -0.0498581256
## 234 59 AI -1.753396565 -1.0912246319
## 235 59 AI 1.089697772 0.3435140136
## 236 59 AI -1.572987550 1.1069309396
## 237 60 AI -0.981532609 -0.5777539935
## 238 60 AI -0.168890359 -1.2212940997
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## 245 62 AI 0.667130877 -1.7787115960
## 246 62 AI -0.298641054 1.4034937483
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## 252 63 AI 2.150891726 1.0250298092
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## 280 70 AI -1.085447971 -0.2011176970
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## 382 96 BLACK -1.976732257 -0.1591540482
## 383 96 BLACK 0.213510746 -0.8804079734
## 384 96 BLACK -0.975439474 0.5320308061
## 385 97 BLACK -1.006869495 0.4055260474
## 386 97 BLACK -1.133158667 -0.3006725213
## 387 97 BLACK -2.186302986 1.1453931873
## 388 97 BLACK -0.297888844 -1.0087213143
## 389 98 BLACK -0.597091499 -0.6439034991
## 390 98 BLACK 1.068124276 -0.1879958797
## 391 98 BLACK 0.023762709 -0.3028674567
## 392 98 BLACK -1.502461508 0.4581871035
## 393 99 BLACK 2.846883510 1.2964149279
## 394 99 BLACK 1.527294074 -1.7372952711
## 395 99 BLACK 1.729306075 -1.6662322124
## 396 99 BLACK 1.552024551 2.1717812182
## 397 100 BLACK 0.205174335 2.5130324775
## 398 100 BLACK 1.458999222 -0.5103773882
## 399 100 BLACK -0.630119509 -0.3143448466
## 400 100 BLACK 1.089992955 0.2653279862