Use rbind() in nested for loop with apply() in r - r

Question

How can you use rbind in a for loop that runs through a list of dataframes? I tried to follow Looping through list of data frames in R but receive the following:
Error in apply(dataFramesList, 2, function(x) { :
dim(X) must have a positive length
I have two dataframes, dfTraining and dfAccuracy (code to reproduce dataframes is below), and need to add a row for any of the crop types missing from either of two columns, CROP or CROP_LABEL. I believe my problem is in my last line of code.
My code block is:
dataFramesList <- list(dfTraining, dfAccuracy)
apply(dataFramesList, 2, function(x){
cropNumbers <- seq(1,23, by = 1)
cropNumbers <- cropNumbers[-c(3)]
cropNumbers <- append(cropNumbers, 34)
listofCROPandCROP_LABELColumns <- list(dataFrameList$CROP, dataFrameList$CROP_LABEL)
missingCROP <- NULL
for (i in listofCROPandCROP_LABELColumns){
for (j in cropNumbers){
if (!j %in% i){
# If crop number is missing from CROP_LABEL, add missingCROP observation (row)
# Make row for missing crop type
missingCrop <- list(FREQUENCY = 0, AA = 1, CROP = j, CROP_LABEL = j, ACRES = 0)
dataFrameList <- rbind(dataFrameList, missingCrop)
}
}
}
})
My dfAccuracy dataframe:
structure(list(FREQUENCY = c(4L, 2L, 1L, 1L, 1L, 1L, 65L, 1L,
1L, 4L, 1L, 5L, 5L, 2L, 4L, 1L, 1L, 1L, 1L, 4L, 9L, 2L, 1L, 1L,
1L, 2L, 4L, 1L, 2L, 18L, 1L, 10L, 3L, 1L, 7L, 1L, 1L, 1L, 3L,
1L, 7L, 1L), AA = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
CROP = c(1L, 4L, 12L, 13L, 14L, 18L, 1L, 1L, 1L, 1L, 1L,
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 18L, 18L, 18L, 18L, 18L, 19L,
19L, 21L, 21L, 21L, 21L), CROP_LABEL = c(1L, 4L, 14L, 13L,
12L, 18L, 1L, 4L, 5L, 6L, 18L, 1L, 4L, 6L, 14L, 18L, 12L,
14L, 18L, 1L, 6L, 14L, 18L, 18L, 4L, 6L, 13L, 21L, 12L, 14L,
18L, 1L, 6L, 14L, 18L, 21L, 1L, 19L, 6L, 13L, 21L, 34L),
ACRES = c(331.737184484, 193.772138572, 26.48543619, 73.2696289437,
112.470306056, 66.6556450342, 3905.71121736, 24.9581079934,
39.9287379709, 259.662359273, 85.2786247851, 306.051491303,
368.342995232, 154.82030835, 265.754349805, 70.3722566979,
35.4066607701, 139.336463432, 58.4307705147, 251.070357093,
471.031628349, 150.965736858, 28.2780117926, 35.3426930108,
34.5730542194, 67.7383953308, 144.442123948, 33.2746560126,
69.4072817311, 1219.65459596, 92.4840910734, 582.983473317,
191.957841327, 35.708775262, 319.638682538, 60.6889287642,
82.6244195055, 36.2898952104, 267.422844756, 72.8352758659,
489.746546145, 65.5392893502)), row.names = c(25L, 26L, 27L,
29L, 30L, 31L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L,
70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L,
83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L
), class = "data.frame")
and my dfTraining dataframe is:
structure(list(FREQUENCY = c(7L, 1L, 1L, 4L, 2L, 6L, 1L, 107L,
1L, 21L, 1L, 1L, 1L, 2L, 1L, 19L, 3L, 1L, 1L, 12L, 1L, 2L, 32L,
2L, 2L, 29L, 2L, 18L, 1L), AA = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L), CROP = c(1L, 1L, 4L, 4L, 12L, 13L, 21L,
1L, 1L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 7L, 12L, 13L, 14L, 14L,
14L, 18L, 18L, 18L, 19L, 21L, 34L), CROP_LABEL = c(1L, 4L, 1L,
4L, 12L, 13L, 21L, 1L, 6L, 4L, 6L, 1L, 5L, 14L, 18L, 6L, 14L,
1L, 12L, 13L, 1L, 6L, 14L, 6L, 14L, 18L, 19L, 21L, 34L), ACRES = c(624.940370218,
26.9188766351, 37.8773839813, 291.79294767, 140.949264214, 391.571023675,
44.5217011939, 6806.02216989, 72.7500299887, 1676.12121152, 14.8739557721,
67.0700291739, 59.7438207953, 82.6713019474, 75.62666152, 1370.78710769,
145.215281276, 41.7380537313, 66.5236760194, 679.91208779, 70.9661875374,
38.8514254734, 1749.63365551, 109.917242057, 79.7758083723, 1660.85759895,
96.8771921798, 1428.71888481, 69.473161379)), row.names = c(18L,
19L, 20L, 21L, 22L, 23L, 24L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L), class = "data.frame")