On learning resources, see The Big Book of R. A good introductory course is R Basics. For tutoring, there's a jobs board here (see this example).
Every R problem can be thought of with advantage as the interaction of three objects— an existing object, x , a desired object,y , and a function, f, that will return a value of y given x as an argument. In other words, school algebra— f(x) = y. Any of the objects can be composites.
Here's an illustration of a problem similar to yours, except with rows, rather than columns.
# x is a large data frame composed of integers
#
# simulated data to substitute for the actual x
set.seed(42)
(DF <- rbind(sample(-20:400,20),sample(-20:400,20),sample(-20:400,20),sample(-20:400,20),sample(-20:400,20)))
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 28 300 132 53 207 125 101 400 107 282 3 306 335 68
#> [2,] 276 68 262 88 -16 191 327 339 238 293 277 3 137 278
#> [3,] 125 88 327 176 -17 205 334 194 224 389 93 241 369 109
#> [4,] 117 19 -16 12 82 207 88 308 136 55 244 14 200 -5
#> [5,] 61 387 348 381 304 89 339 275 128 36 79 277 397 70
#> [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] 144 89 -1 349 346 366
#> [2,] 378 385 391 115 271 303
#> [3,] 351 -18 353 237 337 165
#> [4,] 336 199 227 304 97 109
#> [5,] 248 160 33 318 267 187
# Substitute a row with no values outside the range
DF[3,] <- 1:20
# y is a smaller data frame composed of integers lies outside the range 1:365, so let's create an object to represent that range
boring <- 1:365
# Every row of y is also a row of x, so y is a subset of x. If, and only if,
# there is no integer in x outside a specified range of integers is identical(x,y) equal to TRUE. The objective is to find all rows that have one or more integers outside the specified range of the interval.
#
# f is a function that needs to be composed, so let's start
# determine whether a single integer is outside the specified range
find_outsider <- function(x) !(x %in% boring)
# example
find_outsider(400)
#> [1] TRUE
# determine which integers in a single row are outside the range
find_outsider(DF[1,])
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
#> [13] FALSE FALSE FALSE FALSE TRUE FALSE FALSE TRUE
# but we're only really interested if there is at least one
sum(find_outsider(DF[1,])) > 0
#> [1] TRUE
# let's glue this together
pick_rows <- function(x,y) sum(find_outsider(x[y,])) > 0
# create a vector to hold results
hits <- vector()
# loop over rows
for(i in 1:nrow(DF)) hits[i] = pick_rows(DF,i)
# use the hits logical vector to subset DF
DF[hits,]
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12] [,13] [,14]
#> [1,] 28 300 132 53 207 125 101 400 107 282 3 306 335 68
#> [2,] 276 68 262 88 -16 191 327 339 238 293 277 3 137 278
#> [3,] 117 19 -16 12 82 207 88 308 136 55 244 14 200 -5
#> [4,] 61 387 348 381 304 89 339 275 128 36 79 277 397 70
#> [,15] [,16] [,17] [,18] [,19] [,20]
#> [1,] 144 89 -1 349 346 366
#> [2,] 378 385 391 115 271 303
#> [3,] 336 199 227 304 97 109
#> [4,] 248 160 33 318 267 187
This makes use of the subset operator [. which allows easy selection of rows, columns, both rows and columns.
mtcars[1:2,]
#> mpg cyl disp hp drat wt qsec vs am gear carb
#> Mazda RX4 21 6 160 110 3.9 2.620 16.46 0 1 4 4
#> Mazda RX4 Wag 21 6 160 110 3.9 2.875 17.02 0 1 4 4
mtcars[,3:4]
#> disp hp
#> Mazda RX4 160.0 110
#> Mazda RX4 Wag 160.0 110
#> Datsun 710 108.0 93
#> Hornet 4 Drive 258.0 110
#> Hornet Sportabout 360.0 175
#> Valiant 225.0 105
#> Duster 360 360.0 245
#> Merc 240D 146.7 62
#> Merc 230 140.8 95
#> Merc 280 167.6 123
#> Merc 280C 167.6 123
#> Merc 450SE 275.8 180
#> Merc 450SL 275.8 180
#> Merc 450SLC 275.8 180
#> Cadillac Fleetwood 472.0 205
#> Lincoln Continental 460.0 215
#> Chrysler Imperial 440.0 230
#> Fiat 128 78.7 66
#> Honda Civic 75.7 52
#> Toyota Corolla 71.1 65
#> Toyota Corona 120.1 97
#> Dodge Challenger 318.0 150
#> AMC Javelin 304.0 150
#> Camaro Z28 350.0 245
#> Pontiac Firebird 400.0 175
#> Fiat X1-9 79.0 66
#> Porsche 914-2 120.3 91
#> Lotus Europa 95.1 113
#> Ford Pantera L 351.0 264
#> Ferrari Dino 145.0 175
#> Maserati Bora 301.0 335
#> Volvo 142E 121.0 109
mtcars[1,1]
#> [1] 21