Check monotonically increasing per row

Question

I have a dataframe as the following :

   COL_1 COL_2 COL_3 COL_4 COL_5 COL_6
   <int> <int> <int> <int> <int> <int>
 1     1     1     1     1     1     1
 2     1     1     1     1     1     2
 3     1     1     1     1     1     3
 4     1     1     1     1     1     4
 5     1     2     1     1     1     5
 6     1     1     1     1     1     6
 7     1     3     4     5     6     7
 8     1     1     1     1     1     8
 9     1     1     9     1     1     9
10     1     3     5     7     9    10

I'd like to filter this dataset to keep only values from COL_1 to COL_6 strictly increasing, so it would be as the following:

   COL_1 COL_2 COL_3 COL_4 COL_5 COL_6
   <int> <int> <int> <int> <int> <int>
 7     1     3     4     5     6     7
10     1     3     5     7     9    10

EDIT : The code should be used in a function with a dynamic number of columns (which will be named from COL_1 to COL_N). A "basic" code such as

df %>% filter(COL_6 > COL_5 & ... & COL_2 > COL_1)

will not work in my situation. Thank you very much

Answer 1

Method 1: apply by rows

df[!colSums(apply(df, 1, diff) <= 0), ]

#    COL_1 COL_2 COL_3 COL_4 COL_5 COL_6
# 7      1     3     4     5     6     7
# 10     1     3     5     7     9    10

The trick is that: ! + numeric vector will convert non-zeros to FALSE and zeros to TRUE.

!(-3:3)
# [1] FALSE FALSE FALSE  TRUE FALSE FALSE FALSE

---

Method 2: Map+Reduce (Much Faster)

df[Reduce(`&`, Map(`>`, df[-1], df[-ncol(df)])), ]

---

Benchmark on a larger dataset

library(microbenchmark)

bm <- microbenchmark(
  MrFlick = df[Reduce(\(x, y) { list(y, x[[2]] & (x[[1]] < y)) }, df, init = list(df[[1]]-1, TRUE))[[2]], ],
  Darren_2 = df[Reduce(`&`, Map(`>`, df[-1], df[-ncol(df)])), ],
  LMc = df[lapply(df, increasing())[[length(df)]], ],
  Thomas_2 = df[rowSums(df[-1] > df[-ncol(df)]) == ncol(df) - 1, ],
  Thomas_1 = df[rowMeans(df[-1] > df[-ncol(df)]) == 1, ],
  zx8754_1 = df[ apply(df, 1, \(i) !is.unsorted(i, strictly = TRUE)), ],
  Darren_1 = df[!colSums(apply(df, 1, diff) <= 0), ],
  zx8754_2 = df[ apply(df, 1, \(i) all(rank(i) == seq.int(ncol(df)))), ],
  setup = {
    nr <- 1e4
    df <- as.data.frame(matrix(runif(1e6, 0, 100), nr, 1e2))
    # pick 100 rows to sort
    ind <- sample(1:nr, 100)
    df[ind, ] <- t(apply(df[ind, ], 1, sort))
  },
  unit = "relative"
)

# Unit: relative
#      expr       min        lq      mean    median        uq       max neval
#   MrFlick  1.000000  1.000000  1.000000  1.000000  1.000000 1.0000000   100
#  Darren_2  1.106325  1.094651  1.076697  1.087586  1.005100 1.4959953   100
#       LMc  1.343194  1.341192  1.250389  1.331452  1.154049 0.9628282   100
#  Thomas_2  1.582195  1.572858  1.521608  1.574595  1.382800 1.3160589   100
#  Thomas_1  1.580272  1.563802  1.375415  1.552095  1.301315 1.1092026   100
#  zx8754_1  6.116539  6.362767  5.427589  6.896683  4.752581 2.2093543   100
#  Darren_1 23.723654 25.009280 20.208686 25.972528 19.187991 4.9937860   100
#  zx8754_2 44.612050 46.052300 34.288879 45.622591 29.601381 7.9472758   100

Answer 2

With a bit of work, you could use Reduce for this. For example

keep <- Reduce(function(x, y) {
  list(y, x[[2]] & (x[[1]] < y))
}, dd, init=list(dd[[1]]-1, TRUE))[[2]]
which(keep)
# [1]  7 10
dd[keep, ]
#    COL_1 COL_2 COL_3 COL_4 COL_5 COL_6
# 7      1     3     4     5     6     7
# 10     1     3     5     7     9    10

Tested with

dd <- read.table(text="
COL_1 COL_2 COL_3 COL_4 COL_5 COL_6
1     1     1     1     1     1     1
2     1     1     1     1     1     2
3     1     1     1     1     1     3
4     1     1     1     1     1     4
5     1     2     1     1     1     5
6     1     1     1     1     1     6
7     1     3     4     5     6     7
8     1     1     1     1     1     8
9     1     1     9     1     1     9
10     1     3     5     7     9    10", header=TRUE)