Cutting data by group

Usage

cut_by_group(df, col_data, col_group, group, cat_col)

Arguments

df: (dataframe) with a column of data to be cut and a column specifying the group of each observation.
col_data: (character) the column containing the data to be cut.
col_group: (character) the column containing the names of the groups according to which the data should be split.
group: (nested list) providing for each parameter value that should be analyzed in a categorical way: the name of the parameter (character), a series of breakpoints (numeric) where the first breakpoints is typically -Inf and the last Inf, and a series of name which will describe each category (character).
cat_col: (character) the name of the new column in which the cut label should he stored.

Value

data.frame with a column containing categorical values.

Details

Function used to categorize numeric data stored in long format depending on their group. Intervals are closed on the right (and open on the left).

Examples

group <- list(
  list(
    "Height",
    c(-Inf, 150, 170, Inf),
    c("=<150", "150-170", ">170")
  ),
  list(
    "Weight",
    c(-Inf, 65, Inf),
    c("=<65", ">65")
  ),
  list(
    "Age",
    c(-Inf, 31, Inf),
    c("=<31", ">31")
  ),
  list(
    "PreCondition",
    c(-Inf, 1, Inf),
    c("=<1", "<1")
  )
)
data <- data.frame(
  SUBJECT = rep(letters[1:10], 4),
  PARAM = rep(c("Height", "Weight", "Age", "other"), each = 10),
  AVAL = c(rnorm(10, 165, 15), rnorm(10, 65, 5), runif(10, 18, 65), rnorm(10, 0, 1)),
  index = 1:40
)

cut_by_group(data, "AVAL", "PARAM", group, "my_new_categories")
#>    SUBJECT  PARAM         AVAL index my_new_categories
#> 1        a Height 174.43473063     1              >170
#> 2        b Height 195.97537343     2              >170
#> 3        c Height 140.53515897     3             =<150
#> 4        d Height 172.68640425     4              >170
#> 5        e Height 137.05482762     5             =<150
#> 6        f Height 157.16981228     6           150-170
#> 7        g Height 164.21097135     7           150-170
#> 8        h Height 173.14494514     8              >170
#> 9        i Height 151.28887759     9           150-170
#> 10       j Height 172.02231631    10              >170
#> 11       a Weight  66.81475628    11               >65
#> 12       b Weight  58.47728227    12              =<65
#> 13       c Weight  68.68888161    13               >65
#> 14       d Weight  74.44252465    14               >65
#> 15       e Weight  64.51277448    15              =<65
#> 16       f Weight  60.32076323    16              =<65
#> 17       g Weight  64.92024844    17              =<65
#> 18       h Weight  60.86605523    18              =<65
#> 19       i Weight  57.43800174    19              =<65
#> 20       j Weight  69.67681595    20               >65
#> 21       a    Age  44.79211270    21               >31
#> 22       b    Age  33.77879679    22               >31
#> 23       c    Age  46.02435108    23               >31
#> 24       d    Age  27.00134748    24              =<31
#> 25       e    Age  62.54490507    25               >31
#> 26       f    Age  43.49657921    26               >31
#> 27       g    Age  43.59635949    27               >31
#> 28       h    Age  31.09406624    28               >31
#> 29       i    Age  38.99501605    29               >31
#> 30       j    Age  35.46102567    30               >31
#> 31       a  other  -1.91008747    31              <NA>
#> 32       b  other  -0.27923724    32              <NA>
#> 33       c  other  -0.31344598    33              <NA>
#> 34       d  other   1.06730788    34              <NA>
#> 35       e  other   0.07003485    35              <NA>
#> 36       f  other  -0.63912332    36              <NA>
#> 37       g  other  -0.04996490    37              <NA>
#> 38       h  other  -0.25148344    38              <NA>
#> 39       i  other   0.44479712    39              <NA>
#> 40       j  other   2.75541758    40              <NA>