Description Usage Arguments Value Functions Examples
Doesn't make any allowance for factors.
1 2 3 4 5 6 7 | binary_col_names(x, invert = FALSE)
two_cat_col_names(x, invert = FALSE, ignore_na = FALSE, trim = TRUE)
binary_cols(x, invert = FALSE)
two_cat_cols(x, invert = FALSE)
|
x |
data frame |
invert |
single logical, if true, will return non-binary columns |
ignore_na |
If TRUE, then return columns with two distinct values in addition to NA. Default is FALSE, i.e. NA is counted as a distinct item. |
trim |
If character column found, then trim white space before assessing |
vector of column names
two_cat_col_names
: Get the columns which have exactly two
categories therein, not including NA values. This would catch 0,1 "Yes",
"No", etc.
binary_cols
: Get the data frame containing just the binary
columns.
two_cat_cols
: Get the data frame containing only columns of
input which have two categories
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | dat <- data.frame(
c("a", "b"), c(TRUE, FALSE), c(1, 0), c(1L, 0L),
c(1L, 2L), c(0.1, 0.2), c("9", "8")
)
names(dat) <- c(
"char", "bin", "binfloat", "binint",
"int", "float", "charint"
)
binary_cols(dat)
binary_col_names(dat)
binary_col_names(dat, invert = TRUE)
df <- data.frame(
x = c("A", "B", "A", "B"),
y = letters[1:4],
z = c("y", NA, "y", NA),
stringsAsFactors = FALSE
)
two_cat_col_names(df)
df[1, 1] <- NA
df[2, 2] <- NA
df
stopifnot(two_cat_col_names(df) == "z")
stopifnot(two_cat_col_names(df, ignore_na = TRUE) == "x")
|
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