Choose interesting annotation colnames from a data.frame

Choose interesting annotation colnames from a data.frame

Usage

choose_annotation_colnames(
  df,
  min_reps = 2,
  min_values = 2,
  max_values = Inf,
  keep_numeric = FALSE,
  simplify = TRUE,
  max_colnames = 20,
  ...
)

Arguments

df

data.frame with annotations that could be interesting to display at the top or side of a heatmap.

min_reps

numeric minimum number of replicates required for a column to be considered interesting. For example, min_reps=3 would require any value in a column to be repeated at least 3 times for that column to be interesting. This filter is intended to remove columns whose values are all unique, such as row identifiers.

min_values

numeric minimum number of unique values required for a column to be considered interesting.

max_values

numeric maximum number of unique values required for a column to be considered interesting. Too many values and the interest is lost. Also, too many values, and the color key becomes unbearable with too many labels.

keep_numeric

logical indicating whether to keep columns with numeric values. When keep_numeric == TRUE it will override the rules above.

simplify

logical indicating whether to filter out columns whose data already matches another column with 1:1 cardinality. This step requires platjam::cardinality() until that function is moved into the jamba package.

max_colnames

numeric maximum number of colnames to return. Note that columns are not sorted for priority, so they will be returned in the order they appear in df after applying the relevant criteria.

...

additional arguments are ignored.

Value

character vector of colnames in df that meet the criteria. If no colnames meet the criteria, this function returns NULL.

See also

Other jamses utilities: contrast2comp_dev(), fold_to_log2fold(), intercalate(), list2im_opt(), log2fold_to_fold(), make_block_arrow_polygon(), mark_stat_hits(), matrix_normalize(), point_handedness(), point_slope_intercept(), shortest_unique_abbreviation(), shrinkDataFrame(), shrink_df(), shrink_matrix(), sort_samples(), strsplitOrdered(), sub_split_vector(), update_function_params(), update_list_elements()

Examples

df <- data.frame(
   threereps=paste0("threereps_", letters[c(1,1,1,3,5,7,7)]),
   time=paste0("time_", letters[c(1:7)]),
   tworeps=paste0("tworeps_", letters[c(12,12,14,14,15,15,16)]),
   num=sample(1:7),
   class=paste0("class_", LETTERS[c(1,1,1,3,5,7,7)]),
   blah=rep("blah", 7),
   maxvalues=c("one", "two", "three", "four", "five", "six", "six"))
df
#>     threereps   time   tworeps num   class blah maxvalues
#> 1 threereps_a time_a tworeps_l   5 class_A blah       one
#> 2 threereps_a time_b tworeps_l   4 class_A blah       two
#> 3 threereps_a time_c tworeps_n   7 class_A blah     three
#> 4 threereps_c time_d tworeps_n   2 class_C blah      four
#> 5 threereps_e time_e tworeps_o   3 class_E blah      five
#> 6 threereps_g time_f tworeps_o   1 class_G blah       six
#> 7 threereps_g time_g tworeps_p   6 class_G blah       six

choose_annotation_colnames(df)
#> [1] "threereps" "tworeps"   "maxvalues"
df[,choose_annotation_colnames(df)]
#>     threereps   tworeps maxvalues
#> 1 threereps_a tworeps_l       one
#> 2 threereps_a tworeps_l       two
#> 3 threereps_a tworeps_n     three
#> 4 threereps_c tworeps_n      four
#> 5 threereps_e tworeps_o      five
#> 6 threereps_g tworeps_o       six
#> 7 threereps_g tworeps_p       six

choose_annotation_colnames(df, max_values=5)
#> [1] "threereps" "tworeps"  
df[,choose_annotation_colnames(df, max_values=5)]
#>     threereps   tworeps
#> 1 threereps_a tworeps_l
#> 2 threereps_a tworeps_l
#> 3 threereps_a tworeps_n
#> 4 threereps_c tworeps_n
#> 5 threereps_e tworeps_o
#> 6 threereps_g tworeps_o
#> 7 threereps_g tworeps_p

choose_annotation_colnames(df, simplify=FALSE)
#>   threereps     tworeps       class   maxvalues 
#> "threereps"   "tworeps"     "class" "maxvalues" 
df[,choose_annotation_colnames(df, simplify=FALSE)]
#>     threereps   tworeps   class maxvalues
#> 1 threereps_a tworeps_l class_A       one
#> 2 threereps_a tworeps_l class_A       two
#> 3 threereps_a tworeps_n class_A     three
#> 4 threereps_c tworeps_n class_C      four
#> 5 threereps_e tworeps_o class_E      five
#> 6 threereps_g tworeps_o class_G       six
#> 7 threereps_g tworeps_p class_G       six

choose_annotation_colnames(df, min_reps=3)
#> [1] "threereps"

choose_annotation_colnames(df, min_reps=1)
#> [1] "threereps" "time"      "tworeps"   "maxvalues"

choose_annotation_colnames(df, keep_numeric=TRUE)
#> [1] "threereps" "tworeps"   "num"       "maxvalues"

choose_annotation_colnames(df, min_reps=1)
#> [1] "threereps" "time"      "tworeps"   "maxvalues"

choose_annotation_colnames(df, min_reps=1, keep_numeric=TRUE)
#> [1] "threereps" "time"      "tworeps"   "num"       "maxvalues"