This function will add a new column rank where the ranking is produced as follows: 1. sorts the H2OFrame by columns sorted in by columns specified in group_by_cols and sort_cols in the directions specified by the ascending for the sort_cols. The sort directions for the group_by_cols are ascending only. 2. A new rank column is added to the frame which will contain a rank assignment performed next. The user can choose to assign a name to this new column. The default name is New_Rank_column. 3. For each groupby groups, a rank is assigned to the row starting from 1, 2, ... to the end of that group. 4. If sort_cols_sorted is TRUE, a final sort on the frame will be performed frame according to the sort_cols and the sort directions in ascending. If sort_cols_sorted is FALSE (by default), the frame from step 3 will be returned as is with no extra sort. This may provide a small speedup if desired.

Source: R/frame.R

This function will add a new column rank where the ranking is produced as follows: 1. sorts the H2OFrame by columns sorted in by columns specified in group_by_cols and sort_cols in the directions specified by the ascending for the sort_cols. The sort directions for the group_by_cols are ascending only. 2. A new rank column is added to the frame which will contain a rank assignment performed next. The user can choose to assign a name to this new column. The default name is New_Rank_column. 3. For each groupby groups, a rank is assigned to the row starting from 1, 2, ... to the end of that group. 4. If sort_cols_sorted is TRUE, a final sort on the frame will be performed frame according to the sort_cols and the sort directions in ascending. If sort_cols_sorted is FALSE (by default), the frame from step 3 will be returned as is with no extra sort. This may provide a small speedup if desired.

h2o.rank_within_group_by(
  x,
  group_by_cols,
  sort_cols,
  ascending = NULL,
  new_col_name = "New_Rank_column",
  sort_cols_sorted = FALSE
)

Arguments

x

The H2OFrame input to be sorted.
group_by_cols

a list of column names or indices to form the groupby groups
sort_cols

a list of column names or indices for sorting
ascending

a list of Boolean to determine if ascending sort (set to TRUE) is needed for each column in sort_cols (optional). Default is ascending sort for all. To perform descending sort, set value to FALSE
new_col_name

new column name for the newly added rank column if specified (optional). Default name is New_Rank_column.
sort_cols_sorted

Boolean to determine if the final returned frame is to be sorted according to the sort_cols and sort directions in ascending. Default is FALSE.

The following example is generated by Nidhi Mehta.

If the input frame is train:

ID Group_by_column num data Column_to_arrange_by num_1 fdata 12 1 2941.552 1 3 -3177.9077 1 12 1 2941.552 1 5 -13311.8247 1 12 2 -22722.174 1 3 -3177.9077 1 12 2 -22722.174 1 5 -13311.8247 1 13 3 -12776.884 1 5 -18421.6171 0 13 3 -12776.884 1 4 28080.1607 0 13 1 -6049.830 1 5 -18421.6171 0 13 1 -6049.830 1 4 28080.1607 0 15 3 -16995.346 1 1 -9781.6373 0 16 1 -10003.593 0 3 -61284.6900 0 16 3 26052.495 1 3 -61284.6900 0 16 3 -22905.288 0 3 -61284.6900 0 17 2 -13465.496 1 2 12094.4851 1 17 2 -13465.496 1 3 -11772.1338 1 17 2 -13465.496 1 3 -415.1114 0 17 2 -3329.619 1 2 12094.4851 1 17 2 -3329.619 1 3 -11772.1338 1 17 2 -3329.619 1 3 -415.1114 0

If the following commands are issued: rankedF1 <- h2o.rank_within_group_by(train, c("Group_by_column"), c("Column_to_arrange_by"), c(TRUE)) h2o.summary(rankedF1)

The returned frame rankedF1 will look like this: ID Group_by_column num fdata Column_to_arrange_by num_1 fdata.1 New_Rank_column 12 1 2941.552 1 3 -3177.9077 1 1 16 1 -10003.593 0 3 -61284.6900 0 2 13 1 -6049.830 0 4 28080.1607 0 3 12 1 2941.552 1 5 -13311.8247 1 4 13 1 -6049.830 0 5 -18421.6171 0 5 17 2 -13465.496 0 2 12094.4851 1 1 17 2 -3329.619 0 2 12094.4851 1 2 12 2 -22722.174 1 3 -3177.9077 1 3 17 2 -13465.496 0 3 -11772.1338 1 4 17 2 -13465.496 0 3 -415.1114 0 5 17 2 -3329.619 0 3 -11772.1338 1 6 17 2 -3329.619 0 3 -415.1114 0 7 12 2 -22722.174 1 5 -13311.8247 1 8 15 3 -16995.346 1 1 -9781.6373 0 1 16 3 26052.495 0 3 -61284.6900 0 2 16 3 -22905.288 1 3 -61284.6900 0 3 13 3 -12776.884 1 4 28080.1607 0 4 13 3 -12776.884 1 5 -18421.6171 0 5

If the following commands are issued: rankedF1 <- h2o.rank_within_group_by(train, c("Group_by_column"), c("Column_to_arrange_by"), c(TRUE), sort_cols_sorted=TRUE) h2o.summary(rankedF1)

The returned frame will be sorted according to sortCols and hence look like this instead: ID Group_by_column num fdata Column_to_arrange_by num_1 fdata.1 New_Rank_column 15 3 -16995.346 1 1 -9781.6373 0 1 17 2 -13465.496 0 2 12094.4851 1 1 17 2 -3329.619 0 2 12094.4851 1 2 12 1 2941.552 1 3 -3177.9077 1 1 12 2 -22722.174 1 3 -3177.9077 1 3 16 1 -10003.593 0 3 -61284.6900 0 2 16 3 26052.495 0 3 -61284.6900 0 2 16 3 -22905.288 1 3 -61284.6900 0 3 17 2 -13465.496 0 3 -11772.1338 1 4 17 2 -13465.496 0 3 -415.1114 0 5 17 2 -3329.619 0 3 -11772.1338 1 6 17 2 -3329.619 0 3 -415.1114 0 7 13 3 -12776.884 1 4 28080.1607 0 4 13 1 -6049.830 0 4 28080.1607 0 3 12 1 2941.552 1 5 -13311.8247 1 4 12 2 -22722.174 1 5 -13311.8247 1 8 13 3 -12776.884 1 5 -18421.6171 0 5 13 1 -6049.830 0 5 -18421.6171 0 5