# Compute Weighted Mean by Group This function computes the weighted mean of variable groups from a data.table. computeWeightedMean is performance optimized and designed to work well in bulk operations. The function returns a data.table. ```r computeWeightedMeans(data_table, variables, weight, by) ``` ## Arguments - `data_table`: a data.table - `variables`: character name of the variable(s) to focus on. The variables must be in the data.table - `weight`: character name of the data.table column that contains a weight. - `by`: character vector of the columns to group by ## Examples ```r # TODO: add new weight columns to BTS demo # load library and dataset library(panelaggregation) data(btsdemo) head(btsdemo) # adapt the levels to positive, equal and negative # in order to suit the naming defaults. other levels work too, # but you'd need to specify multipliers in computeBalance then levels(btsdemo$question_1) <- c("pos","eq","neg") # compute the weighted shares and display store in wide format # to get a basis for further steps level1 <- computeShares(btsdemo,"question_1","weight", by = c("date_qtrly","group", "altGroup", "sClass")) # compute balance, don't have to do much here, because # (pos, eq, neg) is the default for the possible answers level1_wbalance <- computeBalance(level1) # Select a particular grouping combination and a timeseries that # should be extracted from the level 1 aggregation. ts1 <- extractTimeSeries(level1_wbalance, "date_qtrly", list(group = "C", altGroup = "a", sClass = "S"), freq = 4, item = "balance", variable = "question_1") ts1 # Plot a standard R ts using the plot method for ts plot(ts1, main = attributes(ts1)$ts_key) # Add weight column to the aggregated results # In order to join the tables, we need to know what weight to assign to each row. # This is done by having via a common key, for example c('group', 'altGroup'). # In this example we would assign a different weight for each # c('group', 'altGroup') combination (e.g. c('A', 'a')). btsweight1 <- btsdemo[, list(weight = sum(weight)), by = 'group'] btsagg1 <- joinDataTables(level1_wbalance, btsweight1, 'group') # Compute second level aggregation, this time on fewer columns and using a different set of weights. level2_balance <- computeWeightedMeans(btsagg1, c('item_pos', 'item_eq', 'item_neg', 'balance'), 'weight', c("date_qtrly","group", "sClass")) # Select a particular grouping combination and a timeseries that # should be extracted from the level 2 aggregation. ts2 <- extractTimeSeries(level2_balance, "date_qtrly", list(group = "C", sClass = "S"), freq = 4, item = "balance", variable = "question_1") ts2 # Plot a standard R ts using the plot method for ts plot(ts2, main = attributes(ts2)$ts_key) # Add weight column to the aggregated results # In order to join the tables, we need to know what weight to assign to each row. # This is done by having via a common key, for example c('group', 'altGroup'). # In this example we would assign a different weight for each # c('group', 'altGroup') combination (e.g. c('A', 'a')). btsweight2 <- btsdemo[, list(weight = sum(weight)), by = 'sClass'] btsagg2 <- joinDataTables(level2_balance, btsweight2, 'sClass') # Compute third level of aggregation, on the whole sector, using yet another set of weights. level3_balance <- computeWeightedMeans(btsagg2, 'balance', 'weight', c("date_qtrly", "sClass")) # Select a particular grouping combination and a timeseries that # should be extracted from the level 2 aggregation. ts3 <- extractTimeSeries(level3_balance, "date_qtrly", list(sClass = "S"), freq = 4, item = "balance", variable = "question_1") ts3 # Plot a standard R ts using the plot method for ts plot(ts3, main = attributes(ts3)$ts_key) ``` ## Author(s) Matthias Bannert, Gabriel Bucur

computeWeightedMeans function