Aggregate indicators in a coin — Aggregate.coin • COINr

Aggregates a named data set specified by dset using aggregation function(s) f_ag, weights w, and optional function parameters f_ag_para. Note that COINr has a number of aggregation functions built in, all of which are of the form a_*(), e.g. a_amean(), a_gmean() and friends.

Usage

# S3 method for class 'coin'
Aggregate(
  x,
  dset,
  f_ag = NULL,
  w = NULL,
  f_ag_para = NULL,
  dat_thresh = NULL,
  by_df = FALSE,
  out2 = "coin",
  write_to = NULL,
  ...
)

Arguments

x: A coin class object.
dset: The name of the data set to apply the function to, which should be accessible in .$Data.
f_ag: The name of an aggregation function, a string. This can either be a single string naming a function to use for all aggregation levels, or else a character vector of function names of length n-1, where n is the number of levels in the index structure. In this latter case, a different aggregation function may be used for each level in the index: the first in the vector will be used to aggregate from Level 1 to Level 2, the second from Level 2 to Level 3, and so on.
w: An optional data frame of weights. If f_ag does not require accept weights, set to "none". Alternatively, can be the name of a weight set found in .$Meta$Weights. This can also be specified as a list specifying the aggregation weights for each level, in the same way as the previous parameters.
f_ag_para: Optional parameters to pass to f_ag, other than x and w. As with f_ag, this can specified to have different parameters for each aggregation level by specifying as a nested list of length n-1. See details.
dat_thresh: An optional data availability threshold, specified as a number between 0 and 1. If a row within an aggregation group has data availability lower than this threshold, the aggregated value for that row will be NA. Data availability, for a row x_row is defined as sum(!is.na(x_row))/length(x_row), i.e. the fraction of non-NA values. Can also be specified as a vector of length n-1, where n is the number of levels in the index structure, to specify different data availability thresholds by level.
by_df: Controls whether to send a numeric vector to f_ag (if FALSE, default) or a data frame (if TRUE) - see details. Can also be specified as a logical vector of length n-1, where n is the number of levels in the index structure.
out2: Either "coin" (default) to return updated coin or "df" to output the aggregated data set.
write_to: If specified, writes the aggregated data to .$Data[[write_to]]. Default write_to = "Aggregated".
...: arguments passed to or from other methods.

Value

An updated coin with aggregated data set added at .$Data[[write_to]] if out2 = "coin", else if out2 = "df" outputs the aggregated data set as a data frame.

Details

When by_df = FALSE, aggregation is performed row-wise using the function f_ag, such that for each row x_row, the output is f_ag(x_row, f_ag_para), and for the whole data frame, it outputs a numeric vector. Otherwise if by_df = TRUE, the entire data frame of each indicator group is passed to f_ag.

The function f_ag must be supplied as a string, e.g. "a_amean", and it must take as a minimum an input x which is either a numeric vector (if by_df = FALSE), or a data frame (if by_df = TRUE). In the former case f_ag should return a single numeric value (i.e. the result of aggregating x), or in the latter case a numeric vector (the result of aggregating the whole data frame in one go).

Weights are passed to the function f_ag as an argument named w. This means that the function should have arguments that look like f_ag(x, w, ...), where ... are possibly other input arguments to the function. If the aggregation function doesn't use weights, you can set w = "none", and no weights will be passed to it.

f_ag can optionally have other parameters, apart from x and w, specified as a list in f_ag_para.

The aggregation specifications can be set to be different for each level of aggregation: the arguments f_ag, f_ag_para, dat_thresh, w and by_df can all be optionally specified as vectors or lists of length n-1, where n is the number of levels in the index. In this case, the first value in each vector/list will be used for the first round of aggregation, i.e. from indicators to the aggregates at level 2. The next will be used to aggregate from level 2 to level 3, and so on.

When different functions are used for different levels, it is important to get the list syntax correct. For example, in a case with three aggregations using different functions, say we want to use a_amean() for the first two levels, then a custom function f_cust() for the last. f_cust() has some additional parameters a and b. In this case, we would specify e.g. f_ag_para = list(NULL, NULL, list(a = 2, b = 3)) - this is becauase a_amean() requires no additional parameters, so we pass NULL.

Note that COINr has a number of aggregation functions built in, all of which are of the form a_*(), e.g. a_amean(), a_gmean() and friends. To see a list browse COINr functions alphabetically or type a_ in the R Studio console and press the tab key (after loading COINr), or see the online documentation.

Optionally, a data availability threshold can be assigned below which the aggregated value will return NA (see dat_thresh argument). If by_df = TRUE, this will however be ignored because aggregation is not done on individual rows. Note that more complex constraints could be built into f_ag if needed.

Examples

# build example up to normalised data set
coin <- build_example_coin(up_to = "Normalise")
#> iData checked and OK.
#> iMeta checked and OK.
#> Written data set to .$Data$Raw
#> Written data set to .$Data$Denominated
#> Written data set to .$Data$Imputed
#> Written data set to .$Data$Screened
#> Written data set to .$Data$Treated
#> Written data set to .$Data$Normalised

# aggregate normalised data set
coin <- Aggregate(coin, dset = "Normalised")
#> Written data set to .$Data$Aggregated