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Screen units based on data availability

Source: R/screen_units.R
Screen.coin.Rd

Screens units based on a data availability threshold and presence of zeros. Units can be optionally "forced" to be included or excluded, making exceptions for the data availability threshold.

Usage

# S3 method for coin
Screen(
  x,
  dset,
  unit_screen,
  dat_thresh = NULL,
  nonzero_thresh = NULL,
  Force = NULL,
  out2 = "coin",
  write_to = NULL,
  ...
)

Arguments

x

A coin

dset

The data set to be checked/screened

unit_screen

Specifies whether and how to screen units based on data availability or zero values.

  • If set to "byNA", screens units with data availability below dat_thresh

  • If set to "byzeros", screens units with non-zero values below nonzero_thresh

  • If set to "byNAandzeros", screens units based on either of the previous two criteria being true.

dat_thresh

A data availability threshold (>= 1 and <= 0) used for flagging low data and screening units if unit_screen != "none". Default 0.66.

nonzero_thresh

As dat_thresh but for non-zero values. Defaults to 0.05, i.e. it will flag any units with less than 5% non-zero values (equivalently more than 95% zero values).

Force

A data frame with any additional countries to force inclusion or exclusion. Required columns uCode (unit code(s)) and Include (logical: TRUE to include and FALSE to exclude). Specifications here override exclusion/inclusion based on data rules.

out2

Where to output the results. If "COIN" (default for COIN input), appends to updated COIN, otherwise if "list" outputs to data frame.

write_to

If specified, writes the aggregated data to .$Data[[write_to]]. Default write_to = "Screened".

...

arguments passed to or from other methods.

Value

An updated coin with data frames showing missing data in .$Analysis, and a new data set .$Data$Screened. If out2 = "list" wraps missing data stats and screened data set into a list.

Details

The two main criteria of interest are NA values, and zeros. The summary table gives percentages of NA values for each unit, across indicators, and percentage zero values (as a percentage of non-NA values). Each unit is flagged as having low data or too many zeros based on thresholds.

See also vignette("screening").

Examples

# build example coin
coin <- build_example_coin(up_to = "new_coin", quietly = TRUE)

# screen units from raw dset
coin <- Screen(coin, dset = "Raw", unit_screen = "byNA",
               dat_thresh = 0.85, write_to = "Filtered_85pc")
#> Written data set to .$Data$Filtered_85pc

# some details about the coin by calling its print method
coin
#> --------------
#> A coin with...
#> --------------
#> Input:
#>   Units: 51 (AUS, AUT, BEL, ...)
#>   Indicators: 49 (Goods, Services, FDI, ...)
#>   Denominators: 4 (Area, Energy, GDP, ...)
#>   Groups: 4 (GDP_group, GDPpc_group, Pop_group, ...)
#> 
#> Structure:
#>   Level 1 Indicator: 49 indicators (FDI, ForPort, Goods, ...) 
#>   Level 2 Pillar: 8 groups (ConEcFin, Instit, P2P, ...) 
#>   Level 3 Sub-index: 2 groups (Conn, Sust) 
#>   Level 4 Index: 1 groups (Index) 
#> 
#> Data sets:
#>   Raw (51 units)
#>   Filtered_85pc (48 units)