Only compute means or sums for cases with enough nonmissings

These functions have been written as equivalents of SPSS' MEAN.x and SUM.x functions, which only compute means and sums if enough cases have valid values.

validMeans(...,
           requiredValidValues = 0,
           returnIfInvalid = NA,
           silent = FALSE)
validSums(...,
          requiredValidValues = 0,
          returnIfInvalid = NA,
          silent = FALSE)

Arguments

...	Either a dataframe or vectors for which to compute the mean or sum.
requiredValidValues	How many values must be valid (i.e. nonmissing) to compute the mean or sum. If a number lower than 1 is provided, it is interpreted as proportion, and the number of variables is computed. For example, if requiredValidValues=.8, 80% of the variables must have valid values. If 'all' is specified, all values must be valid (in which case the functions are equal to rowMeans and rowSums).
returnIfInvalid	Wat to return for cases that don't have enough valid values.
silent	Whether to show the number of cases that have to be valid if requiredValidValues is a proportion.

Value

A numeric vector with the resulting means or sums.

See also

rowMeans, rowSums

Examples

validMeans(mtcars$cyl, mtcars$disp);
#> Argument 'requiredValidValues' was set to a proportion (0), so only computing a mean for cases where that proportion of variables (i.e. 0%, or 0 variables) have valid values.
#>  [1]  83.00  83.00  56.00 132.00 184.00 115.50 184.00  75.35  72.40  86.80
#> [11]  86.80 141.90 141.90 141.90 240.00 234.00 224.00  41.35  39.85  37.55
#> [21]  62.05 163.00 156.00 179.00 204.00  41.50  62.15  49.55 179.50  75.50
#> [31] 154.50  62.50
validSums(mtcars$cyl, mtcars$disp, requiredValidValues = .8);
#> Argument 'requiredValidValues' was set to a proportion (0.8), so only computing a mean for cases where that proportion of variables (i.e. 80%, or 2 variables) have valid values.
#>  [1] 166.0 166.0 112.0 264.0 368.0 231.0 368.0 150.7 144.8 173.6 173.6 283.8
#> [13] 283.8 283.8 480.0 468.0 448.0  82.7  79.7  75.1 124.1 326.0 312.0 358.0
#> [25] 408.0  83.0 124.3  99.1 359.0 151.0 309.0 125.0

### Or specifying a dataframe
validSums(mtcars);
#> Argument 'requiredValidValues' was set to a proportion (0), so only computing a mean for cases where that proportion of variables (i.e. 0%, or 0 variables) have valid values.
#>           Mazda RX4       Mazda RX4 Wag          Datsun 710      Hornet 4 Drive 
#>             328.980             329.795             259.580             426.135 
#>   Hornet Sportabout             Valiant          Duster 360           Merc 240D 
#>             590.310             385.540             656.920             270.980 
#>            Merc 230            Merc 280           Merc 280C          Merc 450SE 
#>             299.570             350.460             349.660             510.740 
#>          Merc 450SL         Merc 450SLC  Cadillac Fleetwood Lincoln Continental 
#>             511.500             509.850             728.560             726.644 
#>   Chrysler Imperial            Fiat 128         Honda Civic      Toyota Corolla 
#>             725.695             213.850             195.165             206.955 
#>       Toyota Corona    Dodge Challenger         AMC Javelin          Camaro Z28 
#>             273.775             519.650             506.085             646.280 
#>    Pontiac Firebird           Fiat X1-9       Porsche 914-2        Lotus Europa 
#>             631.175             208.215             272.570             273.683 
#>      Ford Pantera L        Ferrari Dino       Maserati Bora          Volvo 142E 
#>             670.690             379.590             694.710             288.890