Compute a row-wise mean across multiple variables

javg() computes the mean of values across multiple variables for each case (row) in the data frame. This is typically used to create scale means from a set of related items.

By default, cases with any missing values receive NA. Use the min.valid argument to allow partial means — for example, min.valid = 1 computes the mean of available values as long as at least one item is non-missing.

By default, the denominator is the number of non-missing values for each case. Use fixed = TRUE to always divide by the total number of variables regardless of missing values.

Variables can be listed individually or using colon notation to select a range of consecutive columns (e.g. Attitude1:Attitude6).

Usage

javg(data, ..., min.valid = NULL, fixed = FALSE, var.label = NULL)

Arguments

data: A data frame, or omit to use the juse() default.
...: Unquoted variable names. Use colon notation (e.g. Attitude1:Attitude6) to select a range of consecutive columns.
min.valid: Integer (optional). The minimum number of non-missing values required to compute a mean. If a case has fewer non-missing values, the result is NA. If omitted, all values must be non-missing (equivalent to setting min.valid to the number of variables).
fixed: Logical. If FALSE (default), the denominator for each case is the number of non-missing values (i.e. the mean adjusts for missing data). If TRUE, the denominator is always the total number of variables (i.e. missing values effectively count as zero).
var.label: Character string (optional). A variable label to attach to the result. If omitted, an auto-generated label is used.

Value

A numeric vector the same length as nrow(data), suitable for assigning to a new column: MyData$ScaleMean <- javg(Var1, Var2, Var3).

Examples

# Set the default data frame (so you can omit it in function calls)
juse(community)
#> Default data frame set to: community

# Mean of three variables (all must be non-missing)
community$EnvAvg <- javg(Environment1, Environment3, Environment4)
#> Mean of 3 variables computed for 103 cases
#> (19 set to NA due to missing values).
#> Mean of the new variable: 3.175.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   community$<name> <- javg(...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# Mean with partial data allowed (at least 2 non-missing)
community$EnvAvg <- javg(Environment1, Environment3, Environment4,
                         min.valid = 2)
#> Mean of 3 variables computed for 103 cases (min.valid = 2: 14 cases used
#> partial data, 5 set to NA due to missing values).
#> Mean of the new variable: 3.180.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   community$<name> <- javg(...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# Mean using colon range for consecutive columns
community$ScaleMean <- javg(Environment1:Environment5)
#> Mean of 5 variables computed for 103 cases
#> (19 set to NA due to missing values).
#> Mean of the new variable: 3.031.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   mydata <- Environment1:Environment5
#>   mydata$<name> <- javg(mydata, ...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# Mix colon ranges and explicit names (e.g. after reverse-coding an item)
community$Environment2R <- jrecode(community, Environment2,
                                   map = "1=5; 2=4; 3=3; 4=2; 5=1")
#> Note: This call changes community only if you assign the result:
#>   community$<name> <- jrecode(...)
#> To check the recode landed correctly, compare jfreq() on the original and the
#> new column.
community$ScaleMean <- javg(Environment1, Environment2R,
                            Environment3:Environment5)
#> Mean of 5 variables computed for 103 cases
#> (19 set to NA due to missing values).
#> Mean of the new variable: 3.083.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   community$<name> <- javg(...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# Fixed denominator (always divide by total number of variables)
community$EnvAvg <- javg(Environment1, Environment3, Environment4,
                         min.valid = 2, fixed = TRUE)
#> Mean of 3 variables computed for 103 cases (fixed denominator) (min.valid = 2:
#> 14 cases used partial data, 5 set to NA due to missing values).
#> Mean of the new variable: 3.027.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   community$<name> <- javg(...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# With a custom variable label
community$ScaleMean <- javg(Environment1:Environment5,
                            var.label = "Environment Scale Mean")
#> Mean of 5 variables computed for 103 cases
#> (19 set to NA due to missing values).
#> Mean of the new variable: 3.031.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   mydata <- Environment1:Environment5
#>   mydata$<name> <- javg(mydata, ...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# With an explicit data frame (instead of using juse default),
# positional or named
community$EnvAvg <- javg(community, Environment1, Environment3,
                         Environment4)
#> Mean of 3 variables computed for 103 cases
#> (19 set to NA due to missing values).
#> Mean of the new variable: 3.175.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   community$<name> <- javg(...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.
community$EnvAvg <- javg(data = community, Environment1, Environment3,
                         Environment4)
#> Mean of 3 variables computed for 103 cases
#> (19 set to NA due to missing values).
#> Mean of the new variable: 3.175.
#> 
#> Note: javg() returns the scores; assign them to a column to keep them:
#>   community$<name> <- javg(...)
#> For the full distribution (min, max, SD), run jdesc() on the new column.

# juse(NULL) clears the default dataset -- not normally needed. You'd
# clear a default or registration only to undo a mistake, or -- as in
# this example -- to reset state for testing.
juse(NULL)
#> Default data frame cleared.

Compute a row-wise mean across multiple variables

Usage

Arguments

Value

See also

Examples