Parallel / row-wise sums and means

Given one or more vectors, psum() and pmean() return row-wise sums or means. They are analogous to base::pmin() and pmax(). Like pmin() / pmax(), these functions:

• accept multiple vectors via ... (as opposed to rowSums() and rowMeans(), which accept a single array). This allows them to be used in data-masking contexts (e.g., inside dplyr::mutate()).

• return NA when all values are NA and na.rm = TRUE. (as opposed to rowSums(), which returns 0 in this situation).

Usage

psum(..., na.rm = FALSE)

pmean(..., na.rm = FALSE)

Arguments

...

one or more numeric or logical vectors of equal length or length 1.

na.rm

logical. Should missing values (including NaN) be removed?

Value

A numeric vector.

Details

Both psum() and psum_across() can be used in data-masking contexts, but have different capabilities and limitations. See the "Details" section of ?psum_across.

Note that, unlike pmin(), pmax(), rowSums(), and rowMeans(), these functions do not currently support objects with more than one dimension (e.g., matrices, arrays, or data frames).

See also

• psum_across() for variants that support tidyselect expressions. See the "Details" section for relative strengths of psum() / pmean() vs. psum_across() / pmean_across().

• base::pmin() for analogous pmin() and pmax() functions

• sum_if_any() for non-parallel sums with similar NA handling

Examples

psum(1:5, 6:10)
#> [1]  7  9 11 13 15
pmean(1:5, 6:10)
#> [1] 3.5 4.5 5.5 6.5 7.5

dat <- tibble::tribble(
  ~product,    ~price1, ~price2, ~price3,
  "Product 1", 20,      25,      22,
  "Product 2", NA,      30,      29,
  "Product 3", 15,      NA,      NA,
  "Product 4", NA,      NA,      NA
)

# contrast w `rowSums()` / `rowMeans()`:
# no need for `pick()` and different `NA` behavior
dat %>%
  dplyr::mutate(
    rowSums = rowSums(pick(price1, price2, price3), na.rm = TRUE),
    psum = psum(price1, price2, price3, na.rm = TRUE),
    rowMeans = rowMeans(pick(price1, price2, price3), na.rm = TRUE),
    pmean = pmean(price1, price2, price3, na.rm = TRUE)
  )
#> # A tibble: 4 × 8
#>   product   price1 price2 price3 rowSums  psum rowMeans pmean
#>   <chr>      <dbl>  <dbl>  <dbl>   <dbl> <dbl>    <dbl> <dbl>
#> 1 Product 1     20     25     22      67    67     22.3  22.3
#> 2 Product 2     NA     30     29      59    59     29.5  29.5
#> 3 Product 3     15     NA     NA      15    15     15    15  
#> 4 Product 4     NA     NA     NA       0    NA    NaN    NA