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Sum values in numeric column sorted by variable groupings.

summed_output.Rd

This function sums column of numeric type using dplyr pipeline. Users can set grouping hierarchies and filter conditions to specify calculations.

Usage

summed_output(
  data,
  group_vars = NULL,
  sum_var,
  filter_vars = TRUE,
  na.rm = TRUE
)

Arguments

data

A dataframe that the function will be applied to.

group_vars

variable(s)/column name(s) of a dataframe that is used to group the observations based on common characteristics. "group_vars" specifies the type of operation (group_by). Set to "NULL" as default.

sum_var

a character string that represents a column name/variable containing numeric values. "sum_var" specifies the type of operation (sum) for the variable.

filter_vars

variable(s)/column(s) called on to set conditions for filtering. "filter_vars" specifies the type of operation (filter) for the selected variables. Set to TRUE as default so user can select filter conditions or leave the argument unused.

na.rm

argument specifying how to handle NA values in data when performing sum operation; set to "TRUE" as default to prevent an NA output in the sum operation when the column is passed through the function.

Value

A dataframe with a new column named "cumulative" containing the summed output value for each grouping.

Examples

# Using the function without any grouping or filtering conditions
summed_output(data = palmerpenguins::penguins,
              sum_var = "body_mass_g")
#> # A tibble: 1 × 1
#>   cumulative
#>        <int>
#> 1    1437000

# Applying filters by storing filter conditions as an object in the function call
sex_male_year_2007 <- palmerpenguins::penguins$sex == "male" & palmerpenguins::penguins$year == 2007

summed_output(data = palmerpenguins::penguins,
              group_vars = c("island", "species"),
              sum_var = "bill_depth_mm",
              filter_vars = sex_male_year_2007,
              na.rm = TRUE)
#> # A tibble: 5 × 3
#> # Groups:   island [3]
#>   island    species   cumulative
#>   <fct>     <fct>          <dbl>
#> 1 Biscoe    Adelie          91.5
#> 2 Biscoe    Gentoo         261. 
#> 3 Dream     Adelie         194. 
#> 4 Dream     Chinstrap      249. 
#> 5 Torgersen Adelie         143.