Package {ReportSubtotal}

ReportSubtotal: Adds Subtotals to Data Reports

Description

Adds subtotal rows / sections (a la the 'SAS' 'Proc Tabulate' All option) to a Group By output by running a series of Group By functions with partial sets of the same variables and combining the results with the original. Can be used to add comprehensive information to a data report or to quickly aggregate Group By outputs used to gain a greater understanding of data.

Author(s)

Maintainer: Yoni Aboody yoniaboody@gmail.com [copyright holder]

Authors:

• Yoni Aboody yoniaboody@gmail.com [copyright holder]

Duplicate Subtotal Row Removal Function

Description

Removes duplicate subtotal rows, which may be created by totalling a variable with only one level. In tables with many variables, some may have only one level within one section and many in other sections.

Usage

subtotal_dupe_removal(
  data,
  column,
  iterator = 2,
  skip = 0,
  remove = "Total",
  lead_name = "Lead_Column"
)

Arguments

Details

Adds a leading version of the requested column, which places each row level in the same row as the row level two levels ahead. This is usually the value 2 rows ahead of the current row, although if not the iterator parameter can be used to account for this. Then, if both the original and leading column equal the value to be removed, then the row is considered a duplicate subtotal and is removed. Note: Columns named Lead will receive a temporary suffix.

Value

The data report without duplicate subtotal rows.

Examples

library(dplyr)

group_by(mtcars, cyl, vs) %>% summarise(Sum_Wt = sum(wt), .groups = "keep") %>%
subtotal_row(mtcars, "wt") %>%
subtotal_dupe_removal(2)
group_by(mtcars, cyl, vs, am) %>% summarise(Mean_HP = mean(hp), .groups = "keep") %>%
subtotal_row(mtcars, "hp", "mean") %>%
subtotal_dupe_removal(3, skip = 1)
group_by(mtcars, cyl, vs, am) %>% summarise(Mean_HP = mean(hp), .groups = "keep") %>%
subtotal_row(mtcars, "hp", "mean") %>%
subtotal_dupe_removal(3, skip = 1)

Subtotal Row Addition Function

Description

Adds subtotal rows to a data report. Designed to work with reports generated by group_by + summarise.

Usage

subtotal_row(
  report,
  frame,
  vars = "Population",
  aggregator = "sum",
  exclude = numeric(0),
  agg_parameter = list(),
  subtotal_label = "Total"
)

Arguments

Details

The dataset and report are factorized, and a series of reports are generated. These reports are structured similarly to the original report - using the same grouping variables, aggregating the given variables with the given aggregation function and optional parameters. For best results, choose the same variables and aggregator used to generate the original report. Each generated report has some grouping variables replaced by a chosen subtotal label, effectively concentrating all levels of those variables into one subtotal row for those variables. The subtotal reports are all combined with the original report, and the combined report is grouped and sorted, sorting the subtotal label to the top for all variables.

Value

The grouped data report with subtotal rows included.

Examples

library(dplyr)

group_by(iris, Species, Petal.Width) %>%
summarise(Sum_P_Len = sum(Petal.Length), .groups = "keep") %>%
subtotal_row(iris, vars = "Petal.Length")

group_by(iris, Species, Petal.Width) %>%
summarise(Mean_S_Width = mean(Sepal.Width), .groups = "keep") %>%
subtotal_row(iris, vars = "Sepal.Width", aggregator = "mean")

group_by(mtcars, cyl, gear, carb) %>%
summarise(Med_Wt = median(wt), Med_Hp = median(hp), .groups = "keep") %>%
subtotal_row(mtcars, vars = c("wt", "hp"), aggregator = "median")

group_by(mtcars, cyl, gear, carb) %>%
summarise(Med_Weight = median(wt), Med_Hrspw = median(hp), .groups = "keep") %>%
subtotal_row(mtcars, vars = c("wt", "hp"), aggregator = "median", exclude = 1)

group_by(mtcars, vs, am, drat, carb) %>%
summarise(Min_Mpg = min(mpg), Min_Disp = min(disp), Min_Carb = min(carb), .groups = "keep") %>%
subtotal_row(mtcars, vars = c("mpg", "disp", "carb"),
aggregator = "min", exclude = c(2, 4),
subtotal_label = "Min_Cars_Total", agg_parameter = list(na.rm = TRUE))

Subtotal Section Addition Function

Description

Adds subtotal sections to a data report. Designed to work with reports generated by group_by + summarise.

Usage

subtotal_section(
  report,
  frame,
  vars = "Population",
  aggregator = "sum",
  exclude = numeric(0),
  agg_parameter = list(),
  subtotal_label = "Total"
)

Arguments

Details

The dataset and report are factorized, and a series of reports are generated. These reports are structured similarly to the original report - using the same grouping variables, aggregating the given variables with the given aggregation function and optional parameters. For best results, choose the same variables and aggregator used to generate the original report. Each generated report has some grouping variables replaced by a chosen subtotal label, effectively concentrating all levels of those variables into one subtotal row for those variables - ultimately making up entire subtotal sections for some variables. The subtotal reports are all combined with the original report, and the combined report is grouped and sorted, sorting the subtotal label to the top for all variables.

Value

The grouped data report with subtotal sections included.

Examples

library(dplyr)

group_by(iris, Species) %>%
summarise(Sum_P_Len = sum(Petal.Length), .groups = "keep") %>%
subtotal_section(iris, vars = "Petal.Length")

group_by(mtcars, cyl, gear) %>%
summarise(Mean_Mpg = mean(mpg), .groups = "keep") %>%
subtotal_section(mtcars, vars = "mpg", aggregator = "mean")

group_by(mtcars, cyl, gear) %>%
summarise(Mean_Mpg = mean(mpg), Mean_Wt = mean(wt), .groups = "keep") %>%
subtotal_section(mtcars, vars = c("mpg", "wt"),
aggregator = "mean", exclude = 1:2)

group_by(iris, Species, Petal.Width) %>%
summarise(Max_S_Width = max(Sepal.Width), Max_S_Length = max(Sepal.Length), .groups = "keep") %>%
subtotal_section(iris, vars = c("Sepal.Width", "Sepal.Length"),
aggregator = "max", agg_parameter = list(na.rm = TRUE))

group_by(mtcars, qsec, carb, hp, gear) %>%
summarise(Min_Cyl = min(cyl), Min_Drat = min(drat), Min_Wt = min(wt), .groups = "keep") %>%
subtotal_section(mtcars, vars = c("cyl", "drat", "wt"),
aggregator = "min", agg_parameter = list(na.rm = TRUE),
subtotal_label = "Cars_Total", exclude = c(1, 4))