| Title: | 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. |
| Authors: | Yoni Aboody [aut, cre, cph] |
| Maintainer: | Yoni Aboody <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.1.2 |
| Built: | 2025-03-02 05:34:20 UTC |
| Source: | https://github.com/cran/ReportSubtotal |
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.
subtotal_dupe_removal( data, column, iterator = 2, skip = 0, remove = "All", lead_name = "Lead_Column" )subtotal_dupe_removal( data, column, iterator = 2, skip = 0, remove = "All", lead_name = "Lead_Column" )
data |
Data frame or tibble to remove duplicate row labels from. |
column |
Column containing duplicate row labels. |
iterator |
Minimum number of rows meant to be between each section. Usually two. |
skip |
Number of rows to skip removing rows from. Usually zero. Can be used to dodge NA values. |
remove |
Label of subtotals to be removed. Usually "All". |
lead_name |
Default name for lead column used to filter duplicates. |
Adds a leading version of the requested column, which places each observation in the same row as the next observation. Usually the observation 2 rows on - determined by the iterator. From here if both the original and leading column equal the value to be removed, then the row is a duplicate subtotal and is removed. Note: the last few rows will have NA values in the leading column, so they are covered separately. Note: If you already have columns named Lead or Index, they receive a temporary suffix.
The data report without duplicate subtotal rows.
library(dplyr) group_by(mtcars, cyl, vs) %>% summarise(sum(wt), .groups = "keep") %>% subtotal_row(mtcars, "wt") %>% subtotal_dupe_removal(2) group_by(mtcars, cyl, vs, am) %>% summarise(mean(hp), .groups = "keep") %>% subtotal_row(mtcars, "hp", "mean") %>% subtotal_dupe_removal(3, skip = 1) group_by(mtcars, cyl, vs, am) %>% summarise(mean(hp), .groups = "keep") %>% subtotal_row(mtcars, "hp", "mean") %>% subtotal_dupe_removal(3, skip = 1)library(dplyr) group_by(mtcars, cyl, vs) %>% summarise(sum(wt), .groups = "keep") %>% subtotal_row(mtcars, "wt") %>% subtotal_dupe_removal(2) group_by(mtcars, cyl, vs, am) %>% summarise(mean(hp), .groups = "keep") %>% subtotal_row(mtcars, "hp", "mean") %>% subtotal_dupe_removal(3, skip = 1) group_by(mtcars, cyl, vs, am) %>% summarise(mean(hp), .groups = "keep") %>% subtotal_row(mtcars, "hp", "mean") %>% subtotal_dupe_removal(3, skip = 1)
Adds subtotal rows to a data report.
subtotal_row( report, frame, vars = "Population", aggregator = "sum", exclude = numeric(0), agg_parameter = character(0), subtotal_label = "All" )subtotal_row( report, frame, vars = "Population", aggregator = "sum", exclude = numeric(0), agg_parameter = character(0), subtotal_label = "All" )
report |
A data report. |
frame |
Data frame summarised by the data report. |
vars |
Names of column(s) in the data frame aggregated in the data report. |
aggregator |
Function to aggregate the data with. |
exclude |
Vector of column indices determining which variables don't require subtotal rows. |
agg_parameter |
Optional parameter for the aggregation function to use. |
subtotal_label |
Label to be used for subtotal rows. |
The dataset and report are factorized, and a series of reports with the same variables are then generated, but with some variables replaced by a subtotal label, which effectively concentrates 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 sorted, sorting the subtotal label to the top for all variables.
The data report with subtotal rows included.
library(dplyr) group_by(iris, Species, Petal.Width) %>% summarise(sum(Petal.Length), .groups = "keep") %>% subtotal_row(iris, vars = "Petal.Length") group_by(iris, Species, Petal.Width) %>% summarise(mean(Sepal.Width), .groups = "keep") %>% subtotal_row(iris, vars = "Sepal.Width", aggregator = "mean") group_by(mtcars, cyl, gear, carb) %>% summarise(median(wt), 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(disp), 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 = "na.rm")library(dplyr) group_by(iris, Species, Petal.Width) %>% summarise(sum(Petal.Length), .groups = "keep") %>% subtotal_row(iris, vars = "Petal.Length") group_by(iris, Species, Petal.Width) %>% summarise(mean(Sepal.Width), .groups = "keep") %>% subtotal_row(iris, vars = "Sepal.Width", aggregator = "mean") group_by(mtcars, cyl, gear, carb) %>% summarise(median(wt), 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(disp), 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 = "na.rm")
Adds subtotal sections to a data report.
subtotal_section( report, frame, vars = "Population", aggregator = "sum", exclude = numeric(0), agg_parameter = character(0), subtotal_label = "All" )subtotal_section( report, frame, vars = "Population", aggregator = "sum", exclude = numeric(0), agg_parameter = character(0), subtotal_label = "All" )
report |
A data report. |
frame |
Data frame summarised by the data report. |
vars |
Names of column(s) in the data frame aggregated in the data report. |
aggregator |
Function to aggregate the data with. |
exclude |
Vector of column indices determining which variables only require subtotal rows (as opposed to sections). |
agg_parameter |
Optional parameter for the aggregation function to use. |
subtotal_label |
Label to be used for subtotal rows. |
The dataset and report are factorized, and a series of reports with the same variables are then generated, but with some variables replaced by a subtotal label, which effectively concentrates 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 sorted, sorting the subtotal label to the top for all variables.
The data report with subtotal sections included.
library(dplyr) group_by(iris, Species) %>% summarise(sum(Petal.Length), .groups = "keep") %>% subtotal_section(iris, vars = "Petal.Length") group_by(mtcars, cyl, gear) %>% summarise(mean(mpg), .groups = "keep") %>% subtotal_section(mtcars, vars = "mpg", aggregator = "mean") group_by(mtcars, cyl, gear) %>% summarise(mean(mpg), mean(wt), .groups = "keep") %>% subtotal_section(mtcars, vars = c("mpg", "wt"), aggregator = "mean", exclude = 1:2) group_by(iris, Species, Petal.Width) %>% summarise(max(Sepal.Width), max(Sepal.Length), .groups = "keep") %>% subtotal_section(iris, vars = c("Sepal.Width", "Sepal.Length"), aggregator = "max", agg_parameter = "na.rm") group_by(mtcars, qsec, carb, hp, gear) %>% summarise(min(cyl), min(drat), min(wt), .groups = "keep") %>% subtotal_section(mtcars, vars = c("cyl", "drat", "wt"), aggregator = "min", agg_parameter = "na.rm", subtotal_label = "Cars_Total", exclude = c(1, 4))library(dplyr) group_by(iris, Species) %>% summarise(sum(Petal.Length), .groups = "keep") %>% subtotal_section(iris, vars = "Petal.Length") group_by(mtcars, cyl, gear) %>% summarise(mean(mpg), .groups = "keep") %>% subtotal_section(mtcars, vars = "mpg", aggregator = "mean") group_by(mtcars, cyl, gear) %>% summarise(mean(mpg), mean(wt), .groups = "keep") %>% subtotal_section(mtcars, vars = c("mpg", "wt"), aggregator = "mean", exclude = 1:2) group_by(iris, Species, Petal.Width) %>% summarise(max(Sepal.Width), max(Sepal.Length), .groups = "keep") %>% subtotal_section(iris, vars = c("Sepal.Width", "Sepal.Length"), aggregator = "max", agg_parameter = "na.rm") group_by(mtcars, qsec, carb, hp, gear) %>% summarise(min(cyl), min(drat), min(wt), .groups = "keep") %>% subtotal_section(mtcars, vars = c("cyl", "drat", "wt"), aggregator = "min", agg_parameter = "na.rm", subtotal_label = "Cars_Total", exclude = c(1, 4))