Combine columns into a nested report stub. The report stub
is a common feature of statistical reports. The stub is created with
the stub function, and frequently appears in combination with the
label_row and indent parameters from the
define function. These elements work together to define
the appearance of the stub.
stub( x, vars, label = "", label_align = NULL, align = "left", width = NULL, standard_eval = FALSE )
| x | The table spec. |
|---|---|
| vars | A vector of quoted or unquoted variable names from
which to create the stub. If you want to pass an R variable of names,
escape the values with double curly braces, i.e. |
| label | The label for the report stub. The default label is an empty string. |
| label_align | The alignment for the stub column label.
Valid values are 'left', 'right', 'center', and 'centre'. Default follows
the |
| align | How to align the stub column. Valid values are 'left', 'right', 'center', and 'centre'. Default is 'left'. |
| width | The width of the stub, in report units of measure. |
| standard_eval | A TRUE or FALSE value that indicates whether to
use standard or non-standard evaluation of the |
The modified table spec.
The table stub is a nested set of labels that identify rows on the table. The stub is created by combining two or more columns into a single stub column. The relationship between the columns is typically visualized as a hierarchy, with lower level concepts indented under higher level concepts.
A typical stub is created with the following steps:
Prepare the data.
Create the table object.
Define the stub on the table using the stub function,
and identify the variables to be combined.
Identify higher level concepts with the label_row parameter
on the define function.
Identify lower level concepts using the indent parameter
on the define function.
The stub will be automatically added as an identity variable on the report, and will always appear as the leftmost column. There can only be one stub defined on a report.
If you wish to create multiple levels of nested labels, use an NA value to prevent lower level labels from overwriting higher level labels.
For example, the following data:
continent country state_province "North America" NA NA "North America" "Canada" NA "North America" "Canada" "Ontario" "North America" "USA" NA "North America" "USA" "New York" "South America" NA NA "South America" "Brazil" NA "South America" "Brazil" "Amazonas" "South America" "Brazil" "Bahia"
Will produce the following stub:
North America
Canada
Ontario
USA
New York
South America
Brazil
Amazonas
Bahia
With the following code:
tbl <- create_table(dat) %>% stub(c(continent, country, state_province)) %>% define(country, indent = .25) %>% define(state_province, indent = .5)
Other table:
column_defaults(),
create_table(),
define(),
print.table_spec(),
spanning_header()
library(reporter) library(magrittr) # Create temporary path tmp <- file.path(tempdir(), "stub.txt") # Read in prepared data df <- read.table(header = TRUE, text = ' var label A B "ampg" "N" "19" "13" "ampg" "Mean" "18.8 (6.5)" "22.0 (4.9)" "ampg" "Median" "16.4" "21.4" "ampg" "Q1 - Q3" "15.1 - 21.2" "19.2 - 22.8" "ampg" "Range" "10.4 - 33.9" "14.7 - 32.4" "cyl" "8 Cylinder" "10 ( 52.6%)" "4 ( 30.8%)" "cyl" "6 Cylinder" "4 ( 21.1%)" "3 ( 23.1%)" "cyl" "4 Cylinder" "5 ( 26.3%)" "6 ( 46.2%)"') # Create table tbl <- create_table(df, first_row_blank = TRUE) %>% stub(c(var, label)) %>% define(var, blank_after = TRUE, label_row = TRUE, format = c(ampg = "Miles Per Gallon", cyl = "Cylinders")) %>% define(label, indent = .25) %>% define(A, label = "Group A", align = "center", n = 19) %>% define(B, label = "Group B", align = "center", n = 13) # Create report and add content rpt <- create_report(tmp, orientation = "portrait") %>% page_header(left = "Client: Motor Trend", right = "Study: Cars") %>% titles("Table 1.0", "MTCARS Summary Table") %>% add_content(tbl) %>% footnotes("* Motor Trend, 1974") %>% page_footer(left = Sys.time(), center = "Confidential", right = "Page [pg] of [tpg]") # Write out report write_report(rpt) # View report in console writeLines(readLines(tmp, encoding = "UTF-8")) # Client: Motor Trend Study: Cars # Table 1.0 # MTCARS Summary Table # # Group A Group B # (N=19) (N=13) # ------------------------------------------- # # Miles Per Gallon # N 19 13 # Mean 18.8 (6.5) 22.0 (4.9) # Median 16.4 21.4 # Q1 - Q3 15.1 - 21.2 19.2 - 22.8 # Range 10.4 - 33.9 14.7 - 32.4 # # Cylinders # 8 Cylinder 10 ( 52.6%) 4 ( 30.8%) # 6 Cylinder 4 ( 21.1%) 3 ( 23.1%) # 4 Cylinder 5 ( 26.3%) 6 ( 46.2%) # # ... # # # * Motor Trend, 1974 # # 2020-08-30 03:50:02 Confidential Page 1 of 1 #