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
)

Arguments

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. vars = {{myvar}}. The curly brace escape is useful when writing functions that construct reports dynamically.

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 parameter.

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 vars, from, and to parameters. Set standard_eval to TRUE if you want to pass the column names as variables. Default is FALSE, meaning it will use non-standard (unquoted) evaluation.

Value

The modified table spec.

Details

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)

See also

Examples

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
#