define_keywords: Modify Keywords Used In Outputs

Description

As an alternative to use_custom_lang, this allows temporarily modifying the keywords used in the outputs.

Usage

define_keywords(...)

Arguments

…

one or more pairs of keywords and their new values see Details.

Details

On systems with GUI capabilities, a window will pop-up, allowing the modification of the custom column. The changes will be active as long as the package is loaded. A dialog will show up prompting the user to save the modified set of keywords in a custom csv language file.

Here is the full list of modifiable keywords. Check out the language_template.csv file in the package<U+2019>s includes directory.

title.freq: main heading for freq()
title.freq.weighted: main heading for freq() (weighted)
title.ctable: main heading for ctable()
title.ctable.weighted: main heading ctable() (weighted)
title.ctable.row: indicates what proportions are displayed
title.ctable.col: indicates what proportions are displayed
title.ctable.tot: indicates what proportions are displayed
title.descr: main heading for descr()
title.descr.weighted: main heading for descr() (weighted)
title.dfSummary: main heading for dfSummary()
n: heading item used in descr()
dimensions: heading item used in dfSummary()
duplicates: heading item used in dfSummary()
data.frame: heading item (all functions)
label: heading item (all functions) & column name in dfSummary()
variable: heading item (all functions) & column name in dfSummary()
group: heading item (all functions when used with stby()
by: heading item for descr() when used with stby()
weights: heading item - descr() & freq()
type: heading item for freq()
logical: heading item - type in freq()
character: heading item - type in freq()
numeric: heading item - type in freq()
factor: heading item - type in freq()
factor.ordered: heading item - type in freq()
date: heading item - type in freq()
datetime: heading item - type in freq()
freq: column name in freq()
pct: column name in freq() when report.nas=FALSE
pct.valid.f: column name in freq()
pct.valid.cum: column name in freq()
pct.total: column name in freq()
pct.total.cum: column name in freq()
pct.cum: column name in freq()
valid: column name in freq() and dfSummary() & column content in dfSummary()
invalid: column content in dfSummary() (emails)
total: column grouping in freq(), html version
mean: row name in descr()
sd.long: row name in descr()
sd: cell content (dfSummary)
min: row name in descr()
q1: row name in descr() - 1st quartile
med: row name in descr()
q3: row name in descr() - 3rd quartile
max: row name in descr()
mad: row name in descr() - Median Absolute Deviation
iqr: row name in descr() - Inter-Quartile Range
cv: row name in descr() - Coefficient of Variation
skewness: row name in descr()
se.skewness: row name in descr() - Std. Error for Skewness
kurtosis: row name in descr()
n.valid: row name in descr() - Count of non-missing values
pct.valid: row name in descr() - pct. of non-missing values
no: column name in dfSummary() - position of column in the data frame
stats.values: column name in dfSummary()
freqs.pct.valid: column name in dfSummary()
graph: column name in dfSummary()
missing: column name in dfSummary()
distinct.value: cell content in dfSummary() - singular form
distinct.values: cell content in dfSummary() - plural form
all.nas: cell content in dfSummary() - column has only NAs
all.empty.str: cell content in dfSummary() - column has only empty strings
all.empty.str.nas: cell content in dfSummary() - col. has only NAs and empty strings
no.levels.defined: cell content in dfSummary() - factor has no levels defined
int.sequence: cell content in dfSummary()
rounded: cell content in dfSummary() - note appearing in Stats/Values
others: cell content in dfSummary() - nbr of values not displayed
codes: cell content in dfSummary() - When UPC codes are detected
mode: cell content in dfSummary() - mode = most frequent value
med.short: cell content in dfSummary() - median (shortened term)
start: cell content in dfSummary() - earliest date for date-type cols
end: cell content in dfSummary() - latest date for data-type cols
emails: cell content in dfSummary()
generated.by: footnote content
version: footnote content
date.fmt: footnote - date format (see strptime)

Examples

Run this code

# NOT RUN {
define_keywords(n = "Nb. Obs.")
# }

Run the code above in your browser using DataLab