| Title: | Data Menu for Radiant: Business Analytics using R and Shiny |
| Version: | 1.6.7 |
| Date: | 2024-10-22 |
| Description: | The Radiant Data menu includes interfaces for loading, saving, viewing, visualizing, summarizing, transforming, and combining data. It also contains functionality to generate reproducible reports of the analyses conducted in the application. |
| Depends: | R (≥ 4.3.0), magrittr (≥ 1.5), ggplot2 (≥ 3.4.2), lubridate (≥ 1.7.4), tidyr (≥ 0.8.2), dplyr (≥ 1.1.2) |
| Imports: | tibble (≥ 1.4.2), rlang (≥ 0.4.10), broom (≥ 0.5.2), car (≥ 3.0-0), knitr (≥ 1.20), markdown (≥ 1.7), rmarkdown(≥ 2.22), shiny (≥ 1.8.1), jsonlite (≥ 1.0), shinyAce (≥ 0.4.1), psych (≥ 1.8.4), DT (≥ 0.28), readr (≥ 1.1.1), readxl (≥ 1.0.0), writexl (≥ 0.2), scales (≥ 0.4.0), curl (≥ 2.5), rstudioapi (≥ 0.7), import (≥ 1.1.0), plotly (≥ 4.7.1), glue (≥ 1.3.0), shinyFiles (≥ 0.9.1), stringi (≥ 1.2.4), randomizr (≥ 0.20.0), patchwork (≥ 1.0.0), bslib (≥ 0.5.0), png, MASS, base64enc |
| Suggests: | arrow (≥ 12.0.1), dbplyr (≥ 2.1.1), DBI (≥ 0.7), RSQLite (≥ 2.0), RPostgres (≥ 1.4.4), webshot (≥ 0.5.0), testthat (≥ 2.0.0), pkgdown (≥ 1.1.0) |
| URL: | https://github.com/radiant-rstats/radiant.data/, https://radiant-rstats.github.io/radiant.data/, https://radiant-rstats.github.io/docs/ |
| BugReports: | https://github.com/radiant-rstats/radiant.data/issues/ |
| License: | AGPL-3 | file LICENSE |
| LazyData: | true |
| Encoding: | UTF-8 |
| Language: | en-US |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2024-10-23 00:12:36 UTC; vnijs |
| Author: | Vincent Nijs [aut, cre], Niklas von Hertzen [aut] (html2canvas library) |
| Maintainer: | Vincent Nijs <radiant@rady.ucsd.edu> |
| Repository: | CRAN |
| Date/Publication: | 2024-10-23 04:20:02 UTC |
Convenience function to add a class
Description
Convenience function to add a class
Usage
add_class(x, cl)
Arguments
x |
Object |
cl |
Vector of class labels to add |
Examples
foo <- "some text" %>% add_class("text")
foo <- "some text" %>% add_class(c("text", "another class"))
Convenience function to add a markdown description to a data.frame
Description
Convenience function to add a markdown description to a data.frame
Usage
add_description(df, md = "", path = "")
Arguments
df |
A data.frame or tibble |
md |
Data description in markdown format |
path |
Path to a text file with the data description in markdown format |
See Also
See also register
Examples
if (interactive()) {
mt <- mtcars |> add_description(md = "# MTCARS\n\nThis data.frame contains information on ...")
describe(mt)
}
Arrange data with user-specified expression
Description
Arrange data with user-specified expression
Usage
arrange_data(dataset, expr = NULL)
Arguments
dataset |
Data frame to arrange |
expr |
Expression to use arrange rows from the specified dataset |
Details
Arrange data, likely in combination with slicing
Value
Arranged data frame
Wrapper for as.character
Description
Wrapper for as.character
Usage
as_character(x)
Arguments
x |
Input vector |
Distance in kilometers or miles between two locations based on lat-long Function based on http://www.movable-type.co.uk/scripts/latlong.html. Uses the haversine formula
Description
Distance in kilometers or miles between two locations based on lat-long Function based on http://www.movable-type.co.uk/scripts/latlong.html. Uses the haversine formula
Usage
as_distance(
lat1,
long1,
lat2,
long2,
unit = "km",
R = c(km = 6371, miles = 3959)[[unit]]
)
Arguments
lat1 |
Latitude of location 1 |
long1 |
Longitude of location 1 |
lat2 |
Latitude of location 2 |
long2 |
Longitude of location 2 |
unit |
Measure kilometers ("km", default) or miles ("miles") |
R |
Radius of the earth |
Value
Distance between two points
Examples
as_distance(32.8245525, -117.0951632, 40.7033127, -73.979681, unit = "km")
as_distance(32.8245525, -117.0951632, 40.7033127, -73.979681, unit = "miles")
Convert input in day-month-year format to date
Description
Convert input in day-month-year format to date
Usage
as_dmy(x)
Arguments
x |
Input variable |
Value
Date variable of class Date
Examples
as_dmy("1-2-2014")
Convert input in day-month-year-hour-minute format to date-time
Description
Convert input in day-month-year-hour-minute format to date-time
Usage
as_dmy_hm(x)
Arguments
x |
Input variable |
Value
Date-time variable of class Date
Examples
as_mdy_hm("1-1-2014 12:15")
Convert input in day-month-year-hour-minute-second format to date-time
Description
Convert input in day-month-year-hour-minute-second format to date-time
Usage
as_dmy_hms(x)
Arguments
x |
Input variable |
Value
Date-time variable of class Date
Examples
as_mdy_hms("1-1-2014 12:15:01")
Wrapper for lubridate's as.duration function. Result converted to numeric
Description
Wrapper for lubridate's as.duration function. Result converted to numeric
Usage
as_duration(x)
Arguments
x |
Time difference |
Wrapper for factor with ordered = FALSE
Description
Wrapper for factor with ordered = FALSE
Usage
as_factor(x, ordered = FALSE)
Arguments
x |
Input vector |
ordered |
Order factor levels (TRUE, FALSE) |
Convert input in hour-minute format to time
Description
Convert input in hour-minute format to time
Usage
as_hm(x)
Arguments
x |
Input variable |
Value
Time variable of class Period
Examples
as_hm("12:45")
## Not run:
as_hm("12:45") %>% minute()
## End(Not run)
Convert input in hour-minute-second format to time
Description
Convert input in hour-minute-second format to time
Usage
as_hms(x)
Arguments
x |
Input variable |
Value
Time variable of class Period
Examples
as_hms("12:45:00")
## Not run:
as_hms("12:45:00") %>% hour()
as_hms("12:45:00") %>% second()
## End(Not run)
Convert variable to integer avoiding potential issues with factors
Description
Convert variable to integer avoiding potential issues with factors
Usage
as_integer(x)
Arguments
x |
Input variable |
Value
Integer
Examples
as_integer(rnorm(10))
as_integer(letters)
as_integer(as.factor(5:10))
as.integer(as.factor(5:10))
as_integer(c("a", "b"))
as_integer(c("0", "1"))
as_integer(as.factor(c("0", "1")))
Convert input in month-day-year format to date
Description
Convert input in month-day-year format to date
Usage
as_mdy(x)
Arguments
x |
Input variable |
Details
Use as.character if x is a factor
Value
Date variable of class Date
Examples
as_mdy("2-1-2014")
## Not run:
as_mdy("2-1-2014") %>% month(label = TRUE)
as_mdy("2-1-2014") %>% week()
as_mdy("2-1-2014") %>% wday(label = TRUE)
## End(Not run)
Convert input in month-day-year-hour-minute format to date-time
Description
Convert input in month-day-year-hour-minute format to date-time
Usage
as_mdy_hm(x)
Arguments
x |
Input variable |
Value
Date-time variable of class Date
Examples
as_mdy_hm("1-1-2014 12:15")
Convert input in month-day-year-hour-minute-second format to date-time
Description
Convert input in month-day-year-hour-minute-second format to date-time
Usage
as_mdy_hms(x)
Arguments
x |
Input variable |
Value
Date-time variable of class Date
Examples
as_mdy_hms("1-1-2014 12:15:01")
Convert variable to numeric avoiding potential issues with factors
Description
Convert variable to numeric avoiding potential issues with factors
Usage
as_numeric(x)
Arguments
x |
Input variable |
Value
Numeric
Examples
as_numeric(rnorm(10))
as_numeric(letters)
as_numeric(as.factor(5:10))
as.numeric(as.factor(5:10))
as_numeric(c("a", "b"))
as_numeric(c("3", "4"))
as_numeric(as.factor(c("3", "4")))
Convert input in year-month-day format to date
Description
Convert input in year-month-day format to date
Usage
as_ymd(x)
Arguments
x |
Input variable |
Value
Date variable of class Date
Examples
as_ymd("2013-1-1")
Convert input in year-month-day-hour-minute format to date-time
Description
Convert input in year-month-day-hour-minute format to date-time
Usage
as_ymd_hm(x)
Arguments
x |
Input variable |
Value
Date-time variable of class Date
Examples
as_ymd_hm("2014-1-1 12:15")
Convert input in year-month-day-hour-minute-second format to date-time
Description
Convert input in year-month-day-hour-minute-second format to date-time
Usage
as_ymd_hms(x)
Arguments
x |
Input variable |
Value
Date-time variable of class Date
Examples
as_ymd_hms("2014-1-1 12:15:01")
## Not run:
as_ymd_hms("2014-1-1 12:15:01") %>% as.Date()
as_ymd_hms("2014-1-1 12:15:01") %>% month()
as_ymd_hms("2014-1-1 12:15:01") %>% hour()
## End(Not run)
Avengers
Description
Avengers
Usage
data(avengers)
Format
A data frame with 7 rows and 4 variables
Details
List of avengers. The dataset is used to illustrate data merging / joining. Description provided in attr(avengers,"description")
Center
Description
Center
Usage
center(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
If x is a numeric variable return x - mean(x)
Choose a directory interactively
Description
Choose a directory interactively
Usage
choose_dir(...)
Arguments
... |
Arguments passed to utils::choose.dir on Windows |
Details
Open a file dialog to select a directory. Uses JavaScript on Mac, utils::choose.dir on Windows, and dirname(file.choose()) on Linux
Value
Path to the directory selected by the user
Examples
## Not run:
choose_dir()
## End(Not run)
Choose files interactively
Description
Choose files interactively
Usage
choose_files(...)
Arguments
... |
Strings used to indicate which file types should be available for selection (e.g., "csv" or "pdf") |
Details
Open a file dialog. Uses JavaScript on Mac, utils::choose.files on Windows, and file.choose() on Linux
Value
Vector of paths to files selected by the user
Examples
## Not run:
choose_files("pdf", "csv")
## End(Not run)
Labels for confidence intervals
Description
Labels for confidence intervals
Usage
ci_label(alt = "two.sided", cl = 0.95, dec = 3)
Arguments
alt |
Type of hypothesis ("two.sided","less","greater") |
cl |
Confidence level |
dec |
Number of decimals to show |
Value
A character vector with labels for a confidence interval
Examples
ci_label("less", .95)
ci_label("two.sided", .95)
ci_label("greater", .9)
Values at confidence levels
Description
Values at confidence levels
Usage
ci_perc(dat, alt = "two.sided", cl = 0.95)
Arguments
dat |
Data |
alt |
Type of hypothesis ("two.sided","less","greater") |
cl |
Confidence level |
Value
A vector with values at a confidence level
Examples
ci_perc(0:100, "less", .95)
ci_perc(0:100, "greater", .95)
ci_perc(0:100, "two.sided", .80)
Combine datasets using dplyr's bind and join functions
Description
Combine datasets using dplyr's bind and join functions
Usage
combine_data(
x,
y,
by = "",
add = "",
type = "inner_join",
data_filter = "",
arr = "",
rows = NULL,
envir = parent.frame(),
...
)
Arguments
x |
Dataset |
y |
Dataset to combine with x |
by |
Variables used to combine 'x' and 'y' |
add |
Variables to add from 'y' |
type |
The main bind and join types from the dplyr package are provided. inner_join returns all rows from x with matching values in y, and all columns from x and y. If there are multiple matches between x and y, all match combinations are returned. left_join returns all rows from x, and all columns from x and y. If there are multiple matches between x and y, all match combinations are returned. right_join is equivalent to a left join for datasets y and x. full_join combines two datasets, keeping rows and columns that appear in either. semi_join returns all rows from x with matching values in y, keeping just columns from x. A semi join differs from an inner join because an inner join will return one row of x for each matching row of y, whereas a semi join will never duplicate rows of x. anti_join returns all rows from x without matching values in y, keeping only columns from x. bind_rows and bind_cols are also included, as are intersect, union, and setdiff. See https://radiant-rstats.github.io/docs/data/combine.html for further details |
data_filter |
Expression used to filter the dataset. This should be a string (e.g., "price > 10000") |
arr |
Expression to arrange (sort) the data on (e.g., "color, desc(price)") |
rows |
Rows to select from the specified dataset |
envir |
Environment to extract data from |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/data/combine.html for an example in Radiant
Value
Combined dataset
Examples
avengers %>% combine_data(superheroes, type = "bind_cols")
combine_data(avengers, superheroes, type = "bind_cols")
avengers %>% combine_data(superheroes, type = "bind_rows")
avengers %>% combine_data(superheroes, add = "publisher", type = "bind_rows")
Source all package functions
Description
Source all package functions
Usage
copy_all(.from)
Arguments
.from |
The package to pull the function from |
Details
Equivalent of source with local=TRUE for all package functions. Adapted from functions by smbache, author of the import package. See https://github.com/rticulate/import/issues/4/ for a discussion. This function will be deprecated when (if) it is included in https://github.com/rticulate/import/
Examples
copy_all(radiant.data)
Copy attributes from one object to another
Description
Copy attributes from one object to another
Usage
copy_attr(to, from, attr)
Arguments
to |
Object to copy attributes to |
from |
Object to copy attributes from |
attr |
Vector of attributes. If missing all attributes will be copied |
Source for package functions
Description
Source for package functions
Usage
copy_from(.from, ...)
Arguments
.from |
The package to pull the function from |
... |
Functions to pull |
Details
Equivalent of source with local=TRUE for package functions. Written by smbache, author of the import package. See https://github.com/rticulate/import/issues/4/ for a discussion. This function will be deprecated when (if) it is included in https://github.com/rticulate/import/
Examples
copy_from(radiant.data, get_data)
Coefficient of variation
Description
Coefficient of variation
Usage
cv(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Coefficient of variation
Examples
cv(runif(100))
Deregister a data.frame or list in Radiant
Description
Deregister a data.frame or list in Radiant
Usage
deregister(
dataset,
shiny = shiny::getDefaultReactiveDomain(),
envir = r_data,
info = r_info
)
Arguments
dataset |
String containing the name of the data.frame to deregister |
shiny |
Check if function is called from a shiny application |
envir |
Environment to remove data from |
info |
Reactive list with information about available data in radiant |
Show dataset description
Description
Show dataset description
Usage
describe(dataset, envir = parent.frame())
Arguments
dataset |
Dataset with "description" attribute |
envir |
Environment to extract data from |
Details
Show dataset description, if available, in html form in Rstudio viewer or the default browser. The description should be in markdown format, attached to a data.frame as an attribute with the name "description"
Diamond prices
Description
Diamond prices
Usage
data(diamonds)
Format
A data frame with 3000 rows and 10 variables
Details
A sample of 3,000 from the diamonds dataset bundled with ggplot2. Description provided in attr(diamonds,"description")
Does a vector have non-zero variability?
Description
Does a vector have non-zero variability?
Usage
does_vary(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Logical. TRUE is there is variability
Examples
summarise_all(diamonds, does_vary) %>% as.logical()
Method to create datatables
Description
Method to create datatables
Usage
dtab(object, ...)
Arguments
object |
Object of relevant class to render |
... |
Additional arguments |
See Also
See dtab.data.frame to create an interactive table from a data.frame
See dtab.explore to create an interactive table from an explore object
See dtab.pivotr to create an interactive table from a pivotr object
Create an interactive table to view, search, sort, and filter data
Description
Create an interactive table to view, search, sort, and filter data
Usage
## S3 method for class 'data.frame'
dtab(
object,
vars = "",
filt = "",
arr = "",
rows = NULL,
nr = NULL,
na.rm = FALSE,
dec = 3,
perc = "",
filter = "top",
pageLength = 10,
dom = "",
style = "bootstrap4",
rownames = FALSE,
caption = NULL,
envir = parent.frame(),
...
)
Arguments
object |
Data.frame to display |
vars |
Variables to show (default is all) |
filt |
Filter to apply to the specified dataset. For example "price > 10000" if dataset is "diamonds" (default is "") |
arr |
Expression to arrange (sort) the data on (e.g., "color, desc(price)") |
rows |
Select rows in the specified dataset. For example "1:10" for the first 10 rows or "n()-10:n()" for the last 10 rows (default is NULL) |
nr |
Number of rows of data to include in the table. This function will be mainly used in reports so it is best to keep this number small |
na.rm |
Remove rows with missing values (default is FALSE) |
dec |
Number of decimal places to show. Default is no rounding (NULL) |
perc |
Vector of column names to be displayed as a percentage |
filter |
Show column filters in DT table. Options are "none", "top", "bottom" |
pageLength |
Number of rows to show in table |
dom |
Table control elements to show on the page. See https://datatables.net/reference/option/dom |
style |
Table formatting style ("bootstrap" or "default") |
rownames |
Show data.frame rownames. Default is FALSE |
caption |
Table caption |
envir |
Environment to extract data from |
... |
Additional arguments |
Details
View, search, sort, and filter a data.frame. For styling options see https://rstudio.github.io/DT/functions.html
Examples
## Not run:
dtab(mtcars)
## End(Not run)
Make an interactive table of summary statistics
Description
Make an interactive table of summary statistics
Usage
## S3 method for class 'explore'
dtab(
object,
dec = 3,
searchCols = NULL,
order = NULL,
pageLength = NULL,
caption = NULL,
...
)
Arguments
object |
Return value from |
dec |
Number of decimals to show |
searchCols |
Column search and filter |
order |
Column sorting |
pageLength |
Page length |
caption |
Table caption |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/data/explore.html for an example in Radiant
See Also
pivotr to create a pivot table
summary.pivotr to show summaries
Examples
## Not run:
tab <- explore(diamonds, "price:x") %>% dtab()
tab <- explore(diamonds, "price", byvar = "cut", fun = c("n_obs", "skew"), top = "byvar") %>%
dtab()
## End(Not run)
Make an interactive pivot table
Description
Make an interactive pivot table
Usage
## S3 method for class 'pivotr'
dtab(
object,
format = "none",
perc = FALSE,
dec = 3,
searchCols = NULL,
order = NULL,
pageLength = NULL,
caption = NULL,
...
)
Arguments
object |
Return value from |
format |
Show Color bar ("color_bar"), Heat map ("heat"), or None ("none") |
perc |
Display numbers as percentages (TRUE or FALSE) |
dec |
Number of decimals to show |
searchCols |
Column search and filter |
order |
Column sorting |
pageLength |
Page length |
caption |
Table caption |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/data/pivotr.html for an example in Radiant
See Also
pivotr to create the pivot table
summary.pivotr to print the table
Examples
## Not run:
pivotr(diamonds, cvars = "cut") %>% dtab()
pivotr(diamonds, cvars = c("cut", "clarity")) %>% dtab(format = "color_bar")
pivotr(diamonds, cvars = c("cut", "clarity"), normalize = "total") %>%
dtab(format = "color_bar", perc = TRUE)
## End(Not run)
Convert categorical variables to factors and deal with empty/missing values
Description
Convert categorical variables to factors and deal with empty/missing values
Usage
empty_level(x)
Arguments
x |
Categorical variable used in table |
Value
Variable with updated levels
Explore and summarize data
Description
Explore and summarize data
Usage
explore(
dataset,
vars = "",
byvar = "",
fun = c("mean", "sd"),
top = "fun",
tabfilt = "",
tabsort = "",
tabslice = "",
nr = Inf,
data_filter = "",
arr = "",
rows = NULL,
envir = parent.frame()
)
Arguments
dataset |
Dataset to explore |
vars |
(Numeric) variables to summarize |
byvar |
Variable(s) to group data by |
fun |
Functions to use for summarizing |
top |
Use functions ("fun"), variables ("vars"), or group-by variables as column headers |
tabfilt |
Expression used to filter the table (e.g., "Total > 10000") |
tabsort |
Expression used to sort the table (e.g., "desc(Total)") |
tabslice |
Expression used to filter table (e.g., "1:5") |
nr |
Number of rows to display |
data_filter |
Expression used to filter the dataset before creating the table (e.g., "price > 10000") |
arr |
Expression to arrange (sort) the data on (e.g., "color, desc(price)") |
rows |
Rows to select from the specified dataset |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/data/explore.html for an example in Radiant
Value
A list of all variables defined in the function as an object of class explore
See Also
See summary.explore to show summaries
Examples
explore(diamonds, c("price", "carat")) %>% str()
explore(diamonds, "price:x")$tab
explore(diamonds, c("price", "carat"), byvar = "cut", fun = c("n_missing", "skew"))$tab
Filter data with user-specified expression
Description
Filter data with user-specified expression
Usage
filter_data(dataset, filt = "", drop = TRUE)
Arguments
dataset |
Data frame to filter |
filt |
Filter expression to apply to the specified dataset |
drop |
Drop unused factor levels after filtering (default is TRUE) |
Details
Filters can be used to view a sample from a selected dataset. For example, runif(nrow(.)) > .9 could be used to sample approximately 10
Value
Filtered data frame
Examples
select(diamonds, 1:3) %>% filter_data(filt = "price > max(.$price) - 100")
select(diamonds, 1:3) %>% filter_data(filt = "runif(nrow(.)) > .995")
Find Dropbox folder
Description
Find Dropbox folder
Usage
find_dropbox(account = 1)
Arguments
account |
Integer. If multiple accounts exist, specify which one to use. By default, the first account listed is used |
Details
Find the path for Dropbox if available
Value
Path to Dropbox account
Find Google Drive folder
Description
Find Google Drive folder
Usage
find_gdrive()
Details
Find the path for Google Drive if available
Value
Path to Google Drive folder
Find user directory
Description
Find user directory
Usage
find_home()
Details
Returns /Users/x and not /Users/x/Documents
Find the Rstudio project folder
Description
Find the Rstudio project folder
Usage
find_project(mess = TRUE)
Arguments
mess |
Show or hide messages (default mess = TRUE) |
Details
Find the path for the Rstudio project folder if available. The returned path is normalized (see normalizePath)
Value
Path to Rstudio project folder if available or else and empty string. The returned path is normalized
Ensure column names are valid
Description
Ensure column names are valid
Usage
fix_names(x, lower = FALSE)
Arguments
x |
Data.frame or vector of (column) names |
lower |
Set letters to lower case (TRUE or FALSE) |
Details
Remove symbols, trailing and leading spaces, and convert to valid R column names. Opinionated version of make.names
Examples
fix_names(c(" var-name ", "$amount spent", "100"))
Replace smart quotes etc.
Description
Replace smart quotes etc.
Usage
fix_smart(text, all = FALSE)
Arguments
text |
Text to be parsed |
all |
Should all non-ascii characters be removed? Default is FALSE |
Flip the DT table to put Function, Variable, or Group by on top
Description
Flip the DT table to put Function, Variable, or Group by on top
Usage
flip(expl, top = "fun")
Arguments
expl |
Return value from |
top |
The variable (type) to display at the top of the table ("fun" for Function, "var" for Variable, and "byvar" for Group by. "fun" is the default |
Details
See https://radiant-rstats.github.io/docs/data/explore.html for an example in Radiant
See Also
explore to calculate summaries
summary.explore to show summaries
dtab.explore to create the DT table
Examples
explore(diamonds, "price:x", top = "var") %>% summary()
explore(diamonds, "price", byvar = "cut", fun = c("n_obs", "skew"), top = "byvar") %>% summary()
Format a data.frame with a specified number of decimal places
Description
Format a data.frame with a specified number of decimal places
Usage
format_df(tbl, dec = NULL, perc = FALSE, mark = "", na.rm = FALSE, ...)
Arguments
tbl |
Data.frame |
dec |
Number of decimals to show |
perc |
Display numbers as percentages (TRUE or FALSE) |
mark |
Thousand separator |
na.rm |
Remove missing values |
... |
Additional arguments for format_nr |
Value
Data.frame for printing
Examples
data.frame(x = c("a", "b"), y = c(1L, 2L), z = c(-0.0005, 3)) %>%
format_df(dec = 4)
data.frame(x = c(1L, 2L), y = c(0.06, 0.8)) %>%
format_df(dec = 2, perc = TRUE)
data.frame(x = c(1L, 2L, NA), y = c(NA, 1.008, 2.8)) %>%
format_df(dec = 2)
Format a number with a specified number of decimal places, thousand sep, and a symbol
Description
Format a number with a specified number of decimal places, thousand sep, and a symbol
Usage
format_nr(x, sym = "", dec = 2, perc = FALSE, mark = ",", na.rm = TRUE, ...)
Arguments
x |
Number or vector |
sym |
Symbol to use |
dec |
Number of decimals to show |
perc |
Display number as a percentage |
mark |
Thousand separator |
na.rm |
Remove missing values |
... |
Additional arguments passed to |
Value
Character (vector) in the desired format
Examples
format_nr(2000, "$")
format_nr(2000, dec = 4)
format_nr(.05, perc = TRUE)
format_nr(c(.1, .99), perc = TRUE)
format_nr(data.frame(a = c(.1, .99)), perc = TRUE)
format_nr(data.frame(a = 1:10), sym = "$", dec = 0)
format_nr(c(1, 1.9, 1.008, 1.00))
format_nr(c(1, 1.9, 1.008, 1.00), drop0trailing = TRUE)
format_nr(NA)
format_nr(NULL)
Get variable class
Description
Get variable class
Usage
get_class(dat)
Arguments
dat |
Dataset to evaluate |
Details
Get variable class information for each column in a data.frame
Value
Vector with class information for each variable
Examples
get_class(mtcars)
Select variables and filter data
Description
Select variables and filter data
Usage
get_data(
dataset,
vars = "",
filt = "",
arr = "",
rows = NULL,
data_view_rows = NULL,
na.rm = TRUE,
rev = FALSE,
envir = c()
)
Arguments
dataset |
Dataset or name of the data.frame |
vars |
Variables to extract from the data.frame |
filt |
Filter to apply to the specified dataset |
arr |
Expression to use to arrange (sort) the specified dataset |
rows |
Select rows in the specified dataset |
data_view_rows |
Vector of rows to select. Only used by Data > View in Radiant. Users should use "rows" instead |
na.rm |
Remove rows with missing values (default is TRUE) |
rev |
Reverse filter and row selection (i.e., get the remainder) |
envir |
Environment to extract data from |
Details
Function is used in radiant to select variables and filter data based on user input in string form
Value
Data.frame with specified columns and rows
Examples
get_data(mtcars, vars = "cyl:vs", filt = "mpg > 25")
get_data(mtcars, vars = c("mpg", "cyl"), rows = 1:10)
get_data(mtcars, vars = c("mpg", "cyl"), arr = "desc(mpg)", rows = "1:5")
Create data.frame summary
Description
Create data.frame summary
Usage
get_summary(dataset, dc = get_class(dataset), dec = 3)
Arguments
dataset |
Data.frame |
dc |
Class for each variable |
dec |
Number of decimals to show |
Details
Used in Radiant's Data > Transform tab
Work around to avoid (harmless) messages from ggplotly
Description
Work around to avoid (harmless) messages from ggplotly
Usage
ggplotly(...)
Arguments
... |
Arguments to pass to the |
See Also
See the ggplotly function in the plotly package for details (?plotly::ggplotly)
Find index corrected for missing values and filters
Description
Find index corrected for missing values and filters
Usage
indexr(dataset, vars = "", filt = "", arr = "", rows = NULL, cmd = "")
Arguments
dataset |
Dataset |
vars |
Variables to select |
filt |
Data filter |
arr |
Expression to arrange (sort) the data on (e.g., "color, desc(price)") |
rows |
Selected rows |
cmd |
A command used to customize the data |
Install webshot and phantomjs
Description
Install webshot and phantomjs
Usage
install_webshot()
Calculate inverse of a variable
Description
Calculate inverse of a variable
Usage
inverse(x)
Arguments
x |
Input variable |
Value
1/x
Is a variable empty
Description
Is a variable empty
Usage
is.empty(x, empty = "\\s*")
Arguments
x |
Character value to evaluate |
empty |
Indicate what 'empty' means. Default is empty string (i.e., "") |
Details
Is a variable empty
Value
TRUE if empty, else FALSE
Examples
is.empty("")
is.empty(NULL)
is.empty(NA)
is.empty(c())
is.empty("none", empty = "none")
is.empty("")
is.empty(" ")
is.empty(" something ")
is.empty(c("", "something"))
is.empty(c(NA, 1:100))
is.empty(mtcars)
Is input a double (and not a date type)?
Description
Is input a double (and not a date type)?
Usage
is_double(x)
Arguments
x |
Input |
Value
TRUE if double and not a type of date, else FALSE
Convenience function for is.null or is.na
Description
Convenience function for is.null or is.na
Usage
is_not(x)
Arguments
x |
Input |
Examples
is_not(NA)
is_not(NULL)
is_not(c())
is_not(list())
is_not(data.frame())
Is input a string?
Description
Is input a string?
Usage
is_string(x)
Arguments
x |
Input |
Value
TRUE if string, else FALSE
Examples
is_string(" ")
is_string("data")
is_string(c("data", ""))
is_string(NULL)
is_string(NA)
Create a vector of interaction terms for linear and logistic regression
Description
Create a vector of interaction terms for linear and logistic regression
Usage
iterms(vars, nway = 2, sep = ":")
Arguments
vars |
Labels to use |
nway |
2-way (2) or 3-way (3) interaction labels to create |
sep |
Separator to use between variable names (e.g., :) |
Value
Character vector of interaction term labels
Examples
paste0("var", 1:3) %>% iterms(2)
paste0("var", 1:3) %>% iterms(3)
paste0("var", 1:3) %>% iterms(2, sep = ".")
Launch radiant apps
Description
Launch radiant apps
Usage
launch(package = "radiant.data", run = "viewer", state, ...)
Arguments
package |
Radiant package to start. One of "radiant.data", "radiant.design", "radiant.basics", "radiant.model", "radiant.multivariate", or "radiant" |
run |
Run a radiant app in an external browser ("browser"), an Rstudio window ("window"), or in the Rstudio viewer ("viewer") |
state |
Path to statefile to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Details
See https://radiant-rstats.github.io/docs/ for radiant documentation and tutorials
Examples
## Not run:
launch()
launch(run = "viewer")
launch(run = "window")
launch(run = "browser")
## End(Not run)
Generate list of levels and unique values
Description
Generate list of levels and unique values
Usage
level_list(dataset, ...)
Arguments
dataset |
A data.frame |
... |
Unquoted variable names to evaluate |
Examples
data.frame(a = c(rep("a", 5), rep("b", 5)), b = c(rep(1, 5), 6:10)) %>% level_list()
level_list(mtcars, mpg, cyl)
Natural log
Description
Natural log
Usage
ln(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
Remove missing values (default is TRUE) |
Value
Natural log of vector
Examples
ln(runif(10, 1, 2))
Load data through clipboard on Windows or macOS
Description
Load data through clipboard on Windows or macOS
Usage
load_clip(delim = "\t", text, suppress = TRUE)
Arguments
delim |
Delimiter to use (tab is the default) |
text |
Text input to convert to table |
suppress |
Suppress warnings |
Details
Extract data from the clipboard into a data.frame on Windows or macOS
See Also
See the save_clip
Generate arrange commands from user input
Description
Generate arrange commands from user input
Usage
make_arrange_cmd(expr, dataset = "")
Arguments
expr |
Expression to use arrange rows from the specified dataset |
dataset |
String with dataset name |
Details
Form arrange command from user input
Value
Arrange command
Generate a variable used to selected a training sample
Description
Generate a variable used to selected a training sample
Usage
make_train(n = 0.7, nr = NULL, blocks = NULL, seed = 1234)
Arguments
n |
Number (or fraction) of observations to label as training |
nr |
Number of rows in the dataset |
blocks |
A vector to use for blocking or a data.frame from which to construct a blocking vector |
seed |
Random seed |
Value
0/1 variables for filtering
Examples
make_train(.5, 10)
make_train(.5, 10) %>% table()
make_train(100, 1000) %>% table()
make_train(.15, blocks = mtcars$vs) %>% table() / nrow(mtcars)
make_train(.10, blocks = iris$Species) %>% table() / nrow(iris)
make_train(.5, blocks = iris[, c("Petal.Width", "Species")]) %>% table()
Convert a string of numbers into a vector
Description
Convert a string of numbers into a vector
Usage
make_vec(x)
Arguments
x |
A string of numbers that may include fractions |
Examples
make_vec("1 2 4")
make_vec("1/2 2/3 4/5")
make_vec(0.1)
Margin of error
Description
Margin of error
Usage
me(x, conf_lev = 0.95, na.rm = TRUE)
Arguments
x |
Input variable |
conf_lev |
Confidence level. The default is 0.95 |
na.rm |
If TRUE missing values are removed before calculation |
Value
Margin of error
Examples
me(rnorm(100))
Margin of error for proportion
Description
Margin of error for proportion
Usage
meprop(x, conf_lev = 0.95, na.rm = TRUE)
Arguments
x |
Input variable |
conf_lev |
Confidence level. The default is 0.95 |
na.rm |
If TRUE missing values are removed before calculation |
Value
Margin of error
Examples
meprop(c(rep(1L, 10), rep(0L, 10)))
Calculate the mode (modal value) and return a label
Description
Calculate the mode (modal value) and return a label
Usage
modal(x, na.rm = TRUE)
Arguments
x |
A vector |
na.rm |
If TRUE missing values are removed before calculation |
Details
From https://www.tutorialspoint.com/r/r_mean_median_mode.htm
Examples
modal(c("a", "b", "b"))
modal(c(1:10, 5))
modal(as.factor(c(letters, "b")))
modal(runif(100) > 0.5)
Add ordered argument to lubridate::month
Description
Add ordered argument to lubridate::month
Usage
month(x, label = FALSE, abbr = TRUE, ordered = FALSE)
Arguments
x |
Input date vector |
label |
Month as label (TRUE, FALSE) |
abbr |
Abbreviate label (TRUE, FALSE) |
ordered |
Order factor (TRUE, FALSE) |
See Also
See the month function in the lubridate package for additional details
Add transformed variables to a data frame with the option to include a custom variable name extension
Description
Add transformed variables to a data frame with the option to include a custom variable name extension
Usage
mutate_ext(.tbl, .funs, ..., .ext = "", .vars = c())
Arguments
.tbl |
Data frame to add transformed variables to |
.funs |
Function(s) to apply (e.g., log) |
... |
Variables to transform |
.ext |
Extension to add for each variable |
.vars |
A list of columns generated by dplyr::vars(), or a character vector of column names, or a numeric vector of column positions. |
Details
Wrapper for dplyr::mutate_at that allows custom variable name extensions
Examples
mutate_ext(mtcars, .funs = log, mpg, cyl, .ext = "_ln")
mutate_ext(mtcars, .funs = log, .ext = "_ln")
mutate_ext(mtcars, .funs = log)
mutate_ext(mtcars, .funs = log, .ext = "_ln", .vars = vars(mpg, cyl))
Number of missing values
Description
Number of missing values
Usage
n_missing(x, ...)
Arguments
x |
Input variable |
... |
Additional arguments |
Value
number of missing values
Examples
n_missing(c("a", "b", NA))
Number of observations
Description
Number of observations
Usage
n_obs(x, ...)
Arguments
x |
Input variable |
... |
Additional arguments |
Value
number of observations
Examples
n_obs(c("a", "b", NA))
Normalize a variable x by a variable y
Description
Normalize a variable x by a variable y
Usage
normalize(x, y)
Arguments
x |
Input variable |
y |
Normalizing variable |
Value
x/y
Calculate percentiles
Description
Calculate percentiles
Usage
p01(x, na.rm = TRUE)
p025(x, na.rm = TRUE)
p05(x, na.rm = TRUE)
p10(x, na.rm = TRUE)
p25(x, na.rm = TRUE)
p75(x, na.rm = TRUE)
p90(x, na.rm = TRUE)
p95(x, na.rm = TRUE)
p975(x, na.rm = TRUE)
p99(x, na.rm = TRUE)
Arguments
x |
Numeric vector |
na.rm |
If TRUE missing values are removed before calculation |
Examples
p01(0:100)
Parse file path into useful components
Description
Parse file path into useful components
Usage
parse_path(path, chr = "", pdir = getwd(), mess = TRUE)
Arguments
path |
Path to be parsed |
chr |
Character to wrap around path for display |
pdir |
Project directory if available |
mess |
Print messages if Dropbox or Google Drive not found |
Details
Parse file path into useful components (i.e., file name, file extension, relative path, etc.)
Examples
list.files(".", full.names = TRUE)[1] %>% parse_path()
Summarize a set of numeric vectors per row
Description
Summarize a set of numeric vectors per row
Usage
pfun(..., fun, na.rm = TRUE)
psum(..., na.rm = TRUE)
pmean(..., na.rm = TRUE)
pmedian(..., na.rm = TRUE)
psd(..., na.rm = TRUE)
pvar(..., na.rm = TRUE)
pcv(..., na.rm = TRUE)
pp01(..., na.rm = TRUE)
pp025(..., na.rm = TRUE)
pp05(..., na.rm = TRUE)
pp10(..., na.rm = TRUE)
pp25(..., na.rm = TRUE)
pp75(..., na.rm = TRUE)
pp95(..., na.rm = TRUE)
pp975(..., na.rm = TRUE)
pp99(..., na.rm = TRUE)
Arguments
... |
Numeric vectors of the same length |
fun |
Function to apply |
na.rm |
a logical indicating whether missing values should be removed. |
Details
Calculate summary statistics of the input vectors per row (or 'parallel')
Value
A vector of 'parallel' summaries of the argument vectors.
See Also
Examples
pfun(1:10, fun = mean)
psum(1:10, 10:1)
Create a pivot table
Description
Create a pivot table
Usage
pivotr(
dataset,
cvars = "",
nvar = "None",
fun = "mean",
normalize = "None",
tabfilt = "",
tabsort = "",
tabslice = "",
nr = Inf,
data_filter = "",
arr = "",
rows = NULL,
envir = parent.frame()
)
Arguments
dataset |
Dataset to tabulate |
cvars |
Categorical variables |
nvar |
Numerical variable |
fun |
Function to apply to numerical variable |
normalize |
Normalize the table by row total, column totals, or overall total |
tabfilt |
Expression used to filter the table (e.g., "Total > 10000") |
tabsort |
Expression used to sort the table (e.g., "desc(Total)") |
tabslice |
Expression used to filter table (e.g., "1:5") |
nr |
Number of rows to display |
data_filter |
Expression used to filter the dataset before creating the table (e.g., "price > 10000") |
arr |
Expression to arrange (sort) the data on (e.g., "color, desc(price)") |
rows |
Rows to select from the specified dataset |
envir |
Environment to extract data from |
Details
Create a pivot-table. See https://radiant-rstats.github.io/docs/data/pivotr.html for an example in Radiant
Examples
pivotr(diamonds, cvars = "cut") %>% str()
pivotr(diamonds, cvars = "cut")$tab
pivotr(diamonds, cvars = c("cut", "clarity", "color"))$tab
pivotr(diamonds, cvars = "cut:clarity", nvar = "price")$tab
pivotr(diamonds, cvars = "cut", nvar = "price")$tab
pivotr(diamonds, cvars = "cut", normalize = "total")$tab
Plot method for the pivotr function
Description
Plot method for the pivotr function
Usage
## S3 method for class 'pivotr'
plot(
x,
type = "dodge",
perc = FALSE,
flip = FALSE,
fillcol = "blue",
opacity = 0.5,
...
)
Arguments
x |
Return value from |
type |
Plot type to use ("fill" or "dodge" (default)) |
perc |
Use percentage on the y-axis |
flip |
Flip the axes in a plot (FALSE or TRUE) |
fillcol |
Fill color for bar-plot when only one categorical variable has been selected (default is "blue") |
opacity |
Opacity for plot elements (0 to 1) |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/data/pivotr for an example in Radiant
See Also
pivotr to generate summaries
summary.pivotr to show summaries
Examples
pivotr(diamonds, cvars = "cut") %>% plot()
pivotr(diamonds, cvars = c("cut", "clarity")) %>% plot()
pivotr(diamonds, cvars = c("cut", "clarity", "color")) %>% plot()
Calculate proportion
Description
Calculate proportion
Usage
prop(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Proportion of first level for a factor and of the maximum value for numeric
Examples
prop(c(rep(1L, 10), rep(0L, 10)))
prop(c(rep(4, 10), rep(2, 10)))
prop(rep(0, 10))
prop(factor(c(rep("a", 20), rep("b", 10))))
Comic publishers
Description
Comic publishers
Usage
data(publishers)
Format
A data frame with 3 rows and 2 variables
Details
List of comic publishers from https://stat545.com/join-cheatsheet.html. The dataset is used to illustrate data merging / joining. Description provided in attr(publishers,"description")
Create a qscatter plot similar to Stata
Description
Create a qscatter plot similar to Stata
Usage
qscatter(dataset, xvar, yvar, lev = "", fun = "mean", bins = 20)
Arguments
dataset |
Data to plot (data.frame or tibble) |
xvar |
Character indicating the variable to display along the X-axis of the plot |
yvar |
Character indicating the variable to display along the Y-axis of the plot |
lev |
Level in yvar to use if yvar is of type character of factor. If lev is empty then the first level is used |
fun |
Summary measure to apply to both the x and y variable |
bins |
Number of bins to use |
Examples
qscatter(diamonds, "price", "carat")
qscatter(titanic, "age", "survived")
Create a vector of quadratic and cubed terms for use in linear and logistic regression
Description
Create a vector of quadratic and cubed terms for use in linear and logistic regression
Usage
qterms(vars, nway = 2)
Arguments
vars |
Variables labels to use |
nway |
quadratic (2) or cubic (3) term labels to create |
Value
Character vector of (regression) term labels
Examples
qterms(c("a", "b"), 3)
qterms(c("a", "b"), 2)
radiant.data
Description
Launch the radiant.data app in the default web browser
Usage
radiant.data(state, ...)
Arguments
state |
Path to statefile to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Examples
## Not run:
radiant.data()
radiant.data("https://github.com/radiant-rstats/docs/raw/gh-pages/examples/demo-dvd-rnd.state.rda")
radiant.data("viewer")
## End(Not run)
Deprecated function(s) in the radiant.data package
Description
These functions are provided for compatibility with previous versions of radiant but will be removed
Usage
mean_rm(...)
Arguments
... |
Parameters to be passed to the updated functions |
Details
Replace
mean_rmbymeanReplace
median_rmbymedianReplace
min_rmbyminReplace
max_rmbymaxReplace
sd_rmbysdReplace
var_rmbyvarReplace
sum_rmbysumReplace
getdatabyget_dataReplace
filterdatabyfilter_dataReplace
combinedatabycombine_dataReplace
viewdatabyview_dataReplace
toFctbyto_fctReplace
fixMSbyfix_smartReplace
rounddfbyround_dfReplace
formatdfbyformat_dfReplace
formatnrbyformat_nrReplace
getclassbyget_classReplace
is_numericbyis_doubleReplace
is_emptybyis.empty
Start radiant.data app but do not open a browser
Description
Start radiant.data app but do not open a browser
Usage
radiant.data_url(state, ...)
Arguments
state |
Path to statefile to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Examples
## Not run:
radiant.data_url()
## End(Not run)
Launch the radiant.data app in the Rstudio viewer
Description
Launch the radiant.data app in the Rstudio viewer
Usage
radiant.data_viewer(state, ...)
Arguments
state |
Path to statefile to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Examples
## Not run:
radiant.data_viewer()
## End(Not run)
Launch the radiant.data app in an Rstudio window
Description
Launch the radiant.data app in an Rstudio window
Usage
radiant.data_window(state, ...)
Arguments
state |
Path to statefile to load |
... |
additional arguments to pass to shiny::runApp (e.g, port = 8080) |
Examples
## Not run:
radiant.data_window()
## End(Not run)
Generate code to read a file
Description
Generate code to read a file
Usage
read_files(
path,
pdir = "",
type = "rmd",
to = "",
clipboard = TRUE,
radiant = FALSE
)
Arguments
path |
Path to file. If empty, a file browser will be opened |
pdir |
Project dir |
type |
Generate code for _Report > Rmd_ ("rmd") or _Report > R_ ("r") |
to |
Name to use for object. If empty, will use file name to derive an object name |
clipboard |
Return code to clipboard (not available on Linux) |
radiant |
Should returned code be formatted for use with other code generated by Radiant? |
Details
Return code to read a file at the specified path. Will open a file browser if no path is provided
Examples
if (interactive()) {
read_files(clipboard = FALSE)
}
Objects exported from other packages
Description
These objects are imported from other packages. Follow the links below to see their documentation.
- broom
- bslib
- glue
- knitr
- lubridate
- patchwork
- png
- psych
- tibble
Remove/reorder levels
Description
Remove/reorder levels
Usage
refactor(x, levs = levels(x), repl = NA)
Arguments
x |
Character or Factor |
levs |
Set of levels to use |
repl |
String (or NA) used to replace missing levels |
Details
Keep only a specific set of levels in a factor. By removing levels the base for comparison in, e.g., regression analysis, becomes the first level. To relabel the base use, for example, repl = 'other'
Examples
refactor(diamonds$cut, c("Premium", "Ideal")) %>% head()
refactor(diamonds$cut, c("Premium", "Ideal"), "Other") %>% head()
Register a data.frame or list in Radiant
Description
Register a data.frame or list in Radiant
Usage
register(
new,
org = "",
descr = "",
shiny = shiny::getDefaultReactiveDomain(),
envir = r_data
)
Arguments
new |
String containing the name of the data.frame to register |
org |
Name of the original data.frame if a (working) copy is being made |
descr |
Data description in markdown format |
shiny |
Check if function is called from a shiny application |
envir |
Environment to assign data to |
See Also
See also add_description to add a description in markdown format
to a data.frame
Base method used to render htmlwidgets
Description
Base method used to render htmlwidgets
Usage
render(object, ...)
Arguments
object |
Object of relevant class to render |
... |
Additional arguments |
Method to render DT tables
Description
Method to render DT tables
Usage
## S3 method for class 'datatables'
render(object, shiny = shiny::getDefaultReactiveDomain(), ...)
Arguments
object |
DT table |
shiny |
Check if function is called from a shiny application |
... |
Additional arguments |
Method to render plotly plots
Description
Method to render plotly plots
Usage
## S3 method for class 'plotly'
render(object, shiny = shiny::getDefaultReactiveDomain(), ...)
Arguments
object |
plotly object |
shiny |
Check if function is called from a shiny application |
... |
Additional arguments |
Round doubles in a data.frame to a specified number of decimal places
Description
Round doubles in a data.frame to a specified number of decimal places
Usage
round_df(tbl, dec = 3)
Arguments
tbl |
Data frame |
dec |
Number of decimals to show |
Value
Data frame with rounded doubles
Examples
data.frame(x = as.factor(c("a", "b")), y = c(1L, 2L), z = c(-0.0005, 3.1)) %>%
round_df(dec = 2)
Save data to clipboard on Windows or macOS
Description
Save data to clipboard on Windows or macOS
Usage
save_clip(dataset)
Arguments
dataset |
Dataset to save to clipboard |
Details
Save a data.frame or tibble to the clipboard on Windows or macOS
See Also
See the load_clip
Standard deviation for the population
Description
Standard deviation for the population
Usage
sdpop(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Standard deviation for the population
Examples
sdpop(rnorm(100))
Standard deviation for proportion
Description
Standard deviation for proportion
Usage
sdprop(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Standard deviation for proportion
Examples
sdprop(c(rep(1L, 10), rep(0L, 10)))
Standard error
Description
Standard error
Usage
se(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Standard error
Examples
se(rnorm(100))
Search for a pattern in all columns of a data.frame
Description
Search for a pattern in all columns of a data.frame
Usage
search_data(dataset, pattern, ignore.case = TRUE, fixed = FALSE)
Arguments
dataset |
Data.frame to search |
pattern |
String to match |
ignore.case |
Should search be case sensitive or not (default is FALSE) |
fixed |
Allow regular expressions or not (default is FALSE) |
See Also
See grepl for a detailed description of the function arguments
Examples
publishers %>% filter(search_data(., "^m"))
Standard error for proportion
Description
Standard error for proportion
Usage
seprop(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Standard error for proportion
Examples
seprop(c(rep(1L, 10), rep(0L, 10)))
Alias used to add an attribute
Description
Alias used to add an attribute
Usage
set_attr(x, which, value)
Arguments
x |
Object |
which |
Attribute name |
value |
Value to set |
Examples
foo <- data.frame(price = 1:5) %>% set_attr("description", "price set in experiment ...")
Show all rows with duplicated values (not just the first or last)
Description
Show all rows with duplicated values (not just the first or last)
Usage
show_duplicated(.tbl, ...)
Arguments
.tbl |
Data frame to add transformed variables to |
... |
Variables used to evaluate row uniqueness |
Details
If an entire row is duplicated use "duplicated" to show only one of the duplicated rows. When using a subset of variables to establish uniqueness it may be of interest to show all rows that have (some) duplicate elements
Examples
bind_rows(mtcars, mtcars[c(1, 5, 7), ]) %>%
show_duplicated(mpg, cyl)
bind_rows(mtcars, mtcars[c(1, 5, 7), ]) %>%
show_duplicated()
Add stars based on p.values
Description
Add stars based on p.values
Usage
sig_stars(pval)
Arguments
pval |
Vector of p-values |
Value
A vector of stars
Examples
sig_stars(c(.0009, .049, .009, .4, .09))
Slice data with user-specified expression
Description
Slice data with user-specified expression
Usage
slice_data(dataset, expr = NULL, drop = TRUE)
Arguments
dataset |
Data frame to slice |
expr |
Expression to use select rows from the specified dataset |
drop |
Drop unused factor levels after filtering (default is TRUE) |
Details
Select only a slice of the data to work with
Value
Sliced data frame
Calculate square of a variable
Description
Calculate square of a variable
Usage
square(x)
Arguments
x |
Input variable |
Value
x^2
Hide warnings and messages and return invisible
Description
Hide warnings and messages and return invisible
Usage
sshh(...)
Arguments
... |
Inputs to keep quite |
Details
Hide warnings and messages and return invisible
Examples
sshh(library(dplyr))
Hide warnings and messages and return result
Description
Hide warnings and messages and return result
Usage
sshhr(...)
Arguments
... |
Inputs to keep quite |
Details
Hide warnings and messages and return result
Examples
sshhr(library(dplyr))
Standardize
Description
Standardize
Usage
standardize(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
If x is a numeric variable return (x - mean(x)) / sd(x)
Method to store variables in a dataset in Radiant
Description
Method to store variables in a dataset in Radiant
Usage
store(dataset, object = "deprecated", ...)
Arguments
dataset |
Dataset |
object |
Object of relevant class that has information to be stored |
... |
Additional arguments |
Deprecated: Store method for the explore function
Description
Deprecated: Store method for the explore function
Usage
## S3 method for class 'explore'
store(dataset, object, name, ...)
Arguments
dataset |
Dataset |
object |
Return value from |
name |
Name to assign to the dataset |
... |
further arguments passed to or from other methods |
Details
Return the summarized data. See https://radiant-rstats.github.io/docs/data/explore.html for an example in Radiant
See Also
explore to generate summaries
Deprecated: Store method for the pivotr function
Description
Deprecated: Store method for the pivotr function
Usage
## S3 method for class 'pivotr'
store(dataset, object, name, ...)
Arguments
dataset |
Dataset |
object |
Return value from |
name |
Name to assign to the dataset |
... |
further arguments passed to or from other methods |
Details
Return the summarized data. See https://radiant-rstats.github.io/docs/data/pivotr.html for an example in Radiant
See Also
pivotr to generate summaries
Work around to avoid (harmless) messages from subplot
Description
Work around to avoid (harmless) messages from subplot
Usage
subplot(..., margin = 0.04)
Arguments
... |
Arguments to pass to the |
margin |
Default margin to use between plots |
See Also
See the subplot in the plotly package for details (?plotly::subplot)
Summary method for the explore function
Description
Summary method for the explore function
Usage
## S3 method for class 'explore'
summary(object, dec = 3, ...)
Arguments
object |
Return value from |
dec |
Number of decimals to show |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/data/explore.html for an example in Radiant
See Also
explore to generate summaries
Examples
result <- explore(diamonds, "price:x")
summary(result)
result <- explore(diamonds, "price", byvar = "cut", fun = c("n_obs", "skew"))
summary(result)
explore(diamonds, "price:x", byvar = "color") %>% summary()
Summary method for pivotr
Description
Summary method for pivotr
Usage
## S3 method for class 'pivotr'
summary(object, perc = FALSE, dec = 3, chi2 = FALSE, shiny = FALSE, ...)
Arguments
object |
Return value from |
perc |
Display numbers as percentages (TRUE or FALSE) |
dec |
Number of decimals to show |
chi2 |
If TRUE calculate the chi-square statistic for the (pivot) table |
shiny |
Did the function call originate inside a shiny app |
... |
further arguments passed to or from other methods |
Details
See https://radiant-rstats.github.io/docs/data/pivotr.html for an example in Radiant
See Also
pivotr to create the pivot-table using dplyr
Examples
pivotr(diamonds, cvars = "cut") %>% summary(chi2 = TRUE)
pivotr(diamonds, cvars = "cut", tabsort = "desc(n_obs)") %>% summary()
pivotr(diamonds, cvars = "cut", tabfilt = "n_obs > 700") %>% summary()
pivotr(diamonds, cvars = "cut:clarity", nvar = "price") %>% summary()
Super heroes
Description
Super heroes
Usage
data(superheroes)
Format
A data frame with 7 rows and 4 variables
Details
List of super heroes from https://stat545.com/join-cheatsheet.html. The dataset is used to illustrate data merging / joining. Description provided in attr(superheroes,"description")
Create data.frame from a table
Description
Create data.frame from a table
Usage
table2data(dataset, freq = tail(colnames(dataset), 1))
Arguments
dataset |
Data.frame |
freq |
Column name with frequency information |
Examples
data.frame(price = c("$200", "$300"), sale = c(10, 2)) %>% table2data()
Survival data for the Titanic
Description
Survival data for the Titanic
Usage
data(titanic)
Format
A data frame with 1043 rows and 10 variables
Details
Survival data for the Titanic. Description provided in attr(titanic,"description")
Convert characters to factors
Description
Convert characters to factors
Usage
to_fct(dataset, safx = 30, nuniq = 100, n = 100)
Arguments
dataset |
Data frame |
safx |
Ratio of number of rows to number of unique values |
nuniq |
Cutoff for number of unique values |
n |
Cutoff for small dataset |
Details
Convert columns of type character to factors based on a set of rules. By default columns will be converted for small datasets (<= 100 rows) with more rows than unique values. For larger datasets, columns are converted only when the number of unique values is <= 100 and there are 30 or more rows in the data for every unique value
Examples
tibble(a = c("a", "b"), b = c("a", "a"), c = 1:2) %>% to_fct()
Variance for the population
Description
Variance for the population
Usage
varpop(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Variance for the population
Examples
varpop(rnorm(100))
Variance for proportion
Description
Variance for proportion
Usage
varprop(x, na.rm = TRUE)
Arguments
x |
Input variable |
na.rm |
If TRUE missing values are removed before calculation |
Value
Variance for proportion
Examples
varprop(c(rep(1L, 10), rep(0L, 10)))
View data in a shiny-app
Description
View data in a shiny-app
Usage
view_data(
dataset,
vars = "",
filt = "",
arr = "",
rows = NULL,
na.rm = FALSE,
dec = 3,
envir = parent.frame()
)
Arguments
dataset |
Data.frame or name of the dataframe to view |
vars |
Variables to show (default is all) |
filt |
Filter to apply to the specified dataset |
arr |
Expression to arrange (sort) data |
rows |
Select rows in the specified dataset |
na.rm |
Remove rows with missing values (default is FALSE) |
dec |
Number of decimals to show |
envir |
Environment to extract data from |
Details
View, search, sort, etc. your data
See Also
See get_data and filter_data
Examples
## Not run:
view_data(mtcars)
## End(Not run)
Visualize data using ggplot2 https://ggplot2.tidyverse.org/
Description
Visualize data using ggplot2 https://ggplot2.tidyverse.org/
Usage
visualize(
dataset,
xvar,
yvar = "",
comby = FALSE,
combx = FALSE,
type = ifelse(is.empty(yvar), "dist", "scatter"),
nrobs = -1,
facet_row = ".",
facet_col = ".",
color = "none",
fill = "none",
size = "none",
fillcol = "blue",
linecol = "black",
pointcol = "black",
bins = 10,
smooth = 1,
fun = "mean",
check = "",
axes = "",
alpha = 0.5,
theme = "theme_gray",
base_size = 11,
base_family = "",
labs = list(),
xlim = NULL,
ylim = NULL,
data_filter = "",
arr = "",
rows = NULL,
shiny = FALSE,
custom = FALSE,
envir = parent.frame()
)
Arguments
dataset |
Data to plot (data.frame or tibble) |
xvar |
One or more variables to display along the X-axis of the plot |
yvar |
Variable to display along the Y-axis of the plot (default = "none") |
comby |
Combine yvars in plot (TRUE or FALSE, FALSE is the default) |
combx |
Combine xvars in plot (TRUE or FALSE, FALSE is the default) |
type |
Type of plot to create. One of Distribution ('dist'), Density ('density'), Scatter ('scatter'), Surface ('surface'), Line ('line'), Bar ('bar'), or Box-plot ('box') |
nrobs |
Number of data points to show in scatter plots (-1 for all) |
facet_row |
Create vertically arranged subplots for each level of the selected factor variable |
facet_col |
Create horizontally arranged subplots for each level of the selected factor variable |
color |
Adds color to a scatter plot to generate a 'heat map'. For a line plot one line is created for each group and each is assigned a different color |
fill |
Display bar, distribution, and density plots by group, each with a different color. Also applied to surface plots to generate a 'heat map' |
size |
Numeric variable used to scale the size of scatter-plot points |
fillcol |
Color used for bars, boxes, etc. when no color or fill variable is specified |
linecol |
Color for lines when no color variable is specified |
pointcol |
Color for points when no color variable is specified |
bins |
Number of bins used for a histogram (1 - 50) |
smooth |
Adjust the flexibility of the loess line for scatter plots |
fun |
Set the summary measure for line and bar plots when the X-variable is a factor (default is "mean"). Also used to plot an error bar in a scatter plot when the X-variable is a factor. Options are "mean" and/or "median" |
check |
Add a regression line ("line"), a loess line ("loess"), or jitter ("jitter") to a scatter plot |
axes |
Flip the axes in a plot ("flip") or apply a log transformation (base e) to the y-axis ("log_y") or the x-axis ("log_x") |
alpha |
Opacity for plot elements (0 to 1) |
theme |
ggplot theme to use (e.g., "theme_gray" or "theme_classic") |
base_size |
Base font size to use (default = 11) |
base_family |
Base font family to use (e.g., "Times" or "Helvetica") |
labs |
Labels to use for plots |
xlim |
Set limit for x-axis (e.g., c(0, 1)) |
ylim |
Set limit for y-axis (e.g., c(0, 1)) |
data_filter |
Expression used to filter the dataset. This should be a string (e.g., "price > 10000") |
arr |
Expression used to sort the data. Likely used in combination for 'rows' |
rows |
Rows to select from the specified dataset |
shiny |
Logical (TRUE, FALSE) to indicate if the function call originate inside a shiny app |
custom |
Logical (TRUE, FALSE) to indicate if ggplot object (or list of ggplot objects) should be returned. This option can be used to customize plots (e.g., add a title, change x and y labels, etc.). See examples and https://ggplot2.tidyverse.org for options. |
envir |
Environment to extract data from |
Details
See https://radiant-rstats.github.io/docs/data/visualize.html for an example in Radiant
Value
Generated plots
Examples
visualize(diamonds, "price:cut", type = "dist", fillcol = "red")
visualize(diamonds, "carat:cut",
yvar = "price", type = "scatter",
pointcol = "blue", fun = c("mean", "median"), linecol = c("red", "green")
)
visualize(diamonds,
yvar = "price", xvar = c("cut", "clarity"),
type = "bar", fun = "median"
)
visualize(diamonds,
yvar = "price", xvar = c("cut", "clarity"),
type = "line", fun = "max"
)
visualize(diamonds,
yvar = "price", xvar = "carat", type = "scatter",
size = "table", custom = TRUE
) + scale_size(range = c(1, 10), guide = "none")
visualize(diamonds, yvar = "price", xvar = "carat", type = "scatter", custom = TRUE) +
labs(title = "A scatterplot", x = "price in $")
visualize(diamonds, xvar = "price:carat", custom = TRUE) %>%
wrap_plots(ncol = 2) + plot_annotation(title = "Histograms")
visualize(diamonds,
xvar = "cut", yvar = "price", type = "bar",
facet_row = "cut", fill = "cut"
)
Add ordered argument to lubridate::wday
Description
Add ordered argument to lubridate::wday
Usage
wday(x, label = FALSE, abbr = TRUE, ordered = FALSE)
Arguments
x |
Input date vector |
label |
Weekday as label (TRUE, FALSE) |
abbr |
Abbreviate label (TRUE, FALSE) |
ordered |
Order factor (TRUE, FALSE) |
See Also
See the lubridate::wday() function in the lubridate package for additional details
Weighted standard deviation
Description
Weighted standard deviation
Usage
weighted.sd(x, wt, na.rm = TRUE)
Arguments
x |
Numeric vector |
wt |
Numeric vector of weights |
na.rm |
Remove missing values (default is TRUE) |
Details
Calculate weighted standard deviation
Index of the maximum per row
Description
Index of the maximum per row
Usage
which.pmax(...)
Arguments
... |
Numeric or character vectors of the same length |
Details
Determine the index of the maximum of the input vectors per row. Extension of which.max
Value
Vector of rankings
See Also
See also which.max and which.pmin
Examples
which.pmax(1:10, 10:1)
which.pmax(2, 10:1)
which.pmax(mtcars)
Index of the minimum per row
Description
Index of the minimum per row
Usage
which.pmin(...)
Arguments
... |
Numeric or character vectors of the same length |
Details
Determine the index of the minimum of the input vectors per row. Extension of which.min
Value
Vector of rankings
See Also
See also which.min and which.pmax
Examples
which.pmin(1:10, 10:1)
which.pmin(2, 10:1)
which.pmin(mtcars)
Workaround to store description file together with a parquet data file
Description
Workaround to store description file together with a parquet data file
Usage
write_parquet(x, file, description = attr(x, "description"))
Arguments
x |
A data frame to write to disk |
file |
Path to store parquet file |
description |
Data description |
Split a numeric variable into a number of bins and return a vector of bin numbers
Description
Split a numeric variable into a number of bins and return a vector of bin numbers
Usage
xtile(x, n = 5, rev = FALSE, type = 7)
Arguments
x |
Numeric variable |
n |
number of bins to create |
rev |
Reverse the order of the bin numbers |
type |
An integer between 1 and 9 to select one of the quantile algorithms described in the help for the stats::quantile function |
See Also
See quantile for a description of the different algorithm types
Examples
xtile(1:10, 5)
xtile(1:10, 5, rev = TRUE)
xtile(c(rep(1, 6), 7:10), 5)