Package {MazamaCoreUtils}

MazamaCoreUtils

Description

Utility functions for production R code.

Details

MazamaCoreUtils provides a standardized set of utility functions used by MazamaScience packages and R-based operational systems.

Functionality includes:

• Python-style logging

• simple error messaging

• cache management

• API key handling

• date parsing and formatting

• longitude/latitude validation

• unique location ID creation

• source code linting

Author(s)

Maintainer: Jonathan Callahan jonathan.s.callahan@gmail.com

Other contributors:

• Eli Grosman eligrosman1@gmail.com [contributor]

• Spencer Pease spencerpease618@gmail.com [contributor]

• Thomas Bergamaschi tbergama@gmail.com [contributor]

See Also

Useful links:

• https://github.com/MazamaScience/MazamaCoreUtils

• Report bugs at https://github.com/MazamaScience/MazamaCoreUtils/issues

Pipe operator

Description

See %>% for details.

Usage

lhs %>% rhs

API keys for data services

Description

Internal session state used to store API keys for web services.

Format

A named list of character strings.

Details

Users can set API keys with setAPIKey(). Keys are remembered for the duration of the R session and can be retrieved with getAPIKey().

This provides a small abstraction layer for dependent packages so that data access functions can test for and retrieve provider-specific API keys with generic code.

See Also

getAPIKey(), setAPIKey(), showAPIKeys()

Create one or more unique location IDs

Description

Create a location ID for each longitude/latitude pair using a geohash.

Usage

createLocationID(
  longitude = NULL,
  latitude = NULL,
  precision = 10,
  algorithm = c("geohash", "digest"),
  invalidID = as.character(NA)
)

Arguments

Details

Each location ID is unique within a geohash grid cell. The precision argument determines the size of the grid cell. At the equator, approximate grid cell widths are:

precision   maximum grid cell width
        5   ~ 4.9 km
        6   ~ 1.2 km
        7   ~ 153 m
        8   ~ 38 m
        9   ~ 4.8 m
       10   ~ 1.2 m

Invalid locations are assigned the value specified by invalidID, typically NA.

Value

Character vector of location IDs.

References

https://michaelchirico.github.io/geohashTools/index.html

Examples

longitude <- c(-122.5, 0, NA, -122.5, -122.5)
latitude <- c(47.5, 0, 47.5, NA, 47.5)

createLocationID(longitude, latitude)
createLocationID(longitude, latitude, precision = 7)
createLocationID(longitude, latitude, invalidID = "bad")

Create a mask of valid locations

Description

Create a logical mask identifying valid longitude/latitude pairs.

Usage

createLocationMask(
  longitude = NULL,
  latitude = NULL,
  lonRange = c(-180, 180),
  latRange = c(-90, 90),
  removeZeroZero = TRUE
)

Arguments

Details

The returned logical vector contains TRUE for valid locations and FALSE for invalid locations. This is useful for filtering data frames to retain only records with valid geographic coordinates.

Longitude and latitude values are considered valid when they:

• fall within lonRange and latRange

• are not missing

• are not located at ⁠(0, 0)⁠ when removeZeroZero = TRUE

The lonRange and latRange arguments can be used to restrict valid locations to a rectangular geographic region.

Value

Logical vector identifying valid locations.

Examples

createLocationMask(
  longitude = c(-120, NA, -120, -220, -120, 0),
  latitude = c(45, 45, NA, 45, 100, 0)
)

createLocationMask(
  longitude = -120:-90,
  latitude = 20:50,
  lonRange = c(-110, -100),
  latRange = c(30, 40)
)

Create a POSIXct date range

Description

Create a two-element POSIXct vector representing a date/time range in a specified timezone.

Usage

dateRange(
  startdate = NULL,
  enddate = NULL,
  timezone = NULL,
  unit = "sec",
  ceilingStart = FALSE,
  ceilingEnd = FALSE,
  days = 7
)

Arguments

Details

The returned range is ordered from earliest to latest. The first element represents the beginning of the requested date range and the second element represents the end of the requested date range at the requested temporal precision.

By default, the returned end time is one unit before the beginning of enddate. For example:

dateRange(20190101, 20190102, timezone = "UTC")
[1] "2019-01-01 00:00:00 UTC"
[2] "2019-01-01 23:59:59 UTC"

Setting ceilingEnd = TRUE includes the entirety of enddate:

dateRange(
  20190101,
  20190101,
  timezone = "UTC",
  ceilingEnd = TRUE
)
[1] "2019-01-01 00:00:00 UTC"
[2] "2019-01-01 23:59:59 UTC"

The ceilingEnd argument addresses ambiguity in phrases such as "August 1-8". With ceilingEnd = FALSE (default), the range extends through the end of August 7, stopping at the midnight boundary where August 8 begins. With ceilingEnd = TRUE, the range extends through the end of August 8.

Input dates are parsed with parseDatetime() using the specified timezone.

Value

Two-element POSIXct vector ordered from earliest to latest.

Default arguments

If either startdate or enddate is missing, the missing boundary is calculated using days.

If both are missing, enddate defaults to the current day in timezone and startdate is calculated as enddate - days.

End-of-day units

The returned end time is adjusted to the last representable value within the requested unit:

unit = "day"

End time is midnight at the start of the final day.

unit = "hour"

End time is 23:00:00.

unit = "min"

End time is 23:59:00.

unit = "sec"

End time is 23:59:59.

POSIXct inputs

When startdate or enddate are already POSIXct values, they are first converted to timezone with lubridate::with_tz() without changing the represented instant in time.

Parameter precedence

When parameters conflict, the following rules apply:

1. If both startdate and enddate are supplied, days is ignored.

2. If startdate is missing, ceilingStart is ignored.

3. If enddate is missing, ceilingEnd is ignored.

Examples

dateRange("2019-01-08", timezone = "UTC")

dateRange("2019-01-08", unit = "min", timezone = "UTC")

dateRange("2019-01-08", unit = "hour", timezone = "UTC")

dateRange("2019-01-08", unit = "day", timezone = "UTC")

dateRange("2019-01-08", "2019-01-11", timezone = "UTC")

dateRange(
  enddate = 20190112,
  days = 3,
  unit = "day",
  timezone = "America/Los_Angeles"
)

Create a POSIXct date sequence

Description

Create a sequence of local-midnight POSIXct datetimes in a specified timezone.

Usage

dateSequence(
  startdate = NULL,
  enddate = NULL,
  timezone = NULL,
  ceilingEnd = FALSE
)

Arguments

Details

The returned sequence begins at midnight local time on startdate and ends at midnight local time on enddate, i.e. the beginning of enddate.

The ceilingEnd argument addresses ambiguity in date ranges such as "August 1-8". With ceilingEnd = FALSE (default), the sequence ends at the beginning of August 8. With ceilingEnd = TRUE, the sequence includes the entirety of August 8, ending at the midnight that begins August 9.

Input dates are parsed with parseDatetime() using the specified timezone. Any hour-minute-second information is removed after parsing.

Value

A vector of POSIXct datetimes at local midnight.

POSIXct inputs

When startdate or enddate are already POSIXct values, they are first converted to timezone with lubridate::with_tz() without changing the represented instant in time. They are then floored to local midnight.

Note

This function preserves local clock-time midnight boundaries across daylight savings transitions. This differs from seq.Date(..., by = "day"), which advances by fixed 24-hour intervals and can drift away from midnight local time during daylight savings changes.

Examples

dateSequence(
  "2019-11-01",
  "2019-11-08",
  timezone = "America/Los_Angeles"
)

dateSequence(
  "2019-11-01",
  "2019-11-07",
  timezone = "America/Los_Angeles",
  ceilingEnd = TRUE
)

# Observe daylight savings handling
datetime <- dateSequence(
  "2019-11-01",
  "2019-11-08",
  timezone = "America/Los_Angeles"
)

datetime
lubridate::with_tz(datetime, "UTC")

# POSIXct inputs preserve the represented instant before flooring
jst <- dateSequence(
  20190307,
  20190315,
  timezone = "Asia/Tokyo"
)

jst

dateSequence(
  jst[1],
  jst[7],
  timezone = "UTC"
)

Get API key

Description

Return the API key associated with a web service provider.

Usage

getAPIKey(provider = NULL)

Arguments

Details

If provider = NULL, all currently stored API keys are returned.

Value

API key string, NULL, or a named list of all provider/key pairs.

See Also

APIKeys, setAPIKey(), showAPIKeys()

Extract links from an HTML page

Description

Parse an HTML page and return all ⁠<a href="...">...</a>⁠ links as a data frame.

Usage

html_getLinks(url = NULL, relative = TRUE)

html_getLinkNames(url = NULL)

html_getLinkUrls(url = NULL, relative = TRUE)

Arguments

Details

The returned data frame contains the human-readable link text in linkName and the href value in linkUrl. This is useful for extracting links from index pages, including web-accessible directories that list downloadable files.

Wrapper functions html_getLinkNames() and html_getLinkUrls() return the corresponding columns as character vectors.

Value

A tibble with linkName and linkUrl columns.

html_getLinkNames() returns a character vector of link names.

html_getLinkUrls() returns a character vector of link URLs.

Examples

## Not run: 

# If you want to download lots of USCensus shapefiles
url <- "https://www2.census.gov/geo/tiger/GENZ2019/shp/"

browseURL(url)

dataLinks <- html_getLinks(url)

dataLinks <-
  dataLinks %>%
  dplyr::filter(stringr::str_detect(linkName, "us_county"))

head(dataLinks, 10)

html_getLinkNames(url)
html_getLinkUrls(url, relative = FALSE)

## End(Not run)

Extract tables from an HTML page

Description

Parse an HTML page and return all ⁠<table>⁠ elements as a list of data frames.

Usage

html_getTables(url = NULL, header = NA)

html_getTable(url = NULL, header = NA, index = 1)

Arguments

Details

The url argument may be either a remote URL or a local file path. Tables are parsed with rvest::html_table(). To extract a single table, use html_getTable().

Value

List of data frames, one for each HTML table.

A single data frame containing the requested HTML table.

Examples

## Not run: 
url <- "https://en.wikipedia.org/wiki/List_of_tz_database_time_zones"

tables <- html_getTables(url)
firstTable <- tables[[1]]

head(firstTable)
nrow(firstTable)

## End(Not run)

Initialize standard log files

Description

Create a standard set of MazamaCoreUtils log files.

Usage

initializeLogging(logDir = NULL, filePrefix = "", createDir = TRUE)

Arguments

Details

This convenience function creates or validates a log directory, archives any existing standard log files by appending a UTC timestamp, and then initializes logging with logger.setup().

Standard log files include:

TRACE.log
DEBUG.log
INFO.log
WARN.log
ERROR.log

When filePrefix is supplied, it is prepended to each log file name.

Value

No return value. Called for side effects.

See Also

logger.setup()

Lint function calls for required named arguments

Description

Parse R source code and identify calls to selected functions that are missing required named arguments.

Usage

lintFunctionArgs_file(filePath = NULL, rules = NULL, fullPath = FALSE)

lintFunctionArgs_dir(dirPath = "./R", rules = NULL, fullPath = FALSE)

Arguments

Details

Rules are supplied as a named list where each name is a function to check and each value is a character vector of required argument names. A function call passes when all required arguments are supplied by name.

This linter only checks whether arguments are named in the call. It does not evaluate code, inspect argument values, or detect unnamed positional arguments.

Value

A tibble describing matching function calls, with columns:

file

Source file path or file name.

line_number

Line number where the function call begins.

column_number

Column number where the function call begins.

function_name

Name of the function being checked.

named_args

List column containing named arguments used in the call.

includes_required

Logical indicating whether all required named arguments were supplied.

Limitations

This linter only detects named arguments. For example, foo(x = bar, "baz") is treated as specifying the named argument x, but the value bar and the unnamed argument "baz" are not inspected.

Examples

## Not run: 
rules <- list(
  fn_one = "x",
  fn_two = c("foo", "bar")
)

lintFunctionArgs_file(
  filePath = "local_test/timezone_lint_test_script.R",
  rules = rules
)

lintFunctionArgs_dir(
  dirPath = "./R",
  rules = MazamaCoreUtils::timezoneLintRules
)

## End(Not run)

Load R data from a URL or local file

Description

Load a pre-generated R binary data file from either a local directory or a remote URL.

Usage

loadDataFile(
  filename = NULL,
  dataUrl = NULL,
  dataDir = NULL,
  priority = c("dataDir", "dataUrl")
)

Arguments

Details

This function is intended for use by package-level ⁠*_load()⁠ helper functions. It allows locally cached data files to be used when available, avoiding unnecessary internet access.

If both dataDir and dataUrl are provided, priority determines which source is tried first. If loading from the first source fails, the other source is used as a fallback.

Value

Object loaded from the .rda file.

Examples

## Not run: 
filename <- "USCensusStates_02.rda"
dataDir <- "~/Data/Spatial"
dataUrl <- "http://data.mazamascience.com/MazamaSpatialUtils/Spatial_0.8"

# Load local file
USCensusStates <- loadDataFile(filename, dataDir = dataDir)

# Load remote file
USCensusStates <- loadDataFile(filename, dataUrl = dataUrl)

# Load local file with remote file as backup
USCensusStates <- loadDataFile(
  filename,
  dataDir = dataDir,
  dataUrl = dataUrl,
  priority = "dataDir"
)

# Load remote file with local file as backup
USCensusStates <- loadDataFile(
  filename,
  dataDir = dataDir,
  dataUrl = dataUrl,
  priority = "dataUrl"
)

## End(Not run)

Log levels

Description

Logging level constants used by the MazamaCoreUtils logging API.

Usage

FATAL

Format

An object of class integer of length 1.

Details

Available log levels include:

FATAL
ERROR
WARN
INFO
DEBUG
TRACE

These constants are retained for backwards compatibility with the original MazamaCoreUtils logging system.

Python-style logging statements

Description

Emit a DEBUG level log message.

Usage

logger.debug(msg, ...)

Arguments

Details

Logging must first be initialized with logger.setup().

Value

No return value. Called for side effects.

See Also

logger.setup()

Python-style logging statements

Description

Emit an ERROR level log message.

Usage

logger.error(msg, ...)

Arguments

Details

Logging must first be initialized with logger.setup().

Value

No return value. Called for side effects.

See Also

logger.setup()

Python-style logging statements

Description

Emit a FATAL level log message.

Usage

logger.fatal(msg, ...)

Arguments

Details

Logging must first be initialized with logger.setup().

Value

No return value. Called for side effects.

See Also

logger.setup()

Python-style logging statements

Description

Emit an INFO level log message.

Usage

logger.info(msg, ...)

Arguments

Details

Logging must first be initialized with logger.setup().

Value

No return value. Called for side effects.

See Also

logger.setup()

Check whether logging has been initialized

Description

Determine whether logger.setup() has already been called.

Usage

logger.isInitialized()

Details

This function is useful in package code that conditionally emits log statements only when logging has been configured.

Value

Logical scalar indicating whether logging has been initialized.

See Also

logger.setup()

Examples

## Not run: 
logger.isInitialized()

logger.setup()

logger.isInitialized()

## End(Not run)

Set console log level

Description

Set the minimum log level displayed in the console.

Usage

logger.setLevel(level)

Arguments

Details

By default, only FATAL messages are displayed in the console. This function allows users to display additional log messages interactively.

Available log levels are:

TRACE
DEBUG
INFO
WARN
ERROR
FATAL

Value

No return value. Called for side effects.

Note

All functionality is implemented with the excellent logger package.

See Also

logger.setup()

Examples

## Not run: 
# Enable console logging
logger.setup()

# Show DEBUG and higher messages in the console
logger.setLevel(DEBUG)

## End(Not run)

Set up Python-style logging

Description

Configure level-specific log files using the package logging API.

Usage

logger.setup(
  traceLog = NULL,
  debugLog = NULL,
  infoLog = NULL,
  warnLog = NULL,
  errorLog = NULL,
  fatalLog = NULL
)

Arguments

Details

Logging is built on top of the logger package while retaining the historical MazamaCoreUtils logging interface.

Separate log files can be created for different log levels so that, for example, an errorLog contains only ERROR and FATAL messages while a debugLog contains DEBUG messages as well as all higher-severity messages.

Any log file argument left as NULL is disabled and no file will be created for that level.

After initialization, logging statements can be generated with: logger.trace(), logger.debug(), logger.info(), logger.warn(), logger.error(), and logger.fatal().

Log messages are formatted with:

LEVEL [YYYY-MM-DD HH:MM:SS UTC] message

Console logging is enabled by default only for FATAL messages. Use logger.setLevel() to display additional log messages in the console.

Value

No return value. Called for side effects.

Note

All functionality is implemented with the excellent logger package.

See Also

logger.trace(), logger.debug(), logger.info(), logger.warn(), logger.error(), logger.fatal()

Examples

## Not run: 
# Create three log files
logger.setup(
  debugLog = "debug.log",
  infoLog = "info.log",
  errorLog = "error.log"
)

# Generate log messages
logger.trace("trace statement #%d", 1)
logger.debug("debug statement")
logger.info("info statement %s %s", "with", "arguments")
logger.warn("warn statement: %s", "about to try something risky")

result <- try(1 / "a", silent = TRUE)
logger.error("error message: %s", geterrmessage())
logger.fatal("fatal statement: %s", "THE END")

cat(readLines("debug.log"), sep = "\n")
cat(readLines("info.log"), sep = "\n")
cat(readLines("error.log"), sep = "\n")

## End(Not run)

Python-style logging statements

Description

Emit a TRACE level log message.

Usage

logger.trace(msg, ...)

Arguments

Details

Logging must first be initialized with logger.setup().

Value

No return value. Called for side effects.

See Also

logger.setup()

Python-style logging statements

Description

Emit a WARN level log message.

Usage

logger.warn(msg, ...)

Arguments

Details

Logging must first be initialized with logger.setup().

Value

No return value. Called for side effects.

See Also

logger.setup()

Manage cache size

Description

Remove old or excess files from a cache directory.

Usage

manageCache(
  cacheDir = NULL,
  extensions = c("html", "json", "pdf", "png"),
  maxCacheSize = 100,
  sortBy = "atime",
  maxFileAge = NULL
)

Arguments

Details

Files are eligible for removal when their extension matches extensions. Matching is case-sensitive and extensions may be supplied with or without a leading dot.

Files can be removed for two reasons:

• files older than maxFileAge days are removed first

• if the remaining cache exceeds maxCacheSize, additional files are removed until the cache is under the requested size

When removing files to satisfy maxCacheSize, files are ordered by the timestamp specified by sortBy.

Timestamp meanings are:

atime

File access time, updated when a file is opened.

ctime

File change time, updated when file metadata changes.

mtime

File modification time, updated when file contents change.

Value

Invisibly returns the number of files removed.

Examples

CACHE_DIR <- tempdir()

write.csv(matrix(1, 400, 500), file = file.path(CACHE_DIR, "m1.csv"))
write.csv(matrix(2, 400, 500), file = file.path(CACHE_DIR, "m2.csv"))
write.csv(matrix(3, 400, 500), file = file.path(CACHE_DIR, "m3.csv"))
write.csv(matrix(4, 400, 500), file = file.path(CACHE_DIR, "m4.csv"))

for (file in list.files(CACHE_DIR, pattern = "\\.csv$", full.names = TRUE)) {
  print(file.info(file)[, c("size", "mtime")])
}

# Remove files based on access time until the cache is under 1 MB
manageCache(
  CACHE_DIR,
  extensions = "csv",
  maxCacheSize = 1,
  sortBy = "atime"
)

for (file in list.files(CACHE_DIR, pattern = "\\.csv$", full.names = TRUE)) {
  print(file.info(file)[, c("size", "mtime")])
}

Run package checks

Description

Convenience wrappers around devtools::check() for package checking at different levels of thoroughness.

Usage

check(pkg = ".")

check_fast(pkg = ".")

check_faster(pkg = ".")

check_fastest(pkg = ".")

check_slow(pkg = ".")

check_slower(pkg = ".")

check_slowest(pkg = ".")

Arguments

Details

These functions make it easy to run quick checks during active development and more thorough checks before merging or releasing package changes.

The functions are ordered from most thorough to fastest:

check_slowest()

Builds the manual, runs donttest and dontrun examples, and uses --use-gct.

check_slower()

Builds the manual and runs donttest and dontrun examples.

check_slow()

Builds the manual and runs donttest examples.

check()

Standard development check without building the manual or running donttest examples.

check_fast()

Skips vignette building and ignores vignettes during checking.

check_faster()

Skips vignette building, ignores vignettes, and skips examples.

check_fastest()

Skips vignette building, ignores vignettes, skips examples, and skips tests.

Value

Invisibly returns the result from devtools::check().

See Also

devtools::check()

Parse datetimes

Description

Convert character, numeric, integer, or POSIXct datetimes to POSIXct.

Usage

parseDatetime(
  datetime = NULL,
  timezone = NULL,
  expectAll = FALSE,
  isJulian = FALSE,
  quiet = TRUE
)

Arguments

Details

This function accepts a variety of compact date/time formats commonly used in Mazama Science packages, including Y, Ym, Ymd, YmdH, YmdHM, and YmdHMS. Inputs may be mixed within the same vector.

Examples of equivalent inputs include:

20181012130900
"2018-10-12-13-09-00"
"2018 Oct. 12 13:09:00"

All incoming datetimes are interpreted in the specified timezone. If datetime is already POSIXct, it is converted to the requested timezone with lubridate::with_tz().

If a character datetime includes signed offset information, such as "-07:00", that offset is used by lubridate::parse_date_time() when determining the equivalent instant.

Value

A POSIXct vector.

Mazama Science conventions

Within Mazama Science packages, datetimes not already in POSIXct format are often represented as compact decimal values with no separators, such as 20181012 or 20181012130900, either as numbers or strings.

Implementation

parseDatetime() is a wrapper around lubridate::parse_date_time() that defines the datetime formats supported by MazamaCoreUtils.

See Also

lubridate::parse_date_time()

Examples

# All Y[mdHMS] formats are accepted
parseDatetime(2018, timezone = "America/Los_Angeles")
parseDatetime(201808, timezone = "America/Los_Angeles")
parseDatetime(20180807, timezone = "America/Los_Angeles")
parseDatetime(2018080718, timezone = "America/Los_Angeles")
parseDatetime(201808071812, timezone = "America/Los_Angeles")
parseDatetime(20180807181215, timezone = "America/Los_Angeles")

parseDatetime("2018-08-07 18:12:15", timezone = "America/Los_Angeles")
parseDatetime("2018-08-07 18:12:15-07:00", timezone = "UTC")

# Julian days are accepted
parseDatetime(
  2018219181215,
  timezone = "America/Los_Angeles",
  isJulian = TRUE
)

# Mixed vector inputs are accepted
parseDatetime(
  c("2018-10-24 12:00", "201810311200", "2018-11-07 12:00"),
  timezone = "America/New_York"
)

badInput <- c("20181013", NA, "20181015", "181016", "10172018")

# Return NA for dates that cannot be parsed
parseDatetime(badInput, timezone = "UTC", expectAll = FALSE)

## Not run: 
# Fail if any non-missing dates cannot be parsed
parseDatetime(badInput, timezone = "UTC", expectAll = TRUE)

## End(Not run)

Set API key

Description

Set the API key associated with a web service provider.

Usage

setAPIKey(provider = NULL, key = NULL)

Arguments

Details

API keys are stored in package session state and are remembered only for the duration of the current R session.

Value

Invisibly returns the previous value of the API key.

See Also

getAPIKey(), showAPIKeys()

Set a variable to a default value if it is NULL

Description

Returns default when target is NULL; otherwise returns target unchanged.

Usage

setIfNull(target, default, enforcedType = NULL)

Arguments

Details

This is useful for assigning default values to optional arguments while preserving any user-supplied value exactly as provided.

Optionally, enforcedType may be used to coerce the returned value to a specific type. This coercion is applied after the NULL check and affects both target and default.

Value

The value of target if it is not NULL; otherwise default.

If enforcedType is specified, the returned value is coerced using the corresponding ⁠as.*()⁠ function.

Examples

setIfNull(NULL, "foo")
setIfNull(10, 0)
setIfNull("15", 0)

# User-supplied values are returned unchanged
setIfNull("15", 0)
setIfNull("mean", 0)
setIfNull(mean, 0)

# Optional type enforcement
setIfNull("15", 0, enforcedType = "double")
setIfNull(NULL, "15", enforcedType = "integer")

Show API keys

Description

Print all currently set API keys.

Usage

showAPIKeys()

Value

No return value. Called for side effects.

See Also

getAPIKey(), setAPIKey()

Stop if an object is NULL

Description

Convenience function for validating that an object is not NULL.

Usage

stopIfNull(target, msg = NULL)

Arguments

Details

If target is not NULL, it is returned invisibly. If target is NULL, the function stops with either a default or user-supplied error message.

This function is especially useful for validating required function arguments or for guarding intermediate results in pipelines.

Value

Invisibly returns target when it is not NULL.

Examples

# Return input invisibly if not NULL
x <- stopIfNull(5)
print(x)

# Useful in pipelines
y <- 1:10
y_mean <-
  y %>%
  stopIfNull() %>%
  mean()

## Not run: 
# Trigger the default error message
testVar <- NULL
stopIfNull(testVar)

# Trigger a custom error message
stopIfNull(testVar, msg = "This is NULL")

# Make a failing pipeline
z <- NULL
z_mean <-
  z %>%
  stopIfNull("This has failed.") %>%
  mean()

## End(Not run)

Stop on try-error

Description

Generate a consistent error message from the result of a try() block.

Usage

stopOnError(
  result,
  err_msg = "",
  prefix = "",
  maxLength = 500,
  truncatedLength = 120,
  call. = FALSE
)

Arguments

Details

This function is intended for production code where potentially fragile operations are wrapped in try(..., silent = TRUE). If result inherits from "try-error", a cleaned and optionally customized error message is generated and passed to stop().

If result is not a "try-error", the function returns NULL.

Value

Returns NULL if result is not a "try-error"; otherwise stops with an error.

Note

If logging has been initialized, the final error message is logged with logger.error() before calling stop().

Examples

## Not run: 
myFunc <- function(x) {
  log(x)
}

result <- try({
  myFunc("ten")
}, silent = TRUE)

stopOnError(result)

try({
  myFunc("ten")
}, silent = TRUE) %>%
  stopOnError(err_msg = "Unable to process user input")

try({
  myFunc("ten")
}, silent = TRUE) %>%
  stopOnError(
    prefix = "USER_INPUT_ERROR",
    maxLength = 40,
    truncatedLength = 32
  )

## End(Not run)

Data pronoun for tidy evaluation

Description

See rlang::.data for details.

Create a POSIXct time range

Description

Create an ordered two-element POSIXct time range from start and end datetime values.

Usage

timeRange(
  starttime = NULL,
  endtime = NULL,
  timezone = NULL,
  unit = "sec",
  ceilingStart = FALSE,
  ceilingEnd = FALSE
)

Arguments

Details

Input values are converted with parseDatetime() using the required timezone argument. The resulting start and end times are sorted so the earlier time is always returned first.

By default, both times are rounded down with lubridate::floor_date() using the requested unit. Set ceilingStart = TRUE or ceilingEnd = TRUE to round either endpoint up with lubridate::ceiling_date() instead.

Value

Two-element POSIXct vector ordered from earliest to latest.

POSIXct inputs

When startdate or enddate are already POSIXct values, they are first converted to timezone with lubridate::with_tz() without changing the represented instant in time.

Examples

timeRange(
  starttime = "2019-01-08 10:12:15",
  endtime = 20190109102030,
  timezone = "UTC"
)

timeRange(
  starttime = "2019-01-08 10:12:15",
  endtime = "2019-01-09 10:20:30",
  timezone = "UTC",
  unit = "hour"
)

Create character timestamps

Description

Convert datetimes to compact character timestamps suitable for file names, identifiers, labels, and other reproducible text output.

Usage

timeStamp(datetime = NULL, timezone = NULL, unit = "sec", style = "ymdhms")

Arguments

Details

Input values are converted with parseDatetime() using the required timezone argument. When datetime = NULL, the current UTC time is used and timezone defaults to "UTC".

The unit argument controls the precision of the output timestamp. The style argument controls the output format.

Supported unit values are:

"year"
"month"
"day"
"hour"
"min"
"sec"
"msec"

Supported style values are:

"ymdhms"   compact calendar time
"ymdThms"  compact calendar time with "T" separator
"julian"   year and Julian day
"clock"    ISO-like clock time

For style = "julian" and unit = "month", the timestamp uses the Julian day associated with the beginning of the month.

Value

Character vector of timestamps.

POSIXct inputs

When startdate or enddate are already POSIXct values, they are first converted to timezone with lubridate::with_tz() without changing the represented instant in time.

Examples

datetime <- parseDatetime("2019-01-08 12:30:15", timezone = "UTC")

timeStamp()
timeStamp(datetime, "UTC", unit = "year")
timeStamp(datetime, "UTC", unit = "month")
timeStamp(datetime, "UTC", unit = "month", style = "julian")
timeStamp(datetime, "UTC", unit = "day")
timeStamp(datetime, "UTC", unit = "day", style = "julian")
timeStamp(datetime, "UTC", unit = "hour")
timeStamp(datetime, "UTC", unit = "min")
timeStamp(datetime, "UTC", unit = "sec")
timeStamp(datetime, "UTC", unit = "sec", style = "ymdThms")
timeStamp(datetime, "UTC", unit = "sec", style = "julian")
timeStamp(datetime, "UTC", unit = "sec", style = "clock")
timeStamp(datetime, "America/Los_Angeles", unit = "sec", style = "clock")
timeStamp(datetime, "America/Los_Angeles", unit = "msec", style = "clock")

Timezone linting rules

Description

Rules used by lintFunctionArgs_file() and lintFunctionArgs_dir() to find date/time function calls that should explicitly specify timezone arguments.

Usage

timezoneLintRules

Format

A named list of function/argument pairs.

Details

Each list name is a function to check. Each value is the required named timezone-related argument for that function.

Entries with "DEPRECATED" are used to flag functions that should generally be avoided in package code because they depend on the local system clock or timezone.

Examples

str(timezoneLintRules)

Validate longitude and latitude values

Description

Validate a single longitude/latitude pair to ensure both values are numeric scalars and fall within valid geographic bounds.

Usage

validateLonLat(longitude = NULL, latitude = NULL)

Arguments

Details

Longitudes must fall between -180 and 180 degrees and latitudes must fall between -90 and 90 degrees. If validation fails, an error is generated.

Value

Invisibly returns TRUE if validation succeeds.

Examples

validateLonLat(-122.5, 47.5)

## Not run: 
validateLonLat(-200, 47.5)
validateLonLat(-122.5, NA)

## End(Not run)

Validate longitude and latitude vectors

Description

Validate longitude and latitude vectors to ensure they are numeric, have matching lengths, and contain values within valid geographic bounds.

Usage

validateLonsLats(longitude = NULL, latitude = NULL, na.rm = FALSE)

Arguments

Details

Longitudes must fall between -180 and 180 degrees and latitudes must fall between -90 and 90 degrees. If validation fails, an error is generated.

Value

Invisibly returns TRUE if validation succeeds.

Examples

longitude <- c(-122.5, -122.4)
latitude <- c(47.5, 47.6)

validateLonsLats(longitude, latitude)

# Remove missing values before validation
validateLonsLats(
  c(-122.5, NA),
  c(47.5, NA),
  na.rm = TRUE
)

## Not run: 
validateLonsLats(c(-200, 0), c(45, 46))

## End(Not run)