| Title: | Non-Astonishing Results in R |
|---|---|
| Description: | Simple tools for converting columns to new data types. Intuitive functions for columns with missing values. |
| Authors: | David Sjoberg |
| Maintainer: | David Sjoberg <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.3.2 |
| Built: | 2025-02-24 03:32:24 UTC |
| Source: | https://github.com/davidsjoberg/hablar |
Support functions for the convert function. These functions coerces vectors to a new data type, e.g. as.numeric
except that it converts factors to character first.
See convert for more information.
as_reliable_num(.x, ...) as_reliable_int(.x, ...) as_reliable_lgl(.x, ...) as_reliable_dte(.x, origin = "1970-01-01", ...) as_reliable_dtm(.x, origin = "1970-01-01", tz = "UTC", ...) as_reliable_int(.x, ...) as_reliable_lgl(.x, ...) as_reliable_dte(.x, origin = "1970-01-01", ...) as_reliable_dtm(.x, origin = "1970-01-01", tz = "UTC", ...)as_reliable_num(.x, ...) as_reliable_int(.x, ...) as_reliable_lgl(.x, ...) as_reliable_dte(.x, origin = "1970-01-01", ...) as_reliable_dtm(.x, origin = "1970-01-01", tz = "UTC", ...) as_reliable_int(.x, ...) as_reliable_lgl(.x, ...) as_reliable_dte(.x, origin = "1970-01-01", ...) as_reliable_dtm(.x, origin = "1970-01-01", tz = "UTC", ...)
.x |
vector |
... |
additional arguments |
origin |
argument to set origin for date/date time. |
tz |
argument to set time zone for date/date time. Default is UTC. |
vector
vignette("convert"), vignette("hablar")
x <- as.factor(c("1", "3.5")) as_reliable_num(x) x <- as.factor(c("9", "7")) as_reliable_int(x) x <- as.factor(c("1", "0")) as_reliable_lgl(x)x <- as.factor(c("1", "3.5")) as_reliable_num(x) x <- as.factor(c("9", "7")) as_reliable_int(x) x <- as.factor(c("1", "0")) as_reliable_lgl(x)
Returns TRUE if data frame have the specified special cases. For example, find_duplicates() returns TRUE if any rows are duplicates. If variables are passed to the function then TRUE or FALSE is returned for those variables.
check_duplicates(.data, ...) check_na(.data, ...) check_irrational(.data, ...) check_nan(.data, ...) check_inf(.data, ...) check_complete_set(.data, ...)check_duplicates(.data, ...) check_na(.data, ...) check_irrational(.data, ...) check_nan(.data, ...) check_inf(.data, ...) check_complete_set(.data, ...)
.data |
a data frame |
... |
variables that should be considered. If empty, all variables are used. |
irrational values are Inf and NaN. 'check_complete_set' tests if all combinations of elements exists in the data frame.
TRUE or FALSE
find_in_df to return rows instead of TRUE or FALSE.
vignette("s"), vignette("hablar")
## Not run: df <- data.frame(a = c("A", NA, "B", "C", "C"), b = c(7, 8, 2, 3, 3), c = c(NA, 1, NaN, 3, 2), stringsAsFactors = FALSE) # Returns FALSE because there is no duplicates df %>% check_duplicates() # Returns TRUE because there is duplicates in column a through b df %>% check_duplicates(a:b) # Returns FALSE because there is no NA column b df %>% check_na(b) # Returns TRUE because there is no NaN column c df %>% check_nan(c) ## End(Not run)## Not run: df <- data.frame(a = c("A", NA, "B", "C", "C"), b = c(7, 8, 2, 3, 3), c = c(NA, 1, NaN, 3, 2), stringsAsFactors = FALSE) # Returns FALSE because there is no duplicates df %>% check_duplicates() # Returns TRUE because there is duplicates in column a through b df %>% check_duplicates(a:b) # Returns FALSE because there is no NA column b df %>% check_na(b) # Returns TRUE because there is no NaN column c df %>% check_nan(c) ## End(Not run)
Convert data type of columns
num(..., .args = list()) chr(..., .args = list()) lgl(..., .args = list()) int(..., .args = list()) dbl(..., .args = list()) fct(..., .args = list()) dtm(..., .args = list()) dte(..., .args = list()) convert(.x, ...)num(..., .args = list()) chr(..., .args = list()) lgl(..., .args = list()) int(..., .args = list()) dbl(..., .args = list()) fct(..., .args = list()) dtm(..., .args = list()) dte(..., .args = list()) convert(.x, ...)
... |
Scoping functions, see details |
.args |
extra argument to be passed to support function. |
.x |
A data.frame |
a tbl data frame
vignette("convert"), vignette("hablar")
## Not run: # Change one column to numeric and another to character mtcars %>% convert(num(gear), chr(mpg)) # Changing multiple data types on multiple columns mtcars %>% convert(int(hp, wt), fct(qsec, cyl, drat)) # Also works with tidyselect convenience functions mtcars %>% convert(int(vs:carb), fct(last_col())) ## End(Not run)## Not run: # Change one column to numeric and another to character mtcars %>% convert(num(gear), chr(mpg)) # Changing multiple data types on multiple columns mtcars %>% convert(int(hp, wt), fct(qsec, cyl, drat)) # Also works with tidyselect convenience functions mtcars %>% convert(int(vs:carb), fct(last_col())) ## End(Not run)
Tests if vector could be a another data type without errors.
could_chr_be_num(.x) could_chr_be_int(.x) could_num_be_int(.x) could_chr_be_dtm(.x) could_chr_be_dte(.x)could_chr_be_num(.x) could_chr_be_int(.x) could_num_be_int(.x) could_chr_be_dtm(.x) could_chr_be_dte(.x)
.x |
vector of the data type that should be tested. |
The name logic of could_chr_be_num should be interpreted as:
Could this character vector be a numeric vector?
The same logic goes for all functions named could_this_be_that.
TRUE or FALSE
vignette("s"), vignette("hablar")
x <- c("1", "3", "7") could_chr_be_num(x) could_chr_be_int(x) x <- c("abc", "3", "Hello world") could_chr_be_num(x) x <- c(NA, "3.45", "5,98") could_chr_be_num(x) could_chr_be_int(x) x <- as.numeric(c(3.45, 1.5)) could_num_be_int(x) x <- as.numeric(c(7, 2)) could_num_be_int(x)x <- c("1", "3", "7") could_chr_be_num(x) could_chr_be_int(x) x <- c("abc", "3", "Hello world") could_chr_be_num(x) x <- c(NA, "3.45", "5,98") could_chr_be_num(x) could_chr_be_int(x) x <- as.numeric(c(3.45, 1.5)) could_num_be_int(x) x <- as.numeric(c(7, 2)) could_num_be_int(x)
Creates a vector of the integers 1 and 0. If condition is true it returns 1. If false 0. If condition returns NA it returns NA, if not explicitly not stated than NA should be replaced.
dummy(condition, missing = NA) dummy_(condition, missing = 0L) dummy_(condition, missing = 0L)dummy(condition, missing = NA) dummy_(condition, missing = 0L) dummy_(condition, missing = 0L)
condition |
a predicament |
missing |
a replacement if condition is NA |
a vector of the integers 1, 0 and NA (if not dummy_ is used).
vignette("hablar")
v <- c(10, 5, 3, NA, 9) dummy(v > 5) dummy_(v > 5)v <- c(10, 5, 3, NA, 9) dummy(v > 5) dummy_(v > 5)
cumulative functions. 'cumsum_' is the cumulative sum ignoring missing values. 'cum_unique' counts the cumulative unique value including NA as ONE value. 'cum_unique_' ignores missing values
cumsum_(.v, ignore_na = TRUE) cummean_(.v, ignore_na = TRUE) cum_unique(.v, ignore_na = FALSE) cum_unique_(.v, ignore_na = TRUE)cumsum_(.v, ignore_na = TRUE) cummean_(.v, ignore_na = TRUE) cum_unique(.v, ignore_na = FALSE) cum_unique_(.v, ignore_na = TRUE)
.v |
a vector |
ignore_na |
should missing values be ignores? |
a vector
Filters a data frame for special cases. For example, find_duplicates() returns all rows that are duplicates. If variables are passed to the function then duplicates for those variables are returned.
find_duplicates(.data, ...) find_na(.data, ...) find_irrational(.data, ...) find_nan(.data, ...) find_inf(.data, ...)find_duplicates(.data, ...) find_na(.data, ...) find_irrational(.data, ...) find_nan(.data, ...) find_inf(.data, ...)
.data |
a data frame |
... |
variables that should be considered. If empty, all variables are used. |
irrational values are Inf and NaN
a filtered data frame
vignette("s"), vignette("hablar")
check_df to return TRUE or FALSE instead of rows.
## Not run: df <- data.frame(a = c("A", NA, "B", "C", "C"), b = c(NA, 1, 1, 3, 3), c = c(7, 8, 2, 3, 3), stringsAsFactors = FALSE) # Returns duplicated rows df %>% find_duplicates() # Returns duplicates in specific variables df %>% find_duplicates(b:c) # Returns rows where NA in variable b df %>% find_na(b) ## End(Not run)## Not run: df <- data.frame(a = c("A", NA, "B", "C", "C"), b = c(NA, 1, 1, 3, 3), c = c(7, 8, 2, 3, 3), stringsAsFactors = FALSE) # Returns duplicated rows df %>% find_duplicates() # Returns duplicates in specific variables df %>% find_duplicates(b:c) # Returns rows where NA in variable b df %>% find_na(b) ## End(Not run)
Simple function that filters a vector while helping with missing values. Replacing expression like 'x[x > 3 & !is.null(x)]'
given(.x, .y, ignore_na = FALSE) given_(.x, .y, ignore_na = TRUE)given(.x, .y, ignore_na = FALSE) given_(.x, .y, ignore_na = TRUE)
.x |
the vector to filter |
.y |
a logical vector to filter with |
ignore_na |
should NA be removed? |
a vector
## Not run: x <- c(1, 2, NA, 4) x %>% given_(x >= 2) ## End(Not run)## Not run: x <- c(1, 2, NA, 4) x %>% given_(x >= 2) ## End(Not run)
A vectorised if or else function. It checks that the true or false (or the optional missing) arguments have the same type. However it accepts a generic NA. Built upon dplyr's [if_else()] function. The only difference is that the user do not have to specify the type of NA. if_else_ is faster than base [ifelse()] and a tad slower than dplyr's [if_else()]. Attributes are taken from either true or false because one generic NA.
if_else_(condition, true, false, missing = NULL)if_else_(condition, true, false, missing = NULL)
condition |
logical vector |
true |
value to replace if condition is true. Must be same length as condition or 1. |
false |
value to replace if condition is false. Must be same length as condition or 1. |
missing |
optional. a replacement if condition returns NA. Must be same length as condition or 1. |
If the returning vector have attributes (e.g. for factors) it returns the attributes for the first non-generic NA in the order true, false and then missing.
a vector
vignette("s"), vignette("hablar")
v <- c(TRUE, FALSE, TRUE, FALSE) if_else_(v, "true", "false") v <- c(TRUE, FALSE, NA, FALSE) if_else_(v, 1, NA, 999)v <- c(TRUE, FALSE, TRUE, FALSE) if_else_(v, "true", "false") v <- c(TRUE, FALSE, NA, FALSE) if_else_(v, 1, NA, 999)
Simplifying math functions are simple wrappers of math function (- +). If any of the left-hand side or right-hand side is NA, Inf or NaN it returns any rational value, if there is any.
However, if the both values are irrational it returns NA. The result is then passed to the corresponding math function.
.x %minus_% .y .x %plus_% .y.x %minus_% .y .x %plus_% .y
.x |
numeric or integer element |
.y |
numeric or integer element |
a single value
vignette("s"), vignette("hablar")
## Not run: # The simplest case 3 %minus_% 2 # But with NA it returns 3 as if the NA were zero 3 %minus_% NA # It doesnt matter if the irrational number is on left- or right-hand. NA %plus_% 5 ## End(Not run)## Not run: # The simplest case 3 %minus_% 2 # But with NA it returns 3 as if the NA were zero 3 %minus_% NA # It doesnt matter if the irrational number is on left- or right-hand. NA %plus_% 5 ## End(Not run)
Simple wrapper for length(unique(.x)). If you use n_unique_(.x) then NA is ignored when counting.
n_unique(.x, ignore_na = FALSE) n_unique_(.x, ignore_na = TRUE) n_unique_(.x, ignore_na = TRUE)n_unique(.x, ignore_na = FALSE) n_unique_(.x, ignore_na = TRUE) n_unique_(.x, ignore_na = TRUE)
.x |
a vector |
ignore_na |
a logical indicating whether missing values should be removed |
a single numeric vector of the same length as the data frame it is applied to.
vignette("s"), vignette("hablar")
# Simple n_unique(c(1, 2, 2, 3)) # Same result as above eventhough vector includes NA n_unique_(c(1, 2, 2, 3, NA))# Simple n_unique(c(1, 2, 2, 3)) # Same result as above eventhough vector includes NA n_unique_(c(1, 2, 2, 3, NA))
rationalize transforms all numeric elements to be rational values or NA,
thus removes all NaN,Inf and replaces them with NA.Only allow rational values in numeric vectors
rationalize transforms all numeric elements to be rational values or NA,
thus removes all NaN,Inf and replaces them with NA.
rationalize(.x, ...) ## Default S3 method: rationalize(.x, ...) ## S3 method for class 'numeric' rationalize(.x, ...) ## S3 method for class 'data.frame' rationalize(.x, ...)rationalize(.x, ...) ## Default S3 method: rationalize(.x, ...) ## S3 method for class 'numeric' rationalize(.x, ...) ## S3 method for class 'data.frame' rationalize(.x, ...)
.x |
vector or data.frame |
... |
columns to be evaluated. Only applicable if .x is a data frame. |
#' If a non-numeric vector is passed, it is unchanged. If a data.frame is passed, it evaluates all columns separately.
For vectors: same data type/class as .x.
For data.frame: a tbl data frame.
NULL
NULL
NULL
s, rationalize, vignette("s"), vignette("hablar")
x <- c(3, -Inf, 6.56, 9.3, NaN, 5, -Inf) rationalize(x) df <- data.frame(num_col = c(Inf, 3, NaN), chr_col = c("a", "b", "c"), stringsAsFactors = FALSE) df rationalize(df)x <- c(3, -Inf, 6.56, 9.3, NaN, 5, -Inf) rationalize(x) df <- data.frame(num_col = c(Inf, 3, NaN), chr_col = c("a", "b", "c"), stringsAsFactors = FALSE) df rationalize(df)
Repeats a data frame n times. Useful for testing on large data frames.
repeat_df(.df, n, id = NULL)repeat_df(.df, n, id = NULL)
.df |
a data frame |
n |
times the data frame should be repeated |
id |
a character element that creates a column with a number for each repetition |
a vector of the integers 1, 0 and NA (if not dummy_ is used).
vignette("hablar")
repeat_df(mtcars, 2)repeat_df(mtcars, 2)
If-this-type-then replace with x. And the other way around; replace with x if this.
if_na(.x, replacement, missing = NULL) if_nan(.x, replacement, missing = NULL) if_inf(.x, replacement, missing = NULL) if_zero(.x, replacement, missing = NULL) na_if(.x, condition, replace_na = FALSE) nan_if(.x, condition, replace_na = FALSE) inf_if(.x, condition, replace_na = FALSE) zero_if(.x, condition, replace_na = FALSE) if_not_na(.x, replacement, missing = NULL) if_inf(.x, replacement, missing = NULL) if_nan(.x, replacement, missing = NULL) if_zero(.x, replacement, missing = NULL) na_if(.x, condition, replace_na = FALSE) inf_if(.x, condition, replace_na = FALSE) nan_if(.x, condition, replace_na = FALSE) zero_if(.x, condition, replace_na = FALSE)if_na(.x, replacement, missing = NULL) if_nan(.x, replacement, missing = NULL) if_inf(.x, replacement, missing = NULL) if_zero(.x, replacement, missing = NULL) na_if(.x, condition, replace_na = FALSE) nan_if(.x, condition, replace_na = FALSE) inf_if(.x, condition, replace_na = FALSE) zero_if(.x, condition, replace_na = FALSE) if_not_na(.x, replacement, missing = NULL) if_inf(.x, replacement, missing = NULL) if_nan(.x, replacement, missing = NULL) if_zero(.x, replacement, missing = NULL) na_if(.x, condition, replace_na = FALSE) inf_if(.x, condition, replace_na = FALSE) nan_if(.x, condition, replace_na = FALSE) zero_if(.x, condition, replace_na = FALSE)
.x |
a vector |
replacement |
a replacement if condition is TRUE |
missing |
a value that replace missing values in condition. |
condition |
a predicament |
replace_na |
if TRUE, missing values in condition will be replaced as well |
a vector
vignette("s"), vignette("hablar")
v <- c(1, NA, 2) if_na(v, 100) v <- c(999, NA, 2) zero_if(v, v == 999)v <- c(1, NA, 2) if_na(v, 100) v <- c(999, NA, 2) zero_if(v, v == 999)
retype transforms all elements into simple classes. The simple classes
are date, numeric and character. By transforming all elements to these
classes no information is lost, while simplifying the object. See details below for
more information or type vignette("retype") in the console.
retype(.x, ...) ## Default S3 method: retype(.x, ...) ## S3 method for class 'logical' retype(.x, ...) ## S3 method for class 'integer' retype(.x, ...) ## S3 method for class 'Date' retype(.x, ...) ## S3 method for class 'POSIXct' retype(.x, ...) ## S3 method for class 'numeric' retype(.x, ...) ## S3 method for class 'list' retype(.x, ...) ## S3 method for class 'data.frame' retype(.x, ...)retype(.x, ...) ## Default S3 method: retype(.x, ...) ## S3 method for class 'logical' retype(.x, ...) ## S3 method for class 'integer' retype(.x, ...) ## S3 method for class 'Date' retype(.x, ...) ## S3 method for class 'POSIXct' retype(.x, ...) ## S3 method for class 'numeric' retype(.x, ...) ## S3 method for class 'list' retype(.x, ...) ## S3 method for class 'data.frame' retype(.x, ...)
.x |
vector or data.frame |
... |
column names to be evaluated. Only if .x is a data frame. |
Each vector past to retype is reclassified into the highest position in
a simplification hierarchy without loosing any information. This means that:
Factors are converted to characters.
However, character vectors (or vectors changed to character initially)
are checked to see if they could be a numeric vector without error.
If so, it is transformed into a numeric vector which is higher in the hierarchy.
Vectors of class logical, integer are changed to numerical.
Dates and date time (POSIXct) goes through the same procedure.
Lists and complex vectors are left unchanged because the are neither simple nor complicated.
For vectors: simple class of .x.
For data.frame: a tbl data frame with simple classes.
NULL
NULL
NULL
NULL
NULL
NULL
NULL
NULL
s, rationalize #' vignette("retype"), vignette("s"), vignette("hablar")
# Dates dte <- as.Date(c("2018-01-01", "2016-03-21", "1970-01-05")) retype(dte) retype(dte) # Factors fct <- as.factor(c("good", "bad", "average")) retype(dte) # Character that only contains numeric elements num_chr <- c("3","4.0", "3,5") retype(num_chr) # Logical lgl <- c(TRUE, FALSE, TRUE) retype(lgl) # Data frame with all the above vectors df <- data.frame(dte = dte, fct = fct, num_chr = num_chr, lgl = lgl, stringsAsFactos = FALSE) df retype(df)# Dates dte <- as.Date(c("2018-01-01", "2016-03-21", "1970-01-05")) retype(dte) retype(dte) # Factors fct <- as.factor(c("good", "bad", "average")) retype(dte) # Character that only contains numeric elements num_chr <- c("3","4.0", "3,5") retype(num_chr) # Logical lgl <- c(TRUE, FALSE, TRUE) retype(lgl) # Data frame with all the above vectors df <- data.frame(dte = dte, fct = fct, num_chr = num_chr, lgl = lgl, stringsAsFactos = FALSE) df retype(df)
s means simple and short. It removes all non-values, i.e. NA,Inf,NaN from a vector.
However, if the length is 0 it returns NA.
It is useful in combination with summary functions, e.g. mean, sum or min, when
an answer is desired, if there is one in the data. In any other case NA is returned.
Type vignette("s") in the console for more information.
s(.x, ignore_na = TRUE)s(.x, ignore_na = TRUE)
.x |
one vector. Does not work for factors. |
ignore_na |
if TRUE then NA omitted from results, as long as any non-NA element is left. |
a shortened and simplified vector
retype, rationalize, vignette("s"), vignette("hablar")
## Not run: library(dplyr) ## s on a weird numeric vector vector <- c(7, NaN, 6, -Inf, 5, 4, NA) s(vector) ## Sum vector with non-rational values vector <- c(7, NaN, -Inf, 4) # Base R sum(vector) # With s sum(s(vector)) ## Max of vector with only NA # Base R max(vector, na.rm = TRUE) # With s max(s(vector)) ## First of vector when NA is first element vector <- c(NA, "X", "Y") # dplyr R first(vector) # With s first(s(vector)) ## Use of s when NA should not be removes vector <- c(7, Inf, NA, 4) # Base R sum(vector) # With s sum(s(vector, ignore_na = FALSE)) ## s when summarizing a weird data.frame df_test <- data.frame(a = c(NaN, 1, -Inf, 3), b = c(NA, "Q", "P", "P"), c = c(NA, NA, NA, NA), stringsAsFactors = FALSE) df_test # Base R aggregation with dplyr's summarize summarise(df_test, mean_a = mean(a), min_c = min(c, na.rm = TRUE)) # With s summarise(df_test, mean_a = mean(s(a)), min_c = min(s(c))) ## End(Not run)## Not run: library(dplyr) ## s on a weird numeric vector vector <- c(7, NaN, 6, -Inf, 5, 4, NA) s(vector) ## Sum vector with non-rational values vector <- c(7, NaN, -Inf, 4) # Base R sum(vector) # With s sum(s(vector)) ## Max of vector with only NA # Base R max(vector, na.rm = TRUE) # With s max(s(vector)) ## First of vector when NA is first element vector <- c(NA, "X", "Y") # dplyr R first(vector) # With s first(s(vector)) ## Use of s when NA should not be removes vector <- c(7, Inf, NA, 4) # Base R sum(vector) # With s sum(s(vector, ignore_na = FALSE)) ## s when summarizing a weird data.frame df_test <- data.frame(a = c(NaN, 1, -Inf, 3), b = c(NA, "Q", "P", "P"), c = c(NA, NA, NA, NA), stringsAsFactors = FALSE) df_test # Base R aggregation with dplyr's summarize summarise(df_test, mean_a = mean(a), min_c = min(c, na.rm = TRUE)) # With s summarise(df_test, mean_a = mean(s(a)), min_c = min(s(c))) ## End(Not run)
Sets working directory to the path where the R-script is located. Only works inside [Rstudio] and in a script (i.e. not in the console). Additionally, the R-script needs to be saved in a path to work.
set_wd_to_script_path()set_wd_to_script_path()
NULL. In the background the working directory has changed if not any errors occurred.
Returns the current day, month or year. Day and month returns dates and year a 4 digit number.
this_day() this_month() this_year()this_day() this_month() this_year()
a date or number
this_day() this_month() this_year()this_day() this_month() this_year()
[summary function_*] functions are simple wrappers of aggregate function
and the s function. s removes all non-values,
i.e. NA,Inf,NaN from a vector.
However, if the length is 0 it returns NA. The result is then passed to the
corresponding aggregation function. For example,
min_(x) is identical to min(s(x)). Please read vignette("s") for more information.
max_(.x, ignore_na = TRUE) min_(.x, ignore_na = TRUE) sum_(.x, ignore_na = TRUE) mean_(.x, ignore_na = TRUE) median_(.x, ignore_na = TRUE) sd_(.x, ignore_na = TRUE) var_(.x, ignore_na = TRUE) first_(.x, ignore_na = TRUE) last_(.x, ignore_na = TRUE) first_non_na(.x) squeeze(.x, ignore_na = FALSE) squeeze_(.x, ignore_na = TRUE)max_(.x, ignore_na = TRUE) min_(.x, ignore_na = TRUE) sum_(.x, ignore_na = TRUE) mean_(.x, ignore_na = TRUE) median_(.x, ignore_na = TRUE) sd_(.x, ignore_na = TRUE) var_(.x, ignore_na = TRUE) first_(.x, ignore_na = TRUE) last_(.x, ignore_na = TRUE) first_non_na(.x) squeeze(.x, ignore_na = FALSE) squeeze_(.x, ignore_na = TRUE)
.x |
a single vector |
ignore_na |
if false missing values are not omitted. |
'first_non_na' is a faster version of 'first' since it only search for a non NA value until it finds one. 'squeeze' on the other hand checks if all elements are equal and then returns only that value.
a single aggregated value
vignette("convert"), vignette("hablar")
## sum_ on non-rational numeric vector vector <- c(7, NaN, -Inf, 4) sum_(vector) ## Min of vector with length 0 vector <- c() # With a wrapped s min_(vector) ## Max of vector with only NA # With a wrapped s max_(vector) ## Use of s when NA should not be removed vector <- c(7, Inf, NA, 4) # With a wrapped s sum_(vector, ignore_na = FALSE)## sum_ on non-rational numeric vector vector <- c(7, NaN, -Inf, 4) sum_(vector) ## Min of vector with length 0 vector <- c() # With a wrapped s min_(vector) ## Max of vector with only NA # With a wrapped s max_(vector) ## Use of s when NA should not be removed vector <- c(7, Inf, NA, 4) # With a wrapped s sum_(vector, ignore_na = FALSE)