Reliable conversion to another data type

Description

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.

Usage

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", ...)

Arguments

Value

vector

See Also

vignette("convert"), vignette("hablar")

Examples

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)

Special checks

Description

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.

Usage

check_duplicates(.data, ...)

check_na(.data, ...)

check_irrational(.data, ...)

check_nan(.data, ...)

check_inf(.data, ...)

check_complete_set(.data, ...)

Arguments

Details

irrational values are Inf and NaN. 'check_complete_set' tests if all combinations of elements exists in the data frame.

Value

TRUE or FALSE

See Also

find_in_df to return rows instead of TRUE or FALSE. vignette("s"), vignette("hablar")

Examples

## 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

Description

Convert data type of columns

Usage

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, ...)

Arguments

Value

a tbl data frame

See Also

vignette("convert"), vignette("hablar")

Examples

## 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 is a vector could be of another data type

Description

Tests if vector could be a another data type without errors.

Usage

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)

Arguments

Details

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.

Value

TRUE or FALSE

See Also

vignette("s"), vignette("hablar")

Examples

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)

Create a simple dummy

Description

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.

Usage

dummy(condition, missing = NA)
dummy_(condition, missing = 0L)

dummy_(condition, missing = 0L)

Arguments

Value

a vector of the integers 1, 0 and NA (if not dummy_ is used).

See Also

vignette("hablar")

Examples

v <- c(10, 5, 3, NA, 9)
dummy(v > 5)
dummy_(v > 5)

cumulative_

Description

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

Usage

cumsum_(.v, ignore_na = TRUE)

cummean_(.v, ignore_na = TRUE)

cum_unique(.v, ignore_na = FALSE)

cum_unique_(.v, ignore_na = TRUE)

Arguments

Value

a vector

Special filters

Description

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.

Usage

find_duplicates(.data, ...)

find_na(.data, ...)

find_irrational(.data, ...)

find_nan(.data, ...)

find_inf(.data, ...)

Arguments

Details

irrational values are Inf and NaN

Value

a filtered data frame

See Also

vignette("s"), vignette("hablar")

check_df to return TRUE or FALSE instead of rows.

Examples

## 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)

given

Description

Simple function that filters a vector while helping with missing values. Replacing expression like 'x[x > 3 & !is.null(x)]'

Usage

given(.x, .y, ignore_na = FALSE)

given_(.x, .y, ignore_na = TRUE)

Arguments

Value

a vector

Examples

## Not run: 
x <- c(1, 2, NA, 4)
x %>% given_(x >= 2)

## End(Not run)

if_this_else_that_

Description

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.

Usage

if_else_(condition, true, false, missing = NULL)

Arguments

Details

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.

Value

a vector

See Also

vignette("s"), vignette("hablar")

Examples

v <- c(TRUE, FALSE, TRUE, FALSE)
if_else_(v, "true", "false")

v <- c(TRUE, FALSE, NA, FALSE)
if_else_(v, 1, NA, 999)

Ignore NA in math

Description

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.

Usage

.x %minus_% .y

.x %plus_% .y

Arguments

Value

a single value

See Also

vignette("s"), vignette("hablar")

Examples

## 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)

n_unique

Description

Simple wrapper for length(unique(.x)). If you use n_unique_(.x) then NA is ignored when counting.

Usage

n_unique(.x, ignore_na = FALSE)

n_unique_(.x, ignore_na = TRUE)

n_unique_(.x, ignore_na = TRUE)

Arguments

Value

a single numeric vector of the same length as the data frame it is applied to.

See Also

vignette("s"), vignette("hablar")

Examples

# Simple
n_unique(c(1, 2, 2, 3))

# Same result as above eventhough vector includes NA
n_unique_(c(1, 2, 2, 3, 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.

Description

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.

Usage

rationalize(.x, ...)

## Default S3 method:
rationalize(.x, ...)

## S3 method for class 'numeric'
rationalize(.x, ...)

## S3 method for class 'data.frame'
rationalize(.x, ...)

Arguments

Details

#' If a non-numeric vector is passed, it is unchanged. If a data.frame is passed, it evaluates all columns separately.

Value

For vectors: same data type/class as .x.

For data.frame: a tbl data frame.

NULL

NULL

NULL

See Also

s, rationalize, vignette("s"), vignette("hablar")

Examples

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)

repeat_df

Description

Repeats a data frame n times. Useful for testing on large data frames.

Usage

repeat_df(.df, n, id = NULL)

Arguments

Value

a vector of the integers 1, 0 and NA (if not dummy_ is used).

See Also

vignette("hablar")

Examples

repeat_df(mtcars, 2)

replacemnt and specials

Description

If-this-type-then replace with x. And the other way around; replace with x if this.

Usage

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)

Arguments

Value

a vector

See Also

vignette("s"), vignette("hablar")

Examples

v <- c(1, NA, 2)
if_na(v, 100)

v <- c(999, NA, 2)
zero_if(v, v == 999)

Return simple data types

Description

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.

Usage

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, ...)

Arguments

Details

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.

Value

For vectors: simple class of .x.

For data.frame: a tbl data frame with simple classes.

NULL

NULL

NULL

NULL

NULL

NULL

NULL

NULL

See Also

s, rationalize #' vignette("retype"), vignette("s"), vignette("hablar")

Examples

# 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)

Make vector shorter and simpler

Description

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.

Usage

s(.x, ignore_na = TRUE)

Arguments

Value

a shortened and simplified vector

See Also

retype, rationalize, vignette("s"), vignette("hablar")

Examples

## 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)

Set wd to script path

Description

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.

Usage

set_wd_to_script_path()

Value

NULL. In the background the working directory has changed if not any errors occurred.

this_date

Description

Returns the current day, month or year. Day and month returns dates and year a 4 digit number.

Usage

this_day()

this_month()

this_year()

Value

a date or number

Examples

this_day() 
this_month() 
this_year() 

Combine aggregate functions and s

Description

[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.

Usage

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)

Arguments

Details

'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.

Value

a single aggregated value

See Also

vignette("convert"), vignette("hablar")

Examples

## 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)