Package 'baizer' reference manual

Title:	Useful Functions for Data Processing
Description:	In ancient Chinese mythology, Bai Ze is a divine creature that knows the needs of everything. 'baizer' provides data processing functions frequently used by the author. Hope this package also knows what you want!
Authors:	William Song [aut, cre]
Maintainer:	William Song <[email protected]>
License:	MIT + file LICENSE
Version:	0.8.1
Built:	2025-02-19 04:30:02 UTC
Source:	https://github.com/william-swl/baizer

equal calculation operator, support NA

Description

equal calculation operator, support NA

Usage

x %eq% y
x %eq% y

Arguments

`x`	value x
`y`	value y

Value

logical value, TRUE if x and y are not equal

Examples

NA %eq% NA
NA %eq% NA

not equal calculation operator, support NA

Description

not equal calculation operator, support NA

Usage

x %neq% y
x %neq% y

Arguments

`x`	value x
`y`	value y

Value

logical value, TRUE if x and y are not equal

Examples

1 %neq% NA
1 %neq% NA

not in calculation operator

Description

not in calculation operator

Usage

left %nin% right
left %nin% right

Arguments

`left`	left element
`right`	right element

Value

logical value, TRUE if left is not in right

Examples

0 %nin% 1:4
0 %nin% 1:4

expand a number vector according to the adjacent two numbers

Description

expand a number vector according to the adjacent two numbers

Usage

adjacent_div(v, n_div = 10, .unique = FALSE)
adjacent_div(v, n_div = 10, .unique = FALSE)

Arguments

`v`	number vector
`n_div`	how many divisions expanded by two numbers
`.unique`	only keep unique numbers

Value

new number vector

Examples

adjacent_div(10^c(1:3), n_div = 10)
adjacent_div(10^c(1:3), n_div = 10)

use aliases for function arguments

Description

use aliases for function arguments

Usage

alias_arg(..., default = NULL)
alias_arg(..., default = NULL)

Arguments

`...`	aliases of an argument
`default`	a alias with a default value

Value

the finally value of this argument across all aliases

Examples


# set y, z as aliases of x when create a function
func <- function(x = 1, y = NULL, z = NULL) {
  x <- alias_arg(x, y, z, default = x)
  return(x)
}
# set y, z as aliases of x when create a function
func <- function(x = 1, y = NULL, z = NULL) {
  x <- alias_arg(x, y, z, default = x)
  return(x)
}

trans a tibble into markdown format table

Description

trans a tibble into markdown format table

Usage

as_md_table(x, show = TRUE)
as_md_table(x, show = TRUE)

Arguments

`x`	tibble
`show`	show result instead of return the markdown string, TRUE as default

Value

NULL or markdown string

Examples


mini_diamond %>%
  head(5) %>%
  as_md_table()

mini_diamond %>%
  head(5) %>%
  as_md_table()

trans a table in markdown format into tibble

Description

trans a table in markdown format into tibble

Usage

as_tibble_md(x)
as_tibble_md(x)

Arguments

`x`	character string

Value

tibble

Examples


x <- "
col1 | col2 | col3 |
| ---- | ---- | ---- |
| v1   | v2   | v3   |
| r1   | r2   | r3   |
"

as_tibble_md(x)

x <- "
col1 | col2 | col3 |
| ---- | ---- | ---- |
| v1   | v2   | v3   |
| r1   | r2   | r3   |
"

as_tibble_md(x)

whether the expression is an atomic one

Description

whether the expression is an atomic one

Usage

atomic_expr(ex)
atomic_expr(ex)

Arguments

`ex`	expression

Value

logical value

Examples

atomic_expr(rlang::expr(x))

atomic_expr(rlang::expr(!x))

atomic_expr(rlang::expr(x + y))

atomic_expr(rlang::expr(x > 1))

atomic_expr(rlang::expr(!x + y))

atomic_expr(rlang::expr(x > 1 | y < 2))

atomic_expr(rlang::expr(x))

atomic_expr(rlang::expr(!x))

atomic_expr(rlang::expr(x + y))

atomic_expr(rlang::expr(x > 1))

atomic_expr(rlang::expr(!x + y))

atomic_expr(rlang::expr(x > 1 | y < 2))

broadcast the vector into length n

Description

broadcast the vector into length n

Usage

broadcast_vector(x, n)
broadcast_vector(x, n)

Arguments

`x`	vector
`n`	target length

Value

vector

Examples

broadcast_vector(1:3, 5)

broadcast_vector(1:3, 5)

wrapper of tibble::column_to_rownames

Description

wrapper of tibble::column_to_rownames

Usage

c2r(df, col = "")
c2r(df, col = "")

Arguments

`df`	tibble
`col`	a col name

Value

data.frame

Examples

mini_diamond %>% c2r("id")
mini_diamond %>% c2r("id")

check arguments by custom function

Description

check arguments by custom function

Usage

check_arg(..., n = 2, fun = not.null)
check_arg(..., n = 2, fun = not.null)

Arguments

`...`	arguments
`n`	how many arguments should meet the custom conditions
`fun`	custom conditions defined by a function

Value

logical value

Examples

x <- 1
y <- 3
z <- NULL

func <- function(x = NULL, y = NULL, z = NULL) {
  if (check_arg(x, y, z, n = 2)) {
    print("As expected, two arguments is not NULL")
  }

  if (check_arg(x, y, z, n = 1, method = ~ .x < 2)) {
    print("As expected, one argument less than 2")
  }
}

x <- 1
y <- 3
z <- NULL

func <- function(x = NULL, y = NULL, z = NULL) {
  if (check_arg(x, y, z, n = 2)) {
    print("As expected, two arguments is not NULL")
  }

  if (check_arg(x, y, z, n = 1, method = ~ .x < 2)) {
    print("As expected, one argument less than 2")
  }
}

get the command line arguments

Description

get the command line arguments

Usage

cmdargs(x = NULL)
cmdargs(x = NULL)

Arguments

`x`	one of 'wd, R_env, script_path, script_dir, env_configs'

Value

list of all arguments, or single value of select argument

Examples

cmdargs()

cmdargs()

dump a named vector into character

Description

dump a named vector into character

Usage

collapse_vector(named_vector, front_name = TRUE, collapse = ",")
collapse_vector(named_vector, front_name = TRUE, collapse = ",")

Arguments

`named_vector`	a named vector
`front_name`	if TRUE, put names to former
`collapse`	collapse separator

Value

character

Examples

collapse_vector(c(e = 1:4), front_name = TRUE, collapse = ";")
collapse_vector(c(e = 1:4), front_name = TRUE, collapse = ";")

combine multiple vectors into one

Description

combine multiple vectors into one

Usage

combn_vector(..., method = "first", invalid = NA)
combn_vector(..., method = "first", invalid = NA)

Arguments

`...`	vectors
`method`	how to combine, should be one of `first\|last`, or one of `sum\|mean\|median` for numeric vector, or some characters (e.g. `⁠,\|.\| \|;⁠`) for character vector
`invalid`	invalid value to ignore, `NA` as default

Value

combined vector

Examples

x1 <- c(1, 2, NA, NA)
x2 <- c(3, NA, 2, NA)
x3 <- c(4, NA, NA, 3)

combn_vector(x1, x2, x3, method = "sum")

x1 <- c(1, 2, NA, NA)
x2 <- c(3, NA, 2, NA)
x3 <- c(4, NA, NA, 3)

combn_vector(x1, x2, x3, method = "sum")

correct the numbers to a target ratio

Description

correct the numbers to a target ratio

Usage

correct_ratio(raw, target, digits = 0)
correct_ratio(raw, target, digits = 0)

Arguments

`raw`	the raw numbers
`target`	the target ratio
`digits`	the result digits

Value

corrected number vector

Examples

correct_ratio(c(10, 10), c(3, 5))

# support ratio as a float
correct_ratio(c(100, 100), c(0.2, 0.8))

# more numbers
correct_ratio(10:13, c(2, 3, 4, 6))

# with digits after decimal point
correct_ratio(c(10, 10), c(1, 4), digits = 1)
correct_ratio(c(10, 10), c(3, 5))

# support ratio as a float
correct_ratio(c(100, 100), c(0.2, 0.8))

# more numbers
correct_ratio(10:13, c(2, 3, 4, 6))

# with digits after decimal point
correct_ratio(c(10, 10), c(1, 4), digits = 1)

count two columns as a cross-tabulation table

Description

count two columns as a cross-tabulation table

Usage

cross_count(df, row, col, method = "n", digits = 2)
cross_count(df, row, col, method = "n", digits = 2)

Arguments

`df`	tibble
`row`	the column as rownames in the output
`col`	the column as colnames in the output
`method`	one of `⁠n\|count, rowr\|row_ratio, colr\|col_ratio⁠`
`digits`	the digits of ratios

Value

data.frame

Examples

cross_count(mini_diamond, cut, clarity)

# show the ratio in the row
cross_count(mini_diamond, cut, clarity, method = "rowr")

# show the ratio in the col
cross_count(mini_diamond, cut, clarity, method = "colr")

cross_count(mini_diamond, cut, clarity)

# show the ratio in the row
cross_count(mini_diamond, cut, clarity, method = "rowr")

# show the ratio in the col
cross_count(mini_diamond, cut, clarity, method = "colr")

detect possible duplication in a vector, ignore case, blank and special character

Description

detect possible duplication in a vector, ignore case, blank and special character

Usage

detect_dup(vector, index = FALSE)
detect_dup(vector, index = FALSE)

Arguments

`vector`	vector possibly with duplication
`index`	return duplication index

Value

duplication sub-vector

Examples

detect_dup(c("a", "C_", "c -", "#A"))
detect_dup(c("a", "C_", "c -", "#A"))

the index of different character

Description

the index of different character

Usage

diff_index(s1, s2, nth = NULL, ignore_case = FALSE)
diff_index(s1, s2, nth = NULL, ignore_case = FALSE)

Arguments

`s1`	string1
`s2`	string2
`nth`	just return nth index
`ignore_case`	ignore upper or lower cases

Value

list of different character indices

Examples

diff_index("AAAA", "ABBA")
diff_index("AAAA", "ABBA")

differences between two tibbles

Description

differences between two tibbles

Usage

diff_tb(old, new)
diff_tb(old, new)

Arguments

`old`	old tibble
`new`	new tibble

Value

differences tibble, 'a, d, c' in diff_type stand for 'add, delete, change' compared to the old tibble

Examples

tb1 <- gen_tb(fill = "int", seed = 1)

tb2 <- gen_tb(fill = "int", seed = 3)

diff_tb(tb1, tb2)

tb1 <- gen_tb(fill = "int", seed = 1)

tb2 <- gen_tb(fill = "int", seed = 3)

diff_tb(tb1, tb2)

diagnosis a tibble for character NA, NULL, all T/F column, blank in cell

Description

diagnosis a tibble for character NA, NULL, all T/F column, blank in cell

Usage

dx_tb(x)
dx_tb(x)

Arguments

x

tibble

Value

list

Examples

x <- tibble::tibble(
  c1 = c("NA", NA, "a", "b"),
  c2 = c("c", "d", "e", "NULL"),
  c3 = c("T", "F", "F", "T"),
  c4 = c("T", "F", "F", NA),
  c5 = c("", " ", "\t", "\n")
)

dx_tb(x)

x <- tibble::tibble(
  c1 = c("NA", NA, "a", "b"),
  c2 = c("c", "d", "e", "NULL"),
  c3 = c("T", "F", "F", "T"),
  c4 = c("T", "F", "F", NA),
  c5 = c("", " ", "\t", "\n")
)

dx_tb(x)

detect whether directory is empty recursively

Description

detect whether directory is empty recursively

Usage

empty_dir(dir)
empty_dir(dir)

Arguments

dir

the directory

Value

logical value

Examples

# create an empty directory
dir.create("some/deep/path/in/a/folder", recursive = TRUE)
empty_dir("some/deep/path/in/a/folder")

# create an empty file
file.create("some/deep/path/in/a/folder/there_is_a_file.txt")
empty_dir("some/deep/path/in/a/folder")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)

# create a file with only character of length 0
write("", "some/deep/path/in/a/folder/there_is_a_file.txt")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt")

# clean
unlink("some", recursive = TRUE)

# create an empty directory
dir.create("some/deep/path/in/a/folder", recursive = TRUE)
empty_dir("some/deep/path/in/a/folder")

# create an empty file
file.create("some/deep/path/in/a/folder/there_is_a_file.txt")
empty_dir("some/deep/path/in/a/folder")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)

# create a file with only character of length 0
write("", "some/deep/path/in/a/folder/there_is_a_file.txt")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt")

# clean
unlink("some", recursive = TRUE)

detect whether file is empty recursively

Description

detect whether file is empty recursively

Usage

empty_file(path, strict = FALSE)
empty_file(path, strict = FALSE)

Arguments

`path`	the path of file
`strict`	`FALSE` as default. If `TRUE`, a file with only one character of length 0 will be considered as not empty

Value

logical value

Examples

# create an empty directory
dir.create("some/deep/path/in/a/folder", recursive = TRUE)
empty_dir("some/deep/path/in/a/folder")

# create an empty file
file.create("some/deep/path/in/a/folder/there_is_a_file.txt")
empty_dir("some/deep/path/in/a/folder")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)

# create a file with only character of length 0
write("", "some/deep/path/in/a/folder/there_is_a_file.txt")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt")

# clean
unlink("some", recursive = TRUE)

# create an empty directory
dir.create("some/deep/path/in/a/folder", recursive = TRUE)
empty_dir("some/deep/path/in/a/folder")

# create an empty file
file.create("some/deep/path/in/a/folder/there_is_a_file.txt")
empty_dir("some/deep/path/in/a/folder")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)

# create a file with only character of length 0
write("", "some/deep/path/in/a/folder/there_is_a_file.txt")
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt", strict = TRUE)
empty_file("some/deep/path/in/a/folder/there_is_a_file.txt")

# clean
unlink("some", recursive = TRUE)

generate a matrix to show whether the item in each element of a list

Description

generate a matrix to show whether the item in each element of a list

Usage

exist_matrix(x, n_lim = 0, n_top = NULL, sort_items = NULL)
exist_matrix(x, n_lim = 0, n_top = NULL, sort_items = NULL)

Arguments

`x`	list of character vectors
`n_lim`	n limit to keep items in result
`n_top`	only keep top n items in result
`sort_items`	function to sort the items, item frequency by default

Value

tibble

Examples

x <- 1:5 %>% purrr::map(
  ~ gen_char(to = "k", n = 5, random = TRUE, seed = .x)
)
exist_matrix(x)

x <- 1:5 %>% purrr::map(
  ~ gen_char(to = "k", n = 5, random = TRUE, seed = .x)
)
exist_matrix(x)

pileup the subexpressions which is atomic

Description

pileup the subexpressions which is atomic

Usage

expr_pileup(ex)
expr_pileup(ex)

Arguments

`ex`	expression

Value

the character vector of subexpressions

Examples

ex <- rlang::expr(a == 2 & b == 3 | !b & x + 2)
expr_pileup(ex)

ex <- rlang::expr(a == 2 & b == 3 | !b & x + 2)
expr_pileup(ex)

extract key and values for a character vector

Description

extract key and values for a character vector

Usage

extract_kv(v, sep = ": ", key_loc = 1, value_loc = 2)
extract_kv(v, sep = ": ", key_loc = 1, value_loc = 2)

Arguments

`v`	character vector
`sep`	separator between key and value
`key_loc`	key location
`value_loc`	value location

Value

a named character vector

Examples

extract_kv(c("x: 1", "y: 2"))
extract_kv(c("x: 1", "y: 2"))

fancy count to show an extended column

Description

fancy count to show an extended column

Usage

fancy_count(df, ..., ext = NULL, ext_fmt = "count", sort = FALSE, digits = 2)
fancy_count(df, ..., ext = NULL, ext_fmt = "count", sort = FALSE, digits = 2)

Arguments

`df`	tibble
`...`	other arguments from `dplyr::count()`
`ext`	extended column
`ext_fmt`	`count\|ratio\|clean`, output format of extended column
`sort`	sort by frequency or not
`digits`	if `ext_fmt=ratio`, the digits of ratio

Value

count tibble

Examples

fancy_count(mini_diamond, cut, ext = clarity)

fancy_count(mini_diamond, cut, ext = clarity, ext_fmt = "ratio")

fancy_count(mini_diamond, cut, ext = clarity, ext_fmt = "clean")

fancy_count(mini_diamond, cut, ext = clarity, sort = FALSE)

fancy_count(mini_diamond, cut, clarity, ext = id) %>% head(5)
fancy_count(mini_diamond, cut, ext = clarity)

fancy_count(mini_diamond, cut, ext = clarity, ext_fmt = "ratio")

fancy_count(mini_diamond, cut, ext = clarity, ext_fmt = "clean")

fancy_count(mini_diamond, cut, ext = clarity, sort = FALSE)

fancy_count(mini_diamond, cut, clarity, ext = id) %>% head(5)

fetch character from strings

Description

fetch character from strings

Usage

fetch_char(s, index_list, na.rm = FALSE, collapse = FALSE)
fetch_char(s, index_list, na.rm = FALSE, collapse = FALSE)

Arguments

`s`	strings
`index_list`	index of nth character, can be output of `diff_index` or `same_index`
`na.rm`	remove NA values from results or not
`collapse`	optional string used to combine the characters from a same string

Value

list of characters

Examples

fetch_char(rep("ABC", 3), list(1, 2, 3))
fetch_char(rep("ABC", 3), list(1, 2, 3))

apply tbflt on dplyr filter

Description

apply tbflt on dplyr filter

Usage

filterC(.data, tbflt = NULL, .by = NULL, usecol = TRUE)
filterC(.data, tbflt = NULL, .by = NULL, usecol = TRUE)

Arguments

`.data`	tibble
`tbflt`	tbflt object
`.by`	group by, same as `.by` argument in `dplyr::filter`
`usecol`	if `TRUE` (default), use the default behavior of `dplyr::filter()`, which allows the usage of same variable in colnames, and filter by the data column. If `FALSE`, will check whether the variables on the right side of `⁠==,>,<,>=,<=⁠` have same names as columns and raise error, for the sake of more predictable results. You can always ignore this argument if you know how to use `.env` or `⁠!!⁠`

Value

tibble

Examples

c1 <- tbflt(cut == "Fair")

c2 <- tbflt(x > 8)

mini_diamond %>%
  filterC(c1) %>%
  head(5)

mini_diamond %>% filterC(c1 & c2)



x <- 8
cond <- tbflt(y > x)

# variable `x` not used because of column `x` in `mini_diamond`
filterC(mini_diamond, cond)

# will raise error because `x`  is on the right side of `>`
# filterC(mini_diamond, cond, usecol=FALSE)

# if you know how to use `.env` or `!!`, forget argument `usecol`!
cond <- tbflt(y > !!x)
filterC(mini_diamond, cond)

cond <- tbflt(y > .env$x)
filterC(mini_diamond, cond)

c1 <- tbflt(cut == "Fair")

c2 <- tbflt(x > 8)

mini_diamond %>%
  filterC(c1) %>%
  head(5)

mini_diamond %>% filterC(c1 & c2)



x <- 8
cond <- tbflt(y > x)

# variable `x` not used because of column `x` in `mini_diamond`
filterC(mini_diamond, cond)

# will raise error because `x`  is on the right side of `>`
# filterC(mini_diamond, cond, usecol=FALSE)

# if you know how to use `.env` or `!!`, forget argument `usecol`!
cond <- tbflt(y > !!x)
filterC(mini_diamond, cond)

cond <- tbflt(y > .env$x)
filterC(mini_diamond, cond)

trans fixed string into regular expression string

Description

trans fixed string into regular expression string

Usage

fix_to_regex(p)
fix_to_regex(p)

Arguments

`p`	raw fixed pattern

Value

regex pattern

Examples

fix_to_regex("ABC|?(*)")
fix_to_regex("ABC|?(*)")

from float number to percent number

Description

from float number to percent number

Usage

float_to_percent(x, digits = 2)
float_to_percent(x, digits = 2)

Arguments

`x`	number
`digits`	hold n digits after the decimal point

Value

percent character of x

Examples

float_to_percent(0.12)
float_to_percent(0.12)

farthest point sampling (FPS) for a vector

Description

farthest point sampling (FPS) for a vector

Usage

fps_vector(v, n, method = "round")
fps_vector(v, n, method = "round")

Arguments

`v`	vector
`n`	sample size
`method`	`round\|floor\|ceiling`, the method used when trans to integer

Value

sampled vector

Examples

fps_vector(1:10, 4)
fps_vector(1:10, 4)

like `dplyr::full_join` while ignore the same columns in right tibble

Description

like dplyr::full_join while ignore the same columns in right tibble

Usage

full_expand(x, y, by = NULL)
full_expand(x, y, by = NULL)

Arguments

`x`	left tibble
`y`	right tibble
`by`	columns to join by

Value

tibble

Examples


tb1 <- head(mini_diamond, 4)
tb2 <- tibble::tibble(
  id = c("id-2", "id-4", "id-5"),
  carat = 1:3,
  price = c(1000, 2000, 3000),
  newcol = c("new2", "new4", "new5")
)

left_expand(tb1, tb2, by = "id")

full_expand(tb1, tb2, by = "id")

inner_expand(tb1, tb2, by = "id")
tb1 <- head(mini_diamond, 4)
tb2 <- tibble::tibble(
  id = c("id-2", "id-4", "id-5"),
  carat = 1:3,
  price = c(1000, 2000, 3000),
  newcol = c("new2", "new4", "new5")
)

left_expand(tb1, tb2, by = "id")

full_expand(tb1, tb2, by = "id")

inner_expand(tb1, tb2, by = "id")

generate characters

Description

generate characters

Usage

gen_char(
  from = NULL,
  to = NULL,
  n = NULL,
  random = FALSE,
  allow_dup = TRUE,
  add = NULL,
  seed = NULL
)
gen_char(
  from = NULL,
  to = NULL,
  n = NULL,
  random = FALSE,
  allow_dup = TRUE,
  add = NULL,
  seed = NULL
)

Arguments

`from`	left bound, lower case letter
`to`	right bound, lower case letter
`n`	number of characters to generate
`random`	random generation
`allow_dup`	allow duplication when random generation
`add`	add extra characters other than `base::letters`
`seed`	random seed

Value

generated characters

Examples

gen_char(from = "g", n = 5)
gen_char(to = "g", n = 5)
gen_char(from = "g", to = "j")
gen_char(from = "t", n = 5, random = TRUE)
gen_char(
  from = "x", n = 5, random = TRUE,
  allow_dup = FALSE, add = c("+", "-")
)

gen_char(from = "g", n = 5)
gen_char(to = "g", n = 5)
gen_char(from = "g", to = "j")
gen_char(from = "t", n = 5, random = TRUE)
gen_char(
  from = "x", n = 5, random = TRUE,
  allow_dup = FALSE, add = c("+", "-")
)

generate all combinations

Description

generate all combinations

Usage

gen_combn(x, n = 2)
gen_combn(x, n = 2)

Arguments

`x`	vector
`n`	numbers of element to combine

Value

all combinations

Examples

gen_combn(1:4, n = 2)

gen_combn(1:4, n = 2)

generate outliers from a series of number

Description

generate outliers from a series of number

Usage

gen_outlier(
  x,
  n,
  digits = 0,
  side = "both",
  lim = NULL,
  assign_n = NULL,
  only_out = TRUE
)
gen_outlier(
  x,
  n,
  digits = 0,
  side = "both",
  lim = NULL,
  assign_n = NULL,
  only_out = TRUE
)

Arguments

`x`	number vector
`n`	number of outliers to generate
`digits`	the digits of outliers
`side`	should be one of `⁠both, low, high⁠`
`lim`	a two-length vector to assign the limitations of the outliers if method is `both`, the outliers will be limited in [lim[1], low_outlier_threshold] and [high_outlier_threshold, lim[2]] ; if method is `low`, the outliers will be limited in [lim[1], min(low_outlier_threshold, lim[2])] ; if method is `high`, the outliers will be limited in [max(high_outlier_threshold, lim[1]), lim[2]]
`assign_n`	manually assign the number of low outliers or high outliers when method is `both`
`only_out`	only return outliers

Value

number vector of outliers

Examples

x <- seq(0, 100, 1)

gen_outlier(x, 10)

# generation limits
gen_outlier(x, 10, lim = c(-80, 160))

# assign the low and high outliers
gen_outlier(x, 10, lim = c(-80, 160), assign_n = c(0.1, 0.9))

# just generate low outliers
gen_outlier(x, 10, side = "low")

# return with raw vector
gen_outlier(x, 10, only_out = FALSE)

x <- seq(0, 100, 1)

gen_outlier(x, 10)

# generation limits
gen_outlier(x, 10, lim = c(-80, 160))

# assign the low and high outliers
gen_outlier(x, 10, lim = c(-80, 160), assign_n = c(0.1, 0.9))

# just generate low outliers
gen_outlier(x, 10, side = "low")

# return with raw vector
gen_outlier(x, 10, only_out = FALSE)

generate strings

Description

generate strings

Usage

gen_str(n = 1, len = 3, seed = NULL)
gen_str(n = 1, len = 3, seed = NULL)

Arguments

`n`	number of strings to generate
`len`	string length
`seed`	random seed

Value

string

Examples

gen_str(n = 2, len = 3)
gen_str(n = 2, len = 3)

generate tibbles

Description

generate tibbles

Usage

gen_tb(nrow = 3, ncol = 4, fill = "float", colnames = NULL, seed = NULL, ...)
gen_tb(nrow = 3, ncol = 4, fill = "float", colnames = NULL, seed = NULL, ...)

Arguments

`nrow`	number of rows
`ncol`	number of columns
`fill`	fill by, one of `⁠float, int, char, str⁠`
`colnames`	names of columns
`seed`	random seed
`...`	parameters of `⁠rnorm, gen_char, gen_str⁠`

Value

tibble

Examples

gen_tb()

gen_tb(fill = "str", nrow = 3, ncol = 4, len = 3)
gen_tb()

gen_tb(fill = "str", nrow = 3, ncol = 4, len = 3)

generate ticks for a number vector

Description

generate ticks for a number vector

Usage

generate_ticks(x, expect_ticks = 10)
generate_ticks(x, expect_ticks = 10)

Arguments

`x`	number vector
`expect_ticks`	expected number of ticks, may be a little different from the result

Value

ticks number

Examples

generate_ticks(c(176, 198, 264))
generate_ticks(c(176, 198, 264))

geometric mean

Description

geometric mean

Usage

geom_mean(x, na.rm = TRUE)
geom_mean(x, na.rm = TRUE)

Arguments

`x`	value
`na.rm`	remove NA or not

Value

geometric mean value

Examples

geom_mean(1, 9)
geom_mean(1, 9)

group character vector by a regex pattern

Description

group character vector by a regex pattern

Usage

group_vector(x, pattern = "\\w")
group_vector(x, pattern = "\\w")

Arguments

`x`	character vector
`pattern`	regex pattern, '\w' as default

Value

list

Examples

v <- c(
  stringr::str_c("A", c(1, 2, 9, 10, 11, 12, 99, 101, 102)),
  stringr::str_c("B", c(1, 2, 9, 10, 21, 32, 99, 101, 102))
) %>% sample()

group_vector(v)

group_vector(v, pattern = "\\w\\d")

group_vector(v, pattern = "\\w(\\d)")

# unmatched part will alse be stored
group_vector(v, pattern = "\\d{2}")

v <- c(
  stringr::str_c("A", c(1, 2, 9, 10, 11, 12, 99, 101, 102)),
  stringr::str_c("B", c(1, 2, 9, 10, 21, 32, 99, 101, 102))
) %>% sample()

group_vector(v)

group_vector(v, pattern = "\\w\\d")

group_vector(v, pattern = "\\w(\\d)")

# unmatched part will alse be stored
group_vector(v, pattern = "\\d{2}")

separate numeric x into bins

Description

separate numeric x into bins

Usage

hist_bins(x, bins = 10, lim = c(min(x), max(x)), breaks = NULL, sort = FALSE)
hist_bins(x, bins = 10, lim = c(min(x), max(x)), breaks = NULL, sort = FALSE)

Arguments

`x`	numeric vector
`bins`	bins number, defaults to 10
`lim`	the min and max limits of bins, default as `c(min(x), max(x))`
`breaks`	assign breaks directly and will ignore `bins` and `lim`
`sort`	sort the result tibble

Value

tibble

Examples


x <- dplyr::pull(mini_diamond, price, id)

hist_bins(x, bins = 20)

x <- dplyr::pull(mini_diamond, price, id)

hist_bins(x, bins = 20)

like `dplyr::inner_join` while ignore the same columns in right tibble

Description

like dplyr::inner_join while ignore the same columns in right tibble

Usage

inner_expand(x, y, by = NULL)
inner_expand(x, y, by = NULL)

Arguments

`x`	left tibble
`y`	right tibble
`by`	columns to join by

Value

tibble

Examples


tb1 <- head(mini_diamond, 4)
tb2 <- tibble::tibble(
  id = c("id-2", "id-4", "id-5"),
  carat = 1:3,
  price = c(1000, 2000, 3000),
  newcol = c("new2", "new4", "new5")
)

left_expand(tb1, tb2, by = "id")

full_expand(tb1, tb2, by = "id")

inner_expand(tb1, tb2, by = "id")
tb1 <- head(mini_diamond, 4)
tb2 <- tibble::tibble(
  id = c("id-2", "id-4", "id-5"),
  carat = 1:3,
  price = c(1000, 2000, 3000),
  newcol = c("new2", "new4", "new5")
)

left_expand(tb1, tb2, by = "id")

full_expand(tb1, tb2, by = "id")

inner_expand(tb1, tb2, by = "id")

trans numbers to a fixed integer digit length

Description

trans numbers to a fixed integer digit length

Usage

int_digits(x, digits = 2, scale_factor = FALSE)
int_digits(x, digits = 2, scale_factor = FALSE)

Arguments

`x`	number
`digits`	integer digit length
`scale_factor`	return the scale_factor instead of value

Value

number

Examples

int_digits(0.0332, 1)
int_digits(0.0332, 1)

if a number only have zeros

Description

if a number only have zeros

Usage

is.zero(x)
is.zero(x)

Arguments

x

number

Value

all zero or not

Examples

is.zero(c("0.000", "0.102", NA))
is.zero(c("0.000", "0.102", NA))

like `dplyr::left_join` while ignore the same columns in right tibble

Description

like dplyr::left_join while ignore the same columns in right tibble

Usage

left_expand(x, y, by = NULL)
left_expand(x, y, by = NULL)

Arguments

`x`	left tibble
`y`	right tibble
`by`	columns to join by

Value

tibble

Examples


tb1 <- head(mini_diamond, 4)
tb2 <- tibble::tibble(
  id = c("id-2", "id-4", "id-5"),
  carat = 1:3,
  price = c(1000, 2000, 3000),
  newcol = c("new2", "new4", "new5")
)

left_expand(tb1, tb2, by = "id")

full_expand(tb1, tb2, by = "id")

inner_expand(tb1, tb2, by = "id")
tb1 <- head(mini_diamond, 4)
tb2 <- tibble::tibble(
  id = c("id-2", "id-4", "id-5"),
  carat = 1:3,
  price = c(1000, 2000, 3000),
  newcol = c("new2", "new4", "new5")
)

left_expand(tb1, tb2, by = "id")

full_expand(tb1, tb2, by = "id")

inner_expand(tb1, tb2, by = "id")

trans list into data.frame

Description

trans list into data.frame

Usage

list2df(x, rownames = TRUE, colnames = NULL, method = "row")
list2df(x, rownames = TRUE, colnames = NULL, method = "row")

Arguments

`x`	list
`rownames`	use rownames or not
`colnames`	colnames of the output
`method`	one of `⁠row, col⁠`, set each item as row or col, default as row

Value

tibble

Examples

x <- list(
  c("a", "1"),
  c("b", "2"),
  c("c", "3")
)

list2df(x, colnames = c("char", "num"))

x <- list(
  c("a", "b", "c"),
  c("1", "2", "3")
)

list2df(x, method = "col")
x <- list(
  c("a", "1"),
  c("b", "2"),
  c("c", "3")
)

list2df(x, colnames = c("char", "num"))

x <- list(
  c("a", "b", "c"),
  c("1", "2", "3")
)

list2df(x, method = "col")

max depth of a list

Description

max depth of a list

Usage

max_depth(x)
max_depth(x)

Arguments

x

list

Value

number

Examples


max_depth(list(a = list(b = list(c = 1), d = 2, e = 3)))

max_depth(list(a = list(b = list(c = 1), d = 2, e = 3)))

melt a vector into single value

Description

melt a vector into single value

Usage

melt_vector(x, method = "first", invalid = NA)
melt_vector(x, method = "first", invalid = NA)

Arguments

`x`	vector
`method`	how to melt, should be one of `first\|last`, or one of `sum\|mean\|median` for numeric vector, or some characters (e.g. `⁠,\|.\| \|;⁠`) for character vector
`invalid`	invalid value to ignore, `NA` as default

Value

melted single value

Examples


melt_vector(c(NA, 2, 3), method = "first")

melt_vector(c(NA, 2, 3), method = "sum")

melt_vector(c(NA, 2, 3), method = ",")

melt_vector(c(NA, 2, Inf), invalid = c(NA, Inf))

melt_vector(c(NA, 2, 3), method = "first")

melt_vector(c(NA, 2, 3), method = "sum")

melt_vector(c(NA, 2, 3), method = ",")

melt_vector(c(NA, 2, Inf), invalid = c(NA, Inf))

Minimal tibble dataset adjusted from diamond

Description

Minimal tibble dataset adjusted from diamond

Usage

mini_diamond
mini_diamond

Format

`mini_diamond`

A data frame with 100 rows and 7 columns:

id: unique id
cut, clarity: 2 category variables
carat, price, x, y: 4 continuous variables

...

Source

adjusted from ggplot2

max-min normalization

Description

max-min normalization

Usage

mm_norm(x, low = 0, high = 1)
mm_norm(x, low = 0, high = 1)

Arguments

`x`	numeric vector
`low`	low limit of result, 0 as default
`high`	high limit of result, 1 as default

Value

normed vector

Examples


mm_norm(c(1, 3, 4))

mm_norm(c(1, 3, 4))

move selected rows to target location

Description

move selected rows to target location

Usage

move_row(df, rows, .after = FALSE, .before = FALSE)
move_row(df, rows, .after = FALSE, .before = FALSE)

Arguments

`df`	tibble
`rows`	selected rows indexes
`.after`	`TRUE` will move selected rows to the last row, or you can pass a target row index
`.before`	`TRUE` will move selected rows to the first row, or you can pass a target row index

Value

reordered tibble

Examples

move_row(mini_diamond, 3:5, .after = 8)
move_row(mini_diamond, 3:5, .after = 8)

the ticks near a number

Description

the ticks near a number

Usage

near_ticks(x, level = NULL, div = 2)
near_ticks(x, level = NULL, div = 2)

Arguments

`x`	number
`level`	the level of ticks, such as 1, 10, 100, etc.
`div`	number of divisions

Value

number vector of ticks

Examples

near_ticks(3462, level = 10)
near_ticks(3462, level = 10)

the nearest ticks around a number

Description

the nearest ticks around a number

Usage

nearest_tick(x, side = "both", level = NULL, div = 2)
nearest_tick(x, side = "both", level = NULL, div = 2)

Arguments

`x`	number
`side`	default as 'both', can be 'both\|left\|right'
`level`	the level of ticks, such as 1, 10, 100, etc.
`div`	number of divisions

Value

nearest tick number

Examples

nearest_tick(3462, level = 10)
nearest_tick(3462, level = 10)

not NA

Description

not NA

Usage

not.na(x)
not.na(x)

Arguments

x

value

Value

logical value

Examples

not.na(NA)
not.na(NA)

not NULL

Description

not NULL

Usage

not.null(x)
not.null(x)

Arguments

x

value

Value

logical value

Examples

not.null(NULL)
not.null(NULL)

wrapper of the functions to process number string with prefix and suffix

Description

wrapper of the functions to process number string with prefix and suffix

Usage

number_fun_wrapper(
  x,
  fun = ~.x,
  prefix_ext = NULL,
  suffix_ext = NULL,
  verbose = FALSE
)
number_fun_wrapper(
  x,
  fun = ~.x,
  prefix_ext = NULL,
  suffix_ext = NULL,
  verbose = FALSE
)

Arguments

`x`	number string vector with prefix and suffix
`fun`	process function
`prefix_ext`	prefix extension
`suffix_ext`	suffix extension
`verbose`	print more details

Value

processed number with prefix and suffix

Examples

number_fun_wrapper(">=2.134%", function(x) round(x, 2))
number_fun_wrapper(">=2.134%", function(x) round(x, 2))

slice a tibble by an ordered vector

Description

slice a tibble by an ordered vector

Usage

ordered_slice(df, by, ordered_vector, na.rm = FALSE, dup.rm = FALSE)
ordered_slice(df, by, ordered_vector, na.rm = FALSE, dup.rm = FALSE)

Arguments

`df`	tibble
`by`	slice by this column, this value must has no duplicated value
`ordered_vector`	ordered vector
`na.rm`	remove NA or unknown values from ordered vector
`dup.rm`	remove duplication values from ordered vector

Value

sliced tibble

Examples

ordered_slice(mini_diamond, id, c("id-3", "id-2"))
ordered_slice(mini_diamond, id, c("id-3", "id-2"))

from percent number to float number

Description

from percent number to float number

Usage

percent_to_float(x, digits = 2, to_double = FALSE)
percent_to_float(x, digits = 2, to_double = FALSE)

Arguments

`x`	percent number character
`digits`	hold n digits after the decimal point
`to_double`	use double output

Value

float character or double of x

Examples

percent_to_float("12%")
percent_to_float("12%")

pileup another logical vector on the TRUE values of first vector

Description

pileup another logical vector on the TRUE values of first vector

Usage

pileup_logical(x, v)
pileup_logical(x, v)

Arguments

`x`	logical vector
`v`	another logical vector

Value

logical vector

Examples


# first vector have 2 TRUE value
v1 <- c(TRUE, FALSE, TRUE)

# the length of second vector should also be 2
v2 <- c(FALSE, TRUE)

pileup_logical(v1, v2)

# first vector have 2 TRUE value
v1 <- c(TRUE, FALSE, TRUE)

# the length of second vector should also be 2
v2 <- c(FALSE, TRUE)

pileup_logical(v1, v2)

information of packages

Description

information of packages

Usage

pkginfo(...)
pkginfo(...)

Arguments

...

case-insensitive package names

Examples

baizer::pkginfo(dplyr)
baizer::pkginfo(dplyr)

load packages as a batch

Description

load packages as a batch

Usage

pkglib(...)
pkglib(...)

Arguments

...

pkgs

Examples

baizer::pkglib(dplyr, purrr)
baizer::pkglib(dplyr, purrr)

versions of packages

Description

versions of packages

Usage

pkgver(...)
pkgver(...)

Arguments

...

case-insensitive package names

Examples

baizer::pkgver(dplyr, purrr)
baizer::pkgver(dplyr, purrr)

split a positive integer number as a number vector

Description

split a positive integer number as a number vector

Usage

pos_int_split(x, n, method = "average")
pos_int_split(x, n, method = "average")

Arguments

`x`	positive integer
`n`	length of the output
`method`	should be one of `⁠average, random⁠`, or a number vector which length is n

Value

number vector

Examples

pos_int_split(12, 3, method = "average")

pos_int_split(12, 3, method = "random")

pos_int_split(12, 3, method = c(1, 2, 3))

pos_int_split(12, 3, method = "average")

pos_int_split(12, 3, method = "random")

pos_int_split(12, 3, method = c(1, 2, 3))

wrapper of tibble::rownames_to_column

Description

wrapper of tibble::rownames_to_column

Usage

r2c(df, col = "")
r2c(df, col = "")

Arguments

`df`	tibble
`col`	a col name

Value

tibble

Examples

mini_diamond %>%
  c2r("id") %>%
  r2c("id")
mini_diamond %>%
  c2r("id") %>%
  r2c("id")

read excel file

Description

read excel file

Usage

read_excel(...)
read_excel(...)

Arguments

...

arguments of readxl::read_excel

Value

tibble

read multi-sheet excel file as a list of tibbles

Description

read multi-sheet excel file as a list of tibbles

Usage

read_excel_list(x)
read_excel_list(x)

Arguments

x

path

Value

list

read front matter markdown

Description

read front matter markdown

Usage

read_fmmd(x, rm_blank_line = TRUE)
read_fmmd(x, rm_blank_line = TRUE)

Arguments

`x`	path
`rm_blank_line`	remove leading and trailing blank lines

Value

list

relevel a target column by another reference column

Description

relevel a target column by another reference column

Usage

ref_level(x, col, ref)
ref_level(x, col, ref)

Arguments

`x`	tibble
`col`	target column
`ref`	reference column

Value

tibble

Examples


cut_level <- mini_diamond %>%
  dplyr::pull(cut) %>%
  unique()

mini_diamond %>%
  dplyr::mutate(cut = factor(cut, cut_level)) %>%
  dplyr::mutate(cut0 = stringr::str_c(cut, "xxx")) %>%
  ref_level(cut0, cut)
cut_level <- mini_diamond %>%
  dplyr::pull(cut) %>%
  unique()

mini_diamond %>%
  dplyr::mutate(cut = factor(cut, cut_level)) %>%
  dplyr::mutate(cut0 = stringr::str_c(cut, "xxx")) %>%
  ref_level(cut0, cut)

join the matched parts into string

Description

join the matched parts into string

Usage

reg_join(x, pattern, sep = "")
reg_join(x, pattern, sep = "")

Arguments

`x`	character
`pattern`	regex pattern
`sep`	separator

Value

character

Examples

reg_join(c("A_12.B", "C_3.23:2"), "[A-Za-z]+")

reg_join(c("A_12.B", "C_3.23:2"), "\\w+")

reg_join(c("A_12.B", "C_3.23:2"), "\\d+", sep = ",")

reg_join(c("A_12.B", "C_3.23:2"), "\\d", sep = ",")

reg_join(c("A_12.B", "C_3.23:2"), "[A-Za-z]+")

reg_join(c("A_12.B", "C_3.23:2"), "\\w+")

reg_join(c("A_12.B", "C_3.23:2"), "\\d+", sep = ",")

reg_join(c("A_12.B", "C_3.23:2"), "\\d", sep = ",")

regex match

Description

regex match

Usage

reg_match(x, pattern, group = 1)
reg_match(x, pattern, group = 1)

Arguments

`x`	vector
`pattern`	regex pattern
`group`	regex group, 1 as default. when group=-1, return full matched tibble

Value

vector or tibble

Examples

v <- stringr::str_c("id", 1:3, c("A", "B", "C"))

reg_match(v, "id(\\d+)(\\w)")

reg_match(v, "id(\\d+)(\\w)", group = 2)

reg_match(v, "id(\\d+)(\\w)", group = -1)

v <- stringr::str_c("id", 1:3, c("A", "B", "C"))

reg_match(v, "id(\\d+)(\\w)")

reg_match(v, "id(\\d+)(\\w)", group = 2)

reg_match(v, "id(\\d+)(\\w)", group = -1)

remove columns by the ratio of an identical single value (NA supported)

Description

remove columns by the ratio of an identical single value (NA supported)

Usage

remove_monocol(df, max_ratio = 1)
remove_monocol(df, max_ratio = 1)

Arguments

`df`	tibble
`max_ratio`	the max single value ratio to keep this column, default is 1

Value

tibble

Examples

# remove_monocol(df)
# remove_monocol(df)

remove columns by the ratio of NA

Description

remove columns by the ratio of NA

Usage

remove_nacol(df, max_ratio = 1)
remove_nacol(df, max_ratio = 1)

Arguments

`df`	tibble
`max_ratio`	the max NA ratio to keep this column, default is 1 have NA

Value

tibble

Examples

# remove_nacol(df)
# remove_nacol(df)

remove rows by the ratio of NA

Description

remove rows by the ratio of NA

Usage

remove_narow(df, ..., max_ratio = 1)
remove_narow(df, ..., max_ratio = 1)

Arguments

`df`	tibble
`...`	only remove rows according to these columns, refer to `dplyr::select()`
`max_ratio`	the max NA ratio to keep this row, default is 1 have NA

Value

tibble

Examples

# remove_narow(df)
# remove_narow(df)

remove outliers and NA

Description

remove outliers and NA

Usage

remove_outliers(df, col, .by = NULL)
remove_outliers(df, col, .by = NULL)

Arguments

`df`	tibble
`col`	columns to remove outliers
`.by`	group by

Value

tibble

Examples

remove_outliers(mini_diamond, price)
remove_outliers(mini_diamond, price)

replace the items of one object by another

Description

replace the items of one object by another

Usage

replace_item(x, y, keep_extra = FALSE)
replace_item(x, y, keep_extra = FALSE)

Arguments

`x`	number, character or list
`y`	another object, the class of y should be same as x
`keep_extra`	whether keep extra items in y

Value

replaced object

Examples


x <- list(A = 1, B = 3)
y <- list(A = 9, C = 10)

replace_item(x, y)

replace_item(x, y, keep_extra = TRUE)

x <- list(A = 1, B = 3)
y <- list(A = 9, C = 10)

replace_item(x, y)

replace_item(x, y, keep_extra = TRUE)

rewrite the NA values in a tibble by another tibble

Description

rewrite the NA values in a tibble by another tibble

Usage

rewrite_na(x, y, by)
rewrite_na(x, y, by)

Arguments

`x`	raw tibble
`y`	replace reference tibble
`by`	columns to align the tibbles

Value

tibble

Examples


tb1 <- tibble::tibble(
  id = c("id-1", "id-2", "id-3", "id-4"),
  group = c("a", "b", "a", "b"),
  price = c(0, -200, 3000, NA),
  type = c("large", "none", "small", "none")
)

tb2 <- tibble::tibble(
  id = c("id-1", "id-2", "id-3", "id-4"),
  group = c("a", "b", "a", "b"),
  price = c(1, 2, 3, 4),
  type = c("l", "x", "x", "m")
)

rewrite_na(tb1, tb2, by = c("id", "group"))
tb1 <- tibble::tibble(
  id = c("id-1", "id-2", "id-3", "id-4"),
  group = c("a", "b", "a", "b"),
  price = c(0, -200, 3000, NA),
  type = c("large", "none", "small", "none")
)

tb2 <- tibble::tibble(
  id = c("id-1", "id-2", "id-3", "id-4"),
  group = c("a", "b", "a", "b"),
  price = c(1, 2, 3, 4),
  type = c("l", "x", "x", "m")
)

rewrite_na(tb1, tb2, by = c("id", "group"))

trans range character into seq characters

Description

trans range character into seq characters

Usage

rng2seq(x, sep = "-")
rng2seq(x, sep = "-")

Arguments

`x`	range character
`sep`	range separator

Value

seq characters

Examples

rng2seq(c("1-5", "2"))
rng2seq(c("1-5", "2"))

from float number to fixed digits character

Description

from float number to fixed digits character

Usage

round_string(x, digits = 2)
round_string(x, digits = 2)

Arguments

`x`	number
`digits`	hold n digits after the decimal point

Value

character

Examples

round_string(1.1, 2)
round_string(1.1, 2)

add #' into each line of codes for roxygen examples

Description

add #' into each line of codes for roxygen examples

Usage

roxygen_fmt(x)
roxygen_fmt(x)

Arguments

x

codes

Examples


roxygen_fmt(
  "
code line1
code line2
"
)

roxygen_fmt(
  "
code line1
code line2
"
)

the index of identical character

Description

the index of identical character

Usage

same_index(s1, s2, nth = NULL, ignore_case = FALSE)
same_index(s1, s2, nth = NULL, ignore_case = FALSE)

Arguments

`s1`	string1
`s2`	string2
`nth`	just return nth index
`ignore_case`	ignore upper or lower cases

Value

list of identical character indices

Examples

same_index("AAAA", "ABBA")
same_index("AAAA", "ABBA")

dataframe rows seriation, which will reorder the rows in a better pattern

Description

dataframe rows seriation, which will reorder the rows in a better pattern

Usage

seriate_df(x)
seriate_df(x)

Arguments

x

dataframe

Value

seriated dataframe

Examples

x <- mini_diamond %>%
  dplyr::select(id, dplyr::where(is.numeric)) %>%
  dplyr::mutate(
    dplyr::across(
      dplyr::where(is.numeric),
      ~ round(.x / max(.x), 4)
    )
  ) %>%
  c2r("id")

seriate_df(x)

x <- mini_diamond %>%
  dplyr::select(id, dplyr::where(is.numeric)) %>%
  dplyr::mutate(
    dplyr::across(
      dplyr::where(is.numeric),
      ~ round(.x / max(.x), 4)
    )
  ) %>%
  c2r("id")

seriate_df(x)

connection parameters to remote server via sftp

Description

connection parameters to remote server via sftp

Usage

sftp_connect(
  server = "localhost",
  port = 22,
  user = NULL,
  password = NULL,
  wd = "~"
)
sftp_connect(
  server = "localhost",
  port = 22,
  user = NULL,
  password = NULL,
  wd = "~"
)

Arguments

`server`	remote server
`port`	SSH port, 22 as default
`user`	username
`password`	password
`wd`	workdir

Value

sftp_connection object

Examples

# sftp_con <- sftp_connect(server='remote_host', port=22,
#     user='username', password = "password", wd='~')
# sftp_con <- sftp_connect(server='remote_host', port=22,
#     user='username', password = "password", wd='~')

download file from remote server via sftp

Description

download file from remote server via sftp

Usage

sftp_download(sftp_con, path = NULL, to = basename(path))
sftp_download(sftp_con, path = NULL, to = basename(path))

Arguments

`sftp_con`	sftp_connection created by sftp_connect()
`path`	remote file path
`to`	local target path

Examples

# sftp_download(sftp_con,
#   path=c('t1.txt', 't2.txt'),
#   to=c('path1.txt', 'path2.txt')
# sftp_download(sftp_con,
#   path=c('t1.txt', 't2.txt'),
#   to=c('path1.txt', 'path2.txt')

list files from remote server via sftp

Description

list files from remote server via sftp

Usage

sftp_ls(sftp_con, path = NULL, all = FALSE)
sftp_ls(sftp_con, path = NULL, all = FALSE)

Arguments

`sftp_con`	sftp_connection created by sftp_connect()
`path`	remote directory path
`all`	list hidden files or not

Value

files in the dir

Examples


# sftp_ls(sftp_con, 'your/dir')

# sftp_ls(sftp_con, 'your/dir')

signif while use ceiling

Description

signif while use ceiling

Usage

signif_ceiling(x, digits = 2)
signif_ceiling(x, digits = 2)

Arguments

`x`	number
`digits`	digits

Value

number

Examples

signif_ceiling(3.11, 2)
signif_ceiling(3.11, 2)

signif while use floor

Description

signif while use floor

Usage

signif_floor(x, digits = 2)
signif_floor(x, digits = 2)

Arguments

`x`	number
`digits`	digits

Value

number

Examples

signif_floor(3.19, 2)
signif_floor(3.19, 2)

signif or round string depend on the character length

Description

signif or round string depend on the character length

Usage

signif_round_string(
  x,
  digits = 2,
  format = "short",
  full_large = TRUE,
  full_small = FALSE
)
signif_round_string(
  x,
  digits = 2,
  format = "short",
  full_large = TRUE,
  full_small = FALSE
)

Arguments

`x`	number
`digits`	signif or round digits
`format`	short or long
`full_large`	keep full digits for large number
`full_small`	keep full digits for small number

Value

signif or round strings

Examples

signif_round_string(1.214, 2)
signif_round_string(1.214, 2)

from float number to fixed significant digits character

Description

from float number to fixed significant digits character

Usage

signif_string(x, digits = 2)
signif_string(x, digits = 2)

Arguments

`x`	number
`digits`	hold n significant digits

Value

character

Examples

signif_string(1.1, 2)
signif_string(1.1, 2)

slice character vector

Description

slice character vector

Usage

slice_char(x, from = x[1], to = x[length(x)], unique = FALSE)
slice_char(x, from = x[1], to = x[length(x)], unique = FALSE)

Arguments

`x`	character vector
`from`	from
`to`	to
`unique`	remove the duplicated boundary characters

Value

sliced vector

Examples

x <- c("A", "B", "C", "D", "E")
slice_char(x, "A", "D")
slice_char(x, "D", "A")

x <- c("A", "B", "C", "C", "A", "D", "D", "E", "A")
slice_char(x, "B", "E")
# duplicated element as boundary will throw an error
# slice_char(x, 'A', 'E')
# unique=TRUE to remove the duplicated boundary characters
slice_char(x, "A", "E", unique = TRUE)

x <- c("A", "B", "C", "D", "E")
slice_char(x, "A", "D")
slice_char(x, "D", "A")

x <- c("A", "B", "C", "C", "A", "D", "D", "E", "A")
slice_char(x, "B", "E")
# duplicated element as boundary will throw an error
# slice_char(x, 'A', 'E')
# unique=TRUE to remove the duplicated boundary characters
slice_char(x, "A", "E", unique = TRUE)

sort by a function

Description

sort by a function

Usage

sortf(x, func, group_pattern = NULL)
sortf(x, func, group_pattern = NULL)

Arguments

`x`	vector
`func`	a function used by the sort
`group_pattern`	a regex pattern to group by, only available if x is a character vector

Value

vector

Examples

sortf(c(-2, 1, 3), abs)

v <- stringr::str_c("id", c(1, 2, 9, 10, 11, 12, 99, 101, 102)) %>% sample()

sortf(v, function(x) reg_match(x, "\\d+") %>% as.double())

sortf(v, ~ reg_match(.x, "\\d+") %>% as.double())

v <- c(
  stringr::str_c("A", c(1, 2, 9, 10, 11, 12, 99, 101, 102)),
  stringr::str_c("B", c(1, 2, 9, 10, 21, 32, 99, 101, 102))
) %>% sample()

sortf(v, ~ reg_match(.x, "\\d+") %>% as.double(), group_pattern = "\\w")

sortf(c(-2, 1, 3), abs)

v <- stringr::str_c("id", c(1, 2, 9, 10, 11, 12, 99, 101, 102)) %>% sample()

sortf(v, function(x) reg_match(x, "\\d+") %>% as.double())

sortf(v, ~ reg_match(.x, "\\d+") %>% as.double())

v <- c(
  stringr::str_c("A", c(1, 2, 9, 10, 11, 12, 99, 101, 102)),
  stringr::str_c("B", c(1, 2, 9, 10, 21, 32, 99, 101, 102))
) %>% sample()

sortf(v, ~ reg_match(.x, "\\d+") %>% as.double(), group_pattern = "\\w")

split a column and return a longer tibble

Description

split a column and return a longer tibble

Usage

split_column(df, name_col, value_col, sep = ",")
split_column(df, name_col, value_col, sep = ",")

Arguments

`df`	tibble
`name_col`	repeat this as name column
`value_col`	expand by this value column
`sep`	separator in the string

Value

expanded tibble

Examples

fancy_count(mini_diamond, cut, ext = clarity) %>%
  split_column(name_col = cut, value_col = clarity)
fancy_count(mini_diamond, cut, ext = clarity) %>%
  split_column(name_col = cut, value_col = clarity)

split a path into ancestor paths recursively

Description

split a path into ancestor paths recursively

Usage

split_path(path)
split_path(path)

Arguments

path

path to split

Value

character vectors of ancestor paths

Examples

split_path("/home/someone/a/test/path.txt")
split_path("/home/someone/a/test/path.txt")

split vector into list

Description

split vector into list

Usage

split_vector(vector, breaks, bounds = "(]")
split_vector(vector, breaks, bounds = "(]")

Arguments

`vector`	vector
`breaks`	split breaks
`bounds`	"(]" as default, can also be "[), []"

Value

list

Examples

split_vector(1:10, c(3, 7))
split_vector(stringr::str_split("ABCDEFGHIJ", "") %>% unlist(),
  c(3, 7),
  bounds = "[)"
)
split_vector(1:10, c(3, 7))
split_vector(stringr::str_split("ABCDEFGHIJ", "") %>% unlist(),
  c(3, 7),
  bounds = "[)"
)

fold change calculation which returns a extensible tibble

Description

fold change calculation which returns a extensible tibble

Usage

stat_fc(
  df,
  y,
  x,
  method = "mean",
  .by = NULL,
  rev_div = FALSE,
  digits = 2,
  fc_fmt = "short",
  suffix = "x"
)
stat_fc(
  df,
  y,
  x,
  method = "mean",
  .by = NULL,
  rev_div = FALSE,
  digits = 2,
  fc_fmt = "short",
  suffix = "x"
)

Arguments

`df`	tibble
`y`	value
`x`	sample test group
`method`	`'mean'\|'median'\|'geom_mean'`, the summary method
`.by`	super-group
`rev_div`	reverse division
`digits`	fold change digits
`fc_fmt`	fold change format, one of short, signif, round
`suffix`	suffix of fold change, `x` as default

Value

fold change result tibble

Examples

stat_fc(mini_diamond, y = price, x = cut, .by = clarity)
stat_fc(mini_diamond, y = price, x = cut, .by = clarity)

calculate phi coefficient of two binary variables

Description

calculate phi coefficient of two binary variables

Usage

stat_phi(x)
stat_phi(x)

Arguments

`x`	2x2 matrix or dataframe

Value

phi coefficient

Examples

data <- matrix(c(10, 8, 14, 18), nrow = 2)
stat_phi(data)
data <- matrix(c(10, 8, 14, 18), nrow = 2)
stat_phi(data)

statistical test which returns a extensible tibble

Description

statistical test which returns a extensible tibble

Usage

stat_test(
  df,
  y,
  x,
  .by = NULL,
  trans = "identity",
  paired = FALSE,
  paired_by = NULL,
  alternative = "two.sided",
  exclude_func = NULL,
  method = "wilcoxon",
  ns_symbol = "NS",
  digits = 2
)
stat_test(
  df,
  y,
  x,
  .by = NULL,
  trans = "identity",
  paired = FALSE,
  paired_by = NULL,
  alternative = "two.sided",
  exclude_func = NULL,
  method = "wilcoxon",
  ns_symbol = "NS",
  digits = 2
)

Arguments

`df`	tibble
`y`	value
`x`	sample test group
`.by`	super-group
`trans`	scale transformation
`paired`	paired samples or not
`paired_by`	a column for pair
`alternative`	one of "two.sided" (default), "greater" or "less"
`exclude_func`	a function has two arguments and return bool value, used if paired=TRUE and will keep the comparation pairs which return TRUE by this function.
`method`	test method, 'wilcoxon' as default, one of `t\|wilcoxon`
`ns_symbol`	symbol of nonsignificant, 'NS' as default
`digits`	significant figure digits of p value If the data pair of a single test returns TRUE, then exclude this pair

Value

test result tibble

Examples

stat_test(mini_diamond, y = price, x = cut, .by = clarity)
stat_test(mini_diamond, y = price, x = cut, .by = clarity)

replace specific characters in a string by their locations

Description

replace specific characters in a string by their locations

Usage

str_replace_loc(x, start = 1, end = nchar(x), replacement = " ")
str_replace_loc(x, start = 1, end = nchar(x), replacement = " ")

Arguments

`x`	string
`start`	start
`end`	end
`replacement`	replacement

Value

replaced string

Examples

str_replace_loc("abcde", 1, 3, "A")
str_replace_loc("abcde", 1, 3, "A")

swap the names and values of a vector

Description

swap the names and values of a vector

Usage

swap_vecname(x)
swap_vecname(x)

Arguments

`x`	vector without duplicated values

Value

swapped vector

Examples

v <- c("a" = "A", "b" = "B", "c" = "C")
swap_vecname(v)

v <- c("a" = "A", "b" = "B", "c" = "C")
swap_vecname(v)

create a tbflt object to save filter conditions

Description

tbflt() can save a series of filter conditions, and support logical operating among conditions

Usage

tbflt(x = expression(), .env = NULL)
tbflt(x = expression(), .env = NULL)

Arguments

`x`	any expression
`.env`	environment

Value

tbflt

Examples

c1 <- tbflt(cut == "Fair")

c2 <- tbflt(x > 8)

!c1

c1 | c2

c1 & c2

c1 <- tbflt(cut == "Fair")

c2 <- tbflt(x > 8)

!c1

c1 | c2

c1 & c2

transpose a dataframe

Description

transpose a dataframe

Usage

tdf(x, colnames = NULL)
tdf(x, colnames = NULL)

Arguments

`x`	dataframe
`colnames`	column names of the transposed dataframe

Value

dataframe

Examples


x <- c2r(mini_diamond, "id")
tdf(x)

x <- c2r(mini_diamond, "id")
tdf(x)

return top n items with highest frequency

Description

return top n items with highest frequency

Usage

top_item(x, n = 1)
top_item(x, n = 1)

Arguments

`x`	character
`n`	top n

Value

character

Examples


top_item(c("a", "b", "c", "b"))

top_item(c("a", "b", "c", "b"))

only keep unique vector values and its names

Description

only keep unique vector values and its names

Usage

uniq(x)
uniq(x)

Arguments

x

vector

Value

vector

Examples


x <- c(a = 1, b = 2, c = 3, b = 2, a = 1)

uniq(x)

x <- c(a = 1, b = 2, c = 3, b = 2, a = 1)

uniq(x)

count unique values in each column

Description

count unique values in each column

Usage

uniq_in_cols(x)
uniq_in_cols(x)

Arguments

x

tibble

Value

tibble

Examples


uniq_in_cols(mini_diamond)

uniq_in_cols(mini_diamond)

write a tibble into an excel file

Description

write a tibble into an excel file

Usage

write_excel(df, filename, sheetname = NULL, creator = "")
write_excel(df, filename, sheetname = NULL, creator = "")

Arguments

`df`	tibble or a list of tibbles
`filename`	the output filename
`sheetname`	the names of sheets. If not given, will use 'sheet1', or the names of list
`creator`	creator

Value

return status

Examples

# write_excel(mini_diamond, "mini_diamond.xlsx")
# write_excel(mini_diamond, "mini_diamond.xlsx")

Package 'baizer'

Help Index

equal calculation operator, support NA

Description

Usage

Arguments

Value

Examples

not equal calculation operator, support NA

Description

Usage

Arguments

Value

Examples

not in calculation operator

Description

Usage

Arguments

Value

Examples

expand a number vector according to the adjacent two numbers

Description

Usage

Arguments

Value

Examples

use aliases for function arguments

Description

Usage

Arguments

Value

Examples

trans a tibble into markdown format table

Description

Usage

Arguments

Value

Examples

trans a table in markdown format into tibble

Description

Usage

Arguments

Value

Examples

whether the expression is an atomic one

Description

Usage

Arguments

Value

Examples

broadcast the vector into length n

Description

Usage

Arguments

Value

Examples

wrapper of tibble::column_to_rownames

Description

Usage

Arguments

Value

Examples

check arguments by custom function

Description

Usage

Arguments

Value

Examples

get the command line arguments

Description

Usage

Arguments

Value

Examples

dump a named vector into character

Description

Usage

Arguments

Value

Examples