Package 'pcutils' reference manual

Title:	Some Useful Functions for Statistics and Visualization
Description:	Offers a range of utilities and functions for everyday programming tasks. 1.Data Manipulation. Such as grouping and merging, column splitting, and character expansion. 2.File Handling. Read and convert files in popular formats. 3.Plotting Assistance. Helpful utilities for generating color palettes, validating color formats, and adding transparency. 4.Statistical Analysis. Includes functions for pairwise comparisons and multiple testing corrections, enabling perform statistical analyses with ease. 5.Graph Plotting, Provides efficient tools for creating doughnut plot and multi-layered doughnut plot; Venn diagrams, including traditional Venn diagrams, upset plots, and flower plots; Simplified functions for creating stacked bar plots, or a box plot with alphabets group for multiple comparison group.
Authors:	Chen Peng [aut, cre]
Maintainer:	Chen Peng <[email protected]>
License:	GPL-3
Version:	0.2.7
Built:	2024-11-21 05:39:20 UTC
Source:	https://github.com/asa12138/pcutils

Add alpha for a Rcolor

Description

Add alpha for a Rcolor

Usage

add_alpha(color, alpha = 0.3)
add_alpha(color, alpha = 0.3)

Arguments

`color`	Rcolor
`alpha`	alpha, default 0.3

Value

8 hex color

Examples

add_alpha("red", 0.3)
add_alpha("red", 0.3)

Add a global gg_theme and colors for plots

Description

Add a global gg_theme and colors for plots

Usage

add_theme(set_theme = NULL)
add_theme(set_theme = NULL)

Arguments

set_theme

your theme

Value

No return value

Examples

add_theme()
add_theme()

Change factor levels

Description

Change factor levels

Usage

change_fac_lev(x, levels = NULL, last = FALSE)
change_fac_lev(x, levels = NULL, last = FALSE)

Arguments

`x`	vector
`levels`	custom levels
`last`	put the custom levels to the last

Value

factor

Examples

change_fac_lev(letters[1:5], levels = c("c", "a"))
change_fac_lev(letters[1:5], levels = c("c", "a"))

Plot correlation

Description

Plot correlation

Usage

cor_plot(
  env,
  env2 = NULL,
  geom = ggcor::geom_square,
  mode = 1,
  method = "pearson",
  mode_param = NULL,
  colors = get_cols(pal = "bluered")[2:10]
)
cor_plot(
  env,
  env2 = NULL,
  geom = ggcor::geom_square,
  mode = 1,
  method = "pearson",
  mode_param = NULL,
  colors = get_cols(pal = "bluered")[2:10]
)

Arguments

`env`	dataframe1
`env2`	dataframe2 (default:NULL)
`geom`	geom, default: `geom_square`
`mode`	plot mode (1~4)
`method`	one of "pearson","kendall","spearman"
`mode_param`	parameters parse to `geom` (mode=1~2) or `corrplot` (mode=3)
`colors`	color, default is `get_cols(pal="bluered")[2:10]`

Value

ggplot

Examples


if (interactive()) {
  data(otutab)
  cor_plot(metadata[, 3:10])
  cor_plot(metadata[, 3:10], mode = 2)
  cor_plot(metadata[, 3:10], mode = 3)
  cor_plot(t(otutab)[, 1:50], mode = 4)
}

if (interactive()) {
  data(otutab)
  cor_plot(metadata[, 3:10])
  cor_plot(metadata[, 3:10], mode = 2)
  cor_plot(metadata[, 3:10], mode = 3)
  cor_plot(t(otutab)[, 1:50], mode = 4)
}

Like `uniq -c` in shell to count a vector

Description

Like uniq -c in shell to count a vector

Usage

count2(df)
count2(df)

Arguments

`df`	two columns: first is type, second is number

Value

two columns: first is type, second is number

Examples

count2(data.frame(group = c("A", "A", "B", "C", "C", "A"), value = c(2, 2, 2, 1, 3, 1)))
count2(data.frame(group = c("A", "A", "B", "C", "C", "A"), value = c(2, 2, 2, 1, 3, 1)))

Print some message with =

Description

Print some message with =

Usage

dabiao(
  str = "",
  ...,
  n = 80,
  char = "=",
  mode = c("middle", "left", "right"),
  print = FALSE
)
dabiao(
  str = "",
  ...,
  n = 80,
  char = "=",
  mode = c("middle", "left", "right"),
  print = FALSE
)

Arguments

`str`	output strings
`...`	strings will be paste together
`n`	the number of output length
`char`	side chars default:=
`mode`	"middle", "left" or "right"
`print`	print or message?

Value

No return value

Examples

dabiao("Start running!")

dabiao("Start running!")

Convert Three-column Data to Distance Matrix

Description

This function converts a data frame with three columns (from, to, count) into a distance matrix. The rows and columns of the matrix are all unique names from the 'from' and 'to' columns, and the matrix values are filled with counts.

Usage

df2distance(data)
df2distance(data)

Arguments

data

A data frame containing three columns: from, to, count.

Value

A distance matrix where rows and columns are all unique names from 'from' and 'to' columns.

Examples

data <- data.frame(
  from = c("A", "A", "B", "D"),
  to = c("B", "C", "A", "B"),
  count = c(1, 2, 3, 4)
)
df2distance(data)
data <- data.frame(
  from = c("A", "A", "B", "D"),
  to = c("B", "C", "A", "B"),
  count = c(1, 2, 3, 4)
)
df2distance(data)

df to link table

Description

df to link table

Usage

df2link(test, fun = sum)
df2link(test, fun = sum)

Arguments

`test`	df with at least 3 columns
`fun`	function to summary the elements number, defalut: `sum`, you can choose `mean`.

Value

data.frame

Examples

data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, ] -> test
df2link(test)
data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, ] -> test
df2link(test)

Convert a distance matrix to a data frame

Description

This function converts a distance matrix into a data frame with three columns: from, to, count. The rows and columns of the matrix are all unique names from the 'from' and 'to' columns,

Usage

distance2df(distance_matrix)
distance2df(distance_matrix)

Arguments

distance_matrix

A distance matrix where rows and columns are all unique names from 'from' and 'to' columns.

Value

A data frame containing three columns: from, to, count.

Examples

distance_matrix <- matrix(c(0, 1, 2, 3, 4, 5, 6, 7, 8), nrow = 3)
distance2df(distance_matrix)
distance_matrix <- matrix(c(0, 1, 2, 3, 4, 5, 6, 7, 8), nrow = 3)
distance2df(distance_matrix)

Download genome files from NCBI based on accession number

Description

This function downloads specific genomic files from NCBI's FTP server based on the provided accession number. It supports downloading different types of files, or the entire directory containing the files.

Usage

download_ncbi_genome_file(
  accession,
  out_dir = ".",
  type = "gff",
  file_suffix = NULL,
  timeout = 300
)
download_ncbi_genome_file(
  accession,
  out_dir = ".",
  type = "gff",
  file_suffix = NULL,
  timeout = 300
)

Arguments

`accession`	A character string representing the NCBI accession number (e.g., "GCF_001036115.1_ASM103611v1" or "GCF_001036115.1"). The accession can start with "GCF" or "GCA".
`out_dir`	A character string representing the directory where the downloaded files will be saved. Defaults to the current working directory (".").
`type`	A character string representing the type of file to download. Supported types are "all", "gff", "fna". If "all" is specified, the function will prompt the user to use command line tools to download the entire directory. Defaults to "gff".
`file_suffix`	A character string representing the specific file suffix to download. If specified, this will override the `type` parameter. Defaults to NULL.
`timeout`	A numeric value representing the maximum time in seconds to wait for the download. Defaults to 300.

Details

If the provided accession does not contain the version suffix (e.g., "GCF_001036115.1"), the function will query the NCBI FTP server to determine the full accession name.

When type is set to "all", the function cannot download the entire directory directly but provides a command line example for the user to download the directory using tools like wget.

Value

No value

Examples

## Not run: 
download_ncbi_genome_file("GCF_001036115.1", out_dir = "downloads", type = "gff")
download_ncbi_genome_file("GCF_001036115.1", out_dir = "downloads", file_suffix = "_genomic.fna.gz")

## End(Not run)

## Not run: 
download_ncbi_genome_file("GCF_001036115.1", out_dir = "downloads", type = "gff")
download_ncbi_genome_file("GCF_001036115.1", out_dir = "downloads", file_suffix = "_genomic.fna.gz")

## End(Not run)

Download File

Description

This function downloads a file from the provided URL and saves it to the specified location.

Usage

download2(url, file_path, timeout = 300, force = FALSE, proxy = FALSE, ...)
download2(url, file_path, timeout = 300, force = FALSE, proxy = FALSE, ...)

Arguments

`url`	The URL from which to download the file.
`file_path`	The full path to the file.
`timeout`	timeout, 300s
`force`	FALSE, if TRUE, overwrite existed file
`proxy`	use proxy, default is FALSE
`...`	add

Value

No value

Explode a data.frame if there are split charter in one column

Description

Explode a data.frame if there are split charter in one column

Usage

explode(df, column, split = ",")
explode(df, column, split = ",")

Arguments

`df`	data.frame
`column`	column
`split`	split string

Value

data.frame

Examples


df <- data.frame(a = 1:2, b = c("a,b", "c"), c = 3:4)
explode(df, "b", ",")

df <- data.frame(a = 1:2, b = c("a,b", "c"), c = 3:4)
explode(df, "b", ",")

Fit a distribution

Description

Fit a distribution

Usage

fittest(a)
fittest(a)

Arguments

`a`	a numeric vector

Value

distribution

Generate labels position

Description

Generate labels position

Usage

generate_labels(
  labels = NULL,
  input = c(0, 0),
  nrows = NULL,
  ncols = NULL,
  x_offset = 0.3,
  y_offset = 0.15,
  just = 1
)
generate_labels(
  labels = NULL,
  input = c(0, 0),
  nrows = NULL,
  ncols = NULL,
  x_offset = 0.3,
  y_offset = 0.15,
  just = 1
)

Arguments

`labels`	labels
`input`	c(0,0)
`nrows`	default: NULL
`ncols`	default: NULL
`x_offset`	0.3
`y_offset`	0.15
`just`	0~5

Value

matrix

Examples

library(ggplot2)
labels <- vapply(1:8, \(i)paste0(sample(LETTERS, 4), collapse = ""), character(1))
df <- data.frame(label = labels, generate_labels(labels))
ggplot(data = df) +
  geom_label(aes(x = X1, y = X2, label = label))
library(ggplot2)
labels <- vapply(1:8, \(i)paste0(sample(LETTERS, 4), collapse = ""), character(1))
df <- data.frame(label = labels, generate_labels(labels))
ggplot(data = df) +
  geom_label(aes(x = X1, y = X2, label = label))

Get n colors

Description

Get n colors

Usage

get_cols(n = 11, pal = NULL, n_break = 5)
get_cols(n = 11, pal = NULL, n_break = 5)

Arguments

`n`	how many colors you need
`pal`	"col1", "col2", "col3"; or a vector of colors, you can get from: `RColorBrewer::brewer.pal(5,"Set2")` or `ggsci::pal_aaas()(5)`
`n_break`	default: 5

Value

a vector of n colors

Examples

get_cols(10, "col2") -> my_cols
scales::show_col(my_cols)

scales::show_col(get_cols(15, RColorBrewer::brewer.pal(5, "Set2")))

get_cols(10, "col2") -> my_cols
scales::show_col(my_cols)

scales::show_col(get_cols(15, RColorBrewer::brewer.pal(5, "Set2")))

Heatmap by ggplot

Description

Heatmap by ggplot

Usage

ggheatmap(
  otutab,
  pal = NULL,
  scale = "none",
  rowname = TRUE,
  colname = TRUE,
  tile_params = list(),
  row_cluster = FALSE,
  col_cluster = FALSE,
  row_annotation = NULL,
  col_annotation = NULL,
  annotation_pal = NULL
)
ggheatmap(
  otutab,
  pal = NULL,
  scale = "none",
  rowname = TRUE,
  colname = TRUE,
  tile_params = list(),
  row_cluster = FALSE,
  col_cluster = FALSE,
  row_annotation = NULL,
  col_annotation = NULL,
  annotation_pal = NULL
)

Arguments

`otutab`	otutab
`pal`	the main color pal, a vector of colors
`scale`	"none", "row", "column"
`rowname`	show row names?
`colname`	show column names?
`tile_params`	tile_params parsed to `geom_tile`
`row_cluster`	cluster the row?
`col_cluster`	cluster the column?
`row_annotation`	row annotation
`col_annotation`	column annotation
`annotation_pal`	the annotation color pal, a list. e.g. list(Group=c("red","blue"))

Value

a ggplot

Examples

if (interactive()) {
  data(otutab)
  ggheatmap(otutab[1:30, ],
    scale = "row", row_annotation = otutab[1:30, 1:2],
    col_annotation = metadata[, c(2, 4)]
  )
}
if (interactive()) {
  data(otutab)
  ggheatmap(otutab[1:30, ],
    scale = "row", row_annotation = otutab[1:30, 1:2],
    col_annotation = metadata[, c(2, 4)]
  )
}

gg histogram

Description

gg histogram

Usage

gghist(x, text_pos = c(0.8, 0.8), ...)
gghist(x, text_pos = c(0.8, 0.8), ...)

Arguments

`x`	vector
`text_pos`	text postion, default is c(0.8, 0.8)
`...`	parameters parse to `gghistogram`

Value

ggplot

Examples

if (requireNamespace("ggpubr")) {
  gghist(rnorm(100))
}
if (requireNamespace("ggpubr")) {
  gghist(rnorm(100))
}

Plot a doughnut chart

Description

Plot a doughnut chart

Usage

gghuan(
  tab,
  reorder = TRUE,
  mode = "1",
  topN = 5,
  name = TRUE,
  percentage = TRUE,
  bar_params = NULL,
  text_params = NULL,
  text_params2 = NULL
)
gghuan(
  tab,
  reorder = TRUE,
  mode = "1",
  topN = 5,
  name = TRUE,
  percentage = TRUE,
  bar_params = NULL,
  text_params = NULL,
  text_params2 = NULL
)

Arguments

`tab`	two columns: first is type, second is number
`reorder`	reorder by number?
`mode`	plot style, 1~3
`topN`	plot how many top items
`name`	label the name
`percentage`	label the percentage
`bar_params`	parameters parse to `geom_rect`, for mode=1,3 or `geom_col` for mode=2.
`text_params`	parameters parse to `geom_text`
`text_params2`	parameters parse to `geom_text`, for name=TRUE & mode=1,3

Value

a ggplot

Examples

a <- data.frame(type = letters[1:6], num = c(1, 3, 3, 4, 5, 10))
gghuan(a) + scale_fill_pc()
gghuan(a,
  bar_params = list(col = "black"),
  text_params = list(col = "#b15928", size = 3),
  text_params2 = list(col = "#006d2c", size = 5)
) + scale_fill_pc()
gghuan(a, mode = 2) + scale_fill_pc()
gghuan(a, mode = 3) + scale_fill_pc()
a <- data.frame(type = letters[1:6], num = c(1, 3, 3, 4, 5, 10))
gghuan(a) + scale_fill_pc()
gghuan(a,
  bar_params = list(col = "black"),
  text_params = list(col = "#b15928", size = 3),
  text_params2 = list(col = "#006d2c", size = 5)
) + scale_fill_pc()
gghuan(a, mode = 2) + scale_fill_pc()
gghuan(a, mode = 3) + scale_fill_pc()

gghuan2 for multi-doughnut chart

Description

gghuan2 for multi-doughnut chart

Usage

gghuan2(
  tab = NULL,
  huan_width = 1,
  circle_width = 1,
  space_width = 0.2,
  circle_label = NULL,
  name = TRUE,
  percentage = FALSE,
  text_params = NULL,
  circle_label_params = NULL,
  bar_params = NULL
)
gghuan2(
  tab = NULL,
  huan_width = 1,
  circle_width = 1,
  space_width = 0.2,
  circle_label = NULL,
  name = TRUE,
  percentage = FALSE,
  text_params = NULL,
  circle_label_params = NULL,
  bar_params = NULL
)

Arguments

`tab`	a dataframe with hierarchical structure
`huan_width`	the huan width (numeric vector)
`circle_width`	the center circle width
`space_width`	the space width between doughnuts (0~1).
`circle_label`	the center circle label
`name`	label the name
`percentage`	label the percentage
`text_params`	parameters parse to `geom_text`
`circle_label_params`	parameters parse to `geom_text`
`bar_params`	parameters parse to `geom_rect`

Value

a ggplot

Examples

data.frame(
  a = c("a", "a", "b", "b", "c"), b = c("a", LETTERS[2:5]), c = rep("a", 5),
  number = 1:5
) %>% gghuan2()
data.frame(
  a = c("a", "a", "b", "b", "c"), b = c("a", LETTERS[2:5]), c = rep("a", 5),
  number = 1:5
) %>% gghuan2()

ggmosaic for mosaic plot

Description

ggmosaic for mosaic plot

Usage

ggmosaic(
  tab,
  rect_params = list(),
  rect_space = 0,
  show_number = c("number", "percentage", "none")[1],
  number_params = list(),
  x_label = c("top", "bottom", "none")[1],
  y_label = c("right", "left", "none")[1],
  label_params = list(),
  chisq_test = TRUE
)
ggmosaic(
  tab,
  rect_params = list(),
  rect_space = 0,
  show_number = c("number", "percentage", "none")[1],
  number_params = list(),
  x_label = c("top", "bottom", "none")[1],
  y_label = c("right", "left", "none")[1],
  label_params = list(),
  chisq_test = TRUE
)

Arguments

`tab`	your dataframe, must have 3 columns, the third column must be numeric
`rect_params`	parameters parse to `geom_rect`
`rect_space`	rect_space, defalut 0.
`show_number`	show "number" or "percentage" or "none"
`number_params`	parameters parse to `geom_text`
`x_label`	show x label on "top" or "bottom" or "none"
`y_label`	show y label on "right" or "left" or "none"
`label_params`	parameters parse to `geom_text`
`chisq_test`	whether show chisq test

Value

a ggplot

Examples

data(mtcars)
tab <- dplyr::count(mtcars, gear, cyl)
ggmosaic(tab,
  show_number = "number", x_label = "top",
  y_label = "right", chisq_test = TRUE
)
data(mtcars)
tab <- dplyr::count(mtcars, gear, cyl)
ggmosaic(tab,
  show_number = "number", x_label = "top",
  y_label = "right", chisq_test = TRUE
)

Get a ggplot xlim and ylim

Description

Get a ggplot xlim and ylim

Usage

ggplot_lim(p)
ggplot_lim(p)

Arguments

p

ggplot

Value

list

Translate axis label of a ggplot

Description

Translate axis label of a ggplot

Usage

ggplot_translator(
  gg,
  which = c("x", "y"),
  from = "en",
  to = "zh",
  keep_original_label = FALSE,
  original_sep = "\n",
  verbose = TRUE
)
ggplot_translator(
  gg,
  which = c("x", "y"),
  from = "en",
  to = "zh",
  keep_original_label = FALSE,
  original_sep = "\n",
  verbose = TRUE
)

Arguments

`gg`	a ggplot object to be translated
`which`	vector contains one or more of 'x', 'y', 'label', 'fill', 'color'..., or 'facet_x', 'facet_y', 'labs' and 'all' to select which texts to be translated.
`from`	source language
`to`	target language
`keep_original_label`	keep the source language labels
`original_sep`	default, '\n'
`verbose`	verbose

Value

ggplot

Examples

## Not run: 
df <- data.frame(
  Subject = c("English", "Math"),
  Score = c(59, 98), Motion = c("sad", "happy")
)
ggp <- ggplot(df, mapping = aes(x = Subject, y = Score, label = Motion)) +
  geom_text() +
  geom_point() +
  labs(x = "Subject", y = "Score", title = "Final Examination")
ggplot_translator(ggp, which = "all")

## End(Not run)
## Not run: 
df <- data.frame(
  Subject = c("English", "Math"),
  Score = c(59, 98), Motion = c("sad", "happy")
)
ggp <- ggplot(df, mapping = aes(x = Subject, y = Score, label = Motion)) +
  geom_text() +
  geom_point() +
  labs(x = "Subject", y = "Score", title = "Final Examination")
ggplot_translator(ggp, which = "all")

## End(Not run)

Grepl applied on a data.frame

Description

Grepl applied on a data.frame

Usage

grepl.data.frame(pattern, x, ...)
grepl.data.frame(pattern, x, ...)

Arguments

`pattern`	search pattern
`x`	your data.frame
`...`	addtitional arguments for gerpl()

Value

a logical matrix

Examples

matrix(letters[1:6], 2, 3) |> as.data.frame() -> a
grepl.data.frame("c", a)
grepl.data.frame("\\w", a)
matrix(letters[1:6], 2, 3) |> as.data.frame() -> a
grepl.data.frame("c", a)
grepl.data.frame("\\w", a)

Plot a boxplot

Description

Plot a boxplot

Usage

group_box(
  tab,
  group = NULL,
  metadata = NULL,
  mode = 1,
  group_order = NULL,
  facet_order = NULL,
  paired = FALSE,
  paired_line_param = list(),
  alpha = FALSE,
  method = "wilcox",
  alpha_param = list(),
  point_param = NULL,
  p_value1 = FALSE,
  p_value2 = FALSE,
  only_sig = TRUE,
  stat_compare_means_param = NULL,
  trend_line = FALSE,
  trend_line_param = list()
)
group_box(
  tab,
  group = NULL,
  metadata = NULL,
  mode = 1,
  group_order = NULL,
  facet_order = NULL,
  paired = FALSE,
  paired_line_param = list(),
  alpha = FALSE,
  method = "wilcox",
  alpha_param = list(),
  point_param = NULL,
  p_value1 = FALSE,
  p_value2 = FALSE,
  only_sig = TRUE,
  stat_compare_means_param = NULL,
  trend_line = FALSE,
  trend_line_param = list()
)

Arguments

`tab`	your dataframe
`group`	which colname choose for group or a vector
`metadata`	the dataframe contains the group
`mode`	1~9, plot style, try yourself
`group_order`	the order of x group
`facet_order`	the order of the facet
`paired`	if paired is TRUE, points in different groups will be connected by lines. So the row names order is important.
`paired_line_param`	parameters parse to `geom_line`.
`alpha`	whether plot a group alphabeta by test of method
`method`	test method:wilcox, tukeyHSD, LSD, (default: wilcox), see `multitest`
`alpha_param`	parameters parse to `geom_text`
`point_param`	parameters parse to `geom_point`,
`p_value1`	multi-test of all group
`p_value2`	two-test of each pair
`only_sig`	only_sig for p_value2
`stat_compare_means_param`	parameters parse to `stat_compare_means`
`trend_line`	add a trend line
`trend_line_param`	parameters parse to `geom_smooth`

Value

a ggplot

Examples

a <- data.frame(a = 1:18, b = runif(18, 0, 5))
group_box(a, group = rep(c("a", "b", "c"), each = 6))
a <- data.frame(a = 1:18, b = runif(18, 0, 5))
group_box(a, group = rep(c("a", "b", "c"), each = 6))

Performs multiple mean comparisons for a data.frame

Description

Performs multiple mean comparisons for a data.frame

Usage

group_test(
  df,
  group,
  metadata = NULL,
  method = "wilcox.test",
  pattern = NULL,
  p.adjust.method = "none",
  threads = 1,
  verbose = TRUE
)
group_test(
  df,
  group,
  metadata = NULL,
  method = "wilcox.test",
  pattern = NULL,
  p.adjust.method = "none",
  threads = 1,
  verbose = TRUE
)

Arguments

`df`	a data.frame
`group`	The compare group (categories) in your data, one column name of metadata when metadata exist or a vector whose length equal to columns number of df.
`metadata`	sample information dataframe contains group
`method`	the type of test. Default is wilcox.test. Allowed values include: `t.test` (parametric) and `wilcox.test` (non-parametric). Perform comparison between two groups of samples. If the grouping variable contains more than two levels, then a pairwise comparison is performed. `anova` (parametric) and `kruskal.test` (non-parametric). Perform one-way ANOVA test comparing multiple groups. `chisq.test`, performs chi-squared contingency table tests and goodness-of-fit tests. 'pearson', 'kendall', or 'spearman' (correlation), see `cor`.
`pattern`	a named vector matching the group, e.g. c('G1'=1,'G2'=3,'G3'=2), use the correlation analysis with specific pattern to calculate p-value.
`p.adjust.method`	p.adjust.method, see `p.adjust`, default BH.
`threads`	default 1
`verbose`	logical

Value

data.frame

Examples

data(otutab)
group_test(otutab, metadata$Group, method = "kruskal.test")
group_test(otutab[, 1:12], metadata$Group[1:12], method = "wilcox.test")
data(otutab)
group_test(otutab, metadata$Group, method = "kruskal.test")
group_test(otutab[, 1:12], metadata$Group[1:12], method = "wilcox.test")

Gsub applied on a data.frame

Description

Gsub applied on a data.frame

Usage

gsub.data.frame(pattern, replacement, x, ...)
gsub.data.frame(pattern, replacement, x, ...)

Arguments

`pattern`	search pattern
`replacement`	a replacement for matched pattern
`x`	your data.frame
`...`	additional arguments for gerpl()

Value

a data.frame

Examples

matrix(letters[1:6], 2, 3) |> as.data.frame() -> a
gsub.data.frame("c", "a", a)
matrix(letters[1:6], 2, 3) |> as.data.frame() -> a
gsub.data.frame("c", "a", a)

Filter your data

Description

Filter your data

Usage

guolv(tab, sum = 10, exist = 1)
guolv(tab, sum = 10, exist = 1)

Arguments

`tab`	dataframe
`sum`	the rowsum should bigger than sum (default:10)
`exist`	the exist number bigger than exist (default:1)

Value

input object

Examples

data(otutab)
guolv(otutab)
data(otutab)
guolv(otutab)

Group your data

Description

Group your data

Usage

hebing(otutab, group, margin = 2, act = "mean", metadata = NULL)
hebing(otutab, group, margin = 2, act = "mean", metadata = NULL)

Arguments

`otutab`	data.frame
`group`	group vector or one of colnames(metadata)
`margin`	1 for row and 2 for column(default: 2)
`act`	do (default: mean)
`metadata`	metadata

Value

data.frame

Examples

data(otutab)
hebing(otutab, metadata$Group)
hebing(otutab, "Group", metadata = metadata, act = "sum")
data(otutab)
hebing(otutab, metadata$Group)
hebing(otutab, "Group", metadata = metadata, act = "sum")

How to set font for ggplot

Description

How to set font for ggplot

Usage

how_to_set_font_for_plot()
how_to_set_font_for_plot()

Value

No return value

How to set options in a package

Description

How to set options in a package

Usage

how_to_set_options(package = "My_package")
how_to_set_options(package = "My_package")

Arguments

package

package name

Value

No return value

How to update parameters

Description

How to update parameters

Usage

how_to_update_parameters()
how_to_update_parameters()

Value

No return value

How to use parallel

Description

How to use parallel

Usage

how_to_use_parallel(
  loop = function(i) {
     return(mean(rnorm(100)))
 }
)
how_to_use_parallel(
  loop = function(i) {
     return(mean(rnorm(100)))
 }
)

Arguments

loop

the main function

Value

No return value

How to use sbatch

Description

How to use sbatch

Usage

how_to_use_sbatch(mode = 1)
how_to_use_sbatch(mode = 1)

Arguments

mode

1~3

Value

No return value

Translate text of igraph

Description

Translate text of igraph

Usage

igraph_translator(
  ig,
  from = "en",
  to = "zh",
  which = c("vertex", "edge", "all")[1],
  verbose = TRUE
)
igraph_translator(
  ig,
  from = "en",
  to = "zh",
  which = c("vertex", "edge", "all")[1],
  verbose = TRUE
)

Arguments

`ig`	igraph object to be translated
`from`	source language
`to`	target language
`which`	vertex, edge, or all
`verbose`	verbose

Value

igraph object

Examples

## Not run: 
library(igraph)
ig <- make_graph(c("happy", "sad", "sad", "angry", "sad", "worried"))
plot(ig)
ig2 <- igraph_translator(ig)
font_file <- "/System/Library/Fonts/Supplemental/Songti.ttc"
sysfonts::font_add("Songti", font_file)
plot(ig2, vertex.label.family = "Songti")

## End(Not run)
## Not run: 
library(igraph)
ig <- make_graph(c("happy", "sad", "sad", "angry", "sad", "worried"))
plot(ig)
ig2 <- igraph_translator(ig)
font_file <- "/System/Library/Fonts/Supplemental/Songti.ttc"
sysfonts::font_add("Songti", font_file)
plot(ig2, vertex.label.family = "Songti")

## End(Not run)

Judge if a characteristic is Rcolor

Description

Judge if a characteristic is Rcolor

Usage

is.ggplot.color(color)
is.ggplot.color(color)

Arguments

color

characteristic

Value

TRUE or FALSE

Examples

is.ggplot.color("red")
is.ggplot.color("notcolor")
is.ggplot.color(NA)
is.ggplot.color("#000")
is.ggplot.color("red")
is.ggplot.color("notcolor")
is.ggplot.color(NA)
is.ggplot.color("#000")

Scale a legend size

Description

Scale a legend size

Usage

legend_size(scale = 1)
legend_size(scale = 1)

Arguments

scale

default: 1.

Value

"theme" "gg"

Attach packages or install packages have not benn installed

Description

Attach packages or install packages have not benn installed

Usage

lib_ps(p_list, ..., all_yes = FALSE, library = TRUE)
lib_ps(p_list, ..., all_yes = FALSE, library = TRUE)

Arguments

`p_list`	a vector of packages list
`...`	packages
`all_yes`	all install try set to yes?
`library`	should library the package or just get Namespace ?

Value

No return value

My cat

Description

my little cat named Guo Dong which drawn by my girlfriend.

Format

rastergrob object.

Get coefficients of linear regression model

Description

This function fits a linear regression model using the given data and formula, and returns the coefficients.

Usage

lm_coefficients(data, formula, standardize = FALSE, each = TRUE)
lm_coefficients(data, formula, standardize = FALSE, each = TRUE)

Arguments

`data`	A data frame containing the response variable and predictors.
`formula`	A formula specifying the structure of the linear regression model.
`standardize`	Whether to standardize the data before fitting the model.
`each`	each variable do a lm or whole multi-lm

Value

coefficients The coefficients of the linear regression model.

Examples

data <- data.frame(
  response = c(2, 4, 6, 7, 9),
  x1 = c(1, 2, 3, 4, 5),
  x2 = c(2, 3, 6, 8, 9),
  x3 = c(3, 6, 5, 12, 12)
)
coefficients_df <- lm_coefficients(data, response ~ x1 + x2 + x3)
print(coefficients_df)
plot(coefficients_df)
data <- data.frame(
  response = c(2, 4, 6, 7, 9),
  x1 = c(1, 2, 3, 4, 5),
  x2 = c(2, 3, 6, 8, 9),
  x3 = c(3, 6, 5, 12, 12)
)
coefficients_df <- lm_coefficients(data, response ~ x1 + x2 + x3)
print(coefficients_df)
plot(coefficients_df)

Make a Gitbook using bookdown

Description

Make a Gitbook using bookdown

Usage

make_gitbook(
  book_n,
  root_dir = "~/Documents/R/",
  mode = c("gitbook", "bs4")[1],
  author = "Asa12138",
  bib = "~/Documents/R/pc_blog/content/bib/My Library.bib",
  csl = "~/Documents/R/pc_blog/content/bib/science.csl"
)
make_gitbook(
  book_n,
  root_dir = "~/Documents/R/",
  mode = c("gitbook", "bs4")[1],
  author = "Asa12138",
  bib = "~/Documents/R/pc_blog/content/bib/My Library.bib",
  csl = "~/Documents/R/pc_blog/content/bib/science.csl"
)

Arguments

`book_n`	project name
`root_dir`	root directory
`mode`	"gitbook","bs4"
`author`	author
`bib`	cite papers bib, from Zotero
`csl`	cite papers format, default science.csl

Value

No return value

Make a R-analysis project

Description

Make a R-analysis project

Usage

make_project(pro_n, root_dir = "~/Documents/R/")
make_project(pro_n, root_dir = "~/Documents/R/")

Arguments

`pro_n`	project name
`root_dir`	root directory

Value

No return value

Make a new python package

Description

Make a new python package

Usage

make_py_pkg(
  pkg_name,
  path = ".",
  author = "Your Name",
  email = "[email protected]",
  description = "A brief description of your library",
  license = "MIT"
)
make_py_pkg(
  pkg_name,
  path = ".",
  author = "Your Name",
  email = "[email protected]",
  description = "A brief description of your library",
  license = "MIT"
)

Arguments

`pkg_name`	package name
`path`	project path, default "."
`author`	author
`email`	email
`description`	description
`license`	license

Value

No return value

Examples

if (interactive()) {
  make_py_pkg("my_python_package", path = "~/projects",
  author = "John Doe", description = "My Python library",
  license = "MIT")
}
if (interactive()) {
  make_py_pkg("my_python_package", path = "~/projects",
  author = "John Doe", description = "My Python library",
  license = "MIT")
}

Match otutab and metadata

Description

Match otutab and metadata

Usage

match_df(otutab, metadata)
match_df(otutab, metadata)

Arguments

`otutab`	otutab, rownames are features, colnames are samples
`metadata`	metadata, rownames are samples

Value

list

Examples

data(otutab)
match_df(otutab, metadata)
data(otutab)
match_df(otutab, metadata)

test data for pcutils package

Description

an otutab, metadata and a taxonomy table.

Format

contains an otutab, metadata and a taxonomy table.

otutab: contians otutable rawdata
metadata: contians metadata
taxonomy: contians taxonomy table

Min_Max scale

Description

Min_Max scale

Usage

mmscale(x, min_s = 0, max_s = 1, n = 1, plot = FALSE)
mmscale(x, min_s = 0, max_s = 1, n = 1, plot = FALSE)

Arguments

`x`	a numeric vector
`min_s`	scale min
`max_s`	scale max
`n`	linear transfer for n=1; the slope will change if n>1 or n<1
`plot`	whether plot the transfer?

Value

a numeric vector

Examples

x <- runif(10)
mmscale(x, 5, 10)
x <- runif(10)
mmscale(x, 5, 10)

Multiple regression/ variance decomposition analysis

Description

Multiple regression/ variance decomposition analysis

Usage

multireg(formula, data, TopN = 3)
multireg(formula, data, TopN = 3)

Arguments

`formula`	formula
`data`	dataframe
`TopN`	give top variable importance

Value

ggplot

Examples

if (requireNamespace("relaimpo") && requireNamespace("aplot")) {
  data(otutab)
  multireg(env1 ~ Group * ., data = metadata[, 2:7])
}
if (requireNamespace("relaimpo") && requireNamespace("aplot")) {
  data(otutab)
  multireg(env1 ~ Group * ., data = metadata[, 2:7])
}

Multi-groups test

Description

anova (parametric) and kruskal.test (non-parametric). Perform one-way ANOVA test comparing multiple groups. LSD and TukeyHSD are post hoc test of anova. dunn and nemenyi are post hoc test of kruskal.test. t.test or wilcox is just perform t.test or wilcox.test in each two group (no p.adjust).

Usage

multitest(var, group, print = TRUE, return = FALSE)
multitest(var, group, print = TRUE, return = FALSE)

Arguments

`var`	numeric vector
`group`	more than two-levels group vector
`print`	whether print the result
`return`	return which method result (tukeyHSD or LSD or wilcox?)

Value

No value or a dataframe.

Examples

if (requireNamespace("multcompView")) {
  multitest(runif(30), rep(c("A", "B", "C"), each = 10), return = "wilcox")
}
if (requireNamespace("multcompView")) {
  multitest(runif(30), rep(c("A", "B", "C"), each = 10), return = "wilcox")
}

Show my little cat named Guo Dong which drawn by my girlfriend.

Description

Show my little cat named Guo Dong which drawn by my girlfriend.

Usage

my_cat(mode = 1, picture = 1)
my_cat(mode = 1, picture = 1)

Arguments

`mode`	1~2
`picture`	1~2

Value

a ggplot

My Circle packing plot

Description

My Circle packing plot

Usage

my_circle_packing(
  test,
  anno = NULL,
  mode = 1,
  Group = "level",
  Score = "weight",
  label = "label",
  show_level_name = "all",
  show_tip_label = TRUE,
  str_width = 10
)
my_circle_packing(
  test,
  anno = NULL,
  mode = 1,
  Group = "level",
  Score = "weight",
  label = "label",
  show_level_name = "all",
  show_tip_label = TRUE,
  str_width = 10
)

Arguments

`test`	a dataframe with hierarchical structure
`anno`	annotation tablewith rowname for color or fill.
`mode`	1~2
`Group`	fill for mode2
`Score`	color for mode1
`label`	the labels column
`show_level_name`	show which level name? a vector contains some column names.
`show_tip_label`	show_tip_label, logical
`str_width`	str_width

Value

ggplot

Examples


data(otutab)
cbind(taxonomy, weight = rowSums(otutab))[1:10, ] -> test
if (requireNamespace("igraph") && requireNamespace("ggraph")) {
  my_circle_packing(test)
}

data(otutab)
cbind(taxonomy, weight = rowSums(otutab))[1:10, ] -> test
if (requireNamespace("igraph") && requireNamespace("ggraph")) {
  my_circle_packing(test)
}

My circo plot

Description

My circo plot

Usage

my_circo(
  df,
  reorder = TRUE,
  pal = NULL,
  mode = c("circlize", "chorddiag")[1],
  legend = TRUE,
  ...
)
my_circo(
  df,
  reorder = TRUE,
  pal = NULL,
  mode = c("circlize", "chorddiag")[1],
  legend = TRUE,
  ...
)

Arguments

`df`	dataframe with three column
`reorder`	reorder by number?
`pal`	a vector of colors, you can get from here too: `RColorBrewer::brewer.pal(5,"Set2")` or `ggsci::pal_aaas()(5)`
`mode`	"circlize","chorddiag"
`legend`	plot legend?
`...`	`chordDiagram`

Value

chordDiagram

Examples


if (requireNamespace("circlize")) {
  data.frame(
    a = c("a", "a", "b", "b", "c"),
    b = c("a", LETTERS[2:5]), c = 1:5
  ) %>% my_circo(mode = "circlize")
  data(otutab)
  cbind(taxonomy, num = rowSums(otutab))[1:10, c(2, 6, 8)] -> test
  my_circo(test)
}


if (requireNamespace("circlize")) {
  data.frame(
    a = c("a", "a", "b", "b", "c"),
    b = c("a", LETTERS[2:5]), c = 1:5
  ) %>% my_circo(mode = "circlize")
  data(otutab)
  cbind(taxonomy, num = rowSums(otutab))[1:10, c(2, 6, 8)] -> test
  my_circo(test)
}

Fit a linear model and plot

Description

Fit a linear model and plot

Usage

my_lm(tab, var, metadata = NULL, smooth_param = list(), facet = TRUE, ...)
my_lm(tab, var, metadata = NULL, smooth_param = list(), facet = TRUE, ...)

Arguments

`tab`	your dataframe
`var`	which colname choose for var or a vector
`metadata`	the dataframe contains the var
`smooth_param`	parameters parse to `geom_smooth`
`facet`	whether facet?
`...`	parameters parse to `geom_point`

Value

a ggplot

Examples


if (requireNamespace("ggpmisc")) {
  my_lm(runif(50), var = 1:50)
  my_lm(c(1:50) + runif(50, 0, 5), var = 1:50)
}

if (requireNamespace("ggpmisc")) {
  my_lm(runif(50), var = 1:50)
  my_lm(c(1:50) + runif(50, 0, 5), var = 1:50)
}

My Sankey plot

Description

My Sankey plot

Usage

my_sankey(
  test,
  mode = c("sankeyD3", "ggsankey"),
  topN = "all",
  space = 1,
  width = 0.1,
  str_width = 20,
  notshow = c(),
  D3_params = NULL,
  ...
)
my_sankey(
  test,
  mode = c("sankeyD3", "ggsankey"),
  topN = "all",
  space = 1,
  width = 0.1,
  str_width = 20,
  notshow = c(),
  D3_params = NULL,
  ...
)

Arguments

`test`	a dataframe with hierarchical structure
`mode`	"sankeyD3","ggsankey"
`topN`	"all" or numeric vector, determine how many topN shows in each column
`space`	space width for ggsankey
`width`	width
`str_width`	str_width
`notshow`	notshow
`D3_params`	look for parameters in `sankeyNetwork`
`...`	additional parameters

Value

ggplot or htmlwidget

Examples


if (interactive()) {
  data.frame(
    a = c("a", "a", "b", "b", "c"),
    aa = rep("a", 5),
    b = c("a", LETTERS[2:5]),
    c = 1:5
  ) %>%
    my_sankey(., "gg", num = TRUE)
  data(otutab)
  cbind(taxonomy, num = rowSums(otutab))[1:10, ] -> test
  my_sankey(test)
}

if (interactive()) {
  data.frame(
    a = c("a", "a", "b", "b", "c"),
    aa = rep("a", 5),
    b = c("a", LETTERS[2:5]),
    c = 1:5
  ) %>%
    my_sankey(., "gg", num = TRUE)
  data(otutab)
  cbind(taxonomy, num = rowSums(otutab))[1:10, ] -> test
  my_sankey(test)
}

My Sunburst plot

Description

My Sunburst plot

Usage

my_sunburst(test, ...)
my_sunburst(test, ...)

Arguments

`test`	a dataframe with hierarchical structure
`...`	look for parameters in `plot_ly`

Value

htmlwidget

Examples


data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, ] -> test
if (requireNamespace("plotly")) {
  my_sunburst(test)
}

data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, ] -> test
if (requireNamespace("plotly")) {
  my_sunburst(test)
}

My Treemap plot

Description

My Treemap plot

Usage

my_treemap(test, ...)
my_treemap(test, ...)

Arguments

`test`	a three-columns dataframe with hierarchical structure
`...`	look for parameters in `plot_ly`

Value

htmlwidget

Examples


data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, c(4, 7, 8)] -> test
if (requireNamespace("treemap")) {
  my_treemap(test)
}

data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, c(4, 7, 8)] -> test
if (requireNamespace("treemap")) {
  my_treemap(test)
}

My Voronoi treemap plot

Description

My Voronoi treemap plot

Usage

my_voronoi_treemap(test, ...)
my_voronoi_treemap(test, ...)

Arguments

`test`	a three-columns dataframe with hierarchical structure
`...`	look for parameters in `vt_d3`

Value

htmlwidget

Examples


data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, c(4, 7, 8)] -> test
if (requireNamespace("voronoiTreemap")) {
  my_voronoi_treemap(test)
}

data(otutab)
cbind(taxonomy, num = rowSums(otutab))[1:10, c(4, 7, 8)] -> test
if (requireNamespace("voronoiTreemap")) {
  my_voronoi_treemap(test)
}

test data for pcutils package

Description

an otutab, metadata and a taxonomy table.

Format

contains an otutab, metadata and a taxonomy table.

otutab: contians otutable rawdata
metadata: contians metadata
taxonomy: contians taxonomy table

Plot coefficients as a bar chart or lollipop chart

Description

This function takes the coefficients and generates a plot to visualize their magnitudes.

Usage

## S3 method for class 'coefficients'
plot(x, mode = 1, number = FALSE, x_order = NULL, ...)
## S3 method for class 'coefficients'
plot(x, mode = 1, number = FALSE, x_order = NULL, ...)

Arguments

`x`	The coefficients to be plotted.
`mode`	The mode of the plot: 1 for bar chart, 2 for lollipop chart.
`number`	show number
`x_order`	order of variables
`...`	add

Value

ggplot

Plot a gif

Description

Plot a gif

Usage

plotgif(plist, file, speed = 1, ...)
plotgif(plist, file, speed = 1, ...)

Arguments

`plist`	plot list
`file`	prefix of your .gif file
`speed`	1
`...`	add

Value

No return value

Plot a multi-pages pdf

Description

Plot a multi-pages pdf

Usage

plotpdf(
  plist,
  file,
  width = 8,
  height = 7,
  browser = "/Applications/Microsoft Edge.app/Contents/MacOS/Microsoft Edge",
  ...
)
plotpdf(
  plist,
  file,
  width = 8,
  height = 7,
  browser = "/Applications/Microsoft Edge.app/Contents/MacOS/Microsoft Edge",
  ...
)

Arguments

`plist`	plot list
`file`	prefix of your .pdf file
`width`	width
`height`	height
`browser`	the path of Google Chrome, Microsoft Edge or Chromium in your computer.
`...`	additional arguments

Value

No return value

Prepare a numeric string

Description

Prepare a numeric string

Usage

pre_number_str(str, split_str = ",", continuous_str = "-")
pre_number_str(str, split_str = ",", continuous_str = "-")

Arguments

`str`	a string contain ',' and '-'
`split_str`	split_str ","
`continuous_str`	continuous_str "-"

Value

vector

Examples

pre_number_str("a1,a3,a5,a6-a10")
pre_number_str("a1,a3,a5,a6-a10")

Prepare a package

Description

Prepare a package

Usage

prepare_package(
  pkg_dir = ".",
  exclude = "print.R",
  indent_by = 2,
  check = TRUE,
  ...
)
prepare_package(
  pkg_dir = ".",
  exclude = "print.R",
  indent_by = 2,
  check = TRUE,
  ...
)

Arguments

`pkg_dir`	defalut: "."
`exclude`	vector for excluding .R files
`indent_by`	indent_by, default: 2
`check`	check or not, default: TRUE
`...`	other parameters for devtools::check

Value

No value

Read fasta file

Description

Read fasta file

Usage

read_fasta(fasta_file)
read_fasta(fasta_file)

Arguments

fasta_file

file path

Value

data.frame

Read some special format file

Description

Read some special format file

Usage

read.file(
  file,
  format = NULL,
  just_print = FALSE,
  all_yes = FALSE,
  density = 120,
  ...
)
read.file(
  file,
  format = NULL,
  just_print = FALSE,
  all_yes = FALSE,
  density = 120,
  ...
)

Arguments

`file`	file path
`format`	"blast", "diamond", "fa", "fasta", "fna", "faa", "bib", "gff", "gtf","jpg", "png", "pdf", "svg"...
`just_print`	just print the file
`all_yes`	all_yes?
`density`	the resolution for reading pdf or svg
`...`	additional arguments

Value

data.frame

Remove outliers

Description

Remove outliers

Usage

remove.outliers(x, factor = 1.5)
remove.outliers(x, factor = 1.5)

Arguments

`x`	a numeric vector
`factor`	default 1.5

Value

a numeric vector

Examples

remove.outliers(c(1, 10:15))
remove.outliers(c(1, 10:15))

Transform a rgb vector to a Rcolor code

Description

Transform a rgb vector to a Rcolor code

Usage

rgb2code(x, rev = FALSE)
rgb2code(x, rev = FALSE)

Arguments

`x`	vector or three columns data.frame
`rev`	reverse,transform a Rcolor code to a rgb vector

Value

Rcolor code like "#69C404"

Examples

rgb2code(c(12, 23, 34))
rgb2code("#69C404", rev = TRUE)
rgb2code(c(12, 23, 34))
rgb2code("#69C404", rev = TRUE)

Remove the low relative items in each column

Description

Remove the low relative items in each column

Usage

rm_low(otutab, relative_threshold = 0.0001)
rm_low(otutab, relative_threshold = 0.0001)

Arguments

`otutab`	otutab
`relative_threshold`	threshold, default: 1e-4

Value

data.frame

Examples

data(otutab)
rm_low(otutab)
data(otutab)
rm_low(otutab)

Plot the sampling map

Description

Plot the sampling map

Usage

sample_map(
  metadata,
  mode = 1,
  map_params = list(),
  group = NULL,
  point_params = list(),
  label = NULL,
  label_params = list(),
  leaflet_pal = NULL,
  shp_file = NULL,
  crs = 4326,
  xlim = NULL,
  ylim = NULL,
  add_scale = TRUE,
  scale_params = list(),
  add_north_arrow = TRUE,
  north_arrow_params = list()
)
sample_map(
  metadata,
  mode = 1,
  map_params = list(),
  group = NULL,
  point_params = list(),
  label = NULL,
  label_params = list(),
  leaflet_pal = NULL,
  shp_file = NULL,
  crs = 4326,
  xlim = NULL,
  ylim = NULL,
  add_scale = TRUE,
  scale_params = list(),
  add_north_arrow = TRUE,
  north_arrow_params = list()
)

Arguments

`metadata`	metadata must contains "Longitude","Latitude"
`mode`	1~3. 1 use basic data from ggplot2. 2 use a shp_file. 3 use the leaflet.
`map_params`	parameters parse to geom_polygon (mode=1) or geom_sf (mode=2)
`group`	one column name of metadata which mapping to point color
`point_params`	parameters parse to geom_point
`label`	one column name of metadata which mapping to point label
`label_params`	parameters parse to geom_sf_text
`leaflet_pal`	leaflet color palette
`shp_file`	a geojson file parse to `sf::read_sf`
`crs`	crs coordinate: `https://asa-blog.netlify.app/p/r-map/#crs`
`xlim`	xlim
`ylim`	ylim
`add_scale`	add annotation_scale
`scale_params`	parameters parse to `ggspatial::annotation_scale`
`add_north_arrow`	add annotation_north_arrow
`north_arrow_params`	parameters parse to `ggspatial::annotation_north_arrow`

Value

map

Examples


data(otutab)
anno_df <- metadata[, c("Id", "long", "lat", "Group")]
colnames(anno_df) <- c("Id", "Longitude", "Latitude", "Group")
if (requireNamespace("ggspatial")) {
  sample_map(anno_df, mode = 1, group = "Group", xlim = c(90, 135), ylim = c(20, 50))
}

data(otutab)
anno_df <- metadata[, c("Id", "long", "lat", "Group")]
colnames(anno_df) <- c("Id", "Longitude", "Latitude", "Group")
if (requireNamespace("ggspatial")) {
  sample_map(anno_df, mode = 1, group = "Group", xlim = c(90, 135), ylim = c(20, 50))
}

Three-line table

Description

Three-line table

Usage

sanxian(
  df,
  digits = 3,
  nrow = 10,
  ncol = 10,
  fig = FALSE,
  mode = 1,
  background = "#D7261E",
  ...
)
sanxian(
  df,
  digits = 3,
  nrow = 10,
  ncol = 10,
  fig = FALSE,
  mode = 1,
  background = "#D7261E",
  ...
)

Arguments

`df`	a data.frame
`digits`	how many digits should remain
`nrow`	show how many rows
`ncol`	show how many columns
`fig`	output as a figure
`mode`	1~2
`background`	background color
`...`	additional arguments e.g.(rows=NULL)

Value

a ggplot

Examples


if (require("kableExtra")) {
  data(otutab)
  sanxian(otutab)
}

if (require("kableExtra")) {
  data(otutab)
  sanxian(otutab)
}

Scale a fill color

Description

Scale a fill color

Usage

scale_color_pc(
  palette = c("col1", "col2", "col3", "bluered"),
  alpha = 1,
  n = 11,
  ...
)
scale_color_pc(
  palette = c("col1", "col2", "col3", "bluered"),
  alpha = 1,
  n = 11,
  ...
)

Arguments

`palette`	col1~3; or a vector of colors, you can get from: `RColorBrewer::brewer.pal(5,"Set2")` or `ggsci::pal_aaas()(5)`
`alpha`	alpha
`n`	how many colors you need
`...`	additional

Value

scale_color

Scale a fill color

Description

Scale a fill color

Usage

scale_fill_pc(
  palette = c("col1", "col2", "col3", "bluered"),
  alpha = 1,
  n = 11,
  ...
)
scale_fill_pc(
  palette = c("col1", "col2", "col3", "bluered"),
  alpha = 1,
  n = 11,
  ...
)

Arguments

`palette`	col1~3; or a vector of colors, you can get from: `RColorBrewer::brewer.pal(5,"Set2")` or `ggsci::pal_aaas()(5)`
`alpha`	alpha
`n`	how many colors you need
`...`	additional

Value

scale_fill

Search and browse the web for specified terms

Description

This function takes a vector of search terms, an optional search engine (default is Google), and an optional base URL to perform web searches. It opens the default web browser with search results for each term.

Usage

search_browse(search_terms, engine = "google", base_url = NULL)
search_browse(search_terms, engine = "google", base_url = NULL)

Arguments

`search_terms`	A character vector of search terms to be searched.
`engine`	A character string specifying the search engine to use (default is "google"). Supported engines: "google", "bing".
`base_url`	A character string specifying the base URL for web searches. If not provided, the function will use a default URL based on the chosen search engine.

Value

No return value

Examples

## Not run: 
search_terms <- c(
  "s__Pandoraea_pnomenusa",
  "s__Alicycliphilus_sp._B1"
)

# Using Google search engine
search_browse(search_terms, engine = "google")

# Using Bing search engine
search_browse(search_terms, engine = "bing")

## End(Not run)

## Not run: 
search_terms <- c(
  "s__Pandoraea_pnomenusa",
  "s__Alicycliphilus_sp._B1"
)

# Using Google search engine
search_browse(search_terms, engine = "google")

# Using Bing search engine
search_browse(search_terms, engine = "bing")

## End(Not run)

Set config

Description

Set config

Usage

set_pcutils_config(item, value)
set_pcutils_config(item, value)

Arguments

`item`	item
`value`	value

Value

No value

Show config

Description

Show config

Usage

show_pcutils_config()
show_pcutils_config()

Value

config

Split text into parts, each not exceeding a specified character count

Description

Split text into parts, each not exceeding a specified character count

Usage

split_text(text, nchr_each = 200)
split_text(text, nchr_each = 200)

Arguments

`text`	Original text
`nchr_each`	Maximum character count for each part

Value

List of divided parts

Examples


original_text <- paste0(sample(c(letters, "\n"), 400, replace = TRUE), collapse = "")
parts <- split_text(original_text, nchr_each = 200)
lapply(parts, nchar)

original_text <- paste0(sample(c(letters, "\n"), 400, replace = TRUE), collapse = "")
parts <- split_text(original_text, nchr_each = 200)
lapply(parts, nchar)

Squash one column in a data.frame using other columns as id.

Description

Squash one column in a data.frame using other columns as id.

Usage

squash(df, column, split = ",")
squash(df, column, split = ",")

Arguments

`df`	data.frame
`column`	column name, not numeric position
`split`	split string

Value

data.frame

Examples

df <- data.frame(a = c(1:2, 1:2), b = letters[1:4])
squash(df, "b", ",")
df <- data.frame(a = c(1:2, 1:2), b = letters[1:4])
squash(df, "b", ",")

Plot a stack plot

Description

Plot a stack plot

Plot a area plot

Usage

stackplot(
  otutab,
  metadata = NULL,
  group = "Group",
  get_data = FALSE,
  bar_params = list(width = 0.7, position = "stack"),
  topN = 8,
  others = TRUE,
  relative = TRUE,
  legend_title = "",
  stack_order = TRUE,
  group_order = FALSE,
  facet_order = FALSE,
  style = c("group", "sample")[1],
  flow = FALSE,
  flow_params = list(lode.guidance = "frontback", color = "darkgray"),
  number = FALSE,
  repel = FALSE,
  format_params = list(digits = 2),
  text_params = list(position = position_stack())
)

areaplot(
  otutab,
  metadata = NULL,
  group = "Group",
  get_data = FALSE,
  bar_params = list(position = "stack"),
  topN = 8,
  others = TRUE,
  relative = TRUE,
  legend_title = "",
  stack_order = TRUE,
  group_order = FALSE,
  facet_order = FALSE,
  style = c("group", "sample")[1],
  number = FALSE,
  format_params = list(digits = 2),
  text_params = list(position = position_stack())
)
stackplot(
  otutab,
  metadata = NULL,
  group = "Group",
  get_data = FALSE,
  bar_params = list(width = 0.7, position = "stack"),
  topN = 8,
  others = TRUE,
  relative = TRUE,
  legend_title = "",
  stack_order = TRUE,
  group_order = FALSE,
  facet_order = FALSE,
  style = c("group", "sample")[1],
  flow = FALSE,
  flow_params = list(lode.guidance = "frontback", color = "darkgray"),
  number = FALSE,
  repel = FALSE,
  format_params = list(digits = 2),
  text_params = list(position = position_stack())
)

areaplot(
  otutab,
  metadata = NULL,
  group = "Group",
  get_data = FALSE,
  bar_params = list(position = "stack"),
  topN = 8,
  others = TRUE,
  relative = TRUE,
  legend_title = "",
  stack_order = TRUE,
  group_order = FALSE,
  facet_order = FALSE,
  style = c("group", "sample")[1],
  number = FALSE,
  format_params = list(digits = 2),
  text_params = list(position = position_stack())
)

Arguments

`otutab`	otutab
`metadata`	metadata
`group`	one group name of columns of metadata
`get_data`	just get the formatted data?
`bar_params`	parameters parse to `geom_bar`
`topN`	plot how many top species
`others`	should plot others?
`relative`	transfer to relative or absolute
`legend_title`	fill legend_title
`stack_order`	the order of stack fill
`group_order`	the order of x group, can be T/F, or a vector of x, or a name, or "cluster"
`facet_order`	the order of the facet
`style`	"group" or "sample"
`flow`	should plot a flow plot?
`flow_params`	parameters parse to `geom_flow`
`number`	show the number?
`repel`	use the ggrepel::geom_text_repel instead of geom_text
`format_params`	parameters parse to `format`
`text_params`	parameters parse to `geom_text`

Value

a ggplot

Examples

data(otutab)
stackplot(otutab, metadata, group = "Group")

if (interactive()) {
  stackplot(otutab, metadata,
    group = "Group", style = "sample",
    group_order = TRUE, flow = TRUE, relative = FALSE
  )
}

data(otutab)
areaplot(otutab, metadata, group = "Id")

areaplot(otutab, metadata,
  group = "Group", style = "sample",
  group_order = TRUE, relative = FALSE
)

data(otutab)
stackplot(otutab, metadata, group = "Group")

if (interactive()) {
  stackplot(otutab, metadata,
    group = "Group", style = "sample",
    group_order = TRUE, flow = TRUE, relative = FALSE
  )
}

data(otutab)
areaplot(otutab, metadata, group = "Id")

areaplot(otutab, metadata,
  group = "Group", style = "sample",
  group_order = TRUE, relative = FALSE
)

Split Composite Names

Description

Split Composite Names

Usage

strsplit2(x, split, colnames = NULL, ...)
strsplit2(x, split, colnames = NULL, ...)

Arguments

`x`	character vector
`split`	character to split each element of vector on, see `strsplit`
`colnames`	colnames for the result
`...`	other arguments are passed to `strsplit`

Value

data.frame

Examples

strsplit2(c("a;b", "c;d"), ";", colnames = c("col1", "col2"))
strsplit2(c("a;b", "c;d"), ";", colnames = c("col1", "col2"))

Transpose data.frame

Description

Transpose data.frame

Usage

t2(data)
t2(data)

Arguments

data

data.frame

Value

data.frame

Pie plot

Description

Pie plot

Usage

tax_pie(otutab, topN = 6, ...)
tax_pie(otutab, topN = 6, ...)

Arguments

`otutab`	otutab
`topN`	topN
`...`	add

Value

a ggplot

Examples


data(otutab)
tax_pie(otutab, topN = 7) + scale_fill_pc()

data(otutab)
tax_pie(otutab, topN = 7) + scale_fill_pc()

Radar plot

Description

Radar plot

Usage

tax_radar(group_df, ...)
tax_radar(group_df, ...)

Arguments

`group_df`	group_df
`...`	add

Value

ggplot

Examples


if (interactive()) {
  data(otutab)
  tax_radar(otutab[1:6, 1:4])
}

if (interactive()) {
  data(otutab)
  tax_radar(otutab[1:6, 1:4])
}

test data for pcutils package

Description

an otutab, metadata and a taxonomy table.

Format

contains an otutab, metadata and a taxonomy table.

otutab: contians otutable rawdata
metadata: contians metadata
taxonomy: contians taxonomy table

Replace a vector by named vector

Description

Replace a vector by named vector

Usage

tidai(x, y, fac = FALSE, keep_origin = FALSE)
tidai(x, y, fac = FALSE, keep_origin = FALSE)

Arguments

`x`	a vector need to be replaced
`y`	named vector
`fac`	consider the factor?
`keep_origin`	keep_origin?

Value

vector

Examples

tidai(c("a", "a", "b", "d"), c("a" = "red", b = "blue"))
tidai(c("a", "a", "b", "c"), c("red", "blue"))
tidai(c("A" = "a", "B" = "b"), c("a" = "red", b = "blue"))
tidai(factor(c("A" = "a", "B" = "b", "C" = "c")), c("a" = "red", b = "blue", c = "green"))
tidai(c("a", "a", "b", "d"), c("a" = "red", b = "blue"))
tidai(c("a", "a", "b", "c"), c("red", "blue"))
tidai(c("A" = "a", "B" = "b"), c("a" = "red", b = "blue"))
tidai(factor(c("A" = "a", "B" = "b", "C" = "c")), c("a" = "red", b = "blue", c = "green"))

Transfer Geographical latitude and longitude to XY(m)

Description

Transfer Geographical latitude and longitude to XY(m)

Usage

toXY(geo)
toXY(geo)

Arguments

geo

a two-columns dataframe, first is latitude, second is longitude

Value

data.frame

Examples


if (interactive()) {
  data.frame(row.names = letters[1:18], x = runif(18, 30, 35), y = runif(18, 40, 45)) -> geo
  toXY(geo)
}

if (interactive()) {
  data.frame(row.names = letters[1:18], x = runif(18, 30, 35), y = runif(18, 40, 45)) -> geo
  toXY(geo)
}

Trans format your data

Description

Trans format your data

Usage

trans(df, method = "normalize", margin = 2, ...)
trans(df, method = "normalize", margin = 2, ...)

Arguments

`df`	dataframe
`method`	"cpm","minmax","acpm","total","log", "max", "frequency", "normalize", "range", "rank", "rrank", "standardize", "pa", "chi.square", "hellinger", "log", "clr", "rclr", "alr"
`margin`	1 for row and 2 for column(default: 2)
`...`	additional

Value

data.frame

Examples

data(otutab)
trans(otutab, method = "cpm")

data(otutab)
trans(otutab, method = "cpm")

Transfer the format of file

Description

Transfer the format of file

Usage

trans_format(
  file,
  to_format,
  format = NULL,
  ...,
  browser = "/Applications/Microsoft Edge.app/Contents/MacOS/Microsoft Edge"
)
trans_format(
  file,
  to_format,
  format = NULL,
  ...,
  browser = "/Applications/Microsoft Edge.app/Contents/MacOS/Microsoft Edge"
)

Arguments

`file`	input file
`to_format`	transfer to
`format`	input file format
`...`	additional argument
`browser`	the path of Google Chrome, Microsoft Edge or Chromium in your computer.

Value

file at work directory

Translator

Description

language: en, zh, jp, fra, th..., see https://www.cnblogs.com/pieguan/p/10338255.html

Usage

translator(words, from = "en", to = "zh", split = TRUE, verbose = TRUE)
translator(words, from = "en", to = "zh", split = TRUE, verbose = TRUE)

Arguments

`words`	words
`from`	source language, default "en"
`to`	target language, default "zh"
`split`	split to blocks when your words are too much
`verbose`	verbose

Value

vector

Examples

## Not run: 
translator(c("love", "if"), from = "en", to = "zh")

## End(Not run)
## Not run: 
translator(c("love", "if"), from = "en", to = "zh")

## End(Not run)

Two-group test

Description

Two-group test

Usage

twotest(var, group)
twotest(var, group)

Arguments

`var`	numeric vector
`group`	two-levels group vector

Value

No return value

Examples

twotest(runif(20), rep(c("a", "b"), each = 10))
twotest(runif(20), rep(c("a", "b"), each = 10))

Update the NEW.md for a package

Description

Update the NEW.md for a package

Usage

update_NEWS_md(
  package_dir = ".",
  new_features = character(),
  bug_fixes = character(),
  other_changes = character(),
  ...
)
update_NEWS_md(
  package_dir = ".",
  new_features = character(),
  bug_fixes = character(),
  other_changes = character(),
  ...
)

Arguments

`package_dir`	default: "."
`new_features`	new_features
`bug_fixes`	bug_fixes
`other_changes`	other_changes
`...`	additional info

Value

No value

Update the parameters

Description

Keep the different parameters while use the same name in update first.

Usage

update_param(default, update)
update_param(default, update)

Arguments

`default`	default (data.frame, list, vector)
`update`	update (data.frame, list, vector)

Value

same class of your input (data.frame, list or vector)

Examples

update_param(list(a = 1, b = 2), list(b = 5, c = 5))

update_param(list(a = 1, b = 2), list(b = 5, c = 5))

Plot a general venn (upset, flower)

Description

Plot a general venn (upset, flower)

Usage

venn(...)

## S3 method for class 'list'
venn(aa, mode = "venn", elements_label = TRUE, ...)

## S3 method for class 'data.frame'
venn(otutab, mode = "venn", elements_label = TRUE, ...)
venn(...)

## S3 method for class 'list'
venn(aa, mode = "venn", elements_label = TRUE, ...)

## S3 method for class 'data.frame'
venn(otutab, mode = "venn", elements_label = TRUE, ...)

Arguments

`...`	add
`aa`	list
`mode`	"venn", "venn2", "euler", "upset", "flower", "network"
`elements_label`	logical, show elements label in network?
`otutab`	table

Value

a plot

Examples


if (interactive()) {
  aa <- list(a = 1:3, b = 3:7, c = 2:4)
  venn(aa, mode = "venn")
  venn(aa, mode = "euler")
  venn(aa, mode = "network")
  venn(aa, mode = "upset")
  data(otutab)
  venn(otutab, mode = "flower")
}

if (interactive()) {
  aa <- list(a = 1:3, b = 3:7, c = 2:4)
  venn(aa, mode = "venn")
  venn(aa, mode = "euler")
  venn(aa, mode = "network")
  venn(aa, mode = "upset")
  data(otutab)
  venn(otutab, mode = "flower")
}

Write a data.frame to fasta

Description

Write a data.frame to fasta

Usage

write_fasta(df, file_path, str_per_line = 70)
write_fasta(df, file_path, str_per_line = 70)

Arguments

`df`	data.frame
`file_path`	output file path
`str_per_line`	how many base or animo acid in one line, if NULL, one sequence in one line.

Value

No return value

`analysis_n`	analysis name
`title`	Rmd file title
`pro_dir`	project directory, default is current directory

`china_shp`	china.json file
`download_dir`	download_dir, "pcutils_temp"
`text_param`	parameters parse to `geom_text`

Package 'pcutils'

Help Index

Add alpha for a Rcolor

Description

Usage

Arguments

Value

Examples

Add an analysis for a project

Description

Usage

Arguments

Value

Add a global gg_theme and colors for plots

Description

Usage

Arguments

Value

Examples

Change factor levels

Description

Usage

Arguments

Value

Examples

Plot china map

Description

Usage

Arguments

Value

Copy a data.frame

Description

Usage

Arguments

Value

Copy a vector

Description

Usage

Arguments

Value

Plot correlation

Description

Usage

Arguments

Value

Examples

Like uniq -c in shell to count a vector

Description

Usage

Arguments

Value

Examples

Print some message with =

Description

Usage

Arguments

Value

Examples

Detach packages

Description

Usage

Arguments

Value

Convert Three-column Data to Distance Matrix

Description

Usage

Arguments

Value

Examples

df to link table

Description

Usage

Arguments

Value

Examples

Convert a distance matrix to a data frame

Description

Usage

Arguments

Value

Like `uniq -c` in shell to count a vector