| Title: | Personal R package for Jordi Rosell |
|---|---|
| Description: | Useful functions for personal usage. |
| Authors: | Jordi Rosell [aut, cre] |
| Maintainer: | Jordi Rosell <[email protected]> |
| License: | CC0 |
| Version: | 0.0.0.9011 |
| Built: | 2024-10-28 18:23:20 UTC |
| Source: | https://github.com/jrosell/jrrosell |
It sorts the column names, it hash every row and add the column.
add_row_hash(df, primary_keys)add_row_hash(df, primary_keys)
df |
a data.frame |
primary_keys |
the column anmes of the primary key |
df <- data.frame( id = c(1, 2, 3), name = c("AAAAA", "BBBB", "CCC") ) add_row_hash(df, id)df <- data.frame( id = c(1, 2, 3), name = c("AAAAA", "BBBB", "CCC") ) add_row_hash(df, id)
Get the bit representation of a double number
as.bitstring(x)as.bitstring(x)
x |
A numeric vetor. |
Get the bit representation of a double number Using rev() ensures that the bit order is correct, and the binary representation aligns with the usual convention of having the MSB first and the LSB last. This is because numToBits() returns the bits in the reverse order, and without rev(), we end up with the LSB first and the MSB last.
https://youtu.be/J4DnzjIFj8w
0.1 + 0.2 == 0.3 as.bitstring(0.1 + 0.2) as.bitstring(0.3)0.1 + 0.2 == 0.3 as.bitstring(0.1 + 0.2) as.bitstring(0.3)
Multiple aside functions with base R pipe
aside(x, ...)aside(x, ...)
x |
An object |
... |
functions to run aside |
n_try <- 1 rnorm(200) |> matrix(ncol = 2) |> aside( print("Matrix prepared"), print(n_try) ) |> colSums()n_try <- 1 rnorm(200) |> matrix(ncol = 2) |> aside( print("Matrix prepared"), print(n_try) ) |> colSums()
From a data frame, it returns the minimal split proportion for validation.
calc_split_prop(df)calc_split_prop(df)
df |
A data frame |
The calc_validation_size function returns the optimal split proportion according to the number of rows for your validation set.
https://stats.stackexchange.com/a/305063/7387
calc_split_prop(data.frame(row = 1:891))calc_split_prop(data.frame(row = 1:891))
From binary classification problems, with the desired std_err it returns the minimal assesment/validation set size.
calc_split_size(std_err = 0.001)calc_split_size(std_err = 0.001)
std_err |
The desired std_err numeric (default 0.001) |
The calc_validation_size function returns the minimal validation size for expected probabilities and desired error. s
https://stats.stackexchange.com/a/304996/7387
calc_split_size() calc_split_size(std_err = 0.02)calc_split_size() calc_split_size(std_err = 0.02)
Create a vector of characters from a string
chars(x, ...)chars(x, ...)
x |
a vector of characters of length 1. |
... |
unused |
chars expects a single string as input. To create a list of these,
consider lapply(strings, chars).
a vector of characters
https://github.com/jonocarroll/charcuterie
chars("hola")chars("hola")
Check if the last main github version is installed.
check_installed_gihub(repo)check_installed_gihub(repo)
repo |
a github repo/package. Ex: check_installed_gihub("tidyverse/dplyr") |
if (FALSE) { check_installed_gihub("jrosell/jrrosell") }if (FALSE) { check_installed_gihub("jrosell/jrrosell") }
Count the number of duplicated rows
count_duplicated_rows(df)count_duplicated_rows(df)
df |
a data.frame |
count_duplicated_rows(data.frame(a = c(1, 2, 3), b = c(3, 4, 5))) count_duplicated_rows(data.frame(a = c(1, 2, 3), b = c(1, 4, 5)))count_duplicated_rows(data.frame(a = c(1, 2, 3), b = c(3, 4, 5))) count_duplicated_rows(data.frame(a = c(1, 2, 3), b = c(1, 4, 5)))
It returns the ordered counts of the variable in the data.frame.
count_sorted(df, ...)count_sorted(df, ...)
df |
a data.frame |
... |
the variables to use and other arguments to count |
data.frame(a = c("x", "y", "x"), b = c("z", "z", "n")) |> count_sorted(a)data.frame(a = c("x", "y", "x"), b = c("z", "z", "n")) |> count_sorted(a)
Select cores in max/min of the available cores.
detect_cores(max = 10, min = 2)detect_cores(max = 10, min = 2)
max |
An integer with the max desired cores (default 10) |
min |
An integer with the min desired cores (default 2) |
The detect_cores function uses parallelly package. It returns the desired max cores if available or it fails if not min cores are available.
cores <- detect_cores(max = 5, min = 1) print(cores) if (FALSE) { library(jrrosell) library(future) plan(multisession, workers = detect_cores(max = 10, min = 2)) plan(sequential) }cores <- detect_cores(max = 5, min = 1) print(cores) if (FALSE) { library(jrrosell) library(future) plan(multisession, workers = detect_cores(max = 10, min = 2)) plan(sequential) }
Fit a workflow with specific parameters
fit_results(wf, resamples, param_info = NULL, grid = 10, fn = "tune_grid", ...)fit_results(wf, resamples, param_info = NULL, grid = 10, fn = "tune_grid", ...)
wf |
workflow |
resamples |
rset |
param_info |
for tune_* functions |
grid |
for tune_* functions |
fn |
the name of the function to run when tuning |
... |
Optional engine arguments |
library(tidymodels) library(xgboost) library(modeldata) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = case) train <- training(split) resamples <- vfold_cv(train, v = 2, strata = case) wf_spec <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow(boost_tree(mode = "classification")) res_spec <- wf_spec |> fit_results(resamples) res_spec |> collect_metrics()library(tidymodels) library(xgboost) library(modeldata) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = case) train <- training(split) resamples <- vfold_cv(train, v = 2, strata = case) wf_spec <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow(boost_tree(mode = "classification")) res_spec <- wf_spec |> fit_results(resamples) res_spec |> collect_metrics()
Glimpse multiple datasets
glimpses(...)glimpses(...)
... |
Multiple data.frame |
df1 <- data.frame(a = c(1, 2)) df2 <- data.frame(b = c(3, 4)) glimpses(df1, df2)df1 <- data.frame(a = c(1, 2)) df2 <- data.frame(b = c(3, 4)) glimpses(df1, df2)
Do the last fit and get the metrics
last_fit_metrics(res, split, metric)last_fit_metrics(res, split, metric)
res |
Tune results |
split |
The initial split object |
metric |
What metric to use to select the best workflow |
library(tidymodels) library(xgboost) library(modeldata) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = class) train <- training(split) folds <- vfold_cv(train, v = 2, strata = class) wf <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow_boost_tree() res <- wf |> tune::tune_grid( resamples = folds, grid = 2, metrics = metric_set(roc_auc), control = tune::control_grid(save_workflow = TRUE, verbose = FALSE) ) res |> collect_metrics() res |> last_fit_metrics(split, "roc_auc") best <- res |> fit_best() best |> augment(testing(split)) |> roc_auc(case, .pred_Test) |> pull(.estimate)library(tidymodels) library(xgboost) library(modeldata) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = class) train <- training(split) folds <- vfold_cv(train, v = 2, strata = class) wf <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow_boost_tree() res <- wf |> tune::tune_grid( resamples = folds, grid = 2, metrics = metric_set(roc_auc), control = tune::control_grid(save_workflow = TRUE, verbose = FALSE) ) res |> collect_metrics() res |> last_fit_metrics(split, "roc_auc") best <- res |> fit_best() best |> augment(testing(split)) |> roc_auc(case, .pred_Test) |> pull(.estimate)
Name unnamed chunks in .Rmd or .qmd files Use with caution.
name_unnamed_chunks(file_path)name_unnamed_chunks(file_path)
file_path |
the file name |
Center and scale double vectors
normalize_vec(...)normalize_vec(...)
... |
a double vector or multiple double vectors |
normalize_vec(1, 2, 3, )normalize_vec(1, 2, 3, )
The notify_finished make a sound using beepr::beep, compose and email and send it returing the blastula::smtp_send call results.
notify_finished(name, body = "", ..., sound = 1, tictoc_result = NULL)notify_finished(name, body = "", ..., sound = 1, tictoc_result = NULL)
name |
The process name (Required) |
body |
The contents of the email (Default "") |
... |
Additional arguments to pass to the template function. If you're using the default template, you can use font_family to control the base font, and content_width to control the width of the main content; see blastula_template(). By default, the content_width is set to 1000px. Using widths less than 600px is generally not advised but, if necessary, be sure to test such HTML emails with a wide range of email clients before sending to the intended recipients. The Outlook mail client (Windows, Desktop) does not respect content_width. |
sound |
The sound for beepr::beep call (Default 1) |
tictoc_result |
the result from tictoc::toc (Default NULL) |
The following environment variables should be set:
MY_SMTP_USER from
MY_SMTP_RECIPIENT to
MY_SMTP_PASSWORD service password (for gmail you can use https://myaccount.google.com/apppasswords)
MY_SMTP_PROVIDER blastula provider (gmail if not set)
if (exists("not_run")) { tictoc::tic() Sys.sleep(1) jrrosell::notify_finished("job", "Well done", sound = "fanfare", tictoc_result = tictoc::toc()) }if (exists("not_run")) { tictoc::tic() Sys.sleep(1) jrrosell::notify_finished("job", "Well done", sound = "fanfare", tictoc_result = tictoc::toc()) }
Get the name of the package from the DESCRIPTION file of the master branch in the github repo
package_github_name(x, file_lines = NULL)package_github_name(x, file_lines = NULL)
x |
a single repo/package to check Ex: package_github_name("tidyverse/dplyr") |
file_lines |
(default = NULL, internal) |
if (FALSE) { package_github_name("jrosell/jrrosell") }if (FALSE) { package_github_name("jrosell/jrrosell") }
Get the version from the DESCRIPTION file of the master branch in the github repo
package_github_version(x, file_lines = NULL)package_github_version(x, file_lines = NULL)
x |
a single repo/package to check Ex: package_github_version("tidyverse/dplyr") |
file_lines |
(default = NULL, internal) |
if (FALSE) { package_github_version("jrosell/jrrosell") }if (FALSE) { package_github_version("jrosell/jrrosell") }
Plot bars for non double columns
plot_bars(df, ..., top_values = 50)plot_bars(df, ..., top_values = 50)
df |
a data.frame |
... |
optional parameters to geom_histogram |
top_values |
fist most common values (default 50) |
plot_bars(data.frame(a = c("x", "y"), b = c("z", "z")))plot_bars(data.frame(a = c("x", "y"), b = c("z", "z")))
Plot histograms for double columns
plot_histograms(df, ...)plot_histograms(df, ...)
df |
a data.frame |
... |
optional parameters to geom_histogram |
plot_histograms(data.frame(a = c(1, 2), b = c(1, 3)))plot_histograms(data.frame(a = c(1, 2), b = c(1, 3)))
Plot missing values
plot_missing(df)plot_missing(df)
df |
a data.frame |
plot_missing(data.frame(a = c(1, NA), b = c(NA, 4)))plot_missing(data.frame(a = c(1, NA), b = c(NA, 4)))
It returns a bar or a histogram of the variable
plot_variable(df, variable, ..., type = "numeric")plot_variable(df, variable, ..., type = "numeric")
df |
a data.frame |
variable |
the variable to use. |
... |
params passed to geom_* |
type |
numeric (default) or nominal. |
data.frame(a = c("x", "y", "y"), b = c("z", "z", "x")) |> plot_variable(a)data.frame(a = c("x", "y", "y"), b = c("z", "z", "x")) |> plot_variable(a)
Prep, juice and glimpse a recipe or workflow
prep_juice(object)prep_juice(object)
object |
A recipe or a workflow object with a recipe |
https://recipes.tidymodels.org/reference/update.step.html
recipes::recipe(spray ~ ., data = InsectSprays) |> prep_juice() recipes::recipe(spray ~ ., data = InsectSprays) |> workflows::workflow(parsnip::linear_reg()) |> prep_juice()recipes::recipe(spray ~ ., data = InsectSprays) |> prep_juice() recipes::recipe(spray ~ ., data = InsectSprays) |> workflows::workflow(parsnip::linear_reg()) |> prep_juice()
Prep, juice and get cols from a recipe or workflow
prep_juice_ncol(object)prep_juice_ncol(object)
object |
A recipe or a workflow object with a recipe |
recipes::recipe(spray ~ ., data = InsectSprays) |> prep_juice_ncol()recipes::recipe(spray ~ ., data = InsectSprays) |> prep_juice_ncol()
It's useful for reading the most common types of flat file data, comma separated values and tab separated values.
read_chr(file, delim = ",", locale = NULL, ...)read_chr(file, delim = ",", locale = NULL, ...)
file |
Either a path to a file, a connection, or literal data (either a single string or a raw vector). |
delim |
Single character used to separate fields within a record. |
locale |
The locale controls defaults that vary from place to place. The default locale is US-centric (like R), but you can use locale() to create your own locale that controls things like the default time zone, encoding, decimal mark, big mark, and day/month names. |
... |
Other parameters to readr::read_delim. |
The read_chr function works like readr::read_delim, except that
column sreturned would be characters and with clean names. It requires readr and janitor packages installed.
es <- readr::locale("es", tz = "Europe/Madrid", decimal_mark = ",", grouping_mark = ".") read_chr(readr::readr_example("mtcars.csv"), delim = ",", locale = es)es <- readr::locale("es", tz = "Europe/Madrid", decimal_mark = ",", grouping_mark = ".") read_chr(readr::readr_example("mtcars.csv"), delim = ",", locale = es)
It's useful for getting the text for webpages in a single character vector.
read_url(url, sleep = 0)read_url(url, sleep = 0)
url |
Full url including http or https protocol and the page path. |
sleep |
Seconds to sleep after the request is done and before returning the result. |
The read_url function works uses rvest::read_html and purr::possibly and it's fault tolearnt.
if (FALSE) read_url("https://www.google.cat/", sleep = 1)if (FALSE) read_url("https://www.google.cat/", sleep = 1)
It's useful for reading a single sheets from a Excel/Openoffice file.
read_xlsx(xlsxFile, ..., sheet = 1, startRow = 1)read_xlsx(xlsxFile, ..., sheet = 1, startRow = 1)
xlsxFile |
The name of the file. |
... |
Other parameters to openxls::read.xlsx function |
sheet |
The name or index of the sheet (default 1) |
startRow |
The number of the starting reading row (default 1) |
The write_xlsx it's a wroapper for openxls::write.xlsx.
l <- list("IRIS" = iris, "MTCATS" = mtcars, matrix(runif(1000), ncol = 5)) tmp_file <- tempfile(fileext = ".xlsx") write_xlsx(l, tmp_file, colWidths = c(NA, "auto", "auto")) read_xlsx(tmp_file) file.remove(tmp_file)l <- list("IRIS" = iris, "MTCATS" = mtcars, matrix(runif(1000), ncol = 5)) tmp_file <- tempfile(fileext = ".xlsx") write_xlsx(l, tmp_file, colWidths = c(NA, "auto", "auto")) read_xlsx(tmp_file) file.remove(tmp_file)
It generates slugs URLs as WordPress does
sanitize_title_with_dashes(title)sanitize_title_with_dashes(title)
title |
the title |
sanitize_title_with_dashes("Hello world")sanitize_title_with_dashes("Hello world")
Tune a recipe using glmnet and lightgbm and stacks
score_recipe(rec, resamples, grids = list(10, 10), metric = "accuracy")score_recipe(rec, resamples, grids = list(10, 10), metric = "accuracy")
rec |
recipe |
resamples |
rset |
grids |
for glmnet and lightgbm tuning |
metric |
to be compared |
spain_ccaas
spain_ccaasspain_ccaas
spain_ccaasA sf object with 19 rows and 4 columns:
https://github.com/koldLight/curso-r-dataviz/blob/master/dat/spain_ccaas.geojson
library(sf) data(spain_ccaas) head(spain_ccaas)library(sf) data(spain_ccaas) head(spain_ccaas)
spain_provinces
spain_provincesspain_provinces
spain_provincesA sf object with 60 rows and 4 columns:
https://github.com/koldLight/curso-r-dataviz/blob/master/dat/spain_provinces.geojson
library(sf) data(spain_provinces) head(spain_provinces)library(sf) data(spain_provinces) head(spain_provinces)
Sum the missing values from a data.frame
sum_missing(...)sum_missing(...)
... |
one or multiple data.frame |
sum_missing(data.frame(a = c(1, 2), b = c(3, 4))) sum_missing(data.frame(a = c(1, NA), b = c(3, 4))) sum_missing(data.frame(a = c(1, NA), b = c(NA, 4))) sum_missing(data.frame(a = c(NA, NA), b = c(NA, NA)))sum_missing(data.frame(a = c(1, 2), b = c(3, 4))) sum_missing(data.frame(a = c(1, NA), b = c(3, 4))) sum_missing(data.frame(a = c(1, NA), b = c(NA, 4))) sum_missing(data.frame(a = c(NA, NA), b = c(NA, NA)))
Select constant columns from a data.frame
summarize_n_distinct(df)summarize_n_distinct(df)
df |
a data.frame |
summarize_n_distinct(data.frame(a = c(1, 2), b = c(2, 3))) summarize_n_distinct(data.frame(a = c(1, 1), b = c(2, 3)))summarize_n_distinct(data.frame(a = c(1, 2), b = c(2, 3))) summarize_n_distinct(data.frame(a = c(1, 1), b = c(2, 3)))
Pipe a value forward into a functio or call expression and return the original value instead of the result. This is useful when an expression is used for its side-effect, say plotting or printing.
tee(x, expr)tee(x, expr)
x |
An object |
expr |
An expresion |
The tee pipe works like |>, except the
return value is x itself, and not the result of expr call.
I want to give credit to Michael Milton and Matthew Kay for the idea and the code.
https://mastodon.social/@[email protected]/109555362766969210
rnorm(200) |> matrix(ncol = 2) |> as.data.frame() |> tee(\(x) { ggplot(x, aes(V1, V2)) + geom_point() }) |> colSums()rnorm(200) |> matrix(ncol = 2) |> as.data.frame() |> tee(\(x) { ggplot(x, aes(V1, V2)) + geom_point() }) |> colSums()
It requires roboto fonts installed in your O.S. and run z
theme_roboto( base_size = 11, strip_text_size = 12, strip_text_margin = 5, subtitle_size = 13, subtitle_margin = 10, plot_title_size = 16, plot_title_margin = 10, ... )theme_roboto( base_size = 11, strip_text_size = 12, strip_text_margin = 5, subtitle_size = 13, subtitle_margin = 10, plot_title_size = 16, plot_title_margin = 10, ... )
base_size |
= 11 |
strip_text_size |
= 12 |
strip_text_margin |
= 5 |
subtitle_size |
= 13 |
subtitle_margin |
= 10 |
plot_title_size |
= 16 |
plot_title_margin |
= 10 |
... |
Other parameters passed to theme_set |
library(jrrosell) library(ggplot2) theme_set(theme_roboto()) ggplot(iris, aes(Species)) + geom_bar()library(jrrosell) library(ggplot2) theme_set(theme_roboto()) ggplot(iris, aes(Species)) + geom_bar()
Sets a dark blue colored dark minimal theme using the Roboto font family
theme_set_roboto_darkblue(...)theme_set_roboto_darkblue(...)
... |
Other parameters passed to theme_set |
library(jrrosell) library(ggplot2) theme_set_roboto_darkblue() ggplot(iris, aes(Species)) + geom_bar()library(jrrosell) library(ggplot2) theme_set_roboto_darkblue() ggplot(iris, aes(Species)) + geom_bar()
Update the vaules of a specific recipe step located by id
update_step(object, target_id, ...)update_step(object, target_id, ...)
object |
A recipe or a workflow object with a recipe |
target_id |
The id name of the step |
... |
The arguments to update the step. |
recipes::recipe(spray ~ ., data = InsectSprays) |> recipes::step_ns(count, deg_free = hardhat::tune(), id = "ns") |> update_step("ns", deg_free = 1)recipes::recipe(spray ~ ., data = InsectSprays) |> recipes::step_ns(count, deg_free = hardhat::tune(), id = "ns") |> update_step("ns", deg_free = 1)
Create an xgboost tunable workflow for regression and classification
workflow_boost_tree(rec, engine = "xgboost", counts = TRUE, ...)workflow_boost_tree(rec, engine = "xgboost", counts = TRUE, ...)
rec |
prerocessing recipe to build the workflow |
engine |
xgboost, lightgbm (xgboost by default) |
counts |
Optional logic argument wether mtry use counts or not |
... |
optional engine arguments |
library(tidymodels) library(xgboost) library(modeldata) library(future) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = class) train <- training(split) folds <- vfold_cv(train, v = 2, strata = class) wf <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow_boost_tree() doFuture::registerDoFuture() plan(sequential) res <- wf |> tune::tune_grid( folds, grid = 2, metrics = metric_set(roc_auc), control = tune::control_grid(save_workflow = TRUE, verbose = FALSE) ) res |> collect_metrics() res |> last_fit_metrics(split, "roc_auc") best <- res |> fit_best() best |> augment(testing(split)) |> roc_auc(case, .pred_Test) |> pull(.estimate)library(tidymodels) library(xgboost) library(modeldata) library(future) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = class) train <- training(split) folds <- vfold_cv(train, v = 2, strata = class) wf <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow_boost_tree() doFuture::registerDoFuture() plan(sequential) res <- wf |> tune::tune_grid( folds, grid = 2, metrics = metric_set(roc_auc), control = tune::control_grid(save_workflow = TRUE, verbose = FALSE) ) res |> collect_metrics() res |> last_fit_metrics(split, "roc_auc") best <- res |> fit_best() best |> augment(testing(split)) |> roc_auc(case, .pred_Test) |> pull(.estimate)
Create a tuneable glmnet worfklow for regression and classification
workflow_elasticnet(rec, engine = "glmnet", ...)workflow_elasticnet(rec, engine = "glmnet", ...)
rec |
prerocessing recipe to build the workflow |
engine |
glmnet, spark, brulee (glmnet by default) |
... |
Optional engine arguments |
library(tidymodels) library(glmnet) library(modeldata) library(future) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = class) train <- training(split) folds <- vfold_cv(train, v = 2, strata = class) wf <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow_elasticnet() doFuture::registerDoFuture() plan(sequential) res <- wf |> tune::tune_grid( folds, grid = 2, metrics = metric_set(roc_auc), control = tune::control_grid(save_workflow = TRUE, verbose = FALSE) ) res |> collect_metrics() res |> last_fit_metrics(split, "roc_auc") best <- res |> fit_best() best |> augment(testing(split)) |> roc_auc(case, .pred_Test) |> pull(.estimate)library(tidymodels) library(glmnet) library(modeldata) library(future) data(cells) split <- cells |> mutate(across(where(is.character), as.factor)) |> sample_n(500) |> initial_split(strata = class) train <- training(split) folds <- vfold_cv(train, v = 2, strata = class) wf <- train |> recipe(case ~ .) |> step_integer(all_nominal_predictors()) |> workflow_elasticnet() doFuture::registerDoFuture() plan(sequential) res <- wf |> tune::tune_grid( folds, grid = 2, metrics = metric_set(roc_auc), control = tune::control_grid(save_workflow = TRUE, verbose = FALSE) ) res |> collect_metrics() res |> last_fit_metrics(split, "roc_auc") best <- res |> fit_best() best |> augment(testing(split)) |> roc_auc(case, .pred_Test) |> pull(.estimate)
It's useful for saving multiple data to a multiple sheets of a single Excel/Openoffice/libreoffice file.
write_xlsx(data, distfile, ...)write_xlsx(data, distfile, ...)
data |
A named list of tibbles |
distfile |
The name of the destination file. |
... |
Other parameters to openxls::write.xlsx function |
The write_xlsx it's a wroapper for openxls::write.xlsx.
l <- list("IRIS" = iris, "MTCATS" = mtcars, matrix(runif(1000), ncol = 5)) tmp_file <- tempfile(fileext = ".xlsx") write_xlsx(l, tmp_file, colWidths = c(NA, "auto", "auto")) file.remove(tmp_file)l <- list("IRIS" = iris, "MTCATS" = mtcars, matrix(runif(1000), ncol = 5)) tmp_file <- tempfile(fileext = ".xlsx") write_xlsx(l, tmp_file, colWidths = c(NA, "auto", "auto")) file.remove(tmp_file)