Add Variables to ease data usage in a Pivot Table

Description

Adds dummy columns to reach the number specified by the user. this is mostly useful to ensure straightforward and easy data updating when using pivot tables in Excel. It allows replacement of the previous data sheet by the new one, without having to take care about the number of columns, which will always be the same.

Usage

cols_pad(data, nCols = 100, colPrefix = "x_")

Arguments

Value

A data frame with the specified total number of columns.

Examples

table <- data.frame(a = 1:5, b = letters[1:5])
extraTable <- cols_pad(table, nCols = 6, colPrefix = "extra_")
print(extraTable)

Compare Table Variables

Description

Compares column names in two tables based on a given pattern. Provides information about which columns are present in which tables.

Usage

compareVars(x, y, pattern = "")

Arguments

Value

A list containing:

• all: All column names from both tables.

• common: Column names found in both tables.

• onlyX: Column names found only in the first table (x).

• onlyY: Column names found only in the second table (y).

• exclusive: Column names found in only one of the two tables.

Examples

# Example tables
table1 <- data.frame(exclusive_1 = 1:5, common_1 = 6:10, common_2 = 11:15)
table2 <- data.frame(common_1 = 16:20, common_2 = 21:25, exclusive_2 = 26:30)

# Compare all columns (no pattern given)
compare_all <- compareVars(table1, table2)
compare_all$common
compare_all$exclusive
compare_all$onlyX
compare_all$onlyY

# compare only columns following a specific pattern
compare_wPattern <- compareVars(table1, table2, pattern = "1")
compare_wPattern$all
compare_wPattern$common

create an incremented Counter, based on Start/Stop Markers

Description

This function aims at identifying sections and sub-sections numbers, based on markers of section starts and ends.

Given a data frame, and the name of a column giving the start/stop markers, it will add columns giving infos about the successive section levels

Usage

countSwitches(
  data,
  colNm,
  sttMark,
  endMark,
  includeStt = TRUE,
  includeEnd = TRUE
)

Arguments

Value

A modified version of the input data frame with additional columns including:

• 'catLvl': The current series level calculated as the difference between the cumulative counts of start and end markers.

• 'lvl_1', 'lvl_2', 'lvl_3': Final series counts returned for each respective level.

Note

This function is currently mostly useful internally, to perform foldAllBr().

Examples

# example code
library(dplyr)
tribble(
~step
, "start"
, "content of section 1"
, "start"
, "subsection 1.1"
, "end"
, "end"
, "out of any section"
, "start"
, "section 2"
, "start"
, "subsection 2.1"
, "end"
, "start"
, "subsection 2.2"
, "end"
, "end"
) %>%
	countSwitches(colNm = "step", "start", "end")

Easily Fold Code Parts

Description

This function works with code split into parts identified by brackets. The format is as follows:

{
  ...
  code from part 1
  ...
} # part 1
{
  ...
} # part 2

It automatically identifies parts to fold/unfold easily.

Shortcuts required:

• "fold all brackets": shift + alt + S (Windows) / ctrl + shift + up (Mac)

• "expand fold": shift + alt + D (Windows) / ctrl + shift + down (Mac)

Usage

foldAllBr(time = FALSE, debug_getTbl = FALSE)

Arguments

Value

A list containing:

• debug_info: A data frame with debugging information if debug_getTbl = TRUE.

• timer_plot: A ggplot object visualizing execution times if time = TRUE.

If both parameters are FALSE, the function returns a list with NULL values.

Function to Import and Concatenate Multiple data files

Description

Imports multiple files into a list, concatenates them into a single table, and adds an 'fName' variable.

The files can be selected either by giving a file list (character vector), or by specifying a pattern.

Usage

importAll(
  path = ".",
  pattern = "",
  ignore.case = FALSE,
  importFunction = NULL,
  fill = FALSE,
  fileList = NULL
)

Arguments

Value

A data frame containing the concatenated table with the fName column

Examples

# Directory containing test files
test_path <- tempdir()

# Create test files
write.csv( data.frame(a = 1:3, b = 4:6)    , file.path(test_path, "file1.csv"))
write.csv( data.frame(a = 7:9, b = 10:12)  , file.path(test_path, "file2.csv"))
write.csv( data.frame(a = 3:5, b = 8:10)   , file.path(test_path, "file3.csv"))
saveRDS(   data.frame(a = 1:5, b = 6:10)   , file.path(test_path, "file1.rds"))
saveRDS(   data.frame(a = 11:15, b = 16:20), file.path(test_path, "file2.rds"))

# Example 1 : Import all csv files
result <- importAll(path = test_path, pattern = "\\.csv$")
print(result)

# Example 2: Import only selected files
file_list <- c("file1.csv", "file2.csv")
result <- importAll(path = test_path, fileList = file_list)
print(result)

# Example 3: Import all .rds files
result <- importAll(path = test_path, pattern = "\\.rds$")
print(result)

# Example 4: Use a custom import function
custom_import <- function(file) {
  data <- read.csv(file, stringsAsFactors = FALSE)
  return(data)
}
result <- importAll(path = test_path, pattern = "\\.csv$", importFunction = custom_import)
print(result)

Left Join with Validation Checks

Description

a custom usage of left_join, with more detailed checks. Performs a left join and verifies that no unexpected duplicates or mismatches occur. In cas of unexpected results, gives details about what caused the problem.

Usage

left_join_checks(
  x,
  y,
  ...,
  req_xAllMatch = 1,
  req_preserved_x = 1,
  behavior = "error",
  showNotFound = FALSE,
  showProblems = TRUE,
  time = FALSE
)

Arguments

Value

A data.table containing the joined table.

Examples

library(data.table)
library(dplyr)

# Example 1: Simple left join with all matches
table_left <- data.table(id = 1:3, value_left = c("A", "B", "C"))
table_right <- data.table(id = 1:3, value_right = c("X", "Y", "Z"))
result <- left_join_checks(table_left, table_right, by = "id", req_preserved_x = TRUE)
print(result) # Ensures all rows in table_left are preserved

# Example 2: Left join with missing matches
table_left <- data.table(id = 1:5, value_left = c("A", "B", "C", "D", "E"))
table_right <- data.table(id = c(1, 3, 5), value_right = c("X", "Y", "Z"))
result <- left_join_checks(
  table_left,
  table_right,
  by = "id",
  req_preserved_x = TRUE,
  showNotFound = TRUE,
  behavior = "warning"
)
print(result) # Rows from table_left with no matches in table_right are shown

Adjust the Brightness of the Graphics Window for confortable viewing when using ggplot2

Description

Modifies the brightness level of the active graphics window by adjusting its background color.

This is especially useful when using dark RStudio themes, where a 100 graphic window creates an unconfortable contrast.

Usage

lum_0_100(lum = NULL)

Arguments

Value

no return value : only apply the theme_set() function

Save File in a Directory storing saves, prefixing it with current date

Description

Saves a file with current date in its name in a sub directory located in the same directory as the original file. Optionally, a note is added after the file name.

Usage

quickSave(
  saveDir,
  filePath = NULL,
  saveNote = NULL,
  overwrite = FALSE,
  verbose = FALSE
)

Arguments

Value

the output value of the function used to copy file

Generate Random Dates, with a similar usage as the r* functions

Description

Generates a vector of random dates within a specified range. This function tries to replicate the usage of the r* functions from stats package, such as runif(), rpois(), ...

Usage

rdate(
  x,
  min = paste0(format(Sys.Date(), "%Y"), "-01-01"),
  max = paste0(format(Sys.Date(), "%Y"), "-12-31"),
  sort = FALSE,
  include_hours = FALSE
)

Arguments

Value

A vector of random dates of length 'x'.

Examples

# Generate 5 random dates between two specific dates, sorted
rdate(5, min = as.Date("2020-01-01"), max = as.Date("2020-12-31"), sort = TRUE)

# Generate 7 random datetime values (with hours)
rdate(7, include_hours = TRUE)

Adjust the Brightness of a Hex Color

Description

Modifies the brightness of a color by multiplying its RGB components by a specified factor.

Mostly for internal usage inside lum_0_100 function.

Usage

ret_lum(hexCol, rgbFact)

Arguments

Value

A modified hex color in hexadecimal format.

Examples

# Example 1: Lightening a color
ret_lum("#FF5733", 1.5)  # Returns a lighter version of the input color

# Example 2: Darkening a color
ret_lum("#FF5733", 0.7)  # Returns a darker version of the input color

Get Root Directory of Current Source File

Description

Returns the directory path where the current source code file is located.

It is especially useful when the same source code is used by multiple users, each using his own environment, with different file paths.

the aim is to avoid writing full paths in raw text inside source codes.

Usage

root()

Value

A character string representing the absolute path of the directory containing the current source file.

Quick Number Formatting with Custom Defaults

Description

A wrapper for the 'format' function, designed to format numbers with custom defaults for thousands separator, number of significant digits, and scientific notation.

Usage

sepThsd(x, big.mark = " ", digits = 1, scientific = FALSE)

Arguments

Value

A character vector of formatted numbers.

Examples

# Format with a comma as a thousands separator and 3 significant digits
sepThsd(1234567.89, big.mark = ",", digits = 3)
# Use scientific notation
sepThsd(1234567.89, scientific = TRUE)

Set Shiny Background and Sidebar Colors to a Chosen Shade of Grey

Description

Adjust the background color of a Shiny app's main body and sidebar based on a specified luminosity level.

The purpose is the same as lum_0_100() function, avoiding problems with high contrast between with graphic windows and dark themes.

Usage

shiny_lum_0_100(lum)

Arguments

Value

The HTML tags for setting the background and sidebar colors.

allow organized tracking of R code execution time

Description

The 'timer' function allows you to append timeStamps to a data.table, and include additional metadata provided as arguments. The last call calculates time differences between timeStamps.

Usage

timer(timer_table = data.table(), end = FALSE, ...)

Arguments

Value

A 'data.table' containing the original data, plus one new timeStamp, and optionally computed time differences :

• 'timeStamp': The current timeStamp ('POSIXct').

• 'timeStamp_num': timeStamp converted to numeric, useful for intermediary calculations.

• 'dt_num': The time difference in seconds between consecutive rows as a numeric value.

• 'dt_text': The formatted time difference in seconds with milliseconds as a character string.

• Additional columns for any information provided by the user via '...'. It allows documentation about the current step running, substeps, which version is being tested, ...

Examples

# compare code speed between using a loop, or the mean() function
library(data.table)
library(dplyr)
tmr <- data.table()  # Initialize timer
vec <- rnorm(1e6)    # Example vector

tmr <- timer(tmr, method = "loop")   # timeStamp : 1st step =================
total <- 0
for (i in seq_along(vec)) total <- total + vec[i]
mean_loop <- total / length(vec)

tmr <- timer(tmr, method = "mean()") # timeStamp : 1st step =================
mean_func <- mean(vec)

tmr <- timer(tmr, end = TRUE)        # timeStamp : close timer ==============

t_step1 <- tmr[method == "loop"]$dt_num
t_step2 <- tmr[method == "mean()"]$dt_num
diff_pc <- (t_step2/t_step1 - 1) * 100
diff_txt <- format(diff_pc, nsmall = 0, digits = 1)

# view speed difference
print(tmr %>% select(-matches("_num$")))
paste0("speed difference : ", diff_txt, "%")