| Title: | Solutions for Common Problems in Base R |
|---|---|
| Description: | Contains functions for solving commonly encountered problems while programming in R. This package is intended to provide a lightweight supplement to Base R, and will be useful for almost any R user. |
| Authors: | David Bosak [aut, cre], Duong Tran [ctb] |
| Maintainer: | David Bosak <[email protected]> |
| License: | CC0 |
| Version: | 1.1.3 |
| Built: | 2025-01-29 05:09:11 UTC |
| Source: | https://github.com/dbosak01/common |
The goal of the %eq% operator is to return a TRUE
or FALSE value when any two objects are compared. The function provides a
simple, reliable equality check that allows comparing
of NULLs, NA values, and atomic data types without error.
The function also allows comparing of data frames. It will return TRUE if all values in the data frames are equal, and ignores differences in attributes.
x1 %eq% x2x1 %eq% x2
x1 |
The first object to compare |
x2 |
The second object to compare |
A single TRUE or FALSE value depending on whether the objects are equal.
# Comparing of NULLs and NA NULL %eq% NULL # TRUE NULL %eq% NA # FALSE NA %eq% NA # TRUE 1 %eq% NULL # FALSE 1 %eq% NA # FALSE # Comparing of atomic values 1 %eq% 1 # TRUE "one" %eq% "one" # TRUE 1 %eq% "one" # FALSE 1 %eq% Sys.Date() # FALSE # Comparing of vectors v1 <- c("A", "B", "C") v2 <- c("A", "B", "D") v1 %eq% v1 # TRUE v1 %eq% v2 # FALSE # Comparing of data frames mtcars %eq% mtcars # TRUE mtcars %eq% iris # FALSE iris %eq% iris[1:50,] # FALSE # Mixing it up mtcars %eq% NULL # FALSE v1 %eq% NA # FALSE 1 %eq% v1 # FALSE# Comparing of NULLs and NA NULL %eq% NULL # TRUE NULL %eq% NA # FALSE NA %eq% NA # TRUE 1 %eq% NULL # FALSE 1 %eq% NA # FALSE # Comparing of atomic values 1 %eq% 1 # TRUE "one" %eq% "one" # TRUE 1 %eq% "one" # FALSE 1 %eq% Sys.Date() # FALSE # Comparing of vectors v1 <- c("A", "B", "C") v2 <- c("A", "B", "D") v1 %eq% v1 # TRUE v1 %eq% v2 # FALSE # Comparing of data frames mtcars %eq% mtcars # TRUE mtcars %eq% iris # FALSE iris %eq% iris[1:50,] # FALSE # Mixing it up mtcars %eq% NULL # FALSE v1 %eq% NA # FALSE 1 %eq% v1 # FALSE
paste0()
This function provides an infix operator for the
paste0 function to concatenate strings. The operator
will concatenate a vector of one or more values. The functionality
is identical to paste0(), but more convenient to use in some
situations.
x %p% yx %p% y
x |
A value for the left side of the paste infix operator. |
y |
A value for the right side of the paste infix operator. |
The concatenated or pasted value. No spaces will be inserted in between the values to paste. If a vector of values is supplied, a vector of pasted values will be returned.
# Paste together two strings str <- "Hello" %p% "World" str # [1] "HelloWorld" # Paste together number and string str <- 100 %p% " Kittens" str # [1] "100 Kittens" # Paste together two vectors v1 <- c("A", "B", "C") v2 <- c(1, 2, 3) str <- v1 %p% v2 str # [1] "A1" "B2" "C3"# Paste together two strings str <- "Hello" %p% "World" str # [1] "HelloWorld" # Paste together number and string str <- 100 %p% " Kittens" str # [1] "100 Kittens" # Paste together two vectors v1 <- c("A", "B", "C") v2 <- c(1, 2, 3) str <- v1 %p% v2 str # [1] "A1" "B2" "C3"
The changed function identifies changes in a vector or
data frame. The function is used to locate grouping boundaries. It will
return a TRUE each time the current value is different from the previous
value. The changed function is similar to the Base R duplicated
function, except the changed function will return TRUE even if
the changed value is not unique.
changed(x, reverse = FALSE, simplify = FALSE)changed(x, reverse = FALSE, simplify = FALSE)
x |
A vector of values in which to identify changed values. Also accepts a data frame. In the case of a data frame, the function will use all columns. Input data can be any data type. |
reverse |
Reverse the direction of the scan to identify the last value in a group instead of the first. |
simplify |
If the input data to the function is a data frame, the simplify option will return a single vector of indicator values instead of a data frame of indicator values. |
For a data frame, by default, the function will return another data frame with an equal number of change indicator columns. The column names will be the original column names, with a ".changed" suffix.
To collapse the multiple change indicators into one vector, use the "simplify" option. In this case, the returned vector will essentially be an "or" operation across all columns.
A vector of TRUE or FALSE values indicating the grouping boundaries of the vector or data frame. If the input data is a data frame and the "simplify" parameter is FALSE, the return value will be a data frame of logical vectors describing changed values for each column.
# Create sample vector v1 <- c(1, 1, 1, 2, 2, 3, 3, 3, 1, 1) # Identify changed values res1 <- changed(v1) # View results res1 # [1] TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE # Create sample data frame v2 <- c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B") dat <- data.frame(v1, v2) # View original data frame dat # v1 v2 # 1 1 A # 2 1 A # 3 1 A # 4 2 A # 5 2 A # 6 3 A # 7 3 B # 8 3 B # 9 1 B # 10 1 B # Get changed values for each column res2 <- changed(dat) # View results res2 # v1.changed v2.changed # 1 TRUE TRUE # 2 FALSE FALSE # 3 FALSE FALSE # 4 TRUE FALSE # 5 FALSE FALSE # 6 TRUE FALSE # 7 FALSE TRUE # 8 FALSE FALSE # 9 TRUE FALSE # 10 FALSE FALSE # Get changed values for all columns res3 <- changed(dat, simplify = TRUE) # View results res3 # [1] TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE # Get last items in each group instead of first res4 <- changed(dat, reverse = TRUE) # View results res4 # v1.changed v2.changed # 1 FALSE FALSE # 2 FALSE FALSE # 3 TRUE FALSE # 4 FALSE FALSE # 5 TRUE FALSE # 6 FALSE TRUE # 7 FALSE FALSE # 8 TRUE FALSE # 9 FALSE FALSE # 10 TRUE TRUE# Create sample vector v1 <- c(1, 1, 1, 2, 2, 3, 3, 3, 1, 1) # Identify changed values res1 <- changed(v1) # View results res1 # [1] TRUE FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE # Create sample data frame v2 <- c("A", "A", "A", "A", "A", "A", "B", "B", "B", "B") dat <- data.frame(v1, v2) # View original data frame dat # v1 v2 # 1 1 A # 2 1 A # 3 1 A # 4 2 A # 5 2 A # 6 3 A # 7 3 B # 8 3 B # 9 1 B # 10 1 B # Get changed values for each column res2 <- changed(dat) # View results res2 # v1.changed v2.changed # 1 TRUE TRUE # 2 FALSE FALSE # 3 FALSE FALSE # 4 TRUE FALSE # 5 FALSE FALSE # 6 TRUE FALSE # 7 FALSE TRUE # 8 FALSE FALSE # 9 TRUE FALSE # 10 FALSE FALSE # Get changed values for all columns res3 <- changed(dat, simplify = TRUE) # View results res3 # [1] TRUE FALSE FALSE TRUE FALSE TRUE TRUE FALSE TRUE FALSE # Get last items in each group instead of first res4 <- changed(dat, reverse = TRUE) # View results res4 # v1.changed v2.changed # 1 FALSE FALSE # 2 FALSE FALSE # 3 TRUE FALSE # 4 FALSE FALSE # 5 TRUE FALSE # 6 FALSE TRUE # 7 FALSE FALSE # 8 TRUE FALSE # 9 FALSE FALSE # 10 TRUE TRUE
A function to copy column attributes from one data frame to another. The function will copy all attributes attached to each column. The column order does not matter, and the data frames do not need identical structures. The matching occurs by column name, not position. Any existing attributes on the target data frame that do not match the source data frame will be retained unaltered.
copy.attributes(source, target)copy.attributes(source, target)
source |
A data frame to copy attributes from. |
target |
A data frame to copy attributes to. |
The data frame in the target parameter, with updated
attributes from source.
Other overrides:
labels.data.frame(),
sort.data.frame()
# Prepare data dat1 <- mtcars dat2 <- mtcars # Set labels for dat1 labels(dat1) <- list(mpg = "Miles Per Gallon", cyl = "Cylinders", disp = "Displacement") # Copy labels from dat1 to dat2 dat2 <- copy.attributes(dat1, dat2) # View results labels(dat2) # $mpg # [1] "Miles Per Gallon" # # $cyl # [1] "Cylinders" # # $disp # [1] "Displacement"# Prepare data dat1 <- mtcars dat2 <- mtcars # Set labels for dat1 labels(dat1) <- list(mpg = "Miles Per Gallon", cyl = "Cylinders", disp = "Displacement") # Copy labels from dat1 to dat2 dat2 <- copy.attributes(dat1, dat2) # View results labels(dat2) # $mpg # [1] "Miles Per Gallon" # # $cyl # [1] "Cylinders" # # $disp # [1] "Displacement"
A function to find directories on the file system. The function
starts from the directory specified in the path parameter,
and searches outward in a radiating pattern
for the ending directory name in the pattern parameter.
The up and down parameters define the scope of the search.
Results are returned as a vector of full paths in the order encountered.
This function has an advantage over list.dirs in that it can
search both up and down the file system, and limit the scope of the search.
dir.find(path = ".", pattern = NULL, up = 5, down = 2)dir.find(path = ".", pattern = NULL, up = 5, down = 2)
path |
The directory to start searching from. Default is the current working directory. |
pattern |
A full or partial name of the directory to find. If partial, use the question mark (?) or asterisk (*) characters to indicate the missing piece. Default is NULL, which will return all directories. |
up |
The number of levels above the base path to search. A value of zero (0) means that you do not want to search up. A value of -1 means you do not want to include the base directory in the search results. Default is 5 levels up. |
down |
The number of levels below the base path to search. A value of zero (0) means that you do not want to search down. A value of -1 means you do not want to include the base directory in the search results. Default is 2 levels down. |
The dir.find function attempts to find a directory based on
a full or partial directory name. The directory name is passed
on the pattern parameter.
The pattern accepts both the single character question mark
wild card (?), and the asterisk, multi-character wild card (*).
Searches are case-insensitive.
Starting from the base path specified in the path parameter,
the function searches
both above and below the base path in an alternating
pattern. The function will first search the base path, then up one level,
then down one level, and so on. The boundaries of
the search can be controlled by the up and down parameters.
You can control whether or not you want the base directory
included in the results. To include this directory, ensure both up
and down parameters are zero or greater. If either of these
parameters is set to -1, the base path will be excluded. For example,
up = 3, down = 1 will search up three levels, and down one level.
up = 3, down = 0 will search up three levels and not search down,
but will include the base directory. up = 3, down = -1 will search
up three levels, not search down, and not include the base directory in
the results.
A vector of one or more full directory paths that met the search criteria. The paths in the vector are returned in the order of matches, according to the search algorithm. That means the first directory found will be in position one, and the last directory found will be at the end of the vector. A NULL is returned if no directories met the search criteria.
Other fileops:
Sys.path(),
file.find(),
source.all()
# Search for a directory named "prod" file.find(pattern = "prod") # Search for a directory that starts with "dat" file.find(pattern = "dat*") # Search for a directory up only file.find(pattern = "dat*", up = 3, down = 0) # Search for directories up only, skipping the current working directory file.find(pattern = "dat*", up = 3, down = -1)# Search for a directory named "prod" file.find(pattern = "prod") # Search for a directory that starts with "dat" file.find(pattern = "dat*") # Search for a directory up only file.find(pattern = "dat*", up = 3, down = 0) # Search for directories up only, skipping the current working directory file.find(pattern = "dat*", up = 3, down = -1)
A function to find files on the file system. The function
starts from the directory specified in the path parameter,
and searches outward in a radiating pattern
for the file name in the pattern parameter.
Results are returned as a vector of full paths in the order encountered.
The up and down parameters define the scope of the search.
This function has an advantage over list.files in that it can
search both up and down the file system, and limit the scope of the search.
file.find(path = ".", pattern = NULL, up = 3, down = 1)file.find(path = ".", pattern = NULL, up = 3, down = 1)
path |
The directory to start searching from. Default is the current working directory. |
pattern |
A full or partial name of the file to find. If partial, use the question mark (?) or asterisk (*) characters to indicate the missing piece. Default is NULL, which will return all files. |
up |
The number of levels above the base path to search. A value of zero (0) means that you do not want to search up. A value of -1 means you do not want to include the base directory in the search results. Default is 3 levels up. |
down |
The number of levels below the base path to search. A value of zero (0) means that you do not want to search down. A value of -1 means you do not want to include the base directory in the search results. Default is 1 level down. |
The file.find function attempts to find a file based on
a full or partial file name. The file name is passed
on the pattern parameter.
The pattern accepts both the single character question mark
wild card (?), and the asterisk multi-character wild card (*).
Searches are case-insensitive.
Starting from the base path specified in the path parameter,
the function searches
both above and below the base path in an alternating
pattern. The function will first search the base path, then up one level,
then down one level, and so on. The boundaries of
the search can be controlled by the up and down parameters.
You can control whether or not you want files from the base directory
included in the results. To include these files, ensure both up
and down parameters are zero or greater. If either of these
parameters is set to -1, the base path will be excluded. For example,
up = 3, down = 1 will search up three levels, and down one level.
up = 3, down = 0 will search up three levels and not search down,
but will include the base directory. up = 3, down = -1 will search
up three levels, not search down, and not include the base directory in
the results.
A vector of one or more full file paths that met the search criteria. The paths in the vector are returned in the order of matches, according to the search algorithm. That means the first file found will be in position one, and the last file found will be at the end of the vector. A NULL is returned if no files met the search criteria.
Other fileops:
Sys.path(),
dir.find(),
source.all()
# Search for a file named "globals.R" file.find(getwd(), "globals.R") # Search for Rdata files file.find(getwd(), "*.Rdata") # Search for Rdata files up only file.find(getwd(), "*.Rdata", up = 3, down = 0) # Search for Rdata files up only, skipping the current working directory file.find(getwd(), "*.Rdata", up = 3, down = -1)# Search for a file named "globals.R" file.find(getwd(), "globals.R") # Search for Rdata files file.find(getwd(), "*.Rdata") # Search for Rdata files up only file.find(getwd(), "*.Rdata", up = 3, down = 0) # Search for Rdata files up only, skipping the current working directory file.find(getwd(), "*.Rdata", up = 3, down = -1)
A function to search for variable names in a data.frame or tibble. The function features wild card pattern matching, start and end boundaries, and names to exclude.
find.names( x, pattern = NULL, exclude = NULL, start = NULL, end = NULL, ignore.case = TRUE )find.names( x, pattern = NULL, exclude = NULL, start = NULL, end = NULL, ignore.case = TRUE )
x |
A data frame or tibble whose names to search. Parameter also accepts a character vector of names. |
pattern |
A vector of patterns to search for. The asterisk (*) and question mark (?) characters may be used to indicate partial matches. |
exclude |
A vector of patterns to exclude from the search results. The asterisk (*) and question mark (?) characters may be used to indicate partial matches. |
start |
A variable name or position to start the search. Default is 1. |
end |
A variable name or position to end the search. Default is the length of the name vector. |
ignore.case |
Whether to perform a case sensitive or insensitive search. Valid values are TRUE and FALSE. Default is TRUE. |
A vector of variable names that met the search criteria.
# Show all names for reference names(mtcars) # [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb" # Names that start with "c" find.names(mtcars, "c*") # [1] "cyl" "carb" # Names that start with "c" or "d" find.names(mtcars, c("c*", "d*")) # [1] "cyl" "carb" "disp" "drat" # Names between "disp" and "qsec" find.names(mtcars, start = "disp", end = "qsec") # [1] "disp" "hp" "drat" "wt" "qsec" # Names that start with "c" or "d" after position 5 find.names(mtcars, c("c*", "d*"), start = 5) # [1] "carb" "drat" # Names between "disp" and "qsec" excluding "wt" find.names(mtcars, start = "disp", end = "qsec", exclude = "wt") # [1] "disp" "hp" "drat" "qsec"# Show all names for reference names(mtcars) # [1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear" "carb" # Names that start with "c" find.names(mtcars, "c*") # [1] "cyl" "carb" # Names that start with "c" or "d" find.names(mtcars, c("c*", "d*")) # [1] "cyl" "carb" "disp" "drat" # Names between "disp" and "qsec" find.names(mtcars, start = "disp", end = "qsec") # [1] "disp" "hp" "drat" "wt" "qsec" # Names that start with "c" or "d" after position 5 find.names(mtcars, c("c*", "d*"), start = 5) # [1] "carb" "drat" # Names between "disp" and "qsec" excluding "wt" find.names(mtcars, start = "disp", end = "qsec", exclude = "wt") # [1] "disp" "hp" "drat" "qsec"
The labels function extracts all assigned labels from a
data frame, and returns them in a named list. The function also
assigns labels from a named list. This function is a data frame-specific
implementation of the Base R labels function.
## S3 method for class 'data.frame' labels(object, ...) labels(x) <- value## S3 method for class 'data.frame' labels(object, ...) labels(x) <- value
object |
A data frame or tibble. |
... |
Follow-on parameters. Required for generic function. |
x |
A data frame or tibble |
value |
A named list of labels. The labels must be quoted strings. |
If labels are assigned to the "label" attributes of the data frame
columns, the labels function will extract those labels. The
function will return the labels in a named list, where the names
correspond to the name of the column that the label was assigned to.
If a column does not have a label attribute assigned, that column
will not be included in the list.
When used on the receiving side of an assignment, the function will assign labels to a data frame. The labels should be in a named list, where each name corresponds to the data frame column to assign the label to.
Finally, if you wish to clear out the label attributes, assign
a NULL value to the labels function.
A named list of labels. The labels must be quoted strings.
Other overrides:
copy.attributes(),
sort.data.frame()
# Take subset of data df1 <- mtcars[1:10, c("mpg", "cyl")] # Assign labels labels(df1) <- list(mpg = "Mile Per Gallon", cyl = "Cylinders") # Examine attributes str(df1) # 'data.frame': 10 obs. of 2 variables: # $ mpg: num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 # ..- attr(*, "label")= chr "Mile Per Gallon" # $ cyl: num 6 6 4 6 8 6 8 4 4 6 # ..- attr(*, "label")= chr "Cylinders" # View assigned labels labels(df1) # $mpg # [1] "Mile Per Gallon" # # $cyl # [1] "Cylinders" # Clear labels labels(df1) <- NULL # Display Cleared Labels labels(df1) # list()# Take subset of data df1 <- mtcars[1:10, c("mpg", "cyl")] # Assign labels labels(df1) <- list(mpg = "Mile Per Gallon", cyl = "Cylinders") # Examine attributes str(df1) # 'data.frame': 10 obs. of 2 variables: # $ mpg: num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 # ..- attr(*, "label")= chr "Mile Per Gallon" # $ cyl: num 6 6 4 6 8 6 8 4 4 6 # ..- attr(*, "label")= chr "Cylinders" # View assigned labels labels(df1) # $mpg # [1] "Mile Per Gallon" # # $cyl # [1] "Cylinders" # Clear labels labels(df1) <- NULL # Display Cleared Labels labels(df1) # list()
A function that rounds positive numbers up when the last digit is a 5. For negative numbers ending in 5, the function actually rounds down. "Round away from zero" is the most accurate description of this function.
roundup(x, digits = 0)roundup(x, digits = 0)
x |
A vector of values to round. Also accepts a data frame. In the case of a data frame, the function will round all numeric columns. |
digits |
A number of decimal places to round to. Default is zero. |
The rounded data vector.
# Round to even round(2.4) # 2 round(2.5) # 2 round(-2.5) # -2 round(2.6) # 3 # Round up roundup(2.4) # 2 roundup(2.5) # 3 roundup(-2.5) # -3 roundup(2.6) # 3# Round to even round(2.4) # 2 round(2.5) # 2 round(-2.5) # -2 round(2.6) # 3 # Round up roundup(2.4) # 2 roundup(2.5) # 3 roundup(-2.5) # -3 roundup(2.6) # 3
An overload to the Base R sort function for
data frames. Allows multiple columns to be sorted easily. Also
allows you to control the sort direction for each column independently.
## S3 method for class 'data.frame' sort( x, decreasing = FALSE, ..., by = NULL, ascending = TRUE, na.last = TRUE, index.return = FALSE )## S3 method for class 'data.frame' sort( x, decreasing = FALSE, ..., by = NULL, ascending = TRUE, na.last = TRUE, index.return = FALSE )
x |
The input data frame to sort. |
decreasing |
This parameter was added to conform to the S3 generic
method signature of the |
... |
This parameter is required for the generic method signature. Anything passed on it will be ignored. |
by |
A vector of column names to sort by. If this parameter is not supplied, the function will sort by all columns in order from left to right. |
ascending |
A vector of TRUE or FALSE values corresponding
to the variables on the |
na.last |
Whether to put NA values first or last in the sort. If TRUE, NA values will sort to the bottom. If FALSE, NA values will sort to the top. The default is TRUE. |
index.return |
Whether to return the sorted data frame or a vector of sorted index values. If this parameter is TRUE, the function will return sorted index values. By default, the parameter is FALSE, and will return the sorted data frame. |
The function returns either a sorted data frame or a
sorted vector of row index values, depending on the value of the
index.return parameter. If index.return is FALSE,
the function will return the sorted data frame.
If the index.return parameter is TRUE, it will return a vector
of row indices.
Other overrides:
copy.attributes(),
labels.data.frame()
# Prepare unsorted sample data dt <- mtcars[1:10, 1:3] dt # mpg cyl disp # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Datsun 710 22.8 4 108.0 # Hornet 4 Drive 21.4 6 258.0 # Hornet Sportabout 18.7 8 360.0 # Valiant 18.1 6 225.0 # Duster 360 14.3 8 360.0 # Merc 240D 24.4 4 146.7 # Merc 230 22.8 4 140.8 # Merc 280 19.2 6 167.6 # Sort by mpg ascending dt1 <- sort(dt, by = "mpg") dt1 # mpg cyl disp # Duster 360 14.3 8 360.0 # Valiant 18.1 6 225.0 # Hornet Sportabout 18.7 8 360.0 # Merc 280 19.2 6 167.6 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Hornet 4 Drive 21.4 6 258.0 # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Merc 240D 24.4 4 146.7 # Sort by mpg descending dt1 <- sort(dt, by = "mpg", ascending = FALSE) dt1 # mpg cyl disp # Merc 240D 24.4 4 146.7 # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Hornet 4 Drive 21.4 6 258.0 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Merc 280 19.2 6 167.6 # Hornet Sportabout 18.7 8 360.0 # Valiant 18.1 6 225.0 # Duster 360 14.3 8 360.0 # Sort by cyl then mpg dt1 <- sort(dt, by = c("cyl", "mpg")) dt1 # mpg cyl disp # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Merc 240D 24.4 4 146.7 # Valiant 18.1 6 225.0 # Merc 280 19.2 6 167.6 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Hornet 4 Drive 21.4 6 258.0 # Duster 360 14.3 8 360.0 # Hornet Sportabout 18.7 8 360.0 # Sort by cyl descending then mpg ascending dt1 <- sort(dt, by = c("cyl", "mpg"), ascending = c(FALSE, TRUE)) dt1 # mpg cyl disp # Duster 360 14.3 8 360.0 # Hornet Sportabout 18.7 8 360.0 # Valiant 18.1 6 225.0 # Merc 280 19.2 6 167.6 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Hornet 4 Drive 21.4 6 258.0 # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Merc 240D 24.4 4 146.7# Prepare unsorted sample data dt <- mtcars[1:10, 1:3] dt # mpg cyl disp # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Datsun 710 22.8 4 108.0 # Hornet 4 Drive 21.4 6 258.0 # Hornet Sportabout 18.7 8 360.0 # Valiant 18.1 6 225.0 # Duster 360 14.3 8 360.0 # Merc 240D 24.4 4 146.7 # Merc 230 22.8 4 140.8 # Merc 280 19.2 6 167.6 # Sort by mpg ascending dt1 <- sort(dt, by = "mpg") dt1 # mpg cyl disp # Duster 360 14.3 8 360.0 # Valiant 18.1 6 225.0 # Hornet Sportabout 18.7 8 360.0 # Merc 280 19.2 6 167.6 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Hornet 4 Drive 21.4 6 258.0 # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Merc 240D 24.4 4 146.7 # Sort by mpg descending dt1 <- sort(dt, by = "mpg", ascending = FALSE) dt1 # mpg cyl disp # Merc 240D 24.4 4 146.7 # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Hornet 4 Drive 21.4 6 258.0 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Merc 280 19.2 6 167.6 # Hornet Sportabout 18.7 8 360.0 # Valiant 18.1 6 225.0 # Duster 360 14.3 8 360.0 # Sort by cyl then mpg dt1 <- sort(dt, by = c("cyl", "mpg")) dt1 # mpg cyl disp # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Merc 240D 24.4 4 146.7 # Valiant 18.1 6 225.0 # Merc 280 19.2 6 167.6 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Hornet 4 Drive 21.4 6 258.0 # Duster 360 14.3 8 360.0 # Hornet Sportabout 18.7 8 360.0 # Sort by cyl descending then mpg ascending dt1 <- sort(dt, by = c("cyl", "mpg"), ascending = c(FALSE, TRUE)) dt1 # mpg cyl disp # Duster 360 14.3 8 360.0 # Hornet Sportabout 18.7 8 360.0 # Valiant 18.1 6 225.0 # Merc 280 19.2 6 167.6 # Mazda RX4 21.0 6 160.0 # Mazda RX4 Wag 21.0 6 160.0 # Hornet 4 Drive 21.4 6 258.0 # Datsun 710 22.8 4 108.0 # Merc 230 22.8 4 140.8 # Merc 240D 24.4 4 146.7
A function to source all programs in a specified directory. The function will run each R program file in the directory, and then return a data frame of results of the run.
source.all(path = ".", pattern = NULL, exclude = NULL, isolate = TRUE)source.all(path = ".", pattern = NULL, exclude = NULL, isolate = TRUE)
path |
The directory to source programs from. Default is the current working directory. |
pattern |
A full or partial name of the programs to source. If partial, use the question mark (?) or asterisk (*) characters to indicate the missing piece(s). Default is NULL, which will return all programs. You may pass multiple patterns as a vector. In that case, the function will perform an "or" operation on each pattern. Note that it is not necessary to include the ".R" file extension in your patterns. It is assumed that all source files have a ".R" extension. |
exclude |
A vector of patterns to exclude from the included programs. The exclusion patterns can be the names of specific programs or a wild card exclusion. The asterisk (*) and question mark (?) characters may be used to indicate partial matches. Similar to the "pattern" parameter, the ".R" file extension can be ignored. |
isolate |
Whether to isolate each source call to its own environment. Valid values are TRUE, FALSE, or an environment to run in. If the isolate parameter is FALSE, the programs will run in the global environment. Default is TRUE. |
The source.all function attempts to run all programs in a directory.
This function is useful for batch runs. It has parameters to control
which programs are run or not run. By default, the function will run
all programs in the working directory. You can use the "pattern" and
"exclude" parameters to specify individual program names, or wild card matches.
Inclusion and exclusion patterns are case-insensitive.
Note that the function will run all programs, regardless of any errors. Errors will be indicated in the "Status" and "Message" columns of the result dataset.
A data frame of the results of the source operation. The data frame will show each file sourced, the time started, the time ended, the status, and any error messages. The status value is either 0 (no errors) or 1 (errors).
The source.all function returns a dataset showing the results of the
source operation. There will be one row for each program executed. The
return dataset has the following columns:
Filename: The name of the program.
StartTime: The date and time execution started.
EndTime: The date and time execution ended.
Status: A numeric value indicating whether or not
the program returned errors or warnings. A zero (0) value indicates that
no errors occurred. A one (1) value indicates that an error
occurred. Warnings can also be generated along with an error, but
the status will still be one (1). A two (2) value indicates
that warnings occurred but no errors. Note that capture of warnings
is less reliable than the capture of errors. It is possible that a program
may generate a warning and still return a zero (0) status. If you want to
ensure that warnings are detected, convert them to errors with
options(warn = 2).
Message: If errors or warnings are returned from the program, they will be shown in this column. Multiple messages will be separated with a semi-colon (;) and a carriage return.
In addition to the information shown above, the results dataset will have
attributes assigned with the parameter values passed to the function. Those
attributes can be observed with the Base R attributes() function.
Multiple programs running in the same environment have a risk of conflicting
variables or data. Variables created by the first program can possibly interfere
with the running of the next program. Or they could conflict with variables
in the global environment. To avoid such conflicts, each program
is run in its own environment by default. isolate = TRUE starts each program
with a clean workspace, and is the best choice for running programs in
batch.
There may be situations, however, where you do not want to isolate the source calls. For example, if you are loading functions from a utility library, you may actually wanted them loaded into the global environment so they can by accessed by you or your programs. In this case, set the "isolate" parameter to FALSE.
Lastly, there may be situations where you want to intentionally share an environment, or extract values create by the running programs. In this case, you can instantiate a new environment yourself, and pass that to the "isolate" parameter instead of TRUE or FALSE. Note that this environment will be shared by all programs, but will not have access to the global environment.
Other fileops:
Sys.path(),
dir.find(),
file.find()
# Create temp directory tmp <- tempdir() # Write program 1 p1 <- file(file.path(tmp, "prog1.R")) writeLines("print('Hello from program 1')", p1) close(p1) # Write program 2 p2 <- file(file.path(tmp, "prog2.R")) writeLines("stop('Error from program 2')", p2) close(p2) # Write program 3 p3 <- file(file.path(tmp, "prog3.R")) writeLines("print('Hello from program 3')", p3) close(p3) # Example #1: Run all programs res1 <- source.all(tmp) # [1] "Hello from program 1" # [1] "Hello from program 3" # View results res1 # Filename StartTime EndTime Status Message # 1 prog1.R 2024-03-05 10:12:04 2024-03-05 10:12:04 0 Success # 2 prog2.R 2024-03-05 10:12:04 2024-03-05 10:12:04 1 Error from program 2 # 3 prog3.R 2024-03-05 10:12:04 2024-03-05 10:12:04 0 Success #' # Example #2: Exclusion criteria res2 <- source.all(tmp, exclude = "prog2") # [1] "Hello from program 1" # [1] "Hello from program 3" # View results res2 # Filename StartTime EndTime Status Message # 1 prog1.R 2024-03-05 10:13:24 2024-03-05 10:13:24 0 Success # 2 prog3.R 2024-03-05 10:13:24 2024-03-05 10:13:24 0 Success # Example #3: Inclusion criteria res3 <- source.all(tmp, pattern = "*2") # View results res3 # Filename StartTime EndTime Status Message # 1 prog2.R 2024-03-05 10:16:41 2024-03-05 10:16:41 1 Error from program 2 # View attributes attributes(res3) # $names # [1] "Filename" "StartTime" "EndTime" "Status" "Message" # # $class # [1] "data.frame" # # $row.names # [1] 1 # # $path # [1] "C:\Users\dbosa\AppData\Local\Temp\RtmpGAXYJl" # # $pattern # [1] "*2.R" # # $errors # [1] 1# Create temp directory tmp <- tempdir() # Write program 1 p1 <- file(file.path(tmp, "prog1.R")) writeLines("print('Hello from program 1')", p1) close(p1) # Write program 2 p2 <- file(file.path(tmp, "prog2.R")) writeLines("stop('Error from program 2')", p2) close(p2) # Write program 3 p3 <- file(file.path(tmp, "prog3.R")) writeLines("print('Hello from program 3')", p3) close(p3) # Example #1: Run all programs res1 <- source.all(tmp) # [1] "Hello from program 1" # [1] "Hello from program 3" # View results res1 # Filename StartTime EndTime Status Message # 1 prog1.R 2024-03-05 10:12:04 2024-03-05 10:12:04 0 Success # 2 prog2.R 2024-03-05 10:12:04 2024-03-05 10:12:04 1 Error from program 2 # 3 prog3.R 2024-03-05 10:12:04 2024-03-05 10:12:04 0 Success #' # Example #2: Exclusion criteria res2 <- source.all(tmp, exclude = "prog2") # [1] "Hello from program 1" # [1] "Hello from program 3" # View results res2 # Filename StartTime EndTime Status Message # 1 prog1.R 2024-03-05 10:13:24 2024-03-05 10:13:24 0 Success # 2 prog3.R 2024-03-05 10:13:24 2024-03-05 10:13:24 0 Success # Example #3: Inclusion criteria res3 <- source.all(tmp, pattern = "*2") # View results res3 # Filename StartTime EndTime Status Message # 1 prog2.R 2024-03-05 10:16:41 2024-03-05 10:16:41 1 Error from program 2 # View attributes attributes(res3) # $names # [1] "Filename" "StartTime" "EndTime" "Status" "Message" # # $class # [1] "data.frame" # # $row.names # [1] 1 # # $path # [1] "C:\Users\dbosa\AppData\Local\Temp\RtmpGAXYJl" # # $pattern # [1] "*2.R" # # $errors # [1] 1
A function to create a string of some number of blank spaces. This function is useful when trying to align things.
spaces(num)spaces(num)
num |
The desired number of spaces. |
A single character vector of blank spaces.
# Create spaces spaces(10) # [1] " " # Use spaces to separate something str <- "Left" %p% spaces(40) %p% "Right" str # [1] "Left Right"# Create spaces spaces(10) # [1] " " # Use spaces to separate something str <- "Left" %p% spaces(40) %p% "Right" str # [1] "Left Right"
The subsc function translates a normal character to a UTF-8
subscript character. The function can be used to
generate subscripts for many common characters. All
numeric characters and some lower case letters have UTF-8 subscripts.
There are no upper case subscript letters.
This function is useful
because it saves you from having to look up the subscript character
code, or copy and paste from the internet. If a corresponding
subscript character code does not exist, a question mark
will be returned for that character.
subsc(x)subsc(x)
x |
A string to be converted to subscript. |
The subscript version of the string passed to the function, if one exists. Otherwise, a question mark will be returned.
# Date string paste0("December 5", subsc("th")) # Chemistry paste0("H", subsc("2"), "SO", subsc("4"))# Date string paste0("December 5", subsc("th")) # Chemistry paste0("H", subsc("2"), "SO", subsc("4"))
The supsc function translates a normal character to a UTF-8
superscript character. The function can be used to
generate superscripts for many common characters. Most alphabetic
and numeric characters have UTF-8 superscripts. This function is useful
because it saves you from having to look up the superscript character
code, or copy and pasting from the internet. If a corresponding
superscript character code does not exist, a question mark
will be returned for that character.
supsc(x)supsc(x)
x |
A string to be converted to superscript. |
The superscript version of the string passed to the function, if one exists. Otherwise, a question mark will be returned.
# Single letter paste0(supsc("a"), "Footnote") # Single number paste0(supsc("1"), "Footnote") # Character string paste0("December 5", supsc("th")) # Formula paste0("x", supsc("(a+1)"))# Single letter paste0(supsc("a"), "Footnote") # Single number paste0(supsc("1"), "Footnote") # Character string paste0("December 5", supsc("th")) # Formula paste0("x", supsc("(a+1)"))
The symbol function gets UTF-8 symbol characters. You
can call this function to look up trademarks, Greek letters,
and mathematical operators. The function uses HTML entity
keywords to indicate which symbol to return. You may pass more than
one keyword in a single call to get a combined result. Any characters not
recognized as a keyword will be left alone. Characters surrounded by
square brackets ([]) will be subscripted. Characters surrounded by square
brackets and prefixed with an up arrow (^[]) will be superscripted.
symbol(keyword)symbol(keyword)
keyword |
A symbol keyword. This keyword follows HTML conventions. See the Keywords section for a complete list of all supported keywords. |
The requested UTF-8 symbol character or characters.
The following symbol keywords are available:
Trademark and Copyright: copy, reg, trade
Financial: cent, euro, pound, rupee, ruble, yen, yuan
Mathmatical: asymp, bcong, cong, coprod, empty, fnof, ge, int, Int, infin, le, ncong, ne, not, part, plusmn, prod, radic, sime, sum
Logical: and, cap, cup, comp, cuvee, cuwed, exist, forall, fork, isin, nexist, ni, notin, notni, nsub, nsup, or, sub, sup, xcap, xcup, xvee, xwedge
Greek uppercase letters: Alpha, Beta, Gamma, Delta, Epsilon, Zeta, Eta, Theta, Iota, Kappa, Lambda, Mu, Nu, Xi, Omicron, Pi, Rho, Sigma, Tau, Upsilon, Phi, Chi, Psi, Omega
Greek lowercase letters: alpha, beta, gamma, delta, epsilon, zeta, eta, theta, iota, kappa, lambda, mu, nu, xi, omicron, pi, rho, sigma, tau, upsilon, phi, chi, psi, omega
Arrows: rarr, larr, barr, uarr, darr, harr, rArr, lArr, uArr, dArr, hArr
Other Symbols: dagger, ddagger, deg, permil, pertenk, sect
# Trademark symbol symbol("My Companytrade") # Registered Trademark symbol symbol("My Companyreg") # Dagger symbol concatenated paste0(symbol("dagger"), "My footnotes") # Alpha squared symbol("alpha^[2]") # Greek Symbols symbol("SigmaPsiZeta") # Useful Math Symbols symbol("asymp ge le ne plusmn empty fnof radic sum") # Useful Logical Symbols symbol("forall isin notin cup cap and or") # Chemistry symbol("2H[2] + O[2] barr 2H[2]O")# Trademark symbol symbol("My Companytrade") # Registered Trademark symbol symbol("My Companyreg") # Dagger symbol concatenated paste0(symbol("dagger"), "My footnotes") # Alpha squared symbol("alpha^[2]") # Greek Symbols symbol("SigmaPsiZeta") # Useful Math Symbols symbol("asymp ge le ne plusmn empty fnof radic sum") # Useful Logical Symbols symbol("forall isin notin cup cap and or") # Chemistry symbol("2H[2] + O[2] barr 2H[2]O")
A function that gets the full path of the currently running program. If the function fails to retrieve the path for some reason, it will return a NULL. The function takes no parameters.
Sys.path()Sys.path()
The full path of the currently running program, or a NULL.
Other fileops:
dir.find(),
file.find(),
source.all()
# Get current path pth <- Sys.path() pth # [1] "C:/programs/myprogram.R"# Get current path pth <- Sys.path() pth # [1] "C:/programs/myprogram.R"
A function to quote and combine unquoted values. The function will return a vector of quoted values. This function allows you to use non-standard evaluation for any parameter that accepts a string or vector of strings.
v(...)v(...)
... |
One or more unquoted values. |
A vector of quoted values.
# Combine unquoted values v(var1, var2, var3) # [1] "var1" "var2" "var3" # Data frame subset dat <- mtcars[1:5, v(mpg, cyl, disp)] dat # mpg cyl disp # Mazda RX4 21.0 6 160 # Mazda RX4 Wag 21.0 6 160 # Datsun 710 22.8 4 108 # Hornet 4 Drive 21.4 6 258 # Hornet Sportabout 18.7 8 360 # Data frame sort dat2 <- sort(dat, by = v(cyl, mpg)) dat2 # mpg cyl disp # Datsun 710 22.8 4 108 # Mazda RX4 21.0 6 160 # Mazda RX4 Wag 21.0 6 160 # Hornet 4 Drive 21.4 6 258 # Hornet Sportabout 18.7 8 360# Combine unquoted values v(var1, var2, var3) # [1] "var1" "var2" "var3" # Data frame subset dat <- mtcars[1:5, v(mpg, cyl, disp)] dat # mpg cyl disp # Mazda RX4 21.0 6 160 # Mazda RX4 Wag 21.0 6 160 # Datsun 710 22.8 4 108 # Hornet 4 Drive 21.4 6 258 # Hornet Sportabout 18.7 8 360 # Data frame sort dat2 <- sort(dat, by = v(cyl, mpg)) dat2 # mpg cyl disp # Datsun 710 22.8 4 108 # Mazda RX4 21.0 6 160 # Mazda RX4 Wag 21.0 6 160 # Hornet 4 Drive 21.4 6 258 # Hornet Sportabout 18.7 8 360