Append datasets
Description
![[Deprecated]](data:image/svg+xml;base64,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)
Usage
append(...)append(...)
Arguments
... |
Data frames to combine. |
Details
append stacks multiple datasets.
Value
A data frame
See Also
Other Data Management:
codebook(),
count_functions,
cut(),
tag_duplicates()
Examples
append(airquality, mtcars)append(airquality, mtcars)
Generate a codebook
Description
![[Stable]](data:image/svg+xml;base64,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)
The codebook function generates a codebook for the given dataset. It provides a summary
of the dataset's structure and characteristics, including variable names, types, missing
values, completeness percentages, unique value counts, and variable labels (if available).
Usage
codebook(data)codebook(data)
Arguments
data |
The dataset for which the codebook is to be generated. |
Value
The input dataset is returned invisibly,
allowing codebook() to be used within a data pipe line.
See Also
Other Data Management:
append(),
count_functions,
cut(),
tag_duplicates()
Examples
codebook(mtcars) codebook(iris) labelled::var_label(iris) <- c( "sepal length", "sepal width", "petal length", "petal width", "species" ) codebook(iris)codebook(mtcars) codebook(iris) labelled::var_label(iris) <- c( "sepal length", "sepal width", "petal length", "petal width", "species" ) codebook(iris)
Apply functions to multiple variables in a data frame
Description
The coder() function applies a set of functions to multiple variables in a data frame using dplyr's mutate() and across() functions.
Usage
coder(.data, .fns)coder(.data, .fns)
Arguments
.data |
The input data frame. |
.fns |
A set of functions to be applied to the variables in the data frame. |
Value
A modified data frame with the functions applied to the variables.
Examples
# Apply the `mean()` function to multiple variables in the `mtcars` data frame coder(mtcars, mean)# Apply the `mean()` function to multiple variables in the `mtcars` data frame coder(mtcars, mean)
Count from n to N
Description
Usage
n_(...) N_(...)n_(...) N_(...)
Arguments
... |
Columns to pick. You can't pick grouping columns because they are already automatically
handled by the verb (i.e. |
Details
These functions are used for indexing observations or generating sequences of numbers.
-
n_()generates a running counter within a group of variables and represents the number of the current observation. -
N_()provides the total count within each group of variables.
You can do these operations using dplyr::n() in this way.
See examples below using iris dataset.
iris |> mutate(.N_ = n()) |> head() iris |> mutate(.n_ = 1:n()) |> head() iris |> group_by(Species) |> mutate(.n_ = 1:n()) |> slice(1:5) |> ungroup()
Value
A numeric vector representing the count from n to N.
See Also
Other Data Management:
append(),
codebook(),
cut(),
tag_duplicates()
Examples
# Example with a custom dataset df <- data.frame( x = c(1, 1, 2, 2, 2, 3, 4, 4, 4, 4), y = letters[1:10] ) library(dplyr) # Generate a running counter for each observation within the "x" group using mutate() mutate(df, n = n_(x)) # Generate a running counter for each observation for all columns using mutate() mutate(df, n = n_(everything())) # Generate the total count of observations using summarise() reframe(df, n = n_(x)) # Generate the total count of observations within the "x" group using summarise() mutate(df, N = N_(everything())) mutate(df, N = N_(x)) reframe(df, N = N_(x)) # iris dataset mutate(iris, n = n_(everything())) mutate(iris, N = N_(everything()))# Example with a custom dataset df <- data.frame( x = c(1, 1, 2, 2, 2, 3, 4, 4, 4, 4), y = letters[1:10] ) library(dplyr) # Generate a running counter for each observation within the "x" group using mutate() mutate(df, n = n_(x)) # Generate a running counter for each observation for all columns using mutate() mutate(df, n = n_(everything())) # Generate the total count of observations using summarise() reframe(df, n = n_(x)) # Generate the total count of observations within the "x" group using summarise() mutate(df, N = N_(everything())) mutate(df, N = N_(x)) reframe(df, N = N_(x)) # iris dataset mutate(iris, n = n_(everything())) mutate(iris, N = N_(everything()))
Cut numeric vector into factor vector
Description
Usage
cut(x, at, label = NULL, ...)cut(x, at, label = NULL, ...)
Arguments
x |
A numeric vector to be cut into factors. |
at |
A numeric vector specifying the breakpoints or categories
for cutting the vector. If a single value is provided,
the function will create breaks using the same method as |
label |
Optional labels for the resulting factor levels. If not provided, labels will be automatically generated based on the breaks. |
... |
Additional arguments to be passed to |
Details
This function cuts a numeric vector into factors
based on specified breaks or categories.
If the input vector is not numeric, the function delegates
to the base R cut function.
Value
A factor representing the cut vector with factor levels assigned based on the breaks or categories.
See Also
Other Data Management:
append(),
codebook(),
count_functions,
tag_duplicates()
Examples
x <- c(1, 2, 3, 4, 5) cut(x, 2)x <- c(1, 2, 3, 4, 5) cut(x, 2)
Decode variables in a data frame
Description
The decode() function decodes selected variables in a data frame. It converts the variables to character type, sets labels from the original data, and returns the modified data frame.
Usage
decode(.data, ...)decode(.data, ...)
Arguments
.data |
The input data frame. |
... |
The variables to be decoded. |
Value
A modified data frame with the selected variables decoded.
Examples
# Decode selected variables in the `mtcars` data frame decode(mtcars, mpg, cyl)# Decode selected variables in the `mtcars` data frame decode(mtcars, mpg, cyl)
Convert a continuous variable into groups
Description
This function was deprecated because we realized that it's
a special case of the cut function.
Usage
egen(data, var, at = NULL, label = NULL, new_var = NULL, ...)egen(data, var, at = NULL, label = NULL, new_var = NULL, ...)
Arguments
data |
data.frame |
var |
existing variable |
at |
either a number or a numeric vector |
label |
Labels for the groups |
new_var |
Name of the new variable |
... |
Additional arguments to be passed to |
See Also
Examples
data <- data.frame(x = 1:10) egen(data, x, at = c(3, 7), label = c("low", "medium", "high")) egen(data, x, at = c(3, 7), label = c("low", "medium", "high"), new_var = "group")data <- data.frame(x = 1:10) egen(data, x, at = c(3, 7), label = c("low", "medium", "high")) egen(data, x, at = c(3, 7), label = c("low", "medium", "high"), new_var = "group")
Encode variables in a data frame
Description
The encode() function encodes selected variables in a data frame. It converts the variables to numeric type, sets labels from the original data, and returns the modified data frame.
Usage
encode(.data, ...)encode(.data, ...)
Arguments
.data |
The input data frame. |
... |
The variables to be encoded. |
Value
A modified data frame with the selected variables encoded.
Examples
# Encode selected variables in the `mtcars` data frame encode(mtcars, mpg, cyl)# Encode selected variables in the `mtcars` data frame encode(mtcars, mpg, cyl)
Attach labels to data and variables
Description
This function manipulates labels. It supports different classes of objects, including default objects, data frames, and other types.
Usage
label(x, label = NULL)label(x, label = NULL)
Arguments
x |
The object to which the label will be added or modified. |
label |
A character string specifying the label to be assigned to the variable. |
Details
When used with dplyr's [mutate] function,
this function allows for easy labeling of variables within a data frame.
If used with a data frame, the function labels the dataset itself, and
the label can be checked using the [codebook] function.
Value
The modified object with the updated label.
Examples
library(dplyr) iris |> mutate(Species = label(Species, 'Species of iris flower')) |> codebook() iris |> label("Iris dataset") |> codebook()library(dplyr) iris |> mutate(Species = label(Species, 'Species of iris flower')) |> codebook() iris |> label("Iris dataset") |> codebook()
Tag Duplicates
Description
Usage
tag_duplicates(..., .add_tags = TRUE)tag_duplicates(..., .add_tags = TRUE)
Arguments
... |
Columns to use for identifying duplicates. |
.add_tags |
logical to return three indicator columns: |
Details
This function identifies and tags duplicate observations based on specified variables.
This function mimics the functionality of Stata's duplicates command in R.
It calculates the number of duplicates and provides a report of duplicates
based on the specified variables. The function utilizes the n_ and N_ functions
for counting and grouping the observations.
Value
A tibble with three columns: .n_, .N_, and .dup_.
-
.n_represents the running counter within each group of variables, indicating the number of the current observation. -
.N_represents the total number of observations within each group of variables. -
.dup_is a logical column indicating whether the observation is a duplicate (TRUE) or not (FALSE).
See Also
Other Data Management:
append(),
codebook(),
count_functions,
cut()
Examples
library(dplyr) # Example with a custom dataset data <- data.frame( x = c(1, 1, 2, 2, 3, 4, 4, 5), y = letters[1:8] ) # Identify and tag duplicates based on the "x" variable data %>% mutate(tag_duplicates(x)) # Identify and tag duplicates based on multiple variables data %>% mutate(tag_duplicates(x, y)) # Identify and tag duplicates based on all variables data %>% mutate(tag_duplicates(everything())) ## Not run: ## STATA example dupxmpl <- haven::read_dta("https://www.stata-press.com/data/r18/dupxmpl.dta") dupxmpl |> mutate(tag_duplicates(everything())) ## End(Not run)library(dplyr) # Example with a custom dataset data <- data.frame( x = c(1, 1, 2, 2, 3, 4, 4, 5), y = letters[1:8] ) # Identify and tag duplicates based on the "x" variable data %>% mutate(tag_duplicates(x)) # Identify and tag duplicates based on multiple variables data %>% mutate(tag_duplicates(x, y)) # Identify and tag duplicates based on all variables data %>% mutate(tag_duplicates(everything())) ## Not run: ## STATA example dupxmpl <- haven::read_dta("https://www.stata-press.com/data/r18/dupxmpl.dta") dupxmpl |> mutate(tag_duplicates(everything())) ## End(Not run)