Package 'furniture' reference manual

Title:	Furniture for Quantitative Scientists
Description:	Contains four main functions (i.e., four pieces of furniture): table1() which produces a well-formatted table of descriptive statistics common as Table 1 in research articles, tableC() which produces a well-formatted table of correlations, tableF() which provides frequency counts, and washer() which is helpful in cleaning up the data. These furniture-themed functions are designed to simplify common tasks in quantitative analysis. Other data summary and cleaning tools are also available.
Authors:	Tyson S. Barrett [aut, cre] , Emily Brignone [aut], Daniel J. Laxman [aut]
Maintainer:	Tyson S. Barrett <[email protected]>
License:	GPL-3
Version:	1.10.0
Built:	2024-11-13 05:14:48 UTC
Source:	https://github.com/tysonstanley/furniture

furniture

Description

The furniture package offers simple functions (i.e. pieces of furniture) and an operator that are aimed at helping applied researchers explore and communicate their data as well as clean their data in a tidy way. The package follows similar semantics to the "tidyverse" packages. It contains several table functions (table1()) being the core one.

Details

table1 provides a well-formatted descriptive table often seen as table 1 in academic journals (also a version that simplifies the output is available as simple_table1),
washer provides a simple way to clean up data where there are placeholder values, and
%xt% is an operator that takes two factor variables and creates a cross tabulation and tests for significance via a chi-square test.

Table 1 is the main function in furniture. It is useful in both data exploration and data communication. With minimal cleaning, the outputted table can be put into an academic, peer reviewed journal manuscript. As such, it is very useful in exploring your data when you have a stratifying variable. For example, if you are exploring whether the means of several demographic and behavioral characteristics are related to a health condition, the health condition (i.e. "yes" or "no"; "low", "mid", or "high"; or a list of conditions) as the stratifying variable. With little code, you can test for associations and check means or counts by the stratifying variable. See the vignette for more information.

Note: furniture is meant to make life more comfortable and beautiful. In like manner, this package is designed to be "furniture" for quantitative research.

Author(s)

Maintainer: Tyson S. Barrett [email protected] (ORCID)

Authors:

Emily Brignone
Daniel J. Laxman

Examples

## Not run: 

library(furniture)

## Table 1
data %>%
  table1(var1, var2, var3, 
         splitby = ~groupvar,
         test = TRUE)

## Table F
data %>%
  tableF(var1)

## Washer
x = washer(x, 7, 8, 9)
x = washer(x, is.na, value=0)


## End(Not run)

## Not run: 

library(furniture)

## Table 1
data %>%
  table1(var1, var2, var3, 
         splitby = ~groupvar,
         test = TRUE)

## Table F
data %>%
  tableF(var1)

## Washer
x = washer(x, 7, 8, 9)
x = washer(x, is.na, value=0)


## End(Not run)

Wide to Long Data Reshaping

Description

long() is a wrapper of stats::reshape() that takes the data from a wide format to a long format. It can also handle unbalanced data (where some measures have different number of "time points").

Usage

long(
  data,
  ...,
  v.names = NULL,
  id = NULL,
  timevar = NULL,
  times = NULL,
  sep = ""
)
long(
  data,
  ...,
  v.names = NULL,
  id = NULL,
  timevar = NULL,
  times = NULL,
  sep = ""
)

Arguments

`data`	the data.frame containing the wide format data
`...`	the variables that are time-varying that are to be placed in long format, needs to be in the format `c("x1", "x2"), c("z1", "z2"), etc.`. If the data is unbalanced (e.g., there are three time points measured for one variable but only two for another), using the placeholder variable `miss`, helps fix this.
`v.names`	a vector of the names for the newly created variables (length same as number of vectors in `varying`)
`id`	the ID variable in quotes
`timevar`	the column with the "time" labels
`times`	the labels of the `timevar` (default is numeric)
`sep`	the separating character between the wide format variable names (default is `""`); e.g. "x1" and "x2" would create the variable name of "x"; only applicable if `v.names`

Author(s)

Tyson S. Barrett

Examples


x1 <- runif(1000)
x2 <- runif(1000)
x3 <- runif(1000)
y1 <- rnorm(1000)
y2 <- rnorm(1000)
z  <- factor(sample(c(0,1), 1000, replace=TRUE))
a  <- factor(sample(c(1,2), 1000, replace=TRUE))
b  <- factor(sample(c(1,2,3,4), 1000, replace=TRUE))
df  <- data.frame(x1, x2, x3, y1, y2, z, a, b)

## "Balanced" Data
ldf1 <- long(df, 
             c("x1", "x2"), c("y1", "y2"),
             v.names = c("x", "y"))

## "Unbalanced" Data
ldf2 = long(df, 
            c("x1", "x2", "x3"), c("y1", "y2", "miss"),
            v.names = c("x", "y"))


x1 <- runif(1000)
x2 <- runif(1000)
x3 <- runif(1000)
y1 <- rnorm(1000)
y2 <- rnorm(1000)
z  <- factor(sample(c(0,1), 1000, replace=TRUE))
a  <- factor(sample(c(1,2), 1000, replace=TRUE))
b  <- factor(sample(c(1,2,3,4), 1000, replace=TRUE))
df  <- data.frame(x1, x2, x3, y1, y2, z, a, b)

## "Balanced" Data
ldf1 <- long(df, 
             c("x1", "x2"), c("y1", "y2"),
             v.names = c("x", "y"))

## "Unbalanced" Data
ldf2 = long(df, 
            c("x1", "x2", "x3"), c("y1", "y2", "miss"),
            v.names = c("x", "y"))

NHANES 2009-2010

Description

A dataset containing information on health, healthcare, and demographics of adolescents aged 18 - 30 in the United States from 2009 to 2010. This is a cleaned dataset which is only a subset of the 2009-2010 data release of the National Health and Nutrition Examination Survey (NHANES).

Usage

nhanes_2010
nhanes_2010

Format

A data frame with 1417 rows and 24 variables:

id: individual ID
gen_health: general health indicator with five levels
mod_active: minutes of moderate activity
vig_active: minutes of vigorous activity
home_meals: number of home meals a week
gender: gender of the individual (factor with "male" or "female")
age: age of the individual in years
marijuana: whether the individual has used marijuana
illicit: whether the individual has used illicit drugs
rehab: whether the individual has been to rehab for their drug usage
asthma: whether the individual has asthma
overweight: whether the individual is overweight
cancer: whether the individual has cancer
low_int: rating of whether the individual has low interest in things
down: rating of whether the individual has felt down
sleeping: rating of whether the individual has had trouble sleeping
low_energy: rating of whether the individual has low energy
appetite: rating of whether the individual has lost appetite
feel_bad: rating of whether the individual has felt bad
no_con: rating of whether the individual has felt no confidence
speak_move: rating of whether the individual has trouble speaking/moving
dead: rating of whether the individual has wished he/she was dead
difficulty: rating of whether the individual has felt difficulty from the previous conditions
active: minutes of vigorous or moderate activity

Source

https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/default.aspx?BeginYear=2009

Get Row Means

Description

Does what rowMeans() does but without having to cbind the variables. Makes it easier to use with the tidyverse

Usage

rowmeans(..., na.rm = FALSE)
rowmeans(..., na.rm = FALSE)

Arguments

`...`	the variables (unquoted) to be included in the row means
`na.rm`	should the missing values be ignored? default is FALSE

Value

the row means

Examples


## Not run: 

library(furniture)
library(tidyverse)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- data %>%
  mutate(y_z_mean = rowmeans(y, z))
data2 <- data %>%
  mutate(y_z_mean = rowmeans(y, z, na.rm=TRUE))


## End(Not run)

## Not run: 

library(furniture)
library(tidyverse)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- data %>%
  mutate(y_z_mean = rowmeans(y, z))
data2 <- data %>%
  mutate(y_z_mean = rowmeans(y, z, na.rm=TRUE))


## End(Not run)

Get Row Means With N Missing Values Per Row

Description

Does what furniture::rowmeans() does while allowing a certain number (n) to have missing values.

Usage

rowmeans.n(..., n)
rowmeans.n(..., n)

Arguments

`...`	the variables (unquoted) to be included in the row means
`n`	the number of values without missingness required to get the row mean

Value

the row means

Examples


## Not run: 

library(furniture)
library(dplyr)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- mutate(data, x_y_z_mean = rowmeans.n(x, y, z, n = 2))


## End(Not run)

## Not run: 

library(furniture)
library(dplyr)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- mutate(data, x_y_z_mean = rowmeans.n(x, y, z, n = 2))


## End(Not run)

Get Row Sums

Description

Does what rowSums() does but without having to cbind the variables. Makes it easier to use with the tidyverse

Usage

rowsums(..., na.rm = FALSE)
rowsums(..., na.rm = FALSE)

Arguments

`...`	the variables to be included in the row sums
`na.rm`	should the missing values be ignored? default is FALSE

Value

the row sums

Examples


## Not run: 

library(furniture)
library(tidyverse)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- data %>%
  mutate(y_z_sum = rowsums(y, z))
data2 <- data %>%
  mutate(y_z_sum = rowsums(y, z, na.rm=TRUE))


## End(Not run)


## Not run: 

library(furniture)
library(tidyverse)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- data %>%
  mutate(y_z_sum = rowsums(y, z))
data2 <- data %>%
  mutate(y_z_sum = rowsums(y, z, na.rm=TRUE))


## End(Not run)

Get Row Sums With N Missing Values Per Row

Description

Does what furniture::rowsums() does while allowing a certain number (n) to have missing values.

Usage

rowsums.n(..., n)
rowsums.n(..., n)

Arguments

`...`	the variables (unquoted) to be included in the row means
`n`	the number of values without missingness required to get the row mean

Value

the row sums

Examples


## Not run: 

library(furniture)
library(dplyr)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- mutate(data, x_y_z_mean = rowsums.n(x, y, z, n = 2))


## End(Not run)

## Not run: 

library(furniture)
library(dplyr)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100),
  z = rnorm(100)
)

data2 <- mutate(data, x_y_z_mean = rowsums.n(x, y, z, n = 2))


## End(Not run)

Table 1 for Simple and Stratified Descriptive Statistics

Description

Produces a descriptive table, stratified by an optional categorical variable, providing means/frequencies and standard deviations/percentages. It is well-formatted for easy transition to academic article or report. Can be used within the piping framework [see library(magrittr)].

Usage

table1(
  .data,
  ...,
  splitby = NULL,
  FUN = NULL,
  FUN2 = NULL,
  total = FALSE,
  second = NULL,
  row_wise = FALSE,
  test = FALSE,
  param = TRUE,
  header_labels = NULL,
  type = "pvalues",
  output = "text",
  rounding_perc = 1,
  digits = 1,
  var_names = NULL,
  format_number = FALSE,
  NAkeep = NULL,
  na.rm = TRUE,
  booktabs = TRUE,
  caption = NULL,
  align = NULL,
  float = "ht",
  export = NULL,
  label = NULL
)
table1(
  .data,
  ...,
  splitby = NULL,
  FUN = NULL,
  FUN2 = NULL,
  total = FALSE,
  second = NULL,
  row_wise = FALSE,
  test = FALSE,
  param = TRUE,
  header_labels = NULL,
  type = "pvalues",
  output = "text",
  rounding_perc = 1,
  digits = 1,
  var_names = NULL,
  format_number = FALSE,
  NAkeep = NULL,
  na.rm = TRUE,
  booktabs = TRUE,
  caption = NULL,
  align = NULL,
  float = "ht",
  export = NULL,
  label = NULL
)

Arguments

`.data`	the data.frame that is to be summarized
`...`	variables in the data set that are to be summarized; unquoted names separated by commas (e.g. age, gender, race) or indices. If indices, it needs to be a single vector (e.g. c(1:5, 8, 9:20) instead of 1:5, 8, 9:20). As it is currently, it CANNOT handle both indices and unquoted names simultaneously. Finally, any empty rows (where the row is NA for each variable selected) will be removed for an accurate n count.
`splitby`	the categorical variable to stratify (in formula form `splitby = ~gender`) or quoted `splitby = "gender"`; instead, `dplyr::group_by(...)` can be used within a pipe (this is the default when the data object is a grouped data frame from `dplyr::group_by(...)`).
`FUN`	the function to be applied to summarize the numeric data; default is to report the means and standard deviations
`FUN2`	a secondary function to be applied to summarize the numeric data; default is to report the medians and 25% and 75% quartiles
`total`	whether a total (not stratified with the `splitby` or `group_by()`) should also be reported in the table
`second`	a vector or list of quoted continuous variables for which the `FUN2` should be applied
`row_wise`	how to calculate percentages for factor variables when `splitby != NULL`: if `FALSE` calculates percentages by variable within groups; if `TRUE` calculates percentages across groups for one level of the factor variable.
`test`	logical; if set to `TRUE` then the appropriate bivariate tests of significance are performed if splitby has more than 1 level. A message is printed when the variances of the continuous variables being tested do not meet the assumption of Homogeneity of Variance (using Breusch-Pagan Test of Heteroskedasticity) and, therefore, the argument 'var.equal = FALSE' is used in the test.
`param`	logical; if set to `TRUE` then the appropriate parametric bivariate tests of significance are performed (if 'test = TRUE'). For continuous variables, it is a t-test or ANOVA (depending on the number of levels of the group). If set to `FALSE`, the Kruskal-Wallis Rank Sum Test is performed for the continuous variables. Either way, the chi-square test of independence is performed for categorical variables.
`header_labels`	a character vector that renames the header labels (e.g., the blank above the variables, the p-value label, and test value label).
`type`	what is displayed in the table; a string or a vector of strings. Two main sections can be inputted: 1. if test = TRUE, can write "pvalues", "full", or "stars" and 2. can state "simple" and/or "condense". These are discussed in more depth in the details section below.
`output`	how the table is output; can be "text" or "text2" for regular console output or any of `kable()`'s options from `knitr` (e.g., "latex", "markdown", "pandoc"). A new option, `'latex2'`, although more limited, allows the variable name to show and has an overall better appearance.
`rounding_perc`	the number of digits after the decimal for percentages; default is 1
`digits`	the number of significant digits for the numerical variables (if using default functions); default is 1.
`var_names`	custom variable names to be printed in the table. Variable names can be applied directly in the list of variables.
`format_number`	default is FALSE; if TRUE, then the numbers are formatted with commas (e.g., 20,000 instead of 20000)
`NAkeep`	when set to `TRUE` it also shows how many missing values are in the data for each categorical variable being summarized (deprecated; use `na.rm`)
`na.rm`	when set to `FALSE` it also shows how many missing values are in the data for each categorical variable being summarized
`booktabs`	when `output != "text"`; option is passed to `knitr::kable`
`caption`	when `output != "text"`; option is passed to `knitr::kable`
`align`	when `output != "text"`; option is passed to `knitr::kable`
`float`	the float applied to the table in Latex when output is `latex2`, default is "ht".
`export`	character; when given, it exports the table to a CSV file to folder named "table1" in the working directory with the name of the given string (e.g., "myfile" will save to "myfile.csv")
`label`	for `output == "latex2"`, this provides a table reference label for latex

Details

In defining type, 1. options are "pvalues" that display the p-values of the tests, "full" which also shows the test statistics, or "stars" which only displays stars to highlight significance with *** < .001 ** .01 * .05; and 2. "simple" then only percentages are shown for categorical variable and "condense" then continuous variables' means and SD's will be on the same line as the variable name and dichotomous variables only show counts and percentages for the reference category.

Value

A table with the number of observations, means/frequencies and standard deviations/percentages is returned. The object is a table1 class object with a print method. Can be printed in LaTex form.

Examples


## Fictitious Data ##
library(furniture)
library(dplyr)

x  <- runif(1000)
y  <- rnorm(1000)
z  <- factor(sample(c(0,1), 1000, replace=TRUE))
a  <- factor(sample(c(1,2), 1000, replace=TRUE))
df <- data.frame(x, y, z, a)

## Simple
table1(df, x, y, z, a)

## Stratified
## all three below are the same
table1(df, x, y, z,
       splitby = ~ a)
table1(df, x, y, z,
       splitby = "a")

## With Piping
df %>%
  table1(x, y, z, 
         splitby = ~a) 
         
df %>%
  group_by(a) %>%
  table1(x, y, z)

## Adjust variables within function and assign name
table1(df, 
       x2 = ifelse(x > 0, 1, 0), z = z)

## Fictitious Data ##
library(furniture)
library(dplyr)

x  <- runif(1000)
y  <- rnorm(1000)
z  <- factor(sample(c(0,1), 1000, replace=TRUE))
a  <- factor(sample(c(1,2), 1000, replace=TRUE))
df <- data.frame(x, y, z, a)

## Simple
table1(df, x, y, z, a)

## Stratified
## all three below are the same
table1(df, x, y, z,
       splitby = ~ a)
table1(df, x, y, z,
       splitby = "a")

## With Piping
df %>%
  table1(x, y, z, 
         splitby = ~a) 
         
df %>%
  group_by(a) %>%
  table1(x, y, z)

## Adjust variables within function and assign name
table1(df, 
       x2 = ifelse(x > 0, 1, 0), z = z)

gt output for table1

Description

This takes a table1 object and outputs a 'gt' version.

Usage

table1_gt(tab, spanner = NULL)
table1_gt(tab, spanner = NULL)

Arguments

`tab`	the table1 object
`spanner`	the label above the grouping variable (if table1 is grouped) or any label you want to include over the statistics column(s)

Author(s)

Tyson S. Barrett

Examples


library(furniture)
library(dplyr)

data('nhanes_2010')
nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab, na.rm = FALSE) %>%
  table1_gt(spanner = "Asthma")

library(furniture)
library(dplyr)

data('nhanes_2010')
nhanes_2010 %>%
  group_by(asthma) %>%
  table1(age, marijuana, illicit, rehab, na.rm = FALSE) %>%
  table1_gt(spanner = "Asthma")

Correlation Table

Description

Correlations printed in a nicely formatted table.

Usage

tableC(
  .data,
  ...,
  cor_type = "pearson",
  na.rm = FALSE,
  rounding = 3,
  output = "text",
  booktabs = TRUE,
  caption = NULL,
  align = NULL,
  float = "htb"
)
tableC(
  .data,
  ...,
  cor_type = "pearson",
  na.rm = FALSE,
  rounding = 3,
  output = "text",
  booktabs = TRUE,
  caption = NULL,
  align = NULL,
  float = "htb"
)

Arguments

`.data`	the data frame containing the variables
`...`	the unquoted variable names to be included in the correlations
`cor_type`	the correlation type; default is "pearson", other option is "spearman"
`na.rm`	logical (default is `FALSE`); if set to `TRUE`, the correlations use the "complete.obs" methods option from `stats::cor()`
`rounding`	the value passed to `round` for the output of both the correlation and p-value; default is 3
`output`	how the table is output; can be "text" for regular console output, "latex2" for specialized latex output, or any of `kable()`'s options from `knitr` (e.g., "latex", "markdown", "pandoc").
`booktabs`	when `output != "text"`; option is passed to `knitr::kable`
`caption`	when `output != "text"`; option is passed to `knitr::kable`
`align`	when `output != "text"`; option is passed to `knitr::kable`
`float`	when `output == "latex2"` it controls the floating parameter (h, t, b, H)

Frequency Table

Description

Provides in-depth frequency counts and percentages.

Usage

tableF(.data, x, n = 20, splitby = NULL)
tableF(.data, x, n = 20, splitby = NULL)

Arguments

`.data`	the data frame containing the variable
`x`	the bare variable name (not quoted)
`n`	the number of values shown int he table
`splitby`	the stratifying variable

Value

a list of class tableF containing the frequency table(s)

Examples


## Not run: 

library(furniture)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100)
)

## Basic Use
tableF(data, x)
tableF(data, y)

## Adjust the number of items shown
tableF(data, y, n = 10)

## Add splitby
tableF(data, x, splitby = y)


## End(Not run)

## Not run: 

library(furniture)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = rnorm(100)
)

## Basic Use
tableF(data, x)
tableF(data, y)

## Adjust the number of items shown
tableF(data, y, n = 10)

## Add splitby
tableF(data, x, splitby = y)


## End(Not run)

Table X (for Cross-Tabs)

Description

Provides a pipe-able, clean, flexible version of table().

Usage

tableX(.data, x1, x2, type = "count", na.rm = FALSE, format_number = FALSE)
tableX(.data, x1, x2, type = "count", na.rm = FALSE, format_number = FALSE)

Arguments

`.data`	the data frame containing the variables
`x1`	the first bare (not quoted) variable found in .data
`x2`	the second bare (not quoted) variable found in .data
`type`	the summarized output type; can be "count", "cell_perc", "row_perc", or "col_perc"
`na.rm`	logical; whether missing values should be removed
`format_number`	default is FALSE; if TRUE, then the numbers are formatted with commas (e.g., 20,000 instead of 20000)

Examples


## Not run: 

library(furniture)
library(tidyverse)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = sample(c(0,1), 100, replace=TRUE)
)

tableX(data, x, y)

data %>%
  tableX(x, y)

data %>%
  tableX(x, y, na.rm = TRUE)


## End(Not run)

## Not run: 

library(furniture)
library(tidyverse)

data <- data.frame(
  x = sample(c(1,2,3,4), 100, replace=TRUE),
  y = sample(c(0,1), 100, replace=TRUE)
)

tableX(data, x, y)

data %>%
  tableX(x, y)

data %>%
  tableX(x, y, na.rm = TRUE)


## End(Not run)

From Table 1 to Latex 2

Description

Internal table1() and tableC() function for providing output = "latex2"

Usage

to_latex(
  tab,
  caption,
  align,
  len,
  splitby,
  float,
  booktabs,
  label,
  total = FALSE,
  cor_type = NULL
)
to_latex(
  tab,
  caption,
  align,
  len,
  splitby,
  float,
  booktabs,
  label,
  total = FALSE,
  cor_type = NULL
)

Arguments

`tab`	the table1 object
`caption`	caption character vector
`align`	align character vector
`len`	the number of levels of the grouping factor
`splitby`	the name of the grouping factor
`float`	argument for latex formatting
`booktabs`	add booktabs to latex table
`label`	latex label option
`total`	is there a total column (from Table 1) to be printed?
`cor_type`	optional argument regarding the correlation type (for tableC)

Wash Your Data

Description

Washes the data by replacing values with either NA's or other values set by the user. Useful for replacing values such as 777's or 999's that represent missing values in survey research. Can also perform many useful functions on factors (e.g., removing a level, replacing a level, etc.)

Usage

washer(x, ..., value = NA)
washer(x, ..., value = NA)

Arguments

`x`	the variable to have values adjusted
`...`	the values in the variable that are to be replaced by either NA's or the value set by the user. Can be a function (or multiple functions) to specify values to change (e.g., is.nan(), is.na()).
`value`	(optional) if specified, the values in ... will be replaced by this value (must be a single value)

Value

the original vector (although if the original was a factor, it was changed to a character) with the values changed where indicated.

Examples

x = c(1:20, NA, NaN)
washer(x, 9, 10)
washer(x, 9, 10, value=0)
washer(x, 1:10)
washer(x, is.na, is.nan, value=0)
washer(x, is.na, is.nan, 1:3, value=0)

x = c(1:20, NA, NaN)
washer(x, 9, 10)
washer(x, 9, 10, value=0)
washer(x, 1:10)
washer(x, is.na, is.nan, value=0)
washer(x, is.na, is.nan, 1:3, value=0)

Long to Wide Data Reshaping

Description

wide() is a wrapper of stats::reshape() that takes the data from a long format to a wide format.

Usage

wide(data, v.names, timevar, id = NULL)
wide(data, v.names, timevar, id = NULL)

Arguments

`data`	the data.frame containing the wide format data
`v.names`	the variable names in quotes of the measures to be separated into multiple columns based on the time variable
`timevar`	the variable name in quotes of the time variable
`id`	the ID variable name in quotes

Author(s)

Tyson S. Barrett

Package 'furniture'

Help Index

furniture

Description

Details

Author(s)

Examples

Wide to Long Data Reshaping

Description

Usage

Arguments

Author(s)

See Also

Examples

NHANES 2009-2010

Description

Usage

Format

Source

Get Row Means

Description

Usage

Arguments

Value

Examples

Get Row Means With N Missing Values Per Row

Description

Usage

Arguments

Value

Examples

Get Row Sums

Description

Usage

Arguments

Value

Examples

Get Row Sums With N Missing Values Per Row

Description

Usage

Arguments

Value

Examples

Table 1 for Simple and Stratified Descriptive Statistics

Description

Usage

Arguments

Details

Value

Examples

gt output for table1

Description

Usage

Arguments

Author(s)

Examples

Correlation Table

Description

Usage

Arguments

See Also

Frequency Table

Description

Usage

Arguments

Value

Examples

Table X (for Cross-Tabs)

Description

Usage

Arguments

Examples

From Table 1 to Latex 2

Description

Usage

Arguments

Wash Your Data

Description

Usage

Arguments