The goal of this package is to iteratively build a customizable data table, one row at a time. This package will allow a user to input a data object, specify the rows and columns to use for the summary table, and select the type of data to use for each individual row. Missing data, overall statistics, and comparison tests can be calculated using this package as well.
install.packages("tangram.pipe")
suppressPackageStartupMessages(require(tangram.pipe)) suppressPackageStartupMessages(require(knitr)) suppressPackageStartupMessages(require(kableExtra))
The first step to using this package is to initialize the data table to create.
Here, the user will select the name of the dataset to be analyzed in the table and
specify the variable name to use for the columns. In addition, the user will need
to determine whether to account for missing data, calculate overall statistics
across all columns, or conduct comparison tests across the columns for each row.
The arguments for missing
, overall
, and comparison
will be used as the
defaults for each subsequent row added to the table; however, a user can specify
a different entry for each argument for individual rows if desired. Finally,
the user can choose the default summary function to use for each type of row.
This vignette will use the built-in iris
dataset, which is a well-known dataset
containing flower measurements for three species of iris flowers. Since most of
the data in iris
is numerical, we will add in two made-up variables
(flower color and stem size) in order to demonstrate table-building
functions for non-numeric data. Note that the additional columns are made-up
purely for demonstration of this package.
set.seed(04082022)
iris$color <- sample(c("Blue", "Purple"), size=150, replace=TRUE) iris$Stem.Size <- sample(c("Small", "Medium", "Medium", "Large"), size=150, replace=TRUE) iris[149,5] <- NA iris[150,c(1:4, 6:7)] <- NA head(iris) %>% kable(escape=F, align="c") %>% trimws %>% kable_styling(c("striped","bordered"))
For this example, the variable 'Species' will be chosen as the column variable;
missing
and comparison
will be set to FALSE
to generate a simple example.
We will also set each type of summary function to the default setting used by the
package.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE, digits = 2, default_num_summary = num_default, default_cat_summary = cat_default, default_binary_summary = binary_default)
Using this function creates a list object that stores the user preferences for building the table going forward; in addition to the nine elements listed here, the number of rows is also saved to the list. Subsequent entries to the list will store information for the rows, which will ultimately be compiled to create the final table after all row information has been added.
tbl_start
arguments are set to the following defaults. Aside from data
and col_var
,
the remaining arguments do not need to be specified if they match the following
default values:
missing
: FALSE
overall
: TRUE
comparison
: FALSE
digits
: 2
default_num_summary
: num_default
default_cat_summary
: cat_default
default_binary_summary
: binary_default
To start off, we will first add a numeric row to the table. The function num_row
reads in data that is numeric in form, and by default calculates the five-number
summary statistics (minimum, first quartile, median, third quartile, maximum), as
well as the mean and standard deviation for the numeric variable within each
column. Since we specified overall=TRUE
in the initialization step, an overall
summary row will be included as well. The default summary function is num_default
,
but the user may write their own function to calculate different summary statistics
from what is shown here.
Currently, there are five summary functions available for use within num_row
.
The default summary to use for each row can be specified in tbl_start
, or determined
using the summary
argument of each row
num_default
: Calculates the five-number summary, mean, and standard deviation
num_minmax
: Calculates the minimum and maximum values
num_medianiqr
: Calculates the median and interquartile range
num_mean_sd
: Calculates the mean and standard deviation
num_date
: Calculates the five-number summary for a date object
More information on writing your own summary functions can be found in the accompanying package vignette "Writing User-Defined Summary Functions"
Let's start by calculating summary statistics for the Sepal Length in the iris
dataset. Since it makes more sense to display the variable name as "Sepal Length"
rather than the R-generated "Sepal.Length", we will use the rowlabel
argument
to make this change for the table. Note that if you have a dataframe with
labelled variables as columns, leaving rowlabel
blank will automatically
input the variable's label as the rowlabel. To output the final object, we
use the function tbl_out
to display the table.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% num_row(row_var="Sepal.Length", rowlabel="Sepal Length") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
By default, each row function will use two decimal places in reported statistics.
We can use the digits
argument to specify more or fewer significant digits in
the reported table.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% num_row(row_var="Sepal.Length", rowlabel="Sepal Length", digits=4) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
There is a small amount of missing data within the iris
dataset. Currently,
num_row
filters out the missing data and only considers data with complete cases
of the row and column variables. To see how much missing data there is in the
sepal length, we specify missing=TRUE
.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% num_row(row_var="Sepal.Length", rowlabel="Sepal Length", missing=TRUE) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
The function above tells us that the dataset is missing a sepal length measurement for one of the virginica flowers. Note that the function cannot locate instances of missingness in the column variable.
Finally, suppose we want to look at the differences in means across all species.
The function num_diff
for the comparison
argument will calculated the mean
difference in sepal length for each row compared to a reference category, which
is coded as the first column variable in the table. Here, versicolor and virginica
will be compared to setosa. The function also provides a 95% Confidence interval
to accompany the mean difference. Currently, num_diff
is the only built-in comparison
function for num_row
.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% num_row(row_var="Sepal.Length", rowlabel="Sepal Length", comparison=num_diff) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% num_row(row_var="Sepal.Length", rowlabel="Sepal Length", comparison=num_diff) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered")) %>% column_spec(c(7:9), width_min = "1.5in")
Now, we will look at adding categorical variables. The function cat_row
reads in data that is categorical in form, and by default calculates the number
of instances for each row category within each column category, as well as the
column-wise proportions. The default summary function is cat_default
,
but the user may write their own function to calculate different summary statistics
from what is shown here.
Currently, there are four summary functions available for use within cat_row
.
The default summary to use for each row can be specified in tbl_start
, or determined
using the summary
argument of each row.
cat_default
: Calculates the cell counts and column-wise proportions
cat_pct
: Calculates the cell counts and column-wise percentages
cat_count
: Calculates the cell counts
cat_jama
: Calculates the column-wise percentages and cell counts divided by column
totals. This is the style used by the Journal of the American Medical Association.
We will demonstrate this function by looking at Stem.Size
in the iris
dataset.
Note that cat_row
and num_row
have nearly identical arguments, but cat_row
allows you to choose the number of spaces to indent category names using the
indent
argument. The default setting is 5 spaces.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% cat_row("Stem.Size", rowlabel="Stem Size") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
Setting missing=TRUE
will reveal the proportion of each species that does not
have a corresponding entry for stem size. When missing data is accounted for,
the missingness will be recorded as the percentage of each column that is
designated as missing data.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% cat_row("Stem.Size", rowlabel="Stem Size", missing=TRUE) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
We can also sort a categorical row in ascending or descending order by category
counts for a specified column. The ordering
argument will sort the row variable,
and sortcol
specifies which column we could like to sort our row by. Permissible
arguments for ordering
are c("ascending", "descending")
; by default, the row
function will sort by the overall cell counts unless a valid column category name
is inputted into sortcol
. If an invalid category name is used, the row function
will sort by the overall cell counts instead.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% cat_row("Stem.Size", rowlabel="Stem Size (Ascending by versicolor)", missing=TRUE, ordering = "ascending", sortcol = "versicolor") %>% cat_row("Stem.Size", rowlabel="Stem Size (Descending by overall count)", missing=TRUE, ordering = "descending") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
Finally, let's look at a comparison test for a categorical row. The default
comparison function is cat_comp_default
, which will calculate the relative
entropy between each column and the reference category, as well as conduct
a Chi-Square Goodness of Fit test on the data present. Currently,
cat_comp_default
is the only built-in function for categorical data, but a
user may write their own function to use instead.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% cat_row("Stem.Size", rowlabel="Stem Size", comparison=cat_comp_default) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% cat_row("Stem.Size", rowlabel="Stem Size", comparison=cat_comp_default) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered")) %>% column_spec(c(2:6), width_min = "1.1in") %>% column_spec(7, width_min = "1.5in")
The final type of data we will examine here is binary data; this is when a variable
can only take on two possible values. In a table, it can be helpful to only include
one of the options if the second entry can be deduced from looking at the first.
This is done using the binary_row
function. The default summary function is binary_default
,
but the user may write their own function to calculate different summary statistics
from what is shown here.
Currently, there are four summary functions available for use within binary_row
.
The default summary to use for each row can be specified in tbl_start
, or determined
using the summary
argument of each row.
binary_default
: Calculates the cell counts and column-wise proportions
binary_pct
: Calculates the cell counts and column-wise percentages
binary_count
: Calculates the cell counts
binary_jama
: Calculates the column-wise percentages and cell counts divided by column
totals. This is the style used by the Journal of the American Medical Association.
Note that a user may use cat_row
to process binary data if they wish
to see both row entries included in the table.
We will now demonstrate the use of binary_row
on the color variable in iris
.
In the dataset, the available colors are blue and purple, so we do not wish
to include both entries here.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", rowlabel = "Color") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
The binary_row
function includes all of the same arguments as the previous row
functions, but additionally includes three new arguments. reference
allows
a user to choose which group will appear on the table. By default, the
alphabetically first row group will appear on the table, which is why 'Blue'
appeared above. If we want to see the statistics for purple flowers, we can
run the following code.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", rowlabel = "Color", reference="Purple") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
Notice in the previous examples that the binary row is contained entirely within
one row. This is because many tables in professional journals will often abbreviate
binary data to fit within a single row of data. If you do not wish to do this within
your table, you can set the additional argument compact
to be FALSE and display
the row information in more than one row.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", rowlabel = "Color", compact = FALSE) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
As of package version 1.1.2, the user can now choose to remove the reference group
label from the table if they do not want it to be present. The argument ref.label
allows a user to toggle the name of the reference group in the table; by default,
this is set to on
, but a user can input off
to remove it.
Finally, let's look at some comparison functions used for binary data. By default,
this row function will calculate the difference in proportions by using binary_diff
if comparison=TRUE
during initialization. This will calculate differences in
proportions across columns; the calculations will also include 95% Confidence intervals.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", comparison=binary_diff) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", comparison=binary_diff) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered")) %>% column_spec(1, width_min = "1in") %>% column_spec(c(2:6), width_min = "1.1in") %>% column_spec(7, width_min = "1.75in") %>% column_spec(c(8:9), width_min = "1.5in")
The package has two additional options for comparison tests using binary data.
Odds ratios can be calculated using binary_or
, and risk ratios can be calculated
with binary_rr
. Note that if comparison=TRUE
is initialized in tbl_start
and
a user wants to use an odds ratio or risk ratio here, comparison
must be set to
either of those two options in this row addition, since excluding the argument
will lead to binary_diff
being called by default.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", comparison=binary_or) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", comparison=binary_or) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered")) %>% column_spec(1, width_min = "1in") %>% column_spec(c(2:7), width_min = "1.1in") %>% column_spec(c(8:9), width_min = "1.25in")
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", comparison=binary_rr) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% binary_row("color", comparison=binary_rr) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered")) %>% column_spec(1, width_min = "1in") %>% column_spec(c(2:7), width_min = "1.1in") %>% column_spec(c(8:9), width_min = "1.25in")
The n_row
function will count the number of rows in your dataset, as well as the
total instances of each column variable. Note that you can decide whether or not
you want this function to include the missing data as part of your row count. For
the example below we will not include rows from missing data.
tbl1 <- tbl_start(data=iris, col_var="Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% n_row() %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
The empty_row
function will add a blank row to the final table. This is useful
if a user wants to include blank space between some of table's rows. The user only
needs to specify the name of the list object in order to create the blank row.
An optional argument is a header to include, should the user want to create a label
for the subsequent rows that follow in the table.
tbl1 <- tbl1 %>% empty_row()
The following code will generate a finalized table for the iris
dataset. It
will include all four numeric variables (sepal length, sepal width, petal length,
petal width), as well as stem size and color. The final table itself is generated
using tbl_out
. Below is an example of a customized table report that can be produced
using tangram.pipe. Annotations for the unique elements of the rows are created
by inserting the comments into the header argument for the empty_row()
command.
tbl1 <- tbl_start( data = iris, col_var = "Species", missing=FALSE, overall=TRUE, comparison=TRUE, default_num_summary = num_default, default_cat_summary = cat_pct, default_binary_summary = binary_default) %>% n_row() %>% num_row("Sepal.Length", rowlabel="Sepal Length") %>% empty_row('<i>No rowlabel, 3 decimal places</i>') %>% num_row("Sepal.Width", digits=3) %>% empty_row("<i>No comparison test used, Min-Max summary</i>") %>% num_row("Petal.Length", rowlabel="Petal Length", summary = num_minmax, comparison=FALSE) %>% empty_row("<i>Missing data considered, mean/Std. Dev summary</i>") %>% num_row("Petal.Width", rowlabel="Petal Width", summary = num_mean_sd, missing=TRUE) %>% cat_row("Stem.Size", rowlabel="Stem Size", missing=TRUE) %>% empty_row("<i>No rowlabels, indent 3 spaces, odds ratio as test</i>") %>% binary_row("color", missing = TRUE, comparison=binary_or, indent=3) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
tbl1 <- tbl_start( data = iris, col_var = "Species", missing=FALSE, overall=TRUE, comparison=TRUE, default_num_summary = num_default, default_cat_summary = cat_pct, default_binary_summary = binary_default) %>% n_row() %>% num_row("Sepal.Length", rowlabel="Sepal Length") %>% empty_row('<i>No rowlabel, 3 decimal places</i>') %>% num_row("Sepal.Width", digits=3) %>% empty_row("<i>No comparison test used, Min-Max summary</i>") %>% num_row("Petal.Length", rowlabel="Petal Length", summary = num_minmax, comparison=FALSE) %>% empty_row("<i>Missing data considered, mean/Std. Dev summary</i>") %>% num_row("Petal.Width", rowlabel="Petal Width", summary = num_mean_sd, missing=TRUE) %>% cat_row("Stem.Size", rowlabel="Stem Size", missing=TRUE) %>% empty_row("<i>No rowlabels, indent 3 spaces, odds ratio as test</i>") %>% binary_row("color", missing = TRUE, comparison=binary_or, indent=3) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered")) %>% column_spec(1, width_min = "1.5in") %>% column_spec(c(2:6), width_min = "1.3in") %>% column_spec(c(7:9), width_min = "1.5in")
The package can handle cases where a user only wants a single summary column of
data. In the iris
dataset, if we set the column variable to be NULL in tbl_start
,
we can obtain just one summary column for the dataset without breaking the table
up by columns. Note that comparison functions will not run here, even if the
comparison
argument is set to TRUE.
tbl1 <- tbl_start(iris, NULL, missing=FALSE, overall=TRUE, comparison=FALSE) %>% n_row() %>% num_row("Sepal.Length", rowlabel="Sepal Length") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
This package allows for an individual row to use a different dataset from the one
initialized in tbl_start
. Use the newdata
argument to specify the new dataset
to use, then define the rows and columns for the new data. Note that if a new row
is added after the row with the differing dataset, the new row will automatically
return to using the initialized dataset from tbl_start
unless the user specifies
otherwise in newdata
.
For this example, we will split the iris
dataset so that the sepal and petal
variables are in separate datasets, and show that the newdata
argument can
allow the information from both datasets to be combined in one table.
sepaldat <- iris %>% select(-c(Petal.Length, Petal.Width)) petaldat <- iris %>% select(-c(Sepal.Length, Sepal.Width))
tbl1 <- tbl_start(sepaldat, "Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% num_row("Sepal.Length", rowlabel="Sepal Length") %>% num_row("Sepal.Width", rowlabel="Sepal Width") %>% empty_row(header="Switch to Petal Dataset") %>% num_row(row_var="Petal.Length", col_var="Species", newdata=petaldat, rowlabel="Petal Length") %>% num_row(row_var="Petal.Width", col_var="Species", newdata=petaldat, rowlabel="Petal Width") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
Notice that in this example, the column variable for sepaldat
was the same as
that for petaldat
. If the columns used had differed between the datasets,
all columns would be included in the table, but only columns corresponding to
the data used in the rows would have values filled in.
A common useage for the newdata
argument is when you want to make a table
which combines summary statistics for subsets of data. Suppose we were to display
the sepal measures for the entire dataset, then show these same measurements
for two subsets of data which are determined by the petal length. Here, we
divide the dataset into two subsets; petal length > 4.3 and petal length <= 4.3.
petal.small <- iris %>% filter(Petal.Length <= 4.3) petal.large <- iris %>% filter(Petal.Length > 4.3)
tbl1 <- tbl_start(iris, "Species", missing=FALSE, overall=TRUE, comparison=FALSE) %>% empty_row(header="All Data") %>% n_row() %>% num_row("Sepal.Length", rowlabel=" Sepal Length") %>% num_row("Sepal.Width", rowlabel=" Sepal Width") %>% empty_row(header="Petal Length less than 4.3") %>% n_row(newdata=petal.small) %>% num_row("Sepal.Length", rowlabel=" Sepal Length", col_var="Species", newdata=petal.small) %>% num_row("Sepal.Width", rowlabel=" Sepal Width", col_var="Species", newdata=petal.small) %>% empty_row(header="Petal Length greater than 4.3") %>% n_row(newdata=petal.large) %>% num_row("Sepal.Length", rowlabel=" Sepal Length", col_var="Species", newdata=petal.large) %>% num_row("Sepal.Width", rowlabel=" Sepal Width", col_var="Species", newdata=petal.large) %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l") %>% trimws %>% kable_styling(c("striped","bordered"))
The knitr
and kableExtra
packages can be used to add styling features to the
finished tables. Captions can be added to the tables using the caption
command,
and tables can also be rendered into a LaTeX format using the format
argument;
both can be used in the kable
function. kable_styling
allows you to use the
font_size
argument to specify how large the table text should be.
tbl1 <- tbl_start(iris, "Species", missing=TRUE, overall=TRUE, comparison=TRUE, default_num_summary = num_minmax, default_cat_summary = cat_pct, default_binary_summary = binary_jama) %>% n_row() %>% num_row("Sepal.Length", rowlabel="Sepal Length") %>% cat_row("Stem.Size", rowlabel="Stem Size") %>% binary_row("color", rowlabel="Color") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l", caption = "Example Summary table", format = "html") %>% trimws %>% kable_styling(c("striped","bordered"), font_size = 12)
tbl1 <- tbl_start(iris, "Species", missing=TRUE, overall=TRUE, comparison=TRUE, default_num_summary = num_minmax, default_cat_summary = cat_pct, default_binary_summary = binary_jama) %>% n_row() %>% num_row("Sepal.Length", rowlabel="Sepal Length") %>% cat_row("Stem.Size", rowlabel="Stem Size") %>% binary_row("color", rowlabel="Color") %>% tbl_out() tbl1 %>% tangram_styling() %>% kable(escape=F, align="l", caption = "Example Summary table", format = "html") %>% trimws %>% kable_styling(c("striped","bordered"), font_size = 12) %>% column_spec(1, width_min = "1.5in") %>% column_spec(c(2:6), width_min = "1.3in") %>% column_spec(c(7:9), width_min = "1.5in")
One of the key features of this package is giving the user the flexibility to supply
custom summary and comparison functions to the package to create tables in formats
not built-in to tangram.pipe
. The accompanying vignette "Writing User-Defined
Summary Functions" outlines the process for how to write functions that will work
well with tangram.pipe
The digits
parameter is now available in tbl_start
for specifying default
digits to use throughout the table.
Added ref.label
argument in binary summary functions to allow user to toggle
reference group labels in binary rows.
Deprecated the print.tangram.pipe
function, as the update to tbl_out
in
version 1.1.1 rendered this function obsolete.
Fixed a bug in num_row
where column category names with spaces would not
format correctly.
Changed binary_row
output to include the rowlabel along with the displayed
category when compact = TRUE
.
Fixed a bug in binary_row
where numeric row category labels would not format
correctly when compact = TRUE
.
Added ordering
and sortcol
arguments to cat_row
.
Edited categorical summary functions to utilize sorting arguments.
Added prewritten summary functions num_date
, cat_count
, and binary_count
.
Edited tbl_out
to output the finalized dataframe object (previous version
only appended the final table to the table information list).
Changed the rowlabels
argument to rowlabel
.
Options overall
, missing
, and comparison
now have default values in tbl_start()
.
Only leading white spaces are formatted to HTML form in tangram_styling
.
Added n_row()
as a row function to the table.
Added prewritten summary functions num_minmax
, num_medianiqr
, num_mean_sd
,
cat_pct
, cat_jama
, binary_pct
, binary_jama
.
Added options default_num_summary
, default_cat_summary
, default_binary_summary
to tbl_start()
. Default values are set to the default summary functions for each row.
Changed the summary
argument within the row functions to automatically use
the default specified in tbl_start()
, unless another function is supplied by the user.
The default in the function argument has changed from a function to NULL.
Summary functions now take on generic arguments specified by an ellipsis (...), but still work the same as before within the row functions.
binary_row()
now has the option to condense to one row (compact
). Default is
TRUE.
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