Title: | A Traceability Focused Grammar of Clinical Data Summary |
---|---|
Description: | A traceability focused tool created to simplify the data manipulation necessary to create clinical summaries. |
Authors: | Eli Miller [aut] , Mike Stackhouse [aut, cre] , Ashley Tarasiewicz [aut], Nathan Kosiba [ctb] , Sadchla Mascary [ctb], Andrew Bates [ctb], Shiyu Chen [ctb], Oleksii Mikryukov [ctb], Atorus Research LLC [cph] |
Maintainer: | Mike Stackhouse <[email protected]> |
License: | MIT + file LICENSE |
Version: | 1.2.1 |
Built: | 2024-10-18 04:46:48 UTC |
Source: | https://github.com/atorus-research/Tplyr |
An anti-join allows a tplyr_meta object to refer to data that should be
extracted from a separate dataset, like the population data of a Tplyr table,
that is unavailable in the target dataset. The primary use case for this is
the presentation of missing subjects, which in a Tplyr table is presented
using the function add_missing_subjects_row()
. The missing subjects
themselves are not present in the target data, and are thus only available in
the population data. The add_anti_join()
function allows you to provide the
meta information relevant to the population data, and then specify the on
variable that should be used to join with the target dataset and find the
values present in the population data that are missing from the target data.
add_anti_join(meta, join_meta, on)
add_anti_join(meta, join_meta, on)
meta |
A tplyr_meta object referring to the target data |
join_meta |
A tplyr_meta object referring to the population data |
on |
A list of quosures containing symbols - most likely set to USUBJID. |
A tplyr_meta object
tm <- tplyr_meta( rlang::quos(TRT01A, SEX, ETHNIC, RACE), rlang::quos(TRT01A == "Placebo", TRT01A == "SEX", ETHNIC == "HISPANIC OR LATINO") ) tm %>% add_anti_join( tplyr_meta( rlang::quos(TRT01A, ETHNIC), rlang::quos(TRT01A == "Placebo", ETHNIC == "HISPANIC OR LATINO") ), on = rlang::quos(USUBJID) )
tm <- tplyr_meta( rlang::quos(TRT01A, SEX, ETHNIC, RACE), rlang::quos(TRT01A == "Placebo", TRT01A == "SEX", ETHNIC == "HISPANIC OR LATINO") ) tm %>% add_anti_join( tplyr_meta( rlang::quos(TRT01A, ETHNIC), rlang::quos(TRT01A == "Placebo", ETHNIC == "HISPANIC OR LATINO") ), on = rlang::quos(USUBJID) )
When working with 'huxtable' tables, column headers can be controlled as if they are rows in the data frame.
add_column_headers
eases the process of introducing these headers.
add_column_headers(.data, s, header_n = NULL)
add_column_headers(.data, s, header_n = NULL)
.data |
The data.frame/tibble on which the headers shall be attached |
s |
The text containing the intended header string |
header_n |
A header_n or generic data.frame to use for binding count values. This is required if you are using the token replacement. |
Headers are created by providing a single string. Columns are specified by delimitting each header with a '|' symbol.
Instead of specifying the destination of each header, add_column_headers
assumes that you have organized the columns
of your data frame before hand. This means that after you use Tplyr::build()
, if you'd like to reorganize the
default column order (which is simply alphabetical), simply pass the build output to a dplyr::select
or dplyr::relocate
statement before passing into add_column_headers
.
Spanning headers are also supported. A spanning header is an overarching header that sits across multiple columns.
Spanning headers are introduced to add_column_header
by providing the spanner text (i.e. the text that
you'd like to sit in the top row), and then the spanned text (the bottom row) within curly brackets ('{}). For example,
take the iris dataset. We have the names:
"Sepal.Length" "Sepal.Width" "Petal.Length" "Petal.Width" "Species"
If we wanted to provide a header string for this dataset, with spanners to help with categorization of the variables, we could provide the following string:
"Sepal {Length | Width} | Petal {Length | Width} | Species"
A data.frame with the processed header string elements attached as the top rows
Make sure you are aware of the order of your variables prior to passing in to add_column_headers
. The only requirement
is that the number of column match. The rest is up to you.
There are a few features of add_column_header
that are intended but not yet supported:
Nested spanners are not yet supported. Only a spanning row and a bottom row can currently be created
Different delimiters and indicators for a spanned group may be used in the future. The current choices were intuitive, but based on feedback it could be determined that less common characters may be necessary.
This function has support for reading values from the header_n object in a Tplyr table
and adding them in the column headers. Note: The order of the parameters
passed in the token is important. They should be first the treatment variable
then any cols
variables in the order they were passed in the table construction.
Use a double asterisk "**" at the begining to start the token and another
double asterisk to close it. You can separate column parameters in the token
with a single underscore. For example, **group1_flag2_param3** will pull the count
from the header_n binding for group1 in the treat_var
, flag2 in the first cols
argument, and param3 in the second cols
argument.
You can pass fewer arguments in the token to get the sum of multiple columns. For example, **group1** would get the sum of the group1 treat_var, and all cols from the header_n.
# Load in pipe library(magrittr) library(dplyr) header_string <- "Sepal {Length | Width} | Petal {Length | Width} | Species" iris2 <- iris %>% mutate_all(as.character) iris2 %>% add_column_headers(header_string) # Example with counts mtcars2 <- mtcars %>% mutate_all(as.character) t <- tplyr_table(mtcars2, vs, cols = am) %>% add_layer( group_count(cyl) ) b_t <- build(t) %>% mutate_all(as.character) count_string <- paste0(" | V N=**0** {auto N=**0_0** | man N=**0_1**} |", " S N=**1** {auto N=**1_0** | man N=**1_1**} | | ") add_column_headers(b_t, count_string, header_n(t))
# Load in pipe library(magrittr) library(dplyr) header_string <- "Sepal {Length | Width} | Petal {Length | Width} | Species" iris2 <- iris %>% mutate_all(as.character) iris2 %>% add_column_headers(header_string) # Example with counts mtcars2 <- mtcars %>% mutate_all(as.character) t <- tplyr_table(mtcars2, vs, cols = am) %>% add_layer( group_count(cyl) ) b_t <- build(t) %>% mutate_all(as.character) count_string <- paste0(" | V N=**0** {auto N=**0_0** | man N=**0_1**} |", " S N=**1** {auto N=**1_0** | man N=**1_1**} | | ") add_column_headers(b_t, count_string, header_n(t))
tplyr_table
objectadd_layer
attaches a tplyr_layer
to a tplyr_table
object. This allows
for a tidy style of programming (using magrittr
piping, i.e. %>%
) with a
secondary advantage - the construction of the layer object may consist of a series of piped
functions itself.
Tplyr
encourages a user to view the construction of a table as a series of "layers".
The construction of each of these layers are isolated and independent of one another - but
each of these layers are children of the table itself. add_layer
isolates the construction
of an individual layer and allows the user to construct that layer and insert it back into the
parent. The syntax for this is intuitive and allows for tidy piping. Simply pipe the current
table object in, and write the code to construct your layer within the layer
parameter.
add_layers
is another approach to attaching layers to a tplyr_table
. Instead of
constructing the entire table at once, add_layers
allows you to construct layers as
different objects. These layers can then be attached into the tplyr_table
all at
once.
add_layer
and add_layers
both additionally allow you to name the layers as you
attach them. This is helpful when using functions like get_numeric_data
or
get_stats_data
when you can access information from a layer directly.
add_layer
has a name parameter, and layers can be named in add_layers
by
submitting the layer as a named argument.
add_layer(parent, layer, name = NULL) add_layers(parent, ...)
add_layer(parent, layer, name = NULL) add_layers(parent, ...)
parent |
A |
layer |
A layer construction function and associated modifier functions |
name |
A name to provide the layer in the table layers container |
... |
Layers to be added |
A tplyr_table
or tplyr_layer
/tplyr_subgroup_layer
with a new layer inserted into the layer
binding
[tplyr_table(), tplyr_layer(), group_count(), group_desc(), group_shift()]
# Load in pipe library(magrittr) ## Single layer t <- tplyr_table(mtcars, cyl) %>% add_layer( group_desc(target_var=mpg) ) ## Single layer with name t <- tplyr_table(mtcars, cyl) %>% add_layer(name='mpg', group_desc(target_var=mpg) ) # Using add_layers t <- tplyr_table(mtcars, cyl) l1 <- group_desc(t, target_var=mpg) l2 <- group_count(t, target_var=cyl) t <- add_layers(t, l1, 'cyl' = l2)
# Load in pipe library(magrittr) ## Single layer t <- tplyr_table(mtcars, cyl) %>% add_layer( group_desc(target_var=mpg) ) ## Single layer with name t <- tplyr_table(mtcars, cyl) %>% add_layer(name='mpg', group_desc(target_var=mpg) ) # Using add_layers t <- tplyr_table(mtcars, cyl) l1 <- group_desc(t, target_var=mpg) l2 <- group_count(t, target_var=cyl) t <- add_layers(t, l1, 'cyl' = l2)
This function calculates the number of subjects missing from a particular group of results. The calculation is done by examining the total number of subjects potentially available from the Header N values within the result column, and finding the difference with the total number of subjects present in the result group. Note that for accurate results, the subject variable needs to be defined using the 'set_distinct_by()' function. As with other methods, this function instructs how distinct results should be identified.
add_missing_subjects_row(e, fmt = NULL, sort_value = NULL)
add_missing_subjects_row(e, fmt = NULL, sort_value = NULL)
e |
A 'count_layer' object |
fmt |
An f_str object used to format the total row. If none is provided, display is based on the layer formatting. |
sort_value |
The value that will appear in the ordering column for total rows. This must be a numeric value. |
tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_missing_subjects_row(f_str("xxxx", n)) ) %>% build()
tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_missing_subjects_row(f_str("xxxx", n)) ) %>% build()
A very common requirement for summary tables is to calculate the risk difference between treatment
groups. add_risk_diff
allows you to do this. The underlying risk difference calculations
are performed using the Base R function prop.test
- so prior to using this function,
be sure to familiarize yourself with its functionality.
add_risk_diff(layer, ..., args = list(), distinct = TRUE)
add_risk_diff(layer, ..., args = list(), distinct = TRUE)
layer |
Layer upon which the risk difference will be attached |
... |
Comparison groups, provided as character vectors where the first group is the comparison, and the second is the reference |
args |
Arguments passed directly into |
distinct |
Logical - Use distinct counts (if available). |
add_risk_diff
can only be attached to a count layer, so the count layer must be constructed
first. add_risk_diff
allows you to compare the difference between treatment group, so all
comparisons should be based upon the values within the specified treat_var
in your
tplyr_table
object.
Comparisons are specified by providing two-element character vectors. You can provide as many of
these groups as you want. You can also use groups that have been constructed using
add_treat_grps
or add_total_group
. The first element provided will be considered
the 'reference' group (i.e. the left side of the comparison), and the second group will be considered
the 'comparison'. So if you'd like to see the risk difference of 'T1 - Placebo', you would specify
this as c('T1', 'Placebo')
.
Tplyr forms your two-way table in the background, and then runs prop.test
appropriately.
Similar to way that the display of layers are specified, the exact values and format of how you'd like
the risk difference display are set using set_format_strings
. This controls both the values
and the format of how the risk difference is displayed. Risk difference formats are set within
set_format_strings
by using the name 'riskdiff'.
You have 5 variables to choose from in your data presentation:
Probability of the left hand side group (i.e. comparison)
Probability of the right hand side group (i.e. reference)
Difference of comparison - reference
Lower end of the confidence interval (default is 95%, override with the args
paramter)
Upper end of the confidence interval (default is 95%, override with the args
paramter)
Use these variable names when forming your f_str
objects. The default presentation, if no
string format is specified, will be:
f_str('xx.xxx (xx.xxx, xx.xxx)', dif, low, high)
Note - within Tplyr, you can account for negatives by allowing an extra space within your integer side settings. This will help with your alignment.
If columns are specified on a Tplyr table, risk difference comparisons still only take place between
groups within the treat_var
variable - but they are instead calculated treating the cols
variables as by variables. Just like the tplyr layers themselves, the risk difference will then be transposed
and display each risk difference as separate variables by each of the cols
variables.
If distinct
is TRUE (the default), all calculations will take place on the distinct counts, if
they are available. Otherwise, non-distinct counts will be used.
One final note - prop.test
may throw quite a few warnings. This is natural, because it
alerts you when there's not enough data for the approximations to be correct. This may be unnerving
coming from a SAS programming world, but this is R is trying to alert you that the values provided
don't have enough data to truly be statistically accurate.
library(magrittr) ## Two group comparisons with default options applied t <- tplyr_table(mtcars, gear) # Basic risk diff for two groups, using defaults l1 <- group_count(t, carb) %>% # Compare 3 vs. 4, 3 vs. 5 add_risk_diff( c('3', '4'), c('3', '5') ) # Build and show output add_layers(t, l1) %>% build() ## Specify custom formats and display variables t <- tplyr_table(mtcars, gear) # Create the layer with custom formatting l2 <- group_count(t, carb) %>% # Compare 3 vs. 4, 3 vs. 5 add_risk_diff( c('3', '4'), c('3', '5') ) %>% set_format_strings( 'n_counts' = f_str('xx (xx.x)', n, pct), 'riskdiff' = f_str('xx.xxx, xx.xxx, xx.xxx, xx.xxx, xx.xxx', comp, ref, dif, low, high) ) # Build and show output add_layers(t, l2) %>% build() ## Passing arguments to prop.test t <- tplyr_table(mtcars, gear) # Create the layer with args option l3 <- group_count(t, carb) %>% # Compare 3 vs. 4, 4 vs. 5 add_risk_diff( c('3', '4'), c('3', '5'), args = list(conf.level = 0.9, correct=FALSE, alternative='less') ) # Build and show output add_layers(t, l3) %>% build()
library(magrittr) ## Two group comparisons with default options applied t <- tplyr_table(mtcars, gear) # Basic risk diff for two groups, using defaults l1 <- group_count(t, carb) %>% # Compare 3 vs. 4, 3 vs. 5 add_risk_diff( c('3', '4'), c('3', '5') ) # Build and show output add_layers(t, l1) %>% build() ## Specify custom formats and display variables t <- tplyr_table(mtcars, gear) # Create the layer with custom formatting l2 <- group_count(t, carb) %>% # Compare 3 vs. 4, 3 vs. 5 add_risk_diff( c('3', '4'), c('3', '5') ) %>% set_format_strings( 'n_counts' = f_str('xx (xx.x)', n, pct), 'riskdiff' = f_str('xx.xxx, xx.xxx, xx.xxx, xx.xxx, xx.xxx', comp, ref, dif, low, high) ) # Build and show output add_layers(t, l2) %>% build() ## Passing arguments to prop.test t <- tplyr_table(mtcars, gear) # Create the layer with args option l3 <- group_count(t, carb) %>% # Compare 3 vs. 4, 4 vs. 5 add_risk_diff( c('3', '4'), c('3', '5'), args = list(conf.level = 0.9, correct=FALSE, alternative='less') ) # Build and show output add_layers(t, l3) %>% build()
Adding a total row creates an additional observation in the count summary that presents the total counts (i.e. the n's that are summarized). The format of the total row will be formatted in the same way as the other count strings.
add_total_row(e, fmt = NULL, count_missings = TRUE, sort_value = NULL)
add_total_row(e, fmt = NULL, count_missings = TRUE, sort_value = NULL)
e |
A |
fmt |
An f_str object used to format the total row. If none is provided, display is based on the layer formatting. |
count_missings |
Whether or not to ignore the named arguments passed in 'set_count_missing()' when calculating counts total row. This is useful if you need to exclude/include the missing counts in your total row. Defaults to TRUE meaning total row will not ignore any values. |
sort_value |
The value that will appear in the ordering column for total rows. This must be a numeric value. |
Totals are calculated using all grouping variables, including treat_var and
cols from the table level. If by variables are included, the grouping of the
total and the application of denominators becomes ambiguous. You will be
warned specifically if a percent is included in the format. To rectify this,
use set_denoms_by()
, and the grouping of add_total_row()
will
be updated accordingly.
Note that when using add_total_row()
with set_pop_data()
, you
should call add_total_row()
AFTER calling set_pop_data()
,
otherwise there is potential for unexpected behaivior with treatment groups.
# Load in Pipe library(magrittr) tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_total_row(f_str("xxxx", n)) ) %>% build()
# Load in Pipe library(magrittr) tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_total_row(f_str("xxxx", n)) ) %>% build()
Summary tables often present individual treatment groups, but may additionally have a "Treatment vs. Placebo" or "Total" group added to show grouped summary statistics or counts. This set of functions offers an interface to add these groups at a table level and be consumed by subsequent layers.
add_treat_grps(table, ...) add_total_group(table, group_name = "Total") treat_grps(table)
add_treat_grps(table, ...) add_total_group(table, group_name = "Total") treat_grps(table)
table |
A |
... |
A named vector where names will become the new treatment group names, and values will be used to construct those treatment groups |
group_name |
The treatment group name used for the constructed 'Total' group |
add_treat_grps
allows you to specify specific groupings. This is done
by supplying named arguments, where the name becomes the new treatment
group's name, and those treatment groups are made up of the argument's
values.
add_total_group
is a simple wrapper around add_treat_grps
.
Instead of producing custom groupings, it produces a "Total" group by the
supplied name, which defaults to "Total". This "Total" group is made up of
all existing treatment groups within the population dataset.
Note that when using add_treat_grps
or add_total_row()
with
set_pop_data()
, you should call add_total_row()
AFTER calling
set_pop_data()
, otherwise there is potential for unexpected behaivior
with treatment groups.
The function treat_grps
allows you to see the custom treatment groups
available in your tplyr_table
object
The modified table object
tab <- tplyr_table(iris, Species) # A custom group add_treat_grps(tab, "Not Setosa" = c("versicolor", "virginica")) # Add a total group add_total_group(tab) treat_grps(tab) # Returns: # $`Not Setosa` #[1] "versicolor" "virginica" # #$Total #[1] "setosa" "versicolor" "virginica"
tab <- tplyr_table(iris, Species) # A custom group add_treat_grps(tab, "Not Setosa" = c("versicolor", "virginica")) # Add a total group add_total_group(tab) treat_grps(tab) # Returns: # $`Not Setosa` #[1] "versicolor" "virginica" # #$Total #[1] "setosa" "versicolor" "virginica"
Add additional variable names to a tplyr_meta()
object.
add_variables(meta, names) add_filters(meta, filters)
add_variables(meta, names) add_filters(meta, filters)
meta |
A tplyr_meta object |
names |
A list of names, providing variable names of interest. Provide
as a list of quosures using |
filters |
A list of symbols, providing variable names of interest. Provide as a list of quosures using 'rlang::quos()' |
tplyr_meta object
m <- tplyr_meta() m <- add_variables(m, rlang::quos(a, b, c)) m <- add_filters(m, rlang::quos(a==1, b==2, c==3)) m
m <- tplyr_meta() m <- add_variables(m, rlang::quos(a, b, c)) m <- add_filters(m, rlang::quos(a==1, b==2, c==3)) m
append_metadata()
allows a user to extend the Tplyr metadata data frame
with user provided data. In some tables, Tplyr may be able to provided most
of the data, but a user may have to extend the table with other summaries,
statistics, etc. This function allows the user to extend the tplyr_table's
metadata with their own metadata content using custom data frames created
using the tplyr_meta
object.
append_metadata(t, meta)
append_metadata(t, meta)
t |
A tplyr_table object |
meta |
A dataframe fitting the specifications of the details section of this function |
As this is an advanced feature of Tplyr, ownership is on the user to make
sure the metadata data frame is assembled properly. The only restrictions
applied by append_metadata()
are that meta
must have a column named
row_id
, and the values in row_id
cannot be duplicates of any row_id
value already present in the Tplyr metadata dataframe. tplyr_meta()
objects
align with constructed dataframes using the row_id
and output dataset
column name. As such, tplyr_meta()
objects should be inserted into a data
frame using a list column.
A tplyr_table object
t <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt) ) t %>% build(metadata=TRUE) m <- tibble::tibble( row_id = c('x1_1'), var1_3 = list(tplyr_meta(rlang::quos(a, b, c), rlang::quos(a==1, b==2, c==3))) ) append_metadata(t, m)
t <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt) ) t %>% build(metadata=TRUE) m <- tibble::tibble( row_id = c('x1_1'), var1_3 = list(tplyr_meta(rlang::quos(a, b, c), rlang::quos(a==1, b==2, c==3))) ) append_metadata(t, m)
This function allows you to conditionally re-format a string of numbers based on a numeric value within the string itself. By selecting a "format group", which is targeting a specific number within the string, a user can establish a condition upon which a provided replacement string can be used. Either the entire replacement can be used to replace the entire string, or the replacement text can refill the "format group" while preserving the original width and alignment of the target string.
apply_conditional_format( string, format_group, condition, replacement, full_string = FALSE )
apply_conditional_format( string, format_group, condition, replacement, full_string = FALSE )
string |
Target character vector where text may be replaced |
format_group |
An integer representing the targeted numeric field within the string, numbered from left to right |
condition |
An expression, using the variable name 'x' as the target variable within the condition |
replacement |
A string to use as the replacement value |
full_string |
TRUE if the full string should be replaced, FALSE if the replacement should be done within the format group |
A character vector
string <- c(" 0 (0.0%)", " 8 (9.3%)", "78 (90.7%)") apply_conditional_format(string, 2, x == 0, " 0 ", full_string=TRUE) apply_conditional_format(string, 2, x < 1, "(<1%)")
string <- c(" 0 (0.0%)", " 8 (9.3%)", "78 (90.7%)") apply_conditional_format(string, 2, x == 0, " 0 ", full_string=TRUE) apply_conditional_format(string, 2, x < 1, "(<1%)")
The f_str
object in Tplyr is used to drive formatting of the outputs
strings within a Tplyr table. This function allows a user to use the same
interface to apply formatted string on any data frame within a
dplyr::mutate()
context.
apply_formats(format_string, ..., empty = c(.overall = ""))
apply_formats(format_string, ..., empty = c(.overall = ""))
format_string |
The desired display format. X's indicate digits. On the left, the number of x's indicates the integer length. On the right, the number of x's controls decimal precision and rounding. Variables are inferred by any separation of the 'x' values other than a decimal. |
... |
The variables to be formatted using the format specified in
|
empty |
The string to display when the numeric data is not available. Use a single element character vector, with the element named '.overall' to instead replace the whole string. |
Note that auto-precision is not currently supported within apply_formats()
Character vector of formatted values
library(dplyr) mtcars %>% head() %>% mutate( fmt_example = apply_formats('xxx (xx.x)', hp, wt) )
library(dplyr) mtcars %>% head() %>% mutate( fmt_example = apply_formats('xxx (xx.x)', hp, wt) )
Depending on the display package being used, row label values may need to be
blanked out if they are repeating. This gives the data frame supporting the
table the appearance of the grouping variables being grouped together in
blocks. apply_row_masks
does this work by blanking out the value of
any row_label variable where the current value is equal to the value
before it. Note - apply_row_masks
assumes that the data frame has
already be sorted and therefore should only be applied once the data frame is
in its final sort sequence.
apply_row_masks(dat, row_breaks = FALSE, ...)
apply_row_masks(dat, row_breaks = FALSE, ...)
dat |
Data.frame / tibble to mask repeating row_labels |
row_breaks |
Boolean - set to TRUE to insert row breaks |
... |
Variable used to determine where row-breaks should be inserted.
Breaks will be inserted when this group of variables changes values. This
is determined by dataset order, so sorting should be done prior to using
|
Additionally, apply_row_masks
can add row breaks for you between each
layer. Row breaks are inserted as blank rows. This relies on the "break by"
variables (submitted via ...
) constructed in build
still being
attached to the dataset. An additional order variable is attached named
ord_break
, but the output dataset is sorted to properly insert the row
breaks between layers.
tibble with blanked out rows where values are repeating
tplyr_table
The functions used to assemble a tplyr_table
object and
each of the layers do not trigger the processing of any data. Rather, a lazy
execution style is used to allow you to construct your table and then
explicitly state when the data processing should happen. build
triggers this event.
build(x, metadata = FALSE)
build(x, metadata = FALSE)
x |
A |
metadata |
Trigger to build metadata. Defaults to FALSE |
When the build
command is executed, all of the data
processing commences. Any pre-processing necessary within the table
environment takes place first. Next, each of the layers begins executing.
Once the layers complete executing, the output of each layer is stacked into
the resulting data frame.
Once this process is complete, any post-processing necessary within the table
environment takes place, and the final output can be delivered. Metadata and
traceability information are kept within each of the layer environments,
which allows an investigation into the source of the resulting datapoints.
For example, numeric data from any summaries performed is maintained and
accessible within a layer using get_numeric_data
.
The 'metadata' option of build will trigger the construction of traceability metadata for the constructed data frame. Essentially, for every "result" that Tplyr produces, Tplyr can also generate the steps necessary to obtain the source data which produced that result from the input. For more information, see vignette("metadata").
An executed tplyr_table
tplyr_table, tplyr_layer, add_layer, add_layers, layer_constructors
# Load in Pipe library(magrittr) tplyr_table(iris, Species) %>% add_layer( group_desc(Sepal.Length, by = "Sepal Length") ) %>% add_layer( group_desc(Sepal.Width, by = "Sepal Width") ) %>% build()
# Load in Pipe library(magrittr) tplyr_table(iris, Species) %>% add_layer( group_desc(Sepal.Length, by = "Sepal Length") ) %>% add_layer( group_desc(Sepal.Width, by = "Sepal Width") ) %>% build()
This is a generalized post processing function that allows you to take groups of by variables and collapse them into a single column. Repeating values are split into separate rows, and for each level of nesting, a specified indentation level can be applied.
collapse_row_labels(x, ..., indent = " ", target_col = row_label)
collapse_row_labels(x, ..., indent = " ", target_col = row_label)
x |
Input data frame |
... |
Row labels to be collapsed |
indent |
Indentation string to be used, which is multiplied at each indentation level |
target_col |
The desired name of the output column containing collapsed row labels |
data.frame with row labels collapsed into a single column
x <- tibble::tribble( ~row_label1, ~row_label2, ~row_label3, ~row_label4, ~var1, "A", "C", "G", "M", 1L, "A", "C", "G", "N", 2L, "A", "C", "H", "O", 3L, "A", "D", "H", "P", 4L, "A", "D", "I", "Q", 5L, "A", "D", "I", "R", 6L, "B", "E", "J", "S", 7L, "B", "E", "J", "T", 8L, "B", "E", "K", "U", 9L, "B", "F", "K", "V", 10L, "B", "F", "L", "W", 11L ) collapse_row_labels(x, row_label1, row_label2, row_label3, row_label4) collapse_row_labels(x, row_label1, row_label2, row_label3) collapse_row_labels(x, row_label1, row_label2, indent = " ", target_col = rl)
x <- tibble::tribble( ~row_label1, ~row_label2, ~row_label3, ~row_label4, ~var1, "A", "C", "G", "M", 1L, "A", "C", "G", "N", 2L, "A", "C", "H", "O", 3L, "A", "D", "H", "P", 4L, "A", "D", "I", "Q", 5L, "A", "D", "I", "R", 6L, "B", "E", "J", "S", 7L, "B", "E", "J", "T", 8L, "B", "E", "K", "U", 9L, "B", "F", "K", "V", 10L, "B", "F", "L", "W", 11L ) collapse_row_labels(x, row_label1, row_label2, row_label3, row_label4) collapse_row_labels(x, row_label1, row_label2, row_label3) collapse_row_labels(x, row_label1, row_label2, indent = " ", target_col = rl)
f_str
objectf_str
objects are intended to be used within the function
set_format_strings
. The f_str
object carries information that powers a
significant amount of layer processing. The format_string
parameter is
capable of controlling the display of a data point and decimal precision. The
variables provided in ...
control which data points are used to populate
the string formatted output.
f_str(format_string, ..., empty = c(.overall = ""))
f_str(format_string, ..., empty = c(.overall = ""))
format_string |
The desired display format. X's indicate digits. On the left, the number of x's indicates the integer length. On the right, the number of x's controls decimal precision and rounding. Variables are inferred by any separation of the 'x' values other than a decimal. |
... |
The variables to be formatted using the format specified in
|
empty |
The string to display when the numeric data is not available. For desc layers, an unnamed character vector will populate within the provided format string, set to the same width as the fitted numbers. Use a single element character vector, with the element named '.overall' to instead replace the whole string. |
Format strings are one of the most powerful components of 'Tplyr'. Traditionally, converting numeric values into strings for presentation can consume a good deal of time. Values and decimals need to align between rows, rounding before trimming is sometimes forgotten - it can become a tedious mess that is realistically not an important part of the analysis being performed. 'Tplyr' makes this process as simple as we can, while still allowing flexibility to the user.
Tplyr provides both manual and automatic decimal precision formatting. The
display of the numbers in the resulting data frame is controlled by the
format_string
parameter. For manual precision, just like dummy values may
be presented on your mocks, integer and decimal precision is specified by
the user providing a string of 'x's for how you'd like your numbers
formatted. If you'd like 2 integers with 3 decimal places, you specify your
string as 'xx.xxx'. 'Tplyr' does the work to get the numbers in the right
place.
To take this a step further, automatic decimal precision can also be obtained based on the collected precision within the data. When creating tables where results vary by some parameter, different results may call for different degrees of precision. To use automatic precision, use a single 'a' on either the integer and decimal side. If you'd like to use increased precision (i.e. you'd like mean to be collected precision +1), use 'a+1'. So if you'd like both integer and and decimal precision to be based on the data as collected, you can use a format like 'a.a' - or for collected+1 decimal precision, 'a.a+1'. You can mix and match this with manual formats as well, making format strings such as 'xx.a+1'.
If you want two numbers on the same line, you provide two sets of x's. For example, if you're presenting a value like "mean (sd)" - you could provide the string 'xx.xx (xx.xxx)', or perhaps 'a.a+1 (a.a+2). Note that you're able to provide different integer lengths and different decimal precision for the two values. Each format string is independent and relates only to the format specified.
As described above, when using 'x' or 'a', any other character within the
format string will stay stationary. So for example, if your format string
is 'xx (xxx.x)', your number may format as '12 ( 34.5)'. So the left side
parenthesis stays fixed. In some displays, you may want the parenthesis to
'hug' your number. Following this example, when allotting 3 spaces for the
integer within parentheses, the parentehsis should shift to the right,
making the numbers appear '12 (34.5)'. Using f_str()
you can achieve
this by using a capital 'X' or 'A'. For this example, the format string
would be 'xx (XXX.x)'.
There are a two rules when using 'parenthesis hugging':
Capital letters should only be used on the integer side of a number
A character must precede the capital letter, otherwise there's no character to 'hug'
The other parameters of the f_str
call specify what values should fill
the x's. f_str
objects are used slightly differently between different
layers. When declaring a format string within a count layer, f_str()
expects to see the values n
or distinct_n
for event or distinct counts,
pct
or distinct_pct
for event or distinct percentages, or total
or
distinct_total
for denominator calculations. Note that in an f_str()
for a count layer 'A' or 'a' are based on n counts, and therefore don't
make sense to use in percentages. But in descriptive statistic layers,
f_str
parameters refer to the names of the summaries being performed,
either by built in defaults, or custom summaries declared using
set_custom_summaries()
. See set_format_strings()
for some more notes
about layers specific implementation.
An f_str()
may also be used outside of a Tplyr table. The function
apply_formats()
allows you to apply an f_str
within the context of
dplyr::mutate()
or more generally a vectorized function.
A f_str
object
f_str()
Variables by Layer TypeValid variables allowed within the ...
parameter of f_str()
differ by
layer type.
Count layers
n
pct
total
distinct_n
distinct_pct
distinct_total
Shift layers
n
pct
total
Desc layers
n
mean
sd
median
var
min
max
iqr
q1
q3
missing
Custom summaries created by set_custom_summaries()
f_str("xx.x (xx.x)", mean, sd) f_str("a.a+1 (a.a+2)", mean, sd) f_str("xx.a (xx.a+1)", mean, sd) f_str("xx.x, xx.x, xx.x", q1, median, q3) f_str("xx (XXX.x%)", n, pct) f_str("a.a+1 (A.a+2)", mean, sd)
f_str("xx.x (xx.x)", mean, sd) f_str("a.a+1 (a.a+2)", mean, sd) f_str("xx.a (xx.a+1)", mean, sd) f_str("xx.x, xx.x, xx.x", q1, median, q3) f_str("xx (XXX.x%)", n, pct) f_str("a.a+1 (A.a+2)", mean, sd)
Set or return by layer binding
get_by(layer) set_by(layer, by)
get_by(layer) set_by(layer, by)
layer |
A |
by |
A string, a variable name, or a list of variable names supplied
using |
For get_by
, the by
binding of the supplied layer. For
set_by
the modified layer environment.
# Load in pipe library(magrittr) iris$Species2 <- iris$Species lay <- tplyr_table(iris, Species) %>% group_count(Species) %>% set_by(vars(Species2, Sepal.Width))
# Load in pipe library(magrittr) iris$Species2 <- iris$Species lay <- tplyr_table(iris, Species) %>% group_count(Species) %>% set_by(vars(Species2, Sepal.Width))
Get labels for data sets included in Tplyr.
get_data_labels(data)
get_data_labels(data)
data |
A Tplyr data set. |
A data.frame with columns 'name' and 'label' containing the names and labels of each column.
Tplyr provides you with the ability to set table-wide defaults of format
strings. You may wish to reuse the same format strings across numerous
layers. set_desc_layer_formats
and set_count_layer_formats
allow you to apply your desired format strings within the entire scope of the
table.
get_desc_layer_formats(obj) set_desc_layer_formats(obj, ...) get_count_layer_formats(obj) set_count_layer_formats(obj, ...) get_shift_layer_formats(obj) set_shift_layer_formats(obj, ...)
get_desc_layer_formats(obj) set_desc_layer_formats(obj, ...) get_count_layer_formats(obj) set_count_layer_formats(obj, ...) get_shift_layer_formats(obj) set_shift_layer_formats(obj, ...)
obj |
A tplyr_table object |
... |
formats to pass forward |
For descriptive statistic layers, you can also use set_format_strings
and set_desc_layer_formats
together within a table, but not within the
same layer. In the absence of specified format strings, first the table will
be checked for any available defaults, and otherwise the
tplyr.desc_layer_default_formats
option will be used.
set_format_strings
will always take precedence over either. Defaults
cannot be combined between set_format_strings
,
set_desc_layer_formats
, and the
tplyr.desc_layer_default_formats
because the order of presentation of
results is controlled by the format strings, so relying on combinations of
these setting would not be intuitive.
For count layers, you can override the n_counts
or riskdiff
format strings separately, and the narrowest scope available will be used
from layer, to table, to default options.
Given a row_id value and a result column, this function will return the tplyr_meta object associated with that 'cell'.
get_meta_result(x, row_id, column, ...)
get_meta_result(x, row_id, column, ...)
x |
A built Tplyr table or a dataframe |
row_id |
The row_id value of the desired cell, provided as a character string |
column |
The result column of interest, provided as a character string |
... |
additional arguments |
If a Tplyr table is built with the metadata=TRUE
option specified, then
metadata is assembled behind the scenes to provide traceability on each
result cell derived. The functions get_meta_result()
and
get_meta_subset()
allow you to access that metadata by using an ID provided
in the row_id column and the column name of the result you'd like to access.
The purpose is of the row_id variable instead of a simple row index is to
provide a sort resistant reference of the originating column, so the output
Tplyr table can be sorted in any order but the metadata are still easily
accessible.
The tplyr_meta
object provided a list with two elements - names and
filters. The metadata contain every column from the target data.frame of the
Tplyr table that factored into the specified result cell, and the filters
contains all the necessary filters to subset to data summarized to create the
specified result cell. get_meta_subset()
additionally provides a parameter to
specify any additional columns you would like to include in the returned
subset data frame.
A tplyr_meta object
t <- tplyr_table(mtcars, cyl) %>% add_layer( group_desc(hp) ) dat <- t %>% build(metadata = TRUE) get_meta_result(t, 'd1_1', 'var1_4') m <- t$metadata dat <- t$target get_meta_result(t, 'd1_1', 'var1_4')
t <- tplyr_table(mtcars, cyl) %>% add_layer( group_desc(hp) ) dat <- t %>% build(metadata = TRUE) get_meta_result(t, 'd1_1', 'var1_4') m <- t$metadata dat <- t$target get_meta_result(t, 'd1_1', 'var1_4')
Given a row_id value and a result column, this function will return the subset of data referenced by the tplyr_meta object associated with that 'cell', which provides traceability to tie a result to its source.
get_meta_subset(x, row_id, column, add_cols = vars(USUBJID), ...) ## S3 method for class 'data.frame' get_meta_subset( x, row_id, column, add_cols = vars(USUBJID), target = NULL, pop_data = NULL, ... ) ## S3 method for class 'tplyr_table' get_meta_subset(x, row_id, column, add_cols = vars(USUBJID), ...)
get_meta_subset(x, row_id, column, add_cols = vars(USUBJID), ...) ## S3 method for class 'data.frame' get_meta_subset( x, row_id, column, add_cols = vars(USUBJID), target = NULL, pop_data = NULL, ... ) ## S3 method for class 'tplyr_table' get_meta_subset(x, row_id, column, add_cols = vars(USUBJID), ...)
x |
A built Tplyr table or a dataframe |
row_id |
The row_id value of the desired cell, provided as a character string |
column |
The result column of interest, provided as a character string |
add_cols |
Additional columns to include in subset data.frame output |
... |
additional arguments |
target |
A data frame to be subset (if not pulled from a Tplyr table) |
pop_data |
A data frame to be subset through an anti-join (if not pulled from a Tplyr table) |
If a Tplyr table is built with the metadata=TRUE
option specified, then
metadata is assembled behind the scenes to provide traceability on each
result cell derived. The functions get_meta_result()
and
get_meta_subset()
allow you to access that metadata by using an ID provided
in the row_id column and the column name of the result you'd like to access.
The purpose is of the row_id variable instead of a simple row index is to
provide a sort resistant reference of the originating column, so the output
Tplyr table can be sorted in any order but the metadata are still easily
accessible.
The tplyr_meta
object provided a list with two elements - names and
filters. The metadata contain every column from the target data.frame of the
Tplyr table that factored into the specified result cell, and the filters
contains all the necessary filters to subset to data summarized to create the
specified result cell. get_meta_subset()
additionally provides a parameter
to specify any additional columns you would like to include in the returned
subset data frame.
A data.frame
t <- tplyr_table(mtcars, cyl) %>% add_layer( group_desc(hp) ) dat <- t %>% build(metadata = TRUE) get_meta_subset(t, 'd1_1', 'var1_4', add_cols = dplyr::vars(carb)) m <- t$metadata dat <- t$target get_meta_subset(t, 'd1_1', 'var1_4', add_cols = dplyr::vars(carb), target = target)
t <- tplyr_table(mtcars, cyl) %>% add_layer( group_desc(hp) ) dat <- t %>% build(metadata = TRUE) get_meta_subset(t, 'd1_1', 'var1_4', add_cols = dplyr::vars(carb)) m <- t$metadata dat <- t$target get_meta_subset(t, 'd1_1', 'var1_4', add_cols = dplyr::vars(carb), target = target)
Pull out the metadata dataframe from a tplyr_table to work with it directly
get_metadata(t)
get_metadata(t)
t |
A Tplyr table with metadata built |
Tplyr metadata dataframe
t <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt) ) t %>% build(metadata=TRUE) get_metadata(t)
t <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt) ) t %>% build(metadata=TRUE) get_metadata(t)
get_numeric_data
provides access to the un-formatted numeric data for
each of the layers within a tplyr_table
, with options to allow you to
extract distinct layers and filter as desired.
get_numeric_data(x, layer = NULL, where = TRUE, ...)
get_numeric_data(x, layer = NULL, where = TRUE, ...)
x |
A tplyr_table or tplyr_layer object |
layer |
Layer name or index to select out specifically |
where |
Subset criteria passed to dplyr::filter |
... |
Additional arguments to pass forward |
When used on a tplyr_table
object, this method will aggregate the
numeric data from all Tplyr layers. The data will be returned to the user in
a list of data frames. If the data has already been processed (i.e.
build
has been run), the numeric data is already available and will be
returned without reprocessing. Otherwise, the numeric portion of the layer
will be processed.
Using the layer and where parameters, data for a specific layer can be extracted and subset. This is most clear when layers are given text names instead of using a layer index, but a numeric index works as well.
Numeric data from the Tplyr layer
# Load in pipe library(magrittr) t <- tplyr_table(mtcars, gear) %>% add_layer(name='drat', group_desc(drat) ) %>% add_layer(name='cyl', group_count(cyl) ) # Return a list of the numeric data frames get_numeric_data(t) # Get the data from a specific layer get_numeric_data(t, layer='drat') get_numeric_data(t, layer=1) # Choose multiple layers by name or index get_numeric_data(t, layer=c('cyl', 'drat')) get_numeric_data(t, layer=c(2, 1)) # Get the data and filter it get_numeric_data(t, layer='drat', where = gear==3)
# Load in pipe library(magrittr) t <- tplyr_table(mtcars, gear) %>% add_layer(name='drat', group_desc(drat) ) %>% add_layer(name='cyl', group_count(cyl) ) # Return a list of the numeric data frames get_numeric_data(t) # Get the data from a specific layer get_numeric_data(t, layer='drat') get_numeric_data(t, layer=1) # Choose multiple layers by name or index get_numeric_data(t, layer=c('cyl', 'drat')) get_numeric_data(t, layer=c(2, 1)) # Get the data and filter it get_numeric_data(t, layer='drat', where = gear==3)
The precision_by variables are used to collect the integer and decimal precision when auto-precision is used. These by variables are used to group the input data and identify the maximum precision available within the dataset for each by group. The precision_by variables must be a subset of the by variables
get_precision_by(layer) set_precision_by(layer, precision_by)
get_precision_by(layer) set_precision_by(layer, precision_by)
layer |
A |
precision_by |
A string, a variable name, or a list of variable names supplied
using |
For get_precision_by
, the precision_by binding of the supplied
layer. For set_precision_by
the modified layer environment.
# Load in pipe library(magrittr) lay <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg, by=vars(carb, am)) %>% set_precision_by(carb) )
# Load in pipe library(magrittr) lay <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg, by=vars(carb, am)) %>% set_precision_by(carb) )
The precision_on variable is the variable used to establish numeric
precision. This variable must be included in the list of target_var
variables.
get_precision_on(layer) set_precision_on(layer, precision_on)
get_precision_on(layer) set_precision_on(layer, precision_on)
layer |
A |
precision_on |
A string, a variable name, or a list of variable names
supplied using |
For get_precision_on
, the precision_on binding of the supplied
layer. For set_precision_on
the modified layer environment.
# Load in pipe library(magrittr) lay <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(vars(mpg, disp), by=vars(carb, am)) %>% set_precision_on(disp) )
# Load in pipe library(magrittr) lay <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(vars(mpg, disp), by=vars(carb, am)) %>% set_precision_on(disp) )
Like the layer numeric data, Tplyr also stores the numeric data produced from statistics like risk difference. This helper function gives you access to obtain that data from the environment
get_stats_data(x, layer = NULL, statistic = NULL, where = TRUE, ...)
get_stats_data(x, layer = NULL, statistic = NULL, where = TRUE, ...)
x |
A tplyr_table or tplyr_layer object |
layer |
Layer name or index to select out specifically |
statistic |
Statistic name or index to select |
where |
Subset criteria passed to dplyr::filter |
... |
Additional arguments passed to dispatch |
When used on a tplyr_table
object, this method will aggregate the
numeric data from all Tplyr layers and calculate all statistics. The data
will be returned to the user in a list of data frames. If the data has
already been processed (i.e. build
has been run), the numeric data is
already available and the statistic data will simply be returned. Otherwise,
the numeric portion of the layer will be processed.
Using the layer, where, and statistic parameters, data for a specific layer statistic can be extracted and subset, allowing you to directly access data of interest. This is most clear when layers are given text names instead of using a layer index, but a numeric index works as well. If just a statistic is specified, that statistic will be collected and returned in a list of data frames, allowing you to grab, for example, just the risk difference statistics across all layers.
The statistics data of the supplied layer
library(magrittr) t <- tplyr_table(mtcars, gear) %>% add_layer(name='drat', group_desc(drat) ) %>% add_layer(name="cyl", group_count(cyl) ) %>% add_layer(name="am", group_count(am) %>% add_risk_diff(c('4', '3')) ) %>% add_layer(name="carb", group_count(carb) %>% add_risk_diff(c('4', '3')) ) # Returns a list of lists, containing stats data from each layer get_stats_data(t) # Returns just the riskdiff statistics from each layer - NULL # for layers without riskdiff get_stats_data(t, statistic="riskdiff") # Return the statistic data for just the "am" layer - a list get_stats_data(t, layer="am") get_stats_data(t, layer=3) # Return the statistic data for just the "am" and "cyl", layer - a # list of lists get_stats_data(t, layer=c("am", "cyl")) get_stats_data(t, layer=c(3, 2)) # Return just the statistic data for "am" and "cyl" - a list get_stats_data(t, layer=c("am", "cyl"), statistic="riskdiff") get_stats_data(t, layer=c(3, 2), statistic="riskdiff") # Return the riskdiff for the "am" layer - a data frame get_stats_data(t, layer="am", statistic="riskdiff") # Return and filter the riskdiff for the am layer - a data frame get_stats_data(t, layer="am", statistic="riskdiff", where = summary_var==1)
library(magrittr) t <- tplyr_table(mtcars, gear) %>% add_layer(name='drat', group_desc(drat) ) %>% add_layer(name="cyl", group_count(cyl) ) %>% add_layer(name="am", group_count(am) %>% add_risk_diff(c('4', '3')) ) %>% add_layer(name="carb", group_count(carb) %>% add_risk_diff(c('4', '3')) ) # Returns a list of lists, containing stats data from each layer get_stats_data(t) # Returns just the riskdiff statistics from each layer - NULL # for layers without riskdiff get_stats_data(t, statistic="riskdiff") # Return the statistic data for just the "am" layer - a list get_stats_data(t, layer="am") get_stats_data(t, layer=3) # Return the statistic data for just the "am" and "cyl", layer - a # list of lists get_stats_data(t, layer=c("am", "cyl")) get_stats_data(t, layer=c(3, 2)) # Return just the statistic data for "am" and "cyl" - a list get_stats_data(t, layer=c("am", "cyl"), statistic="riskdiff") get_stats_data(t, layer=c(3, 2), statistic="riskdiff") # Return the riskdiff for the "am" layer - a data frame get_stats_data(t, layer="am", statistic="riskdiff") # Return and filter the riskdiff for the am layer - a data frame get_stats_data(t, layer="am", statistic="riskdiff", where = summary_var==1)
Set or return treat_var binding
get_target_var(layer) set_target_var(layer, target_var)
get_target_var(layer) set_target_var(layer, target_var)
layer |
A |
target_var |
A symbol to perform the analysis on |
For treat_var
, the treatment variable binding of the layer
object. For set_treat_var
, the modified layer environment.
# Load in pipe library(magrittr) iris$Species2 <- iris$Species lay <- tplyr_table(iris, Species) %>% group_count(Species) %>% set_target_var(Species2)
# Load in pipe library(magrittr) iris$Species2 <- iris$Species lay <- tplyr_table(iris, Species) %>% group_count(Species) %>% set_target_var(Species2)
This function allows you to extract important regular expressions used inside Tplyr.
get_tplyr_regex(rx = c("format_string", "format_group"))
get_tplyr_regex(rx = c("format_string", "format_group"))
rx |
A character string with either the value 'format_string' or 'format_group' |
There are two important regular expressions used within Tplyr. The format_string expression is the expression to parse format strings. This is what is used to make sense out of strings like 'xx (XX.x%)' or 'a+1 (A.a+2)' by inferring what the user is specifying about number formatting.
The 'format_group' regex is the opposite of this, and when given a string of numbers, such as ' 5 (34%) [9]' will return the separate segments of numbers broken into their format groups, which in this example would be ' 5', '(34%)', and '[9]'.
A regular expression object
get_tplyr_regex('format_string') get_tplyr_regex('format_group')
get_tplyr_regex('format_string') get_tplyr_regex('format_group')
Set or return where binding for layer or table
## S3 method for class 'tplyr_layer' get_where(obj) ## S3 method for class 'tplyr_layer' set_where(obj, where) get_where(obj) ## S3 method for class 'tplyr_table' get_where(obj) set_where(obj, where) ## S3 method for class 'tplyr_table' set_where(obj, where) set_pop_where(obj, where) get_pop_where(obj)
## S3 method for class 'tplyr_layer' get_where(obj) ## S3 method for class 'tplyr_layer' set_where(obj, where) get_where(obj) ## S3 method for class 'tplyr_table' get_where(obj) set_where(obj, where) ## S3 method for class 'tplyr_table' set_where(obj, where) set_pop_where(obj, where) get_pop_where(obj)
obj |
A |
where |
An expression (i.e. syntax) to be used to subset the data. Supply as programming logic (i.e. x < 5 & y == 10) |
For where
, the where binding of the supplied object.
For set_where
, the modified object
# Load in pipe library(magrittr) iris$Species2 <- iris$Species lay <- tplyr_table(iris, Species) %>% group_count(Species) %>% set_where(Petal.Length > 3) %>% # Set logic for pop_data as well set_pop_where(Petal.Length > 3)
# Load in pipe library(magrittr) iris$Species2 <- iris$Species lay <- tplyr_table(iris, Species) %>% group_count(Species) %>% set_where(Petal.Length > 3) %>% # Set logic for pop_data as well set_pop_where(Petal.Length > 3)
count
, desc
, or shift
layer for discrete count
based summaries, descriptive statistics summaries, or shift count summariesThis family of functions specifies the type of summary that is
to be performed within a layer. count
layers are used to create
summary counts of some discrete variable. desc
layers create summary
statistics, and shift
layers summaries the counts of different
changes in states. See the "details" section below for more information.
group_count(parent, target_var, by = vars(), where = TRUE, ...) group_desc(parent, target_var, by = vars(), where = TRUE, ...) group_shift(parent, target_var, by = vars(), where = TRUE, ...)
group_count(parent, target_var, by = vars(), where = TRUE, ...) group_desc(parent, target_var, by = vars(), where = TRUE, ...) group_shift(parent, target_var, by = vars(), where = TRUE, ...)
parent |
Required. The parent environment of the layer. This must be the
|
target_var |
Symbol. Required, The variable name(s) on which the summary
is to be performed. Must be a variable within the target dataset. Enter
unquoted - i.e. target_var = AEBODSYS. You may also provide multiple
variables with |
by |
A string, a variable name, or a list of variable names supplied
using |
where |
Call. Filter logic used to subset the target data when performing a summary. |
... |
Additional arguments to pass forward |
Count layers allow you to create
summaries based on counting values with a variable. Additionally, this
layer allows you to create n (%) summaries where you're also summarizing
the proportion of instances a value occurs compared to some denominator.
Count layers are also capable of producing counts of nested relationships.
For example, if you want to produce counts of an overall outside group, and
then the subgroup counts within that group, you can specify the target
variable as vars(OutsideVariable, InsideVariable). This allows you to do
tables like Adverse Events where you want to see the Preferred Terms within
Body Systems, all in one layer. Further control over denominators is
available using the function set_denoms_by
and distinct
counts can be set using set_distinct_by
Descriptive statistics layers perform summaries on
continuous variables. There are a number of summaries built into Tplyr
already that you can perform, including n, mean, median, standard
deviation, variance, min, max, inter-quartile range, Q1, Q3, and missing
value counts. From these available summaries, the default presentation of a
descriptive statistic layer will output 'n', 'Mean (SD)', 'Median', 'Q1, Q3',
'Min, Max', and 'Missing'. You can change these summaries using
set_format_strings
, and you can also add your own summaries
using set_custom_summaries
. This allows you to implement any
additional summary statistics you want presented.
A
shift layer displays an endpoint's 'shift' throughout the duration of the
study. It is an abstraction over the count layer, however we have provided
an interface that is more efficient and intuitive. Targets are passed as
named symbols using dplyr::vars
. Generally the baseline is passed
with the name 'row' and the shift is passed with the name 'column'. Both
counts (n) and percentages (pct) are supported and can be specified with
the set_format_strings
function. To allow for flexibility
when defining percentages, you can define the denominator using the
set_denoms_by
function. This function takes variable names and
uses those to determine the denominator for the counts.
An tplyr_layer
environment that is a child of the specified
parent. The environment contains the object as listed below.
A tplyr_layer
object
[add_layer, add_layers, tplyr_table, tplyr_layer]
# Load in pipe library(magrittr) t <- tplyr_table(iris, Species) %>% add_layer( group_desc(target_var=Sepal.Width) ) t <- tplyr_table(iris, Species) %>% add_layer( group_desc(target_var=Sepal.Width) ) t <- tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) )
# Load in pipe library(magrittr) t <- tplyr_table(iris, Species) %>% add_layer( group_desc(target_var=Sepal.Width) ) t <- tplyr_table(iris, Species) %>% add_layer( group_desc(target_var=Sepal.Width) ) t <- tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) )
The 'header_n()' functions can be used to automatically pull the header_n derivations from the table or change them for future use.
header_n(table) header_n(x) <- value set_header_n(table, value)
header_n(table) header_n(x) <- value set_header_n(table, value)
table |
A |
x |
A |
value |
A data.frame with columns with the treatment variable, column variabes, and a variable with counts named 'n'. |
header_n |
A data.frame with columns with the treatment variable, column variabes, and a variable with counts named 'n'. |
The 'header_n' object is created by Tplyr when a table is built and intended to be used by the 'add_column_headers()' function when displaying table level population totals. These methods are intended to be used for calling the population totals calculated by Tplyr, and to overwrite them if a user chooses to.
If you have a need to change the header Ns that appear in your table headers, say you know you are working with a subset of the data that doesn't represent the totals, you can replace the data used with 'set_header_n()'.
For tplyr_header_n
the header_n binding of the
tplyr_table
object. For tplyr_header_n<-
and
set_tplyr_header_n
the modified object.
tab <- tplyr_table(mtcars, gear) header_n(tab) <- data.frame( gear = c(3, 4, 5), n = c(10, 15, 45) )
tab <- tplyr_table(mtcars, gear) header_n(tab) <- data.frame( gear = c(3, 4, 5), n = c(10, 15, 45) )
In certain cases you only want a layer to include certain values of a factor. The 'keep_levels()' function allows you to pass character values to be included in the layer. The others are ignored. **NOTE: Denominator calculation is unaffected by this function, see the examples on how to include this logic in your percentages'**
keep_levels(e, ...)
keep_levels(e, ...)
e |
A |
... |
Character values to count int he layer |
The modified Tplyr layer object
library(dplyr) mtcars <- mtcars %>% mutate_all(as.character) t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% keep_levels("4", "8") %>% set_denom_where(cyl %in% c("4", "8")) ) %>% build()
library(dplyr) mtcars <- mtcars %>% mutate_all(as.character) t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% keep_levels("4", "8") %>% set_denom_where(cyl %in% c("4", "8")) ) %>% build()
There are several scenarios where a layer template may be useful. Some tables, like demographics tables, may have many layers that will all essentially look the same. Categorical variables will have the same count layer settings, and continuous variables will have the same desc layer settings. A template allows a user to build those settings once per layer, then reference the template when the Tplyr table is actually built.
new_layer_template(name, template) remove_layer_template(name) get_layer_template(name) get_layer_templates() use_template(name, ..., add_params = NULL)
new_layer_template(name, template) remove_layer_template(name) get_layer_template(name) get_layer_templates() use_template(name, ..., add_params = NULL)
name |
Template name |
template |
Template layer syntax, starting with a layer constructor
|
... |
Arguments passed directly into a layer constructor, matching the target, by, and where parameters. |
add_params |
Additional parameters passed into layer modifier functions. These arguments are specified in a template within curly brackets such as {param}. Supply as a named list, where the element name is the parameter. |
This suite of functions allows a user to create and use layer templates. Layer templates allow a user to pre-build and reuse an entire layer configuration, from the layer constructor down to all modifying functions. Furthermore, users can specify parameters they may want to be interchangeable. Additionally, layer templates are extensible, so a template can be use and then further extended with additional layer modifying functions.
Layers are created using new_layer_template()
. To use a layer, use the
function use_template()
in place of group_count|desc|shift()
. If you want
to view a specific template, use get_layer_template()
. If you want to view
all templates, use get_layer_templates()
. And to remove a layer template use
remove_layer_template()
. Layer templates themselves are stored in the
option tplyr.layer_templates
, but a user should not access this directly
and instead use the Tplyr supplied functions.
When providing the template layer syntax, the layer must start with a layer
constructor. These are one of the function group_count()
, group_desc()
,
or group_shift()
. Instead of passing arguments into these function,
templates are specified using an ellipsis in the constructor, i.e.
group_count(...)
. This is required, as after the template is built a user
supplies these arguments via use_template()
use_template()
takes the group_count|desc|shift()
arguments by default.
If a user specified additional arguments in the template, these are provided
in a list throught the argument add_params
. Provide these arguments exactly
as you would in a normal layer. When creating the template, these parameters
can be specified by using curly brackets. See the examples for details.
op <- options() new_layer_template( "example_template", group_count(...) %>% set_format_strings(f_str('xx (xx%)', n, pct)) ) get_layer_templates() get_layer_template("example_template") tplyr_table(mtcars, vs) %>% add_layer( use_template("example_template", gear) ) %>% build() remove_layer_template("example_template") new_layer_template( "example_template", group_count(...) %>% set_format_strings(f_str('xx (xx%)', n, pct)) %>% set_order_count_method({sort_meth}) %>% set_ordering_cols({sort_cols}) ) get_layer_template("example_template") tplyr_table(mtcars, vs) %>% add_layer( use_template("example_template", gear, add_params = list( sort_meth = "bycount", sort_cols = `1` )) ) %>% build() remove_layer_template("example_template") options(op)
op <- options() new_layer_template( "example_template", group_count(...) %>% set_format_strings(f_str('xx (xx%)', n, pct)) ) get_layer_templates() get_layer_template("example_template") tplyr_table(mtcars, vs) %>% add_layer( use_template("example_template", gear) ) %>% build() remove_layer_template("example_template") new_layer_template( "example_template", group_count(...) %>% set_format_strings(f_str('xx (xx%)', n, pct)) %>% set_order_count_method({sort_meth}) %>% set_ordering_cols({sort_cols}) ) get_layer_template("example_template") tplyr_table(mtcars, vs) %>% add_layer( use_template("example_template", gear, add_params = list( sort_meth = "bycount", sort_cols = `1` )) ) %>% build() remove_layer_template("example_template") options(op)
The population data is used to gather information that may not be available
from the target dataset. For example, missing treatment groups, population N
counts, and proper N counts for denominators will be provided through the
population dataset. The population dataset defaults to the target dataset
unless otherwise specified using set_pop_data
.
pop_data(table) pop_data(x) <- value set_pop_data(table, pop_data)
pop_data(table) pop_data(x) <- value set_pop_data(table, pop_data)
table |
A |
x |
A |
value |
A data.frame with population level information |
pop_data |
A data.frame with population level information |
For tplyr_pop_data
the pop_data binding of the
tplyr_table
object. For tplyr_pop_data<-
nothing is returned,
the pop_data binding is set silently. For set_tplyr_pop_data
the
modified object.
tab <- tplyr_table(iris, Species) pop_data(tab) <- mtcars tab <- tplyr_table(iris, Species) %>% set_pop_data(mtcars)
tab <- tplyr_table(iris, Species) pop_data(tab) <- mtcars tab <- tplyr_table(iris, Species) %>% set_pop_data(mtcars)
The treatment variable used in the target data may be different than the
variable within the population dataset. set_pop_treat_var
allows you
to change this.
pop_treat_var(table) set_pop_treat_var(table, pop_treat_var)
pop_treat_var(table) set_pop_treat_var(table, pop_treat_var)
table |
A |
pop_treat_var |
Variable containing treatment group assignments within the |
For tplyr_pop_treat_var
the pop_treat_var binding of the
tplyr_table
object. For set_tplyr_pop_treat_var
the modified
object.
tab <- tplyr_table(iris, Species) pop_data(tab) <- mtcars set_pop_treat_var(tab, mpg)
tab <- tplyr_table(iris, Species) pop_data(tab) <- mtcars set_pop_treat_var(tab, mpg)
Reformat strings with leading whitespace for HTML
replace_leading_whitespace(x, tab_width = 4)
replace_leading_whitespace(x, tab_width = 4)
x |
Target string |
tab_width |
Number of spaces to compensate for tabs |
String with replaced for leading whitespace
x <- c(" Hello there", " Goodbye Friend ", "\tNice to meet you", " \t What are you up to? \t \t ") replace_leading_whitespace(x) replace_leading_whitespace(x, tab=2)
x <- c(" Hello there", " Goodbye Friend ", "\tNice to meet you", " \t What are you up to? \t \t ") replace_leading_whitespace(x) replace_leading_whitespace(x, tab=2)
This function allows a user to define custom summaries to be performed in a
call to dplyr::summarize()
. A custom summary by the same name as a
default summary will override the default. This allows the user to override
the default behavior of summaries built into 'Tplyr', while also adding new
desired summary functions.
set_custom_summaries(e, ...)
set_custom_summaries(e, ...)
e |
|
... |
Named parameters containing syntax to be used in a call to
|
When programming the logic of the summary function, use the variable name
.var
to within your summary functions. This allows you apply the
summary function to each variable when multiple target variables are
declared.
An important, yet not immediately obvious, part of using
set_custom_summaries
is to understand the link between the named
parameters you set in set_custom_summaries
and the names called in
f_str
objects within set_format_strings
. In
f_str
, after you supply the string format you'd like your
numbers to take, you specify the summaries that fill those strings.
When you go to set your format strings, the name you use to declare a summary
in set_custom_summaries
is the same name that you use in your
f_str
call. This is necessary because
set_format_strings
needs some means of putting two summaries in
the same value, and setting a row label for the summary being performed.
Review the examples to see this put into practice. Note the relationship
between the name created in set_custom_summaries
and the name used in
set_format_strings
within the f_str
call
Binds a variable custom_summaries
to the specified layer
#Load in pipe library(magrittr) tplyr_table(iris, Species) %>% add_layer( group_desc(Sepal.Length, by = "Sepal Length") %>% set_custom_summaries( geometric_mean = exp(sum(log(.var[.var > 0]), na.rm=TRUE) / length(.var)) ) %>% set_format_strings( 'Geometric Mean' = f_str('xx.xx', geometric_mean) ) ) %>% build()
#Load in pipe library(magrittr) tplyr_table(iris, Species) %>% add_layer( group_desc(Sepal.Length, by = "Sepal Length") %>% set_custom_summaries( geometric_mean = exp(sum(log(.var[.var > 0]), na.rm=TRUE) / length(.var)) ) %>% set_format_strings( 'Geometric Mean' = f_str('xx.xx', geometric_mean) ) ) %>% build()
'r lifecycle::badge("defunct")'
This is generally used for missing values. Values like "", NA, "NA" are common ways missing values are presented in a data frame. In certain cases, percentages do not use "missing" values in the denominator. This function notes different values as "missing" and excludes them from the denominators.
set_denom_ignore(e, ...)
set_denom_ignore(e, ...)
e |
A |
... |
Values to exclude from the percentage calculation. If you use 'set_missing_counts()' this should be the name of the parameters instead of the values, see the example below. |
The modified layer object
library(magrittr) mtcars2 <- mtcars mtcars2[mtcars$cyl == 6, "cyl"] <- NA mtcars2[mtcars$cyl == 8, "cyl"] <- "Not Found" tplyr_table(mtcars2, gear) %>% add_layer( group_count(cyl) %>% set_missing_count(f_str("xx ", n), Missing = c(NA, "Not Found")) # This function is currently deprecated. It was replaced with an # argument in set_missing_count # set_denom_ignore("Missing") ) %>% build()
library(magrittr) mtcars2 <- mtcars mtcars2[mtcars$cyl == 6, "cyl"] <- NA mtcars2[mtcars$cyl == 8, "cyl"] <- "Not Found" tplyr_table(mtcars2, gear) %>% add_layer( group_count(cyl) %>% set_missing_count(f_str("xx ", n), Missing = c(NA, "Not Found")) # This function is currently deprecated. It was replaced with an # argument in set_missing_count # set_denom_ignore("Missing") ) %>% build()
By default, denominators in count layers are subset based on the layer level where logic. In some cases this might not be correct. This functions allows the user to override this behavior and pass custom logic that will be used to subset the target dataset when calculating denominators for the layer.
set_denom_where(e, denom_where)
set_denom_where(e, denom_where)
e |
A |
denom_where |
An expression (i.e. syntax) to be used to subset the target dataset for calculating layer denominators. Supply as programming logic (i.e. x < 5 & y == 10). To remove the layer where parameter subsetting for the total row and thus the percentage denominators, pass 'TRUE' to this function. |
The modified Tplyr layer object
library(magrittr) t10 <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl, where = cyl != 6) %>% set_denom_where(TRUE) # The denominators will be based on all of the values, including 6 ) %>% build()
library(magrittr) t10 <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl, where = cyl != 6) %>% set_denom_where(TRUE) # The denominators will be based on all of the values, including 6 ) %>% build()
This function is used when calculating pct in count or shift layers. The percentages default to the treatment variable and any column variables but can be calculated on any variables passed to target_var, treat_var, by, or cols.
set_denoms_by(e, ...)
set_denoms_by(e, ...)
e |
A count/shift layer object |
... |
Unquoted variable names |
The modified layer object
library(magrittr) # Default has matrix of treatment group, additional columns, # and by variables sum to 1 tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build() tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) %>% set_denoms_by(cyl, gear) # Row % sums to 1 ) %>% build() tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) %>% set_denoms_by(cyl, gear, am) # % within treatment group sums to 1 ) %>% build()
library(magrittr) # Default has matrix of treatment group, additional columns, # and by variables sum to 1 tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build() tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) %>% set_denoms_by(cyl, gear) # Row % sums to 1 ) %>% build() tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) %>% set_denoms_by(cyl, gear, am) # % within treatment group sums to 1 ) %>% build()
In some situations, count summaries may want to see distinct counts by a
variable like subject. For example, the number of subjects in a population
who had a particular adverse event. set_distinct_by
allows you to set
the by variables used to determine a distinct count.
set_distinct_by(e, distinct_by)
set_distinct_by(e, distinct_by)
e |
A |
distinct_by |
Variable(s) to get the distinct data. |
When a distinct_by
value is set, distinct counts will be used by
default. If you wish to combine distinct and not distinct counts, you can
choose which to display in your f_str()
objects using n
,
pct
, distinct_n
, and distinct_pct
. Additionally, denominators
may be presented using total
and distinct_total
The layer object with
#Load in pipe library(magrittr) tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_distinct_by(carb) ) %>% build()
#Load in pipe library(magrittr) tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_distinct_by(carb) ) %>% build()
'Tplyr' gives you extensive control over how strings are presented.
set_format_strings
allows you to apply these string formats to your
layer. This behaves slightly differently between layers.
set_format_strings(e, ...) ## S3 method for class 'desc_layer' set_format_strings(e, ..., cap = getOption("tplyr.precision_cap")) ## S3 method for class 'count_layer' set_format_strings(e, ...)
set_format_strings(e, ...) ## S3 method for class 'desc_layer' set_format_strings(e, ..., cap = getOption("tplyr.precision_cap")) ## S3 method for class 'count_layer' set_format_strings(e, ...)
e |
Layer on which to bind format strings |
... |
Named parameters containing calls to |
cap |
A named character vector containing an 'int' element for the cap on integer precision, and a 'dec' element for the cap on decimal precision. |
Format strings are one of the most powerful components of 'Tplyr'. Traditionally, converting numeric values into strings for presentation can consume a good deal of time. Values and decimals need to align between rows, rounding before trimming is sometimes forgotten - it can become a tedious mess that, in the grand scheme of things, is not an important part of the analysis being performed. 'Tplyr' makes this process as simple as we can, while still allowing flexibility to the user.
In a count layer, you can simply provide a single f_str
object to specify how you want your n's, percentages, and denominators formatted.
If you are additionally supplying a statistic, like risk difference using
add_risk_diff
, you specify the count formats using the name
'n_counts'. The risk difference formats would then be specified using the
name 'riskdiff'. In a descriptive statistic layer,
set_format_strings
allows you to do a couple more things:
By naming parameters with character strings, those character strings become a row label in the resulting data frame
The actual summaries that are performed come from the variable names
used within the f_str
calls
Using multiple summaries (declared by your f_str
calls), multiple summary values can appear within the same line. For
example, to present "Mean (SD)" like displays.
Format strings in the desc layer also allow you to configure how
empty values should be presented. In the f_str
call, use the
empty
parameter to specify how missing values should present. A
single element character vector should be provided. If the vector is
unnamed, that value will be used in the format string and fill the space
similar to how the numbers will display. Meaning - if your empty string is
'NA' and your format string is 'xx (xxx)', the empty values will populate
as 'NA ( NA)'. If you name the character vector in the 'empty' parameter
'.overall', like empty = c(.overall='')
, then that exact string will
fill the value instead. For example, providing 'NA' will instead create the
formatted string as 'NA' exactly.
See the f_str
documentation for more details about how this
implementation works.
The layer environment with the format string binding added
tplyr_layer object with formats attached
Returns the modified layer object.
# Load in pipe library(magrittr) # In a count layer tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_format_strings(f_str('xx (xx%)', n, pct)) ) %>% build() # In a descriptive statistics layer tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg) %>% set_format_strings( "n" = f_str("xx", n), "Mean (SD)" = f_str("xx.x", mean, empty='NA'), "SD" = f_str("xx.xx", sd), "Median" = f_str("xx.x", median), "Q1, Q3" = f_str("xx, xx", q1, q3, empty=c(.overall='NA')), "Min, Max" = f_str("xx, xx", min, max), "Missing" = f_str("xx", missing) ) ) %>% build() # In a shift layer tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build()
# Load in pipe library(magrittr) # In a count layer tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_format_strings(f_str('xx (xx%)', n, pct)) ) %>% build() # In a descriptive statistics layer tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg) %>% set_format_strings( "n" = f_str("xx", n), "Mean (SD)" = f_str("xx.x", mean, empty='NA'), "SD" = f_str("xx.xx", sd), "Median" = f_str("xx.x", median), "Q1, Q3" = f_str("xx, xx", q1, q3, empty=c(.overall='NA')), "Min, Max" = f_str("xx, xx", min, max), "Missing" = f_str("xx", missing) ) ) %>% build() # In a shift layer tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build()
When a count layer uses nesting (i.e. triggered by set_nest_count
),
the indentation
argument's value will be used as a prefix for the inner layer's
records
set_indentation(e, indentation)
set_indentation(e, indentation)
e |
A |
indentation |
A character to prefix the row labels in an inner count layer |
The modified count_layer environment
This function allows you to select a combination of by variables or
potentially target variables for which you only want to display values
present in the data. By default, Tplyr will create a cartesian combination of
potential values of the data. For example, if you have 2 by variables
present, then each potential combination of those by variables will have a
row present in the final table. set_limit_data_by()
allows you to choose
the by variables whose combination you wish to limit to values physically
present in the available data.
set_limit_data_by(e, ...)
set_limit_data_by(e, ...)
e |
A tplyr_layer |
... |
Subset of variables within by or target variables |
a tplyr_table
tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_desc(AVAL, by = vars(PECAT, PARAM, AVISIT)) ) %>% build() tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_desc(AVAL, by = vars(PECAT, PARAM, AVISIT)) %>% set_limit_data_by(PARAM, AVISIT) ) %>% build() tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_count(AVALC, by = vars(PECAT, PARAM, AVISIT)) %>% set_limit_data_by(PARAM, AVISIT) ) %>% build() tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_count(AVALC, by = vars(PECAT, PARAM, AVISIT)) %>% set_limit_data_by(PECAT, PARAM, AVISIT) ) %>% build()
tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_desc(AVAL, by = vars(PECAT, PARAM, AVISIT)) ) %>% build() tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_desc(AVAL, by = vars(PECAT, PARAM, AVISIT)) %>% set_limit_data_by(PARAM, AVISIT) ) %>% build() tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_count(AVALC, by = vars(PECAT, PARAM, AVISIT)) %>% set_limit_data_by(PARAM, AVISIT) ) %>% build() tplyr_table(tplyr_adpe, TRT01A) %>% add_layer( group_count(AVALC, by = vars(PECAT, PARAM, AVISIT)) %>% set_limit_data_by(PECAT, PARAM, AVISIT) ) %>% build()
Controls how missing counts are handled and displayed in the layer
set_missing_count(e, fmt = NULL, sort_value = NULL, denom_ignore = FALSE, ...)
set_missing_count(e, fmt = NULL, sort_value = NULL, denom_ignore = FALSE, ...)
e |
A |
fmt |
An f_str object to change the display of the missing counts |
sort_value |
A numeric value that will be used in the ordering column. This should be numeric. If it is not supplied the ordering column will be the maximum value of what appears in the table plus one. |
denom_ignore |
A boolean. Specifies Whether or not to include the missing counts specified within the ... parameter within denominators. If set to TRUE, the values specified within ... will be ignored. |
... |
Parameters used to note which values to describe as missing. Generally NA and "Missing" would be used here. Parameters can be named character vectors where the names become the row label. |
The modified layer
library(magrittr) library(dplyr) mtcars2 <- mtcars %>% mutate_all(as.character) mtcars2[mtcars$cyl == 6, "cyl"] <- NA tplyr_table(mtcars2, gear) %>% add_layer( group_count(cyl) %>% set_missing_count(f_str("xx ", n), Missing = NA) ) %>% build()
library(magrittr) library(dplyr) mtcars2 <- mtcars %>% mutate_all(as.character) mtcars2[mtcars$cyl == 6, "cyl"] <- NA tplyr_table(mtcars2, gear) %>% add_layer( group_count(cyl) %>% set_missing_count(f_str("xx ", n), Missing = NA) ) %>% build()
Set the label for the missing subjects row
set_missing_subjects_row_label(e, missing_subjects_row_label)
set_missing_subjects_row_label(e, missing_subjects_row_label)
e |
A |
missing_subjects_row_label |
A character to label the total row |
The modified count_layer
object
t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_missing_subjects_row() %>% set_missing_subjects_row_label("Missing") ) build(t)
t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_missing_subjects_row() %>% set_missing_subjects_row_label("Missing") ) build(t)
If set to TRUE, the second variable specified in target_var
will be nested inside of the first variable. This allows you to create
displays like those commonly used in adverse event tables, where
one column holds both the labels of the outer categorical variable
and the inside event variable (i.e. AEBODSYS and AEDECOD).
set_nest_count(e, nest_count)
set_nest_count(e, nest_count)
e |
A |
nest_count |
A logical value to set the nest option |
The modified layer
In certain tables, it may be necessary to only include rows that meet numeric conditions. Rows that are less than a certain cutoff can be suppressed from the output. This function allows you to pass a cutoff, a cutoff stat(n, distinct_n, pct, or distinct_pct) to supress values that are lesser than the cutoff.
set_numeric_threshold(e, numeric_cutoff, stat, column = NULL)
set_numeric_threshold(e, numeric_cutoff, stat, column = NULL)
e |
A |
numeric_cutoff |
A numeric value where only values greater than or equal to will be displayed. |
stat |
The statistic to use when filtering out rows. Either 'n', 'distinct_n', or 'pct' are allowable |
column |
If only a particular column should be used to cutoff values, it can be supplied here as a character value. |
The modified Tplyr layer object
mtcars %>% tplyr_table(gear) %>% add_layer( group_count(cyl) %>% set_numeric_threshold(10, "n") %>% add_total_row() %>% set_order_count_method("bycount") )
mtcars %>% tplyr_table(gear) %>% add_layer( group_count(cyl) %>% set_numeric_threshold(10, "n") %>% add_total_row() %>% set_order_count_method("bycount") )
The sorting of a table can greatly vary depending on the situation at hand. For count layers, when creating tables like adverse event summaries, you may wish to order the table by descending occurrence within a particular treatment group. But in other situations, such as AEs of special interest, or subject disposition, there may be a specific order you wish to display values. Tplyr offers solutions to each of these situations.
Instead of allowing you to specify a custom sort order, Tplyr instead
provides you with order variables that can be used to sort your table after
the data are summarized. Tplyr has a default order in which the table will
be returned, but the order variables will always persist. This allows you
to use powerful sorting functions like arrange
to get your desired order, and in double programming situations, helps your
validator understand the how you achieved a particular sort order and where
discrepancies may be coming from.
When creating order variables for a layer, for each 'by' variable Tplyr will search for a <VAR>N version of that variable (i.e. VISIT <-> VISITN, PARAM <-> PARAMN). If available, this variable will be used for sorting. If not available, Tplyr will created a new ordered factor version of that variable to use in alphanumeric sorting. This allows the user to control a custom sorting order by leaving an existing <VAR>N variable in your dataset if it exists, or create one based on the order in which you wish to sort - no custom functions in Tplyr required.
Ordering of results is where things start to differ. Different situations
call for different methods. Descriptive statistics layers keep it simple -
the order in which you input your formats using
set_format_strings
is the order in which the results will
appear (with an order variable added). For count layers, Tplyr offers three
solutions: If there is a <VAR>N version of your target variable, use that.
If not, if the target variable is a factor, use the factor orders. Finally,
you can use a specific data point from your results columns. The result
column can often have multiple data points, between the n counts, percent,
distinct n, and distinct percent. Tplyr allows you to choose which of these
values will be used when creating the order columns for a specified result
column (i.e. based on the treat_var
and cols
arguments). See
the 'Sorting a Table' section for more information.
Shift layers sort very similarly to count layers, but to order your row shift variable, use an ordered factor.
set_order_count_method(e, order_count_method, break_ties = NULL) set_ordering_cols(e, ...) set_result_order_var(e, result_order_var)
set_order_count_method(e, order_count_method, break_ties = NULL) set_ordering_cols(e, ...) set_result_order_var(e, result_order_var)
e |
A |
order_count_method |
The logic determining how the rows in the final layer output will be indexed. Options are 'bycount', 'byfactor', and 'byvarn'. |
break_ties |
In certain cases, a 'bycount' sort will result in conflicts if the counts aren't unique. break_ties will add a decimal to the sorting column so resolve conflicts. A character value of 'asc' will add a decimal based on the alphabetical sorting. 'desc' will do the same but sort descending in case that is the intention. |
... |
Unquoted variables used to select the columns whose values will be extracted for ordering. |
result_order_var |
The numeric value the ordering will be done on. This can be either n, distinct_n, pct, or distinct_pct. Due to the evaluation of the layer you can add a value that isn't actually being evaluated, if this happens this will only error out in the ordering. |
Returns the modified layer object. The 'ord_' columns are added during the build process.
When a table is built, the output has several ordering(ord_) columns that are appended. The first represents the layer index. The index is determined by the order the layer was added to the table. Following are the indices for the by variables and the target variable. The by variables are ordered based on:
The 'by' variable is a factor in the target dataset
If the variable isn't a factor, but has a <VAR>N variable (i.e. VISIT -> VISITN, TRT -> TRTN)
If the variable is not a factor in the target dataset, it is coerced to one and ordered alphabetically.
The target variable is ordered depending on the type of layer. See more below.
There are many ways to order a count layer
depending on the preferences of the table programmer. Tplyr
supports
sorting by a descending amount in a column in the table, sorting by a
<VAR>N variable, and sorting by a custom order. These can be set using the
'set_order_count_method' function.
A selected numeric value from a selected column will be indexed based on the descending numeric value. The numeric value extracted defaults to 'n' but can be changed with 'set_result_order_var'. The column selected for sorting defaults to the first value in the treatment group variable. If there were arguments passed to the 'cols' argument in the table those must be specified with 'set_ordering_columns'.
If the treatment variable has a <VAR>N variable. It can be indexed to that variable.
If a factor is found for the target variable in the target dataset that is used to order, if no factor is found it is coerced to a factor and sorted alphabetically.
If two variables are targeted by a count layer, two methods can be passed to 'set_order_count'. If two are passed, the first is used to sort the blocks, the second is used to sort the "inside" of the blocks. If one method is passed, that will be used to sort both.
The order of a desc layer is mostly set during the object construction. The by variables are resolved and index with the same logic as the count layers. The target variable is ordered based on the format strings that were used when the layer was created.
library(dplyr) # Default sorting by factor t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) ) build(t) # Sorting by <VAR>N mtcars$cylN <- mtcars$cyl t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_order_count_method("byvarn") ) # Sorting by row count t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_order_count_method("bycount") %>% # Orders based on the 6 gear group set_ordering_cols(6) ) # Sorting by row count by percentages t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_order_count_method("bycount") %>% set_result_order_var(pct) ) # Sorting when you have column arguments in the table t <- tplyr_table(mtcars, gear, cols = vs) %>% add_layer( group_count(cyl) %>% # Uses the fourth gear group and the 0 vs group in ordering set_ordering_cols(4, 0) ) # Using a custom factor to order mtcars$cyl <- factor(mtcars$cyl, c(6, 4, 8)) t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% # This is the default but can be used to change the setting if it is #set at the table level. set_order_count_method("byfactor") )
library(dplyr) # Default sorting by factor t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) ) build(t) # Sorting by <VAR>N mtcars$cylN <- mtcars$cyl t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_order_count_method("byvarn") ) # Sorting by row count t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_order_count_method("bycount") %>% # Orders based on the 6 gear group set_ordering_cols(6) ) # Sorting by row count by percentages t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% set_order_count_method("bycount") %>% set_result_order_var(pct) ) # Sorting when you have column arguments in the table t <- tplyr_table(mtcars, gear, cols = vs) %>% add_layer( group_count(cyl) %>% # Uses the fourth gear group and the 0 vs group in ordering set_ordering_cols(4, 0) ) # Using a custom factor to order mtcars$cyl <- factor(mtcars$cyl, c(6, 4, 8)) t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% # This is the default but can be used to change the setting if it is #set at the table level. set_order_count_method("byfactor") )
Set the value of a outer nested count layer to Inf or -Inf
set_outer_sort_position(e, outer_sort_position)
set_outer_sort_position(e, outer_sort_position)
e |
A |
outer_sort_position |
Either 'asc' or 'desc'. If desc the final ordering helper will be set to Inf, if 'asc' the ordering helper is set to -Inf. |
The modified count layer.
In some cases, there may be organizational standards surrounding decimal precision.
For example, there may be a specific standard around the representation of precision relating
to lab results. As such, set_precision_data()
provides an interface to provide integer and
decimal precision from an external data source.
set_precision_data(layer, prec, default = c("error", "auto"))
set_precision_data(layer, prec, default = c("error", "auto"))
layer |
A |
prec |
A dataframe following the structure specified in the function details |
default |
Handling of unspecified by variable groupings. Defaults to 'error'. Set to 'auto' to automatically infer any missing groups. |
The ultimate behavior of this feature is just that of the existing auto precision method, except
that the precision is specified in the provided precision dataset rather than inferred from the source data.
At a minimum, the precision dataset must contain the integer variables max_int
and max_dec
. If by variables
are provided, those variables must be available in the layer by variables.
When the table is built, by default Tplyr will error if the precision dataset is missing by variable groupings
that exist in the target dataset. This can be overriden using the default
parameter. If default
is set to
"auto", any missing values will be automatically inferred from the source data.
prec <- tibble::tribble( ~vs, ~max_int, ~max_dec, 0, 1, 1, 1, 2, 2 ) tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt, by = vs) %>% set_format_strings( 'Mean (SD)' = f_str('a.a+1 (a.a+2)', mean, sd) ) %>% set_precision_data(prec) %>% set_precision_on(wt) ) %>% build()
prec <- tibble::tribble( ~vs, ~max_int, ~max_dec, 0, 1, 1, 1, 2, 2 ) tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt, by = vs) %>% set_format_strings( 'Mean (SD)' = f_str('a.a+1 (a.a+2)', mean, sd) ) %>% set_precision_data(prec) %>% set_precision_on(wt) ) %>% build()
In many cases, treatment groups are represented as columns within a table.
But some tables call for a transposed presentation, where the treatment
groups displayed by row, and the descriptive statistics are represented as
columns. set_stats_as_columns()
allows Tplyr to output a built table
using this transposed format and deviate away from the standard
representation of treatment groups as columns.
set_stats_as_columns(e, stats_as_columns = TRUE)
set_stats_as_columns(e, stats_as_columns = TRUE)
e |
|
stats_as_columns |
Boolean to set stats as columns |
This function leaves all specified by variables intact. The only switch that happens during the build process is that the provided descriptive statistics are transposed as columns and the treatment variable is left as rows. Column variables will remain represented as columns, and multiple target variables will also be respected properly.
The input tplyr_layer
dat <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt, by = vs) %>% set_format_strings( "n" = f_str("xx", n), "sd" = f_str("xx.x", sd, empty = c(.overall = "BLAH")), "Median" = f_str("xx.x", median), "Q1, Q3" = f_str("xx, xx", q1, q3), "Min, Max" = f_str("xx, xx", min, max), "Missing" = f_str("xx", missing) ) %>% set_stats_as_columns() ) %>% build()
dat <- tplyr_table(mtcars, gear) %>% add_layer( group_desc(wt, by = vs) %>% set_format_strings( "n" = f_str("xx", n), "sd" = f_str("xx.x", sd, empty = c(.overall = "BLAH")), "Median" = f_str("xx.x", median), "Q1, Q3" = f_str("xx, xx", q1, q3), "Min, Max" = f_str("xx, xx", min, max), "Missing" = f_str("xx", missing) ) %>% set_stats_as_columns() ) %>% build()
The row label for a total row defaults to "Total", however this can be overriden using this function.
set_total_row_label(e, total_row_label)
set_total_row_label(e, total_row_label)
e |
A |
total_row_label |
A character to label the total row |
The modified count_layer
object
# Load in pipe library(magrittr) t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_total_row() %>% set_total_row_label("Total Cyl") ) build(t)
# Load in pipe library(magrittr) t <- tplyr_table(mtcars, gear) %>% add_layer( group_count(cyl) %>% add_total_row() %>% set_total_row_label("Total Cyl") ) build(t)
These functions allow you to extract segments of information from within a
result string by targetting specific format groups. str_extract_fmt_group()
allows you to pull out the individual format group string, while
str_extract_num()
allows you to pull out that specific numeric result.
str_extract_fmt_group(string, format_group) str_extract_num(string, format_group)
str_extract_fmt_group(string, format_group) str_extract_num(string, format_group)
string |
A string of number results from which to extract format groups |
format_group |
An integer representing format group that should be extracted |
Format groups refer to individual segments of a string. For example, given the string ' 5 (34.4%) [9]', there are three separate format groups, which are ' 5', '(34.4%)', and '[9]'.
A character vector
string <- c(" 0 (0.0%)", " 8 (9.3%)", "78 (90.7%)") str_extract_fmt_group(string, 2) str_extract_num(string, 2)
string <- c(" 0 (0.0%)", " 8 (9.3%)", "78 (90.7%)") str_extract_fmt_group(string, 2) str_extract_num(string, 2)
str_indent_wrap()
leverages stringr::str_wrap()
under the hood, but takes
some extra steps to preserve any indentation that has been applied to a
character element, and use hyphenated wrapping of single words that run
longer than the allotted wrapping width.
str_indent_wrap(x, width = 10, tab_width = 5)
str_indent_wrap(x, width = 10, tab_width = 5)
x |
An input character vector |
width |
The desired width of elements within the output character vector |
tab_width |
The number of spaces to which tabs should be converted |
The function stringr::str_wrap()
is highly efficient, but in the
context of table creation there are two select features missing - hyphenation
for long running strings that overflow width, and respect for pre-indentation
of a character element. For example, in an adverse event table, you may have
body system rows as an un-indented column, and preferred terms as indented
columns. These strings may run long and require wrapping to not surpass the
column width. Furthermore, for crowded tables a single word may be longer
than the column width itself.
This function takes steps to resolve these two issues, while trying to minimize additional overhead required to apply the wrapping of strings.
Note: This function automatically converts tabs to spaces. Tab width varies depending on font, so width cannot automatically be determined within a data frame. As such, users can specify the width
A character vector with string wrapping applied
ex_text1 <- c("RENAL AND URINARY DISORDERS", " NEPHROLITHIASIS") ex_text2 <- c("RENAL AND URINARY DISORDERS", "\tNEPHROLITHIASIS") cat(paste(str_indent_wrap(ex_text1, width=8), collapse="\n\n"),"\n") cat(paste(str_indent_wrap(ex_text2, tab_width=4), collapse="\n\n"),"\n")
ex_text1 <- c("RENAL AND URINARY DISORDERS", " NEPHROLITHIASIS") ex_text2 <- c("RENAL AND URINARY DISORDERS", "\tNEPHROLITHIASIS") cat(paste(str_indent_wrap(ex_text1, width=8), collapse="\n\n"),"\n") cat(paste(str_indent_wrap(ex_text2, tab_width=4), collapse="\n\n"),"\n")
'r lifecycle::badge("experimental")'
'Tplyr' is a package dedicated to simplifying the data manipulation necessary to create clinical reports. Clinical data summaries can often be broken down into two factors - counting discrete variables (or counting shifts in state), and descriptive statistics around a continuous variable. Many of the reports that go into a clinical report are made up of these two scenarios. By abstracting this process away, 'Tplyr' allows you to rapidly build these tables without worrying about the underlying data manipulation.
'Tplyr' takes this process a few steps further by abstracting away most of the programming that goes into proper presentation, which is where a great deal of programming time is spent. For example, 'Tplyr' allows you to easily control:
Different reports warrant different presentation of your strings. Programming this can get tedious, as you typically want to make sure that your decimals properly align. 'Tplyr' abstracts this process away and provides you with a simple interface to specify how you want your data presented
Need a total column? Need to group summaries of multiple treatments? 'Tplyr' makes it simple to add additional treatment groups into your report
n (%) counts often vary based on the summary being performed. 'Tplyr' allows you to easily control what denominators are used based on a few common scenarios
Summarizing data is one thing, but ordering it for presentation. Tplyr automatically derives sorting variable to give you the data you need to order your table properly. This process is flexible so you can easily get what you want by leveraging your data or characteristics of R.
Another powerful aspect of 'Tplyr' are the objects themselves. 'Tplyr' does more than format your data. Metadata about your table is kept under the hood, and functions allow you to access information that you need. For example, 'Tplyr' allows you to calculate and access the raw numeric data of calculations as well, and easily pick out just the pieces of information that you need.
Lastly, 'Tplyr' was built to be flexible, yet intuitive. A common pitfall of building tools like this is over automation. By doing to much, you end up not doing enough. 'Tplyr' aims to hit the sweet spot in between. Additionally, we designed our function interfaces to be clean. Modifier functions offer you flexibility when you need it, but defaults can be set to keep the code concise. This allows you to quickly assemble your table, and easily make changes where necessary.
Maintainer: Mike Stackhouse [email protected] (ORCID)
Authors:
Eli Miller [email protected] (ORCID)
Ashley Tarasiewicz [email protected]
Other contributors:
Nathan Kosiba [email protected] (ORCID) [contributor]
Sadchla Mascary [email protected] [contributor]
Andrew Bates [email protected] [contributor]
Shiyu Chen [email protected] [contributor]
Oleksii Mikryukov [email protected] [contributor]
Atorus Research LLC [copyright holder]
Useful links:
Report bugs at https://github.com/atorus-research/Tplyr/issues
# Load in pipe library(magrittr) # Use just the defaults tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg, by=cyl) ) %>% add_layer( group_count(carb, by=cyl) ) %>% build() # Customize and modify tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg, by=cyl) %>% set_format_strings( "n" = f_str("xx", n), "Mean (SD)" = f_str("a.a+1 (a.a+2)", mean, sd, empty='NA'), "Median" = f_str("a.a+1", median), "Q1, Q3" = f_str("a, a", q1, q3, empty=c(.overall='NA')), "Min, Max" = f_str("a, a", min, max), "Missing" = f_str("xx", missing) ) ) %>% add_layer( group_count(carb, by=cyl) %>% add_risk_diff( c('5', '3'), c('4', '3') ) %>% set_format_strings( n_counts = f_str('xx (xx%)', n, pct), riskdiff = f_str('xx.xxx (xx.xxx, xx.xxx)', dif, low, high) ) %>% set_order_count_method("bycount") %>% set_ordering_cols('4') %>% set_result_order_var(pct) ) %>% build() # A Shift Table tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build()
# Load in pipe library(magrittr) # Use just the defaults tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg, by=cyl) ) %>% add_layer( group_count(carb, by=cyl) ) %>% build() # Customize and modify tplyr_table(mtcars, gear) %>% add_layer( group_desc(mpg, by=cyl) %>% set_format_strings( "n" = f_str("xx", n), "Mean (SD)" = f_str("a.a+1 (a.a+2)", mean, sd, empty='NA'), "Median" = f_str("a.a+1", median), "Q1, Q3" = f_str("a, a", q1, q3, empty=c(.overall='NA')), "Min, Max" = f_str("a, a", min, max), "Missing" = f_str("xx", missing) ) ) %>% add_layer( group_count(carb, by=cyl) %>% add_risk_diff( c('5', '3'), c('4', '3') ) %>% set_format_strings( n_counts = f_str('xx (xx%)', n, pct), riskdiff = f_str('xx.xxx (xx.xxx, xx.xxx)', dif, low, high) ) %>% set_order_count_method("bycount") %>% set_ordering_cols('4') %>% set_result_order_var(pct) ) %>% build() # A Shift Table tplyr_table(mtcars, am) %>% add_layer( group_shift(vars(row=gear, column=carb), by=cyl) %>% set_format_strings(f_str("xxx (xx.xx%)", n, pct)) ) %>% build()
A subset of the PHUSE Test Data Factory ADAE data set.
tplyr_adae
tplyr_adae
A data.frame with 276 rows and 55 columns.
https://github.com/phuse-org/TestDataFactory
[get_data_labels()]
A subset of the PHUSE Test Data Factory ADAS data set.
tplyr_adas
tplyr_adas
A data.frame with 1,040 rows and 40 columns.
https://github.com/phuse-org/TestDataFactory
[get_data_labels()]
A subset of the PHUSE Test Data Factory ADLB data set.
tplyr_adlb
tplyr_adlb
A data.frame with 311 rows and 46 columns.
https://github.com/phuse-org/TestDataFactory
[get_data_labels()]
A mock-up dataset that is fit for testing data limiting
tplyr_adpe
tplyr_adpe
A data.frame with 21 rows and 8 columns.
A subset of the PHUSE Test Data Factory ADSL data set.
tplyr_adsl
tplyr_adsl
A data.frame with 254 rows and 49 columns.
https://github.com/phuse-org/TestDataFactory
[get_data_labels()]
tplyr_layer
objectThis object is the workhorse of the tplyr
package. A
tplyr_layer
can be thought of as a block, or "layer" of a table.
Summary tables typically consist of different sections that require different
summaries. When programming these section, your code will create different
layers that need to be stacked or merged together. A tplyr_layer
is
the container for those isolated building blocks.
When building the tplyr_table
, each layer will execute independently.
When all of the data processing has completed, the layers are brought
together to construct the output.
tplyr_layer
objects are not created directly, but are rather created
using the layer constructor functions group_count
,
group_desc
, and group_shift
.
tplyr_layer(parent, target_var, by, where, type, ...)
tplyr_layer(parent, target_var, by, where, type, ...)
parent |
|
target_var |
Symbol. Required, The variable name on which the summary is to be performed. Must be a variable within the target dataset. Enter unquoted - i.e. target_var = AEBODSYS. |
by |
A string, a variable name, or a list of variable names supplied
using |
where |
Call. Filter logic used to subset the target data when performing a summary. |
type |
"count", "desc", or "shift". Required. The category of layer - either "counts" for categorical counts, "desc" for descriptive statistics, or "shift" for shift table counts |
... |
Additional arguments |
A tplyr_layer
environment that is a child of the specified
parent. The environment contains the object as listed below.
tplyr_layer
Core Object Structuretype
This is an attribute. A string indicating the layer type, which controls the summary that will be performed.
target_var
A quosure of a name, which is the variable on which a summary will be performed.
by
A list of quosures representing either text labels or variable names used in grouping. Variable names must exist within the target dataset Text strings submitted do not need to exist in the target dataset.
cols
A list of quosures used to determine the variables that are used to display in columns.
where
A quosure of a call that containers the
filter logic used to subset the target dataset. This filtering is in
addition to any subsetting done based on where
criteria specified in
tplyr_table
layers
A list with class
tplyr_layer_container
. Initialized as empty, but serves as the
container for any sublayers of the current layer. Used internally.
Different layer types will have some different bindings specific to that layer's needs.
tab <- tplyr_table(iris, Sepal.Width) l <- group_count(tab, by=vars('Label Text', Species), target_var=Species, where= Sepal.Width < 5.5, cols = Species)
tab <- tplyr_table(iris, Sepal.Width) l <- group_count(tab, by=vars('Label Text', Species), target_var=Species, where= Sepal.Width < 5.5, cols = Species)
If a Tplyr table is built with the 'metadata=TRUE' option specified, then metadata is assembled behind the scenes to provide traceability on each result cell derived. The functions 'get_meta_result()' and 'get_meta_subset()' allow you to access that metadata by using an ID provided in the row_id column and the column name of the result you'd like to access. The purpose is of the row_id variable instead of a simple row index is to provide a sort resistant reference of the originating column, so the output Tplyr table can be sorted in any order but the metadata are still easily accessible.
tplyr_meta(names = list(), filters = exprs())
tplyr_meta(names = list(), filters = exprs())
names |
List of symbols |
filters |
List of expressions |
The 'tplyr_meta' object provided a list with two elements - names and filters. The names contain every column from the target data.frame of the Tplyr table that factored into the specified result cell, and the filters contains all the necessary filters to subset the target data to create the specified result cell. 'get_meta_subset()' additionally provides a parameter to specify any additional columns you would like to include in the returned subset data frame.
tplyr_meta object
tplyr_meta( names = rlang::quos(x, y, z), filters = rlang::quos(x == 1, y==2, z==3) )
tplyr_meta( names = rlang::quos(x, y, z), filters = rlang::quos(x == 1, y==2, z==3) )
The tplyr_table
object is the main container upon which a Tplyr table is constructed. Tplyr tables are made up of
one or more layers. Each layer contains an instruction for a summary to be performed. The tplyr_table
object contains
those layers, and the general data, metadata, and logic necessary.
tplyr_table(target, treat_var, where = TRUE, cols = vars())
tplyr_table(target, treat_var, where = TRUE, cols = vars())
target |
Dataset upon which summaries will be performed |
treat_var |
Variable containing treatment group assignments. Supply unquoted. |
where |
A general subset to be applied to all layers. Supply as programming logic (i.e. x < 5 & y == 10) |
cols |
A grouping variable to summarize data by column (in addition to treat_var). Provide multiple
column variables by using |
When a tplyr_table
is created, it will contain the following bindings:
target - The dataset upon which summaries will be performed
pop_data - The data containing population information. This defaults to the target dataset
cols - A categorical variable to present summaries grouped by column (in addition to treat_var)
table_where - The where
parameter provided, used to subset the target data
treat_var - Variable used to distinguish treatment groups.
header_n - Default header N values based on treat_var
pop_treat_var - The treatment variable for pop_data
(if different)
layers - The container for individual layers of a tplyr_table
treat_grps - Additional treatment groups to be added to the summary (i.e. Total)
tplyr_table
allows you a basic interface to instantiate the object. Modifier functions are available to change
individual parameters catered to your analysis. For example, to add a total group, you can use the
add_total_group
.
In future releases, we will provide vignettes to fully demonstrate these capabilities.
A tplyr_table
object
tab <- tplyr_table(iris, Species, where = Sepal.Length < 5.8)
tab <- tplyr_table(iris, Species, where = Sepal.Length < 5.8)
Return or set the treatment variable binding
treat_var(table) set_treat_var(table, treat_var)
treat_var(table) set_treat_var(table, treat_var)
table |
A |
treat_var |
Variable containing treatment group assignments. Supply unquoted. |
For tplyr_treat_var
the treat_var binding of the tplyr_table
object. For set_tplyr_treat_var
the modified object.
tab <- tplyr_table(mtcars, cyl) set_treat_var(tab, gear)
tab <- tplyr_table(mtcars, cyl) set_treat_var(tab, gear)