teal.transform
allows the app user to oversee
transforming a relational set of data objects into the final dataset for
analysis. User actions create a R expression that subsets and merges the
input data.
In the following example we will create an analysis dataset
ANL
by:
AGE
from ADSL
AVAL
and filtering the rows where
PARAMCD
is OS
from ADTTE
Note that primary key columns are maintained when selecting columns from datasets.
Let’s see how to achieve this dynamic select
,
filter
, and merge
operations in a
shiny
app using teal.transform
.
library(teal.transform)
library(teal.data)
#> Loading required package: teal.code
library(shiny)
# Define data.frame objects
ADSL <- rADSL
ADTTE <- rADTTE
# create a list of reactive data.frame objects
datasets <- list(
ADSL = reactive(ADSL),
ADTTE = reactive(ADTTE)
)
# create join_keys
join_keys <- join_keys(
join_key("ADSL", "ADSL", c("STUDYID", "USUBJID")),
join_key("ADSL", "ADTTE", c("STUDYID", "USUBJID")),
join_key("ADTTE", "ADTTE", c("STUDYID", "USUBJID", "PARAMCD"))
)
In the following code block, we create a
data_extract_spec
object for each dataset, as illustrated
above. It is created by the data_extract_spec()
function
which takes in four arguments:
dataname
is the name of the dataset to be
extracted.select
helps specify the columns from which we wish to
allow the app user to select. It can be generated using the function
select_spec()
. In the case of ADSL
, we
restrict the selection to AGE
, SEX
, and
BMRKR1
, with AGE
being the default
selection.filter
helps specify the values of a variable we wish
to filter during extraction. It can be generated using the function
filter_spec()
. In the case of ADTTE
, we filter
the variable PARAMCD
by allowing users to choose from
CRSD
, EFS
, OS
, and
PFS
, with OS
being the default filter.reshape
is a boolean which helps to specify if the data
needs to be reshaped from long to wide format. By default it is set to
FALSE
.adsl_extract <- data_extract_spec(
dataname = "ADSL",
select = select_spec(
label = "Select variable:",
choices = c("AGE", "SEX", "BMRKR1"),
selected = "AGE",
multiple = TRUE,
fixed = FALSE
)
)
adtte_extract <- data_extract_spec(
dataname = "ADTTE",
select = select_spec(
choices = c("AVAL", "AVALC", "ASEQ"),
selected = "AVAL",
multiple = TRUE,
fixed = FALSE
),
filter = filter_spec(
vars = "PARAMCD",
choices = c("CRSD", "EFS", "OS", "PFS"),
selected = "OS"
)
)
data_extracts <- list(adsl_extract = adsl_extract, adtte_extract = adtte_extract)
Here, we define the merge_ui
function, which will be
used to create the UI components for the shiny
app.
Note that we take in the list of data_extract
objects as
input, and make use of the data_extract_ui
function to
create our UI.
merge_ui <- function(id, data_extracts) {
ns <- NS(id)
sidebarLayout(
sidebarPanel(
h3("Encoding"),
tags$div(
data_extract_ui(
ns("adsl_extract"), # must correspond with data_extracts list names
label = "ADSL extract",
data_extracts[[1]]
),
data_extract_ui(
ns("adtte_extract"), # must correspond with data_extracts list names
label = "ADTTE extract",
data_extracts[[2]]
)
)
),
mainPanel(
h3("Output"),
verbatimTextOutput(ns("expr")),
dataTableOutput(ns("data"))
)
)
}
Here, we define the merge_srv
function, which will be
used to create the server logic for the shiny
app.
This function takes as arguments the datasets (as a list of reactive
data.frame
), the data extract specifications created above
(the data_extract
list), and the join_keys
object (read more about the join_keys
in the Join
Keys vignette of teal.data
). We make use of the
merge_expression_srv
function to get a reactive list
containing merge expression and information needed to perform the
transformation - see more in merge_expression_srv
documentation. We print this expression in the UI and also evaluate it
to get the final ANL
dataset which is also displayed as a
table in the UI.
merge_srv <- function(id, datasets, data_extracts, join_keys) {
moduleServer(id, function(input, output, session) {
selector_list <- data_extract_multiple_srv(data_extracts, datasets, join_keys)
merged_data <- merge_expression_srv(
selector_list = selector_list,
datasets = datasets,
join_keys = join_keys,
merge_function = "dplyr::left_join"
)
ANL <- reactive({
data_list <- lapply(datasets, function(ds) ds())
eval(envir = list2env(data_list), expr = as.expression(merged_data()$expr))
})
output$expr <- renderText(paste(merged_data()$expr, collapse = "\n"))
output$data <- renderDataTable(ANL())
})
}
shiny
AppFinally, we include merge_ui
and merge_srv
in the UI and server components of the shinyApp
,
respectively, using the data_extract
s defined in the first
code block and the datasets
object: