The final answer is: To get the resultTbl you can just access the input[x]’s. Here is an example of how you can do it: library(DT) library(shiny) library(dplyr) cars_df <- mtcars selectInputIDa <- paste0("sela", 1:length(cars_df)) selectInputIDb <- paste0("selb", 1:length(cars_df)) initMeta <- dplyr::tibble( variables = names(cars_df), data_class = sapply(selectInputIDa, function(x){as.character(selectInput(inputId = x, label = "", choices = c("numeric", "character", "factor", "logical"), selected = sapply(cars_df, class)))}), usage = sapply(selectInputIDb, function(x){as.character(selectInput(inputId = x, label = "", choices = c("id", "meta", "demo", "sel", "text"), selected = "sel"))}) ) ui <- fluidPage( htmltools::findDependencies(selectizeInput("dummy", label = NULL, choices = NULL)), DT::dataTableOutput(outputId = 'my_table'), br(), verbatimTextOutput("table") ) server <- function(input, output, session) { displayTbl <- reactive({ dplyr::tibble( variables = names(cars_df), data_class = sapply(selectInputIDa, function(x){input[[x]]}), usage = sapply(selectInputIDb, function(x){input[[x]]}) ) }) resultTbl <- reactive({ dplyr::tibble( variables = names(cars_df), data_class = sapply(selectInputIDa, function(x){input[[x]]}), usage = sapply(selectInputIDb, function(x){input[[x]]}) ) }) output$my_table <- DT::renderDataTable({ DT::datatable( initMeta, escape = FALSE, selection = 'none', rownames = FALSE, options = list(paging = FALSE, ordering = FALSE, scrollx = TRUE, dom = "t", preDrawCallback = JS('function() { Shiny.

Implementing Learning Record Store (LRS) with Tin Can API for iPhone Introduction In today’s digital learning landscape, it’s essential to have a robust and standardized way of tracking learner progress and achievements. The Tin Can API, also known as xAPI, is an open standard for learning record stores (LRS). It allows learners to share their experiences with others and provides a framework for institutions to track learner progress. In this article, we’ll explore how to implement LRS with the Tin Can API on iPhone using Objective-C.

Animating a View While It’s Being Moved by TouchesMoved in UIKit When working with touch events on iOS devices, it can be challenging to manage the view’s state while it’s being moved. In this response, we’ll explore how to animate a UIView subclass as it’s being dragged around the screen. Understanding the Problem The problem at hand involves creating an animated transition when a user drags a view around on their device.

Understanding the Requirements for Zooming an Image in an iPad App When developing an iPad app that requires zooming and scrolling of images, it’s essential to understand how to achieve this functionality effectively. In this article, we’ll delve into the details of using UIScrollView to enable zooming and scrolling of images, as well as how to determine the position of the zoomed image. Introduction to UIScrollView A UIScrollView is a view that allows users to scroll through its content.

Understanding Logistic Regression without an Intercept Logistic regression is a widely used statistical technique for modeling binary outcomes. It’s a popular choice in machine learning and data analysis due to its simplicity and interpretability. However, when it comes to logistic regression without an intercept, things can get tricky. In this article, we’ll delve into the world of logistic regression, explore why removing the intercept can lead to warning messages, and discuss potential solutions.

Understanding Request Complexity: 1 vs 2 Requests to a Web Service from an iPhone As a developer, making requests to a web service can be a daunting task, especially when dealing with complex scenarios. In this article, we’ll delve into the intricacies of sending requests to a web service from an iPhone, exploring the pros and cons of two common approaches: 1 request vs 2 requests. Introduction When building an iPhone app, it’s essential to consider how your app will interact with a web service.

Calculating Time Differences by Condition In this article, we will explore how to calculate time differences in a dataset where each row represents a shift for a worker. The goal is to determine the duration of each shift based on the start and finish times. Background When working with time-related data, it’s common to encounter various time-based functions such as dplyr’s summarise function in R or Python’s pandas library. These tools are designed to help you extract insights from your data by grouping and aggregating values based on conditions specified.

Calculating Cumulative Inventory Levels with Nested Index Groups Introduction In this article, we’ll explore the challenges of calculating cumulative inventory levels when working with nested index groups. We’ll delve into the specifics of the problem presented in a Stack Overflow question and provide a solution using Python and the Pandas library. Background The problem involves an inventory model where inputs increase the inventory and outputs decrease it every day. The inventory cannot go below zero.

Plotting Combined Bar and Line Plot with Secondary Y-Axis in Python In this article, we will explore how to create a combined bar and line plot with a secondary y-axis using Python. We’ll discuss two approaches: one where we use a matplotlib workaround and another where we neglect the fact that the points are dates. Introduction When working with data from CSV files, it’s often necessary to visualize the data to gain insights or understand patterns.

Understanding the datetime.date() vs. ‘%Y/%m/%d’ Format Issue in Python In this article, we’ll delve into a specific question on Stack Overflow regarding an issue with formatting dates using datetime.date() and the format string ‘%Y/%m/%d’. We’ll explore what’s happening behind the scenes, why the code isn’t working as expected, and how to fix it. Introduction to Date Formatting in Python Python’s datetime module provides a powerful way to work with dates. The date class is used to represent a date without any time component.