Understanding the Restrictions of Apple’s Camera API When it comes to developing an iPhone app that takes a photo and uploads it to a server, there are several restrictions and guidelines set by Apple to ensure that developers create apps that are secure, private, and respectful of users’ privacy. One such restriction is related to the “use”/“retake” screen that appears after taking a photo. The Problem: Understanding the Use/Retake Screen The use/retake screen in iOS apps is a default implementation provided by Apple’s Camera API.

Scheduling a Script in R with Encoding: A Deep Dive into the taskscheduleR Package Introduction As data analysts and scientists, we often rely on scripts to automate repetitive tasks. In this article, we’ll explore how to schedule a script in R using the taskscheduleR package, while also addressing encoding issues that can arise when working with special characters. What is the taskscheduleR Package? The taskscheduleR package provides a convenient way to schedule R scripts using cron jobs.

Filtering a Grouped Pandas DataFrame: Keeping All Rows with Minimum Value in Column In this article, we’ll explore how to filter a grouped pandas DataFrame while keeping all rows that have the minimum value in a specific column. We’ll examine different approaches and techniques for achieving this goal. Introduction The groupby function is a powerful tool in pandas for grouping data by one or more columns. However, when working with grouped DataFrames, it’s not uncommon to need to filter out rows that don’t meet certain conditions.

Working with Time Stamps in R: Converting HH:MM:SS to HH:MM When working with time stamps in R, it’s not uncommon to encounter timestamps in the format HH:MM:SS. However, in many cases, we want to display or work with time stamps in a more compact format, such as HH:MM. In this article, we’ll explore how to create a column with time HH:MM from a timestamp column with time HH:MM:SS in your dataset using both the data.

Understanding Hibernate Querying with Custom Object and Variable from Properties File Introduction Hibernate is a popular object-relational mapping (ORM) framework that enables developers to interact with databases using Java objects. One of the key features of Hibernate is its ability to query databases using complex queries, allowing for flexible and powerful data retrieval. In this article, we will explore how to return a list of custom objects (CustomEmployee) from a database query in Hibernate, while also incorporating variables from a properties file.

Changing Dates as per Production Shift Timings in R In this article, we will explore how to adjust the dates of a dataset based on production shift timings using R. Introduction Production shifts often have specific start and end times that can affect the date of data entry. For instance, if a company starts operations at 7:00 AM and works till 6:59 PM next day, we might want to count only the duration between these two times as one day.

SQL Aggregation: Selecting the Best Filled Value of Multiple Occurrences of Value Combination When working with data that has multiple occurrences of the same value combination, it’s not uncommon to encounter situations where you need to select the best filled value for a specific category. In this article, we’ll explore how to achieve this using SQL aggregation techniques. Problem Statement Let’s dive into the problem presented in the question: “I have the following piece of SQL code:

Introduction to Calculating Grand Total for Row and Column in a Pivot Table As a technical blogger, I have encountered numerous questions related to data analysis and visualization. One such question that has been on my mind lately is calculating the grand total for row and column in a pivot table or any other method. In this article, we will explore various methods to achieve this, including using pivot tables, grouping sets, and union of two separate queries.

Pandas Data Lookup by Value in Column and then Row ===================================================== In this article, we will explore the concept of data lookup in pandas DataFrame using both column and row values. We will delve into how to perform such lookups efficiently and effectively. Introduction Pandas is a powerful library used for data manipulation and analysis in Python. It provides efficient data structures and operations for handling structured data, including tabular data like tables, spreadsheets, and SQL tables.

Here is the updated solution with a reactive value to store the polygon clicked: library(shiny) library(leaflet) ui <- fluidPage( leafletOutput(outputId = "mymap"), tableOutput(outputId = "myDf_output") ) server <- function(input, output) { # load data cities <- read.csv(textConnection("City,Lat,Long,PC\nBoston,42.3601,-71.0589,645966\nHartford,41.7627,-72.6743,125017\nNew York City,40.7127,-74.0059,8406000\nPhiladelphia,39.9500,-75.1667,1553000\nPittsburgh,40.4397,-79.9764,305841\nProvidence,41.8236,-71.4222,177994")) cities$id <- 1:nrow(cities) # add an 'id' value to each shape # reactive value to store the polygon clicked rv <- reactiveValues() rv$myDf <- NULL output$mymap <- renderLeaflet({ leaflet(cities) %>% addTiles() %>% addCircles(lng = ~Long, lat = ~Lat, weight = 1, radius = ~sqrt(PC) * 30, popup = ~City, layerId = ~id) }) observeEvent(input$mymap_shape_click, { event <- input$mymap_shape_click rv$myDf <- data.