The Mysterious Case of R’s data.entry on OS X El Capitan As a seasoned R user and developer, I’ve encountered my fair share of frustrating issues. However, the enigmatic behavior of R’s data.entry function on OS X El Capitan has left me perplexed for quite some time. In this article, we’ll delve into the world of R package dependencies, X11 support, and the intricacies of macOS installation processes to uncover the root cause of this problem.

Based on the provided code and explanation, here’s a concise version that combines the functions and list processing into a single executable code block: library(dplyr) my_func <- function(df, grp = "wave", hi130 = "hi130", fixrate = "fixrate") { df %>% group_by_(.dots = grp) %>% mutate(hi130_eur = (hi130 / fixrate)) } countries <- list(country1, country2) df_list <- lapply(countries, my_func) for(i in seq_along(df_list)) { assign(paste0("country", i), df_list[[i]]) } This code creates a function my_func that takes a dataframe and optional arguments for grouping and column names.

Oracle While Exists Select Insert into ===================================================== Introduction In this article, we will explore a complex query that involves a while loop, exists clause, and recursive inserts. The goal of the query is to insert data from one table into another based on connections between them. The problem presented in the question is as follows: We have three tables: TEMP_TABLE, ID_TABLE, and CONNECTIONS_TABLE. TEMP_TABLE contains IDs that we want to add or update.

Scaling and Zooming Images in iPhone Applications ===================================================== In this article, we will explore how to scale and zoom images within an iPhone application using various methods. Introduction When it comes to displaying images in mobile applications, there are several factors to consider. Image size can be a significant issue, particularly when dealing with small screens like those found on iPhones. In these situations, scaling and zooming images becomes crucial for ensuring that users can view and interact with the content effectively.

SSRS Report Generation without Selecting All Parameters In SQL Server Reporting Services (SSRS), report parameters are used to filter data based on user input. However, in some cases, you may want to generate a report without selecting all parameters. This can be achieved using the IIF function and a combination of conditional statements. Understanding IIF Function The IIF function is used to perform a condition-based value return. It takes three arguments: the first argument is the condition, the second argument is the value to return if the condition is true, and the third argument is the value to return if the condition is false.

Creating a Footer/Toolbar in an iPhone Web App Using Only CSS Creating a footer or toolbar that sticks to the bottom of the viewport on an iPhone web app can be achieved using HTML, CSS, and JavaScript. However, with the introduction of iOS 5, we have a new set of options available to us. In this article, we will explore how to create a sticky footer using only CSS. Understanding the Problem In iOS 4 and earlier versions, creating a sticky footer was not straightforward.

Optimizing Double for-Loops in R: A Deep Dive As a beginner in R, creating efficient code can be challenging, especially when dealing with nested loops. In this article, we’ll explore the reasons behind slow performance, identify bottlenecks, and provide strategies to optimize double for-loops in R. Understanding the Problem The provided code snippet attempts to calculate the sum of all amounts paid at each day. The loop iterates through a dataset with two columns: amount and days.

Understanding R’s head() Function with Subset Selection In this article, we will delve into the world of data manipulation in R, specifically focusing on the head() function and its ability to subset a dataset based on user-defined categories. Introduction to Data Manipulation in R R is a popular programming language used extensively in data analysis, machine learning, and visualization. One of the fundamental tools in R for working with data is the head() function.

Data Frame Subsetting: Checking for Presence of Values Across Datasets In this article, we will explore how to check if a data frame contains a value defined in another data frame. This is a common problem in data analysis and manipulation, and there are several approaches to solving it. Introduction Data frames are a fundamental data structure in R, used to store and manipulate tabular data. They provide an efficient way to perform various operations on data, including filtering, grouping, and joining.

To create a hover text that includes both the base and feature counts for each class, you can modify the hovertext parameter in the Scatter function to use the hover2 column. Here’s an example of how you can do it: fig.add_traces(go.Scatter(x=df2['num_missed_base'], y=df2['num_missed_feature'], mode='markers', marker=dict(color='red', line=dict(color='black', width=1), size=14), hovertext=df2['hover2'] + "<br>" + df2["hover"], hoverinfo="text", )) This will create a hover text that displays the base and feature counts for each class, with the feature count on one line and the base count on the next.