The error message indicates that the lm function from a custom package or personal function is overriding the base lm function. This can be resolved by either restarting R session, removing all packages and functions with the name “lm” (using rm(list = ls())), or explicitly calling the base lm function using base::lm. Here’s an example of how to resolve the issue: # Create a sample data frame data <- data.frame(Sales = rnorm(10), Discount = rnorm(10)) # Custom lm function lm_func <- function(x) { return(0) } # Call the custom lm function, expecting an error lm_func(data$Sales ~ data$Discount, data = data) # Explicitly call the base lm function to avoid the conflict gt <- base::lm(Sales ~ Discount, data = data) Alternatively, you can remove all packages and functions with the name “lm” using rm(list = ls()):

Reading Data Files with Pandas by Format String Introduction Pandas is a powerful Python library used for data manipulation and analysis. One of its key features is reading data from various file formats, including text files, CSV files, and even binary files like .dat files. In this article, we will explore how to read a fixed-width .dat file using pandas by format string. The Format String Notation In the given Stack Overflow post, the author mentions that the format string notation is based on the C printf convention.

Pandas: Using Fuzzy Matching to Compare Adjacent Rows in a DataFrame Introduction When working with data that contains similar but not identical values, fuzzy matching can be an effective technique for comparing adjacent rows. In this article, we will explore how to use the fuzzywuzzy library, along with pandas, to compare the names of adjacent rows in a DataFrame and update the value based on the similarity. Background The fuzzywuzzy library is a Python package that provides efficient fuzzy matching algorithms for strings.

Adding Totals and Adjusting Row Location in a Data Frame In this article, we will explore how to add totals for rows and columns in a data frame using the janitor package. We’ll also discuss how to adjust the location of rows when dealing with non-numeric values. Introduction The janitor package is a popular choice among R users for adding totals and adjusting row locations in data frames. It provides an easy-to-use interface for performing these tasks, making it a valuable tool in any data analysis workflow.

Understanding Memory Leaks and Zombies in iOS Development Memory leaks are a common issue in iOS development, where an application fails to release memory allocated for objects, leading to increased memory usage over time. This can cause performance issues, crashes, and even affect the overall stability of the device. In this article, we will delve into the world of memory management in iOS, exploring the differences between memory leaks and zombies, and provide guidance on how to identify and fix these issues.

Conditional Sorting in SQL: A Practical Guide When working with data, it’s not uncommon to need to sort a dataset based on specific conditions. This can be particularly useful when you want to prioritize certain items over others or group similar data together. In this article, we’ll explore how to achieve conditional sorting in SQL using various techniques. Introduction to Conditional Sorting Conditional sorting involves selecting rows from a database table where a condition is met, and then sorting the resulting subset of data based on additional criteria.

Missing Values Imputation in DataFrames ===================================================== In this article, we will explore the concept of missing values imputation in dataframes. We will discuss different methods and techniques for handling missing data, including the popular dplyr library in R. Introduction to Missing Values Missing values, also known as null values or NaNs (Not a Number), are a common problem in data analysis. They occur when a value is not available or cannot be measured for a particular observation.

Creating a Boolean DataFrame from Series with Itself in Pandas In this article, we will explore the process of creating a boolean DataFrame where each item serves as both a row and column. We’ll examine the most efficient methods to achieve this task using Pandas. Introduction When working with categorical data, it’s common to encounter situations where you need to create masks or boolean arrays based on specific conditions. In such cases, having an array of categories can be helpful in creating these masks efficiently.

Inserting Python List into Pandas DataFrame Rows and Setting Row Values to NaN In this article, we will explore how to insert a new row with just the ticker date into a specific column of a Pandas DataFrame. We will also discuss how to set remaining values of rows where list values inserted into “Date” column to NaN. Introduction to Pandas DataFrames Before diving into the solution, let’s first cover some basic concepts and terminology related to Pandas DataFrames.

Managing Subscriptions with Sandbox Accounts: A Deep Dive into iOS Development Background In-app purchases and auto-renewable subscription plans are popular features in modern mobile applications, especially for those that rely on recurring revenue streams. Apple’s In App Purchase (IAP) framework provides a convenient way to manage subscriptions, but it also presents some challenges when testing these scenarios. The WWDC 2016 slides demonstrate the Manage Subscription page within iTunes & App Store, allowing users to change their current subscription plan and cancel their subscription.