The issue with the plot is that it’s not properly mapping the factor levels to colors due to missing NA values. To resolve this, we need to explicitly include “NA” as a level in the factor and use scale_fill_manual instead of scale_fill_brewer to map the factor levels to colors. Here’s the corrected code: # Create a new column with "NA" if count is NA states$count[is.na(states$count)] = "NA" # Map the factor to colors using scale_fill_manual ggplot(data = states) + geom_polygon(aes(x = long, y = lat, fill = factor(count, levels=c(0:5,"NA")), group = group), color = "white") + scale_fill_manual(name="counts", values=brewer.

Authentication of Oracle Database from R Script ============================================= In this article, we’ll explore the process of authenticating an Oracle database connection from a R script. This is crucial for securing your data and preventing unauthorized access to your databases. Introduction Many organizations use R scripts to perform various tasks such as data analysis, visualization, and reporting. However, when it comes to interacting with external resources like databases, security becomes a top priority.

Introduction The OECD (Organisation for Economic Co-operation and Development) website provides a wealth of economic data for countries around the world. However, accessing this data can be challenging, especially when dealing with XML-based datasets like SDMX (Statistical Data eXchange). In this article, we will explore how to fetch data from the OECD into R using SDMX/XML. Prerequisites Before diving into the code, ensure that you have the necessary packages installed in your R environment:

SQL SUM and Value Conversion As a technical blogger, it’s not uncommon for readers to reach out with specific questions about SQL queries. One such question that caught my attention recently was about transforming data in a SUM query to acknowledge negative numeric values. The questioner wanted to know how to handle credit transactions that are not explicitly represented as negative in the database, but should be treated as such.

Introduction to iOS 6.0 Facebook Connectivity ============================================= In this article, we will explore how to integrate Facebook connectivity with an iOS application targeting iOS 6.0. We will dive into the steps required to connect to the Facebook platform and share user-generated content. Prerequisites for iOS 6.0 Facebook Connectivity Before we begin, ensure that you have: Xcode 4.5 or later installed on your Mac. An Apple Developer account with a provisioned certificate for the iOS 6.

Understanding the find_nearest Function and DataFrame Column Issues As a data scientist or engineer, working with Pandas DataFrames is a common task. When creating functions to manipulate or analyze these data structures, it’s essential to understand how to access their columns correctly. In this article, we’ll delve into the issue of calling DataFrame column names directly within function definitions and explore potential workarounds. Introduction to DataFrame Columns In Pandas, DataFrames are two-dimensional labeled data structures with rows and columns.

Understanding Prediction in R - GLMM ===================================================== In this article, we will delve into the world of Generalized Linear Mixed Models (GLMM) and explore how to make predictions using these models in R. Introduction to GLMM GLMMs are a type of regression model that extends traditional logistic regression by incorporating random effects. These models are particularly useful when dealing with data that contains correlated or clustered responses, such as repeated measures or panel data.

Generating Autogenerated Columns in PostgreSQL Introduction When working with PostgreSQL, it’s often necessary to create tables and insert data into them. However, sometimes the table schema needs to change, which can lead to issues when trying to insert data from one table to another. In this article, we’ll explore how to generate autogenerated columns in PostgreSQL and solve a specific problem related to inserting values into a table with an autogenerated column.

Window Functions in SQL: Counting Projects and Displaying Against Each Row Introduction SQL is a powerful language for managing and analyzing data, but it can be challenging to work with complex data structures. One such challenge is performing calculations across rows that share common characteristics. This is where window functions come into play. In this article, we’ll explore the concept of window functions in SQL, specifically focusing on counting projects and displaying the results against each row.

Advanced SQL Querying: Getting Average of Nonzero Values Without Spoiling Sum ===================================================== In this article, we’ll explore how to use a specific SQL function to get the average of all nonzero values in a column without spoiling the sum of other values. We’ll also discuss alternative approaches and provide examples to help you understand the concepts better. Understanding the Problem The problem arises when you need to calculate the average of a column, but some values in that column are zero, which would skew the average.