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›- R-bloggers | R news and tutorials contributed by (750) R bloggers
R-bloggers | R news and tutorials contributed by (750) R bloggers
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Repost: R 4.5.0 and Bioconductor 3.21 | R-bloggers
Reposted from the original at https://blog.stephenturner.us/p/r-450-bioconductor-321.Faster package installation, import only the functions you want with use(), built-in Palmer penguins data, grep values shortcut, and lots of new bioinformatics pa...
Exploring `RSQLite` With `DBI`: A Note To Myself | R-bloggers
I messed around with DBI and RSQLite and learned it’s actually pretty simple to use in R - just connect, write tables, and use SQL queries without all the complicated server stuff. Thanks to Alec Wong for suggesting this! Motivation After our last blog, my friend Alec Wong suggested that I switch storing data from CSV files to SQLite when building Plumber API. I had no idea that CSV files can get corrupted when multiple users hit the API at the same time! SQLite handles this automatically and lets you validate your data without needing to set up any complicated server stuff. It’s actually pretty straightforward, here is a note to myself of some simple and frequent functions. Objectives Connecting to A Database List Tables Check Data Add Data Query Data Using glue_sql Remove Data Disconnect Lessons Learnt Connecting to A Database library(DBI) library(RSQLite) library(tidyverse) con mutate(date = as_datetime(date, tz = "America/New_York")) list(df) } Lessons Learnt Lots of goodies on DBI official website Learnt how to set up SQLite on Rpi, incorporated it on the previous migraine logger Definitely need to be comfortable with SQL to use this Might be a good idea to add this to the pressure logger too! Maybe in the same database but different table! If you like this article: please feel free to send me a comment or visit my other blogs please feel free to follow me on BlueSky, twitter, GitHub or Mastodon if you would like collaborate please feel free to contact me
Banking & Insurance Dataset for Data Analysis in RStudio | R-bloggers
When you are working on a project involving data analysis or statistical modeling, it's crucial to understand the dataset you're using. In this guide, we'll explore a synthetic dataset created for customers in the banking and insurance sectors. Whether you're a researcher, a student, or a business analyst, understanding how data is structured and analyzed can make a huge difference. This data comes with a variety of features that offer insights into customer behaviors, financial statuses, and policy preferences. Table of Contents Dataset Origin and ContextThe dataset, designed for analysis in tools like RStudio or SPSS, combines customer details such as age, account balance, and insurance premiums. Businesses in the finance and insurance industries need to help them optimize customer experiences, improve retention rates, and refine risk assessment models.Dataset StructureIn any data analysis, understanding the basic structure of your dataset is key. This dataset consists of 1,000 rows (representing individual customers) and 10 columns. The columns include a mix of categorical (like Gender and Marital Status) and numeric variables (like Account Balance and Credit Score). This combination allows you to explore relationships and trends across various customer attributes.File Formats and AccessThe data is accessible in a CSV format, making it easy to load into tools such as RStudio, Excel, or SPSS. For those who need assistance with data analysis or want to perform statistical tests, this format is ideal for quick importing and processing.Variables Variable Type Description Distribution / Levels CustomerID Categorical Unique identifier for each customer CUST0001 – CUST1000 Gender Categorical Gender of the customer Male, Female (≈49%/51%) MaritalStatus Categorical Marital status Single, Married, Divorced, Widowed EducationLevel Categorical Highest education attained High School, College, Graduate, Post-Graduate, Doctorate IncomeCategory Categorical Annual income bracket 120K PolicyType Categorical Type of insurance policy held Life, Health, Auto, Home, Travel Age Numeric Age in years Normal distribution, μ = 45, σ = 12 AccountBalance Numeric Bank account balance in USD Normal distribution, μ = 20,000, σ = 5,000 CreditScore Numeric FICO credit score Normal distribution, μ = 715, σ = 50 InsurancePremium Numeric Annual premium paid in USD Normal distribution, μ = 1,000, σ = 300 ClaimAmount Numeric Total claims paid in USD per year Normal distribution, μ = 5,000, σ = 2,000 Categorical VariablesCategorical variables are important because they represent grouped or qualitative data. In this dataset, you'll find attributes like Gender (Male/Female), Marital Status (Single, Married, etc.), and Policy Type (Health, Auto, Home, etc.). Understanding these helps in analyzing demographics and preferences. For example, a company could use this information to understand the market distribution of different insurance products.Numeric VariablesNumeric variables like Age, Account Balance, and Credit Score are continuous and provide a clear, measurable view of each customer's financial standing. These variables allow for in-depth statistical analysis, such as regression models or predictive analytics, to forecast customer behavior or policy outcomes. A business could use these variables to assess financial health or risk levels for insurance.Distributional AssumptionsThe data uses normal distributions for numeric variables like Age and Account Balance, meaning the values are centered around a mean with a set standard deviation. This ensures the dataset mirrors real-world scenarios, where values tend to follow a natural spread. Understanding these distributions helps in applying appropriate statistical methods when analyzing the data.Data Quality and ValidationMissing Value TreatmentBefore conducting any analysis, it's essential to address missing data. This dataset has been cleaned and preprocessed to ensure that missing values are handled appropriately, whether by imputation or removal. Having clean data ensures that the results of your analysis are valid and reliable.Outlier Detection and HandlingOutliers can significantly skew the analysis. We use methods like z-scores or boxplots to detect outliers in variables like Insurance Premium or Claim Amount. Once detected, these outliers can be adjusted or removed, ensuring your analysis reflects true patterns rather than anomalies.Consistency Checks (e.g., Income Category vs. Account Balance)Data consistency is crucial for making accurate predictions. For example, customers with an Income Category of ">120K" should logically have a higher Account Balance. We ensure that the dataset aligns with real-world logic by performing consistency checks across variables.Usage and Analysis ExamplesDemographic ProfilingUnderstanding customer demographics helps businesses create targeted marketing campaigns or personalized product offerings. This dataset allows you to analyze how age, marital status, and education level correlate with preferences for certain types of insurance policies or account balances.Credit Risk ModelingOne of the most common applications of this data is in credit risk modeling. By analyzing Credit Scores alongside Account Balance, you can build models to predict a customer's likelihood of defaulting on payments or making insurance claims.Insurance Claim PredictionPredicting Insurance Claims is another use case for this dataset. By studying the relationship between Age, Policy Type, and Claim Amount, businesses can create more accurate models to predict future claims and optimize policy pricing.Documentation and MaintenanceVersioning and Change LogAs datasets evolve, it is important to maintain version control. We ensure that any changes to the dataset are documented with clear versioning and change logs. Hence, users know exactly when and why adjustments were made.Contact and GovernanceIf you require further assistance with data analysis, our team at RStudioDatalab is here to help. Whether you need guidance on statistical tests or further clarification on the dataset, we offer support through Zoom, Google Meet, chat, and email. Bank and insurance.csv 100KB Transform your raw data into actionable insights. Let my expertise in R and advanced data analysis techniques unlock the power of your information. Get a personalized consultation and see how I can streamline your projects, saving you time and driving better decision-making. Contact me today at contact@rstudiodatalab.com or visit to schedule your discovery call. Join Our Community Book a free call