A Fine Slice Of SQL Server
Steven Sanderson goes hunting for strings:
Today I want to discuss a common task in data manipulation: selecting columns containing a specific string. Whether you’re working with base R or popular packages like
stringr,stringi, ordplyr, I’ll show you how to efficiently achieve this. We’ll cover various methods and provide clear examples to help you understand each approach. Let’s get started!
Click through for five examples across the three methods.
Comments closedSteven Sanderson takes a peek:
When working with data frames in R, it’s common to need to check whether a specific column exists. This is particularly useful in data cleaning and preprocessing, to ensure your scripts don’t throw errors if a column is missing. Today, we’ll explore several methods to perform this check efficiently in R, and I encourage you to try these methods out with your own data sets.
Read on for four ways to do this.
Comments closedSteven Sanderson performs a check:
Whether you’re doing some data cleaning or exploring your dataset, checking if a column contains a specific string can be a crucial task. Today, I’ll show you how to do this using both
str_detect()from the stringr package and base R methods. We’ll also tackle finding partial strings and counting occurrences. Let’s dive right in!
Read on for a few variants on the theme.
Comments closedSteven Sanderson builds a list:
When working with data frames in R, you may often encounter scenarios where you need to collapse or concatenate text values based on groups within your dataset. This could involve combining text from multiple rows into a single row per group, which can be useful for summarizing data or preparing it for further analysis. In this post, we’ll explore how to achieve this task using different methods in R—specifically using
base R, thedplyrpackage, and thedata.tablepackage.
This is the R equivalent of T-SQL’s STRING_AGG() function, or the STUFF() + FOR XML PATH approach if you’re still on an older version of SQL Server.
Steven Sanderson counts what doesn’t exist:
Welcome back, R enthusiasts! Today, we’re going to explore a fundamental task in data analysis: counting the number of missing (NA) values in each column of a dataset. This might seem straightforward, but there are different ways to achieve this using different packages and methods in R.
Let’s dive right in and compare how to accomplish this task using base R, dplyr, and data.table. Each method has its own strengths and can cater to different preferences and data handling scenarios.
Read on for 3 1/2 separate methods.
Comments closedSteven Sanderson talks about the Akaike Information Criterion:
In the world of data analysis and statistics, one of the key challenges is selecting the best model to describe and analyze your data. This decision is crucial because it impacts the accuracy and reliability of your results. Among the many tools available, the Akaike Information Criterion (AIC) stands out as a powerful method for comparing different models and choosing the most suitable one.
Today we will go through an example of model selection using the AIC, specifically focusing on its application to various statistical distributions available in the TidyDensity package. TidyDensity, a part of the healthyverse ecosystem, offers a comprehensive suite of tools for data analysis in R, including functions to compute AIC scores for different probability distributions.
Read on for a quick primer on the AIC itself and how you can use it in TidyDensity.
Comments closedSteven Sanderson performs some sampling:
In the area of statistical modeling and Bayesian inference, Markov Chain Monte Carlo (MCMC) methods are indispensable tools for tackling complex problems. The new
tidy_mcmc_sampling()function in the TidyDensity R package simplifies MCMC sampling and visualization, making it accessible to a broader audience of data enthusiasts and analysts.
Read on for a brief primer on MCMC and an example of how the tidy_mcmc_sampling() function works.
Steven Sanderson looks for dupes:
Today, we’re diving into a useful new function from the TidyDensity R package:
check_duplicate_rows(). This function is designed to efficiently identify duplicate rows within a data frame, providing a logical vector that flags each row as either a duplicate or unique. Let’s explore how this function works and see it in action with some illustrative examples.
Read on to see how it works. Though I am curious about whether there’s an option to ignore certain columns, such as row IDs or other “non-essential” columns you don’t want to include for comparison. Also, checking how it handles NA or NULL would be interesting.
Comments closedSteven Sanderson achieves normality:
In data analysis, especially when dealing with multiple samples or distributions, ensuring comparability and removing biases is crucial. One powerful technique for achieving this is quantile normalization. This method aligns the distributions of values across different samples, making them more similar in terms of their statistical properties.
Read on to see how you can use the TidyDensity package to pull this off.
Comments closedSteven Sanderson shows off some more complex string splitting scenarios in R:
In data preprocessing and text manipulation tasks, the
strsplit()function in R is incredibly useful for splitting strings based on specific delimiters. However, what if you need to split a string using multiple delimiters? This is wherestrsplit()can really shine by allowing you to specify a regular expression that defines these delimiters. In this blog post, we’ll dive into how you can usestrsplit()effectively with multiple delimiters to parse strings in your data.
Read on for two examples of complex scenarios.
Comments closed