Including a Function from External Source R in RMarkdown Suppose you have a functions.R script in which you have defined a few functions. Now, you want to include only foo() (and not the whole functions.R) in a chunk in RMarkdown. If you wanted all functions to be included, following a certain answer, you could have done this via: However, you only need foo() in the chunk. How can you do it?

Finding the Polygon Nearest to a Point in R In geospatial analysis, finding the polygon nearest to a point is a fundamental task with numerous applications in geography, urban planning, and more. In this article, we will explore how to achieve this using R and its associated libraries: rgeos for spatial operations and rgdal for working with geospatial data. Introduction When working with spatial data, it’s common to have a set of polygons (e.

Understanding Postgres Grouping Sets PostgreSQL provides a powerful grouping mechanism through its GROUP BY clause. When used with the GROUPING SETS operator, it allows us to group rows in multiple ways, making it easier to calculate aggregates like totals and subtotals. Introduction to GROUP By The GROUP BY clause is used to group rows that have the same values in a specific set of columns. The result is a new row for each unique combination of those column values.

Understanding Data Types in Pandas DataFrames When working with dataframes in pandas, it’s essential to understand the different types of data that can be stored. In this blog post, we’ll delve into the world of data types and explore how to preserve them when saving a dataframe to a csv file. What are Data Types in Pandas? In pandas, data types refer to the type of data stored in a column or series.

Changing the Encoding of a DataFrame Using Map Functions in R ==================================================================== In this article, we will explore how to change the encoding of a DataFrame in R using map functions. We will cover different approaches and techniques for achieving this, including using map_dfc, assignment functions, and the across function from the dplyr package. Introduction Changing the encoding of a DataFrame is an essential step when working with text data that has been encoded in a specific format.

Understanding Time Deltas and DataFrames in Python As a data scientist or engineer, working with time series data is an essential part of many tasks. In this blog post, we will explore how to efficiently find timedeltas in a pandas DataFrame. Introduction to Timedeltas A timedelta is a duration, the difference between two dates or times. In Python’s datetime library, timedelta is used to represent this concept. from datetime import datetime, timedelta current_date = datetime.

Conditional Aggregation in SQL: Checking for a Character in a String Column When working with string columns, one common task is to check if a specific character exists within the data. In this scenario, we have two tables, Booking and BookingDesc, which contain information about bookings and their corresponding routes. We want to create a new column that indicates whether each booking’s route contains the character ‘D’. Understanding Conditional Aggregation Conditional aggregation allows us to perform calculations on grouped data based on conditions.

Advanced String Matching in R: A Deep Dive into grep and lapply In this article, we’ll explore how to perform exact string matching in a vector inside a list using R’s built-in functions grep and lapply. We’ll also discuss some nuances of regular expressions (regex) and their applications in R. Introduction The grep function is a powerful tool for searching for patterns within strings. However, when dealing with vectors inside lists, things can get complex quickly.

Understanding fct_reorder2() in R: A Deep Dive The fct_reorder2() function in R is part of the tidyverse package and is used to reorder factor levels based on a specific variable. However, understanding its purpose can be challenging due to the limited information provided in the documentation. In this article, we will delve into the world of fct_reorder2() and explore what it does, how it works, and when to use it.

Calculating a Partial Dependency Plot with Prediction Intervals for an SAR Model in R Introduction Spatial autoregressive (SAR) models are widely used in geography and spatial analysis to model the relationship between variables at different locations. These models are particularly useful when dealing with spatial data, as they can capture the spatial autocorrelation present in such data. However, one of the limitations of SAR models is that they do not provide a straightforward way to visualize the effect of individual predictor variables on the outcome variable.