To combine the words in a document term matrix (DTM) using the tm package in R, you can create a custom function to replace the old words with the new ones and then apply it to each document. Here’s an example: library(tm) library(stringr) # Define the function to replace words replaceWords <- function(x, from, keep) { regex_pat <- paste(from, collapse = "|") x <- gsub(regex_pat, keep, x) return(x) } # Define the old and new words oldwords <- c("abroad", "access", "accid") newword <- "accid" # Create a corpus from the text data corpus <- Corpus(VectorSource(text_infos$my_docs)) # Convert all texts to lowercase corpus <- tm_map(corpus, tolower) # Remove punctuation and numbers corpus <- tm_map(corpus, removePunctuation) corpus <- tm_map(corpus, removeNumbers) # Create a dictionary of old words to new ones dict <- list(oldword=newword) # Map the function to each document in the corpus corpus <- tm_map(corpus, function(x) { # Remove stopwords x <- tm_remove(x, stopwords(kind = "en")) # Replace words based on the dictionary for (word in names(dict)) { if (grepl(word, x)) { x <- replaceWords(x, word, dict[[word]]) } } return(x) }) # View the updated corpus summary(corpus) This code defines a function replaceWords that takes an input string and two arguments: from and keep.

Understanding pmin and Pattern Matching in R R is a popular programming language for statistical computing and graphics. It provides an extensive set of libraries and tools for data manipulation, analysis, and visualization. In this article, we’ll delve into the world of R’s pmin function, explore its capabilities, and discuss how to apply pattern matching to find minimum values in columns with specific names. Introduction to pmin The pmin function in R returns the smallest value from a list of numeric vectors.

Understanding the Behavior of df.select_dtypes The popular data science library Pandas provides an efficient way to manipulate and analyze data in Python. One of its key features is the ability to select columns based on their data types. In this article, we’ll explore a peculiar behavior of pd.DataFrame.select_dtypes when selecting numeric columns. Background: What are Data Types? Before diving into the specifics of select_dtypes, it’s essential to understand what data types are in Pandas.

Removing Rows from a Pandas DataFrame When the Last One is Equal to the Previous One In this article, we will explore how to remove rows from a Pandas DataFrame when the last row is equal to the previous one. We will cover the concept of boolean indexing and its application in Pandas. Background Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures such as Series (1-dimensional labeled array) and DataFrames (2-dimensional labeled data structure with columns of potentially different types).

PostgreSQL Connection Issues with Docker and Makefile As a developer, working with databases like PostgreSQL can be challenging, especially when trying to automate tasks using makefiles. In this article, we’ll explore the issues of connecting to PostgreSQL from a makefile and running migration scripts. Background on Docker and PostgreSQL To start, let’s briefly discuss how Docker and PostgreSQL work together. Docker is a containerization platform that allows us to package our application code and dependencies into a single container, which can be run independently of the host operating system.

PANDAS Web Scraping Multiple Pages Introduction Web scraping is a technique used to extract data from websites. Pandas, a Python library, provides efficient data structures and operations for manipulating numerical data. In this article, we will explore how to scrape multiple pages of a website using Pandas. Understanding the Problem The problem presented involves scraping data from multiple pages of a website using Beautiful Soup and then extracting that data into DataFrames.

Working with Pandas DataFrames: Slicing Rows Based on Date Indexes In this article, we will explore how to slice rows from a Pandas DataFrame based on date indexes. We’ll dive into the world of data manipulation and examine the various techniques for achieving this goal. Introduction to Pandas DataFrames A Pandas DataFrame is a two-dimensional labeled data structure with columns of potentially different types. It’s a powerful tool for data analysis, and it’s widely used in scientific computing, data science, and business intelligence.

Counting Columns that Match a Condition Rowwise: A Deep Dive Introduction In this article, we will explore how to count the number of columns in each row that match a certain condition. We will use R and the tidyverse package for this example. We are given a data frame demo with several variables (columns) and their corresponding values. The goal is to create a new variable that tells us how many variables of each row equal 10.

Understanding the Problem: Downloading Multiple JPEGS into an Array from URLs in a Data Frame The problem at hand involves downloading multiple JPEG images from their respective URLs and storing them in a data frame as an array. The current implementation using a for loop and tempfile() is not efficient, resulting in the overwrite of previous downloaded images. Background and Context RStudio provides an extensive range of tools for data manipulation, visualization, and analysis.

Choosing Subsets of Factor Groups for Statistical Tests in R Introduction In this article, we will discuss how to select subsets of factor groups from a dataset in R for statistical testing. We will explore various methods and techniques using existing data to test the variances of specific groups. Understanding the Problem The problem at hand is to statistically test the variance (Kruskal-test) for each variable separately in a dataset. The dataset contains 16 groups, but we are only interested in subsets of these groups based on certain criteria.