You want to reduce the space between columns without changing their width. Here’s an updated version of your code with full_width set to FALSE and the column widths adjusted: library(knitr) library(kableExtra) # Create the table tab <- rbind( c("Grp1 &amp; Grp2", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1 &amp; Grp2", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1 &amp; Grp2", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1 &amp; Grp2", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017"), c("Grp1", "Jan 2015 - Dec 2017", "Jan 2016 - Dec 2016", "Jan 2017 - Dec 2017") ) colnames(tab) <- c(' ','A1','A2','A1','A2','A1','A2','A1','A2','A1','A2','A1','A2') rownames(tab) <- NULL tab <- as.

Optimizing Runtime for qbeta in R Introduction The qbeta function in R is a useful tool for generating beta-distributed random variables. However, it can be computationally intensive, especially when used with large sample sizes or complex distributions. In this article, we will explore ways to optimize the runtime of qbeta in R. Background Beta distributions are commonly used in modeling binary data, such as proportions or success rates. The beta distribution is a conjugate prior for the binomial likelihood, making it an attractive choice for Bayesian inference and machine learning algorithms.

Working with UI Components in UIKit: A Step-by-Step Guide to Displaying a DatePicker in an InputView In this tutorial, we will explore how to display a DatePicker in an InputView within an UITextField. This is a common requirement in iOS development and can be achieved using the UIDatePicker class. We will break down the process into smaller sections for clarity and provide explanations and examples where necessary. Understanding the Basics of InputViews and UIDatePickers What are InputViews?

Introduction to Merging Polygon Boundaries using sf in R In recent years, the importance of spatial data has grown exponentially. This is because spatial data can be used in various applications such as environmental monitoring, urban planning, and geographic information systems (GIS). One of the key tools for working with spatial data is the sf package in R. In this article, we will explore how to merge some polygon boundaries using sf in R.

Understanding Plist Files and Changing Data Plist files are a type of property list file used by macOS and iOS applications to store data. They are similar to XML files, but with some key differences. In this article, we will explore how to load plist files into memory as mutable dictionaries, and then change the value of specific keys. What is a Plist File? A plist file is a text-based file that contains key-value pairs, where each key-value pair represents a single piece of data.

Understanding Reused Cells in iOS When it comes to creating user interfaces in iOS, one common technique used is the reuse of cells. In this article, we’ll explore how reused cells work and how they affect the performance of your app. What are Reused Cells? In iOS, a cell is an object that represents a single item in a table view or collection view. When you create a new instance of a cell for each row in your table view or collection view, it can be inefficient, especially when dealing with a large number of rows.

Understanding UTF-16-BE Encoding in Python Introduction When working with files and data storage, it’s essential to understand the encoding schemes used by different operating systems and programming languages. In this article, we’ll delve into the specifics of UTF-16-BE (big-endian Unicode Transformation Format) encoding and provide a step-by-step guide on how to save a file using this encoding in Python. Background: What is UTF-16-BE? UTF-16-BE is a variant of the Unicode character encoding standard.

Cumprod over Multiple Sub-Segments Introduction In this article, we will explore the problem of calculating cumulative products (cumprod) across multiple sub-segments within a dataset. We will delve into the solution provided by using a helper column and grouping with cumprod. Understanding Cumulative Products Before diving into the solution, let’s first understand what cumulative products are. The cumulative product of a set of numbers is the result of multiplying all the numbers in that set together.

Removing Outliers from a DataFrame Using Z-Score Method In this article, we will explore how to remove outliers from a dataset using the Z-score method. The Z-score is a measure of how many standard deviations an element is from the mean. We will discuss the steps involved in removing outliers using the Z-score method and provide examples to illustrate each step. Understanding Outliers An outlier is a data point that is significantly different from the other data points in the dataset.

Creating a DataFrame from a Column of Comma-Separated Values When working with data in Python, it’s not uncommon to encounter columns that contain comma-separated values (CSVs). In this blog post, we’ll explore how to create a DataFrame from such a column using the popular Pandas library. Introduction The question at hand involves a DataFrame df with columns “nome”, “tipo”, and “resumo”. The “resumo” column contains a list of crimes investigated for prosecution in court proceedings, separated by commas.