I can solve this problem. Here is the final code: library(dplyr) df %>% mutate(DateTime = as.POSIXct(DateTime, format = "%d/%m/%Y %H:%M"), Date = as.Date(DateTime)) %>% arrange(DateTime) %>% mutate(class = c("increase", "decrease")[(Area - lag(Area) < 0) + 1]) %>% group_by(Date) %>% mutate(prev_max = max(Area), class = case_when( class == "increase" & Area > prev_max ~ "growth", TRUE ~ class)) %>% select(-prev_max) This code first converts DateTime to POSIXct value and Date to Date.

Understanding Float Data Type in TiDB and MySQL ===================================================== In this article, we will explore the float data type in both MySQL and TiDB, focusing on their differences and how they impact the storage and calculation of decimal numbers. Introduction to Float Data Type The float data type is a numeric type used to store decimal numbers. It is commonly used in applications where precise calculations are not necessary, such as financial transactions or logging data.

Understanding SQL Joins SQL joins are used to combine data from two or more tables based on a related column between them. In this scenario, we have three tables: Table A, Table B, and Table C. What is a LEFT OUTER JOIN? A LEFT OUTER JOIN is used when you want to include all records from the left table (Table C), even if there are no matching records in the right table (Tables A or B).

Understanding the Error: Column Count Doesn’t Match Value Count in MySQL As a developer, we’ve all encountered those frustrating errors that make us scratch our heads. In this article, we’ll dive into one such error: “column count doesn’t match value count at row 1” in MySQL. This issue arises when you try to insert data into a table and provide fewer values than the number of columns defined in the table.

Django ORM vs PostgreSQL Raw SQL Introduction As a developer, it’s common to work with databases in our applications. When working with databases, one of the most important decisions is how to interact with them - whether to use Object-Relational Mapping (ORM) or raw SQL queries. In this article, we’ll explore the pros and cons of using Django ORM versus PostgreSQL raw SQL queries. Understanding Django ORM Django ORM is a high-level interface that allows us to interact with databases without writing raw SQL queries.

Working with Lists of Headers and Rows in Pandas DataFrames When working with data stored in spreadsheets or other tabular formats, it’s often necessary to convert the data into a structured format that can be easily manipulated. In this case, we’re dealing with a list of headers and rows, where each row represents a single data point. In this article, we’ll explore how to convert these lists into a Pandas DataFrame, which is a powerful tool for data analysis and manipulation.

Stratified Table 1 using a svydesign object and tbl_svysummary? Introduction In this article, we’ll explore the process of creating a stratified table in R using the tbl_svysummary() function from the gtsummary package. We’ll start with an example dataset from the mtcars package and then apply the same concepts to your NHANES survey data. Prerequisites Before we begin, make sure you have the necessary packages installed: tidyverse gtsummary You can install these packages using the following command:

Understanding the Problem: Filling Gaps in a Sequence with SQL and Oracle As a database professional, you’ve likely encountered situations where you need to generate a sequence of numbers within a specific range. In this blog post, we’ll delve into one such problem involving an Oracle database and explore how to fill gaps in a sequence using SQL. Background: What’s Behind the Problem? The problem presents a scenario where we have a table with two columns, Batch and _serial_no to to_serial_no, which contain ranges.

Confidence Intervals in Bar Plots: A Deep Dive Introduction Confidence intervals are a crucial concept in statistical inference, representing a range of values within which a population parameter is likely to lie. In the context of bar plots, adding confidence intervals can provide valuable insights into the uncertainty associated with each estimate. However, implementing this in a bar plot setting requires some thought and understanding of the underlying concepts. Understanding Confidence Intervals A confidence interval is a statistical tool that provides a range of values within which a population parameter is likely to lie.

Removing Columns of Equal Variance after dplyr::group_by and before prcomp ===================================================== In this article, we’ll explore how to remove columns of equal variance from the data after grouping using dplyr and before performing a principal component analysis (PCA) with prcomp. We’ll go through a step-by-step guide on how to identify such columns, exclude them, and then perform PCA. Introduction Principal Component Analysis (PCA) is a widely used technique for dimensionality reduction.