Based on the provided code, it seems that you are trying to create a drake::drake_plan with multiple targets and transforms. Here’s an example of how you can structure your plan without any transforms: library(drake) plan <- drake_plan( # Target 1 target = "a", fn1 = function(arg1, arg2) { print("Function 1 executed") }, # Target 2 target = "b", fn2 = function(arg1) { print("Function 2 executed") }, # Target 3 target = "d", fn3 = function(arg1) { print("Function 3 executed") } ) # Desired plan for the run target run_plan <- tibble( target = c("a", "b", "d"), command = list( expr(fn1(c("arg11", "arg12"), c("arg21", "arg22"))), expr(fn2(c("arg11", "arg12"))), expr(fn3(c("arg11", "arg12"))) ), path = NA_character_, country = "1", population_1 = c(rep("population_1_sub1", 2), rep("population_1_sub2", 2)), substudy = c(rep("sub1", 2), rep("sub2", 2)), adjust = c(rep("no", 2), rep("yes", 2)), sex = c(rep("male/female", 4)), pedigree_1 = c(rep("pedigree_1_sub1", 2), rep("pedigree_1_sub2", 2)), covariable_1 = c(rep("covariable_1_sub1", 2), rep("covariable_1_sub2", 2)), model = c("x", "y", "z") ) config <- drake_config(plan, run_plan) vis_drake_graph(config, targets_only = TRUE) As for the issue with map not understanding .

Working with Dataframes in Python: Creating a DataFrame from a List of Tuples with Lists As a data scientist or analyst, working with dataframes is an essential skill. In this article, we will explore how to create a dataframe from a list of tuples with lists using the popular pandas library. Introduction to Pandas and Dataframes The pandas library provides data structures and functions designed for tabular data. A dataframe is a two-dimensional table of data with rows and columns, similar to an Excel spreadsheet or a SQL table.

Refreshing Dataset and Updating the Label with SQL In this article, we will explore how to refresh a dataset after a given time and update the label accordingly. We’ll use a stored procedure to retrieve data from a database and display it on a webpage. The goal is to update the label every 8 hours. Background To understand this topic, let’s first review some essential concepts: Stored Procedures: These are pre-written SQL commands that can be executed on a database server to perform specific tasks.

Understanding Foreign Key Constraints and LINQPad Syntax Foreign key constraints are a fundamental concept in database design, ensuring data consistency between different tables. In this article, we will delve into the world of foreign key constraints, explore their role in maintaining data integrity, and discuss how to use them effectively with LINQPad syntax. What is a Foreign Key? A foreign key is a field or column in a table that references the primary key of another table.

SQL Order Each First Letter by Date ====================================================== Introduction When working with databases, it’s not uncommon to have multiple columns that need to be ordered in a specific manner. In this article, we’ll explore how to achieve the goal of ordering rows where each first letter of the name column is followed by the date column, while also considering sticky items that should be displayed on top of the results.

Inserting a pandas DataFrame into an existing PostgreSQL table =========================================================== In this article, we will discuss how to insert a pandas DataFrame into an existing PostgreSQL table. We will explore the different options available for truncating and inserting data into the database, including manual methods, using pandas.DataFrame.to_sql(), and more. Prerequisites Before we begin, it is assumed that you have a basic understanding of Python, pandas, and SQL. Additionally, you should have a PostgreSQL database set up on your local machine or a remote server.

Introduction to NetCDF Files and Extracting Data NetCDF (Network Common Data Form) files are a popular format for storing scientific data, particularly in fields like meteorology, oceanography, and climate science. These files contain a wealth of information about the Earth’s climate system, including temperature, precipitation, and atmospheric pressure patterns. However, accessing this data can be challenging, especially for those without prior experience with NetCDF files. In recent years, R has emerged as a powerful tool for analyzing and visualizing climate data, thanks in part to the ncdf4 package.

Symbolic Computation Using Regex and Eval() in Python In this blog post, we will explore the use of regular expressions (regex) and the eval() function in Python to perform symbolic computation on financial models. We will delve into the details of how regex can be used to parse and evaluate mathematical expressions, and how this can be applied to build a generic cash flow model. Introduction Symbolic computation is a powerful technique that allows us to perform calculations using mathematical expressions rather than numerical values.

Grouping Last Amount Paid by City and Year When working with financial or transactional data, it’s often necessary to summarize payments by city and year. In this article, we’ll explore how to achieve this using SQL queries. Understanding the Problem Suppose you have a table t containing payment records, including the date of payment (twoMonths), city name (nameCity), and amount paid (payment). You want to retrieve the last amount paid for each year and city combination.

Replacing Columns in DataFrames: A Deep Dive into Index Alignment As a beginner in Python, it’s easy to get stuck when working with DataFrames from popular libraries like Pandas. In this article, we’ll delve into the intricacies of replacing columns between two DataFrames while maintaining their original alignment. Introduction to DataFrames and Indexing DataFrames are a powerful data structure in Pandas that allows for efficient storage and manipulation of structured data.