Tags
Language
Tags
June 2025
Su Mo Tu We Th Fr Sa
1 2 3 4 5 6 7
8 9 10 11 12 13 14
15 16 17 18 19 20 21
22 23 24 25 26 27 28
29 30 1 2 3 4 5
    Attention❗ To save your time, in order to download anything on this site, you must be registered 👉 HERE. If you do not have a registration yet, it is better to do it right away. ✌

    ( • )( • ) ( ͡⚆ ͜ʖ ͡⚆ ) (‿ˠ‿)
    SpicyMags.xyz

    Nonprofit Data Analysis Using R

    Posted By: ELK1nG
    Nonprofit Data Analysis Using R

    Nonprofit Data Analysis Using R
    Published 9/2024
    MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
    Language: English | Size: 4.68 GB | Duration: 7h 54m

    An 80-20 Approach to Proficiency for Beginners

    What you'll learn

    Load data from different sources into R (files, databases)

    Clean and transform data using the tidyverse packages

    Quickly explore and visualize data trends

    Create professional visualizations and reports

    Perform time-series analysis

    Conduct feature engineering for deeper analysis

    Automate reports with Rmarkdown

    Requirements

    No programming or statistical experience necessary.

    Description

    This is the R course for beginners with no coding experience. It is based on the latest research in online learning theory and my personal experience with dozens of online courses. I created this course as the course I wish I would have had when I first started learning R. We will code together and focus on the 20% of code responsible for 80% of the work. At the end of sections, you will have a 'Make It Stick' challenge to apply what you have just learned with a different dataset (based on principles in the book 'Make It Stick'). This course is different from other beginner courses in R in a couple significant ways:Project-based learning with real-world scenarios: All lessons are based on common questions facing data practioners. Content focus: The course outline and lectures are based on everyday workflows of data practioners rather than a bottom-up approach to R programming. Practically, this means we won't spend much time learning about R and core principles of programming; we will immediately start with how you will use it. Current (& continually updated) code: I work in R everyday and make sure you are learning the best and most efficient ways to accomplish the most common and important tasks. For example, the rowwise function in the dplyr package enables you to perform calculations across columns by rows. A single line of code can now accomplish what was previously far more challenging. Keeping it real: I keep the video rolling when I make an error. You can learn a lot from mistakes. R was my first programming language and I quit twice because of too many errors, too much time to learn it, and frustration with online courses that left out important steps or assumed knowledge that simply wasn't there. I try really hard to explain what we're doing while we're doing it and then giving you an opportunity to do it on your own with a different (but related) dataset.      In this course, you will learn to:Load data from different sources (files, databases)Structure data for analysis using the tidyverse packagesQuickly explore and visualize data trendsConduct feature engineering for deeper analysisAnalyze survey dataSelect the right visualization for your dataCreate professional visualizationsCreate and automate reports using RMarkdown

    Overview

    Section 1: Introduction

    Lecture 1 Dataset Introduction

    Section 2: Project Set-Up

    Lecture 2 Download R

    Lecture 3 Download R Studio

    Lecture 4 Download Course Files

    Lecture 5 Set Working Directory

    Lecture 6 Install Packages

    Section 3: Section 1: Jumpstart

    Lecture 7 1.0. Load Libraries and Import Data

    Lecture 8 1.1. Data Wrangling Part 1 (mutate, change data types)

    Lecture 9 1.2. Data Wrangling Part 2 (select, set_names, rename, and separate)

    Lecture 10 1.3. Data Wrangling Part 3 (filter, group_by, and count)

    Lecture 11 1.4. Data Wrangling Part 4 (distinct, slice, and filter by another variable)

    Lecture 12 1.5. Data Visualization Part 1 (core syntax, facet_wrap, geom_text, & scales)

    Lecture 13 1.6. Data Visualization Part 2 (Add theme and labels)

    Lecture 14 1.7. Data Visualization Part 3 (geom_point, geom_smooth, geom_jitter)

    Lecture 15 Challenge 1 Introduction

    Lecture 16 Challenge 1 Explanation

    Section 4: Section 2: Loading, Joining, and Exploring Data

    Lecture 17 Section 2 Introduction

    Lecture 18 Data Type Intro.

    Lecture 19 Data Structure Intro.

    Lecture 20 Load Data from Snowflake Database

    Lecture 21 Mutate (with case_when, if_else)

    Lecture 22 Exploratory Data Analysis Part 1 (Introduction)

    Lecture 23 Exploratory Data Analysis Part 2 (DataExplorer package)

    Lecture 24 Exploratory Data Analysis Part 3 (skimr and GGally packages)

    Section 5: Section 3: Data Transformation

    Lecture 25 Filter Part 1

    Lecture 26 Filter Part 2

    Lecture 27 Pivot_wider and pivot_longer Part 1

    Lecture 28 Pivot_longer Part 2

    Lecture 29 Bind_rows

    Lecture 30 Group_by & Summarize

    Lecture 31 Dates and Times Part 1: Date components

    Lecture 32 Dates and Times Part 2: floor & ceiling_date

    Lecture 33 Dates and Times Part 3: lag & change over time

    Lecture 34 Dates and Times Part 4: rollmean & cumsum

    Lecture 35 Modify Strings Part 1: str_to_lower, str_detect, and str_replace_all

    Lecture 36 Modify Strings Part 2: str_glue

    Lecture 37 Modify Strings Part 3: separate & unite

    Lecture 38 Challenge 3 Introduction

    Lecture 39 Challenge 3 Solutions

    Section 6: Section 4: Feature Engineering

    Lecture 40 Feature Engineering Introduction

    Lecture 41 Cumulative (year-to-date) and Rolling Averages

    Lecture 42 Extracting Time-Based Features

    Lecture 43 Course Option: Functions or Visualizations

    Lecture 44 Functional Programming Part 1: Anonymous functions within a list

    Lecture 45 Functional Programming Part 2: Creating your first function

    Lecture 46 Interpreting a Boxplot and Defining Outliers

    Lecture 47 Functional Programming Part 3: Run a Function on a Single Column

    Lecture 48 Functional Programming Part 4: Run a Function On Multiple Columns

    Lecture 49 Functional Programming Part 4: Adding Function Results to Visualization

    Lecture 50 Functional Programming Part 5: Run Multiple T-Tests on a Dataframe

    Lecture 51 Functional Programming Part 6: Save and Load Functions

    Section 7: Section 5: Data Visualizations and Reports

    Lecture 52 Introduction: Choosing the Right Plot

    Lecture 53 Part 1: Barplot

    Lecture 54 Part 2: Barplot Function

    Lecture 55 Part 3: Scatterplots (& geom_jitter)

    Lecture 56 Part 4: Scatterplot Function

    Lecture 57 Part 5: Density Plot

    Lecture 58 Part 6: Boxplot & Violin Plot

    Lecture 59 Part 7: Line Graph and Sourcing Plot Functions

    Lecture 60 Part 8: Load New Libraries Before Next Section

    Section 8: Section 6: Building Reports

    Lecture 61 R Markdown Introduction

    Lecture 62 Part 1: Creating a Report

    Lecture 63 Part 2: Adding Graphs and Tables to Reports

    Lecture 64 Part 3: Using CSS to Customize Report Layout

    Lecture 65 Part 4: PDF Reports

    Lecture 66 Part 5: Intro to Graph Layout with Patchwork

    Lecture 67 Part 6: Additional Ways to Customize Graph Layout

    Lecture 68 Part 7: Visual Editor Window

    Lecture 69 Part 8: Parameterized Reports for Automation

    Non-profit employees responsible for measuring and understanding program performance.,Employees who work on spreadsheets and are looking for more capacity and efficiency.