Publications by Jamal Rogers
Applied Data Science: Module 3 Lesson 1 Abstraction 3
Table of contents How do you fit a linear model in R? To specify a model A model workflow Predict with your model The tidymodels prediction guarantee! Understand your model The whole game - status update What Makes a Model? Author Jamal Rogers Published August 17, 2023 from the previous section library(tidymodels) library(modeldatatoo) ...
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Applied Data Science: Module 3 Lesson 1 Abstraction 1
Table of contents Load the Packages Data on Chicago taxi trips Which of these variable can we use? Checklist for predictors Data on Chicago taxi trips Preliminaries Author Jamal Rogers Published August 16, 2023 Load the Packages The following packages shall be used for the entire Module 3 of this course. # Install the packages if not avail...
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Applied Data Science: Module 2 Lesson 2 Application
Module 2 Lesson 2 Application Author Jamal Rogers Published May 17, 2023 Let’s load the tidyverse to begin. library(tidyverse) diamonds |> ggplot() + geom_bar(aes(x = cut, fill = color)) + labs( x = "Different cuts of diamonds", y = "Number of diamonds", fill = ...
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Applied Data Science: Module 2 Lesson 2 Abstraction 5
Layout Author Jamal Rogers Published May 17, 2023 We’ll focus more on the use of the ggplot2 package and a little dplyr for data manipulation. 3 Three additional packages will be also be used. library(tidyverse) library(scales) library(ggrepel) library(patchwork) So far we talked about how to create and modify a single plot. What if yo...
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Applied Data Science: Module 2 Lesson 2 Abstraction 1
Labels Author Jamal Rogers Published May 17, 2023 We’ll more on the use of the ggplot2 package and a little dplyr for data manipulation. 3 Three additional packages will be also be used. library(tidyverse) library(scales) library(ggrepel) library(patchwork) The easiest place to start when turning an exploratory graphic into an exposito...
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Applied Data Science: Module 2 Lesson 2 Abstraction 2
Annotations Author Jamal Rogers Published May 17, 2023 We’ll focus more on the use of the ggplot2 package and a little dplyr for data manipulation. 3 Three additional packages will be also be used. library(tidyverse) library(scales) library(ggrepel) library(patchwork) In addition to labelling major components of your plot, it’s often...
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Applied Data Science: Module 2 Lesson 2 Abstraction 4
Themes Author Jamal Rogers Published May 17, 2023 We’ll focus more on the use of the ggplot2 package and a little dplyr for data manipulation. 3 Three additional packages will be also be used. library(tidyverse) library(scales) library(ggrepel) library(patchwork) Finally, you can customize the non-data elements of your plot with a them...
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Applied Data Science: Module 2 Lesson 2 Abstraction 3
Table of contents Default scales Axis ticks and legend keys Legend layout Replacing a scale Zooming Scales Author Jamal Rogers Published May 17, 2023 We’ll focus more on the use of the ggplot2 package and a little dplyr for data manipulation. 3 Three additional packages will be also be used. library(tidyverse) library(scales) library(g...
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Applied Data Science: Module 2 Lesson 1 Abstraction
Table of contents Introduction Questions Variation Typical values Unusual values Unusual Values Covariation A categorical and a numerical variable Two categorical Variables Two numerical variables Patterns and Models Exploratory Data Analysis Author Jamal Rogers Published May 16, 2023 Introduction This book will show you how to use visu...
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Applied Data Science: Module 2 Lesson 1 Application
Module 2 Lesson 1 Application Author Jamal Rogers Published May 16, 2023 Let’s load the tidyverse to begin. library(tidyverse) Among the variables in mpg are: displ: A car’s engine size, in liters. A numerical variable. hwy: A car’s fuel efficiency on the highway, in miles per gallon (mpg). A car with a low fuel efficiency consumes m...
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