A common criticism of cities in the South is their poor walkability and over reliance on the car. Northern cities like New York and D.C. are renowned for their public transit systems which encourage more activity and a relatively healthier lifestyle. For the past two years I’ve been wearing an Apple watch which has coincided with my time living in three different cities: Charlotte, DC, and Charleston, and I was curious if the activity data would reflect these differences. 12.2 Tidy data 12.2.1 Exercises 12.3 Spreading and gathering 12.3.3 Exercises 12.4 Separating and uniting 12.4.3 Exercises 12.5 Missing values 12.5.1 Exercises 12.6 Case Study 12.6.1 Exercises This post covers the content and exercises for Ch 12: Tidy Data from R for Data Science. The chapter teaches how to apply the organizational structure of tidy data to achieve a consistent format for data. 11.2 Getting started 11.2.2 Exercises 11.3 Parsing a vector 11.3.5 Exercises This post covers the content and exercises for Ch 11: Data Import from R for Data Science. The chapter teaches how to read in plain text files of data. library(tidyverse) 11.2 Getting started Using {readr} to load text files. 11.2.2 Exercises What function would you use to read a file where fields were separated with “|”? 10.5 Exercises This post covers the content and exercises for Ch 10: Tibbles from R for Data Science. The chapter teaches how to use the tidyverse version of data frames called tibbles. 10.5 Exercises library(tidyverse) ## Loading tidyverse: ggplot2 ## Loading tidyverse: tibble ## Loading tidyverse: tidyr ## Loading tidyverse: readr ## Loading tidyverse: purrr ## Loading tidyverse: dplyr ## Warning: package 'tibble' was built under R version 3. 7.2 Questions 7.3 Variation 7.3.4 Exercises 7.4 Missing Values 7.4.1 Exercises 7.5 Covariation 7.5.1 A categorical and continuous variable 7.5.1.1 Exercises 7.5.2 Two categorical variables 7.5.2.1 Exercises 7.5.3 Two continuous variables 7.5.3.1 Exercises This post covers the content and exercises for Ch 7: Exploratory Data Analysis from R for Data Science. The chapter teaches how to use visualisation and transformation to explore your data in a systematic way. 5.1 Introduction 5.1.1 Prerequisites 5.1.2 nycflights13 5.1.3 dplyr Basics 5.2 Filter Rows with filter() 5.2.4 Exercises 5.3 Arrange Rows with arrange() 5.3.1 Exercises 5.4 Select Columns with select() 5.4.1 Exercises 5.5 Add new variables with mutate() 5.5.2 Exercises 5.6 Grouped Summaries with summarise() 5.6.7 Exercises 5.7 Grouped Mutates (and Filters) 5.7.1 Exercises This post covers the content and exercises for Ch 5: Data Transformation from R for Data Science. 3.2 First Steps 3.2.4 Exercises 3.3 Aesthetic Mappings 3.3.1 Exercises 3.5 Facets 3.5.1 Exercises 3.6 Geometric Objects 3.6.1 Exercises 3.7 Statistical Transformations 3.7.1 Exercises 3.8 Position Adjustments 3.8.1 Exercises 3.9 Coordinate Systems 3.9.1 Exercises 3.10 The Layered Grammar of Graphics This post covers the content and exercises for Ch 3: Data Visualization from R for Data Science. The chapter teaches the use of the grammar of graphics with ggplot2.
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Patrick O'Malley

I’m a data scientist constantly learning new ways to use data to answer questions. This website is a collection of my learning resources and personal projects.

Data Scientist

Charleston SC