Publications by Datacamp - Jo Hardin
Foundations of Inference
Welcome to the course! Working with the NHANES data Throughout this chapter, you will use the NHANES dataset from the NHANES R package. The data are collected by the Center for Disease Control (CDC, the national public health institute in the United States) and can be thought of as a random sample of US residents. Before moving on to investigate...
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Modeling with Data in the Tidyverse
Background on modeling for explanation Course overview introduction to modeling: theory and terminology regression: simple linear regression multiple regression model assessment General modeling framework formula \(y = f(\vec{x}) + \epsilon\) Where: \(y\): outcome variable of interest \(\vec{x}\): explanatory/predictor variables \(f()\): fun...
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Case Studies: Network Analysis in R
Exploring your data set Dyads Triads 3-digit code count of vertices connected by a bidirectional symmetric edge count of pairs of vertices connected by an asymmetric edge count of pairs of unconnected vertices Letter code C. Cyclic D. Single edges go Down U. Single edges come Up T. Transitive - if any two vertices in a triad are connected to ...
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Supervised Learning in R: Classification
Classification with Nearest Neighbors Recognizing a road sign with kNN After several trips with a human behind the wheel, it is time for the self-driving car to attempt the test course alone. As it begins to drive away, its camera captures the following image: Can you apply a kNN classifier to help the car recognize this sign? The dataset signs...
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Exploratory Data Analysis in R
Exploring categorical data Contingency table review In this chapter you’ll continue working with the comics dataset introduced in the video. This is a collection of characteristics on all of the superheroes created by Marvel and DC comics in the last 80 years. Let’s start by creating a contingency table, which is a useful way to represent th...
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Introduction to Writing Functions in R
Why you should use functions The arguments to mean() Mean has 3 arguments x: a numeric or date-time vector trim: the proportion of outliers from each end to remove before calculating na.rm: remove before calculating Calling mean() Pass arguments by position mean(numbers, 0.1, TRUE) Pass arguments by name mean(na.rm = TRUE, trim = 0.1, x = numbe...
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Working with Dates and Times in R
Introduction to dates Dates 27th Feb 2013 NZ: 27/2/2013 USA: 2/27/2013 ISO 8601 YYYY-MM-DD values ordered from the largest to smallest unit of time each has a fixed number of digits, must be padded with leading zeros either, no separators for computers, or - in dates 1st of January 2011 -> 2011-01-01 Specifying dates As you saw in the video, ...
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Intermediate Importing Data in R
R Markdown Relational Databases What is a relational database? How to connect? How to read table? Database Management System DBMS Open Source MySQL, PostgreSQL, SQLite Proprietary Oracle Database, Microsoft SQL Server SQL = Structured Query Language Databases in R Different R packages MySQL: RMySQL PostgresSQL: RPostgresSQL Oracle Data...
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Introduction to Importing Data in R
Introduction & read.csv read.csv The utils package, which is automatically loaded in your R session on startup, can import CSV files with the read.csv() function. In this exercise, you’ll be working with swimming_pools.csv[http://s3.amazonaws.com/assets.datacamp.com/production/course_1477/datasets/swimming_pools.csv]; it contains data on swimm...
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Intermediate Data Visualization with ggplot2
Stats with geoms ggplot2, course 2 statistics coordinates facets data visualization best practices statistics layer two categories of functions called from within a geom called independently stats_ non-parametric model is default - loess Smoothing To practice on the remaining layers (statistics, coordinates and facets), we’ll continue w...
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