Publications by Datacamp - Jo Hardin
Introduction to R
How it works R makes use of the # sign to add comments, so that you and others can understand what the R code is about. Just like Twitter! Comments are not run as R code, so they will not influence your result. For example, Calculate 3 + 4 in the editor on the right is a comment. You can also execute R commands straight in the console. This is a ...
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foundations of probability in R
Flipping coins in R probability is the study of how data can be generated from a model Flipping a coin in R random draw from a binomial distribution two outcomes, 0 (tails) or 1 (heads) rbinom(1, 1, .5) #[1] 1 parameter 1: number of random draws / outcomes from a random variable parameter 2: number of coins we are flipping on each draw para...
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introduction to data visualization with ggplot2
Your instructor - Rick Scavetta email: office@scavetta.academy twitter: @Rick_Scavetta Drawing your first plot To get a first feel for ggplot2, let’s try to run some basic ggplot2 commands. The mtcars dataset contains information on 32 cars from a 1973 issue of Motor Trend magazine. This dataset is small, intuitive, and contains a variety of ...
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building web applications with Shiny in R
Introduction to Shiny Build a “Hello, world” Shiny app “Hello, World” app input (UI) The “Hello, World!!!” app, while enthusiastic, could be more fun. Extend the app and have it wish a hello to a specific person. A user will enter a name and the app will update to wish that name “Hello”. For users to enter text, you’ll have to ...
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case studies - manipulating time series data in R
Review xts fundamentals Flight data flight delay cancellations, 2010 through 2015 Flight data Now that you’re back in the time series mindset, it’s time to get to work! Your task is to understand the travel patterns of tourists visiting Boston. As a first step, you’ve been assigned to explore patterns in flights arriving at Boston’s Log...
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visualizing time series data in R
Refresher on xts and the plot() function a special class of object to handle time series eXtensible Time Series xts = Time Index + Matrix the index is a time object: Date, POSIX Time, TimeDate. chron plot() and plot.xts() plot() plot.xts() <- can be abbreviated to plot() if object is a time series. plot() function - basic parameters The plot....
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forecasting in R
What you will learn Exploring and visualizing time series Simple benchmark methods for forecasting Exponential smoothing and ARIMA models Advanced forecasting methods Measuring forecast accuracy Choosing the best method Time series data series of data observed over time regularly spaced time series Forecasting is estimating how the sequence...
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ARIMA Models in R
Time series regression models Regression: \(Y_i = \beta X_i + \epsilon_i\), where \(\epsilon_i\) is white noise - assumptions about errors: - independent - normally distributed - homoscedastic - i.e., white noise - white noise: - independent normals with common variance - is basic building block of time series AutoRegression: \(X_t = \phi X_{t-1}...
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time series analysis in R
Welcome to the course! Introduction Time Series: A sequence of data in chronological order. Data is commonly recorded sequentially, over time. Time series data is everywhere Time series data Time series data is dated or time stamped in R print(Time_Series) Time series plots plot(Time_Series) time is indexed on horizontal axis observation...
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manipulating time series data with xts and zoo in R
Introducing xts and zoo objects What is xts? eXtensible Time Series An extended zoo object Index + Matrix Times + Observations The xts constructor tzone: time zone of your series unique: forces times to be unique index is in increasing order of time Special xts behavior xts is a matrix with associated times for each observation subsets p...
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