Publications by Dr. Chelsey Hill
Deep Learning for Text with Covariates
Introduction In real-world data analysis, you will want to analyze both text and non-text data to be able to classify observations. To be able to do this, you will use a multi-input neural network. Preliminary library(tm) # text mining library(caret) # classification library(keras) # deep learning library(tensorflow) # deep learning Cleaning...
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Association Analysis
Preliminary We will use the arules package in the lesson that follows. install.packages("arules") Next, we load the arules package for use. library(arules) In the lesson that follows, we use the Groceries data set from the arules package, which contains 1 month (30 days) of real-world point-of-sale transaction data from a typical local grocery ou...
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Data Quality
Preliminary We will use the DescTools and caret packages to address quality issues with our data. Both packages have very useful and comprehensive vignettes which can be accessed through the help documentation in RStudio. We load the packages for use in the R session using the library() function. library(DescTools) library(caret) In the lesson t...
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Introduction to RStudio: Vectors & Dataframes
Vectors Creating Vectors To create a vector, we concatenate (combine) values using the c() function. First, let’s create some objects (named variables) x <- 2 z <- 4 We can combine the x and z variables to create a vector named b. b <- c(x, z) b ## [1] 2 4 We can add the valuesh values 5 and 8 to the end of the b vector (overwriting the orig...
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Introduction to RStudio: The Basics
Introduction R Software is an integrated suite of open-source software facilities for data manipulation, calculation and graphical display. RStudio is an integrated development environment (IDE) that allows us to interact with R more easily. Below is an example of an RStudio session. Example RStudio Session RStudio has a user-friendly interface...
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Introduction to RStudio: Working Directory, Import & Export
The Working Directory The working directory is the folder on your local computer that you would like to interact with (read and write to) during your RStudio session. The getwd() function can be used to obtain your current working directory. getwd() ## [1] "C:/Users/chh35/OneDrive - Drexel University/Teaching/Drexel/STAT 331/Course Content/Week 1...
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Introduction to RStudio: Plotting & Packages
Plotting: The Basics The plot() function can be used to create basic plots in R. Plots generated in RStudio will display in the ‘Plots’ panel. The window size can be increased to increase the plot size or decreased to decrease the plot size. The type of plot that is produced will depend on the type of data provided to the function. In the exa...
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Data Preprocessing
Preliminary We will use the DescTools and caret packages for preprocessing our data. If you do not already have the caret package installed, you will first install the package using the install.packages() function. Both packages have very useful and comprehensive vignettes which can be accessed through the help documentation in RStudio. install.p...
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Data: Types, Exploration & Visualization
Preliminary We will use the DescTools package to explore and work with our data. If you do not already have these packages installed, you will first install the packages using the install.packages() function. The package has a very useful and comprehensive vignette which can be accessed through the help documentation in RStudio. install.packages(...
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k-Means Cluster Analysis (kMC)
Preliminary We will use the DescTools and caret packages, which were previously installed. In addition, we use the ggplot2 and factoextra for plotting, as well as the cluster and fpc packages. Next, we load all of the necessary libraries for use in the session. library(caret) library(DescTools) library(ggplot2) library(cluster) library(factoe...
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