Publications by Matthew Tillmawitz
Data 607 Web API Assignment
Fetching the NYT Best Sellers List This assignment lends itself better to an actual R Script file but for ease of publishing as well as viewing the results it will be done in R Markdown. Note that this code assumes you have already registered an application and authorized it to call the /lists API as well as stored the API key in your .Renviron. Th...
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Data 624 Assignment 6
9.1 9.1.1 All appear to indicate that the data is white noise as none of the autocorrelations rise to the level of significance, although I will admit the plot of 1000 random numbers is difficult to determine with the naked eye due to the size of the spikes and low critical value. 9.1.2 The critical values decrease with the number of observations...
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Data 607 week 7 assignment
The Data The data is stored in my github in each of the three formats requested. The structure of the data is slightly different in each format due to the differing formatting requirements but is fundamentally the same information. xml_address = "https://raw.githubusercontent.com/Tillmawitz/data_607/refs/heads/main/assignment_7/books.xml" html_addr...
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Data 624 Homework 5
8.1 8.1.1 By using the ETS function with an additive error and no trend or season we model simple exponential smoothing. The optimal α is 0.3221247 and the optimal l[0] is 100646.6 for this model. vic_pigs <- aus_livestock |> filter(Animal == "Pigs", State == "Victoria") |> select(Month, Count) pig_model <- vic_pigs |> model(ETS(Count ~ e...
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Data 607 Project 2 Buffalo Weather Cleanup
Initial Analysis This is a very fun data set to work with, as to the human eye it is a very easily interpretable dataset but it needs some serious coercion to be usable for analysis by a machine. Kevin Havis was kind enough to provide a csv in his post, and deserves credit for said portion. As the raw data can be easily copied from the source into ...
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Data 607 Project 2 Movies Cleanup
Reading the Data The file is actually semicolon separated, but it is simple to read in using read_delim. An initial look shows that overall the data is well structured, but would benefit from collapsing the genre data. raw_data <- read_delim('https://raw.githubusercontent.com/Tillmawitz/data_607/refs/heads/main/project_2/movies.csv', delim = ";") #...
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Data 624 Assignment 4
3.1 3.1.1 Looking at the distrobutions of the predictors in the Glass dataset, we immediately see some interesting scenarios. The Aluminum content appears to be the most normally distributed with Sodium, Calcium, and the Refractive Index being largely normal with a right skew. The Silicon and Magnesium measures also have fairly normal distribution...
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Data 607 Assignment 5
Read in data The data is read in from the csv that was created and can be viewed in the github repository this project resides in. Some filtering is done to remove empty rows in the csv and fill the airline name in. raw_data <- read_csv("flights.csv") marshal <- raw_data |> filter(!if_all(names(raw_data), ~ is.na(.))) |> fill(...1) |> rename(...
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Data 607 Project 1
Reading the data This a project that reads chess tournament results and writes a summarisation to a csv. To begin the data is read in skipping the first several rows that contain what would be the headers. We also want to filter out the dividing rows (those consisting of the “-” character) and trim any leading or trailing whitespace in the colu...
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Data 624 Assignment 3
5.1 5.1.1 The Australian population data is most appropriately modeled using the drift method, and we have forecast the population growth for 4 years. aus_pop <- global_economy |> filter(Country == "Australia") |> select(Country, Year, Population) aus_pop_fc <- aus_pop |> model(RW(Population ~ drift())) |> forecast(h = 4) aus_pop_fc |> ...
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