Publications by Brett McGillivary
Neural Network
library(readr) library(dplyr) ## ## Attaching package: 'dplyr' ## The following objects are masked from 'package:stats': ## ## filter, lag ## The following objects are masked from 'package:base': ## ## intersect, setdiff, setequal, union library(nnet) library(neuralnet) ## ## Attaching package: 'neuralnet' ## The following object is m...
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Assignment 7 BAN340 - McGillivary
I decided to use markdown so I could better comment on the different code chunks and have the results be visible underneath. I did notice to errors in the code chuncks below which is probably why I am assuming you wanted it in a pdf form. # read the data into r and convert the brand to a factor cars <- read.csv("~/RstudioProjects/BAN340/Data sets...
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Assignment 5
This week was a nice change and tables are very useful. Americas Europe statistic Jamaica Trinidad and Tobago Iceland Montenegro population 2531311 1138101 271192 692651 life expectancy 72 69 79 75 Note: Population and life expectancy for the two least populous countries in Americas and Europe. ...
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Assignment 4 - Spatial data
I really threw everything at the wall this week and spent a lot of time and research prepping the data and creating the functions to make them work. I had to combine the death/incidence data and the lat/long centers of each state for the first plot, which I vow to never do again. I jumped to deep in the tank this week and this took way longer tha...
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Assignment 3 BAN350
I did a comparrison by distributor and both total box office and weekend box office I never realized how difficult it can be to utilize temporal data. I was tipped off to do.call and I can see it’s usefulness. I tried a number of different methods to get the left axis to show as dollar amounts but while I was always able to get it to coerce it ...
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Assignment 4 BAN340
Introduce cars data set cars = read.csv("cars.txt", header=TRUE, sep = ',') Convert time.to.60 seconds to time.to.60 minutes in new columns cars$time.to.60.min <- cars$time.to.60/60 Delete columns hp, mpg, year, brand cars$mpg <- NULL cars$hp <- NULL cars$year <- NULL cars$brand <- NULL Min-Max transformation for cars$weightlbs cars$weightlbsminm...
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Assignment 2 (Categorical Data)
Overall I was not particularly happy and I struggled a lot with getting an actionable visual out of this. Visualizing categorical data is so much more difficult and I am not creative enough with R yet to know what is possible. ...
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Assignment_1
## muenster Unintentional Suicide Homicide ## muenster 1.0000000 0.86434680 0.9555510 -0.25343850 ## Unintentional 0.8643468 1.00000000 0.9304585 0.02860829 ## Suicide 0.9555510 0.93045853 1.0000000 -0.23414469 ## Homicide -0.2534385 0.02860829 -...
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Assignment 3
I used a lot of libraries with this. I spent a lot of time playing with different things and by the end I couldn’t remember which ones I needed, so I kept them all knitr::opts_chunk$set(echo = TRUE) # load twitter library - the rtweet library is recommended now over twitteR library(rtweet) # plotting and pipes - tidyverse! library(ggplot2) libr...
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Final_project_summary
I enjoyed the Intoduction to data course for a number of reasons. As someone who is focusing on Business Analytics I found the information about sampling very helpful. Undersatanding the differences in sampling techniques will be important to ensure you collect data in a way that will produce valid results. The unit also gets into more detail abo...
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