Publications by Ethan Hicks
Term Project
This is an extension of the tidytuesday assignment you have already done. Complete the questions below, using the screencast you chose for the tidytuesday assigment. Import Data devtools::install_github("thebioengineer/tidytuesdayR") ## Skipping install of 'tidytuesdayR' from a github remote, the SHA1 (bf28f42c) has not changed since last instal...
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Quiz 5
Replicate a case study of marketing analytics: https://www.linkedin.com/learning/the-data-science-of-marketing/cluster-analysis-with-r?u=2232593 Q1 Import data myClusterData <- read.csv("/cloud/project/cluster-r.csv") myClusterData ## Email Behavior.3 ## 1 nisl@adipiscin...
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TidyTuesday
Choose one of David Robinson’s tidytuesday screencasts, watch the video, and summarise. https://www.youtube.com/channel/UCeiiqmVK07qhY-wvg3IZiZQ Instructions You must follow the instructions below to get credits for this assignment. Read the document posted in Moodle before answering the following questions. Write in your own words. Multiple ...
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Quiz 4
Make sure to include the unit of the values whenever appropriate. Q1 Build a regression model to predict life expectancy using gdp per capita. Hint: The variables are available in the gapminder data set from the gapminder package. Note that the data set and package both have the same name, gapminder. library(tidyverse) options(scipen=999) data(...
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Document
In this exercise you will learn to visualize the pairwise relationships between a set of quantitative variables. To this end, you will make your own note of 8.1 Correlation plots from Data Visualization with R. # import data data(SaratogaHouses, package="mosaicData") # select numeric variables df <- dplyr::select_if(SaratogaHouses, is.numeric) ...
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Quiz2_b
In this exercise you will learn to plot data using the ggplot2 package. To answer the questions below, use Chapter 4.3 Categorical vs. Quantitative Data Visualization with R. # Load packages library(tidyquant) library(tidyverse) # Import stock prices stock_prices <- tq_get(c("AAPL", "MSFT"), get = "stock.prices", from = "2020-01-01") stock_pri...
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Quiz2_a
In this exercise you will learn to clean data using the dplyr package. To this end, you will follow through the codes in one of our e-texts, Data Visualization with R. The given example code below is from Chapter 1.2 Cleaning data. ## # A tibble: 87 x 13 ## name height mass hair_color skin_color eye_color birth_year gender ## <chr> <int>...
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Quiz 1
Use the given code below to answer the questions. Q1 Import Netflix stock prices, instead of Apple. Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol. Find ticker symbols from Yahoo Finance. ## Load package library(tidyverse) # for cleaning, plotting, etc library(tidyquant) # f...
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Quiz1_mock
Use the given code below to answer the questions. Q1 Get Walmart stock prices, instead of Apple. Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol for Walmart. You may find the ticker symbol for Microsoft from Yahoo Finance. ## # A tibble: 1,028 x 7 ## date open high...
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Publish Document
Use the given code below to answer the questions. Q1 Get Amazon stock prices, instead of Apple. Hint: Insert a new code chunk below and type in the code, using the tq_get() function above. Replace the ticker symbol for Microsoft. You may find the ticker symbol for Microsoft from Yahoo Finance. ## Load package library(tidyverse) # for cleaning, p...
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