Publications by Alex Makos
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 library(tidyverse) theme_set(theme_light()) wine_ratings <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-05-28/wi...
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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. library(tidyverse) options(scipen=999) data(gapminder, package="gapminder") houses_lm <- lm(lifeExp ~ gdpPercap, data = gapminder) Q2 Is the coefficient of gdpPercap statistically signif...
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Reading on Regression
Instructions You must follow the instructions below to get credits for this assignment. Read the document (example of regression analysis) posted in Moodle before answering the following questions. Write in your own words. Multiple identical answers will get zero. Elaborate your answer. One or two sentence answers won’t get credit. Make sure t...
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Quiz 3
The data set is from a case-control study of smoking and Alzheimer’s disease. The data set has two variables of main interest: smoking a factor with four levels “None”, “<10”, “10-20”, and “>20” (cigarettes per day) disease a factor with three levels “Alzheimer”, “Other dementias”, and “Other diagnoses”. Q1 Describ...
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Mosaic Plot
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 8.5 Mosaic plots from Data Visualization with R. Mosaic charts can display the relationship between categorical variables using: rectangles whose areas represent the proportion of cases for ...
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Quiz 2
# Load packages library(tidyquant) library(tidyverse) # Import stock prices stock_prices <- tq_get(c("WMT", "TGT", "AMZN"), get = "stock.prices", from = "2020-01-01") # Calculate daily returns stock_returns <- stock_prices %>% group_by(symbol) %>% tq_mutate(select = adjusted, mutate_fun = periodReturn, period = "daily") stock_retur...
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Bivariate Graphs
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. Q1 Plot the distribution of daily returns by stock using kernel density plots. Q2 Plot the distribution of daily returns by stock using boxplots. Q3 Based on the boxplot...
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Introduction to ggplot2
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_pr...
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