Publications by Raven Silver
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()) horror_movies_raw <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2019/2019-10-...
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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@adipisci...
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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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Quiz3
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”. library(ti...
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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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Correlation
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. Q1 What factors have strong positve correlation with home price? The living area and number of bathrooms represent a strong positive cor...
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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. Hint: See the code in 4.3.2 Grouped kernel density plots. Q2 Plot the distribution of daily...
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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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Clean data
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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