Publications by Michael Ripley
Quiz 1 makeup
Use the given code below to answer the questions. ## Load package library(tidyverse) # for cleaning, plotting, etc library(tidyquant) # for financial analysis ## Import data stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01") stocks ## Visualize stocks %>% ggplot(aes(x = date, y = close)) + geom_line() Q1 Import Microsoft s...
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quiz1-redo
Use the given code below to answer the questions. ## Load package library(tidyverse) # for cleaning, plotting, etc library(tidyquant) # for financial analysis ## Import data stocks <- tq_get("AAPL", get = "stock.prices", from = "2016-01-01") stocks ## Visualize stocks %>% ggplot(aes(x = date, y = adjusted)) + geom_line() Q1 Import Netflix ...
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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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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. ## # A tibble: 74 x 9 ## # Groups: symbol [2] ## symbol date open high low close volume adjusted daily.returns ## <chr> <date> <dbl> <dbl> <dbl> <d...
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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) ## Loading required package: lubridate ## ## Attaching package: 'lubridate' ## The following object is masked from 'package:base': ## #...
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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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Quiz 1
Use the given code below to answer the questions. ## # A tibble: 1,032 x 7 ## date open high low close volume adjusted ## <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 2016-01-04 103. 105. 102 105. 67649400 98.2 ## 2 2016-01-05 106. 106. 102. 103. 55791000 95.8 ## 3 2016-01-06 101. 102. 99.9 10...
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