Publications by Daniel Lee
Quiz4_makeup
Make sure to include the unit of the values whenever appropriate. Q1 Build a regression model to predict wages using education. Hint: The variables are available in the CPS85 data set from the mosaicData package. data(gapminder, package ="gapminder") houses_lm <- lm(lifeExp ~ gdpPercap, data = gapminder) summary(houses_lm) ## ##...
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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") library(tidytuesdayR) library(tidyverse) Description of the data and definition of variables The data is the w...
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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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Quiz 3B
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 3-a
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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Introduction to ggplot2 Quiz2B
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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Quiz 2-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 2a
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. ## Load package library(tidyverse) # for cleaning, plotting, etc ## ── Attaching packages ───────────────────────────────────────────── tidyverse 1.3.0 ── ## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3 ##...
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