Publications by Kacey Paquette
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) library(lubridate) theme_set(theme_light()) seattle_pets <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data...
939 sym R (1015 sym/2 pcs) 1 img
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...
599 sym R (53174 sym/6 pcs) 1 img
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(...
2304 sym R (1847 sym/2 pcs)
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...
4327 sym
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...
2038 sym 2 img
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 ...
2079 sym 2 img
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? By default, it creates a ggplot2 graph were darker red indicates strong...
1693 sym 2 img
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...
1836 sym R (3017 sym/10 pcs) 4 img
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_pri...
1134 sym R (2890 sym/10 pcs) 6 img
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>...
892 sym R (2926 sym/7 pcs)