Publications by Daniel Lee
CA3
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0 ## ...
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Apply4
# Load packages library(tidyverse) library(tidyquant) 1 Import stock prices of your choice # Choose stocks symbols <- c("AAPL", "DIS", "GE", "NKE", "SBUX") prices <- tq_get( x = symbols, get = "stock.prices", from = "2012-01-01", to = "2017-01-01") 2 Convert prices to returns by quarterl...
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Code along 3
library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ✔ tidyr 1.3.0 ## ...
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CodeAlong4
# Load packages # Core library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ...
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Week 5 - Code Along 4
Welcome Ch1 Introduction The data science project workflow Prerequisites R RStudio r packages Install the tidyverse package Running R code Getting help Google Stackoverflow Ch2 Introduction to Data Exploration Ch3 Data Visualization Set up library(tidyverse) ## ── Attaching packages ─────────────────...
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Code Along 4
Welcome Ch1 Introduction The data science project workflow Prerequisites R RStudio r packages Install the tidyverse package Running R code 1+2 ## [1] 3 Getting help Google Stackoverflow Ch2 Introduction to Data Exploration Ch3 Data Visualization Set up library(tidyverse) data mpg ## # A tibble: 234 × 11 ## manufacturer model d...
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Apply Data 4
Import data # excel file data <- read_excel("../02_module5/beesdata.xlsx") data State one question Do larger total colonies lose more percentage of colonies than smaller total colonies? (Hypothesis) Larger colonies will lose a larger percentage of colonies. Plot data ggplot(data = data) + geom_smooth(mapping = aes(x = colony_n, y = colo...
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Apply it to your Data 1: Superbowl Commercial
Superbowl commercials: Build a regression model to predict the Youtube like count (like_count). Use the youtube dataset. Import Data youtube <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-02/youtube.csv') ## Rows: 247 Columns: 25 ## ── Column specification ────────�...
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Code along 4
# Load packages # Core library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.3 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.3 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ...
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Week 4: Real World Applications 4
What is happening to your client business’s profit margin? Make your argument based on your analysis of the given charts. My answer is… Grappoone outputPrice = Consumer Price Index for All Urban Consumers: New Vehicles in U.S. City Average inputPrice = Producer Price Index by Industry: New Car Dealers: New Vehicle Sales Bank of New Hampshire...
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