Publications by Daniel Lee
Week 3 Apply to data
Import stock prices stocks <- tq_get(c("AAPL", "NFLX", "AMZN"), get = "stock.prices", from = "2016-01-01") stocks ## # A tibble: 5,823 × 8 ## symbol date open high low close volume adjusted ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2016-01-04 25.7 26.3 ...
83 sym Python (2879 sym/7 pcs) 1 img
Apply3
# Load packages library(tidyverse) library(tidyquant) 1 Get stock prices and convert to returns Ra <- c("NKE", "AAPL", "SBUX") %>% tq_get(get = "stock.prices", from = "2022-01-01") %>% group_by(symbol) %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "monthl...
203 sym R (3054 sym/11 pcs)
Codealong3_Performace Analysis
# Load packages 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 ✔ tid...
142 sym R (5205 sym/12 pcs)
Apply3
# Load packages library(tidyverse) library(tidyquant) 1 Get stock prices and convert to returns Ra <- c("AAPL", "TSLA", "NFLX") %>% tq_get(get = "stock.prices", from = "2010-01-01") %>% group_by(symbol) %>% tq_transmute(select = adjusted, mutate_fun = periodReturn, period = "month...
203 sym R (3082 sym/11 pcs)
Week 4: Real World Applications 3
Economic Dashboard Market Indicators Economic Indicators What is your reading of the economy? Make your argument based on your analysis of the given charts. Discuss timing and depth of changes in the economic data relative to recessions in at least 50 words....
274 sym
DAT3100: Apply 1 - Superbowl commercials
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 ────────�...
352 sym 1 img 5 tbl
Apply 2
## ── 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 ## ✔ purrr 1.0.2...
2142 sym 4 img
Apply 1
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 ## ...
70 sym R (7469 sym/32 pcs) 1 img
Document
Import data flights ## # A tibble: 336,776 × 19 ## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time ## <int> <int> <int> <int> <int> <dbl> <int> <int> ## 1 2013 1 1 517 515 2 830 819 ## 2 2013 1 1 533 529 ...
512 sym Python (19789 sym/51 pcs) 1 img
Apply to data 2
Import stock prices stocks <- tq_get(c("AAPL", "NFLX", "AMZN"), get = "stock.prices", from = "2016-01-01") stocks ## # A tibble: 5,814 × 8 ## symbol date open high low close volume adjusted ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2016-01-04 25.7 26.3 ...
50 sym Python (1086 sym/3 pcs) 1 img