Publications by Daniel Lee
Codealong12
# Load packages # Core library(tidyverse) library(tidyquant) library(readr) # Time series library(lubridate) library(tibbletime) # modeling library(broom) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. five stocks: “SPY”, “EFA”, �...
657 sym R (15829 sym/23 pcs) 2 img
Code Along 12
# Load packages # Core library(tidyverse) library(tidyquant) library(readr) # Time series library(lubridate) library(tibbletime) # modeling library(broom) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. five stocks: “SPY”, “EFA”, “IJS”, �...
597 sym R (10944 sym/22 pcs) 2 img
codealong12FA
# Load packages # Core library(tidyverse) library(tidyquant) library(readr) # Time series library(lubridate) library(tibbletime) # modeling library(broom) Goal Examine how each asset contributes to portfolio standard deviation. This is to ensure that our risk is not concentrated in any one asset. five stocks: “SPY”, “EFA”, “IJS”, �...
607 sym R (14259 sym/30 pcs) 4 img
Code Along 11 FA
# Load packages # Core library(tidyverse) library(nycflights13) library(lubridate) Introduction When should you write a function # For reproducable work set.seed(1234) # Create a data frame df <- tibble::tibble( a = rnorm(10), b = rnorm(10), c = rnorm(10), d = rnorm(10) ) # Rescale each column df$a <- (df$a - min(df$a, na.rm = TRUE)) /...
290 sym R (2433 sym/26 pcs)
Apply11DA
Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_un...
517 sym 4 tbl
Codealong12DA
Ch20 Vectors 1 Introduction 2 Vector basics 3 Important types of atomic vector 4 Using atomic vectors sample(10) + 10 ## [1] 11 19 14 13 12 17 18 15 16 20 1:10 + 1:2 ## [1] 2 4 4 6 6 8 8 10 10 12 1:10 + 1:3 ## Warning in 1:10 + 1:3: longer object length is not a multiple of shorter object ## length ## [1] 2 4 6 5 7 9 8 10 12 11...
360 sym Python (2134 sym/52 pcs)
CodeAlong11
# For Reproducible Work set.seed(1234) df <- tibble::tibble( a = rnorm(10), b = rnorm(10), c = rnorm(10), d = rnorm(10) ) # Rescale Each Column (df$a - min(df$a, na.rm = TRUE)) / (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE)) ## [1] 0.3319492 0.7647291 1.0000000 0.0000000 0.8089534 0.8313814 0.5162933 ## [8] 0.52...
82 sym
Apply 11
Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_un...
524 sym R (3121 sym/18 pcs) 4 tbl
Code Along 11 DA
# Load packages # Core library(tidyverse) library(nycflights13) library(lubridate) Introduction When should you write a function # For reproducable work set.seed(1234) # Create a data frame df <- tibble::tibble( a = rnorm(10), b = rnorm(10), c = rnorm(10), d = rnorm(10) ) # Rescale each column df$a <- (df$a - min(df$a, na.rm = TRUE)) /...
290 sym R (2433 sym/26 pcs)
Apply it 11
Import your data data(flights) flights %>% skimr::skim() Data summary Name Piped data Number of rows 336776 Number of columns 19 _______________________ Column type frequency: character 4 numeric 14 POSIXct 1 ________________________ Group variables None Variable type: character skim_variable n_missing complete_rate min max empty n_u...
526 sym Python (2727 sym/19 pcs) 4 tbl