Publications by Job Boonstoppel
DAT 3000 Code Along 5 Chapter 4
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 ...
542 sym Python (18927 sym/51 pcs) 1 img
DAT 3000 Code Along 5 Chapter 5
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 ...
542 sym Python (18927 sym/51 pcs) 1 img
DAT 3000 - Apply 4
Import data ## # A tibble: 45,090 × 10 ## stock_symbol date open high low close adj_close volume ## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2010-01-04 00:00:00 7.62 7.66 7.58 7.64 6.52 493729600 ## 2 AAPL 2010-01-05 00:00:00 7.66 7.70 7.62 7.6...
623 sym 1 img
DAT 3000 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 ...
1377 sym R (1745 sym/12 pcs) 7 img
Test publishing
Do your work here. library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.2 ✔ readr 2.1.4 ## ✔ forcats 1.0.0 ✔ stringr 1.5.0 ## ✔ ggplot2 3.4.2 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.2 ✔ ...
26 sym R (1626 sym/4 pcs)
DAT 3000 - Apply 3
Import data # csv file #data <- read.csv("data/Salaries.csv") #data # excel file data <- read_excel("data/myData_charts.xlsx") data ## # A tibble: 45,090 × 10 ## stock_symbol date open high low close adj_close volume ## <chr> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 AAPL 2010...
34 sym 1 img
Apply to your data 13
# Load packages # Core library(tidyverse) library(tidyquant) # Source function source("../00_scripts/simulate_accumulation.R") 1 Import stock prices Revise the code below. Replace symbols with your stocks. Replace the from and the to arguments to date from 2012-12-31 to present. symbols <- c("TSLA", "META", "XOM", "AAPL", "PG", "AMZN") prices...
1866 sym R (4790 sym/22 pcs) 1 img
Code Along 13
# Load packages # Core library(tidyverse) library(tidyquant) # time series library(timetk) Goal Simulate future portfolio returns five stocks: “SPY”, “EFA”, “IJS”, “EEM”, “AGG” market: “SPY” from 2012-12-31 to 2017-12-31 1 Import stock prices symbols <- c("SPY", "EFA", "IJS", "EEM", "AGG") prices <- tq_get(x = symbol...
449 sym R (8601 sym/34 pcs) 2 img
Apply to your data 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...
555 sym 6 tbl
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”, �...
687 sym R (15492 sym/23 pcs) 2 img