Publications by Daniel Lee
Apply to your Data 3
Import data # excel file data <- read_excel("data/MyData.xlsx") data ## # A tibble: 100 × 12 ## Brand Model Year Kilometers_Driven Fuel_Type Transmission Owner_Type Mileage ## <chr> <chr> <dbl> <dbl> <chr> <chr> <chr> <dbl> ## 1 Toyo… Coro… 2018 50000 Petrol Manual First 15 ...
40 sym 3 img
Apply it to your data 2
Import stock prices stocks <- tq_get(c("NVDA", "AMD", "MSFT"), get = "stock.prices", from = "2019-09-10", to = "2023-09-10") stocks ## # A tibble: 3,021 × 8 ## symbol date open high low close volume adjusted ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 NVD...
202 sym Python (7208 sym/26 pcs) 2 img
Apply2
Import stock prices stocks <- tq_get(c("GOOGL", "ELF", "COST"), get = "stock.prices", from = "2016-01-01") stocks ## # A tibble: 6,151 × 8 ## symbol date open high low close volume adjusted ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 GOOGL 2016-01-04 38.1 38.1 37.4 3...
212 sym Python (10564 sym/31 pcs) 1 img
Code Along 2
Coding basics R as a calculator 1000 / 10 ## [1] 100 x <- 1000 / 10 # x assignment symbol What’s in a name? Calling functions Use of TAB seq(from = 1, to = 10, by = 1) ## [1] 1 2 3 4 5 6 7 8 9 10 continuation character, + seq(from = 1, to = 10) ## [1] 1 2 3 4 5 6 7 8 9 10 Printing to screen y <- seq(from = 1, to = 10) y...
707 sym Python (19865 sym/58 pcs) 1 img
Code Along 2 ch5
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 ...
370 sym Python (15810 sym/49 pcs) 2 img
Code Along 2 ch4
Coding basics R as a calculator 2000/ 200 * 45 ## [1] 450 # <- 2000 / 200 * 45 # [x] assignment symbol What’s in a name? Calling functions Use of TAB seq (from = 1, to = 10, by = 1) ## [1] 1 2 3 4 5 6 7 8 9 10 continuation character, + seq (from = 1, to = 10) ## [1] 1 2 3 4 5 6 7 8 9 10 Printing to screen y <- seq (fro...
171 sym Python (246 sym/9 pcs)
Apply1
Import stock prices stocks <- tq_get(c("GOOGL", "ELF", "COST"), get = "stock.prices", from = "2016-01-01") stocks ## # A tibble: 6,145 × 8 ## symbol date open high low close volume adjusted ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 GOOGL 2016-01-04 38.1 38.1 37.4 3...
50 sym Python (1055 sym/3 pcs) 1 img
Test Publishing
Do your work here. library(tidyverse) ## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ## ✔ dplyr 1.1.4 ✔ readr 2.1.5 ## ✔ forcats 1.0.0 ✔ stringr 1.5.1 ## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ## ✔ lubridate 1.9.3 ✔ ...
26 sym R (1626 sym/4 pcs)
Code Along 1
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 ...
1379 sym R (1819 sym/13 pcs) 8 img
Apply it to your data 1
Import stock prices stocks <- tq_get(c("NVDA", "AMD", "MSFT"), get = "stock.prices", from = "2019-09-10", to = "2023-09-10") stocks ## # A tibble: 3,021 × 8 ## symbol date open high low close volume adjusted ## <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> ## 1 NVD...
50 sym Python (1090 sym/3 pcs) 1 img