Publications by Job Boonstoppel
DAT 3100 Code Along 3
Goal: to predict the rental prices in the SF rental market Click here for the data. Import Data rent <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv') skimr::skim(rent) Data summary Name rent Number of rows 200796 Number of columns 17 _______________________ Column type frequen...
314 sym R (22971 sym/24 pcs) 5 img 3 tbl
DAT 3100 Apply 2
Click here to read the data manually. Import and Clean Data ikea <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv') skimr::skim(ikea) Data summary Name ikea Number of rows 3694 Number of columns 14 _______________________ Column type frequency: character 7 logical 1 numeric 6...
3240 sym 6 img 4 tbl
DAT 3100 Code Along 2
Goal: to predict the rental prices in the SF rental market Click here for the data. Import Data rent <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv') skimr::skim(rent) Data summary Name rent Number of rows 200796 Number of columns 17 _______________________ Column type frequen...
307 sym R (21562 sym/18 pcs) 4 img 3 tbl
DAT 3100 Apply 1
Click here to read the data manually. Import and Clean Data ikea <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv') skimr::skim(ikea) Data summary Name ikea Number of rows 3694 Number of columns 14 _______________________ Column type frequency: character 7 logical 1 numeric 6...
1422 sym 4 img 4 tbl
DAT 3100 Code Along 1
Goal: to predict the rental prices in the SF rental market Click here for the data. Import Data rent <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv') skimr::skim(rent) Data summary Name rent Number of rows 200796 Number of columns 17 _______________________ Column type frequen...
307 sym 4 img 3 tbl
DAT 3000 Apply 12
Import your data data("mtcars") # My dataset data <- read_excel("myData_charts.xlsx") data %>% skimr::skim() Data summary Name Piped data Number of rows 45090 Number of columns 10 _______________________ Column type frequency: character 1 logical 1 numeric 7 POSIXct 1 ________________________ Group variables None Variable type: charact...
567 sym Python (10522 sym/37 pcs) 2 img 10 tbl
DAT 3000 Apply 11
Import your data # My dataset data(flights) data <- read_excel("myData_charts.xlsx") data %>% skimr::skim() Data summary Name Piped data Number of rows 45090 Number of columns 10 _______________________ Column type frequency: character 1 logical 1 numeric 7 POSIXct 1 ________________________ Group variables None Variable type: characte...
635 sym 9 tbl
DAT 3000 CodeAlong 12
Ch 20 Vectors 1 Introduction Textbook reading. 2 Vector basics Textbook reading. 3 Important types of automic vector Textbook reading. 4 Using automic vectors sample(10) + 10 ## [1] 20 19 15 18 14 16 12 13 17 11 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 short...
462 sym Python (2137 sym/52 pcs)
DAT 3000 CodeAlong 11
Chapter 19 Introduction We should you write a function? # For reproducible 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$a, na.rm = TRUE)) / (max(df$a, na.rm = TRUE) - min(df$a, na.rm = TRUE)) df$b <- (df$b - mi...
301 sym
DAT 3000 Apply 10
Import your data # excel file data <- read_excel("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 2010-01-04 00:00:00 7.62 7.66 7.58 7.64 6.52 4937296...
335 sym 2 img