Publications by Job Boonstoppel
DAT 3100 Code Along 8
knitr::opts_chunk$set(echo = TRUE) Goal is to predict attrition, employees who are likely to leave the company. Import library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package 'dplyr' was built under R version 4.3.1 ## Warning: package 'stringr'...
1428 sym R (23837 sym/56 pcs) 6 img 3 tbl
DAT 3100 Apply 7
The dataset documents the reasons for CEO departure in S&P 1500 firms from 2000 through 2018. Goal is to predict CEO departure (ceo_dismissal) by using the departures dataset. Import Data library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package ...
809 sym R (50568 sym/64 pcs) 7 img 7 tbl
DAT 3100 Code Along 7
knitr::opts_chunk$set(echo = TRUE) Goal is to predict attrition, employees who are likely to leave the company. Import library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package 'dplyr' was built under R version 4.3.1 ## Warning: package 'stringr'...
874 sym R (23083 sym/56 pcs) 6 img 3 tbl
DAT 3100 Apply 6
The dataset documents the reasons for CEO departure in S&P 1500 firms from 2000 through 2018. Goal is to predict CEO departure (ceo_dismissal) by using the departures dataset. Import Data library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package ...
681 sym R (384268 sym/60 pcs) 4 img 7 tbl
DAT 3100 Code Along 6
knitr::opts_chunk$set(echo = TRUE) Goal is to predict attrition, employees who are likely to leave the company. Import library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package 'dplyr' was built under R version 4.3.1 ## Warning: package 'stringr'...
740 sym R (20646 sym/46 pcs) 3 img 3 tbl
DAT 3100 Apply 5
The dataset documents the reasons for CEO departure in S&P 1500 firms from 2000 through 2018. Goal is to predict CEO departure (ceo_dismissal) by using the departures dataset. Import Data library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package ...
485 sym R (7351 sym/26 pcs) 3 img 3 tbl
DAT 3100 Code Along 5
knitr::opts_chunk$set(echo = TRUE) Goal is to predict attrition, employees who are likely to leave the company. Import library(tidyverse) ## Warning: package 'ggplot2' was built under R version 4.3.1 ## Warning: package 'tidyr' was built under R version 4.3.1 ## Warning: package 'dplyr' was built under R version 4.3.1 ## Warning: package 'stringr'...
629 sym R (12792 sym/22 pcs) 3 img 3 tbl
DAT 3100 Apply 4
Data Import and Cleaning ikea <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv') # Clean the data and address missing values data <- ikea %>% filter(!is.na(height), !is.na(width), !is.na(depth)) %>% mutate(across(is.logical, as.factor)) %>% select(-...1, -link, -old_price, -desig...
1242 sym Python (44736 sym/21 pcs) 2 img 4 tbl
DAT 3100 Code Along 4
This template offers an opinionated guide on how to structure a modeling analysis. Your individual modeling analysis may require you to add to, subtract from, or otherwise change this structure, but consider this a general framework to start from. If you want to learn more about using tidymodels, check out our Getting Started guide. In this example...
1827 sym R (8387 sym/50 pcs) 4 img
DAT 3100 Apply 3
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...
3646 sym Python (19944 sym/26 pcs) 8 img 4 tbl