Publications by Derek Howanski
DAT 3100 Apply 1
Let’s build a model for IKEA furniture. We can build a model to predict prices from other furniture characteristics like category and size. Explore data library(tidyverse) ikea <- read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-11-03/ikea.csv") How is the price related to the furniture dimensions...
4047 sym R (6589 sym/24 pcs) 4 img
Code Along 4
Let’s train a model for giant pumpkins, predicting a competition pumpkin’s weight from other characteristics. Import Data pumpkins_raw <- readr::read_csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-10-19/pumpkins.csv") ## Rows: 28065 Columns: 14 ## ── Column specification ────────...
3863 sym R (9363 sym/28 pcs) 4 img
DAT 3100 Apply 2
In this analysis, let’s fit a model to predict the average moving rating from horror movies dating back to the 1950’s. library(tidymodels) ## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ── ## ✔ broom 1.0.5 ✔ rec...
1525 sym R (7286 sym/31 pcs) 3 img
DAT 3100 Code Along 2
In this code along analysis, let’s fit a model to predict Chocolate Ratings from Flavors of Cacao by way of Georgios and Kelsey. library(tidyverse) url <- "https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-18/chocolate.csv" chocolate <- read_csv(url) ## Rows: 2530 Columns: 10 ## ── Column specificatio...
1761 sym R (7742 sym/31 pcs) 4 img
Conclusion
Original Question “Does having a large budget for collegiate sports which has the most participants (both male and female turnout) produce a winning program? Are there any programs that have a small budget and have a high win percentage?” New Question “Does having a large number of sports participants (Male/Female) lead to a higher total...
3383 sym R (8337 sym/34 pcs)
Apply 12
Import your data data("mtcars") mtcars <- as_tibble(mtcars) sports <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-29/sports.csv') ## Rows: 132327 Columns: 28 ## ── Column specification ────────────────────────────────�...
386 sym Python (10096 sym/53 pcs) 1 img
Apply 12
Import your data sports <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-29/sports.csv') ## Rows: 132327 Columns: 28 ## ── Column specification ───────────────────────────────────────────────�...
313 sym 3 tbl
Code Along 12
Ch20 Vectors 1 Introduction 2 Vector basics 3 Important types of automic vectors 4 Using automic vectors sample(10) + 10 ## [1] 14 19 11 12 13 18 20 17 16 15 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 1...
317 sym Python (2605 sym/58 pcs)
Code Along 11
Introduction When 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) ) # Re-scale 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 <...
292 sym
Apply 10
Import your data sports <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-03-29/sports.csv') ## Rows: 132327 Columns: 28 ## ── Column specification ───────────────────────────────────────────────�...
272 sym 2 img