Publications by Max Haussmann
DAT3100_Apply2
Goal: The goal is to predict the Youtube like count. Click here for the data. Import Data youtube <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-02/youtube.csv') ## Rows: 247 Columns: 25 ## ── Column specification ──────────────────────────...
561 sym R (22458 sym/30 pcs) 6 img 11 tbl
DAT3100//Apply1
Goal: The goal is to predict the Youtube like count. Click here for the data. Import Data youtube <- read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-02/youtube.csv') ## Rows: 247 Columns: 25 ## ── Column specification ──────────────────────────...
410 sym 5 img 5 tbl
DAT3100_CodeAlong1
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') ## Rows: 200796 Columns: 17 ## ── Column specification ────────────────────────�...
309 sym 4 img 3 tbl
Apply to your Data 12
Import your data data("mtcars") mtcars <- as_tibble(mtcars) library(readxl) # excel file mydata <- read_excel("../00_data/data/myData.xlsx") mydata ## # A tibble: 9,355 × 12 ## work_year job_title job_category salary_currency salary salary_in_usd ## <dbl> <chr> <chr> <chr> <dbl> <dbl> ## 1 ...
479 sym R (8091 sym/35 pcs) 1 img
Code Along 12
Ch 20 Vectors 1 Introduction 2 Vector basics 3 Important types of atomic vector 4 Using atomic vectors sample(10) + 10 ## [1] 13 14 12 17 20 15 11 19 18 16 1.10 + 1:2 ## [1] 2.1 3.1 1.10 + 1:3 ## [1] 2.1 3.1 4.1 data.frame(a=1:10, b=1:2) ## a b ## 1 1 1 ## 2 2 2 ## 3 3 1 ## 4 4 2 ## 5 5 1 ## 6 6 2 ## 7 7 1 ## 8 8 2 ## 9 9...
330 sym Python (1647 sym/41 pcs)
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...
533 sym R (4218 sym/27 pcs) 4 tbl
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) ) # 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$b - min(df$b, na...
293 sym
Apply to your Data 10
Import your data library(readxl) # excel file data <- read_excel("../00_data/data/myData.xlsx") data ## # A tibble: 9,355 × 12 ## work_year job_title job_category salary_currency salary salary_in_usd ## <dbl> <chr> <chr> <chr> <dbl> <dbl> ## 1 2023 AI Architect Machine Learning… USD ...
336 sym R (5851 sym/19 pcs) 2 img
Code Along 10
Chapter 15 Factors Creating factors General Social Survey gss_cat ## # A tibble: 21,483 × 9 ## year marital age race rincome partyid relig denom tvhours ## <int> <fct> <int> <fct> <fct> <fct> <fct> <fct> <int> ## 1 2000 Never married 26 White $8000 to 9999 Ind,near … Prot… Sout… ...
436 sym 4 img
Apply to your Data 9
Import your data library(readxl) # excel file data <- read_excel("../00_data/data/myData.xlsx") data ## # A tibble: 9,355 × 12 ## work_year job_title job_category salary_currency salary salary_in_usd ## <dbl> <chr> <chr> <chr> <dbl> <dbl> ## 1 2023 AI Architect Machine Learning… USD ...
112 sym R (3372 sym/14 pcs)