Publications by Spencer Murrin
CodeAlong__3
Goal: To predict the rental prices in the SF rental market Click {here for the data} (https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-07-05). Import Data rent <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv') ## Rows: 200796 Columns: 17 ## ── Colum...
400 sym R (23521 sym/27 pcs) 5 img 3 tbl
Apply__2
Import Data chocolate <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-18/chocolate.csv') ## Rows: 2530 Columns: 10 ## ── Column specification ────────────────────────────────────────────────�...
114 sym R (25215 sym/20 pcs) 1 img 3 tbl
Code__Along__2
Goal: To predict the rental prices in the SF rental market Click {here for the data} (https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-07-05). Import Data rent <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv') ## Rows: 200796 Columns: 17 ## ── Colum...
396 sym R (50546 sym/24 pcs) 4 img 3 tbl
Apply__1
Import Data chocolate <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-01-18/chocolate.csv') ## Rows: 2530 Columns: 10 ## ── Column specification ────────────────────────────────────────────────�...
440 sym 1 img 3 tbl
CodeAlong__1
Goal: To predict the rental prices in the SF rental market Click {here for the data} (https://github.com/rfordatascience/tidytuesday/tree/master/data/2022/2022-07-05). Import Data rent <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2022/2022-07-05/rent.csv') ## Rows: 200796 Columns: 17 ## ── Colum...
392 sym 4 img 3 tbl
Apply_12
Import your data data("mtcars") mtcars <- as_tibble(mtcars) # Apply Data choc_data <- read_excel("../01_module4/data/MyData.xlsx") choc_data ## # A tibble: 2,657 × 10 ## REF Compan…¹ Compa…² Revie…³ Count…⁴ Speci…⁵ Cocoa…⁶ Ingre…⁷ Most.…⁸ Rating ## <dbl> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> ...
366 sym Python (7557 sym/34 pcs) 2 img
CodeAlong_12
Ch20 Vectors Introduction Vector Basics Important Types of Automic Vectors Using Automic Vectors sample(10) + 10 ## [1] 17 12 15 18 20 13 11 19 14 16 1:10 + 1:2 ## [1] 2 4 4 6 6 8 8 10 10 12 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 1 ## 10 ...
351 sym Python (2021 sym/49 pcs)
Apply_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...
529 sym 4 tbl
CodeAlong_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....
290 sym
Apply_10
Import your data data <- read_excel("../01_module4/data/MyData.xlsx") data ## # A tibble: 2,657 × 10 ## REF Compan…¹ Compa…² Revie…³ Count…⁴ Speci…⁵ Cocoa…⁶ Ingre…⁷ Most.…⁸ Rating ## <dbl> <chr> <chr> <dbl> <chr> <chr> <dbl> <chr> <chr> <dbl> ## 1 2454 5150 U.S.A. 2019 Tanzan… Koko...
285 sym 3 img