Publications by Paige Biester
Apply 12
Import your data data("mtcars") mtcars < - as_tibble(mtcars) ## mpg cyl disp hp drat wt qsec vs am gear ## Mazda RX4 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## Mazda RX4 Wag FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE ## Datsun 710 FALSE FALSE F...
369 sym Python (11332 sym/35 pcs) 2 img
Week 13; Code Along 12
Ch20 Vectors 1 Introduction 2 Vector basics 3 Important types of atomic vector 4 Using atomic vectors sample(10) + 10 ## [1] 13 17 19 20 16 18 11 12 15 14 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 12 1...
360 sym Python (2238 sym/52 pcs)
Apply 11
FYI, I did this whole assignment under the belief that we were supposed to be using our own data the entire time. Import your data scooby <- read_excel("../00_data/MyData.xlsx") scooby %>% skimr::skim() Data summary Name Piped data Number of rows 603 Number of columns 75 _______________________ Column type frequency: character 58 logica...
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Week 12; Code Along 11
Ch19 Functions 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)) d...
309 sym
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Import your data Chapter 15 Create a factor ## # A tibble: 11 × 2 ## network n ## <chr> <int> ## 1 ABC 281 ## 2 Adult Swim 1 ## 3 Boomerang 74 ## 4 CBS 49 ## 5 Cartoon Network 84 ## 6 Syndication 3 ## 7 TB...
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Week 10, Code Along 9
Ch15 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 $8000 to 9999 Ind,near … Prot… Sout… 12 ...
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Apply 9
Import your data scooby <- read_excel("../00_data/MyData.xlsx") Chapter 14 Tools Detect matches scooby$monster_gender ## [1] "Male" ## [2] "Male" ...
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Week 10; Code Along 9
Introduction String basics chac_data <- "I'm 'very' hungry." stringr::str_length("I am hungry.") ## [1] 12 stringr::str_c(c("I", " am"), collapse = "") ## [1] "I am" stringr::str_c("I", " am", sep = " ;") ## [1] "I ; am" str_sort(c("John", "Mary", "Aaron")) ## [1] "Aaron" "John" "Mary" Matching patterns with regular expressions flights %>% gli...
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Week 9; Apply 8
This was probably my favorite assignment we’ve done yet. Just thought you should know. 1. Import your data Import two related datasets from TidyTuesday Project. details <- read_excel("../00_data/BGDetails.xlsx") ratings <- read_excel("../00_data/BGRatings.xlsx") 2. Make data small Describe the two datasets: Data 1 Details Columns: year, nam...
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Module 9; Code Along 8
Introduction nycflights13 Keys Mutating joins Inner join x <- tribble( ~key, ~val_x, 1, "x1", 2, "x2", 3, "x3" ) y <- tribble( ~key, ~val_y, 1, "y1", 2, "y2", 4, "y3" ) inner_join(x, y) ## Joining with `by = join_by(key)` ## # A tibble: 2 × 3 ## key val_x val_y ## <dbl> <chr> <chr> ## 1 ...
205 sym Python (5397 sym/43 pcs)