Publications by Paige Biester
Apply 7
Note: I reordered some sections Import your data scooby <- read_excel("../00_data/MyData.xlsx") scrappy <- scooby %>% select(monster_type, series_name, title, run_time, imdb) %>% filter(monster_type %in% c("Mechanical")) Pivoting wide to long form scrappy_wide <- scrappy %>% pivot_wider(names_from = imdb, val...
181 sym Python (6824 sym/11 pcs)
Week 8; Code Along 7
Tidy data Pivoting long to wide form table4a_long <- table4a %>% pivot_longer(cols = c(`1999`, `2000`), names_to = "year", values_to = "cases") table4a_long ## # A tibble: 6 × 3 ## country year cases ## <chr> <chr> <dbl> ## 1 Afghanistan 1999 745 ## 2 Afghanistan 2000 2666...
168 sym Python (3877 sym/20 pcs)
Apply 6
Import Data scooby <- read_excel("../00_data/MyData.xlsx") Introduction Questions Some of my columns of categorical data has data that could be replaced with the word various instead of male, male, female, male, female, etc.. Can I please replace the data with the word various and basically make it a miscellaneous category? I feel like I’d h...
625 sym R (2156 sym/19 pcs) 13 img
Week 7; Code Along 6
Introduction Questions Variation Visualizing Distributions diamonds %>% ggplot(aes(x = cut)) + geom_bar() diamonds %>% ggplot(mapping = aes(x = carat)) + geom_histogram(binwidth = 0.5) diamonds %>% filter(carat < 3) %>% ggplot(aes(x = carat)) + geom_histogram(binwidth = 0.5) diamonds %>% ggplot(aes(x ...
285 sym R (2019 sym/20 pcs) 15 img
Apply 5
Import data ## # A tibble: 603 × 75 ## index series_name network season title imdb engagement date_aired ## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm> ## 1 1 Scooby Doo, … CBS 1 What… 8.1 556 1969-09-13 00:00:00 ## 2 2 Scooby Doo, … CBS 1 A Cl… ...
157 sym
Week 6; Code Along 5
Import data flights ## # A tibble: 336,776 × 19 ## year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time ## <int> <int> <int> <int> <int> <dbl> <int> <int> ## 1 2013 1 1 517 515 2 830 819 ## 2 2013 1 1 533 529 ...
525 sym Python (20211 sym/51 pcs) 1 img
Module 5; Apply 4 Amended
Import data # excel file monster_comparison <- read_excel("../00_data/MyData.xlsx") monster_comparison ## # A tibble: 603 × 75 ## index series_name network season title imdb engagement date_aired ## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm> ## 1 1 Scooby Doo, … CBS 1 What�...
571 sym 1 img
Module 5; Apply 4
Import data # excel file monster_comparison <- read_excel("../00_data/MyData.xlsx") monster_comparison ## # A tibble: 603 × 75 ## index series_name network season title imdb engagement date_aired ## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm> ## 1 1 Scooby Doo, … CBS 1 What�...
509 sym 1 img
Week 5; Code Along 4
Welcome Ch1 Introduction The data science project workflow Prerequisites R RStudio r packages Install the tidyverse package Running R code 1+2 ## [1] 3 Getting help Google Stackoverflow Ch2 Introduction to Data Exploration Ch3 Data Visualization Set up library(tidyverse) data mpg ## # A tibble: 234 × 11 ## manufacturer model d...
1380 sym R (1748 sym/12 pcs) 7 img
Week 4; Apply 3
Import data # excel file data <- read_excel("data/MyData.xlsx") data ## # A tibble: 603 × 75 ## index series_name network season title imdb engagement date_aired ## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dttm> ## 1 1 Scooby Doo, … CBS 1 What… 8.1 556 1969-09-13 00:0...
34 sym 1 img