Publications by Derek Howanski
Code Along 10
Chapter 15 Introduction 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...
434 sym 4 img
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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 ───────────────────────────────────────────────�...
113 sym
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 %>% ...
364 sym
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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 ───────────────────────────────────────────────�...
309 sym
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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 ───────────────────────────────────────────────�...
167 sym Python (6744 sym/12 pcs)
Code Along 7
Tidy data Pivoting long to wide form table4a ## # A tibble: 3 × 3 ## country `1999` `2000` ## * <chr> <int> <int> ## 1 Afghanistan 745 2666 ## 2 Brazil 37737 80488 ## 3 China 212258 213766 table4a_long <- table4a %>% pivot_longer(cols = c(`1999`, `2000`), names_to = "year", ...
170 sym Python (4068 sym/22 pcs)
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 (5604 sym/43 pcs)
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Import 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 ─────────────────────────────────────────────────�...
275 sym 2 img
Code Along 6
Introduction Question 2 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...
287 sym R (1940 sym/19 pcs) 15 img
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Import data ## # A tibble: 132,327 × 28 ## year unitid instit…¹ city_…² state…³ zip_t…⁴ class…⁵ class…⁶ class…⁷ ef_ma…⁸ ## <dbl> <dbl> <chr> <chr> <chr> <chr> <dbl> <chr> <chr> <dbl> ## 1 2015 100654 Alabama… Normal AL 35762 2 NCAA D… <NA> 1923 ## 2 2015 100654 A...
158 sym