Publications by rboynton2
CODE ALONG 9
Introduction String basics ## [1] 12 ## [1] "I am" ## [1] "I ; am" ## [1] "Aaron" "John" "Mary" Matching patterns with regular expressions ## Rows: 336,776 ## Columns: 19 ## $ year <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2… ## $ month <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1… ##...
332 sym
CODE ALONG 10
Ch15 Factors Creating factors General Social Survey ## # 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 ## 2 ...
375 sym 3 img
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") wide to long form table4a_long %>% pivot_wider(names_from = year, values_from = cases) ## # A tibble: 3 × 3 ## country `1999` `2000` ## <chr> <dbl> ...
169 sym Python (909 sym/6 pcs)
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") wide to long form table4a_long %>% pivot_wider(names_from = year, values_from = cases) ## # A tibble: 3 × 3 ## country `1999` `2000` ## <chr> <dbl> ...
179 sym Python (2629 sym/16 pcs)
code along 6
Introduction Questions Variation Visualizing distribution] 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 =...
290 sym R (2704 sym/19 pcs) 13 img
apply4
++— title: “Module 5: Apply 4” author: “Riley Boynton” output: html_document: toc: yes pdf_document: default word_document: default editor_options: chunk_output_type: console — Import data ## function (..., list = character(), package = NULL, lib.loc = NULL, ## verbose = getOption("verbose"), envir = .GlobalEnv, overwrite = TRU...
506 sym R (8564 sym/1 pcs) 1 img
APPLY 4
Import data # excel file refugee <- read_excel("../00_data/mydata.xlsx") data State one question What is the relation between year vs number of asylum seekers Plot data ggplot(data = refugee) + geom_point(mapping = aes(x = `The-year-of-data-collection`, y = `The-number-of-asylum-seekers`)) Over the past 7 years the number of asylum seeker...
317 sym 1 img
code along 5 one
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 ...
529 sym Python (20378 sym/55 pcs) 1 img
Code Along 5
Coding basics R as a calculator 1000 / 10 ## [1] 100 x <- 1000 / 10 # x assignment symbol What’s in a name? Calling functions Use of TAB seq(from = 1, to = 10, by = 1) ## [1] 1 2 3 4 5 6 7 8 9 10 continuation character, + seq(from = 1, to = 10) ## [1] 1 2 3 4 5 6 7 8 9 10 Printing to screen y <- seq(from = 1, to = 10) ...
171 sym Python (231 sym/9 pcs)
code along 5 one
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 ...
529 sym Python (20378 sym/55 pcs) 1 img