coRps R Building Blocks Series - 10 - Functions/Iteration

May 16, 2022
coRps R Building Blocks Series - 10 - Functions/Iteration
Part 10 of the coRps R Building Blocks Series (RBBS)

https://usaid-oha-si.github.io/corps/rbbs/2022/05/16/rbb-data-functions-iteration-part1.html

https://usaid-oha-si.github.io/corps/rbbs/2022/05/23/rbb-data-functions-iteration-part2.html

https://usaid-oha-si.github.io//learn/categories/#rbbs

Transcript

2. Learning Objectives • Understand the components of a function • Know the benefits of creating a function • Gain an understanding of the loop vs functional programming (purrr) and their differences
3. FUNCTIONS
4. mutate pivot_wider read_csv unite str_detect ungroup tribble summarize mdy slice_head na_if last_col split filter distinct count bind_rows map_dfr str_detect str_detect crossing drop_na across fill nest drive_ls last_col read_delim excel_sheets drive_auth fct_reorder split What are some tidyverse functions you know?
5. What is a function?
6. basic_fcn <- function(x) { #cool things happen … } Anatomy function name body arguments
7. shake <- function() { print(“show treat \n close fist around treat”) print(“hand out, palm up”) Sys.sleep(2) if(dog_shake == TRUE){ print(“give treat”) } else { print(“oops”) } } Anatomy
8. trick <- function(type) { intro_treat() print_actions(type) outcome_treat() } Anatomy
9. answer_to_the_universe.R Why write a function?
10. answer_to_the_universe.R avoid repeated code blocks Why write a function?
11. answer_to_the_universe.R { } avoid repeated code blocks Why write a function?
12. answer_to_the_universe.R { } Why write a function? explain_existance() easier to understand code
13. answer_to_the_universe.R minimize missing error fixes Why write a function?
14. answer_to_the_universe.R minimize missing error fixes { } Why write a function?
15. answer_to_the_universe.R minimize copy and paste errors Why write a function? uh oh
16. answer_to_the_universe.R minimize copy and paste errors Why write a function? ( ){ }
17. answer_to_the_universe.R { } reuse function elsewhere deep_thought.R Why write a function?
18. You should consider writing a function whenever youʼve copied and pasted a block of code more than twice (i.e. you now have three copies of the same code). “ When should I write a function?
19. Process • Write the script that solves your problem for a real scenario • Wrap the code in function(){script} to save it • Add the name of the real object(s) as an argument(s), function(df, x){script} • Name the function resolve_issue <- function(df, x){ script } • Use the function df_fix <- resolve_issue(df_treat, “Jupiter”)
20. Naming functions guidelines • Ideally, the name of your function will be short, but clearly evoke what the function does • Generally, function names should be verbs, and arguments should be nouns • If your function name is composed of multiple words, I recommend using ʻsnake_caseʼ • If you have a family of functions that do similar things, make sure they have consistent names and arguments. “
21. Functions in Action Goal - Across different sectors, I want to know the overall target achievement Target achievement = cumulative results / targets
22. Functions in Action df_sect1 %>% mutate(achievement = cumulative / targets, achv_disp = percent(achievement, 1))
23. Functions in Action achv <- function(df){ df %>% mutate(achievement = cumulative / targets, achv_disp = percent(achievement, 1)) %>% arrange(desc(targets)) } achv(df_sect1) achv(df_sect2)
24. Functions in Action achv <- function(df){ if(“targets” %in% names(df) || “cumulative %in% names(df) stop(“Missing targets or cumulative column”) df %>% mutate(achievement = cumulative / targets, achv_disp = percent(achievement, 1)) %>% arrange(desc(targets)) }
25. Functions in Action agg_achv <- function(df, low_lvl = .75){ sect_achv <- df %>% summarise(cumulative = sum(cumulative), achievement = sum(cumulative) / sum(targets)) if(sect_achv$achievement < low_lvl){ usethis::ui_warn("Sector only achieved {comma(sect_achv$cumulative)} results, only {percent(sect_achv$achievement,1)} of target achievement") } else { usethis::ui_info("{percent(sect_achv$achievement,1)} of sector's targets achieved") } }
26. ITERATION
27. One tool for reducing duplication is functions, which reduce duplication by identifying repeated patterns of code and extract them out into independent pieces that can be easily reused and updated. Another tool for reducing duplication is iteration, which helps you when you need to do the same thing to multiple inputs: repeating the same operation on different columns, or on different datasets. “ Iterate to reduce duplication
28. Copy and paste method What is the mean value in Sector 3 across the the three indicators, HTS_TST, HTS_TST_POS, and TX_NEW? mean(df_sect3$hts_tst) mean(df_sect3$hts_tst_pos) mean(df_sect3$tx_new)
29. Letʼs iterate with for loop #remove the facility (character) column df <- df_sect3[-1] output <- vector("double", ncol(df)) # 1. output for (i in seq_along(df)) { # 2. sequence output[[i]] <- mean(df[[i]]) # 3. body } > output # 4. print values [1] 463.8 33.5 29.7 critical to allocate sufficient space for the output determine what to loop over actual code being applied output stored/saved to vector we can print
30. Loop options 1. Loop over numeric indices : for (i in seq_along(xs)) 2. Loop over the elements: for (x in xs) 3. Loop over the names: for (nm in names(xs)) 4. Loop over unknown sequence length: while (condition)
31. For loops are not as important in R as they are in other languages because R is a functional programming language. This means that itʼs possible to wrap up for loops in a function, and call that function instead of using the for loop directly. “ Final thoughts on loops
32. Tidyverse packages in the flow
33. list map(.x, .f) map2(.x, .y, .f) pmap(.l, .f) numeric vector map_dbl() map2_dbl() pmap_dbl() integer vector map_int() map2_int() pmap_int() character vector map_chr() map2_chr() pmap_chr() logical vector map_lgl() map2_lgl() pmap_lgl() data frame (by col) map_dfc() map2_dfc() pmap_dfc() data frame (by row) map_dfr() map2_dfr() pmap_dfr() render output not value walk() walk2() pwalk() Apply a function to each/pair/group of element of a list or vector, return a list. One list Two lists Many lists
34. Letʼs explore map map(.x, .f, …) a list a function additional arguments to pass into the function
35. Letʼs explore map map_chr(.x = pepfar_country_list$country, .f = toupper)
36. Letʼs explore map map2_chr(.x = pepfar_country_list$operatingunit, .y = pepfar_country_list$country, .f = ~ ifelse(.x == .y, .x, glue("{.x}/{.y}"))) pass in a custom/named function
37. Map v Loop map_dbl(.x = df_sect3, .f = mean)
38. Use case for map > (files <- list.files(“Data”)) [1] MER_Structured_Datasets_Site_IM_FY50-52_v2_Jupiter.zip [2] MER_Structured_Datasets_Site_IM_FY50-52_v2_Neptune.zip [3] MER_Structured_Datasets_Site_IM_FY50-52_v2_Saturn.zip > df_tx_nn <- map_dfr(.x = files, .f = ~ read_msd(.x) %>% filter(indicator %in% c(“TX_CURR”,"TX_NEW", “TX_NET_NEW”), standardizeddisaggregate == "Total Numerator"), fiscal_year == 2052) > plots <- df_tx_nn %>% count(country, orgunituid, wt = cumulative) %>% pivot_wider(names_from = indicator, values_from = n) %>% split(.$country) %>% map(~ggplot(., aes(tx_net_new, tx_new, size = tx_curr)) + geom_point()) > paths <- str_c(names(plots), ".png") > pwalk(list(paths, plots), ggsave, path = tempdir())
39. Take advantage of RStudio cheat sheets
40. Resources & Sources • R for Data Science, Garrett Grolemund and Hadley Wickham • Master the Tidyverse - 6 - Iteration with purrr - Garrett Grolemund • Cupcakes (for-loops vs map/lapply) - Hadley Wickham • https://www.tidyverse.org/