EVOLUTION-MANAGER

Edit File: pivot.R

## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(tibble.print_max = 10) ## ----setup, message = FALSE--------------------------------------------------- library(tidyr) library(dplyr) library(readr) ## ----------------------------------------------------------------------------- relig_income ## ----------------------------------------------------------------------------- relig_income %>% pivot_longer(!religion, names_to = "income", values_to = "count") ## ----------------------------------------------------------------------------- billboard ## ----------------------------------------------------------------------------- billboard %>% pivot_longer( cols = starts_with("wk"), names_to = "week", values_to = "rank", values_drop_na = TRUE ) ## ---- eval = FALSE------------------------------------------------------------ # billboard %>% # pivot_longer( # cols = starts_with("wk"), # names_to = "week", # names_prefix = "wk", # names_transform = list(week = as.integer), # values_to = "rank", # values_drop_na = TRUE, # ) ## ---- eval = FALSE------------------------------------------------------------ # billboard %>% # pivot_longer( # cols = starts_with("wk"), # names_to = "week", # names_transform = list(week = readr::parse_number), # values_to = "rank", # values_drop_na = TRUE, # ) ## ----------------------------------------------------------------------------- who ## ----------------------------------------------------------------------------- who %>% pivot_longer( cols = new_sp_m014:newrel_f65, names_to = c("diagnosis", "gender", "age"), names_pattern = "new_?(.*)_(.)(.*)", values_to = "count" ) ## ---- eval = FALSE------------------------------------------------------------ # who %>% pivot_longer( # cols = new_sp_m014:newrel_f65, # names_to = c("diagnosis", "gender", "age"), # names_pattern = "new_?(.*)_(.)(.*)", # names_transform = list( # gender = ~ readr::parse_factor(.x, levels = c("f", "m")), # age = ~ readr::parse_factor( # .x, # levels = c("014", "1524", "2534", "3544", "4554", "5564", "65"), # ordered = TRUE # ) # ), # values_to = "count", # ) ## ----------------------------------------------------------------------------- family <- tribble( ~family, ~dob_child1, ~dob_child2, ~gender_child1, ~gender_child2, 1L, "1998-11-26", "2000-01-29", 1L, 2L, 2L, "1996-06-22", NA, 2L, NA, 3L, "2002-07-11", "2004-04-05", 2L, 2L, 4L, "2004-10-10", "2009-08-27", 1L, 1L, 5L, "2000-12-05", "2005-02-28", 2L, 1L, ) family <- family %>% mutate_at(vars(starts_with("dob")), parse_date) family ## ----------------------------------------------------------------------------- family %>% pivot_longer( !family, names_to = c(".value", "child"), names_sep = "_", values_drop_na = TRUE ) ## ----------------------------------------------------------------------------- anscombe ## ----------------------------------------------------------------------------- anscombe %>% pivot_longer(everything(), names_to = c(".value", "set"), names_pattern = "(.)(.)" ) %>% arrange(set) ## ----------------------------------------------------------------------------- pnl <- tibble( x = 1:4, a = c(1, 1,0, 0), b = c(0, 1, 1, 1), y1 = rnorm(4), y2 = rnorm(4), z1 = rep(3, 4), z2 = rep(-2, 4), ) pnl %>% pivot_longer( !c(x, a, b), names_to = c(".value", "time"), names_pattern = "(.)(.)" ) ## ----------------------------------------------------------------------------- df <- tibble(id = 1:3, y = 4:6, y = 5:7, y = 7:9, .name_repair = "minimal") df ## ----------------------------------------------------------------------------- df %>% pivot_longer(!id, names_to = "name", values_to = "value") ## ----------------------------------------------------------------------------- df <- tibble(id = 1:3, x1 = 4:6, x2 = 5:7, y1 = 7:9, y2 = 10:12) df %>% pivot_longer(!id, names_to = ".value", names_pattern = "(.).") ## ----------------------------------------------------------------------------- fish_encounters ## ----------------------------------------------------------------------------- fish_encounters %>% pivot_wider(names_from = station, values_from = seen) ## ----------------------------------------------------------------------------- fish_encounters %>% pivot_wider( names_from = station, values_from = seen, values_fill = 0 ) ## ----------------------------------------------------------------------------- warpbreaks <- warpbreaks %>% as_tibble() %>% select(wool, tension, breaks) warpbreaks ## ----------------------------------------------------------------------------- warpbreaks %>% count(wool, tension) ## ----------------------------------------------------------------------------- warpbreaks %>% pivot_wider(names_from = wool, values_from = breaks) ## ----------------------------------------------------------------------------- warpbreaks %>% pivot_wider( names_from = wool, values_from = breaks, values_fn = list(breaks = mean) ) ## ----------------------------------------------------------------------------- production <- expand_grid( product = c("A", "B"), country = c("AI", "EI"), year = 2000:2014 ) %>% filter((product == "A" & country == "AI") | product == "B") %>% mutate(production = rnorm(nrow(.))) production ## ----------------------------------------------------------------------------- production %>% pivot_wider( names_from = c(product, country), values_from = production ) ## ----------------------------------------------------------------------------- production %>% pivot_wider( names_from = c(product, country), values_from = production, names_sep = ".", names_prefix = "prod." ) production %>% pivot_wider( names_from = c(product, country), values_from = production, names_glue = "prod_{product}_{country}" ) ## ----------------------------------------------------------------------------- us_rent_income ## ----------------------------------------------------------------------------- us_rent_income %>% pivot_wider(names_from = variable, values_from = c(estimate, moe)) ## ----------------------------------------------------------------------------- contacts <- tribble( ~field, ~value, "name", "Jiena McLellan", "company", "Toyota", "name", "John Smith", "company", "google", "email", "john@google.com", "name", "Huxley Ratcliffe" ) ## ----------------------------------------------------------------------------- contacts <- contacts %>% mutate( person_id = cumsum(field == "name") ) contacts ## ----------------------------------------------------------------------------- contacts %>% pivot_wider(names_from = field, values_from = value) ## ----------------------------------------------------------------------------- world_bank_pop ## ----------------------------------------------------------------------------- pop2 <- world_bank_pop %>% pivot_longer(`2000`:`2017`, names_to = "year", values_to = "value") pop2 ## ----------------------------------------------------------------------------- pop2 %>% count(indicator) ## ----------------------------------------------------------------------------- pop3 <- pop2 %>% separate(indicator, c(NA, "area", "variable")) pop3 ## ----------------------------------------------------------------------------- pop3 %>% pivot_wider(names_from = variable, values_from = value) ## ----------------------------------------------------------------------------- multi <- tribble( ~id, ~choice1, ~choice2, ~choice3, 1, "A", "B", "C", 2, "C", "B", NA, 3, "D", NA, NA, 4, "B", "D", NA ) ## ----------------------------------------------------------------------------- multi2 <- multi %>% pivot_longer(!id, values_drop_na = TRUE) %>% mutate(checked = TRUE) multi2 ## ----------------------------------------------------------------------------- multi2 %>% pivot_wider( id_cols = id, names_from = value, values_from = checked, values_fill = FALSE ) ## ----------------------------------------------------------------------------- spec <- relig_income %>% build_longer_spec( cols = !religion, names_to = "income", values_to = "count" ) pivot_longer_spec(relig_income, spec) ## ----------------------------------------------------------------------------- spec ## ----------------------------------------------------------------------------- us_rent_income %>% pivot_wider(names_from = variable, values_from = c(estimate, moe)) ## ----------------------------------------------------------------------------- spec1 <- us_rent_income %>% build_wider_spec(names_from = variable, values_from = c(estimate, moe)) spec1 ## ----------------------------------------------------------------------------- spec2 <- spec1 %>% mutate(.name = paste0(variable, ifelse(.value == "moe", "_moe", ""))) spec2 ## ----------------------------------------------------------------------------- pivot_wider_spec(us_rent_income, spec2) ## ----------------------------------------------------------------------------- construction ## ----------------------------------------------------------------------------- spec <- tribble( ~.name, ~.value, ~units, ~region, "1 unit", "n", "1", NA, "2 to 4 units", "n", "2-4", NA, "5 units or more", "n", "5+", NA, "Northeast", "n", NA, "Northeast", "Midwest", "n", NA, "Midwest", "South", "n", NA, "South", "West", "n", NA, "West", ) ## ----------------------------------------------------------------------------- pivot_longer_spec(construction, spec) ## ----------------------------------------------------------------------------- construction %>% pivot_longer_spec(spec) %>% pivot_wider_spec(spec)