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- # Josua Kugler, Christian Merten
-
- # install.packages("babynames")
- library(tidyverse)
-
- ## Create some data-----------------------------------------------------------
-
- set.seed(1)
-
- baseset <- list()
- baseset$grade <- as.integer(c(5,6,7,8,9,10,11))
- baseset$grade_boost <- c(1,3,5,7,8,9,10)
- baseset$letter <- letters[1:4]
- baseset$letter_boost <- sample(1:5, 4, replace=T)
- babynames::babynames %>%
- group_by(sex, name) %>%
- summarise(n = sum(n)) %>%
- arrange(desc(n)) %>%
- mutate(rank = min_rank(-n)) %>%
- filter (rank <= 3000) ->
- ranked_names
- baseset$name <- ranked_names$name
- baseset$distance <- c(100,200,400,1000)
- baseset$distance_boost <- c(14,12,10,8)
-
- sample_observation <- function(n) {
- res <- list()
- res$name <- sample(baseset$name, n, replace=T)
- res$grade <- sample(baseset$grade, n, replace=T)
- res$letter <- sample(baseset$letter, n, replace=T)
- boost_base <-
- baseset$grade_boost[match(res$grade,baseset$grade)] +
- baseset$letter_boost[match(res$letter,baseset$letter)]
- res$time100 <- sample_time(100, baseset$distance_boost[1] + boost_base)
- res$time200 <- sample_time(200, baseset$distance_boost[2] + boost_base)
- res$time400 <- sample_time(400, baseset$distance_boost[3] + boost_base)
- res$time1000 <- sample_time(1000, baseset$distance_boost[4] + boost_base)
- as_tibble(res)
- }
-
- sample_time <- function(dist, boost) {
- (runif(length(boost))/2+2.5)/boost*dist*2
- }
-
- sports <- sample_observation(1000)
-
- requirements <- tibble(
- level = 1:11,
- min100 = seq(43,23,len=11),
- min1000 = seq(500,300,len=11)
- )
-
-
-
- ## Exercises -----------------------------------------------------------------
-
-
- # a)
- # get all students who failed in 100m or 1000m
- sports %>% left_join(requirements, by = c("grade" = "level")) %>%
- filter(time100 <= min100, time1000 <= min1000)
-
- # b)
- # get names, grade and letter of all students who failed 1000m by less than 1s
- # so you can still let them pass :)
- sports %>% left_join(requirements, by = c("grade" = "level")) %>%
- filter((time1000 - min1000) > 0 & (time1000 - min1000) < 1) %>%
- select(name, grade, letter)
-
- # c)
- # tidy the data:
- # create two columns from all timeXXX-columns:
- # a column "time" with the entries from all timeXXX-columns
- # a column "distance" of the distance the time refers to
- # make sure all columns have a suitable type
- sports %>% pivot_longer(c("time100", "time200", "time400", "time1000"),
- names_to="distanceRaw",
- values_to="time") %>%
- extract(distanceRaw, into="distance", regex="time(.*)", convert=T)
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