Return x percent to y percent of values

near_between(x, from, to)

Arguments

x

numeric vector

from

the lower bound of percentage

to

the upper bound of percentage

Value

logical vector

Examples

x <- runif(20) near_middle(x = x, middle = 0.5, within = 0.2)
#> [1] FALSE FALSE TRUE TRUE FALSE FALSE FALSE FALSE FALSE FALSE TRUE TRUE #> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
library(dplyr) heights %>% features(height_cm, list(min = min)) %>% filter(near_between(min, 0.1, 0.9))
#> # A tibble: 114 x 2 #> country min #> <chr> <dbl> #> 1 Afghanistan 161. #> 2 Albania 168. #> 3 Algeria 166. #> 4 Argentina 167. #> 5 Armenia 164. #> 6 Austria 162. #> 7 Bahrain 161. #> 8 Bangladesh 160. #> 9 Belarus 164. #> 10 Belgium 163. #> # … with 104 more rows
near_quantile(x = x, probs = 0.5, tol = 0.01)
#> [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE #> [13] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
near_quantile(x, c(0.25, 0.5, 0.75), 0.05)
#> [1] FALSE FALSE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE #> [13] FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE
heights %>% features(height_cm, l_five_num) %>% mutate_at(vars(min:max), .funs = near_quantile, 0.5, 0.01) %>% filter(min)
#> # A tibble: 0 x 6 #> # … with 6 variables: country <chr>, min <lgl>, q_25 <lgl>, med <lgl>, #> # q_75 <lgl>, max <lgl>
heights %>% features(height_cm, list(min = min)) %>% mutate(min_near_q3 = near_quantile(min, c(0.25, 0.5, 0.75), 0.01)) %>% filter(min_near_q3)
#> # A tibble: 2 x 3 #> country min min_near_q3 #> <chr> <dbl> <lgl> #> 1 Ethiopia 161. TRUE #> 2 Madagascar 161. TRUE
heights %>% features(height_cm, list(min = min)) %>% filter(near_between(min, 0.1, 0.9))
#> # A tibble: 114 x 2 #> country min #> <chr> <dbl> #> 1 Afghanistan 161. #> 2 Albania 168. #> 3 Algeria 166. #> 4 Argentina 167. #> 5 Armenia 164. #> 6 Austria 162. #> 7 Bahrain 161. #> 8 Bangladesh 160. #> 9 Belarus 164. #> 10 Belgium 163. #> # … with 104 more rows
heights %>% features(height_cm, list(min = min)) %>% filter(near_middle(min, 0.5, 0.1))
#> # A tibble: 14 x 2 #> country min #> <chr> <dbl> #> 1 Brazil 164. #> 2 Cameroon 164. #> 3 Estonia 165. #> 4 Gabon 164. #> 5 Ghana 164. #> 6 Guinea 164. #> 7 Kenya 165. #> 8 Kyrgyzstan 164. #> 9 Latvia 165. #> 10 Lithuania 165. #> 11 Netherlands 164. #> 12 Switzerland 165. #> 13 Tajikistan 165. #> 14 Uganda 165.