每周分享第四期

fruit <- c("apple", "banana", "pear", "pineapple")
"a" %count% fruit
#> [1] 1 3 1 1
c("a", "b", "p", "p") %count% fruit
#> [1] 1 1 1 3

squish(c(-1, 0.5, 1, 2, NA), range = c(0, 1))
#> [1] 0.0 0.5 1.0 1.0  NA

# Useful for setting the `oob` argument for a colour scale with reduced limits
library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, colour = Petal.Length)) +
  geom_point() +
  scale_color_continuous(limit = c(2, 4), oob = scales::squish)

# Load tidylog after dplyr:

library("dplyr")
library("tidylog", warn.conflicts = FALSE)

# Tidylog will give you feedback, for instance when filtering a data frame:

filtered <- filter(mtcars, cyl == 4)
#> filter: removed 21 rows (66%)

library(tsfeatures)
mylist <- list(sunspot.year, WWWusage, AirPassengers, USAccDeaths)
myfeatures <- tsfeatures(mylist)
myfeatures
#> # A tibble: 4 x 20
#>   frequency nperiods seasonal_period trend   spike linearity curvature
#>       <dbl>    <dbl>           <dbl> <dbl>   <dbl>     <dbl>     <dbl>
#> 1         1        0               1 0.125 2.10e-5      3.58      1.11
#> 2         1        0               1 0.985 3.01e-8      4.45      1.10
#> 3        12        1              12 0.989 2.12e-8     11.0       1.10
#> 4        12        1              12 0.796 9.67e-7     -2.13      2.85
#> # ... with 13 more variables: e_acf1 <dbl>, e_acf10 <dbl>, entropy <dbl>,
#> #   x_acf1 <dbl>, x_acf10 <dbl>, diff1_acf1 <dbl>, diff1_acf10 <dbl>,
#> #   diff2_acf1 <dbl>, diff2_acf10 <dbl>, seasonal_strength <dbl>,
#> #   peak <dbl>, trough <dbl>, seas_acf1 <dbl>