Intro to csutil

Splitting

csutil::easy_split(letters[1:20], size_of_each_group = 3)
 #> $`1`
 #> [1] "a" "b" "c"
 #> 
 #> $`2`
 #> [1] "d" "e" "f"
 #> 
 #> $`3`
 #> [1] "g" "h" "i"
 #> 
 #> $`4`
 #> [1] "j" "k" "l"
 #> 
 #> $`5`
 #> [1] "m" "n" "o"
 #> 
 #> $`6`
 #> [1] "p" "q" "r"
 #> 
 #> $`7`
 #> [1] "s" "t"
csutil::easy_split(letters[1:20], number_of_groups = 3)
 #> $`1`
 #> [1] "a" "b" "c" "d" "e" "f" "g"
 #> 
 #> $`2`
 #> [1] "h" "i" "j" "k" "l" "m" "n"
 #> 
 #> $`3`
 #> [1] "o" "p" "q" "r" "s" "t"

Unnesting data.frames

x <- list(
 list(
 "a" = data.frame("v1"=1),
 "b" = data.frame("v2"=3)
 ),
 list(
 "a" = data.frame("v1"=10),
 "b" = data.frame("v2"=30),
 "d" = data.frame("v3"=50)
 )
)
 print(x)
 #> [[1]]
 #> [[1]]$a
 #> v1
 #> 1 1
 #> 
 #> [[1]]$b
 #> v2
 #> 1 3
 #> 
 #> 
 #> [[2]]
 #> [[2]]$a
 #> v1
 #> 1 10
 #> 
 #> [[2]]$b
 #> v2
 #> 1 30
 #> 
 #> [[2]]$d
 #> v3
 #> 1 50
csutil::unnest_dfs_within_list_of_fully_named_lists(x)
 #> $a
 #> v1
 #> 1: 1
 #> 2: 10
 #> 
 #> $b
 #> v2
 #> 1: 3
 #> 2: 30
 #> 
 #> $d
 #> v3
 #> 1: 50

Describing lists

csutil::is_fully_named_list(list(1))
 #> [1] FALSE
csutil::is_fully_named_list(list("a"=1))
 #> [1] TRUE
 
csutil::is_all_list_elements_null_or_df(list(data.frame()))
 #> [1] TRUE
csutil::is_all_list_elements_null_or_df(list(1, NULL))
 #> [1] FALSE
 
csutil::is_all_list_elements_null_or_list(list(1, NULL))
 #> [1] FALSE
csutil::is_all_list_elements_null_or_list(list(list(), NULL))
 #> [1] TRUE
 
csutil::is_all_list_elements_null_or_fully_named_list(list(list(), NULL))
 #> [1] FALSE
csutil::is_all_list_elements_null_or_fully_named_list(list(list("a" = 1), NULL))
 #> [1] TRUE

Apply a function via hash table

This function extracts the unique input values, applies the given function to it to create a hash table (containing unique input/output combinations), and then matches the original input to the hash table to obtain the desired output.

This can dramatically speed up computation if there is a lot of data and a limited amount of unique values.

input <- rep(seq(as.Date("2000年01月01日"), as.Date("2020年01月01日"), 1), 1000)
a1 <- Sys.time()
z <- format(input, "%Y")
a2 <- Sys.time()
a2 - a1
 #> Time difference of 2.071753 secs
 
b1 <- Sys.time()
z <- csutil::apply_fn_via_hash_table(
 input,
 format,
 "%Y"
)
b2 <- Sys.time()
b2 - b1
 #> Time difference of 0.6172273 secs