R Under development (unstable) (2024-05-14 r86553 ucrt) -- "Unsuffered Consequences"
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Platform: x86_64-w64-mingw32/x64

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> total_time <- Sys.time()
> 
> suppressMessages(library(Rcpp))
> suppressMessages(library(dplyr))
> suppressMessages(library(data.table))
> suppressMessages(library(qs))
> suppressMessages(library(stringfish))
> options(warn = 1)
> 
> do_gc <- function() {
+   if (utils::compareVersion(as.character(getRversion()), "3.5.0") != -1) {
+     gc(full = TRUE)
+   } else {
+     gc()
+   }
+ }
> 
> # because sourceCpp uses setwd, we need absolute path to R_TESTS when run within R CMD check
> R_TESTS <- Sys.getenv("R_TESTS") # startup.Rs
> if (nzchar(R_TESTS)) {
+   R_TESTS_absolute <- normalizePath(R_TESTS)
+   Sys.setenv(R_TESTS = R_TESTS_absolute)
+ }
> sourceCpp(code="#include <Rcpp.h>
+ using namespace Rcpp;
+ // [[Rcpp::plugins(cpp11)]]
+ // [[Rcpp::export(rng=false)]]
+ CharacterVector splitstr(std::string x, std::vector<double> cuts){
+   CharacterVector ret(cuts.size() - 1);
+   for(uint64_t i=1; i<cuts.size(); i++) {
+     ret[i-1] = x.substr(std::round(cuts[i-1])-1, std::round(cuts[i])-std::round(cuts[i-1]));
+   }
+   return ret;
+ }
+ // [[Rcpp::export(rng=false)]]
+ int setlev(SEXP x, int i) {
+   return SETLEVELS(x,i);
+ }
+ // [[Rcpp::export(rng=false)]]
+ void setobj(SEXP x, int i) {
+   return SET_OBJECT(x, i);
+ }
+ // [[Rcpp::export(rng=false)]]
+ List generateList(std::vector<int> list_elements){
+   auto randchar = []() -> char
+   {
+     const char charset[] =
+       \"0123456789\"
+       \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"
+       \"abcdefghijklmnopqrstuvwxyz\";
+     const size_t max_index = (sizeof(charset) - 1);
+     return charset[ rand() % max_index ];
+   };
+   List ret(list_elements.size());
+   std::string str(10,0);
+   for(size_t i=0; i<list_elements.size(); i++) {
+     switch(list_elements[i]) {
+     case 1:
+       ret[i] = R_NilValue;
+       break;
+     case 2:
+       std::generate_n( str.begin(), 10, randchar );
+       ret[i] = str;
+       break;
+     case 3:
+       ret[i] = rand();
+       break;
+     case 4:
+       ret[i] = static_cast<double>(rand());
+       break;
+     }
+   }
+   return ret;
+ }")
> if (nzchar(R_TESTS)) Sys.setenv(R_TESTS = R_TESTS)
> 
> args <- commandArgs(T)
> if (nzchar(R_TESTS) || ((length(args) > 0) && args[1] == "check")) { # do fewer tests within R CMD check so it completes within a reasonable amount of time
+   reps <- 2
+   test_points <- c(0, 1, 2, 4, 8, 2^5 - 1, 2^5 + 1, 2^5, 2^8 - 1, 2^8 + 1, 2^8, 2^16 - 1, 2^16 + 1, 2^16, 1e6)
+   test_points_slow <- c(0, 1, 2, 4, 8, 2^5 - 1, 2^5 + 1, 2^5, 2^8 - 1, 2^8 + 1, 2^8, 2^16 - 1, 2^16 + 1, 2^16) # for Character Vector, stringfish and list
+   max_size <- 1e6
+ } else {
+   reps <- 3
+   test_points <- c(0, 1, 2, 4, 8, 2^5 - 1, 2^5 + 1, 2^5, 2^8 - 1, 2^8 + 1, 2^8, 2^16 - 1, 2^16 + 1, 2^16, 1e6, 1e7)
+   test_points_slow <- test_points
+   max_size <- 1e7
+ }
> myfile <- tempfile()
> 
> obj_size <- 0
> get_obj_size <- function() {
+   get("obj_size", envir = globalenv())
+ }
> set_obj_size <- function(x) {
+   assign("obj_size", get_obj_size() + as.numeric(object.size(x)), envir = globalenv())
+   return(get_obj_size());
+ }
> random_object_generator <- function(N, with_envs = FALSE) { # additional input: global obj_size, max_size
+   if (sample(3, 1) == 1) {
+     ret <- as.list(1:N)
+   } else if (sample(2, 1) == 1) {
+     ret <- as.pairlist(1:N)
+   } else {
+     ret <- as.pairlist(1:N)
+     setlev(ret, sample(2L^12L, 1L) - 1L)
+     setobj(ret, 1L)
+   }
+ 
+   for (i in 1:N) {
+     if (get_obj_size() > get("max_size", envir = globalenv())) break;
+     otype <- sample(12, size = 1)
+     z <- NULL
+     is_attribute <- ifelse(i == 1, F, sample(c(F, T), size = 1))
+     if (otype == 1) {z <- rnorm(1e4); set_obj_size(z);}
+     else if (otype == 2) { z <- sample(1e4) - 5e2; set_obj_size(z); }
+     else if (otype == 3) { z <- sample(c(T, F, NA), size = 1e4, replace = T); set_obj_size(z); }
+     else if (otype == 4) { z <- (sample(256, size = 1e4, replace = T) - 1) %>% as.raw; set_obj_size(z); }
+     else if (otype == 5) { z <- replicate(sample(1e4, size = 1), {rep(letters, length.out = sample(10, size = 1)) %>% paste(collapse = "")}); set_obj_size(z); }
+     else if (otype == 6) { z <- rep(letters, length.out = sample(1e4, size = 1)) %>% paste(collapse = ""); set_obj_size(z); }
+     else if (otype == 7) { z <- as.formula("y ~ a + b + c : d", env = globalenv()); attr(z, "blah") <- sample(1e4) - 5e2; set_obj_size(z); }
+     else if (with_envs && otype %in% c(8, 9)) { z <- function(x) {x + runif(1)} }
+     # else if(with_envs && otype %in% c(10,11)) { z <- new.env(); z$x <- random_object_generator(N, with_envs); makeActiveBinding("y", function() runif(1), z) }
+     else { z <- random_object_generator(N, with_envs) }
+     if (is_attribute) {
+       attr(ret[[i - 1]], runif(1) %>% as.character()) <- z
+     } else {
+       ret[[i]] <- z
+     }
+   }
+   return(ret)
+ }
> 
> rand_strings <- function(n) {
+   s <- sample(0:100, size = n, replace = T)
+   x <- lapply(unique(s), function(si) {
+     stringfish::random_strings(sum(s == si), si, vector_mode = "normal")
+   }) %>% unlist %>% sample
+   x[sample(n, size = n/10)] <- NA
+   return(x)
+ }
> 
> nested_tibble <- function() {
+   sub_tibble <- function(nr = 600, nc = 4) {
+     z <- lapply(1:nc, function(i) rand_strings(nr)) %>%
+       setNames(make.unique(paste0(sample(letters, nc), rand_strings(nc)))) %>%
+       bind_cols %>%
+       as_tibble
+   }
+   tibble(
+     col1 = rand_strings(100),
+     col2 = rand_strings(100),
+     col3 = lapply(1:100, function(i) sub_tibble(nr = 600, nc = 4)),
+     col4 = lapply(1:100, function(i) sub_tibble(nr = 600, nc = 4)),
+     col5 = lapply(1:100, function(i) sub_tibble(nr = 600, nc = 4))
+   ) %>% setNames(make.unique(paste0(sample(letters, 5), rand_strings(5))))
+ }
> 
> printCarriage <- function(x) {
+   cat(x, "\r")
+ }
> 
> attributes_serialize_identical <- function(attributes, full_object) {
+   identical(serialize(attributes(full_object), NULL), serialize(attributes, NULL))
+ }
> 
> attributes_identical <- function(attributes, full_object) {
+   identical(attributes, attributes(full_object))
+ }
> 
> ################################################################################################
> 
> qsave_rand <- function(x, file) {
+   alg <- sample(c("lz4", "zstd", "lz4hc", "zstd_stream", "uncompressed"), 1)
+   # alg <- "zstd_stream"
+   nt <- sample(5,1)
+   sc <- sample(0:15,1)
+   cl <- sample(10,1)
+   ch <- sample(c(T,F),1)
+   qsave(x, file = file, preset = "custom", algorithm = alg,
+       compress_level = cl, shuffle_control = sc, nthreads = nt, check_hash = ch)
+ }
> 
> qattributes_rand <- function(file) {
+   # ar <- sample(c(T,F),1)
+   # don't use altrep to avoid serialization differences
+   # attributes_serialize_identical won't pass with ALTREP
+   ar <- FALSE
+   nt <- sample(5,1)
+   qattributes(file, use_alt_rep = ar, nthreads = nt, strict = T)
+ }
> 
> ################################################################################################
> 
> for (q in 1:reps) {
+   cat("Rep",  q, "of", reps, "\n")
+   # String correctness
+   time <- vector("numeric", length = 3)
+   for (tp in test_points) {
+     for (i in 1:3) {
+       x1 <- rep(letters, length.out = tp) %>% paste(collapse = "")
+       x1 <- c(NA, "", x1)
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("strings: %s, %s s",tp, signif(mean(time), 4)))
+   }
+   cat("\n")
+ 
+   # Character vectors
+   time <- vector("numeric", length = 3)
+   for (tp in test_points_slow) {
+     for (i in 1:3) {
+       # qs_use_alt_rep(F)
+       x1 <- rep(as.raw(sample(255)), length.out = tp*10) %>% rawToChar
+       cuts <- sample(tp*10, tp + 1) %>% sort %>% as.numeric
+       x1 <- splitstr(x1, cuts)
+       x1 <- c(NA, "", x1)
+       qsave_rand(x1, file = myfile)
+       time[i] <- Sys.time()
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("Character Vectors: %s, %s s",tp, signif(mean(time), 4)))
+   }
+   cat("\n")
+ 
+   # stringfish character vectors -- require R > 3.5.0
+   if (utils::compareVersion(as.character(getRversion()), "3.5.0") != -1) {
+     time <- vector("numeric", length = 3)
+     for (tp in test_points_slow) {
+       for (i in 1:3) {
+         x1 <- rep(as.raw(sample(255)), length.out = tp*10) %>% rawToChar
+         cuts <- sample(tp*10, tp + 1) %>% sort %>% as.numeric
+         x1 <- splitstr(x1, cuts)
+         x1 <- c(NA, "", x1)
+         x1 <- stringfish::convert_to_sf(x1)
+         qsave_rand(x1, file = myfile)
+         time[i] <- Sys.time()
+         z <- qattributes_rand(file = myfile)
+         time[i] <- Sys.time() - time[i]
+         do_gc()
+         stopifnot(attributes_identical(z, x1))
+       }
+       printCarriage(sprintf("Stringfish: %s, %s s",tp, signif(mean(time), 4)))
+     }
+     cat("\n")
+   }
+ 
+   # Integers
+   time <- vector("numeric", length = 3)
+   for (tp in test_points) {
+     for (i in 1:3) {
+       x1 <- sample(1:tp, replace = T)
+       x1 <- c(NA, x1)
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("Integers: %s, %s s",tp, signif(mean(time), 4)))
+   }
+   cat("\n")
+ 
+   # Doubles
+   time <- vector("numeric", length = 3)
+   for (tp in test_points) {
+     for (i in 1:3) {
+       x1 <- rnorm(tp)
+       x1 <- c(NA, x1)
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("Numeric: %s, %s s",tp, signif(mean(time), 4)))
+   }
+   cat("\n")
+ 
+   # Logical
+   time <- vector("numeric", length = 3)
+   for (tp in test_points) {
+     for (i in 1:3) {
+ 
+       x1 <- sample(c(T, F, NA), replace = T, size = tp)
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("Logical: %s, %s s",tp, signif(mean(time),4)))
+   }
+   cat("\n")
+ 
+   # List
+   time <- vector("numeric", length = 3)
+   for (tp in test_points_slow) {
+     for (i in 1:3) {
+       x1 <- generateList(sample(1:4, replace = T, size = tp))
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("List: %s, %s s",tp, signif(mean(time),4)))
+   }
+   cat("\n")
+ 
+   for (i in 1:3) {
+     x1 <- rep( replicate(1000, { rep(letters, length.out = 2^7 + sample(10, size = 1)) %>% paste(collapse = "") }), length.out = 1e6 )
+     x1 <- data.frame(str = x1,num = runif(1:1000), stringsAsFactors = F)
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     do_gc()
+     stopifnot(attributes_identical(z, x1))
+   }
+   cat("Data.frame test")
+   cat("\n")
+ 
+   for (i in 1:3) {
+     x1 <- rep( replicate(1000, { rep(letters, length.out = 2^7 + sample(10, size = 1)) %>% paste(collapse = "") }), length.out = 1e6 )
+     x1 <- data.table(str = x1,num = runif(1:1e6))
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     do_gc()
+     stopifnot(attributes_serialize_identical(z, x1))
+   }
+   cat("Data.table test")
+   cat("\n")
+ 
+   for (i in 1:3) {
+     x1 <- rep( replicate(1000, { rep(letters, length.out = 2^7 + sample(10, size = 1)) %>% paste(collapse = "") }), length.out = 1e6 )
+     x1 <- tibble(str = x1,num = runif(1:1e6))
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     do_gc()
+     stopifnot(attributes_identical(z, x1))
+   }
+   cat("Tibble test")
+   cat("\n")
+ 
+   # Encoding test
+   if (Sys.info()[['sysname']] != "Windows") {
+     for (i in 1:3) {
+       x1 <- "己所不欲，勿施于人" # utf 8
+       x2 <- x1
+       Encoding(x2) <- "latin1"
+       x3 <- x1
+       Encoding(x3) <- "bytes"
+       x4 <- rep(x1, x2, length.out = 1e4) %>% paste(collapse = ";")
+       x1 <- c(x1, x2, x3, x4)
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage("Encoding test")
+   } else {
+     printCarriage("(Encoding test not run on windows)")
+   }
+   cat("\n")
+ 
+   # complex vectors
+   time <- vector("numeric", length = 3)
+   for (tp in test_points) {
+     for (i in 1:3) {
+       re <- rnorm(tp)
+       im <- runif(tp)
+       x1 <- complex(real = re, imaginary = im)
+       x1 <- c(NA_complex_, x1)
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("Complex: %s, %s s",tp, signif(mean(time), 4)))
+   }
+   cat("\n")
+ 
+   # factors
+   for (tp in test_points) {
+     time <- vector("numeric", length = 3)
+     for (i in 1:3) {
+       x1 <- factor(rep(letters, length.out = tp), levels = sample(letters), ordered = TRUE)
+       time[i] <- Sys.time()
+       qsave_rand(x1, file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1))
+     }
+     printCarriage(sprintf("Factors: %s, %s s",tp, signif(mean(time), 4)))
+   }
+   cat("\n")
+ 
+   # Random objects
+   time <- vector("numeric", length = 8)
+   for (i in 1:8) {
+     # qs_use_alt_rep(sample(c(T, F), size = 1))
+     obj_size <- 0
+     x1 <- random_object_generator(12)
+     printCarriage(sprintf("Random objects: %s bytes", object.size(x1) %>% as.numeric))
+     time[i] <- Sys.time()
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     time[i] <- Sys.time() - time[i]
+     do_gc()
+     stopifnot(attributes_identical(z, x1))
+   }
+   printCarriage(sprintf("Random objects: %s s", signif(mean(time), 4)))
+   cat("\n")
+ 
+   # nested attributes
+   time <- vector("numeric", length = 3)
+   for (i in 1:3) {
+     x1 <- as.list(1:26)
+     attr(x1[[26]], letters[26]) <- rnorm(100)
+     for (i in 25:1) {
+       attr(x1[[i]], letters[i]) <- x1[[i + 1]]
+     }
+     time[i] <- Sys.time()
+     for(j in 1:length(x1)) {
+       qsave_rand(x1[[j]], file = myfile)
+       z <- qattributes_rand(file = myfile)
+       time[i] <- Sys.time() - time[i]
+       do_gc()
+       stopifnot(attributes_identical(z, x1[[j]]))
+     }
+   }
+   printCarriage(sprintf("Nested attributes: %s s", signif(mean(time), 4)))
+   cat("\n")
+ 
+   # alt-rep -- should serialize the unpacked object
+   time <- vector("numeric", length = 3)
+   for (i in 1:3) {
+     x1 <- 1:max_size
+     time[i] <- Sys.time()
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     time[i] <- Sys.time() - time[i]
+     do_gc()
+     stopifnot(attributes_identical(z, x1))
+   }
+   printCarriage(sprintf("Alt rep integer: %s s", signif(mean(time), 4)))
+   cat("\n")
+ 
+ 
+   # Environment test
+   time <- vector("numeric", length = 3)
+   for (i in 1:3) {
+     x1 <- new.env()
+     x1[["a"]] <- 1:max_size
+     x1[["b"]] <- runif(max_size)
+     x1[["c"]] <- stringfish::random_strings(1e4, vector_mode = "normal")
+     time[i] <- Sys.time()
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     stopifnot(attributes_identical(z[["a"]], x1[["a"]]))
+     stopifnot(attributes_identical(z[["b"]], x1[["b"]]))
+     stopifnot(attributes_identical(z[["c"]], x1[["c"]]))
+     time[i] <- Sys.time() - time[i]
+     do_gc()
+   }
+   printCarriage(sprintf("Environment test: %s s", signif(mean(time), 4)))
+   cat("\n")
+ 
+   time <- vector("numeric", length = 3)
+   for (i in 1:3) {
+     x1 <- nested_tibble()
+     time[i] <- Sys.time()
+     qsave_rand(x1, file = myfile)
+     z <- qattributes_rand(file = myfile)
+     stopifnot(attributes_identical(z, x1))
+     time[i] <- Sys.time() - time[i]
+     do_gc()
+   }
+   printCarriage(sprintf("nested tibble test: %s s", signif(mean(time), 4)))
+   cat("\n")
+ }
Rep 1 of 2 
strings: 0, 0.007217 s 
strings: 1, 0.0004599 s 
strings: 2, 0.002523 s 
strings: 4, 0.00268 s 
strings: 8, 0.001814 s 
strings: 31, 0.002919 s 
strings: 33, 0.003587 s 
strings: 32, 0.002328 s 
strings: 255, 0.00161 s 
strings: 257, 0.001183 s 
strings: 256, 0.00123 s 
strings: 65535, 0.001323 s 
strings: 65537, 0.002425 s 
strings: 65536, 0.001967 s 
strings: 1e+06, 0.0046 s 

Character Vectors: 0, 0.001111 s 
Character Vectors: 1, 0.0003827 s 
Character Vectors: 2, 0.000879 s 
Character Vectors: 4, 0.0004067 s 
Character Vectors: 8, 0.00182 s 
Character Vectors: 31, 0.0008973 s 
Character Vectors: 33, 0.0002317 s 
Character Vectors: 32, 0.0006947 s 
Character Vectors: 255, 0.001449 s 
Character Vectors: 257, 0.0002631 s 
Character Vectors: 256, 0.001754 s 
Character Vectors: 65535, 0.00183 s 
Character Vectors: 65537, 0.002068 s 
Character Vectors: 65536, 0.003307 s 

Stringfish: 0, 0.000254 s 
Stringfish: 1, 0.0002207 s 
Stringfish: 2, 0.000266 s 
Stringfish: 4, 0.0003331 s 
Stringfish: 8, 0.0005486 s 
Stringfish: 31, 0.0007336 s 
Stringfish: 33, 0.001053 s 
Stringfish: 32, 0.0001361 s 
Stringfish: 255, 0.0007936 s 
Stringfish: 257, 0.0004103 s 
Stringfish: 256, 0.001186 s 
Stringfish: 65535, 0.001838 s 
Stringfish: 65537, 0.002802 s 
Stringfish: 65536, 0.003379 s 

Integers: 0, 0.003322 s 
Integers: 1, 0.003543 s 
Integers: 2, 0.001598 s 
Integers: 4, 0.002948 s 
Integers: 8, 0.0005316 s 
Integers: 31, 0.00205 s 
Integers: 33, 0.00118 s 
Integers: 32, 0.0008347 s 
Integers: 255, 0.002783 s 
Integers: 257, 0.004468 s 
Integers: 256, 0.001511 s 
Integers: 65535, 0.004802 s 
Integers: 65537, 0.002407 s 
Integers: 65536, 0.004634 s 
Integers: 1e+06, 0.03314 s 

Numeric: 0, 0.00198 s 
Numeric: 1, 0.001468 s 
Numeric: 2, 0.002685 s 
Numeric: 4, 0.003357 s 
Numeric: 8, 0.002156 s 
Numeric: 31, 0.003377 s 
Numeric: 33, 0.0036 s 
Numeric: 32, 0.002865 s 
Numeric: 255, 0.001803 s 
Numeric: 257, 0.003363 s 
Numeric: 256, 0.002591 s 
Numeric: 65535, 0.002345 s 
Numeric: 65537, 0.009909 s 
Numeric: 65536, 0.004537 s 
Numeric: 1e+06, 0.02703 s 

Logical: 0, 0.002135 s 
Logical: 1, 0.006207 s 
Logical: 2, 0.0006104 s 
Logical: 4, 0.002894 s 
Logical: 8, 0.000668 s 
Logical: 31, 0.00142 s 
Logical: 33, 0.001882 s 
Logical: 32, 0.001012 s 
Logical: 255, 0.001497 s 
Logical: 257, 0.001348 s 
Logical: 256, 0.001181 s 
Logical: 65535, 0.01272 s 
Logical: 65537, 0.004227 s 
Logical: 65536, 0.009775 s 
Logical: 1e+06, 0.03613 s 

List: 0, 0.004162 s 
List: 1, 0.001691 s 
List: 2, 0.001257 s 
List: 4, 0.004286 s 
List: 8, 0.001194 s 
List: 31, 0.001152 s 
List: 33, 0.001587 s 
List: 32, 0.000596 s 
List: 255, 0.004278 s 
List: 257, 0.001299 s 
List: 256, 0.001729 s 
List: 65535, 0.02305 s 
List: 65537, 0.01892 s 
List: 65536, 0.01603 s 

Data.frame test
Data.table test
Tibble test
(Encoding test not run on windows) 

Complex: 0, 0.001799 s 
Complex: 1, 0.000804 s 
Complex: 2, 0.0006203 s 
Complex: 4, 0.002286 s 
Complex: 8, 0.002401 s 
Complex: 31, 0.005419 s 
Complex: 33, 0.001176 s 
Complex: 32, 0.001283 s 
Complex: 255, 0.002644 s 
Complex: 257, 0.002944 s 
Complex: 256, 0.001247 s 
Complex: 65535, 0.0161 s 
Complex: 65537, 0.02801 s 
Complex: 65536, 0.02327 s 
Complex: 1e+06, 0.259 s 

Factors: 0, 0.002114 s 
Factors: 1, 0.001736 s 
Factors: 2, 0.00171 s 
Factors: 4, 0.002253 s 
Factors: 8, 0.001886 s 
Factors: 31, 0.0005891 s 
Factors: 33, 0.00099 s 
Factors: 32, 0.001936 s 
Factors: 255, 0.002095 s 
Factors: 257, 0.001806 s 
Factors: 256, 0.0007553 s 
Factors: 65535, 0.001093 s 
Factors: 65537, 0.001698 s 
Factors: 65536, 0.002704 s 
Factors: 1e+06, 0.00766 s 

Random objects: 1081792 bytes 
Random objects: 1012336 bytes 
Random objects: 1067992 bytes 
Random objects: 1074528 bytes 
Random objects: 1069776 bytes 
Random objects: 1047344 bytes 
Random objects: 1012824 bytes 
Random objects: 1047008 bytes 
Random objects: 0.02031 s 

Nested attributes: 571900000 s 

Alt rep integer: 0.03843 s 

Environment test: 0.1481 s 

nested tibble test: 0.5875 s 

Rep 2 of 2 
strings: 0, 0.001853 s 
strings: 1, 0.003211 s 
strings: 2, 0.002972 s 
strings: 4, 0.002094 s 
strings: 8, 0.00272 s 
strings: 31, 0.0007211 s 
strings: 33, 0.001206 s 
strings: 32, 0.0006576 s 
strings: 255, 0.001269 s 
strings: 257, 0.001608 s 
strings: 256, 0.002121 s 
strings: 65535, 0.002095 s 
strings: 65537, 0.001953 s 
strings: 65536, 0.002999 s 
strings: 1e+06, 0.004311 s 

Character Vectors: 0, 0.0001707 s 
Character Vectors: 1, 0.0002393 s 
Character Vectors: 2, 0.0004137 s 
Character Vectors: 4, 0.001001 s 
Character Vectors: 8, 0.0004748 s 
Character Vectors: 31, 0.0002903 s 
Character Vectors: 33, 0.0006113 s 
Character Vectors: 32, 0.000445 s 
Character Vectors: 255, 0.000238 s 
Character Vectors: 257, 0.0003254 s 
Character Vectors: 256, 0.0004984 s 
Character Vectors: 65535, 0.002425 s 
Character Vectors: 65537, 0.002589 s 
Character Vectors: 65536, 0.002016 s 

Stringfish: 0, 0.0005353 s 
Stringfish: 1, 0.0008337 s 
Stringfish: 2, 0.0005767 s 
Stringfish: 4, 0.001309 s 
Stringfish: 8, 0.0005047 s 
Stringfish: 31, 0.0002447 s 
Stringfish: 33, 0.0003267 s 
Stringfish: 32, 0.0007002 s 
Stringfish: 255, 0.0004323 s 
Stringfish: 257, 0.0001326 s 
Stringfish: 256, 0.0003564 s 
Stringfish: 65535, 0.002525 s 
Stringfish: 65537, 0.002991 s 
Stringfish: 65536, 0.003118 s 

Integers: 0, 0.001871 s 
Integers: 1, 0.001993 s 
Integers: 2, 0.002732 s 
Integers: 4, 0.001806 s 
Integers: 8, 0.00159 s 
Integers: 31, 0.002241 s 
Integers: 33, 0.001421 s 
Integers: 32, 0.001361 s 
Integers: 255, 0.00169 s 
Integers: 257, 0.001625 s 
Integers: 256, 0.0009278 s 
Integers: 65535, 0.003115 s 
Integers: 65537, 0.005699 s 
Integers: 65536, 0.006261 s 
Integers: 1e+06, 0.008353 s 

Numeric: 0, 0.002521 s 
Numeric: 1, 0.001102 s 
Numeric: 2, 0.002516 s 
Numeric: 4, 0.003601 s 
Numeric: 8, 0.002429 s 
Numeric: 31, 0.002263 s 
Numeric: 33, 0.002822 s 
Numeric: 32, 0.002025 s 
Numeric: 255, 0.001479 s 
Numeric: 257, 0.001532 s 
Numeric: 256, 0.0007857 s 
Numeric: 65535, 0.002638 s 
Numeric: 65537, 0.002051 s 
Numeric: 65536, 0.008729 s 
Numeric: 1e+06, 0.03753 s 

Logical: 0, 0.002017 s 
Logical: 1, 0.003008 s 
Logical: 2, 0.002592 s 
Logical: 4, 0.002514 s 
Logical: 8, 0.002696 s 
Logical: 31, 0.001738 s 
Logical: 33, 0.002527 s 
Logical: 32, 0.002705 s 
Logical: 255, 0.001116 s 
Logical: 257, 0.001596 s 
Logical: 256, 0.000964 s 
Logical: 65535, 0.01517 s 
Logical: 65537, 0.01019 s 
Logical: 65536, 0.007822 s 
Logical: 1e+06, 0.02569 s 

List: 0, 0.006143 s 
List: 1, 0.002981 s 
List: 2, 0.003365 s 
List: 4, 0.001556 s 
List: 8, 0.001365 s 
List: 31, 0.001542 s 
List: 33, 0.002158 s 
List: 32, 0.004966 s 
List: 255, 0.000638 s 
List: 257, 0.001165 s 
List: 256, 0.00171 s 
List: 65535, 0.01611 s 
List: 65537, 0.01644 s 
List: 65536, 0.01749 s 

Data.frame test
Data.table test
Tibble test
(Encoding test not run on windows) 

Complex: 0, 0.002349 s 
Complex: 1, 0.001658 s 
Complex: 2, 0.002005 s 
Complex: 4, 0.002159 s 
Complex: 8, 0.001552 s 
Complex: 31, 0.001944 s 
Complex: 33, 0.002096 s 
Complex: 32, 0.001223 s 
Complex: 255, 0.001073 s 
Complex: 257, 0.001796 s 
Complex: 256, 0.001299 s 
Complex: 65535, 0.01615 s 
Complex: 65537, 0.00914 s 
Complex: 65536, 0.01331 s 
Complex: 1e+06, 0.2975 s 

Factors: 0, 0.002141 s 
Factors: 1, 0.002278 s 
Factors: 2, 0.001331 s 
Factors: 4, 0.0009733 s 
Factors: 8, 0.00144 s 
Factors: 31, 0.0007894 s 
Factors: 33, 0.0006104 s 
Factors: 32, 0.001471 s 
Factors: 255, 0.003057 s 
Factors: 257, 0.005709 s 
Factors: 256, 0.001379 s 
Factors: 65535, 0.002056 s 
Factors: 65537, 0.002635 s 
Factors: 65536, 0.001397 s 
Factors: 1e+06, 0.0121 s 

Random objects: 1027192 bytes 
Random objects: 1102496 bytes 
Random objects: 1061736 bytes 
Random objects: 1048880 bytes 
Random objects: 1048936 bytes 
Random objects: 1023568 bytes 
Random objects: 1086608 bytes 
Random objects: 1029688 bytes 
Random objects: 0.008632 s 

Nested attributes: 571900000 s 

Alt rep integer: 0.02613 s 

Environment test: 0.06981 s 

nested tibble test: 0.3903 s 

> 
> cat("tests done\n")
tests done
> rm(list = setdiff(ls(), c("total_time", "do_gc")))
> do_gc()
          used (Mb) gc trigger  (Mb) max used  (Mb)
Ncells  666671 35.7    2255092 120.5  2894651 154.6
Vcells 2110144 16.1   17903036 136.6 25842560 197.2
> total_time <- Sys.time() - total_time
> print(total_time)
Time difference of 1.526866 mins
> 
> proc.time()
   user  system elapsed 
  92.12   10.62   91.75