R Under development (unstable) (2023-11-26 r85638 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, 0.004285 s 
strings: 1, 0.002713 s 
strings: 2, 0.002434 s 
strings: 4, 0.0006791 s 
strings: 8, 0.001765 s 
strings: 31, 0.002454 s 
strings: 33, 0.003492 s 
strings: 32, 0.002041 s 
strings: 255, 0.001398 s 
strings: 257, 0.001801 s 
strings: 256, 0.002071 s 
strings: 65535, 0.002093 s 
strings: 65537, 0.002475 s 
strings: 65536, 0.00124 s 
strings: 1e+06, 0.004435 s 

Character Vectors: 0, 0.0007241 s 
Character Vectors: 1, 0.0004996 s 
Character Vectors: 2, 0.000996 s 
Character Vectors: 4, 0.0004276 s 
Character Vectors: 8, 0.0001396 s 
Character Vectors: 31, 0.0003519 s 
Character Vectors: 33, 0.0008553 s 
Character Vectors: 32, 0.001674 s 
Character Vectors: 255, 0.0006882 s 
Character Vectors: 257, 0.0006126 s 
Character Vectors: 256, 0.0007621 s 
Character Vectors: 65535, 0.002752 s 
Character Vectors: 65537, 0.002373 s 
Character Vectors: 65536, 0.002508 s 

Stringfish: 0, 0.0001361 s 
Stringfish: 1, 0.001051 s 
Stringfish: 2, 0.0007301 s 
Stringfish: 4, 0.000966 s 
Stringfish: 8, 0.0005033 s 
Stringfish: 31, 0.000132 s 
Stringfish: 33, 0.0005401 s 
Stringfish: 32, 0.000132 s 
Stringfish: 255, 0.0002019 s 
Stringfish: 257, 0.0004307 s 
Stringfish: 256, 0.001021 s 
Stringfish: 65535, 0.002306 s 
Stringfish: 65537, 0.003788 s 
Stringfish: 65536, 0.003004 s 

Integers: 0, 0.004646 s 
Integers: 1, 0.003299 s 
Integers: 2, 0.00264 s 
Integers: 4, 0.002114 s 
Integers: 8, 0.001566 s 
Integers: 31, 0.002087 s 
Integers: 33, 0.0006483 s 
Integers: 32, 0.001665 s 
Integers: 255, 0.001737 s 
Integers: 257, 0.002048 s 
Integers: 256, 0.001667 s 
Integers: 65535, 0.005952 s 
Integers: 65537, 0.004426 s 
Integers: 65536, 0.007549 s 
Integers: 1e+06, 0.01926 s 

Numeric: 0, 0.001366 s 
Numeric: 1, 0.002675 s 
Numeric: 2, 0.003089 s 
Numeric: 4, 0.0009507 s 
Numeric: 8, 0.0009343 s 
Numeric: 31, 0.001681 s 
Numeric: 33, 0.001543 s 
Numeric: 32, 0.004977 s 
Numeric: 255, 0.0009123 s 
Numeric: 257, 0.001572 s 
Numeric: 256, 0.001576 s 
Numeric: 65535, 0.003527 s 
Numeric: 65537, 0.01383 s 
Numeric: 65536, 0.009889 s 
Numeric: 1e+06, 0.06502 s 

Logical: 0, 0.000795 s 
Logical: 1, 0.0023 s 
Logical: 2, 0.003087 s 
Logical: 4, 0.001448 s 
Logical: 8, 0.002527 s 
Logical: 31, 0.002183 s 
Logical: 33, 0.002563 s 
Logical: 32, 0.001259 s 
Logical: 255, 0.005712 s 
Logical: 257, 0.001521 s 
Logical: 256, 0.002171 s 
Logical: 65535, 0.007729 s 
Logical: 65537, 0.001347 s 
Logical: 65536, 0.008621 s 
Logical: 1e+06, 0.1147 s 

List: 0, 0.004499 s 
List: 1, 0.002055 s 
List: 2, 0.002514 s 
List: 4, 0.0009618 s 
List: 8, 0.001738 s 
List: 31, 0.002824 s 
List: 33, 0.004049 s 
List: 32, 0.001454 s 
List: 255, 0.001411 s 
List: 257, 0.001911 s 
List: 256, 0.002302 s 
List: 65535, 0.01526 s 
List: 65537, 0.0144 s 
List: 65536, 0.009912 s 

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

Complex: 0, 0.007951 s 
Complex: 1, 0.004017 s 
Complex: 2, 0.002802 s 
Complex: 4, 0.001707 s 
Complex: 8, 0.001489 s 
Complex: 31, 0.001962 s 
Complex: 33, 0.0005167 s 
Complex: 32, 0.001809 s 
Complex: 255, 0.001068 s 
Complex: 257, 0.00136 s 
Complex: 256, 0.001106 s 
Complex: 65535, 0.01229 s 
Complex: 65537, 0.007112 s 
Complex: 65536, 0.02094 s 
Complex: 1e+06, 0.209 s 

Factors: 0, 0.001822 s 
Factors: 1, 0.0009041 s 
Factors: 2, 0.001144 s 
Factors: 4, 0.0008463 s 
Factors: 8, 0.002282 s 
Factors: 31, 0.002179 s 
Factors: 33, 0.002815 s 
Factors: 32, 0.001187 s 
Factors: 255, 0.001433 s 
Factors: 257, 0.004131 s 
Factors: 256, 0.00188 s 
Factors: 65535, 0.002669 s 
Factors: 65537, 0.001199 s 
Factors: 65536, 0.004032 s 
Factors: 1e+06, 0.005145 s 

Random objects: 1069520 bytes 
Random objects: 1058248 bytes 
Random objects: 1046056 bytes 
Random objects: 1023528 bytes 
Random objects: 623352 bytes 
Random objects: 1024632 bytes 
Random objects: 1020296 bytes 
Random objects: 1065344 bytes 
Random objects: 0.008669 s 

Nested attributes: 5.67e+08 s 

Alt rep integer: 0.01152 s 

Environment test: 0.08248 s 

nested tibble test: 0.7926 s 

Rep 2 of 2 
strings: 0, 0.007601 s 
strings: 1, 0.002717 s 
strings: 2, 0.002222 s 
strings: 4, 0.006519 s 
strings: 8, 0.0051 s 
strings: 31, 0.002046 s 
strings: 33, 0.005102 s 
strings: 32, 0.002181 s 
strings: 255, 0.002254 s 
strings: 257, 0.001453 s 
strings: 256, 0.001682 s 
strings: 65535, 0.006379 s 
strings: 65537, 0.002468 s 
strings: 65536, 0.001377 s 
strings: 1e+06, 0.003277 s 

Character Vectors: 0, 0.0002358 s 
Character Vectors: 1, 0.0006483 s 
Character Vectors: 2, 0.0008029 s 
Character Vectors: 4, 0.0007227 s 
Character Vectors: 8, 0.0008673 s 
Character Vectors: 31, 0.00092 s 
Character Vectors: 33, 0.000787 s 
Character Vectors: 32, 0.000154 s 
Character Vectors: 255, 0.001123 s 
Character Vectors: 257, 0.0006607 s 
Character Vectors: 256, 0.0004873 s 
Character Vectors: 65535, 0.001829 s 
Character Vectors: 65537, 0.002133 s 
Character Vectors: 65536, 0.003153 s 

Stringfish: 0, 0.0008179 s 
Stringfish: 1, 0.000661 s 
Stringfish: 2, 0.0001733 s 
Stringfish: 4, 0.002309 s 
Stringfish: 8, 0.001167 s 
Stringfish: 31, 0.0007517 s 
Stringfish: 33, 0.0008056 s 
Stringfish: 32, 0.0004024 s 
Stringfish: 255, 0.0009068 s 
Stringfish: 257, 0.0001717 s 
Stringfish: 256, 0.00079 s 
Stringfish: 65535, 0.001719 s 
Stringfish: 65537, 0.00279 s 
Stringfish: 65536, 0.002175 s 

Integers: 0, 0.002136 s 
Integers: 1, 0.001225 s 
Integers: 2, 0.001681 s 
Integers: 4, 0.001617 s 
Integers: 8, 0.001991 s 
Integers: 31, 0.001724 s 
Integers: 33, 0.002754 s 
Integers: 32, 0.001097 s 
Integers: 255, 0.003457 s 
Integers: 257, 0.001595 s 
Integers: 256, 0.002273 s 
Integers: 65535, 0.005941 s 
Integers: 65537, 0.005857 s 
Integers: 65536, 0.004347 s 
Integers: 1e+06, 0.01296 s 

Numeric: 0, 0.002551 s 
Numeric: 1, 0.001323 s 
Numeric: 2, 0.0008134 s 
Numeric: 4, 0.005456 s 
Numeric: 8, 0.005111 s 
Numeric: 31, 0.00165 s 
Numeric: 33, 0.001145 s 
Numeric: 32, 0.00177 s 
Numeric: 255, 0.002199 s 
Numeric: 257, 0.001508 s 
Numeric: 256, 0.001524 s 
Numeric: 65535, 0.002548 s 
Numeric: 65537, 0.01537 s 
Numeric: 65536, 0.003058 s 
Numeric: 1e+06, 0.01929 s 

Logical: 0, 0.002113 s 
Logical: 1, 0.001734 s 
Logical: 2, 0.002381 s 
Logical: 4, 0.005044 s 
Logical: 8, 0.000982 s 
Logical: 31, 0.001216 s 
Logical: 33, 0.001507 s 
Logical: 32, 0.002845 s 
Logical: 255, 0.005598 s 
Logical: 257, 0.0008516 s 
Logical: 256, 0.001437 s 
Logical: 65535, 0.002515 s 
Logical: 65537, 0.008086 s 
Logical: 65536, 0.004626 s 
Logical: 1e+06, 0.0448 s 

List: 0, 0.003517 s 
List: 1, 0.003051 s 
List: 2, 0.001464 s 
List: 4, 0.00346 s 
List: 8, 0.003685 s 
List: 31, 0.002578 s 
List: 33, 0.001687 s 
List: 32, 0.003504 s 
List: 255, 0.005448 s 
List: 257, 0.001777 s 
List: 256, 0.002213 s 
List: 65535, 0.01503 s 
List: 65537, 0.01578 s 
List: 65536, 0.01949 s 

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

Complex: 0, 0.007703 s 
Complex: 1, 0.004035 s 
Complex: 2, 0.00421 s 
Complex: 4, 0.003322 s 
Complex: 8, 0.001886 s 
Complex: 31, 0.002159 s 
Complex: 33, 0.002638 s 
Complex: 32, 0.001156 s 
Complex: 255, 0.002224 s 
Complex: 257, 0.001839 s 
Complex: 256, 0.001504 s 
Complex: 65535, 0.01398 s 
Complex: 65537, 0.01118 s 
Complex: 65536, 0.008236 s 
Complex: 1e+06, 0.3406 s 

Factors: 0, 0.006902 s 
Factors: 1, 0.005459 s 
Factors: 2, 0.00419 s 
Factors: 4, 0.001453 s 
Factors: 8, 0.002786 s 
Factors: 31, 0.001744 s 
Factors: 33, 0.001889 s 
Factors: 32, 0.001435 s 
Factors: 255, 0.001923 s 
Factors: 257, 0.001847 s 
Factors: 256, 0.00137 s 
Factors: 65535, 0.004354 s 
Factors: 65537, 0.003325 s 
Factors: 65536, 0.002077 s 
Factors: 1e+06, 0.008247 s 

Random objects: 1023312 bytes 
Random objects: 1024136 bytes 
Random objects: 1061672 bytes 
Random objects: 1088592 bytes 
Random objects: 1072712 bytes 
Random objects: 1070560 bytes 
Random objects: 1100200 bytes 
Random objects: 1020144 bytes 
Random objects: 0.01098 s 

Nested attributes: 5.67e+08 s 

Alt rep integer: 0.006821 s 

Environment test: 0.07755 s 

nested tibble test: 0.2454 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  647120 34.6    2082345 111.3  2982938 159.4
Vcells 2071796 15.9   17903036 136.6 25842617 197.2
> total_time <- Sys.time() - total_time
> print(total_time)
Time difference of 1.60778 mins
> 
> proc.time()
   user  system elapsed 
  92.68    9.90   96.61