diff --git a/.travis.yml b/.travis.yml
index f3be8bb..a349b99 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -2,10 +2,11 @@ language: R
matrix:
include:
- r: release
- env:
- - HUTILS_BENCHMARK=TRUE
+ env:
+ - HUTILS_BENCHMARK=TRUE
- r: release
- - HUTILS_BENCHMARK=FALSE
+ env:
+ - HUTILS_BENCHMARK=FALSE
- r: devel
cache: packages
latex: true
diff --git a/R/auc.R b/R/auc.R
index 17becad..b354b7b 100644
--- a/R/auc.R
+++ b/R/auc.R
@@ -6,7 +6,7 @@
#' an ordered factor, or the unique values are \code{FALSE} and \code{TRUE} (case-insensitively).
#' Anything else is an error.
#' @param pred Numeric (double) vector the same length as \code{actual} giving the predicted probability of \code{TRUE}. Must be a numeric vector the same length as \code{actual}.
-#' @source Source code based on \code{\link[Metrics]{auc}} from Ben Hamner and Michael Frasco and Erin LeDell from the Metrics package.
+#' @source Source code based on \code{Metrics::auc} from Ben Hamner and Michael Frasco and Erin LeDell from the Metrics package.
#' @export auc
#' @author
@@ -30,7 +30,8 @@ auc <- function(actual, pred) {
if (!is.double(pred)) {
# Switch the values
if (is.logical(pred) && is.double(actual)) {
- # Immeediate return
+ # Immediate return
+ message("`pred` was type logical and `actual` was type double, so interpreting as auc(pred, actual). Set to auc(actual, pred) for standard behaviour.")
return(auc(actual = pred, pred = actual))
}
diff --git a/inst/doc/hutils.R b/inst/doc/hutils.R
index b45bb79..d920417 100644
--- a/inst/doc/hutils.R
+++ b/inst/doc/hutils.R
@@ -39,9 +39,8 @@ na <- sample(letters, size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
cnd <- sample(c(TRUE, FALSE, NA), size = 100e3, replace = TRUE)
yes <- sample(letters, size = 1, replace = TRUE)
@@ -50,9 +49,8 @@ na <- sample(letters, size = 1, replace = TRUE)
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
## ----compare_coalesce----------------------------------------------------
x <- sample(c(letters, NA), size = 100e3, replace = TRUE)
@@ -62,18 +60,16 @@ C <- sample(c(letters, NA), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
## ----compare_coalesce_short_circuit_x------------------------------------
x <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
## ----compare_coalesce_short_circuit_A------------------------------------
x <- sample(c(letters, NA), size = 100e3, replace = TRUE)
@@ -81,9 +77,8 @@ A <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
## ----canonical_drop_DT---------------------------------------------------
DT <- data.table(A = 1:5, B = 1:5, C = 1:5)
diff --git a/inst/doc/hutils.Rmd b/inst/doc/hutils.Rmd
index 31fe5be..9cd722e 100644
--- a/inst/doc/hutils.Rmd
+++ b/inst/doc/hutils.Rmd
@@ -87,9 +87,8 @@ na <- sample(letters, size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
cnd <- sample(c(TRUE, FALSE, NA), size = 100e3, replace = TRUE)
yes <- sample(letters, size = 1, replace = TRUE)
@@ -98,9 +97,8 @@ na <- sample(letters, size = 1, replace = TRUE)
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
This speed advantage also appears to be true of `coalesce`:
@@ -113,9 +111,8 @@ C <- sample(c(letters, NA), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
especially during short-circuits:
@@ -125,9 +122,8 @@ x <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
```{r compare_coalesce_short_circuit_A}
@@ -136,9 +132,8 @@ A <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
diff --git a/inst/doc/hutils.html b/inst/doc/hutils.html
index c4fbdf4..c4304a5 100644
--- a/inst/doc/hutils.html
+++ b/inst/doc/hutils.html
@@ -12,7 +12,7 @@
-
+
Hutils
@@ -70,7 +70,7 @@
Hutils
Hugh Parsonage
-2018-05-04
+2018-05-12
@@ -96,10 +96,6 @@ hutils package
error = function(e) {
opts_chunk$set(eval = FALSE)
})
-## data.table 1.11.0
-## The fastest way to learn (by data.table authors): https://www.datacamp.com/courses/data-analysis-the-data-table-way
-## Documentation: ?data.table, example(data.table) and browseVignettes("data.table")
-## Release notes, videos and slides: http://r-datatable.com
Aliases
These are simple additions to magrittr‘s aliases, including: capitalized forms of and and or that invoke && and || (the ’long-form’ logical operators) and nor / neither functions.
@@ -132,14 +128,12 @@
coalesce and if_else
microbenchmark(
dplyr = dplyr
::if_else(cnd, yes, no, na),
hutils = hutils
::if_else(cnd, yes, no, na),
-
check = my_check)
%T>%
-
print
%>%
-
autoplot
+ check = my_check) %>%
+ print
## Unit: milliseconds
-## expr min lq mean median uq max neval cld
-## dplyr 5.741339 6.519730 10.106987 6.809764 11.375758 154.1358 100 b
-## hutils 1.967590 2.277802 3.541882 2.332767 2.485915 59.7201 100 a
-
+## expr min lq mean median uq max neval cld
+## dplyr 5.336251 6.632064 10.313847 7.036243 12.16995 168.55931 100 b
+## hutils 1.951324 2.073151 2.702137 2.398271 2.51332 14.38661 100 a
cnd <- sample(c(TRUE, FALSE, NA), size = 100e3, replace = TRUE)
yes <- sample(letters, size = 1, replace = TRUE)
no <- sample(letters, size = 100e3, replace = TRUE)
@@ -147,14 +141,12 @@ coalesce and if_else
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
## Unit: milliseconds
-## expr min lq mean median uq max neval cld
-## dplyr 4.985685 5.363662 7.245224 5.404472 10.556256 15.006147 100 b
-## hutils 1.172181 1.396708 1.874132 1.419448 1.442187 8.039921 100 a
-
+## expr min lq mean median uq max neval cld
+## dplyr 4.187562 4.960684 6.705858 5.545116 5.955771 13.898406 100 b
+## hutils 1.167061 1.264641 1.716801 1.455437 1.513413 8.251943 100 a
This speed advantage also appears to be true of coalesce:
x <- sample(c(letters, NA), size = 100e3, replace = TRUE)
A <- sample(c(letters, NA), size = 100e3, replace = TRUE)
@@ -163,43 +155,40 @@ coalesce and if_else
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
## Unit: milliseconds
-## expr min lq mean median uq max neval cld
-## dplyr 2.343760 2.695083 5.814904 2.726406 7.818556 148.89049 100 b
-## hutils 1.000812 1.258770 2.092449 1.284369 1.315390 12.13834 100 a
-
+ check = my_check) %>%
+ print
+## Unit: microseconds
+## expr min lq mean median uq max neval cld
+## dplyr 1885.668 2686.046 4078.867 2770.525 6241.738 9323.229 100 a
+## hutils 993.582 1236.481 3475.583 1275.182 1333.310 167157.033 100 a
especially during short-circuits:
x <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
## Unit: microseconds
-## expr min lq mean median uq max neval
-## dplyr 2081.736 2536.0615 5728.88782 2574.914 7626.5565 149937.386 100
-## hutils 58.730 61.2905 74.99327 67.464 83.1255 160.527 100
+## expr min lq mean median uq max neval
+## dplyr 1690.807 2386.0735 4259.60067 2578.6760 2669.481 60602.191 100
+## hutils 58.429 60.9885 71.95122 65.8065 79.511 127.699 100
## cld
## b
## a
-
x <- sample(c(letters, NA), size = 100e3, replace = TRUE)
A <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
## Unit: microseconds
-## expr min lq mean median uq max neval cld
-## dplyr 2303.402 2681.681 4545.0994 2726.5565 7743.262 9070.248 100 b
-## hutils 574.043 745.564 978.0727 764.9905 790.289 6912.616 100 a
-
+## expr min lq mean median uq max neval
+## dplyr 1813.386 2536.0590 3670.791 2721.5840 2813.8950 8877.487 100
+## hutils 567.718 680.0575 2681.907 764.5375 796.7635 165714.396 100
+## cld
+## a
+## a
Drop columns
@@ -465,9 +454,9 @@
Haversine distance
## expr
## DT1[, `:=`(distance, haversine_distance(lat_orig, lon_orig, lat_dest, lon_dest))]
## DT2[, `:=`(distance, distHaversine(cbind(lon_orig, lat_orig), cbind(lon_orig, lat_orig)))]
-## min lq mean median uq max neval cld
-## 20.92458 21.06041 22.23995 21.13359 21.25361 35.10851 100 a
-## 37.78748 38.29225 49.01212 46.64103 49.33160 199.04370 100 b
+## min lq mean median uq max neval cld
+## 21.24983 21.38235 22.75108 21.4730 21.57962 80.80091 100 a
+## 37.65071 38.64655 48.38211 47.0524 49.93059 215.68083 100 b
diff --git a/man/auc.Rd b/man/auc.Rd
index c398ffe..a6e3c05 100644
--- a/man/auc.Rd
+++ b/man/auc.Rd
@@ -4,7 +4,7 @@
\alias{auc}
\title{AUC}
\source{
-Source code based on \code{\link[Metrics]{auc}} from Ben Hamner and Michael Frasco and Erin LeDell from the Metrics package.
+Source code based on \code{Metrics::auc} from Ben Hamner and Michael Frasco and Erin LeDell from the Metrics package.
}
\usage{
auc(actual, pred)
diff --git a/tests/testthat/test_benchmarks.R b/tests/testthat/test_benchmarks.R
index fbb14da..14f4d4d 100644
--- a/tests/testthat/test_benchmarks.R
+++ b/tests/testthat/test_benchmarks.R
@@ -5,15 +5,19 @@ test_that("Benchmarks", {
skip_if_not(identical(Sys.getenv("HUTILS_BENCHMARK"), "TRUE"))
library(microbenchmark)
invisible(gc(FALSE, reset = TRUE, full = TRUE))
- z <- as.logical(1:10 %% 3)
- len10 <- microbenchmark(if_else(z, 1, 2))
- len10 <- microbenchmark(if_else(z, 1, 2))
- expect_lt(median(len10$time), 4500)
z <- as.logical(1:1e5 %% 3)
- len10 <- microbenchmark(if_else(z, 1:1e5, 1:1e5))
- expect_lt(median(len10$time), 1e6)
+ len1e5c <- microbenchmark(if_else(z, "a", rep_len(letters, 1e5)))
+ len1e5c_ifelse <- microbenchmark(base::ifelse(z, "a", rep_len(letters, 1e5)))
+ expect_lt(median(len1e5c$time) / median(len1e5c_ifelse$time), 0.1)
+
+ z <- as.logical(1:1e4 %% 9)
+ z[1:1e4 %% 8 == 3] <- NA
+ len1e4c <- microbenchmark(if_else(z, "a", rep_len(letters, 1e4)))
+ len1e4c_ifelse <- microbenchmark(base::ifelse(z, "a", rep_len(letters, 1e4)))
+ expect_lt(median(len1e4c$time), 2e6)
})
+
diff --git a/vignettes/hutils.Rmd b/vignettes/hutils.Rmd
index 31fe5be..9cd722e 100644
--- a/vignettes/hutils.Rmd
+++ b/vignettes/hutils.Rmd
@@ -87,9 +87,8 @@ na <- sample(letters, size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
cnd <- sample(c(TRUE, FALSE, NA), size = 100e3, replace = TRUE)
yes <- sample(letters, size = 1, replace = TRUE)
@@ -98,9 +97,8 @@ na <- sample(letters, size = 1, replace = TRUE)
microbenchmark(dplyr = dplyr::if_else(cnd, yes, no, na),
hutils = hutils::if_else(cnd, yes, no, na),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
This speed advantage also appears to be true of `coalesce`:
@@ -113,9 +111,8 @@ C <- sample(c(letters, NA), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
especially during short-circuits:
@@ -125,9 +122,8 @@ x <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```
```{r compare_coalesce_short_circuit_A}
@@ -136,9 +132,8 @@ A <- sample(c(letters), size = 100e3, replace = TRUE)
microbenchmark(dplyr = dplyr::coalesce(x, A, B, C),
hutils = hutils::coalesce(x, A, B, C),
- check = my_check) %T>%
- print %>%
- autoplot
+ check = my_check) %>%
+ print
```