| /dports/math/R-cran-recipes/recipes/tests/testthat/ |
| H A D | test_downsample.R | 5 iris2 <- iris[-(1:45),] globalVar
6 iris2$Species[seq(6, 96, by = 5)] <- NA
7 iris2$Species2 <- sample(iris2$Species)
8 iris2$Species3 <- as.character(sample(iris2$Species))
10 rec <- recipe( ~ ., data = iris2)
24 rec1_p <- prep(rec1, training = iris2)
36 og_xtab <- table(iris2$Species, useNA = "always")
42 expect_warning(prep(rec1, training = iris2), NA)
50 rec2_p <- prep(rec2, training = iris2)
67 rec3_p <- prep(rec3, training = iris2)
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| H A D | test_upsample.R | 5 iris2 <- iris[-(1:45),] globalVar
6 iris2$Species[seq(6, 96, by = 5)] <- NA
7 iris2$Species2 <- sample(iris2$Species)
8 iris2$Species3 <- as.character(sample(iris2$Species))
10 rec <- recipe( ~ ., data = iris2)
23 rec1_p <- prep(rec1, training = iris2)
40 expect_warning(prep(rec1, training = iris2), NA)
48 rec2_p <- prep(rec2, training = iris2)
56 sum(is.na(iris2$Species)))
66 rec3_p <- prep(rec3, training = iris2)
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| H A D | test_sample.R | 7 iris2 <- iris %>% mutate(row = 1:150) globalVar
8 iris_rec <- recipe( ~ ., data = iris2)
16 prep(training = iris2) %>%
24 prep(training = iris2) %>%
32 prep(training = iris2) %>%
40 prep(training = iris2) %>%
48 prep(training = iris2) %>%
56 prep(training = iris2 %>% slice(1:120))
59 expect_equal(bake(smaller_iris, iris2 %>% slice(121:150)) %>% nrow(), 30)
64 prep(training = iris2) %>%
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| /dports/math/R-cran-units/units/tests/testthat/ |
| H A D | test_tidyverse.R | 80 iris2 <- iris globalVar
82 units(iris2[,i]) <- "cm"
84 out <- iris2 %>%
93 exp <- lapply(split(iris2[1:4], iris2$Species), lapply, mean)
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| /dports/devel/R-cran-pillar/pillar/tests/testthat/ |
| H A D | test-glimpse.R | 49 iris2 <- as_override_tbl_sum(iris) globalVar
52 glimpse(iris2),
90 iris2 <- as_unknown_rows(iris) globalVar
91 glimpse(iris2, width = 70L)
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| /dports/devel/R-cran-diffobj/diffobj/tests/ |
| H A D | test-file.R | 81 iris2 <- iris globalVar
82 iris2$Sepal.Length[25] <- 9.9
85 write.csv(iris2, f2, row.names=FALSE)
88 as.character(diffPrint(iris, iris2, tar.banner="f1", cur.banner="f2")),
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| H A D | test-file.Rout.save | 108 > iris2 <- iris
109 > iris2$Sepal.Length[25] <- 9.9
112 > write.csv(iris2, f2, row.names=FALSE)
115 + as.character(diffPrint(iris, iris2, tar.banner="f1", cur.banner="f2")),
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| /dports/devel/R-cran-tibble/tibble/tests/testthat/ |
| H A D | test-zzz-glimpse.R | 113 iris2 <- as_unknown_rows(iris) globalVar
116 glimpse(iris2, width = 70L),
124 iris2 <- as_override_tbl_sum(iris) globalVar
127 glimpse(iris2),
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| H A D | test-zzz-options.R | 95 iris2 <- as_tibble(iris) globalVar
97 withr::with_options(list(max.print = 3), print(iris2)),
98 capture_output(print(iris2)),
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| H A D | test-zzz-tbl-df.R | 315 iris2 <- as_tibble(iris) globalVar
316 expect_equal(iris2[[1, 2]], iris[[1, 2]])
317 expect_equal(iris2[[2, 3]], iris[[2, 3]])
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| /dports/misc/orange3/orange3-3.29.1/Orange/classification/tests/ |
| H A D | test_base.py | 28 cls.iris0, cls.iris1, cls.iris2 = tables
74 val2, prob2 = model(self.iris2, model.ValueProbs)
112 model = lrn()(self.iris2)
114 val2, prob2 = model(self.iris2, model.ValueProbs)
134 model2 = lrn()(self.iris2)
135 valp2 = model2(self.iris2)
143 val2, prob2 = model0(self.iris2, model0.ValueProbs)
157 val2, prob2 = model1(self.iris2, model1.ValueProbs)
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| /dports/math/R-cran-dplyr/dplyr/inst/doc/ |
| H A D | base.R | 98 iris2 <- iris globalVar
99 names(iris2)[2] <- "sepal_width"
102 names(iris2)[names(iris2) == "Sepal.Length"] <- "sepal_length"
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| H A D | base.Rmd | 236 iris2 <- iris
237 names(iris2)[2] <- "sepal_width"
243 names(iris2)[names(iris2) == "Sepal.Length"] <- "sepal_length"
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| /dports/textproc/R-cran-openxlsx/openxlsx/man/ |
| H A D | NamedRegion.Rd | 48 writeData(wb, sheet = 1, x = iris, name = "iris2", startCol = 10)
59 deleteNamedRegion(wb = wb, name = "iris2")
66 df <- read.xlsx(out_file, namedRegion = "iris2")
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| H A D | getNamedRegions.Rd | 33 writeData(wb, sheet = 1, x = iris, name = "iris2", startCol = 10)
46 df <- read.xlsx(out_file, namedRegion = "iris2")
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| /dports/misc/orange3/orange3-3.29.1/Orange/widgets/evaluate/tests/ |
| H A D | test_owpredictions.py | 299 iris2 = self.iris.transform(dom)
301 predictor_iris1 = ConstantLearner()(iris2)
302 predictor_iris2 = ConstantLearner()(iris2)
326 iris2 = self.iris[:100].transform(dom2)
329 predictor_iris2 = ConstantLearner()(iris2)
349 iris2 = self.iris[:100].transform(dom2)
352 predictor_iris2 = ConstantLearner()(iris2)
370 iris2 = self.iris.transform(dom2)
373 predictor_iris2 = ConstantLearner()(iris2)
394 iris2 = self.iris[:100].transform(dom2)
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| /dports/math/R-cran-dplyr/dplyr/man/ |
| H A D | copy_to.Rd | 32 iris2 <- dbplyr::src_memdb() \%>\% copy_to(iris, overwrite = TRUE)
33 iris2
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| /dports/misc/orange3/orange3-3.29.1/Orange/widgets/data/tests/ |
| H A D | test_owselectrows.py | 280 iris2 = iris.transform(Domain(domain.attributes[:2], None))
281 self.send_signal(self.widget.Inputs.data, iris2)
306 iris2 = Table.from_numpy(new_domain, iris.X[non0], iris.Y[non0] - 1)
307 self.send_signal(self.widget.Inputs.data, iris2)
323 iris2 = Table.from_numpy(new_domain, iris.X[non0], iris.Y[non0] - 1)
324 self.send_signal(self.widget.Inputs.data, iris2)
337 iris2 = Table.from_numpy(new_domain, iris.X[non0], iris.Y[non0] - 1)
338 self.send_signal(self.widget.Inputs.data, iris2)
351 iris2 = iris.transform(Domain(domain.attributes[2:], None))
352 self.send_signal(self.widget.Inputs.data, iris2)
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| /dports/misc/orange3/orange3-3.29.1/doc/data-mining-library/source/reference/ |
| H A D | distance.rst | 27 >>> iris2 = iris[100:]
37 >>> dist = dist_model(iris2[:3])
43 The above distances are computed on the first three rows of `iris2`, normalized
46 Here are five closest neighbors of `iris2[0]` from `iris1`::
48 >>> dist0 = dist_model(iris1, iris2[0])
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| /dports/textproc/R-cran-rio/rio/man/ |
| H A D | import.Rd | 91 export(iris, "iris2.csv", col.names = FALSE)
92 iris2 <- import("iris2.csv")
93 identical(names(iris), names(iris2))
101 unlink("iris2.csv")
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| /dports/devel/R-cran-glue/glue/man/ |
| H A D | glue_sql.Rd | 58 iris2 <- iris
59 colnames(iris2) <- gsub("[.]", "_", tolower(colnames(iris)))
60 DBI::dbWriteTable(con, "iris", iris2)
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| /dports/devel/R-cran-pillar/pillar/tests/testthat/_snaps/ |
| H A D | glimpse.md | 193 iris2 <- as_unknown_rows(iris)
194 glimpse(iris2, width = 70L)
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| /dports/math/R-cran-dplyr/dplyr/vignettes/ |
| H A D | base.Rmd | 236 iris2 <- iris
237 names(iris2)[2] <- "sepal_width"
243 names(iris2)[names(iris2) == "Sepal.Length"] <- "sepal_length"
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| /dports/devel/spark/spark-2.1.1/R/pkg/inst/tests/testthat/ |
| H A D | test_mllib.R | 1164 iris2 <- iris[iris$Species != "virginica", ] globalVar
1165 data <- suppressWarnings(createDataFrame(iris2))
1189 iris2$NumericSpecies <- ifelse(iris2$Species == "setosa", 0, 1)
1190 df <- suppressWarnings(createDataFrame(iris2))
1194 expect_equal(iris2$NumericSpecies, as.double(collect(predict(m, df))$prediction))
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| /dports/science/R-cran-e1071/e1071/man/ |
| H A D | svm.Rd | 275 iris2 = scale(iris[,-5])
279 data = iris2, kernel = "linear")
282 plot(Petal.Length ~ Petal.Width, data = iris2, col = setosa)
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