[SPARK-20877][SPARKR] refactor tests to basic tests only for CRAN

## What changes were proposed in this pull request?

Move all existing tests to non-installed directory so that it will never run by installing SparkR package

For a follow-up PR:
- remove all skip_on_cran() calls in tests
- clean up test timer
- improve or change basic tests that do run on CRAN (if anyone has suggestion)

It looks like `R CMD build pkg` will still put pkg\tests (ie. the full tests) into the source package but `R CMD INSTALL` on such source package does not install these tests (and so `R CMD check` does not run them)

## How was this patch tested?

- [x] unit tests, Jenkins
- [x] AppVeyor
- [x] make a source package, install it, `R CMD check` it - verify the full tests are not installed or run

Author: Felix Cheung <felixcheung_m@hotmail.com>

Closes #18264 from felixcheung/rtestset.

R/pkg/inst/tests/testthat/test_basic.R

+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+context("basic tests for CRAN")
+
+test_that("create DataFrame from list or data.frame", {
+  sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
+
+  i <- 4
+  df <- createDataFrame(data.frame(dummy = 1:i))
+  expect_equal(count(df), i)
+
+  l <- list(list(a = 1, b = 2), list(a = 3, b = 4))
+  df <- createDataFrame(l)
+  expect_equal(columns(df), c("a", "b"))
+
+  a <- 1:3
+  b <- c("a", "b", "c")
+  ldf <- data.frame(a, b)
+  df <- createDataFrame(ldf)
+  expect_equal(columns(df), c("a", "b"))
+  expect_equal(dtypes(df), list(c("a", "int"), c("b", "string")))
+  expect_equal(count(df), 3)
+  ldf2 <- collect(df)
+  expect_equal(ldf$a, ldf2$a)
+
+  mtcarsdf <- createDataFrame(mtcars)
+  expect_equivalent(collect(mtcarsdf), mtcars)
+
+  bytes <- as.raw(c(1, 2, 3))
+  df <- createDataFrame(list(list(bytes)))
+  expect_equal(collect(df)[[1]][[1]], bytes)
+
+  sparkR.session.stop()
+})
+
+test_that("spark.glm and predict", {
+  sparkR.session(master = sparkRTestMaster, enableHiveSupport = FALSE)
+
+  training <- suppressWarnings(createDataFrame(iris))
+  # gaussian family
+  model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species)
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+  vals <- collect(select(prediction, "prediction"))
+  rVals <- predict(glm(Sepal.Width ~ Sepal.Length + Species, data = iris), iris)
+  expect_true(all(abs(rVals - vals) < 1e-6), rVals - vals)
+
+  # Gamma family
+  x <- runif(100, -1, 1)
+  y <- rgamma(100, rate = 10 / exp(0.5 + 1.2 * x), shape = 10)
+  df <- as.DataFrame(as.data.frame(list(x = x, y = y)))
+  model <- glm(y ~ x, family = Gamma, df)
+  out <- capture.output(print(summary(model)))
+  expect_true(any(grepl("Dispersion parameter for gamma family", out)))
+
+  # tweedie family
+  model <- spark.glm(training, Sepal_Width ~ Sepal_Length + Species,
+                     family = "tweedie", var.power = 1.2, link.power = 0.0)
+  prediction <- predict(model, training)
+  expect_equal(typeof(take(select(prediction, "prediction"), 1)$prediction), "double")
+  vals <- collect(select(prediction, "prediction"))
+
+  # manual calculation of the R predicted values to avoid dependence on statmod
+  #' library(statmod)
+  #' rModel <- glm(Sepal.Width ~ Sepal.Length + Species, data = iris,
+  #'             family = tweedie(var.power = 1.2, link.power = 0.0))
+  #' print(coef(rModel))
+
+  rCoef <- c(0.6455409, 0.1169143, -0.3224752, -0.3282174)
+  rVals <- exp(as.numeric(model.matrix(Sepal.Width ~ Sepal.Length + Species,
+                                       data = iris) %*% rCoef))
+  expect_true(all(abs(rVals - vals) < 1e-5), rVals - vals)
+
+  sparkR.session.stop()
+})