tests/testthat/test-prepdataremoval.R
In Displayr/flipChart: Single function for calling charts - CChart
context("PrepareData Removing Rows and Columns")
test_that("PrepareData R+C removal: single table, single stat",
{
input.data.table <- structure(c(48.3870967741936, 51.6129032258064, 100, 52.6315789473684,
47.3684210526316, 100, 48.936170212766, 51.063829787234, 100,
42.3076923076923, 57.6923076923077, 100, 55.3191489361702, 44.6808510638298,
100, 50, 50, 100, 41.3793103448276, 58.6206896551724, 100, 58.0645161290323,
41.9354838709677, 100, 50, 50, 100), .Dim = c(3L, 9L), statistic = "Column %", .Dimnames = list(
c("Male", "Female", "NET"), c("Less than 18 + 18 to 24 + 25 to 29",
"30 to 34", "35 to 39", "40 to 44", "45 to 49", "50 to 54",
"55 to 64", "65 or more", "NET")), name = "Q1 by Q2", questions = c("Q1", "Q2"))
QFilter <- structure(TRUE, name = "", label = "Total sample")
QPopulationWeight <- NULL
chart.type <- "Pie"
out <- PrepareData(chart.type, QFilter, QPopulationWeight, input.data.table = input.data.table, column.names.to.remove = NULL,
row.names.to.remove = NULL)
expect_equal(attr(out$data, "statistic"), attr(input.data.table, "statistic"))
expect_is(out$data, "matrix")
expect_equal(dim(out$data), dim(input.data.table))
out <- PrepareData(chart.type, QFilter, QPopulationWeight, input.data.table = input.data.table, row.names.to.remove = "NET",
column.names.to.remove = "NET")
expect_equal(attr(out$data, "statistic"), attr(input.data.table, "statistic"))
expect_is(out$data, "matrix")
expect_equal(dim(out$data), dim(input.data.table) - c(1, 1))
})
test_that("PrepareData R+C removal: multiple existing tables",
{
input.data.tables <- list(structure(c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12), .Names = c("Blueberry",
"Cherry", "Apple", "Boston Cream", "Other", "Vanilla Cream")),
structure(c(42.625, 11.125, 17.875, 9, 2.5, 14.875, 0.75,
1.25, 100), .Dim = 9L, statistic = "%", .Dimnames = list(
c("Coca-Cola", "Diet Coke", "Coke Zero", "Pepsi ", "Diet Pepsi",
"Pepsi Max", "Dislike all cola", "Don't care", "NET")), name = "Preferred cola",
questions = c("Preferred cola",
"SUMMARY")))
out <- PrepareData(input.data.table = NULL, input.data.raw = NULL, input.data.tables = input.data.tables, input.data.other = NULL,
chart.type = "Scatter Plot", row.names.to.remove = "NET", column.names.to.remove = "NET")
expect_length(out$data, 2)
expect_equal(attr(out$data[[2]], "statistic"), "%")
expect_length(out$data[[1]], length(input.data.tables[[1]] ))
expect_length(out$data[[2]], length(input.data.tables[[2]]) - 1)
})
test_that("PredpareData R+C removal: pasted raw data",
{
## list(get0("formPastedData"), get0("formPastedRawData"), get0("formPastedFactor"), get0("formPastedColumnNames"),
## get0("formPastedRowNames"), get0("formPastedDateConvention"))
dat <- rbind(c("", LETTERS[1:4]), cbind(letters[1:3], matrix(as.character(1:12), 3, 4)))
pasted <- list(dat, FALSE, TRUE, TRUE)
out <- PrepareData(input.data.table = NULL, input.data.raw = NULL, input.data.tables = NULL, input.data.other = NULL,
input.data.pasted = pasted, chart.type = "Bubble Chart", row.names.to.remove = "a",
column.names.to.remove = LETTERS[2:3])
expect_is(out$data, "matrix")
expect_equal(colnames(out$data), LETTERS[c(1, 4)])
expect_equal(rownames(out$data), letters[2:3])
})
test_that("PrepareData R+C removal: pasted raw data factor handling",
{
## list(get0("formPastedData"), get0("formPastedRawData"), get0("formPastedFactor"), get0("formPastedColumnNames"),
## get0("formPastedRowNames"), get0("formPastedDateConvention"))
dat <- rbind(c("", LETTERS[1:4]), cbind(letters[1:3], matrix(as.character(1:12), 3, 4)))
dat[-1, 3] <- c("dog", "cat", "dog")
pasted <- list(dat, FALSE, TRUE, TRUE)
out <- suppressWarnings(PrepareData(input.data.pasted = pasted, chart.type = "Table",
column.names.to.remove = "D"))
expect_is(out$data, "matrix")
expect_is(out$data[[2]], "numeric")
out <- suppressWarnings(PrepareData(input.data.pasted = pasted, chart.type = "Table", tidy = FALSE,
column.names.to.remove = "D"))
expect_is(out$data, "data.frame")
expect_is(out$data[[1]], "numeric")
expect_is(out$data[[2]], "character")
})
test_that("PrepareData R+C removal: pasted, non-raw table",
{
dat <- structure(c("", "a", "v", "NET", "SUM", "col 1", "2", "3", "1", "2",
"col 2", "3", "2", "1", "1", "col 3", "3", "2", "1", "1", "col 4",
"3", "2", "1", "1", "col 5", "3", "2", "1", "1", "col 6", "3",
"2", "1", "1", "SUM", "3", "2", "1", "1", "NET", "3", "2",
"1", "1"), .Dim = c(5L, 9L))
pasted <- list(dat, FALSE)
QFilter <- structure(TRUE, name = "", label = "Total sample")
QPopulationWeight <- NULL
chart.type <- "Scatter"
expect_error(out <- PrepareData(input.data.pasted = pasted, chart.type = chart.type,
subset = QFilter, weights = QPopulationWeight, tidy = TRUE), NA)
expect_is(out$data, "matrix")
expect_equal(dim(out$data), dim(dat) - c(3, 3))
})
test_that("PrepareData R+C removal: Binary variable for venn diagram",
{
input.data.other <- structure(list(`Coca-Cola` = c(0, 1, 0, 0, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0,
1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,
1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,
0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1,
0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0), `Diet Coke` = c(1, 0,
1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,
0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,
1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,
0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,
0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,
0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0
), `Coke Zero` = c(0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0,
1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,
1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,
0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,
0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,
0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,
0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0), `Pepsi + Pepsi Max + Diet Pepsi` = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,
1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0,
1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,
1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,
0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,
0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,
1)), class = "data.frame", .Names = c("Coca-Cola", "Diet Coke",
"Coke Zero", "Pepsi + Pepsi Max + Diet Pepsi"),
row.names = c(NA,
800L), questiontype = "PickAny", question = "Brand attitude: Love + Like")
QFilter <- rbinom(nrow(input.data.other), 1, .25)
n.filter <- sum(QFilter== 1)
out <- suppressWarnings(PrepareData(input.data.other = input.data.other, chart.type = "Scatter Plot",
subset = QFilter, column.names.to.remove = colnames(input.data.other)[1:2],
hide.empty.columns = FALSE, hide.empty.rows = FALSE))
expect_is(out$data, "data.frame")
expect_named(out$data, names(input.data.other)[3:4])
expect_equal(nrow(out$data), n.filter)
})
test_that("PrepareData pasted vector entry removal",
{
dat <- cbind(letters[1:5], 1:5)
pasted <- list(dat, FALSE, NULL, NULL, NULL, NULL)
QFilter <- structure(TRUE, name = "", label = "Total sample")
QPopulationWeight <- NULL
chart.type <- "Scatter Plot"
out <- PrepareData(input.data.pasted = pasted, chart.type = chart.type, subset = QFilter,
weights = QPopulationWeight, row.names.to.remove = "a", column.names.to.remove = "b")
expect_is(out$data, "numeric")
expect_null(dim(out$data))
# expect_named(names(out$data), letters[2:5])
pasted[[1]] <- t(dat)
out <- PrepareData(input.data.pasted = pasted, chart.type = chart.type, subset = QFilter,
weights = QPopulationWeight, row.names.to.remove = c("b", "e", "q"), column.names.to.remove = "e")
expect_named(out$data, c("a", "c", "d"))
})
test_that("PrepareData R+C removal: input.data.raw with missing vals",
{
dat <- structure(list(structure.c.2L..1L..2L..2L..2L..1L..1L..1L..2L..2L..2L..1L..2L.. = structure(c(2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L), class = "factor", .Label = c("Male",
"Female"), questiontype = "PickOne", name = "d3", label = "Gender", question = "Gender"),
structure.c.7L..4L..7L..2L..NA..3L..7L..5L..5L..7L..2L..5L..2L.. = structure(c(7L,
4L, 7L, 2L, NA, 3L, 7L, 5L, 5L, 7L, 2L, 5L, 2L, 6L, 4L, 4L,
4L, 2L, 8L, 6L, 5L, 5L, 4L, 3L, NA, 2L, 6L, 5L, 9L, NA, 6L,
NA, 5L, 4L, 7L, NA, 4L, NA, 4L, 3L, 7L, 6L, 1L, 4L, 1L, 4L,
2L, 5L, NA, NA, 4L, 2L, 6L, 4L, 7L, 8L, 9L, 4L, 5L, 6L, 5L,
6L, 2L, 3L, 2L, 3L, 2L, 5L, NA, 4L, 4L, 2L, 3L, 9L, 5L, 7L,
8L, 8L, 9L, 5L, 2L, 7L, 5L, 1L, 3L, 3L, 8L, 4L, 5L, 2L, 1L,
6L, 4L, 6L, 5L, 2L, 3L, 2L, 6L, 9L, 7L, NA, NA, 2L, 8L, 2L,
9L, 3L, 2L, 8L, 2L, 4L, 8L, 6L, 6L, 6L, 3L, 3L, 5L, 1L, 4L,
8L, 6L, 2L, 9L, 7L, NA, 3L, 6L, NA, 4L, 6L, NA, 4L, NA, 2L,
3L, 5L, 6L, 5L, 5L, 6L, 5L, 4L, 1L, 6L, NA, NA, 7L, NA, 5L,
3L, 6L, 8L, 5L, 5L, 8L, NA, 4L, 5L, 2L, 3L, 3L, 4L, 6L, 5L,
NA, 4L, 6L, 7L, 6L, 5L, 4L, 6L, 3L, 5L, 4L, 4L, 8L, 2L, 5L,
5L, 5L, 4L, NA, 6L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
7L, 4L, 4L, 3L, 7L, 4L, NA, 7L, 3L, 7L, 5L, 4L, 4L, 7L, 5L,
2L, 7L, 6L, 5L, 4L, 4L, 2L, 8L, 6L, 5L, 3L, 4L, 5L, 6L, NA,
6L, NA, 5L, 4L, NA, NA, 4L, NA, 4L, 4L, 2L, 3L, 5L, 3L, 4L,
6L, 4L, 4L, 8L, 2L, 5L, NA, 5L, 4L, NA, 5L, 2L, 4L, 8L, 9L,
4L, 6L, NA, 6L, 9L, 3L, NA, 5L, NA, NA, 7L, 4L, 6L, 4L, 4L,
NA, 2L, 5L, 5L, 8L, 8L, 2L, 9L, 3L, 2L, 5L, 5L, 2L, 7L, 5L,
1L, 5L, 3L, 9L, 4L, 5L, 2L, 7L, 6L, 6L, 5L, 4L, 2L, 2L, 5L,
6L, 9L, NA, NA, 3L, NA, 6L, NA, 2L, 2L, 9L, 8L, 6L, 2L, 9L,
4L, 8L, 6L, 6L, 5L, 3L, 5L, 3L, 9L, 5L, 1L, 8L, 5L, 9L, 7L,
9L, NA, 3L, 6L, NA, 4L, 6L, NA, 8L, 7L, 4L, 5L, 5L, 2L, 1L,
5L, 5L, 6L, 1L, 6L, NA, NA, 7L, 5L, 3L, 6L, 9L, 5L, 8L, 4L,
9L, 3L, 2L, 5L, 5L, 5L, NA, 6L, 7L, 4L, 5L, 5L, NA, 3L, 4L,
8L, 5L, 2L, 5L, 5L, 4L, 4L, 4L, 5L, 7L, 8L, 5L, 6L, 4L, 6L,
6L, 6L, 6L, 7L, 4L, 4L, 5L, 4L, 3L, 7L, 4L, NA, 2L, 3L, NA,
3L, 7L, 5L, 5L, 4L, 7L, 2L, 5L, 2L, 7L, 4L, 5L, 4L, 8L, 3L,
4L, 2L, 5L, 2L, 5L, 9L, 6L, 5L, 5L, NA, 7L, NA, NA, NA, 4L,
5L, NA, 4L, 7L, 4L, 1L, 4L, 8L, 1L, 2L, 5L, NA, 5L, 5L, 5L,
NA, 2L, 4L, 7L, 5L, 6L, 5L, 3L, 9L, 3L, 5L, 5L, NA, NA, 7L,
4L, 4L, 3L, 9L, 5L, 8L, 9L, 3L, 2L, 5L, 2L, NA, 8L, 7L, 5L,
1L, 5L, 3L, 3L, 4L, 8L, 9L, 1L, 2L, 7L, 1L, 6L, 4L, 6L, 2L,
5L, 6L, 9L, NA, 6L, 7L, 3L, 3L, 6L, NA, 2L, 8L, 9L, 3L, 2L,
6L, 9L, 6L, NA, 6L, 5L, 3L, 4L, 3L, NA, 5L, 5L, 1L, 4L, 5L,
8L, 9L, 7L, 9L, NA, 7L, 3L, NA, 6L, 5L, NA, 2L, 3L, NA, 4L,
5L, 5L, 6L, 1L, 5L, 4L, 1L, 6L, 8L, NA, NA, 7L, NA, 5L, 3L,
6L, 5L, 5L, 5L, 8L, 4L, 4L, 9L, 5L, 3L, 4L, 5L, 5L, 4L, 7L,
5L, 4L, 6L, 3L, 5L, 5L, 3L, 4L, 3L, 4L, 5L, 5L, 4L, NA, 4L,
7L, 8L, 5L, 6L, 6L, 3L, 6L, 5L, 6L, 7L, 4L, 4L, 5L, 3L, NA,
7L, 2L, NA, 3L, 7L, 7L, 7L, 5L, 4L, 7L, 5L, 7L, 4L, 5L, 4L,
8L, 6L, 5L, 5L, 5L, 3L, NA, 5L, 6L, 9L, NA, 6L, 5L, 5L, NA,
4L, 4L, NA, 3L, NA, 4L, 6L, 4L, 5L, 5L, NA, 5L, NA, 5L, 4L,
6L, 4L, 8L, 4L, 5L, NA, 6L, 2L, 9L, 2L, 2L, NA, 7L, 4L, 4L,
NA, 2L, 9L, 7L, 8L, 8L, 2L, 9L, 3L, 5L, 5L, 5L, 2L, NA, 8L,
7L, 5L, 1L, 4L, 9L, 4L, 7L, 1L, 5L, 2L, 7L, 1L, 6L, 4L, 2L,
3L, 4L, 2L, 5L, 6L, NA, NA, 6L, 3L, 6L, 2L, 2L, 9L, 3L, 2L,
6L, 2L, 4L, NA, 8L, 6L, 6L, NA, 3L, 3L, 4L, 9L, 5L, 4L, 5L,
6L, 8L, 2L, 9L, 7L, 9L, NA, 7L, 3L, 6L, NA, 4L, 6L, NA, 8L,
4L, NA, 2L, 3L, NA, 6L, 5L, 6L, 2L, 1L, 4L, 1L, 8L, NA, 3L,
8L, 5L, 9L, 5L, NA, 9L, 2L, 5L, 2L, 5L, 6L, 5L, 5L, 4L, 6L,
2L, 6L, 6L, 3L, 5L, 5L, 3L, 4L, 4L, 5L, 4L, 5L, 2L, 5L, 5L,
4L, 4L, 5L, 5L, 6L, 4L, 6L, 6L, 6L, 2L, 3L, 5L, 6L, 7L, 4L,
4L, 4L, 4L, 3L), class = "factor", .Label = c("Less than $15,000",
"$15,001 to $30,000", "$30,001 to $45,000", "$45,001 to $60,000",
"$60,001 to $90,000", "$90,001 to $120,000", "$120,001 to $150,000",
"$150,001 to $200,000", "$200,001 or more"), questiontype = "PickOne",
name = "d2", label = "Income", question = "Income")),
.Names = c("Gender", "Income"),
row.names = c(NA, -800L), class = "data.frame")
out <- suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bubble Chart",
column.names.to.remove = "Income"))
expect_is(out$data, "data.frame")
## Bubble Charts don't have aggre. so will remove Income var.
expect_equal(ncol(out$data), ncol(dat) - 1)
expect_equal(dim(out$data), dim(dat) - c(0, 1))
## Bar Charts have aggregation, so Gender, Income aren't removed
## as aggregation happens before removal
expect_error(suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
first.aggregate = FALSE,
column.names.to.remove = c("Gender", "Income"))),
"Removing column 'Gender', 'Income' gives empty input matrix")
pd <- suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
first.aggregate = TRUE,
column.names.to.remove = c("Gender", "Income")))
expect_equal(names(pd$data), c("Gender", "Income"))
z = suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
first.aggregate = TRUE,
row.names.to.remove = c("Male")))
expect_equal(length(z$data), 2)
# Note this unit test is a bit ambitious as it assumes that users will provide a vector
# which is pretty unlikely. However, it is left in as it tests the more general splitting mechanism.
out <- suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
column.names.to.remove = levels(dat[[2L]])[2:3],
split = "$") )# don't split on "," since levels have commas
})
input.data.table <- structure(c(7.08868501529052, 3.84709480122324, 17.4617737003058
), .Dim = 3L, statistic = "Average", .Dimnames = list(c("Colas (e.g., Coca-Cola, Pepsi Max)?",
"Sparkling mineral water", "SUM")), name = "Number Multi", questions = c("Number Multi",
"SUMMARY"))
test_that("PrepareData R+C removal: 1D Q-Table",
{
out <- PrepareData(input.data.table = input.data.table, chart.type = "Bubble Chart")
expect_equal(names(out$data), names(input.data.table)[-3])
})
test_that("PrepareData hide empty rows and columns QTable",
{
input.data.table[1L] <- NA
out <- PrepareData(input.data.table = input.data.table, chart.type = "Bubble Chart")
expect_equal(out$data, input.data.table[2], check.attributes = FALSE)
})
test_that("PrepareData hide empty rows and columns list of tables",
{
input.data.table[1] <- NA
d2 <- input.data.table
attr(d2, "statistic") <- "Column %"
d2[2] <- 0
x <- list(input.data.table, d2)
#expect_error(PrepareData(input.data.tables = x, chart.type = "Bubble Chart"),
# "Hiding empty elements gives empty input vector.")
x[[2]][1] <- 5.3
out <- PrepareData(input.data.tables = x, chart.type = "Donut Chart",
row.names.to.remove = NULL, column.names.to.remove = NULL)
expect_equal(length(out$data[[1]]), sum(!is.na(input.data.table)))
expect_equal(length(out$data[[2]]), sum(!is.na(x[[2]]) & x[[2]] != 0))
})
test_that("PrepareData, hide empty rows and columns, list of factors",
{
data(colas, package = "flipExampleData")
RawData.XFactor.YFactor = list(X = colas$d1, Y = colas$d2)
pd <- suppressWarnings(PrepareData("Bean", input.data.raw = RawData.XFactor.YFactor))
expect_is(pd$data, "list")
expect_is(pd$data[[1L]], "factor")
expect_equal(levels(pd$data[[2L]]), levels(RawData.XFactor.YFactor$X))
expect_true(all(names(pd$data) %in% levels(RawData.XFactor.YFactor$Y)))
})
Displayr/flipChart documentation built on Sept. 20, 2024, 10:56 a.m.
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context("PrepareData Removing Rows and Columns")
test_that("PrepareData R+C removal: single table, single stat",
{
input.data.table <- structure(c(48.3870967741936, 51.6129032258064, 100, 52.6315789473684,
47.3684210526316, 100, 48.936170212766, 51.063829787234, 100,
42.3076923076923, 57.6923076923077, 100, 55.3191489361702, 44.6808510638298,
100, 50, 50, 100, 41.3793103448276, 58.6206896551724, 100, 58.0645161290323,
41.9354838709677, 100, 50, 50, 100), .Dim = c(3L, 9L), statistic = "Column %", .Dimnames = list(
c("Male", "Female", "NET"), c("Less than 18 + 18 to 24 + 25 to 29",
"30 to 34", "35 to 39", "40 to 44", "45 to 49", "50 to 54",
"55 to 64", "65 or more", "NET")), name = "Q1 by Q2", questions = c("Q1", "Q2"))
QFilter <- structure(TRUE, name = "", label = "Total sample")
QPopulationWeight <- NULL
chart.type <- "Pie"
out <- PrepareData(chart.type, QFilter, QPopulationWeight, input.data.table = input.data.table, column.names.to.remove = NULL,
row.names.to.remove = NULL)
expect_equal(attr(out$data, "statistic"), attr(input.data.table, "statistic"))
expect_is(out$data, "matrix")
expect_equal(dim(out$data), dim(input.data.table))
out <- PrepareData(chart.type, QFilter, QPopulationWeight, input.data.table = input.data.table, row.names.to.remove = "NET",
column.names.to.remove = "NET")
expect_equal(attr(out$data, "statistic"), attr(input.data.table, "statistic"))
expect_is(out$data, "matrix")
expect_equal(dim(out$data), dim(input.data.table) - c(1, 1))
})
test_that("PrepareData R+C removal: multiple existing tables",
{
input.data.tables <- list(structure(c(0.12, 0.3, 0.26, 0.16, 0.04, 0.12), .Names = c("Blueberry",
"Cherry", "Apple", "Boston Cream", "Other", "Vanilla Cream")),
structure(c(42.625, 11.125, 17.875, 9, 2.5, 14.875, 0.75,
1.25, 100), .Dim = 9L, statistic = "%", .Dimnames = list(
c("Coca-Cola", "Diet Coke", "Coke Zero", "Pepsi ", "Diet Pepsi",
"Pepsi Max", "Dislike all cola", "Don't care", "NET")), name = "Preferred cola",
questions = c("Preferred cola",
"SUMMARY")))
out <- PrepareData(input.data.table = NULL, input.data.raw = NULL, input.data.tables = input.data.tables, input.data.other = NULL,
chart.type = "Scatter Plot", row.names.to.remove = "NET", column.names.to.remove = "NET")
expect_length(out$data, 2)
expect_equal(attr(out$data[[2]], "statistic"), "%")
expect_length(out$data[[1]], length(input.data.tables[[1]] ))
expect_length(out$data[[2]], length(input.data.tables[[2]]) - 1)
})
test_that("PredpareData R+C removal: pasted raw data",
{
## list(get0("formPastedData"), get0("formPastedRawData"), get0("formPastedFactor"), get0("formPastedColumnNames"),
## get0("formPastedRowNames"), get0("formPastedDateConvention"))
dat <- rbind(c("", LETTERS[1:4]), cbind(letters[1:3], matrix(as.character(1:12), 3, 4)))
pasted <- list(dat, FALSE, TRUE, TRUE)
out <- PrepareData(input.data.table = NULL, input.data.raw = NULL, input.data.tables = NULL, input.data.other = NULL,
input.data.pasted = pasted, chart.type = "Bubble Chart", row.names.to.remove = "a",
column.names.to.remove = LETTERS[2:3])
expect_is(out$data, "matrix")
expect_equal(colnames(out$data), LETTERS[c(1, 4)])
expect_equal(rownames(out$data), letters[2:3])
})
test_that("PrepareData R+C removal: pasted raw data factor handling",
{
## list(get0("formPastedData"), get0("formPastedRawData"), get0("formPastedFactor"), get0("formPastedColumnNames"),
## get0("formPastedRowNames"), get0("formPastedDateConvention"))
dat <- rbind(c("", LETTERS[1:4]), cbind(letters[1:3], matrix(as.character(1:12), 3, 4)))
dat[-1, 3] <- c("dog", "cat", "dog")
pasted <- list(dat, FALSE, TRUE, TRUE)
out <- suppressWarnings(PrepareData(input.data.pasted = pasted, chart.type = "Table",
column.names.to.remove = "D"))
expect_is(out$data, "matrix")
expect_is(out$data[[2]], "numeric")
out <- suppressWarnings(PrepareData(input.data.pasted = pasted, chart.type = "Table", tidy = FALSE,
column.names.to.remove = "D"))
expect_is(out$data, "data.frame")
expect_is(out$data[[1]], "numeric")
expect_is(out$data[[2]], "character")
})
test_that("PrepareData R+C removal: pasted, non-raw table",
{
dat <- structure(c("", "a", "v", "NET", "SUM", "col 1", "2", "3", "1", "2",
"col 2", "3", "2", "1", "1", "col 3", "3", "2", "1", "1", "col 4",
"3", "2", "1", "1", "col 5", "3", "2", "1", "1", "col 6", "3",
"2", "1", "1", "SUM", "3", "2", "1", "1", "NET", "3", "2",
"1", "1"), .Dim = c(5L, 9L))
pasted <- list(dat, FALSE)
QFilter <- structure(TRUE, name = "", label = "Total sample")
QPopulationWeight <- NULL
chart.type <- "Scatter"
expect_error(out <- PrepareData(input.data.pasted = pasted, chart.type = chart.type,
subset = QFilter, weights = QPopulationWeight, tidy = TRUE), NA)
expect_is(out$data, "matrix")
expect_equal(dim(out$data), dim(dat) - c(3, 3))
})
test_that("PrepareData R+C removal: Binary variable for venn diagram",
{
input.data.other <- structure(list(`Coca-Cola` = c(0, 1, 0, 0, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0,
1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0,
1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1,
1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0,
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,
1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1,
1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1,
0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1,
1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1,
0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0), `Diet Coke` = c(1, 0,
1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1,
0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0,
1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1,
1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0,
1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0,
0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0,
0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,
1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 1,
0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1,
1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0,
1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,
1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0,
1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,
0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0,
0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0
), `Coke Zero` = c(0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0,
1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1,
0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0,
1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1,
0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0,
0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0,
0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,
1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1,
1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1,
0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,
1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0,
0, 1, 1, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0,
0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,
0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0,
0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1,
0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1,
0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0,
0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1,
0, 0, 1, 1, 1, 0, 0, 0, 0), `Pepsi + Pepsi Max + Diet Pepsi` = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0,
1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0,
0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0,
0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1,
1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1,
1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1,
1, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1,
1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1,
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0,
1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0,
1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1,
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0,
1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0,
0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1,
0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,
1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1,
1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1,
1)), class = "data.frame", .Names = c("Coca-Cola", "Diet Coke",
"Coke Zero", "Pepsi + Pepsi Max + Diet Pepsi"),
row.names = c(NA,
800L), questiontype = "PickAny", question = "Brand attitude: Love + Like")
QFilter <- rbinom(nrow(input.data.other), 1, .25)
n.filter <- sum(QFilter== 1)
out <- suppressWarnings(PrepareData(input.data.other = input.data.other, chart.type = "Scatter Plot",
subset = QFilter, column.names.to.remove = colnames(input.data.other)[1:2],
hide.empty.columns = FALSE, hide.empty.rows = FALSE))
expect_is(out$data, "data.frame")
expect_named(out$data, names(input.data.other)[3:4])
expect_equal(nrow(out$data), n.filter)
})
test_that("PrepareData pasted vector entry removal",
{
dat <- cbind(letters[1:5], 1:5)
pasted <- list(dat, FALSE, NULL, NULL, NULL, NULL)
QFilter <- structure(TRUE, name = "", label = "Total sample")
QPopulationWeight <- NULL
chart.type <- "Scatter Plot"
out <- PrepareData(input.data.pasted = pasted, chart.type = chart.type, subset = QFilter,
weights = QPopulationWeight, row.names.to.remove = "a", column.names.to.remove = "b")
expect_is(out$data, "numeric")
expect_null(dim(out$data))
# expect_named(names(out$data), letters[2:5])
pasted[[1]] <- t(dat)
out <- PrepareData(input.data.pasted = pasted, chart.type = chart.type, subset = QFilter,
weights = QPopulationWeight, row.names.to.remove = c("b", "e", "q"), column.names.to.remove = "e")
expect_named(out$data, c("a", "c", "d"))
})
test_that("PrepareData R+C removal: input.data.raw with missing vals",
{
dat <- structure(list(structure.c.2L..1L..2L..2L..2L..1L..1L..1L..2L..2L..2L..1L..2L.. = structure(c(2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L,
2L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 1L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 1L,
2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L,
1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L), class = "factor", .Label = c("Male",
"Female"), questiontype = "PickOne", name = "d3", label = "Gender", question = "Gender"),
structure.c.7L..4L..7L..2L..NA..3L..7L..5L..5L..7L..2L..5L..2L.. = structure(c(7L,
4L, 7L, 2L, NA, 3L, 7L, 5L, 5L, 7L, 2L, 5L, 2L, 6L, 4L, 4L,
4L, 2L, 8L, 6L, 5L, 5L, 4L, 3L, NA, 2L, 6L, 5L, 9L, NA, 6L,
NA, 5L, 4L, 7L, NA, 4L, NA, 4L, 3L, 7L, 6L, 1L, 4L, 1L, 4L,
2L, 5L, NA, NA, 4L, 2L, 6L, 4L, 7L, 8L, 9L, 4L, 5L, 6L, 5L,
6L, 2L, 3L, 2L, 3L, 2L, 5L, NA, 4L, 4L, 2L, 3L, 9L, 5L, 7L,
8L, 8L, 9L, 5L, 2L, 7L, 5L, 1L, 3L, 3L, 8L, 4L, 5L, 2L, 1L,
6L, 4L, 6L, 5L, 2L, 3L, 2L, 6L, 9L, 7L, NA, NA, 2L, 8L, 2L,
9L, 3L, 2L, 8L, 2L, 4L, 8L, 6L, 6L, 6L, 3L, 3L, 5L, 1L, 4L,
8L, 6L, 2L, 9L, 7L, NA, 3L, 6L, NA, 4L, 6L, NA, 4L, NA, 2L,
3L, 5L, 6L, 5L, 5L, 6L, 5L, 4L, 1L, 6L, NA, NA, 7L, NA, 5L,
3L, 6L, 8L, 5L, 5L, 8L, NA, 4L, 5L, 2L, 3L, 3L, 4L, 6L, 5L,
NA, 4L, 6L, 7L, 6L, 5L, 4L, 6L, 3L, 5L, 4L, 4L, 8L, 2L, 5L,
5L, 5L, 4L, NA, 6L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L,
7L, 4L, 4L, 3L, 7L, 4L, NA, 7L, 3L, 7L, 5L, 4L, 4L, 7L, 5L,
2L, 7L, 6L, 5L, 4L, 4L, 2L, 8L, 6L, 5L, 3L, 4L, 5L, 6L, NA,
6L, NA, 5L, 4L, NA, NA, 4L, NA, 4L, 4L, 2L, 3L, 5L, 3L, 4L,
6L, 4L, 4L, 8L, 2L, 5L, NA, 5L, 4L, NA, 5L, 2L, 4L, 8L, 9L,
4L, 6L, NA, 6L, 9L, 3L, NA, 5L, NA, NA, 7L, 4L, 6L, 4L, 4L,
NA, 2L, 5L, 5L, 8L, 8L, 2L, 9L, 3L, 2L, 5L, 5L, 2L, 7L, 5L,
1L, 5L, 3L, 9L, 4L, 5L, 2L, 7L, 6L, 6L, 5L, 4L, 2L, 2L, 5L,
6L, 9L, NA, NA, 3L, NA, 6L, NA, 2L, 2L, 9L, 8L, 6L, 2L, 9L,
4L, 8L, 6L, 6L, 5L, 3L, 5L, 3L, 9L, 5L, 1L, 8L, 5L, 9L, 7L,
9L, NA, 3L, 6L, NA, 4L, 6L, NA, 8L, 7L, 4L, 5L, 5L, 2L, 1L,
5L, 5L, 6L, 1L, 6L, NA, NA, 7L, 5L, 3L, 6L, 9L, 5L, 8L, 4L,
9L, 3L, 2L, 5L, 5L, 5L, NA, 6L, 7L, 4L, 5L, 5L, NA, 3L, 4L,
8L, 5L, 2L, 5L, 5L, 4L, 4L, 4L, 5L, 7L, 8L, 5L, 6L, 4L, 6L,
6L, 6L, 6L, 7L, 4L, 4L, 5L, 4L, 3L, 7L, 4L, NA, 2L, 3L, NA,
3L, 7L, 5L, 5L, 4L, 7L, 2L, 5L, 2L, 7L, 4L, 5L, 4L, 8L, 3L,
4L, 2L, 5L, 2L, 5L, 9L, 6L, 5L, 5L, NA, 7L, NA, NA, NA, 4L,
5L, NA, 4L, 7L, 4L, 1L, 4L, 8L, 1L, 2L, 5L, NA, 5L, 5L, 5L,
NA, 2L, 4L, 7L, 5L, 6L, 5L, 3L, 9L, 3L, 5L, 5L, NA, NA, 7L,
4L, 4L, 3L, 9L, 5L, 8L, 9L, 3L, 2L, 5L, 2L, NA, 8L, 7L, 5L,
1L, 5L, 3L, 3L, 4L, 8L, 9L, 1L, 2L, 7L, 1L, 6L, 4L, 6L, 2L,
5L, 6L, 9L, NA, 6L, 7L, 3L, 3L, 6L, NA, 2L, 8L, 9L, 3L, 2L,
6L, 9L, 6L, NA, 6L, 5L, 3L, 4L, 3L, NA, 5L, 5L, 1L, 4L, 5L,
8L, 9L, 7L, 9L, NA, 7L, 3L, NA, 6L, 5L, NA, 2L, 3L, NA, 4L,
5L, 5L, 6L, 1L, 5L, 4L, 1L, 6L, 8L, NA, NA, 7L, NA, 5L, 3L,
6L, 5L, 5L, 5L, 8L, 4L, 4L, 9L, 5L, 3L, 4L, 5L, 5L, 4L, 7L,
5L, 4L, 6L, 3L, 5L, 5L, 3L, 4L, 3L, 4L, 5L, 5L, 4L, NA, 4L,
7L, 8L, 5L, 6L, 6L, 3L, 6L, 5L, 6L, 7L, 4L, 4L, 5L, 3L, NA,
7L, 2L, NA, 3L, 7L, 7L, 7L, 5L, 4L, 7L, 5L, 7L, 4L, 5L, 4L,
8L, 6L, 5L, 5L, 5L, 3L, NA, 5L, 6L, 9L, NA, 6L, 5L, 5L, NA,
4L, 4L, NA, 3L, NA, 4L, 6L, 4L, 5L, 5L, NA, 5L, NA, 5L, 4L,
6L, 4L, 8L, 4L, 5L, NA, 6L, 2L, 9L, 2L, 2L, NA, 7L, 4L, 4L,
NA, 2L, 9L, 7L, 8L, 8L, 2L, 9L, 3L, 5L, 5L, 5L, 2L, NA, 8L,
7L, 5L, 1L, 4L, 9L, 4L, 7L, 1L, 5L, 2L, 7L, 1L, 6L, 4L, 2L,
3L, 4L, 2L, 5L, 6L, NA, NA, 6L, 3L, 6L, 2L, 2L, 9L, 3L, 2L,
6L, 2L, 4L, NA, 8L, 6L, 6L, NA, 3L, 3L, 4L, 9L, 5L, 4L, 5L,
6L, 8L, 2L, 9L, 7L, 9L, NA, 7L, 3L, 6L, NA, 4L, 6L, NA, 8L,
4L, NA, 2L, 3L, NA, 6L, 5L, 6L, 2L, 1L, 4L, 1L, 8L, NA, 3L,
8L, 5L, 9L, 5L, NA, 9L, 2L, 5L, 2L, 5L, 6L, 5L, 5L, 4L, 6L,
2L, 6L, 6L, 3L, 5L, 5L, 3L, 4L, 4L, 5L, 4L, 5L, 2L, 5L, 5L,
4L, 4L, 5L, 5L, 6L, 4L, 6L, 6L, 6L, 2L, 3L, 5L, 6L, 7L, 4L,
4L, 4L, 4L, 3L), class = "factor", .Label = c("Less than $15,000",
"$15,001 to $30,000", "$30,001 to $45,000", "$45,001 to $60,000",
"$60,001 to $90,000", "$90,001 to $120,000", "$120,001 to $150,000",
"$150,001 to $200,000", "$200,001 or more"), questiontype = "PickOne",
name = "d2", label = "Income", question = "Income")),
.Names = c("Gender", "Income"),
row.names = c(NA, -800L), class = "data.frame")
out <- suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bubble Chart",
column.names.to.remove = "Income"))
expect_is(out$data, "data.frame")
## Bubble Charts don't have aggre. so will remove Income var.
expect_equal(ncol(out$data), ncol(dat) - 1)
expect_equal(dim(out$data), dim(dat) - c(0, 1))
## Bar Charts have aggregation, so Gender, Income aren't removed
## as aggregation happens before removal
expect_error(suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
first.aggregate = FALSE,
column.names.to.remove = c("Gender", "Income"))),
"Removing column 'Gender', 'Income' gives empty input matrix")
pd <- suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
first.aggregate = TRUE,
column.names.to.remove = c("Gender", "Income")))
expect_equal(names(pd$data), c("Gender", "Income"))
z = suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
first.aggregate = TRUE,
row.names.to.remove = c("Male")))
expect_equal(length(z$data), 2)
# Note this unit test is a bit ambitious as it assumes that users will provide a vector
# which is pretty unlikely. However, it is left in as it tests the more general splitting mechanism.
out <- suppressWarnings(PrepareData(input.data.raw = dat, chart.type = "Bar Chart",
column.names.to.remove = levels(dat[[2L]])[2:3],
split = "$") )# don't split on "," since levels have commas
})
input.data.table <- structure(c(7.08868501529052, 3.84709480122324, 17.4617737003058
), .Dim = 3L, statistic = "Average", .Dimnames = list(c("Colas (e.g., Coca-Cola, Pepsi Max)?",
"Sparkling mineral water", "SUM")), name = "Number Multi", questions = c("Number Multi",
"SUMMARY"))
test_that("PrepareData R+C removal: 1D Q-Table",
{
out <- PrepareData(input.data.table = input.data.table, chart.type = "Bubble Chart")
expect_equal(names(out$data), names(input.data.table)[-3])
})
test_that("PrepareData hide empty rows and columns QTable",
{
input.data.table[1L] <- NA
out <- PrepareData(input.data.table = input.data.table, chart.type = "Bubble Chart")
expect_equal(out$data, input.data.table[2], check.attributes = FALSE)
})
test_that("PrepareData hide empty rows and columns list of tables",
{
input.data.table[1] <- NA
d2 <- input.data.table
attr(d2, "statistic") <- "Column %"
d2[2] <- 0
x <- list(input.data.table, d2)
#expect_error(PrepareData(input.data.tables = x, chart.type = "Bubble Chart"),
# "Hiding empty elements gives empty input vector.")
x[[2]][1] <- 5.3
out <- PrepareData(input.data.tables = x, chart.type = "Donut Chart",
row.names.to.remove = NULL, column.names.to.remove = NULL)
expect_equal(length(out$data[[1]]), sum(!is.na(input.data.table)))
expect_equal(length(out$data[[2]]), sum(!is.na(x[[2]]) & x[[2]] != 0))
})
test_that("PrepareData, hide empty rows and columns, list of factors",
{
data(colas, package = "flipExampleData")
RawData.XFactor.YFactor = list(X = colas$d1, Y = colas$d2)
pd <- suppressWarnings(PrepareData("Bean", input.data.raw = RawData.XFactor.YFactor))
expect_is(pd$data, "list")
expect_is(pd$data[[1L]], "factor")
expect_equal(levels(pd$data[[2L]]), levels(RawData.XFactor.YFactor$X))
expect_true(all(names(pd$data) %in% levels(RawData.XFactor.YFactor$Y)))
})
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