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Sign upColumns with missing values only in plot_missing() #127
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…ues. This remove visual clutter for datasets where only a small proportion of data is missing.
| ## Declare variable first to pass R CMD check | ||
| pct_missing <- Band <- NULL | ||
| ## Profile missing values | ||
| missing_value <- data.table(profile_missing(data)) | ||
| if (ignore_zeroes) | ||
| missing_value <- missing_value[missing_value$num_missing>0, ] |
boxuancui
Aug 23, 2019
Owner
missing_value is a data.table object. You might want to do:
if (missing_only) {
missing_value <- data.table(profile_missing(data))[num_missing>0]
} else {
missing_value <- data.table(profile_missing(data))
}
missing_value is a data.table object. You might want to do:
if (missing_only) {
missing_value <- data.table(profile_missing(data))[num_missing>0]
} else {
missing_value <- data.table(profile_missing(data))
}| @@ -3,4 +3,7 @@ context("plot missing data profile") | |||
| test_that("test return object", { | |||
| plot_obj <- plot_missing(airquality) | |||
| expect_true(is.ggplot(plot_obj)) | |||
|
|
|||
| plot_obj <- plot_missing(airquality, ignore_zeroes=TRUE) | |||
| expect_true(is.ggplot(plot_obj)) | |||
boxuancui
Aug 23, 2019
Owner
This is not necessary.
This is not necessary.
|
If you would like to submit a PR, please complete the |
boxuancui
changed the title
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Added option to plot_missing() so that it can ignore variables with no missing values. Very useful for datasets like the Kaggle House Prices contest (81 variables, only 19 of them having missing data), where the chart would otherwise be too cluttered to read.
I'm a complete newbie to both Git and R package development, so there's a very high chance that I've got something wrong here - apologies in advance.