Separate groups with missing values
This function is used internally by plotly, but may also be useful to some power users. The details section explains when and why this function is useful.
group2NA( data, groupNames = "group", nested = NULL, ordered = NULL, retrace.first = inherits(data, "GeomPolygon") )
data |
a data frame. |
groupNames |
character vector of grouping variable(s) |
nested |
other variables that group should be nested (i.e., ordered) within. |
ordered |
a variable to arrange by (within nested & groupNames). This is useful primarily for ordering by x |
retrace.first |
should the first row of each group be appended to the last row? This is useful for enclosing polygons with lines. |
If a group of scatter traces share the same non-positional characteristics
(i.e., color, fill, etc), it is more efficient to draw them as a single trace
with missing values that separate the groups (instead of multiple traces),
In this case, one should also take care to make sure
connectgaps
is set to FALSE.
a data.frame with rows ordered by: nested,
then groupNames, then ordered. As long as groupNames
contains valid variable names, new rows will also be inserted to separate
the groups.
# note the insertion of new rows with missing values group2NA(mtcars, "vs", "cyl") # need to group lines by city somehow! plot_ly(txhousing, x = ~date, y = ~median) %>% add_lines() # instead of using group_by(), you could use group2NA() tx <- group2NA(txhousing, "city") plot_ly(tx, x = ~date, y = ~median) %>% add_lines() # add_lines() will ensure paths are sorted by x, but this is equivalent tx <- group2NA(txhousing, "city", ordered = "date") plot_ly(tx, x = ~date, y = ~median) %>% add_paths()
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