def test_mutate(self):
dataf_a = dplyr.DataFrame(mtcars)
dataf_b = dataf_a.mutate(foo=1, bar=rl('gear+1'))
assert type(dataf_b) is dplyr.DataFrame
assert all(a == b
for a, b in zip(dataf_a.rx2('gear'), dataf_b.rx2('gear')))
assert all(a + 1 == b
for a, b in zip(dataf_a.rx2('gear'), dataf_b.rx2('bar')))
TARGET_VERSION = '3.3.'
if not ggplot2.__version__.startswith(TARGET_VERSION):
warnings.warn(
'This was designed againt ggplot2 versions starting with %s but you '
'have %s' % (TARGET_VERSION, ggplot2.__version__))
ggplot2_env = robjects.baseenv['as.environment']('package:ggplot2')
StrVector = robjects.StrVector
def as_symbol(x):
return rlang.sym(x)
_AES_RLANG = rl('ggplot2::aes()')
class GGPlot(robjects.vectors.ListVector):
""" A Grammar of Graphics Plot.
GGPlot instances can be added to one an other in order to construct
the final plot (the method `__add__()` is implemented).
"""
_constructor = ggplot2._env['ggplot']
_rprint = ggplot2._env['print.ggplot']
_add = ggplot2._env['%+%']
@classmethod
def new(cls, data, mapping=_AES_RLANG, **kwargs):
def test_as_labeller(self, labeller):
if isinstance(labeller, dict):
labeller = ggplot2.dict2rvec(labeller)
gp = (ggplot2.ggplot(mtcars) + ggplot2.facet_wrap(
rl('~am'), labeller=ggplot2.as_labeller(labeller)))
assert isinstance(gp, ggplot2.GGPlot)
class TestGGplot(object):
def test_gglot(self):
gp = ggplot2.ggplot(mtcars)
assert isinstance(gp, ggplot2.GGPlot)
def test_gglot_mapping(self):
gp = ggplot2.ggplot(mtcars, ggplot2.aes_string(x='gear'))
assert isinstance(gp, ggplot2.GGPlot)
def test_element_text(self):
et = ggplot2.element_text()
assert isinstance(et, ggplot2.ElementText)
def test_element_text_repr(self):
et = ggplot2.element_text()
assert repr(et).startswith('<instance of')
def test_element_rect(self):
er = ggplot2.element_rect()
assert isinstance(er, ggplot2.ElementRect)
def test_element_blank(self):
eb = ggplot2.element_blank()
assert isinstance(eb, ggplot2.ElementBlank)
def test_element_line(self):
eb = ggplot2.element_line()
assert isinstance(eb, ggplot2.ElementLine)
def test_labs(self):
la = ggplot2.labs()
assert isinstance(la, ggplot2.Labs)
def test_add(self):
gp = ggplot2.ggplot(mtcars)
gp += ggplot2.aes_string(x='wt', y='mpg')
gp += ggplot2.geom_point()
assert isinstance(gp, ggplot2.GGPlot)
def test_aes(self):
gp = ggplot2.ggplot(mtcars)
gp += ggplot2.aes(x='wt', y='mpg')
gp += ggplot2.geom_point()
assert isinstance(gp, ggplot2.GGPlot)
gp = ggplot2.ggplot(mtcars)
gp += ggplot2.aes('wt', 'mpg')
gp += ggplot2.geom_point()
assert isinstance(gp, ggplot2.GGPlot)
def test_vars(self):
gp = (ggplot2.ggplot(mtcars) + ggplot2.aes(x='wt', y='mpg') +
ggplot2.geom_point() + ggplot2.facet_wrap(ggplot2.vars('gears')))
assert isinstance(gp, ggplot2.GGPlot)
@pytest.mark.parametrize('theme_name', [
'theme_grey', 'theme_classic', 'theme_dark', 'theme_grey',
'theme_light', 'theme_bw', 'theme_linedraw', 'theme_void',
'theme_minimal'
])
def test_theme(self, theme_name):
theme = getattr(ggplot2, theme_name)
gp = (ggplot2.ggplot(mtcars) + theme())
assert isinstance(gp, ggplot2.GGPlot)
@pytest.mark.parametrize(
'labeller', (rl('as_labeller(c(`0` = "Zero", `1` = "One"))'), {
'0': 'Zero',
'1': 'One'
}))
def test_as_labeller(self, labeller):
if isinstance(labeller, dict):
labeller = ggplot2.dict2rvec(labeller)
gp = (ggplot2.ggplot(mtcars) + ggplot2.facet_wrap(
rl('~am'), labeller=ggplot2.as_labeller(labeller)))
assert isinstance(gp, ggplot2.GGPlot)
def group_by(self, *args, _add=False,
_drop=robjects.rl('group_by_drop_default(.data)')):
"""Call the R function `dplyr::group_by()`."""
res = dplyr.group_by(self, *args,
**{'.add': _add, '.drop': _drop})
return GroupedDataFrame(res)
def test_mutate_all(self):
dataf_a = dplyr.DataFrame(mtcars)
dataf_b = dataf_a.mutate_all(rl('sqrt'))
assert type(dataf_b) is dplyr.DataFrame
def test_mutate_at(self):
dataf_a = dplyr.DataFrame(mtcars)
dataf_b = dataf_a.mutate_at(StrVector(["gear"]), rl('sqrt'))
assert type(dataf_b) is dplyr.DataFrame
def test_splitmerge_function(self):
dataf = dplyr.DataFrame(mtcars)
dataf_by_gear = dataf.group_by(rl('gear'))
dataf_avg_mpg = dataf_by_gear.summarize(foo=rl('mean(mpg)'))
assert isinstance(dataf_avg_mpg, dplyr.DataFrame)
def test_group_by(self):
dataf_a = dplyr.DataFrame(mtcars)
dataf_g = dataf_a.group_by(rl('gear'))
assert dataf_g.is_grouped_df
assert not dataf_g.ungroup().is_grouped_df
assert dataf_g.is_grouped_df
def test_filter_onefilter_function(self):
dataf = dplyr.DataFrame(mtcars)
ngear_gt_3 = len(tuple(x for x in dataf.rx2('gear') if x > 3))
dataf_filter = dplyr.filter(dataf, rl('gear > 3'))
assert ngear_gt_3 == dataf_filter.nrow
def test_arrange(self):
dataf = dplyr.DataFrame(mtcars)
dataf_arrange = dataf.arrange(rl('mpg'))
assert tuple(sorted(dataf.collect().rx2('mpg'))) == \
tuple(dataf_arrange.collect().rx2('mpg'))
def ungroup(self,
*args,
_add=False,
_drop=robjects.rl('group_by_drop_default(.data)')):
res = dplyr.ungroup(*args, _add=_add, _drop=_drop)
return guess_wrap_type(res)(res)
