# aes_string: set the axis labels and what we're plotting
# opts: sets thickness of the lines
# note the use of **{} to allow setting "legend.key.size" as a keyword
# scale_colour_manual: associate color datasets with actual colors and names
# geom_point and geom_line: thicker points and lines
# scale_linetype_manual: associate perf types with linetypes
for col_i, yscale in enumerate(['log', 'linear']):
vp = grid.viewport(**{'layout.pos.col': col_i + 1, 'layout.pos.row': 1})
pp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='variable', y='Performance', color='color',
shape='PerfType', linetype='PerfType') + \
ggplot2.ggtitle('Performance vs. Color') + \
ggplot2.theme(**{'legend.key.size' : ro.r.unit(1.4, "lines") } ) + \
ggplot2.scale_colour_manual("Color",
values=colormap,
breaks=colormap.names,
labels=[elt[1] for elt in
colormap_labels]) + \
ggplot2.geom_point(size=3) + \
ggplot2.scale_linetype_manual(values=linemap) + \
ggplot2.geom_line(size=1.5)
# custom y-axis lines: major lines ("breaks") are every 10^n; 9
# minor lines ("minor_breaks") between major lines
if (yscale == 'log'):
pp = pp + \
ggplot2.scale_y_log10(breaks = ro.r("10^(%d:%d)" % (gflops_range[0],
gflops_range[1])),
minor_breaks =
ro.r("rep(10^(%d:%d), each=9) * rep(1:9, %d)" %
(gflops_range[0] - 1, gflops_range[1],
# opts: set the title and the thickness of the lines
# note the use of **{} to allow setting "legend.key.size" as a keyword
# scale_colour_manual: associate color datasets with actual colors and names
# geom_point and geom_line: thicker points and lines
# scale_linetype_manual: associate perf types with linetypes
for col_i, yscale in enumerate(['log', 'linear']):
vp = grid.viewport(**{'layout.pos.col':col_i+1, 'layout.pos.row': 1})
pp = ggplot2.ggplot(df) + \
ggplot2.aes_string(x='Date', y='Performance', color='color',
shape='PerfType', linetype='PerfType') + \
ggplot2.opts(**{'title' :
'Performance vs. Color',
'legend.key.size' : ro.r.unit(1.4, "lines") } ) + \
ggplot2.scale_colour_manual("Color",
values=colormap,
breaks=colormap.names,
labels=[elt[1] for elt in
colormap_labels]) + \
ggplot2.geom_point(size=3) + \
ggplot2.scale_linetype_manual(values=linemap) + \
ggplot2.geom_line(size=1.5)
# custom y-axis lines: major lines ("breaks") are every 10^n; 9
# minor lines ("minor_breaks") between major lines
if (yscale == 'log'):
pp = pp + \
ggplot2.scale_y_log10(breaks = ro.r("10^(%d:%d)" % (gflops_range[0],
gflops_range[1])),
minor_breaks =
ro.r("rep(10^(%d:%d), each=9) * rep(1:9, %d)" %
(gflops_range[0] - 1, gflops_range[1],
def plot_volcano_with_r(
data,
xlabel='Estimated effect (change in H/L ratio)',
title='',
max_labels=20,
color_background='#737373',
color_significant='#252525',
color_significant_muted='#252525',
label_only_large_fc=False,
special_labels=None,
special_palette=None,
base_size=12,
label_size=3,
x='logFC',
y='neg_log10_p_adjust',
special_labels_mode='all',
xlim=None,
skip_labels=None,
nudges=None,
):
r_data, r_like_data = transform_data_for_ggplot(
data,
label_only_large_fc=label_only_large_fc,
special_labels=special_labels,
max_labels=max_labels,
special_labels_mode=special_labels_mode,
skip_labels=skip_labels,
nudges=nudges)
plot = r_ggplot2.ggplot(r_data)
plot += r_ggplot2.theme_minimal(base_size=base_size)
plot += r_ggplot2.theme(
**{
'panel.grid.major':
r_ggplot2.element_blank(),
'panel.grid.minor':
r_ggplot2.element_blank(),
'panel.border':
r_ggplot2.element_rect(fill=robjects.rinterface.NA, color="black")
})
plot += r_ggplot2.theme(
text=r_ggplot2.element_text(family='Helvetica', face='plain'))
plot += r_ggplot2.theme(
**{
'plot.title': r_ggplot2.element_text(hjust=0.5),
# 'axis.title.y': r_ggplot2.element_text((t = 0, r = 20, b = 0, l = 0)),
})
aes_points = r_ggplot2.aes_string(x=x, y=y, color='group')
scale_points = r_ggplot2.scale_colour_manual(
aes_points,
values=r_label_palette(
r_like_data,
special_palette,
color_background=color_background,
color_significant=color_significant,
color_significant_muted=color_significant_muted))
plot += aes_points
plot += scale_points
if xlim is not None:
plot += r_ggplot2.scale_x_continuous(
labels=r_custom.formatterFunTwoDigits, limits=robjects.r.c(*xlim))
else:
plot += r_ggplot2.scale_x_continuous(
labels=r_custom.formatterFunTwoDigits)
plot += r_ggplot2.scale_y_continuous(labels=r_custom.formatterFunOneDigit)
plot += r_ggplot2.geom_hline(
yintercept=float(-np.log10(FDR_THRESHOLD_RESPONSE)),
color='#BDBDBD',
alpha=.3)
plot += r_ggplot2.geom_vline(xintercept=float(FC_THRESHOLD_RESPONSE),
color='#BDBDBD',
alpha=.3)
plot += r_ggplot2.geom_vline(xintercept=-float(FC_THRESHOLD_RESPONSE),
color='#BDBDBD',
alpha=.3)
plot += r_ggplot2.geom_point(**{'show.legend': False})
aes_text = r_ggplot2.aes_string(label='label')
plot += aes_text
plot += r_ggrepel.geom_text_repel(
aes_text,
nudge_x=r_dollar(r_data, 'nudgex'),
nudge_y=r_dollar(r_data, 'nudgey'),
size=label_size,
family='Helvetica',
**{
'show.legend': False,
'point.padding': 0.25,
'min.segment.length': 0,
#'max.iter':0,
'segment.color': '#BDBDBD'
},
)
plot += r_ggplot2.labs(x=xlabel,
y='Adjusted p value (-log10)',
title=title)
plot.plot()
group='variable',
colour='variable',
shape='variable',
linetype='variable')
line = ggplot2.geom_line()
point = ggplot2.geom_point()
vert_line_onset = ggplot2.geom_vline(xintercept=-1.5,
linetype=2,
colour="#999999")
vert_line_exhaust = ggplot2.geom_vline(xintercept=5.5,
linetype=2,
colour="#999999")
vert_line_exhaust_FL = ggplot2.geom_vline(xintercept=3.5,
linetype=2,
colour="#999999")
colors = ggplot2.scale_colour_manual(values=robjects.r.palette_lines)
hollow = ggplot2.scale_shape_manual(
values=robjects.r('c(16,17,15,18,6,7,9,3)'))
xlab = ggplot2.labs(x="Months Since First UI Check")
loc_default = robjects.r('c(1,0)')
legend_f = lambda loc=loc_default: ggplot2.theme(**{
'legend.position': loc,
'legend.justification': loc
})
ggsave = lambda filename, plot: robjects.r.ggsave(
filename="../out/" + filename + ".pdf", plot=plot, width=6, height=4)
colors_alt = ggplot2.scale_colour_manual(values=robjects.r.palette_lines[1])
shape_alt = ggplot2.scale_shape_manual(values=17)
ggplot2_env = robjects.baseenv['as.environment']('package:ggplot2')
#alternatives that tried and failed
#base_plot = lambda gr_name = 'variable': ggplot2.aes_string(x='x', y='value',group=gr_name,colour=gr_name, shape = gr_name)
#colors = ggplot2.scale_colour_manual(values=robjects.r.palette_lines, name = ltitle)
pandas2ri.activate()
#set up basic, repetitive plot features
base_plot = ggplot2.aes_string(x='x', y='value',group='variable',colour='variable', shape = 'variable')
line = ggplot2.geom_line()
point = ggplot2.geom_point()
bar = ggplot2.geom_bar(stat="identity")
vert_line_onset = ggplot2.geom_vline(xintercept=-1, linetype=2, colour="red", alpha=0.25)
vert_line_exhaust = ggplot2.geom_vline(xintercept=5, linetype=2, colour="red", alpha=0.25)
ltitle = "crazy"
ltitle_default = 'Variable'
#colors = lambda ltitle = ltitle_default: ggplot2.scale_colour_manual(values=robjects.r.palette_lines, name = ltitle)
colors = ggplot2.scale_colour_manual(values=robjects.r.palette_lines)
legend_t_c = lambda ltitle = ltitle_default: ggplot2.scale_color_discrete(name = ltitle)
legend_t_s = lambda ltitle = ltitle_default: ggplot2.scale_shape_discrete(name = ltitle)
loc_default = robjects.r('c(1,0)')
legend_f = lambda loc = loc_default: ggplot2.theme(**{'legend.position':loc, 'legend.justification':loc})
ggsave = lambda filename, plot: robjects.r.ggsave(filename=out_path + filename + ".pdf", plot=plot, width = 6, height = 4)
colors_alt = ggplot2.scale_colour_manual(values=robjects.r.palette_lines[1])
shape_alt = ggplot2.scale_shape_manual(values=17)
#create a new function straight from the R enviornment
#xx in principle it should be possible to load class using what's already been imported
#above, but I'm not going to worry about that for now
ggplot2_env = robjects.baseenv['as.environment']('package:ggplot2')
class GBaseObject(robjects.RObject):
p.save('./plots/taxi_pickups%02i.png' % i, width=4, height=4.5)
# Create animated .gif of pickups by hour
import imageio
file_names = sorted((fn for fn in os.listdir('./plots') if fn.startswith('taxi_pickups')))
file_names = ['plots/' + s for s in file_names]
images = []
for filename in file_names:
images.append(imageio.imread(filename))
imageio.mimsave('./plots/pickups_movie.gif', images, duration=0.4)
# total pickups by date, color
p1 = ggplot2.ggplot(pandas2ri.py2ri(date_avgs)) + \
ggplot2.aes_string(x='date', y='total_pickups', color='type') + \
ggplot2.scale_colour_manual(values = robjects.StrVector(['green', 'yellow'])) + \
ggplot2.geom_line() + \
ggplot2.theme(legend_position='bottom') + \
ggplot2.labs(y='Total Pickups', x='Date', title='Total Pickups by Date')
p1.save('./plots/pickups_by_date.png', width=6, height=5)
# average fare and tip by date, color
p2 = ggplot2.ggplot(pandas2ri.py2ri(date_avgs)) + \
ggplot2.aes_string(x='date', y='fare_amount', color='type') + \
ggplot2.scale_colour_manual(values = robjects.StrVector(['green', 'yellow'])) + \
ggplot2.geom_line() + \
ggplot2.theme(legend_position='bottom') + \
ggplot2.labs(y='Average Fare ($)', x='Date', title='Average Fare by Date')
p2.save('./plots/fares_by_date.png', width=6, height=5)
p3 = ggplot2.ggplot(pandas2ri.py2ri(date_avgs)) + \