# grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2map_polygon.png',
width=612,
height=612,
antialias="subpixel",
type="cairo")
#-- ggplot2mappolygon-begin
map = importr('maps')
fr = ggplot2.map_data('france')
# add a column indicating which region names have an "o".
fr = fr.cbind(fr, has_o=base.grepl('o', fr.rx2("region"), ignore_case=True))
p = ggplot2.ggplot(fr) + \
ggplot2.geom_polygon(ggplot2.aes(x = 'long', y = 'lat',
group = 'group', fill = 'has_o'),
col="black")
p.plot()
#-- ggplot2mappolygon-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2mtcars_coordtrans.png',
width=936,
height=624,
antialias="subpixel",
type="cairo")
#-- ggplot2mtcarscoordtrans-begin
from rpy2.robjects.lib import grid
grid.newpage()
grid.viewport(layout=grid.layout(2, 3)).push()
# #-- ggplot2coordtransreverse-end
# grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2map_polygon.png',
width=612,
height=612,
antialias=ANTIALIAS,
type="cairo")
#-- ggplot2mappolygon-begin
map = importr('maps')
fr = ggplot2.map_data('france')
# add a column indicating which region names have an "o".
fr = fr.cbind(has_o=base.grepl('o', fr.rx2("region"), ignore_case=True))
p = (ggplot2.ggplot(fr) + ggplot2.geom_polygon(ggplot2.aes_string(
x='long', y='lat', group='group', fill='has_o'),
col="black"))
p.plot()
#-- ggplot2mappolygon-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2mtcars_coordtrans.png',
width=936,
height=624,
antialias=ANTIALIAS,
type="cairo")
#-- ggplot2mtcarscoordtrans-begin
from rpy2.robjects.lib import grid
grid.newpage()
grid.viewport(layout=grid.layout(2, 3)).push()
## loaded data sets can now be accessed through R handle
## note that different from R dot . is not valid for Python variable names!
IL_railroads = robjects.r('IL.railroads')
IL_final = robjects.r('IL.final')
## import device driver from R with importr to plot to PNG
## we can then call any function in the grdevices package
grdevices = importr('grDevices')
grdevices.png(file='/Users/user/Downloads/data/mapplot.png', width=1300, height=1000)
## plot the map
## note that the order matters when we add another layer in ggplot (here IL_railroads): first aes, then data, that's different from R
## (see http://permalink.gmane.org/gmane.comp.python.rpy/2349)
## note that we use dictionary to set the opts to be able to set options as keywords, for example legend.key.size
p_map = ggplot2.ggplot(IL_final) + \
ggplot2.geom_polygon(ggplot2.aes(x = 'long', y = 'lat', group = 'group', color = 'ObamaShare', fill = 'ObamaShare')) + \
ggplot2.scale_fill_gradient(high = 'blue', low = 'red') + \
ggplot2.scale_fill_continuous(name = "Obama Vote Share") + \
ggplot2.scale_colour_continuous(name = "Obama Vote Share") + \
ggplot2.opts(**{'legend.position': 'left', 'legend.key.size': robjects.r.unit(2, 'lines'), 'legend.title' : ggplot2.theme_text(size = 14, hjust=0), \
'legend.text': ggplot2.theme_text(size = 12), 'title' : "Obama Vote Share and Distance to Railroads in IL", \
'plot.title': ggplot2.theme_text(size = 24), 'plot.margin': robjects.r.unit(robjects.r.rep(0,4),'lines'), \
'panel.background': ggplot2.theme_blank(), 'panel.grid.minor': ggplot2.theme_blank(), 'panel.grid.major': ggplot2.theme_blank(), \
'axis.ticks': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.title.y': ggplot2.theme_blank(), \
'axis.title.x': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.text.x': ggplot2.theme_blank(), \
'axis.text.y': ggplot2.theme_blank()} ) + \
ggplot2.geom_line(ggplot2.aes(x='long', y='lat', group='group'), data=IL_railroads, color='grey', size=0.2) + \
ggplot2.coord_equal()
p_map.plot()
# #-- ggplot2coordtransreverse-end
# grdevices.dev_off()
grdevices.png('../../_static/graphics_ggplot2map_polygon.png',
width = 612, height = 612, antialias="subpixel", type="cairo")
#-- ggplot2mappolygon-begin
map = importr('maps')
fr = ggplot2.map_data('france')
# add a column indicating which region names have an "o".
fr = fr.cbind(fr, has_o = base.grepl('o', fr.rx2("region"),
ignore_case = True))
p = ggplot2.ggplot(fr) + \
ggplot2.geom_polygon(ggplot2.aes(x = 'long', y = 'lat',
group = 'group', fill = 'has_o'),
col="black")
p.plot()
#-- ggplot2mappolygon-end
grdevices.dev_off()
grdevices.png('../../_static/graphics_grid.png',
width = 612, height = 612, antialias="subpixel", type="cairo")
#-- grid-begin
grid.newpage()
# create a rows/columns layout
lt = grid.layout(2, 3)
vp = grid.viewport(layout = lt)
IL_railroads = robjects.r('IL.railroads')
IL_final = robjects.r('IL.final')
## import device driver from R with importr to plot to PNG
## we can then call any function in the grdevices package
grdevices = importr('grDevices')
grdevices.png(file='/Users/user/Downloads/data/mapplot.png',
width=1300,
height=1000)
## plot the map
## note that the order matters when we add another layer in ggplot (here IL_railroads): first aes, then data, that's different from R
## (see http://permalink.gmane.org/gmane.comp.python.rpy/2349)
## note that we use dictionary to set the opts to be able to set options as keywords, for example legend.key.size
p_map = ggplot2.ggplot(IL_final) + \
ggplot2.geom_polygon(ggplot2.aes(x = 'long', y = 'lat', group = 'group', color = 'ObamaShare', fill = 'ObamaShare')) + \
ggplot2.scale_fill_gradient(high = 'blue', low = 'red') + \
ggplot2.scale_fill_continuous(name = "Obama Vote Share") + \
ggplot2.scale_colour_continuous(name = "Obama Vote Share") + \
ggplot2.opts(**{'legend.position': 'left', 'legend.key.size': robjects.r.unit(2, 'lines'), 'legend.title' : ggplot2.theme_text(size = 14, hjust=0), \
'legend.text': ggplot2.theme_text(size = 12), 'title' : "Obama Vote Share and Distance to Railroads in IL", \
'plot.title': ggplot2.theme_text(size = 24), 'plot.margin': robjects.r.unit(robjects.r.rep(0,4),'lines'), \
'panel.background': ggplot2.theme_blank(), 'panel.grid.minor': ggplot2.theme_blank(), 'panel.grid.major': ggplot2.theme_blank(), \
'axis.ticks': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.title.y': ggplot2.theme_blank(), \
'axis.title.x': ggplot2.theme_blank(), 'axis.title.x': ggplot2.theme_blank(), 'axis.text.x': ggplot2.theme_blank(), \
'axis.text.y': ggplot2.theme_blank()} ) + \
ggplot2.geom_line(ggplot2.aes(x='long', y='lat', group='group'), data=IL_railroads, color='grey', size=0.2) + \
ggplot2.coord_equal()
p_map.plot()
pickups_late = pickups_late.groupby(['nbhd', 'period'])['passenger_count'].sum().reset_index()
temp_perc = pickups_late.groupby(['nbhd'])['passenger_count'].apply(lambda x: 100*x/float(x.sum())).reset_index()
temp_perc = temp_perc.rename(columns={'passenger_count':'relative_percent'})
pickups_late = pd.concat([pickups_late, temp_perc['relative_percent']], axis=1)
pickups_late = pickups_late[pickups_late['period'] == 'late']
pickups_late = pd.merge(pickups_late, nbhd_borders, how='right', on=['nbhd']).dropna()
# Find top 10 neighborhoods with largest late night % of pickups
print(tabulate(pickups_late[['relative_percent', 'nbhd',
'borough']].drop_duplicates().sort(['relative_percent'], ascending=False).head(10),
tablefmt='pipe', headers='keys', showindex=False))
p4 = ggplot2.ggplot(pandas2ri.py2ri(pickups_late)) + \
ggplot2.aes_string(x='lon', y='lat', group='nbhd', fill='relative_percent') + \
ggplot2.geom_polygon() + \
ggplot2.theme(legend_position='bottom') + \
ggplot2.labs(x='', y='', title='Late Night Pickups (% of All Pickups)')
p4.save('./plots/late_night_pickups.png', width=5, height=6)
# Plot % change (2010 to 2015) in pickups by neighborhood
pickups_change = pickups_hdl.groupby(['nbhd', 'borough', 'year'])['passenger_count'].sum().reset_index()
pickups_change = pickups_change[pickups_change['year'].isin([2010, 2015])].reset_index()
pickups_change = pickups_change.sort_values(['nbhd', 'year']).reset_index()
temp_change = pickups_change.groupby(['nbhd'])['passenger_count'].apply(lambda x: x.pct_change()).reset_index()
temp_change = temp_change.rename(columns={'passenger_count':'percent_change'})
pickups_change = pd.concat([pickups_change, temp_change['percent_change']], axis=1)
pickups_change = pickups_change.dropna()
# Find top 10 neighborhoods with largest percent change
print(tabulate(pickups_change[['percent_change', 'nbhd',
