plotnine.geoms.geom_segment

class plotnine.geoms.geom_segment(mapping: Aes | None = None, data: DataLike | None = None, **kwargs: Any)[source]

Line segments

Usage

geom_segment(mapping=None, data=None, stat='identity', position='identity',
             na_rm=False, inherit_aes=True, show_legend=None, raster=False,
             arrow=None, lineend='butt', **kwargs)

Only the data and mapping can be positional, the rest must be keyword arguments. **kwargs can be aesthetics (or parameters) used by the stat.

Parameters:
mappingaes, optional

Aesthetic mappings created with aes(). If specified and inherit.aes=True, it is combined with the default mapping for the plot. You must supply mapping if there is no plot mapping.

Aesthetic

Default value

x

xend

y

yend

alpha

1

color

'black'

group

linetype

'solid'

size

0.5

The bold aesthetics are required.

datadataframe, optional

The data to be displayed in this layer. If None, the data from from the ggplot() call is used. If specified, it overrides the data from the ggplot() call.

statstr or stat, optional (default: stat_identity)

The statistical transformation to use on the data for this layer. If it is a string, it must be the registered and known to Plotnine.

positionstr or position, optional (default: position_identity)

Position adjustment. If it is a string, it must be registered and known to Plotnine.

na_rmbool, optional (default: False)

If False, removes missing values with a warning. If True silently removes missing values.

inherit_aesbool, optional (default: True)

If False, overrides the default aesthetics.

show_legendbool or dict, optional (default: None)

Whether this layer should be included in the legends. None the default, includes any aesthetics that are mapped. If a bool, False never includes and True always includes. A dict can be used to exclude specific aesthetis of the layer from showing in the legend. e.g show_legend={'color': False}, any other aesthetic are included by default.

rasterbool, optional (default: False)

If True, draw onto this layer a raster (bitmap) object even ifthe final image is in vector format.

lineendstr (default: butt)

Line end style, of of butt, round or projecting. This option is applied for solid linetypes.

arrowplotnine.geoms.geom_path.arrow (default: None)

Arrow specification. Default is no arrow.

See also

plotnine.geoms.geom_path.arrow

for adding arrowhead(s) to segments.

Examples

[1]:

# NOTE: This notebook uses the polars package
import pandas as pd
import pandas.api.types as pdtypes
import numpy as np

from plotnine import *
import polars as pl
from polars import col

Ranges of Similar Variables

Comparing the point to point difference of many similar variables

Read the data.

Source: Pew Research Global Attitudes Spring 2015

[2]:

!head -n 20 'data/survey-social-media.csv'

PSRAID,COUNTRY,Q145,Q146,Q70,Q74
100000,Ethiopia,Female,35,No,
100001,Ethiopia,Female,25,No,
100002,Ethiopia,Male,40,Don’t know,
100003,Ethiopia,Female,30,Don’t know,
100004,Ethiopia,Male,22,No,
100005,Ethiopia,Male,40,No,
100006,Ethiopia,Female,20,No,
100007,Ethiopia,Female,18,No,No
100008,Ethiopia,Male,50,No,
100009,Ethiopia,Male,35,No,
100010,Ethiopia,Female,20,No,
100011,Ethiopia,Female,30,Don’t know,
100012,Ethiopia,Male,60,No,
100013,Ethiopia,Male,18,No,
100014,Ethiopia,Male,40,No,
100015,Ethiopia,Male,28,Don’t know,
100016,Ethiopia,Female,55,Don’t know,
100017,Ethiopia,Male,30,Don’t know,
100018,Ethiopia,Female,22,No,

[3]:

columns = dict(
    COUNTRY='country',
    Q145='gender',
    Q146='age',
    Q70='use_internet',
    Q74='use_social_media'
)

data = pl.scan_csv(
    'data/survey-social-media.csv',
    dtypes=dict(Q146=pl.Utf8),
).rename(
    columns
).select([
    'country',
    'age',
    'use_social_media'
]).collect()

data.sample(10, seed=123)

[3]:
shape: (10, 3)
country age use_social_media
str str str
"Venezuela" "47" "Yes"
"Israel" "63" " "
"Germany" "60" "Yes"
"France" "60" "No"
"Philippines" "25" " "
"China" "40" " "
"Senegal" "20" " "
"Argentina" "47" "Yes"
"India" "53" "No"
"Jordan" "24" " "

Create age groups for users of social media

[4]:

yes_no = ['Yes', 'No']
valid_age_groups = ['18-34', '35-49', '50+']

rdata = data.with_columns([
    pl
    .when(col('age') <= '34').then('18-34')
    .when(col('age') <= '49').then('35-49')
    .when(col('age') < '98').then('50+')
    .otherwise("")
    .alias('age_group'),
    pl.count().over("country").alias('country_count')
]).filter(
    col('age_group').is_in(valid_age_groups) &
    col('use_social_media').is_in(yes_no)
).groupby(['country', 'age_group']).agg([
    # social media use percentage
    ((col('use_social_media') == 'Yes').sum() * 100 / pl.count()).alias('sm_use_percent'),

    # social media question response rate
    (col('use_social_media').is_in(yes_no).sum() * 100 / col('country_count').first()).alias('smq_response_rate')
]).sort(['country', 'age_group'])

rdata.head()

[4]:
shape: (5, 4)
country age_group sm_use_percent smq_response_rate
str str f64 f64
"Argentina" "18-34" 90.883191 35.1
"Argentina" "35-49" 84.40367 21.8
"Argentina" "50+" 67.333333 15.0
"Australia" "18-34" 90.862944 19.621514
"Australia" "35-49" 78.04878 20.418327

Top 14 countries by response rate to the social media question.

[5]:

def col_format(name, fmt):
    # Format useing python formating
    # for more control over
    return col(name).apply(lambda x: fmt.format(x=x))

def float_to_str_round(name):
    return col_format(name, '{x:.0f}')

n = 14

top = rdata.groupby('country').agg([
    col('smq_response_rate').sum().alias('r')
]).sort('r', reverse=True).head(n)
top_countries = top['country']

expr = float_to_str_round('sm_use_percent')
expr_pct = expr + '%'

point_data = rdata.filter(
    col('country').is_in(top_countries)
).with_column(
    pl.when(col('country') == 'France').then(expr_pct).otherwise(expr).alias('sm_use_percent_str')
)

point_data.head()

[5]:
shape: (5, 5)
country age_group sm_use_percent smq_response_rate sm_use_percent_str
str str f64 f64 str
"Australia" "18-34" 90.862944 19.621514 "91"
"Australia" "35-49" 78.04878 20.418327 "78"
"Australia" "50+" 48.479087 52.390438 "48"
"Canada" "18-34" 92.063492 25.099602 "92"
"Canada" "35-49" 75.925926 21.513944 "76" 
[6]:

segment_data = point_data.groupby('country').agg([
    col('sm_use_percent').min().alias('min'),
    col('sm_use_percent').max().alias('max'),
]).with_column(
    (col('max') - col('min')).alias('gap')
).sort(
    'gap',
).with_columns([
    float_to_str_round('min').alias('min_str'),
    float_to_str_round('max').alias('max_str'),
    float_to_str_round('gap').alias('gap_str')
])

segment_data.head()

[6]:
shape: (5, 7)
country min max gap min_str max_str gap_str
str f64 f64 f64 str str str
"Russia" 76.07362 95.151515 19.077896 "76" "95" "19"
"Israel" 55.405405 88.311688 32.906283 "55" "88" "33"
"United Kingdom... 52.74463 86.096257 33.351627 "53" "86" "33"
"United States" 52.597403 88.669951 36.072548 "53" "89" "36"
"Canada" 53.986333 92.063492 38.077159 "54" "92" "38"

Format the floating point data that will be plotted into strings

Set the order of the countries along the y-axis by setting the country variable to an ordered categorical.

[7]:

country_expr = col('country').cast(pl.Categorical)
segment_data = segment_data.with_column(country_expr)
point_data = point_data.with_columns(country_expr)

First plot

[8]:

# The right column (youngest-oldest gap) location
xgap = 112

(ggplot()
 # Range strip
 + geom_segment(
     segment_data,
     aes(x='min', xend='max', y='country', yend='country'),
     size=6,
     color='#a7a9ac'
 )
 # Age group markers
 + geom_point(
     point_data,
     aes('sm_use_percent', 'country', color='age_group', fill='age_group'),
     size=5,
     stroke=0.7,
 )
 # Age group percentages
 + geom_text(
     point_data.filter(col('age_group')=="50+"),
     aes(x='sm_use_percent-2', y='country', label='sm_use_percent_str', color='age_group'),
     size=8,
     ha='right'
 )
 + geom_text(
     point_data.filter(col('age_group')=="35-49"),
     aes(x='sm_use_percent+2', y='country', label='sm_use_percent_str'),
     size=8,
     ha='left',
     va='center',
     color='white'
 )
 + geom_text(
     point_data.filter(col('age_group')=="18-34"),
     aes(x='sm_use_percent+2', y='country', label='sm_use_percent_str', color='age_group'),
     size=8,
     ha='left',
 )
 # gap difference
 + geom_text(
     segment_data,
     aes(x=xgap, y='country', label='gap_str'),
     size=9,
     fontweight='bold',
     format_string='+{}'
 )
)
../_images/geom_segment_14_0.png 
[8]:

<ggplot: (328973255)>

Tweak it

[9]:

# The right column (youngest-oldest gap) location
xgap = 115

# Gallery Plot

(ggplot()
 # Background Strips                                     # new
 + geom_segment(
     segment_data,
     aes(y='country', yend='country'),
     x=0, xend=100,
     size=8.5,
     color='#edece3'
 )
 # vertical grid lines along the strips                  # new
 + annotate(
     'segment',
     x=list(range(10, 100, 10)) * n,
     xend=list(range(10, 100, 10)) * n,
     y=np.tile(np.arange(1, n+1), 9)-.25,
     yend=np.tile(np.arange(1, n+1), 9) + .25,
     color='#CCCCCC'
 )
 # Range strip
 + geom_segment(
     segment_data,
     aes(x='min', xend='max', y='country', yend='country'),
     size=6,
     color='#a7a9ac'
 )
 # Age group markers
 + geom_point(
     point_data,
     aes('sm_use_percent', 'country', color='age_group', fill='age_group'),
     size=5,
     stroke=0.7,
 )
 # Age group percentages
 + geom_text(
     point_data.filter(col('age_group')=="50+"),
     aes(x='sm_use_percent-2', y='country', label='sm_use_percent_str', color='age_group'),
     size=8,
     ha='right',
 )
 + geom_text(
     point_data.filter(col('age_group')=="35-49"),
     aes(x='sm_use_percent+2', y='country', label='sm_use_percent_str'),
     size=8,
     ha='left',
     va='center',
     color='white'
 )
 + geom_text(
     point_data.filter(col('age_group')=="18-34"),
     aes(x='sm_use_percent+2', y='country', label='sm_use_percent_str', color='age_group'),
     size=8,
     ha='left',
 )
 # countries right-hand-size (instead of y-axis)         # new
 + geom_text(
     segment_data,
     aes(y='country', label='country'),
     x=-1,
     size=8,
     ha='right',
     fontweight='bold',
     color='#222222'
 )
 # gap difference
 + geom_vline(xintercept=xgap, color='#edece3', size=32)  # new
 + geom_text(
     segment_data,
     aes(x=xgap, y='country', label='gap_str'),
     size=9,
     fontweight='bold',
     format_string='+{}'
 )
 # Annotations                                            # new
 + annotate('text', x=31, y=n+1.1, label='50+', size=9, color='#ea9f2f', va='top')
 + annotate('text', x=56, y=n+1.1, label='35-49', size=9, color='#6d6e71', va='top')
 + annotate('text', x=85, y=n+1.1, label='18-34', size=9, color='#939c49', va='top')
 + annotate('text', x=xgap, y=n+.5, label='Youngest-\nOldest Gap', size=9, color='#444444', va='bottom', ha='center')
 + annotate('point', x=[31, 56, 85], y=n+.3, alpha=0.85, stroke=0)
 + annotate('segment', x=[31, 56, 85], xend=[31, 56, 85], y=n+.3, yend=n+.8, alpha=0.85)
 + annotate('hline', yintercept=[x+0.5 for x in range(2, n, 2)], alpha=.5, linetype='dotted', size=0.7)

 # Better spacing and color                              # new
 + scale_x_continuous(limits=(-18, xgap+2))
 + scale_y_discrete(expand=(0, 0.25, 0.1, 0))
 + scale_fill_manual(values=['#c3ca8c', '#d1d3d4', '#f2c480'])
 + scale_color_manual(values=['#939c49', '#6d6e71', '#ea9f2f'])
 + guides(color=None, fill=None)
 + theme_void()
 + theme(figure_size=(8, 8.5))
)
../_images/geom_segment_16_0.png 
[9]:

<ggplot: (330813074)>

Instead of looking at this plot as having a country variable on the y-axis and a percentage variable on the x-axis, we can view it as having vertically stacked up many indepedent variables, the values of which have a similar scale.

Protip: Save a pdf file.

Change in Rank

Comparing a group of ranked items at two different times

Read the data.

Source: World Bank - Infanct Mortality Rate (per 1,000 live births)b

[10]:

data = pl.read_csv(
    'data/API_SP.DYN.IMRT.IN_DS2_en_csv_v2/API_SP.DYN.IMRT.IN_DS2_en_csv_v2.csv',
    skip_rows=4,
    null_values="",
)

# Columns as valid python variables
year_columns = {c: f'y{c}' for c in data.columns if c[:2] in {'19', '20'}}
data = data.rename({
    'Country Name': 'country',
    'Country Code': 'code',
    **year_columns
}).drop(['Indicator Name', 'Indicator Code'])
data.head()

[10]:
shape: (5, 59)
country code y1960 y1961 y1962 y1963 y1964 y1965 y1966 y1967 y1968 y1969 y1970 y1971 y1972 y1973 y1974 y1975 y1976 y1977 y1978 y1979 y1980 y1981 y1982 y1983 y1984 y1985 y1986 y1987 y1988 y1989 y1990 y1991 y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 y2014 y2015 y2016
str str f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 str
"Aruba" "ABW" null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null
"Afghanistan" "AFG" null 240.5 236.3 232.3 228.5 224.6 220.7 217.0 213.3 209.8 206.1 202.2 198.2 194.3 190.3 186.6 182.6 178.7 174.5 170.4 166.1 161.8 157.5 153.2 148.7 144.5 140.2 135.7 131.3 126.8 122.5 118.3 114.4 110.9 107.7 105.0 102.7 100.7 98.9 97.2 95.4 93.4 91.2 89.0 86.7 84.4 82.3 80.4 78.6 76.8 75.1 73.4 71.7 69.9 68.1 66.3 null
"Angola" "AGO" null null null null null null null null null null null null null null null null null null null null 138.3 137.5 136.8 136.0 135.3 134.9 134.4 134.1 133.8 133.6 133.5 133.5 133.5 133.4 133.2 132.8 132.3 131.5 130.6 129.5 128.3 126.9 125.5 124.1 122.8 121.2 119.4 117.1 114.7 112.2 109.6 106.8 104.1 101.4 98.8 96.0 null
"Albania" "ALB" null null null null null null null null null null null null null null null null null null 73.0 68.4 64.0 59.9 56.1 52.4 49.1 45.9 43.2 40.8 38.6 36.7 35.1 33.7 32.5 31.4 30.3 29.1 27.9 26.8 25.5 24.4 23.2 22.1 21.0 20.0 19.1 18.3 17.4 16.7 16.0 15.4 14.8 14.3 13.8 13.3 12.9 12.5 null
"Andorra" "AND" null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null 7.5 7.0 6.5 6.1 5.6 5.2 5.0 4.6 4.3 4.1 3.9 3.7 3.5 3.3 3.2 3.1 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.1 null

The data includes regional aggregates. To tell apart the regional aggregates we need the metadata. Every row in the data table has a corresponding row in the metadata table. Where the row has regional aggregate data, the Region column in the metadata table is NaN.

[11]:

def ordered_categorical(s, categories=None):
    """
    Create a categorical ordered according to the categories
    """
    name = getattr(s, 'name', '')
    if categories is None:
        return pl.Series(name, s).cast(pl.Categorical)

    with pl.StringCache():
        pl.Series(categories).cast(pl.Categorical)
        return pl.Series(name, s).cast(pl.Categorical)

columns = {
    'Country Code': 'code',
    'Region': 'region',
    'IncomeGroup': 'income_group'
}

metadata = pl.scan_csv(
    'data/API_SP.DYN.IMRT.IN_DS2_en_csv_v2/Metadata_Country_API_SP.DYN.IMRT.IN_DS2_en_csv_v2.csv'
).rename(
    columns
).select(
    list(columns.values())
).filter(
    # Drop the regional aggregate information
    (col('region') != '') & (col('income_group') != '')
).collect()

cat_order = ['High income', 'Upper middle income', 'Lower middle income', 'Low income']
metadata = metadata.with_columns([
    ordered_categorical(metadata['income_group'], cat_order)
])

metadata.head(10)

[11]:
shape: (10, 3)
code region income_group
str str cat
"ABW" "Latin America ... "High income"
"AFG" "South Asia" "Low income"
"AGO" "Sub-Saharan Af... "Lower middle i...
"ALB" "Europe & Centr... "Upper middle i...
"AND" "Europe & Centr... "High income"
"ARE" "Middle East & ... "High income"
"ARG" "Latin America ... "Upper middle i...
"ARM" "Europe & Centr... "Lower middle i...
"ASM" "East Asia & Pa... "Upper middle i...
"ATG" "Latin America ... "High income"

Remove the regional aggregates, to create a table with only country data

[12]:

country_data = data.join(metadata, on='code')
country_data.head()

[12]:
shape: (5, 61)
country code y1960 y1961 y1962 y1963 y1964 y1965 y1966 y1967 y1968 y1969 y1970 y1971 y1972 y1973 y1974 y1975 y1976 y1977 y1978 y1979 y1980 y1981 y1982 y1983 y1984 y1985 y1986 y1987 y1988 y1989 y1990 y1991 y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 y2014 y2015 y2016 region income_group
str str f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 str str cat
"Aruba" "ABW" null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null "Latin America ... "High income"
"Afghanistan" "AFG" null 240.5 236.3 232.3 228.5 224.6 220.7 217.0 213.3 209.8 206.1 202.2 198.2 194.3 190.3 186.6 182.6 178.7 174.5 170.4 166.1 161.8 157.5 153.2 148.7 144.5 140.2 135.7 131.3 126.8 122.5 118.3 114.4 110.9 107.7 105.0 102.7 100.7 98.9 97.2 95.4 93.4 91.2 89.0 86.7 84.4 82.3 80.4 78.6 76.8 75.1 73.4 71.7 69.9 68.1 66.3 null "South Asia" "Low income"
"Angola" "AGO" null null null null null null null null null null null null null null null null null null null null 138.3 137.5 136.8 136.0 135.3 134.9 134.4 134.1 133.8 133.6 133.5 133.5 133.5 133.4 133.2 132.8 132.3 131.5 130.6 129.5 128.3 126.9 125.5 124.1 122.8 121.2 119.4 117.1 114.7 112.2 109.6 106.8 104.1 101.4 98.8 96.0 null "Sub-Saharan Af... "Lower middle i...
"Albania" "ALB" null null null null null null null null null null null null null null null null null null 73.0 68.4 64.0 59.9 56.1 52.4 49.1 45.9 43.2 40.8 38.6 36.7 35.1 33.7 32.5 31.4 30.3 29.1 27.9 26.8 25.5 24.4 23.2 22.1 21.0 20.0 19.1 18.3 17.4 16.7 16.0 15.4 14.8 14.3 13.8 13.3 12.9 12.5 null "Europe & Centr... "Upper middle i...
"Andorra" "AND" null null null null null null null null null null null null null null null null null null null null null null null null null null null null null null 7.5 7.0 6.5 6.1 5.6 5.2 5.0 4.6 4.3 4.1 3.9 3.7 3.5 3.3 3.2 3.1 2.9 2.8 2.7 2.6 2.5 2.4 2.3 2.2 2.1 2.1 null "Europe & Centr... "High income"

We are interested in the changes in rank between 1960 and 2015. To plot a reasonable sized graph, we randomly sample 35 countries.

[13]:

sampled_data = country_data.drop_nulls(
    subset=['y1960', 'y2015']
).sample(
    n=35,
    seed=123
).with_columns([
    col('y1960').rank(method='ordinal').cast(pl.Int64).suffix('_rank'),
    col('y2015').rank(method='ordinal').cast(pl.Int64).suffix('_rank')
]).sort('y2015_rank', reverse=True)

sampled_data.head()

[13]:
shape: (5, 63)
country code y1960 y1961 y1962 y1963 y1964 y1965 y1966 y1967 y1968 y1969 y1970 y1971 y1972 y1973 y1974 y1975 y1976 y1977 y1978 y1979 y1980 y1981 y1982 y1983 y1984 y1985 y1986 y1987 y1988 y1989 y1990 y1991 y1992 y1993 y1994 y1995 y1996 y1997 y1998 y1999 y2000 y2001 y2002 y2003 y2004 y2005 y2006 y2007 y2008 y2009 y2010 y2011 y2012 y2013 y2014 y2015 y2016 region income_group y1960_rank y2015_rank
str str f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 f64 str str cat i64 i64
"Togo" "TGO" 162.4 159.4 156.4 153.5 150.5 147.7 144.7 141.8 138.8 135.8 132.8 130.0 127.2 124.4 121.8 119.2 116.6 114.1 111.7 109.2 106.9 104.8 102.7 100.7 98.9 97.1 95.5 94.0 92.6 91.4 90.2 89.0 87.9 86.8 85.5 84.2 82.8 81.2 79.6 77.9 76.2 74.4 72.6 70.8 69.1 67.4 65.7 64.1 62.5 60.9 59.3 57.9 56.5 55.0 53.6 52.3 null "Sub-Saharan Af... "Low income" 33 35
"Haiti" "HTI" 194.8 191.5 188.3 185.2 182.2 179.1 176.0 172.9 169.8 166.6 163.4 160.1 156.6 153.0 149.5 146.0 142.6 139.2 135.8 132.5 129.4 126.2 123.0 120.0 117.1 114.3 111.5 108.8 106.1 103.5 101.0 98.4 95.8 93.1 90.4 87.8 85.1 82.4 79.9 77.4 75.0 72.8 70.7 68.9 67.2 65.6 64.1 62.7 61.3 60.0 85.5 57.5 56.2 54.8 53.5 52.2 null "Latin America ... "Low income" 35 34
"Gambia, The" "GMB" 148.4 146.1 143.8 141.5 139.3 137.1 134.9 132.6 130.5 128.3 126.0 123.8 121.5 119.1 116.7 114.4 112.1 109.8 107.6 105.4 103.2 100.9 98.6 96.2 93.7 91.3 88.9 86.5 84.3 82.1 80.0 78.0 76.1 74.3 72.6 70.9 69.3 67.7 66.2 64.8 63.3 62.0 60.6 59.3 58.0 56.8 55.6 54.5 53.6 52.6 51.7 50.9 50.1 49.4 48.6 47.9 null "Sub-Saharan Af... "Low income" 32 33
"Zimbabwe" "ZWE" 92.6 90.1 87.6 85.3 82.8 80.5 78.3 76.3 74.7 73.4 72.4 71.6 71.1 70.7 70.5 70.3 70.1 69.8 69.2 68.1 66.4 64.2 61.6 58.8 56.0 53.6 51.7 50.4 49.8 50.2 51.2 52.6 54.5 56.4 58.1 60.1 61.6 62.7 63.3 63.5 63.5 63.2 62.7 61.9 61.5 61.0 60.3 59.9 58.9 57.7 55.8 54.0 49.4 48.8 47.6 46.6 null "Sub-Saharan Af... "Low income" 19 32
"Zambia" "ZMB" 123.2 120.9 118.7 116.7 115.1 114.0 113.3 112.9 112.2 111.1 109.3 106.7 103.7 100.7 98.1 96.3 95.3 95.1 95.3 95.6 96.1 97.0 98.3 100.2 102.7 105.6 108.3 110.6 112.2 113.1 113.3 113.0 112.4 111.3 109.7 107.8 106.1 104.6 103.1 100.9 97.6 92.7 86.5 80.0 73.9 68.7 64.9 61.3 58.7 55.6 52.9 51.1 49.0 46.5 44.7 43.3 null "Sub-Saharan Af... "Lower middle i... 23 31

First graph

[14]:

(ggplot(sampled_data)
 + geom_text(aes(1, 'y1960_rank', label='country'), ha='right', size=9)
 + geom_text(aes(2, 'y2015_rank', label='country'), ha='left', size=9)
 + geom_point(aes(1, 'y1960_rank', color='income_group'), size=2.5)
 + geom_point(aes(2, 'y2015_rank', color='income_group'), size=2.5)
 + geom_segment(aes(x=1, y='y1960_rank', xend=2, yend='y2015_rank', color='income_group'))
 + scale_y_reverse()
)
../_images/geom_segment_28_0.png 
[14]:

<ggplot: (330848389)>

It has the form we want, but we need to tweak it.

[15]:

# Text colors
black1 = '#252525'
black2 = '#222222'

# Gallery Plot

(ggplot(sampled_data)
 # Slight modifications for the original lines,
 # 1. Nudge the text to either sides of the points
 # 2. Alter the color and alpha values
 + geom_text(aes(1, 'y1960_rank', label='country'), nudge_x=-0.05, ha='right', size=9, color=black1)
 + geom_text(aes(2, 'y2015_rank', label='country'), nudge_x=0.05, ha='left', size=9, color=black1)
 + geom_point(aes(1, 'y1960_rank', color='income_group'), size=2.5, alpha=.7)
 + geom_point(aes(2, 'y2015_rank', color='income_group'), size=2.5, alpha=.7)
 + geom_segment(aes(x=1, y='y1960_rank', xend=2, yend='y2015_rank', color='income_group'), alpha=.7)

 # Text Annotations
 #+ annotate('text', x=1, y=0, label='Rank in 1960', fontweight='bold', ha='right', size=10, color=black2)
 #+ annotate('text', x=2, y=0, label='Rank in 2015', fontweight='bold', ha='left', size=10, color=black2)
 + annotate('text', x=1.5, y=0, label='Lines show change in rank', size=9, color=black1)
 #+ annotate('label', x=1.5, y=3, label='Lower infant\ndeath rates', size=9, color=black1,
 #           label_size=0, fontstyle='italic')
 #+ annotate('label', x=1.5, y=33, label='Higher infant\ndeath rates', size=9, color=black1,
 #           label_size=0, fontstyle='italic')

 # Prevent country names from being chopped off
 + lims(x=(0.35, 2.65))
 + labs(color='Income Group')
 # Countries with lower rates on top
 + scale_y_reverse()
 # Change colors
 + scale_color_brewer(type='qual', palette=2)
 # Removes all decorations
 + theme_void()
 # Changing the figure size prevents the country names from squishing up
 + theme(figure_size=(8, 11))
)
../_images/geom_segment_30_0.png 
[15]:

<ggplot: (330832440)>