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NOTE: This notebook uses the polars package
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An Elaborate Range Plot

In [1]:
# NOTE: This notebook uses the polars package
import numpy as np

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

Comparing the point to point difference of many similar variables

Read the data.

Source: Pew Research Global Attitudes Spring 2015

In [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, 
In [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",
        schema_overrides=dict(Q146=pl.Utf8),
    )
    .rename(columns)
    .select(["country", "age", "use_social_media"])
    .collect()
)

data.sample(10, seed=123)
shape: (10, 3)
country age use_social_media
str str str
"India" "23" " "
"Pakistan" "18" " "
"Peru" "39" "Yes"
"Jordan" "56" " "
"United Kingdom" "35" "Yes"
"Chile" "24" "Yes"
"Israel" "32" "No"
"Pakistan" "39" "No"
"Chile" "26" "Yes"
"Nigeria" "43" "Yes"

Create age groups for users of social media

In [4]:
yes_no = ["Yes", "No"]
valid_age_groups = ["18-34", "35-49", "50+"]

rdata = (
    data.with_columns(
        age_group=pl.when(col("age") <= "34")
        .then(pl.lit("18-34"))
        .when(col("age") <= "49")
        .then(pl.lit("35-49"))
        .when(col("age") < "98")
        .then(pl.lit("50+"))
        .otherwise(pl.lit("")),
        country_count=pl.len().over("country"),
    )
    .filter(
        col("age_group").is_in(valid_age_groups) & col("use_social_media").is_in(yes_no)
    )
    .group_by(["country", "age_group"])
    .agg(
        # social media use percentage
        sm_use_percent=(col("use_social_media") == "Yes").sum() * 100 / pl.len(),
        # social media question response rate
        smq_response_rate=col("use_social_media").is_in(yes_no).sum()
        * 100
        / col("country_count").first(),
    )
    .sort(["country", "age_group"])
)

rdata.head()
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.

In [5]:
def format_column(column, fmt):
    """Format column using python format"""
    def _fmt(s):
        return pl.Series([fmt.format(x) if x is not None else x for x in s])
    return pl.col(column).map_batches(_fmt)

n = 14

top = (
    rdata.group_by("country")
    .agg(r=col("smq_response_rate").sum())
    .sort("r", descending=True)
    .head(n)
)
top_countries = set(top["country"])

point_data = rdata.filter(col("country").is_in(top_countries)).with_columns(
    col("country").cast(pl.Categorical),
    sm_use_percent_str=pl.when(
        col("country")=="United States"
    ).then(
        format_column("sm_use_percent", "{:.0f}%")
    ).otherwise(
        format_column("sm_use_percent", "{:.0f}")
    )
)

point_data.head()
shape: (5, 5)
country age_group sm_use_percent smq_response_rate sm_use_percent_str
cat 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" 
In [6]:
segment_data = (
    point_data.group_by("country")
    .agg(
        min=col("sm_use_percent").min(),
        max=col("sm_use_percent").max(),
    )
    .with_columns(gap=(col("max") - col("min")))
    .sort(
        "gap",
    )
    .with_columns(
        min_str=format_column("min", "{:.0f}"),
        max_str=format_column("max", "{:.0f}"),
        gap_str=format_column("gap", "{:.0f}"),
    )
)

segment_data.head()
shape: (5, 7)
country min max gap min_str max_str gap_str
cat 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

First plot

In [7]:
# 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",
        va="center_baseline",
    )
    + 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_baseline",
        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",
        va="center_baseline",
    )
    # gap difference
    + geom_text(
        segment_data,
        aes(x=xgap, y="country", label="gap_str"),
        size=9,
        fontweight="bold",
        format_string="+{}",
    )
)

Tweak it

In [8]:
# Gallery, elaborate

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

(
    ggplot()
    # Background Strips                                     # new
    + geom_segment(
        segment_data,
        aes(y="country", yend="country"),
        x=0,
        xend=101,
        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) - 0.25,
        yend=np.tile(np.arange(1, n + 1), 9) + 0.25,
        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",
        va="center_baseline",
    )
    + 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_baseline",
        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",
        va="center_baseline",
    )
    # countries right-hand-size (instead of y-axis)         # new
    + geom_text(
        segment_data,
        aes(y="country", label="country"),
        x=-1,
        size=8,
        ha="right",
        va="center_baseline",
        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,
        va="center_baseline",
        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 + 0.5,
        label="Youngest-\nOldest Gap",
        size=9,
        color="#444444",
        va="bottom",
        ha="center",
    )
    + annotate("point", x=[31, 56, 85], y=n + 0.3, alpha=0.85, stroke=0)
    + annotate(
        "segment",
        x=[31, 56, 85],
        xend=[31, 56, 85],
        y=n + 0.3,
        yend=n + 0.8,
        alpha=0.85,
    )
    + annotate(
        "hline",
        yintercept=[x + 0.5 for x in range(2, n, 2)],
        alpha=0.5,
        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))
)

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 Order

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

In [9]:
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="",
    truncate_ragged_lines=True,
)

# 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()
shape: (5, 60)
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 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 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 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 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 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 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.

In [10]:
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)
shape: (10, 3)
code region income_group
str str cat
"ABW" "Latin America & Caribbean" "High income"
"AFG" "South Asia" "Low income"
"AGO" "Sub-Saharan Africa" "Lower middle income"
"ALB" "Europe & Central Asia" "Upper middle income"
"AND" "Europe & Central Asia" "High income"
"ARE" "Middle East & North Africa" "High income"
"ARG" "Latin America & Caribbean" "Upper middle income"
"ARM" "Europe & Central Asia" "Lower middle income"
"ASM" "East Asia & Pacific" "Upper middle income"
"ATG" "Latin America & Caribbean" "High income"

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

In [11]:
country_data = data.join(metadata, on="code")
country_data.head()
shape: (5, 62)
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 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 null "Latin America & Caribbean" "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 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 null "Sub-Saharan Africa" "Lower middle income"
"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 null "Europe & Central Asia" "Upper middle income"
"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 null "Europe & Central Asia" "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.

In [12]:
sampled_data = (
    country_data.drop_nulls(subset=["y1960", "y2015"])
    .sample(n=35, seed=123)
    .with_columns(
        y1960_rank=col("y1960").rank(method="ordinal").cast(pl.Int64),
        y2015_rank=col("y2015").rank(method="ordinal").cast(pl.Int64),
    )
    .sort("y2015_rank", descending=True)
)

sampled_data.head()
shape: (5, 64)
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 str cat i64 i64
"Sierra Leone" "SLE" 223.6 220.5 217.5 214.2 211.0 207.6 204.2 200.8 197.3 194.1 191.0 188.0 185.2 182.6 180.0 177.5 175.3 173.2 171.2 169.2 167.3 165.6 164.1 162.8 161.5 160.4 159.4 158.3 157.6 157.0 156.5 156.1 155.7 155.2 154.5 153.4 152.0 150.1 148.1 145.8 143.3 140.5 137.7 134.6 131.4 128.1 124.5 120.5 116.2 111.7 107.0 102.3 97.9 93.8 90.2 87.1 null null "Sub-Saharan Africa" "Low income" 35 35
"Cote d'Ivoire" "CIV" 208.4 203.0 197.7 192.8 188.0 183.3 178.7 174.2 169.9 165.4 161.0 156.4 151.3 146.1 140.7 135.1 129.7 124.7 120.2 116.6 113.7 111.4 109.5 108.0 106.9 106.1 105.5 105.2 104.9 104.9 104.9 104.8 104.7 104.7 104.6 104.4 104.0 103.3 102.3 101.0 99.5 97.7 95.7 93.6 91.4 88.9 86.7 84.1 81.3 79.0 76.9 75.0 72.8 70.6 68.5 66.6 null null "Sub-Saharan Africa" "Lower middle income" 33 34
"Benin" "BEN" 186.9 183.9 180.6 177.1 173.6 170.2 166.8 164.0 161.5 159.2 157.1 154.9 152.5 149.8 146.8 143.5 140.1 136.7 133.6 130.9 128.7 126.6 124.7 122.8 120.9 118.9 116.9 114.8 112.6 110.4 108.0 105.6 103.2 100.9 98.9 97.2 95.6 94.2 92.7 91.1 89.3 87.4 85.2 83.0 80.8 78.8 76.9 75.2 73.7 72.3 71.0 69.8 68.5 67.2 65.7 64.2 null null "Sub-Saharan Africa" "Low income" 32 33
"Burkina Faso" "BFA" 161.3 159.4 157.5 155.8 154.3 153.0 151.8 150.9 150.2 149.7 149.3 148.5 147.1 144.6 141.0 136.6 131.9 127.4 123.4 120.2 117.6 115.6 113.9 112.4 110.8 109.0 107.1 105.3 103.8 102.9 102.5 102.3 102.4 102.4 102.1 101.4 100.5 99.4 98.3 97.3 96.2 95.0 93.4 91.4 88.9 86.0 82.7 79.2 75.8 72.5 69.7 67.3 65.4 63.7 62.2 60.9 null null "Sub-Saharan Africa" "Low income" 30 32
"Ghana" "GHA" 125.1 123.8 122.7 121.8 121.2 120.8 120.7 120.6 120.6 120.5 120.1 119.5 118.2 116.5 114.2 111.5 108.7 106.0 103.8 102.1 100.9 100.1 99.3 98.4 96.8 94.7 92.1 89.0 85.8 82.7 79.8 77.5 75.6 74.1 73.0 72.0 71.0 69.8 68.4 66.7 64.9 63.0 61.2 59.6 58.1 56.8 55.6 54.4 53.1 51.7 50.2 48.6 47.0 45.5 44.2 42.8 null null "Sub-Saharan Africa" "Lower middle income" 25 31

First graph

In [13]:
(
    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()
)

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

In [14]:
# Text colors
black1 = "#252525"
black2 = "#222222"

# Gallery, elaborate

(
    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=0.7)
    + geom_point(aes(2, "y2015_rank", color="income_group"), size=2.5, alpha=0.7)
    + geom_segment(
        aes(x=1, y="y1960_rank", xend=2, yend="y2015_rank", color="income_group"),
        alpha=0.7,
    )
    # 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))
)