Rank Plot

Author

Hassan Kibirige

# NOTE: This notebook uses the polars package
import numpy as np

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

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

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.

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

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.

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

(
    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.

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


(
    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))
)