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I've been reading EIA report and this plot captured my attention. I now want to be able to create the same type of plot.

enter image description here

It shows the energy productivity evolution between two years (1990-2015) and adds the change value between this two periods.

What is the name of this type of plot? How can I create the same plot (with different countries) in excel?

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  • $\begingroup$ Is this pdf the source? I don't see that figure in it. $\endgroup$ Commented Aug 26, 2019 at 15:03
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    $\begingroup$ I usually call this a dot plot. $\endgroup$ Commented Aug 26, 2019 at 15:07
  • $\begingroup$ Another name is lollipop plot, particularly when the observations have paired data being looked at. $\endgroup$ Commented Aug 26, 2019 at 18:57
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    $\begingroup$ Looks like a dumbbell plot. $\endgroup$ Commented Aug 27, 2019 at 6:57

3 Answers 3

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Some call it a (horizontal) lollipop plot with two groups.

Here is how to make this plot in Python using matplotlib and seaborn (only used for the style), adapted from https://python-graph-gallery.com/184-lollipop-plot-with-2-groups/ and as requested by the OP in the comments.

import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns import io sns.set(style="whitegrid") # set style data = io.StringIO(""""Country" 1990 2015 "Russia" 71.5 101.4 "Canada" 74.4 102.9 "Other non-OECD Europe/Eurasia" 60.9 135.2 "South Korea" 127.0 136.2 "China" 58.5 137.1 "Middle East" 170.9 158.8 "United States" 106.8 169.0 "Australia/New Zealand" 123.6 170.9 "Brazil" 208.5 199.8 "Japan" 181.0 216.7 "Africa" 185.4 222.0 "Other non-OECD Asia" 202.7 236.0 "OECD Europe" 173.8 239.9 "Other non-OECD Americas" 193.1 242.3 "India" 173.8 260.6 "Mexico/Chile" 221.1 269.8""") df = pd.read_csv(data, sep="\s+", quotechar='"') df = df.set_index("Country").sort_values("2015") df["change"] = df["2015"] / df["1990"] - 1 plt.figure(figsize=(12,6)) y_range = np.arange(1, len(df.index) + 1) colors = np.where(df['2015'] > df['1990'], '#d9d9d9', '#d57883') plt.hlines(y=y_range, xmin=df['1990'], xmax=df['2015'], color=colors, lw=10) plt.scatter(df['1990'], y_range, color='#0096d7', s=200, label='1990', zorder=3) plt.scatter(df['2015'], y_range, color='#003953', s=200 , label='2015', zorder=3) for (_, row), y in zip(df.iterrows(), y_range): plt.annotate(f"{row['change']:+.0%}", (max(row["1990"], row["2015"]) + 4, y - 0.25)) plt.legend(ncol=2, bbox_to_anchor=(1., 1.01), loc="lower right", frameon=False) plt.yticks(y_range, df.index) plt.title("Energy productivity in selected countries and regions, 1990 and 2015\nBillion dollars GDP per quadrillion BTU", loc='left') plt.xlim(50, 300) plt.gcf().subplots_adjust(left=0.35) plt.tight_layout() plt.show()

enter image description here

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  • $\begingroup$ thank you however, it is giving me error I have used your data and python (TypeError: 'int' object is not callable) any help Regards Mazin $\endgroup$ Commented Mar 10, 2020 at 2:22
  • $\begingroup$ @MazinAlmurrani In which line does the error appear? $\endgroup$ Commented Mar 10, 2020 at 10:12
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That's a dot plot. It is sometimes called a "Cleveland dot plot" because there is a variant of a histogram made with dots that people sometimes call a dot plot as well. This particular version plots two dots per country (for the two years) and draws a thicker line between them. The countries are sorted by the latter value. The primary reference would be Cleveland's book Visualizing Data. Googling leads me to this Excel tutorial.

I scraped the data, in case anyone wants to play with them. 

 Country 1990 2015 Russia 71.5 101.4 Canada 74.4 102.9 Other non-OECD Europe/Eurasia 60.9 135.2 South Korea 127.0 136.2 China 58.5 137.1 Middle East 170.9 158.8 United States 106.8 169.0 Australia/New Zealand 123.6 170.9 Brazil 208.5 199.8 Japan 181.0 216.7 Africa 185.4 222.0 Other non-OECD Asia 202.7 236.0 OECD Europe 173.8 239.9 Other non-OECD Americas 193.1 242.3 India 173.8 260.6 Mexico/Chile 221.1 269.8
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    $\begingroup$ BTW, "scrape" means estimate the values that the dots in the plot represent. FWIW, I used Web Plot Digitizer. $\endgroup$ Commented Aug 27, 2019 at 13:55
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    $\begingroup$ Or. trivially, dot chart. Precursors seem thin on the ground but do exist. See e.g. Snedecor, G.W. 1937. Statistical Methods Applied to Experiments in Agriculture and Biology. Ames, IA: Collegiate Press. This graph was dropped at some later point in the revision of this well-known text; it doesn't appear in editions with co-author W.G. Cochran, $\endgroup$ Commented Aug 27, 2019 at 14:12
  • $\begingroup$ Minor correction to previous. The last edition with such a plot was 1967 from Snedecor and Cochran. $\endgroup$ Commented Apr 23, 2024 at 8:52
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The answer by @gung is correct in identifying the chart type and providing a link to how to implement in Excel, as requested by the OP. But for others wanting to know how to do this in R/tidyverse/ggplot, below is complete code:

library(dplyr) # for data manipulation library(tidyr) # for reshaping the data frame library(stringr) # string manipulation library(ggplot2) # graphing # create the data frame # (in wide format, as needed for the line segments): dat_wide = tibble::tribble( ~Country, ~Y1990, ~Y2015, 'Russia', 71.5, 101.4, 'Canada', 74.4, 102.9, 'Other non-OECD Europe/Eurasia', 60.9, 135.2, 'South Korea', 127, 136.2, 'China', 58.5, 137.1, 'Middle East', 170.9, 158.8, 'United States', 106.8, 169, 'Australia/New Zealand', 123.6, 170.9, 'Brazil', 208.5, 199.8, 'Japan', 181, 216.7, 'Africa', 185.4, 222, 'Other non-OECD Asia', 202.7, 236, 'OECD Europe', 173.8, 239.9, 'Other non-OECD Americas', 193.1, 242.3, 'India', 173.8, 260.6, 'Mexico/Chile', 221.1, 269.8 ) # a version reshaped to long format (for the points): dat_long = dat_wide %>% gather(key = 'Year', value = 'Energy_productivity', Y1990:Y2015) %>% mutate(Year = str_replace(Year, 'Y', '')) # create the graph: ggplot() + geom_segment(data = dat_wide, aes(x = Y1990, xend = Y2015, y = reorder(Country, Y2015), yend = reorder(Country, Y2015)), size = 3, colour = '#D0D0D0') + geom_point(data = dat_long, aes(x = Energy_productivity, y = Country, colour = Year), size = 4) + labs(title = 'Energy productivity in selected countries \nand regions', subtitle = 'Billion dollars GDP per quadrillion BTU', caption = 'Source: EIA, 2016', x = NULL, y = NULL) + scale_colour_manual(values = c('#1082CD', '#042B41')) + theme_bw() + theme(legend.position = c(0.92, 0.20), legend.title = element_blank(), legend.box.background = element_rect(colour = 'black'), panel.border = element_blank(), axis.ticks = element_line(colour = '#E6E6E6')) ggsave('energy.png', width = 20, height = 10, units = 'cm')

Graph as implemented in ggplot

This could be extended to add value labels and to highlight the colour of the one case where the values swap order, as in the original.

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    $\begingroup$ Also geom_lollipop is available in ggalt and in SciencesPo R packages. $\endgroup$ Commented Apr 5, 2020 at 22:13

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