Quality statistical charts: introduction to data visualisation
Tomasz Przechlewski
May, 2020
Why it is important to know the rules of statistical charts
Lies, damned lies, and statistics
The above quotation is attributed to Benjamin Disraeli (UK Prime Minister)
Fake news hype. Numbers disguised as a result of a statistical analysis are often used to persuade something to somebody. Statistical charts are ubiquitous now and many of them are fake…
Why people believe in numbers uncritically? They believe as they are innumerate. Why people are innumerate?
People are illiterate because they failed to learn how to read and write. Simple…
Perhaps people are innumerate not because of genetic load or some other disaster, but simply becuase they are not educated.
Five stages of statistical data analysis:
Usually students’ attention is concentrated on stages 2 and 3 almost exclusively. In result statistics is regarded as part of math thus 100% reliable while in reality it is not. Stages 1 and 5 are often more of an art than a science and if one do not know the rules of these stages one can easily put excessive trust in the final outcome.
Less theory, more practice, and common sense.
What are statistical charts for?
Statistical charts can be plotted for the following three purposes:
Decoration (to attract somebodys’ attention, document without pictures looks dull, color pictures are better than back-white ones, fanciful drawings are better than simple ones, form is the king and content does not matter)
Explanation (to better explain some phenomenon to somebody. It is claimed that a picture is worth thousand of words in this context)
Exploration (looking for data patterns at the exploratory stage of data analysis)
A visual-art designer not statistician is a right person for the 1st purpose. I am not an art-designer so I will not tell you how to prepare eye-catching pictures. I am a statistician and I will concentrate on effective graphical methods for statistical explanation/exploration. And by effective I mean that one (graphical) method is more effective than another if its quantitative information can be decoded more quickly/easily [Robbins 2005]
Types of charts
Some graphs are better than others:
Recommended: (ordered) dot plots, bar charts, lineplots, histograms and kernel density estimates, stripcharts, multipanel displays (instead of stacked bars multiple line/dot plots) scatterplots (two variables)
Not recommended: Pie charts, bubble charts, stacked bar charts,
Note: bar/line/pie charts were introduced by William Playfair in XVIII century. Dot plots were introduced by John Cleveland (1980s). Box-plots were introduced by John Tukey (1970s)
More Playfair’s charts can be found via google or in Syamnzik’s paper
Ladies corner
Crimean war was waged in 1853–1856 between Russia and the Turkey / France / England coalition and the Kingdom of Sardinia. Won by a coalition, but with shockingly large losses (0.5 million soldiers)
The first modern war; thanks to the invention of the telegraph and photography (Roger Fenton https://pl.qaz.wiki/wiki/Roger_Fenton or https://en.wikipedia.org/wiki/Roger_Fenton; or enter Roger + Fenton + camera in the search engine) broadcast live to the metropolis.
Also famous for an episode known as The charge of the Light Brigade (something like the Polish Samosierra charge, https://en.wikipedia.org/wiki/Battle_of_Somosierra but it ended in defeat)
Florence Nightingale
As is the case in war, it is often a mess and incompetence was the main source of losses. Wounded and sick soldiers were transported to Turkey, but due to poor medical care, the mortality rate was high, and most died of typhus rather than of wounds sustained on the battlefield. Florence Nightingale organized/reformed the care of wounded soldiers, fighting the resistance of military bureaucracy (hospital/Üsküdar near Istanbul). It is believed that many soldiers owed their lives to her.
Moreover she documented her actions, i.e. kept statistics (according to her own project). And it is known that medicine = quackery + statistics.
In particular, Nightingale diagrams are famous, which admittedly They (apparently) didn’t turn out to be particularly useful, but not all new ones the idea is immediately brilliant:
This is kind of like a bar chart except that there are pie slices instead of bars. There are twelve clippings as many as months. The length of the ray and hence the size of the sector’s field depends on the size of the phenomenon, which it represents (cause of death: wounds / diseases / other)
In March 1855 as a result of the Sanitary Commissioners intervention, sanitary facilities improved as shown in the graph:
Pie charts, dot plots and histograms
Nights spent at tourist accommodation establishments by non residents (tour_occ_ninat)
Histograms and kernel density functions
Histograms show the distribution of a set of data. To draw a histogram the numbers (observations) are grouped into bins (intervals or classes). There is a trade-off between showing details or showing an overall picture. When bin width changes the scale at Y-axis changes as well (more bins less observations in each bin). Example number of hotels in Poland (2017):
d <- read.csv("powiaty_wskazniki_woj_regiony.csv", sep = ';', header=T, na.string="NA");
ggplot(d, aes(x = hotele2017)) +
geom_histogram(bins = nclass.Sturges(d$hotele2017))
Histograms with binwidth equal to 20, 10, 5 and 1 respectively:
Drawback of histogram: scale is bin (width) dependent.
Kernel density functions
ggplot(data=d) + geom_density(aes(x=hotele2017))
p1 <- ggplot(data=d) + geom_density(aes(x=hotele2017), adjust=0.25)
p2 <- ggplot(data=d) + geom_density(aes(x=hotele2017), adjust=1.0)
p3 <- ggplot(data=d) + geom_density(aes(x=hotele2017), adjust=2.0)
p4 <- ggplot(data=d) + geom_density(aes(x=hotele2017), adjust=8.0)
ggarrange(p1,p2,p3,p4)
Comparing distributions: box-plots
Box-plots are much better than histograms for comparing distributions of more than one data sets.
Construction of a (typical) box-plot: The middle bar is a median. Top/bottom bars of the rectangle shows the IQR (interquartile range is 1st and 3rd
quartile), the fanciful bars above/below rectangle called whiskers (google: whiskers mustache :-) are 1,5 times the IQR (or minimu/maximum if those values are less than plus/minus 1,5 IQR. The symbols above/below whiskers (usually open circles) are outliers (non typical/extreme values)
Note the trick: outliers are defined not as (for example) top/botom 1% fraction of values (every distribution would has outliers in such a case) but as values less/more than Me - 1,5IQR (distributions with medium variablity would not have outliers)
Example: age of Nobel-prize winners (cf The Nobel Prize API Developer Hub)
nlf <- read.csv("nobel_laureates3.csv", sep = ';', dec = ",", header=T, na.string="NA");
ggplot(nlf, aes(x=category, y=age, fill=category)) + geom_boxplot() + ylab("years") + xlab("");
## Warning: Removed 39 rows containing non-finite values (stat_boxplot).
NUTS and TERYT
The Nomenclature of Territorial Units for Statistics (NUTS) is a geocode standard for referencing the subdivisions of countries for statistical purposes. The standard is developed and regulated by the European Union, and thus only covers the member states of the EU in detail (cf NUTS)
NUTS standard was revised several times (on the average every 4 years :-)), so there is even a page at ec.europa.eu domain dedicated to NUTS (short) history (cf NUTS history)
NUTS1 (level) – macroregion, NUTS2 – state, NUTS3 – subregion (several counties in case of Poland)
Poland is divided into 7 macroregions, 16 states (NUTS2), and 72 subregions (NUTS3).
NUTS1 level is only for statistical purposes (but regions are in fact distinct due to history, economics, natural-conditions, cultural factors etc… )
There is a relevant and interesting page by GUS (Main Statistical Office or Główny Urząd Statystyczny), but unfortunately in Polish (use google translate :-) in case you are interested or mail me) (cf Klasyfikacja NUTS w Polsce )
The above map shows 7 macroregions (NUT1) and 16 provinces (NUTS2). BTW province in Polish is “prowincja” (due to both are from Latin) but actually Polish administrative provice is called “województwo”, from “wodzić” – ie commanding (the armed troops in this context). This is an old term/custom from the 14th century, where Poland was divided into provinces (every province ruled by a “wojewoda” ie chief of that province). More can be found at Wikipedia (cf Administrative divisions of Poland)
NUTS3 consists of 380 counties grouped into 72 subregions.
A Polish county (called “powiat”) is 2-nd level administrative unit.
In ancient Poland powiat was called “starostwo” and the head of a “starostwo was called”starosta“.”Stary" means Old, so “starosta” is an old (and thus wise) person. BTW the head of powiat is “starosta” as 600 years ago:-)
The 3rd level administrative unit is called “gmina” (community).
There are (approximately) 380 counties and 2750 communities in Poland.
As Poland population is 38,5 mln and the area equals 312,7 sq kilometers (120 persons per 1 sqkm) on the average each powiat has 820 sqkm and each community has 113.5 sqkm or approximately 100 thousand persons per “powiat and 14 thousand per”gmina".
TERYT is a Polish NUTS (developed some 50 years ago). It is complex system which includes identification of administrative units. Every unit has (up to) a 7-digit id number: wwppggt where ww = “województwo” id, pp = “powiat” id, gg = “gmina” id and “t” decodes type-of-community (rural, municipal or mixed). Higher units has trailing zeros for irrelevant part of id, so 14 or 1400000 means the same; as well as 1205 and 1205000. Six numbers is enough to identify a community (approx 2750 units).
So you are now experts on administrative division of Poland, and we can go back to statistical charts…
Strip charts
A strip chart (strip plot) shows the distribution of data points along a numerical axis.These plots are suitable compared to box plots when sample sizes are small (because preserve more information about the data).
Example: Number of hotels in powiat by region (NUTS1, 2017):
The biggest potential problem with a dot/scatterplot is overplotting: whenever one has more than a few points, points may be plotted on top of one another. This can severely distort the visual appearance of the plot (left panel)
There is no one solution to this problem, but there are some techniques that can help: use smaller dots, use semi-transparent dots (right panel), use jitter.
Jitter—a small random noise added to data, is shown below (higher jitter on the right panel)
Comparing distributions box-plots vs strip-charts
Number of hotels in powiat by województwo (2017):
More jitter:
Boxplots are better:
Scatter-plots
A scatter-plot (aka scatter diagrams, xyplot) is a basic form used for two (quantitative) variables.
To see the relationship between variables, a line is can be fitted. Least square (LS) line which assumes linear relationship between variables, is fitted by minimizing the sum of squares of the residuals (residual is the difference between a data-point and a relevant line-point ie a point computed from the formula y = a +bx where x is the value of the x-axis variable.)
(Almost) each part of Poland is attractive for tourists, but those counties which are at the seaside (north) or in the mountains (south) are special. There are 11 counties at the seaside (morze = sea) and 18 in the mountains (góry):
##
## Call:
## lm(formula = tz2017 ~ y2017, data = m)
##
## Residuals:
## Min 1Q Median 3Q Max
## -127494 -22228 4466 20779 84551
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -52055 45219 -1.151 0.2793
## y2017 5839 2324 2.513 0.0332 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 60310 on 9 degrees of freedom
## Multiple R-squared: 0.4123, Adjusted R-squared: 0.347
## F-statistic: 6.314 on 1 and 9 DF, p-value: 0.03316
##
## Call:
## lm(formula = tz2017 ~ y2017, data = m)
##
## Residuals:
## Min 1Q Median 3Q Max
## -26224 -5432 2165 5616 25199
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -11071.5 5820.8 -1.902 0.086330 .
## y2017 961.7 161.4 5.960 0.000139 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 13270 on 10 degrees of freedom
## Multiple R-squared: 0.7803, Adjusted R-squared: 0.7584
## F-statistic: 35.52 on 1 and 10 DF, p-value: 0.0001394
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
So each new hotel in the mountains on the average would attract 961.6 foreign tourists, while a new hotel at the seaside would attract 5838 foreign tourists (and both numbers are statistically significant at \(\alpha=0.05\):-) )
Alternatively loess curve can be used which do not assumes linearity but is parameters are not interpretable.
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
Scales
Logarithmic scale makes it possible to plot values with too wide range for a linear scale. Base 10 logarithms squeeze' the numbers more than base 2 logarithms (log10(100)=2 wile log2(100)=6.64. Moreover if the original scale contains multiplications of 10 use log10 to getnice’ log-scale while it contains multiplications of 2 use log2.
Logarithms transforms additive scale to `multiplicative’ one. Example (Nobel prize again):
dA <- read.csv("nobel_laureates3.csv", sep = ';', dec = ",", header=T, na.string="NA");
nrow(dA)
## [1] 934
dS <- subset(dA, (! bornCountryCode == "" )) # by country of birth
nrow(dS) # how many
## [1] 901
aggregate NP-winners by bornCountryCode:
Finally plot the resulting data using various Y-axis scales (arithmetic, log2 and log10)
The exact figures are as follows:
##
## AR AT AU AZ BA BD BE BG BR BY CA CH CL CN CO CR CY CZ DE
## 0 4 17 10 1 2 1 9 1 1 4 19 17 2 12 2 1 1 6 82
## DK DZ EG ES FI FR GB GH GP GR GT HR HU ID IE IL IN IR IS IT
## 12 2 6 7 5 55 100 1 1 1 2 1 9 1 5 6 8 2 1 19
## JP KE KR LC LR LT LU LV MA MG MK MM MX NG NL NO NZ PE PK PL
## 26 1 2 2 2 3 2 1 1 1 1 1 3 1 18 12 3 1 3 25
## PT RO RU SE SI SK TL TR TW UA US VE VN YE ZA ZW
## 2 4 26 29 1 1 2 3 1 5 269 1 1 1 9 1
Graphic features
Vizualize data = encode the quantitative information using graphic features (shapes, angles, colors, position, etc.). Some features are better than others…
From the best to the worst (William S. Cleveland and Robert McGill Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods, JASA 1984):
Position along common scale
Position along common but nonaligned scales
Length
Angle (slope)
Area
Volume
Color (hue), Color (saturation), Color (density of black)
Angle judgement is not precise. Acute angles are underestimated while obtuse angles (greater than 90) are overestimated.
Area judgement is biased as well. It is impossible to distinguish small differences in area, while quite easy when the same date is plotted along common scale
The most accurate of graphic task is positioning along common scale
General design rules
Clear content: Reader/receiver/consumer clearly understands what is graphed: scales/labels/explanations are provided (remember what/when/where?)
Clear form: reader/receiver/consumer clearly sees what is graphed (no cluttered lines, overlapping elements, etc…)
Emphasize the data not grids, labels or pointless arrows. The simpler the better, leave complicated designs for professionals. For example use gray not black ink (default in Excel) for grid lines
Tick marks and axis labels should be placed outward. X-axis values increase always from left to the right, Y-axis values from the bottom to the top never in reverse direction. Do not overdo the number of tick marks.
Preparing color chart think how it will look like when reproduced in B-W (xerox) or half-size or less (smartphone). It is important particularly in electronic print.
Never use more graphic feature than your data set has dimensions. For univariate analysis use length or color not both for example. (Well rare exceptions to this rule are allowed)
Pseudo 3D charts for 2D data should be forbidden as well and without any exception. Virtually no-one can read them.
Use a common baseline wherever possible. Use optimum aspect ratio (banking to 45, see below). Use logaritmic scales when data range is huge, do not break scales and generally always include 0 in numerical axes (not 100% obligatory however.) Do not (generally) use double axes.
Prefer direct labels over using separate legend. Separate legend forces the reader to look back and forth when studying the graph. Of course if there is no room for (long) labels use legend.
Multiline graphs generally are bad idea (different scales, clutter, difficulty with assessing the difference between lines)
Do not use crappy software which do not produces charts in proportion to data
Edward Tufte’s rules
Tufte who is a renown expert on Information Visualization (aka guru) coined two popular rules: (high) data to ink ratio and a lie-factor.
Ink in this definition refers to non-erasable ink used for the presentation of data. If data-ink would be removed from the image, the graphic would lose the content. Non-Data-Ink is accordingly the ink that does not transport the information but it is used for scales, labels and edges.
Good graphics should include only data-Ink. Non-Data-Ink is to be deleted everywhere where possible. The reason for this is to avoid drawing the attention of viewers of the data presentation to irrelevant elements. There is an short an excellent video clip at YouTube which illustrates this rule.
Data to Ink Ratio
Lie factor (LF) is a ratio as well but defined as size of the effect shown in graphics to the size of effect in data. Preferaby LF should equal 100%. According to Tufte, LF greater than 1.05 or less than 0.95 signals significant distortion. This rule can be best explained with an example.
Lie factor example
This giant guy (GG) in the middle is our ex-president. The guy next to him on the left is our current president Duda. Next to Duda is ex-rock star Kukiz, dark-horse of the elections. This is the cover (slightly modified) of influential polish weekly magazine form May 2015, shortly before elections.
The figures are claimed to be in-sync with the recent survey results (sort of a barchart). Could you figure-out from that chart about the proportion of scores of each candidate? How much the giant-guy outperforms the runner-up candidate? Which candidate is supported by this influential magazine (easy:-)?
The lie-factor details:
The line from shoes to top of the head equals (at certain size of course) 204mm for GG, 134mm for Duda and 42.5mm for ex-rock star. So \(204/134=1.5\) and \(204/42.5 \approx 4.8\). As \(44/29 \approx 1.5\) and \(44/9 \approx 4.8\) as well formally the lieFactor is perfect. But should one compares lengths or areas?
If one compares areas not heights, one get significantly different (and correct) results, namely: \((204 * 58) /(134 * 21)= 4.20\) and \((204 *58)/(42.5 *15) \approx 18.56\). Lie factor is \(4.2/1.5 =280\)% and \(18.56/4.8=387\)% respectively. Huge distortion
Moreover two more tricks were applied to boost GG. Can you see them?
BTW: the text in the pink frame claims: “figure ratios are consistent with april-may survey outcome.”" (But what exactly figure ratios means?)
Banking to 45
The ratio between the width and the height of a rectangle is called its aspect ratio.
The aspect ratio describes the area that is occupied by the data in the chart. A change in aspect ratio changes the perception of the graph. The question is which aspect ratio is the best.
We can recognize change most easily if absolute slopes equals to 45 degree angle on the graph. It is much harder to see change if the curves are nearly horizontal/vertical. The idea (Cleveland, 1988) behind banking is therefore to adjust the aspect ratio of the entire plot in such a way that most slopes are at an approximate 45 degree angle.
Setting the aspect ratio so that the average of the values of the orientations is 45 degrees is called “banking the average orientation to 45 degrees”.
Setting the aspect ratio so that the weighted mean of line segments (weighted by segments’ length is approx 45 degrees is called average weighted orientation method (to 45 degrees).
Exercise: assess which slope is the steepest one and which is the smallest one?
BTW: every chart presents the same data on CO2 emission (average for May each year) as provided by US Government’s Earth System Research Laboratory, Global Monitoring Division. (cf CO2 PPM - Trends in Atmospheric Carbon Dioxide)
Crusaders, Knights and Malbork castle
First short explanation about the subject of the analysis ie famous Castle of the Teutonic Order in Malbork which is enlisted at UNESCO heritage list (cf UNESCO heritage list ):
Several religious military orders were formed in the Holy Land during the Crusades Templars, Hospitallers, Teutonic Knights
The Teutonic Knights or the Teutonic Order of the Hospital of St. Mary in Jerusalem, were known in Poland as Krzyżacy on account of the black cross they wore on their white coats.
Established in 1190 to protect German pilgrims in the Holy Land, the order was later transformed in order to fight heretics.
In 1226 the Teutonic Knights came to Poland, invited by Duke Konrad I of Mazovia to fight with the annoying pagan Prussian tribes invading Poland from time-to-time from the north. Teutonic Knights conquered Prussia, exterminated the locals and founded a powerful state with Malbork (Marienburg or Mary’s castle in German) as its capital.
Example of a very bad graphs
There is a research, peer-reviewed paper on tourist traffic in the castle’s museum of Malbork
The determinants of the tourist traffic in the castle’s museum of Malbork
Unfortunately all charts in this paper contains elementary errors. Could you identify them?
if one insists on using piecharts (improved version):
or better, using bar/dot charts:
Even worse graphics (yes we can:-))
Piecharts are notorious for obscurity:
What about this barchart (distribution of seats in Polish parliament (Sejm) after 2015 elections—50% majority is 430 seats)?
Remember dark-horse ex-rock start Kukiz? IMO his bar does not looks like being equal to 50 votes (minus 1.) PO-bar is peculiar as well…
Not mention about strange tilt to the left…
Last example: Why polish teachers protested (recently)
Popular posters distributed during protest to persuade the public opinion that teachers are much worse then they used to be.
Average salary in education sector as % of total avarage
“Improved” variants of this chart:
Public expenditures on education
The source publication of the Polish Main Statistical Office publication (1st version):
The improved (by GUS not by me) version of the above:
There is a life without speadsheet too: R and Rstudio
R is both programming language for statistical computing and graphics and a software (ie application) to execute programs written in R. R was developed in mid 90s at the University of Auckland (New Zealand).
Since then R has become one of the dominant software environments for data analysis and is used by a variety of scientific disiplines.
BTW why it is called so strange (R)? Long time ago it was popular to use short names for computer languages (C for example). At AT&T Bell Labs (John Chambers) in mid 70s a language oriented towards statistical computing was developed and called S (from Statistics). R is one letter before S in an alphabet.
Rstudio is an environment through which to use R. In Rstudio one can simultaneously write code, execute code it, manage data, get help, view plots. Rstudio is a commercial product distributed under dual-license system by RStudio, Inc. Key developer of RStudio is Hadley Wickham another brilliant New Zealander (cf Hadley Wickham )
Microsoft invest heavily into R development recently. It bought Revolution Analytics a key developer of R and provider of commercial versions of the system. With MS support the system is expected to gain more popularity (for example by integrating it with popular MS products)
Reproducible research or how to make statistical computations more meaningfu
Abandoning the habit of secrecy in favor of process transparency and peer review was the crucial step by which alchemy became chemistry. Eric S. Raymond, E. S. The art of UNIX programming: Addison-Wesley.
Replicability vs Reproducibility
Hot topic: google: reproducible research = 158000
Replicability: independent experiment targetting the same question will produce a result consistent with the original study.
Reproducibility: ability to repeat the experiment with exactly the same outcome as originally reported [description of method/code/data is needed to do so].
Computational science is facing a credibility crisis: it’s impossible to verify most of the computational results presented at conferences and in papers today. (Donoho D. et al 2009)
Australopithecus (Current practices)
- Enter data in Excel/OOCalc to clean and/or make explanatory analysis.
Use Excel for data cleaning & descriptive statistics Excel handles missing data inconsistently and sometimes incorrectly Many common functions are poor or missing in Excel
- Import data from Spreadsheet into SPSS/SAS/Stata for serious analysis
Use SPSS/SAS/Stata in point-and-click mode to run serious statistical analyses.
Prepare report/paper: copy and paste output to Word/OpenOffice, add description.
Send to publisher (repeat 1–4 if returned for revision).
Problems
Tedious/time-wasting/costly.
Even small data/method change requires extensive recomputation effort/careful report/paper revision and update.
Error-prone: difficult to record/remember a ‘click history’.
Famous example: Reinhart and Rogoff controversy Countries very high GDP–debt ratio suffer from low growth. However the study suffers serious but easy identifiable flaws which were discovered when RR published the dataset they used in their analysis (cf Growth_in_a_Time_of_Debt)
Homo habilis (Enhanced current practices)
Benefits
Improved: reliability, transparency, automation, maintanability. Lower costs (in the long run).
Solves 1–2 but not 3–4.
Problems: Steeper learning curve. Perhaps higher costs in short run. Duplication of effort (or mess if scripts/programs are poorly documented).
Homo Erectus (Literate statistical programming)
Literate programming concept: Code and description in one document. Create software as works of literature, by embedding source code inside descriptive text, rather than the reverse (as in most programming languages), in an order that is convenient for human readers.
A program is like a WEB tangled and weaved (turned into a document), with relations and connections in the program parts. We express a program as a web of ideas. WEB is a combination of – a document formatting language and – a program language.
General idea of Literate statistical programming mimics Knuth’s WEB system.
Statistical computing code is embedded inside descriptive text. Literate statistical program is weaved (turned) into report/paper by executing code and inserting the results obtained. data/method changes.
Solves 1–4.
Github for the uninitiated
The basic idea is that instead of manually registering changes one has made to data, documents etc, one can use software to help him manage the whole process. Such software is called Version Control Systems or VCS
VCS not only manages content, registering each modification of it, but control access to the content as well. Thus many individuals can work on common project (compare this to common scenario of mailing spreadsheets to each other–highly inefficient at least)
There are highly reliable and publicly available VCS services and GitHub is the most popular of them.
GitHub is owned by Microsoft (do not use if you boycott MS :-))
I use GitHub as an educational tool: to distribute learning content to my students and to store content they produce for me (ie projects)
The free GitHub account is public. It is OK for me. If it is not OK for you, you can buy a license for commercial account or do not use GitHub.
Learning resources and data banks
Learnig resources
Data banks
Questions?
