├── .DS_Store
├── .github
    └── CODEOWNERS
├── .gitignore
├── LICENSE
├── README.md
├── priestley-timeline
    ├── README.md
    ├── composers.csv
    ├── images
    │   └── priestley.png
    ├── index.html
    └── js
    │   └── d3.min.js
└── visual-vocabulary
    ├── .DS_Store
    ├── FT4schools_RGS.pdf
    ├── README.md
    ├── Visual-vocabulary-JP.pdf
    ├── Visual-vocabulary-cn-simplified.pdf
    ├── Visual-vocabulary-cn-traditional.pdf
    ├── Visual-vocabulary-de.pdf
    ├── Visual-vocabulary-en.pdf
    ├── Visual-vocabulary-es.pdf
    ├── Visual-vocabulary-fr.pdf
    └── poster.png


/.DS_Store:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/Financial-Times/chart-doctor/befb9cba21849432a4029d0cfdda938819ae733f/.DS_Store


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/.github/CODEOWNERS:
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1 | * @Financial-Times/visual-data-journalism-admins


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/.gitignore:
--------------------------------------------------------------------------------
1 | 
2 | npm-debug.log
3 | .DS_Store
4 | .DS_Store
5 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | The MIT License (MIT)
 2 | 
 3 | Copyright (c) 2016 FT Interactive News
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
--------------------------------------------------------------------------------
1 | # chart-doctor
2 | Sample files to accompany the FT's [Chart Doctor](http://www.ft.com/chart-doctor) feature.
3 | 
4 | ## Licence
5 | This software is published by the Financial Times under the [MIT licence](http://opensource.org/licenses/MIT). 
6 | 
7 | Please note the MIT licence includes only the software, and does not cover any FT content made available using the software, which is copyright © The Financial Times Limited, all rights reserved. For more information about re-publishing FT content, please contact our [syndication department](http://syndication.ft.com/).
8 | 


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/priestley-timeline/README.md:
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 1 | #Joseph Priestley timeline
 2 | 
 3 | This sample uses the data visualisation library d3.js to create a timeline of dates and durations similar to those popularised by [Jospeh Priestley](https://en.wikipedia.org/wiki/Joseph_Priestley) in the 18th century.
 4 | 
 5 | It takes as its input the datafile [composers.csv](composers.csv) which simply contains the name of a composer, along with birth and death date.
 6 | 
 7 | ![Priestley-style timeline of great composers](images/priestley.png)
 8 | 
 9 | For more information, please visit the [FT Chart Doctor](http://www.ft.com/chart-doctor)
10 | 


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/priestley-timeline/composers.csv:
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 1 | name,born,died
 2 | Haydn,31/03/1732,31/05/1809
 3 | Mozart,27/01/1756,05/12/1791
 4 | Beethoven,17/12/1770,26/03/1827
 5 | Mendelssohn,03/02/1809,04/11/1847
 6 | Schumann,08/06/1810,29/07/1856
 7 | Schubert,31/01/1797,19/11/1828
 8 | Berlioz,11/12/1803,08/03/1869
 9 | Bizet,25/10/1838,03/06/1875
10 | Bruckner,04/09/1824,11/10/1896
11 | Brahms,07/05/1833,03/04/1897
12 | Elgar,02/06/1857,23/02/1934


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/priestley-timeline/images/priestley.png:
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https://raw.githubusercontent.com/Financial-Times/chart-doctor/befb9cba21849432a4029d0cfdda938819ae733f/priestley-timeline/images/priestley.png


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/priestley-timeline/index.html:
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  1 | <html>
  2 | <head>
  3 |     <title>Basic Priestley-style timeline</title>
  4 |     <style>
  5 |         /*some basic CSS*/
  6 |         .axis path{fill:none;stroke:none}
  7 |         .axis line{fill:none;stroke:gray;stroke-width:1px;}
  8 |         .axis text{fill:gray;stroke:none;}
  9 |         text {text-anchor:middle}
 10 |     </style>
 11 | </head>
 12 | <body>
 13 |     <h1>A Specimen of a Priestley-style Timeline</h1>
 14 |     <script src="js/d3.min.js"></script>
 15 |     <script>
 16 |     
 17 |     var file="composers.csv"
 18 |     var width=600,height=400;
 19 |     var margin={top:50,bottom:30,left:20,right:20}
 20 |     
 21 |     //create svg element to hold graphics
 22 |     var svg=d3.select("body").append("svg")
 23 |         .attr("width",width)
 24 |         .attr("height",height)
 25 |     
 26 |     //load the data
 27 |     d3.csv(file,function(data){
 28 |         
 29 |         //define the date format used in the data
 30 |         var parseDate = d3.time.format("%d/%m/%Y");
 31 |         //parse the date information in the data
 32 |         data.forEach(function(d) {
 33 |                 d.born=parseDate.parse(d.born)
 34 |                 d.died=parseDate.parse(d.died)
 35 |         });
 36 |         
 37 |         //sort the lifespans into date-of-birth order - we will use this for the drawing order of the chart
 38 |         data.sort(function(a, b){
 39 |             return a.born-b.born;
 40 |         });
 41 |         
 42 |         //find out the range of the dates
 43 |         //first birth
 44 |         var minDate=d3.min(data, function(d){
 45 |             return d.born;
 46 |         })
 47 |         
 48 |         //last death
 49 |         var maxDate=d3.max(data, function(d){
 50 |             return d.died;
 51 |         })
 52 |         
 53 |         //calculate plot dimensions
 54 |         var plotWidth = width-(margin.left+margin.right)
 55 |         var plotHeight = height-(margin.top+margin.bottom)
 56 |         
 57 |         //create a scale for the timeline
 58 |         var xScale = d3.time.scale()
 59 |             .domain([minDate,maxDate])
 60 |             .range([0,plotWidth])
 61 |         
 62 |         //a scale for vertical arrangement of each lifespan
 63 |         var yScale = d3.scale.ordinal()
 64 |             .domain(data.map(function(d){
 65 |                 return d.name;
 66 |             }))
 67 |             .rangeRoundBands([0,plotHeight],0.6);
 68 |         
 69 |         //define the chart's time axes
 70 |         //primary axis - these will be labelled
 71 |         var xAxis = d3.svg.axis()
 72 |             .scale(xScale)
 73 |             .orient("bottom")
 74 |             .ticks(4)
 75 |             .tickSize(-plotHeight)
 76 |     
 77 |         //secondary axis - more ticks but no labels
 78 |         var xAxis2 = d3.svg.axis()
 79 |             .scale(xScale)
 80 |             .orient("bottom")
 81 |             .tickFormat(d3.time.format(""))
 82 |             .ticks(20)
 83 |         
 84 |         //draw the axes
 85 |         svg.append("g")
 86 |             .attr("class","axis")
 87 |             .attr("transform","translate("+margin.left+","+(margin.top+plotHeight)+")")
 88 |             .call(xAxis)
 89 |         svg.append("g")
 90 |             .attr("class","axis")
 91 |             .attr("transform","translate("+margin.left+","+(margin.top+plotHeight)+")")
 92 |             .call(xAxis2)
 93 |         
 94 |         //offset the textlabels
 95 |         svg.selectAll(".axis text")
 96 |             .attr("dy",20)
 97 |         
 98 |         //create chart geometry
 99 |         //chart group
100 |         var chart = svg.append("g")
101 |             .attr("id","chart")
102 |             .attr("transform","translate("+margin.left+","+margin.top+")")
103 |         
104 |         //a row for each lifespan
105 |         var rowGroups = chart.append("g")
106 |             .attr("id","chart_rows")
107 |             .selectAll("g")
108 |             .data(data)
109 |             .enter()
110 |             .append("g")
111 |             .attr("transform",function(d,i){
112 |                 return "translate(0,"+yScale(d.name)+")"
113 |             })
114 |         
115 |         rowGroups.each(function(d,i){
116 |                 //create a rectangle to show time between birth and and death
117 |                 d3.select(this).append("rect")
118 |                 .attr("x",function(d){
119 |                     return xScale(d.born)
120 |                 })
121 |                 .attr("width",function(d){
122 |                     return xScale(d.died)-xScale(d.born)
123 |                 })
124 |                 .attr("height",yScale.rangeBand)
125 |                 .attr("fill","black")
126 |                 .attr("fill-opacity",0.8)
127 |                 
128 |                 //create a label with each composer's name on it - put it in middle of lifespan
129 |                 d3.select(this).append("text")
130 |                     .attr("x",function(d){
131 |                         //calculate mid point of composer's life
132 |                         var midPoint = new Date((d.born.getTime() + d.died.getTime()) / 2);
133 |                         return xScale(midPoint)
134 |                      })
135 |                     .attr("y",function(d){
136 |                         return -1;
137 |                     })
138 |                     .text(function(d){
139 |                         return d.name
140 |                     })
141 |                 
142 |                 //label the first row with a key
143 |                 if (i==0){
144 |                     var key = d3.select(this).append("g")
145 |                         .attr("class","axis")
146 |                     key.append("text")
147 |                         .attr("x",function(d){
148 |                             return xScale(d.born);
149 |                         })
150 |                         .attr("y",function(d){
151 |                             return -5;
152 |                         })
153 |                         .text("born")
154 |                     key.append("text")
155 |                         .attr("x",function(d){
156 |                             return xScale(d.died);
157 |                         })
158 |                         .attr("y",function(d){
159 |                             return -5;
160 |                         })
161 |                         .text("died")
162 |                 }
163 |         })//end each row
164 |         
165 |     })//end
166 |     
167 |     </script>
168 | </body>
169 | </html>


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  1 | ![Financial Times Visual Vocabulary poster](poster.png)
  2 | 
  3 | 
  4 | # Financial Times Visual Vocabulary
  5 | 
  6 | A poster (available in [English](https://github.com/ft-interactive/chart-doctor/blob/master/visual-vocabulary/Visual-vocabulary.pdf), [Japanese](https://github.com/ft-interactive/chart-doctor/blob/master/visual-vocabulary/Visual-vocabulary-JP.pdf), [traditional Chinese](https://github.com/ft-interactive/chart-doctor/blob/master/visual-vocabulary/Visual-vocabulary-chinese-traditional.pdf) and [simplified Chinese](https://github.com/ft-interactive/chart-doctor/blob/master/visual-vocabulary/Visual-vocabulary-chinese-simplified.pdf)) and [web site](http://ft-interactive.github.io/visual-vocabulary/) to assist designers and journalists to select the optimal symbology for data visualisations, by the [Financial Times Visual Journalism Team](https://www.ft.com/visual-journalism).
  7 | 
  8 | The FT Visual Vocabulary is at the core of a newsroom-wide training session aimed at improving chart literacy. This learning resource is inspired by the [Graphic Continuum](https://policyviz.com/2014/09/09/graphic-continuum/) by Jon Schwabish and Severino Ribecca. This is not an attempt to teach everyone how to make charts, but how to recognise the opportunities to use them effectively alongside words.
  9 | 
 10 | Read the [Chart Doctor](http://www.ft.com/chart-doctor) feature column for full background on why we made this: [Simple techniques for bridging the graphics language gap](https://www.ft.com/content/304419ec-63a3-11e6-8310-ecf0bddad227)
 11 | 
 12 | For D3 templates for producing many of these chart types in FT style, see our [Visual Vocabulary repo](https://github.com/ft-interactive/visual-vocabulary).
 13 | 
 14 | - - -
 15 | 
 16 | ## Related reading
 17 | 
 18 | The full content of the poster, along with links to related material, including research and examples of best practice. _This is a work in progress._
 19 | 
 20 | ### General
 21 | 
 22 | * National Geographic: [Taking data visualisation from eye candy to efficiency](http://news.nationalgeographic.com/2015/09/150922-data-points-visualization-eye-candy-efficiency/)
 23 | * William S. Cleveland and Robert McGill: [Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods](http://info.slis.indiana.edu/~katy/S637-S11/cleveland84.pdf)
 24 | * Hadley Wickham: [A Layered Grammar of Graphics](http://vita.had.co.nz/papers/layered-grammar.pdf)
 25 | * Tracey L. Weissgerber et al: [Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm](http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.1002128)
 26 | * Numeroteca: [Uses and abuses of data visualisations in mass media](http://numeroteca.org/2016/05/18/uses-and-abuses-of-data-visualizations-in-mass-media/)
 27 | * Andy Cotgreave: [The inevitability of data visualization criticism](http://www.computerworld.com/article/3048315/data-analytics/the-inevitability-of-data-visualization-criticism.html)
 28 | * Alberto Cairo: ["Our reader" won't understand something as complicated as that!](http://www.thefunctionalart.com/2016/05/our-reader-wont-understand-something-as.html)
 29 | * Alberto Cairo: [Visualization's expanding vocabulary](http://www.thefunctionalart.com/2016/05/visualizations-expanding-vocabulary.html)
 30 | 
 31 | ### Deviation
 32 | 
 33 | Emphasise variations (+/-) from a fixed reference point. Typically the reference point is zero but it can also be a target or a long-term average. Can also be used to show sentiment (positive/neutral/negative). *Example FT uses:* Trade surplus/deficit, climate change
 34 | 
 35 | #### Diverging bar
 36 | 
 37 | A simple standard bar chart that can handle both negative and positive magnitude values.
 38 | 
 39 | * Chart Doctor: [How the FT explained Brexit](https://www.ft.com/content/3bfc0aac-4ccd-11e6-88c5-db83e98a590a)
 40 | 
 41 | #### Diverging stacked bar
 42 | 
 43 | Perfect for presenting survey results which involve sentiment (eg disagree/neutral/agree).
 44 | 
 45 | #### Spine chart
 46 | 
 47 | Splits a single value into 2 contrasting components (eg Male/Female)
 48 | 
 49 | #### Surplus/deficit filled line
 50 | 
 51 | The shaded area of these charts allows a balance to be shown – either against a baseline or between two series.
 52 | 
 53 | ### Correlation
 54 | 
 55 | Show the relationship between two or more variables. Be mindful that, unless you tell them otherwise, many readers will assume the relationships you show them to be causal (i.e. one causes the other). *Example FT uses:* Inflation & unemployment, income & life expectancy
 56 | 
 57 | * Chart Doctor: [The German election and the trouble with correlation](https://www.ft.com/content/94e3acec-a767-11e7-ab55-27219df83c97)
 58 | 
 59 | #### Scatterplot
 60 | 
 61 | The standard way to show the relationship between two continuous variables, each of which has its own axis.
 62 | 
 63 | * Chart Doctor: [The storytelling genius of unveiling truths through charts](https://www.ft.com/content/e2eba288-ef83-11e6-930f-061b01e23655)
 64 | * Maarten Lambrechts: [7 reasons you should use dot graphs](http://www.maartenlambrechts.com/2015/05/03/to-the-point-7-reasons-you-should-use-dot-graphs.html)
 65 | * Tim Brock: [Too Big Data: Coping with Overplotting](https://www.infragistics.com/community/blogs/tim_brock/archive/2015/04/21/too-big-data-coping-with-overplotting.aspx)
 66 | * Sara Kehaulani Goo: [The art and science of the scatterplot](http://www.pewresearch.org/fact-tank/2015/09/16/the-art-and-science-of-the-scatterplot/)
 67 | * Chart Doctor: [The storytelling genius of unveiling truths through charts](https://www.ft.com/content/e2eba288-ef83-11e6-930f-061b01e23655)
 68 | * *Examples:* [_FT_](https://www.ft.com/content/1ce1a720-ce94-3c32-a689-8d2356388a1f)
 69 | 
 70 | #### Line + Column
 71 | 
 72 | A good way of showing the relationship between an amount (columns) and a rate (line)
 73 | 
 74 | * Data Revelations: [Be Careful with Dual Axis Charts](http://www.datarevelations.com/be-careful-with-dual-axis-charts.html)
 75 | * DataHero: [The Do’s and Don’ts of Dual Axis Charts](https://datahero.com/blog/2015/04/23/the-dos-and-donts-of-dual-axis-charts/)
 76 | * Harvard Business Review: [Beware Spurious Correlations](https://hbr.org/2015/06/beware-spurious-correlations)
 77 | 
 78 | #### Connected scatterplot
 79 | 
 80 | Usually used to show how the relationship between two variables has changed over time.
 81 | 
 82 | * Robert Kosara: [The Connected Scatterplot for Presenting Paired Time Series](https://eagereyes.org/papers/the-connected-scatterplot-for-presenting-paired-time-series)
 83 | * Data Revelations: [Be Careful with Dual Axis Charts](http://www.datarevelations.com/be-careful-with-dual-axis-charts.html)
 84 | * *Examples:* [_Washington Post_](https://www.washingtonpost.com/apps/g/page/business/the-end-of-the-us-oil-boom-told-through-one-texas-companys-bust/1999/)
 85 | 
 86 | #### Bubble
 87 | 
 88 | Like a scatterplot, but adds additional detail by sizing the circles according to a third variable 
 89 | 
 90 | * Chart Doctor: [The storytelling genius of unveiling truths through charts](https://www.ft.com/content/e2eba288-ef83-11e6-930f-061b01e23655)
 91 | * *Examples:* [_FT_](https://ig.ft.com/managements-missing-women-data/)
 92 | 
 93 | #### XY heatmap
 94 | 
 95 | A good way of showing the patterns between 2 categories of data, less good at showing fine differences in amounts.
 96 | 
 97 | * Chart Doctor: [Use fewer maps to illustrate data better](https://www.ft.com/content/de3ef722-9514-11e6-a1dc-bdf38d484582)
 98 | 
 99 | ### Ranking
100 | 
101 | Use where an item’s position in an ordered list is more important than its absolute or relative value. Don’t be afraid to highlight the points of interest. *Example FT uses:* Wealth, deprivation, league tables, constituency election results
102 | 
103 | #### Ordered bar
104 | 
105 | Standard bar charts display the ranks of values much more easily when sorted into order
106 | 
107 | #### Ordered column
108 | 
109 | See above.
110 | 
111 | #### Ordered proportional symbol
112 | 
113 | Use when there are big variations between values and/or seeing fine differences between data is not so important.
114 | 
115 | #### Dot strip plot
116 | 
117 | Dots placed in order on a strip are a space-efficient method of laying out ranks across multiple categories.
118 | 
119 | #### Slope
120 | 
121 | Perfect for showing how ranks have changed over time or vary between categories. 
122 | 
123 | #### Lollipop chart
124 | 
125 | Lollipops draw more attention to the data value than standard bar/column and can also show rank and value effectively.
126 | 
127 | ### Distribution
128 | 
129 | Show values in a dataset and how often they occur. The shape (or ‘skew’) of a distribution can be a memorable way of highlighting the lack of uniformity or equality in the data. *Example FT uses:* Income distribution, population (age/sex) distribution
130 | 
131 | * Joey Cherdarchuk: [Visualising distributions](http://www.darkhorseanalytics.com/blog/visualizing-distributions-3)
132 | 
133 | #### Histogram
134 | 
135 | The standard way to show a statistical distribution - keep the gaps between columns small to highlight the ‘shape’ of the data
136 | 
137 | * Aran Lunzer and Amelia McNamara: [Exploring histograms](http://tinlizzie.org/histograms/)
138 | 
139 | #### Boxplot
140 | 
141 | Summarise multiple distributions by showing the median (centre) and range of the data
142 | 
143 | #### Violin plot
144 | 
145 | Similar to a box plot but more effective with complex distributions (data that cannot be summarised with simple average).
146 | 
147 | #### Population pyramid
148 | 
149 | A standard way for showing the age and sex breakdown of a population distribution; effectively, back to back histograms.
150 | 
151 | #### Dot strip plot
152 | 
153 | Good for showing individual values in a distribution, can be a problem when too many dots have the same value.
154 | 
155 | #### Dot plot
156 | 
157 | A simple way of showing the change or range (min/max) of data across multiple categories. 
158 | 
159 | #### Barcode plot
160 | 
161 | Like dot strip plots, good for displaying all the data in a table,they work best when highlighting individual values.
162 | 
163 | * Maarten Lambrechts: [Interactive strip plots for visualizing demographics](http://www.maartenlambrechts.com/2015/11/30/interactive-strip-plots-for-visualizing-demographics.html)
164 | 
165 | #### Cumulative curve
166 | 
167 | A good way of showing how unequal a distribution is: y axis is always cumulative frequency, x axis is always a measure.
168 | 
169 | ### Change over Time
170 | 
171 | Give emphasis to changing trends. These can be short (intra-day) movements or extended series traversing decades or centuries. Choosing the correct time period is important to provide suitable context for the reader. *Example FT uses:* Share price movements, economic time series
172 | 
173 | * Flowing Data: [11 Ways to Visualize Changes Over Time – A Guide](http://flowingdata.com/2010/01/07/11-ways-to-visualize-changes-over-time-a-guide/)
174 | 
175 | #### Line 
176 | 
177 | The standard way to show a changing time series. If data are irregular, consider markers to represent data points 
178 | 
179 | * Chart Doctor: [A chart’s ability to mislead is off the scale](https://www.ft.com/content/3062d082-e3da-11e6-8405-9e5580d6e5fb)
180 | * Office for National Statistics: [Does the axis have to start at zero? (Part 1 – line charts)](https://blog.ons.digital/2016/06/27/does-the-axis-have-to-start-at-zero-part-1-line-charts/)
181 | * Quartz: [It's OK not to start your y-axis at zero](https://qz.com/418083/its-ok-not-to-start-your-y-axis-at-zero/)
182 | * Vox: [Shut up about the y-axis. It should't always start at zero](https://www.youtube.com/watch?v=14VYnFhBKcY)
183 | * Emily Schuch: [How to Make a Line Chart that Doesn't Lie](http://emschuch.github.io/Planned-Parenthood/)
184 | 
185 | #### Column 
186 | 
187 | Columns work well for showing change over time - but usually best with only one series of data at a time.
188 | 
189 | * Chart Doctor: [A chart’s ability to mislead is off the scale](https://www.ft.com/content/3062d082-e3da-11e6-8405-9e5580d6e5fb)
190 | * Office for National Statistics: [Does the axis have to start at zero? (Part 2 – bar charts)](https://blog.ons.digital/2016/07/19/does-the-axis-have-to-start-at-zero-part-2-bar-charts/)
191 | 
192 | 
193 | #### Line + column 
194 | 
195 | A good way of showing the relationship over time between an amount (columns) and a rate (line)
196 | 
197 | #### Stock price 
198 | 
199 | Usually focused on day-to-day activity, these charts show opening/closing and hi/low points of each day 
200 | 
201 | #### Slope 
202 | 
203 | Good for showing changing data as long as the data can be simplified into 2 or 3 points without missing a key part of story 
204 | 
205 | #### Area chart 
206 | 
207 | Use with care – these are good at showing changes to total, but seeing change in components can be very difficult 
208 | 
209 | #### Fan chart (projection) 
210 | 
211 | Use to show the uncertainty in future projections - usually this grows the further forward to projection 
212 | 
213 | #### Connected scatterplot 
214 | 
215 | A good way of showing changing data for two variables whenever there is a relatively clear pattern of progression. 
216 | 
217 | #### Calendar heatmap 
218 | 
219 | A great way of showing temporal patterns (daily, weekly, monthly) – at the expense of showing precision in quantity. 
220 | 
221 | #### Priestley timeline 
222 | 
223 | Great when date and duration are key elements of the story in the data.
224 | 
225 | * Chart Doctor: [Communicating with data: Timelines](https://www.ft.com/content/6f777c84-322b-11e6-ad39-3fee5ffe5b5b)
226 | * *Examples:* [_FT_](https://www.ft.com/content/e7591532-9338-11e6-a1dc-bdf38d484582)
227 | 
228 | #### Circle timeline 
229 | 
230 | Good for showing discrete values of varying size across multiple categories (eg earthquakes by contintent). 
231 | 
232 | #### Seismogram 
233 | 
234 | Another alternative to the circle timeline for showing series where there are big variations in the data.
235 | 
236 | ### Part-to-whole
237 | 
238 | Show how a single entity can bebroken down into its component elements. If the reader’s interest issolely in the size of the components,consider a magnitude-type chartinstead. *Example FT uses:* Fiscal budgets, company structures,national election results
239 | 
240 | * Flowing Data: [9 Ways to Visualize Proportions – A Guide](http://flowingdata.com/2009/11/25/9-ways-to-visualize-proportions-a-guide/)
241 | 
242 | #### Stacked column
243 | 
244 | A simple way of showing part-to-whole relationships but can be difficult to read with more than a few components.
245 | 
246 | * Robert Kosara: [Stacked bars are the worst](https://eagereyes.org/techniques/stacked-bars-are-the-worst)
247 | 
248 | #### Proportional stacked bar
249 | 
250 | A good way of showing the size and proportion of data at the same time – as long as the data are not too complicated. 
251 | 
252 | * Chart Doctor: [How to apply Marimekko to data](https://www.ft.com/content/3ee98782-9149-11e7-a9e6-11d2f0ebb7f0)
253 | 
254 | #### Pie
255 | 
256 | A common way of showing part-to-whole data – but be aware that it’s difficult to accurately compare the size of the segments.
257 | 
258 | * Robert Kosara: [Ye olde pie chart debate](https://eagereyes.org/blog/2015/ye-olde-pie-chart-debate)
259 | * Robert Kosara: [Pie Charts – Unloved, Unstudied, and Misunderstood](https://eagereyes.org/talk/pie-charts-unloved-unstudied-and-misunderstood)
260 | * Robert Kosara: [An Illustrated Tour of the Pie Chart Study Results](https://eagereyes.org/blog/2016/an-illustrated-tour-of-the-pie-chart-study-results)
261 | * David Robinson: [How to replace a pie chart](http://varianceexplained.org/r/improving-pie-chart/)
262 | * Office for National Statistics: [The humble pie chart: part 1](https://blog.ons.digital/2017/01/24/the-humble-pie-chart-part1/)
263 | * Office for National Statistics: [The humble pie chart: part 2](https://blog.ons.digital/2017/02/23/the-humble-pie-chart-part2/)
264 | * Ian Spence: [No humble pie: The origins and usage of a statistical chart](http://www.psych.utoronto.ca/users/spence/Spence%202005.pdf)
265 | * Jeff Clark: [In defense of pie charts](http://www.neoformix.com/2007/InDefenseOfPieCharts.html)
266 | * Stephen Few: [Save the Pies for Dessert](https://www.perceptualedge.com/articles/visual_business_intelligence/save_the_pies_for_dessert.pdf)
267 | 
268 | 
269 | #### Donut
270 | 
271 | Similar to a pie chart – but the centre can be a good way of making space to include more information about the data (eg. total) 
272 | 
273 | #### Treemap
274 | 
275 | Use for hierarchical part-to-whole relationships; can be difficult to read when there are many small segments.
276 | 
277 | #### Voronoi
278 | 
279 | A way of turning points into areas – any point within each area is closer to the central point than any other centroid.
280 | 
281 | #### Arc
282 | 
283 | A hemicycle, often used for visualising political results in parliaments.
284 | 
285 | #### Gridplot
286 | 
287 | Good for showing % information, they work best when used on whole numbers and work well in multiple layout form.
288 | 
289 | #### Venn
290 | 
291 | Generally only used for schematic representation
292 | 
293 | #### Waterfall
294 | 
295 | Can be useful for showing part-to-whole relationships where some of the components are negative.
296 | 
297 | ### Magnitude
298 | 
299 | Show size comparisons. These can berelative (just being able to seelarger/bigger) or absolute (need tosee fine differences). Usually theseshow a ‘counted’ number (for example, barrels, dollars or people) rather thana calculated rate or per cent. *Example FT uses:* Commodity production, marketcapitalisation
300 | 
301 | #### Column
302 | 
303 | The standard way to compare the size of things. Must always start at 0 on the axis
304 | 
305 | #### Bar
306 | 
307 | See above. Good when the data are not time series and labels have long category names.
308 | 
309 | #### Paired column
310 | 
311 | As per standard column but allows for multiple series. Can become tricky to read with more than 2 series.
312 | 
313 | #### Paired bar
314 | 
315 | See above.
316 | 
317 | #### Proportional stacked bar
318 | 
319 | A good way of showing the size and proportion of data at the same time – as long as the data are not too complicated.
320 | 
321 | * Chart Doctor: [How to apply Marimekko to data](https://www.ft.com/content/3ee98782-9149-11e7-a9e6-11d2f0ebb7f0)
322 | 
323 | #### Proportional symbol
324 | 
325 | Use when there are big variations between values and/or seeing fine differences between data is not so important.
326 | 
327 | #### Isotype (pictogram)
328 | 
329 | Excellent solution in some instances – use only with whole numbers (do not slice off an arm to represent a decimal).
330 | 
331 | #### Lollipop chart
332 | 
333 | Lollipop charts draw more attention to the data value than standard bar/column – does not HAVE to start at zero (but preferable).
334 | 
335 | #### Radar chart
336 | 
337 | A space-efficient way of showing value pf multiple variables– but make sure they are organised in a way that makes sense to reader.
338 | 
339 | #### Parallel coordinates
340 | 
341 | An alternative to radar charts – again, the arrngement of the variables is important. Usually benefits from highlighting values.
342 | 
343 | ### Spatial
344 | 
345 | Used only when precise locations orgeographical patterns in data aremore important to the reader thananything else. *Example FT uses:* Locator maps, population density,natural resource locations, naturaldisaster risk/impact, catchment areas, variation in election results
346 | 
347 | * Chart Doctor: [Use fewer maps to illustrate data better](https://www.ft.com/content/de3ef722-9514-11e6-a1dc-bdf38d484582)
348 | * Matthew Ericson: [When Maps Shouldn’t Be Maps](http://www.ericson.net/content/2011/10/when-maps-shouldnt-be-maps/)
349 | * Mapbox: [7 data visualization techniques for location](https://blog.mapbox.com/7-data-visualizations-techniques-for-location-544c558cc960)
350 | 
351 | #### Basic choropleth (rate/ratio)
352 | 
353 | The standard approach for putting data on a map – should always be rates rather than totals and use a sensible base geography
354 | 
355 | * Vox: [The bad map we see every presidential election](http://www.vox.com/2016/5/17/11686328/bad-election-map)
356 | * Vox: [This “bad” election map? It’s not so bad.](http://www.vox.com/2016/6/2/11828628/election-maps-hard)
357 | * UX•Blog: [Telling the truth](http://uxblog.idvsolutions.com/2011/10/telling-truth.html)
358 | 
359 | #### Proportional symbol (count/magnitde)
360 | 
361 | Use for totals rather than rates – be wary that small differences in data will be hard to see.
362 | 
363 | * Stephen Few: [What Can’t Be Built with Bricks?](https://www.perceptualedge.com/blog/?p=1627)
364 | 
365 | #### Flow map
366 | 
367 | For showing unambiguous movement across a map.
368 | 
369 | #### Contour map
370 | 
371 | For showing areas of equal value on a map. Can use deviation colour schemes for showing +/- values
372 | 
373 | #### Equalised cartogram
374 | 
375 | Converting each unit on a map to a regular and equally-sized shape – good for representing voting regions with equal value.
376 | 
377 | * Chart Doctor: [How the FT explained Brexit](https://www.ft.com/content/3bfc0aac-4ccd-11e6-88c5-db83e98a590a)
378 | * 5W Blog: [The power of cartograms and creating them easily](https://5wvelascoblog.com/2016/10/27/the-power-of-cartograms-and-creating-them-easily/)
379 | 
380 | #### Scaled cartogram (value)
381 | 
382 | Stretching and shrinking a map so that each area is sized according to a particular value.
383 | 
384 | * Chart Doctor: [The search for a better US election map](https://www.ft.com/content/3685bf9e-a4cc-11e6-8b69-02899e8bd9d1)
385 | * 5W Blog: [The power of cartograms and creating them easily](https://5wvelascoblog.com/2016/10/27/the-power-of-cartograms-and-creating-them-easily/)
386 | * Vox: [The bad map we see every presidential election](https://www.youtube.com/watch?v=hlQE4IGFc5A)
387 | 
388 | #### Dot density
389 | 
390 | Used to show the location of individual events/locations – make sure to annotate any patterns the reader should see.
391 | 
392 | * Chart Doctor: [The search for a better US election map](https://www.ft.com/content/3685bf9e-a4cc-11e6-8b69-02899e8bd9d1)
393 | 
394 | #### Heat map
395 | 
396 | Grid-based data values mapped with an intensity colour scale. As choropleth map – but not snapped to an admin/political unit.
397 | 
398 | * 5W Blog: [The power of cartograms and creating them easily](https://5wvelascoblog.com/2016/10/27/the-power-of-cartograms-and-creating-them-easily/)
399 | 
400 | ### Flow
401 | 
402 | Show the reader volumes or intensity of movement between two or more states or conditions. These might belogical sequences or geographical locations. *Example FT uses:* Movement of funds, trade, migrants, lawsuits, information; relationship graphs.
403 | 
404 | * RJ Andrews: [Picturing the Great Migration](https://medium.com/info-we-trust/picturing-the-great-migration-9e4b5a3eca8a)
405 | 
406 | #### Sankey (aka river plot)
407 | 
408 | Shows changes in flows from one condition to at least one other; good for tracing the eventual outcome of a complex process. 
409 | 
410 | * Chart Doctor: [Data visualisation: it is not all about technology](https://www.ft.com/content/aba6c58e-5a8e-11e7-9bc8-8055f264aa8b)
411 | 
412 | #### Waterfall
413 | 
414 | Designed to show the sequencing of data through a flow process, typically budgets. Can include +/- components.
415 | 
416 | #### Chord
417 | 
418 | A complex but powerful diagram which can illustrate 2-way flows (and net winner) in a matrix.
419 | 
420 | #### Network
421 | 
422 | Used for showing the strength and inter-connectedness of relationships of varying types. 
423 | 
424 | - - - 
425 | 
426 | Todo:
427 | 
428 | ### Uncertainty
429 | 
430 | * Scientific American: [Visualising uncertain weather](https://blogs.scientificamerican.com/sa-visual/visualizing-uncertain-weather/)
431 | * Oli Hawkins: [Animating uncertainty](http://olihawkins.com/2013/09/2)
432 | 
433 | ### Animation
434 | 
435 | * Chart Doctor: [The storytelling genius of unveiling truths through charts](https://www.ft.com/content/e2eba288-ef83-11e6-930f-061b01e23655)
436 | * Evan Sinar: [Use Animation to Supercharge Data Visualization](https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4)
437 | 
438 | ### Interactivity
439 | 
440 | * Chart Doctor: [Why the FT creates so few clickable graphics](https://www.ft.com/content/c62b21c6-7feb-11e6-8e50-8ec15fb462f4)
441 | * Gregor Aisch: [In defense of interactive graphics](https://www.vis4.net/blog/posts/in-defense-of-interactive-graphics/)
442 | * Zan Armstrong: [Why choose? Scrollytelling and steppers](https://medium.com/@zanarmstrong/why-choose-scrollytelling-steppers-155a59dd97fe))
443 | 
444 | ### Map projections
445 | 
446 | ### Colour
447 | 
448 | 
449 | 
450 | - - - 
451 | 
452 | ## Licence
453 | 
454 | Copyright &copy; The Financial Times Limited, all rights reserved. For more information about re-publishing FT content, please contact our [syndication department](http://syndication.ft.com/).
455 | 


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