Friends Don't Let Friends Make Bad Graphs

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# Friends Don't Let Friends Make Bad Graphs

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  <a href="/training-material/hall-of-fame/cxli233/" class="contributor-badge contributor-cxli233"><img src="https://avatars.githubusercontent.com/cxli233?s=36" alt="Chenxin Li avatar" width="36" class="avatar" />
    Chenxin Li</a>
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# Friends Don't Let Friends Make Bad Graphs

This is an *opinionated* essay about good and bad practices in data visualization.

This material is MIT licensed and adapted from [cxli233/FriendsDontLetFriends](https://github.com/cxli233/FriendsDontLetFriends).

R Code for generating the graphs is available in that repository.

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- This is an opinionated essay about good and bad practices in data visualization.
- This material is MIT licensed and adapted from cxli233's work
- Please see the linked repository for the source for each of these graphs as well as more links and details.

---

## 1. Friends Don't Let Friends Make Bar Plots for Means Separation

![No Bar Plots for Means Separation](images/dont_bar_plot.png)

.footnote[inspired by many tweets the author saw on limitations of the bar graph. See e.g. <span class="citation"><a href="https://doi.org/10.1371/journal.pbio.1002128">Weissgerber <i>et al.</i> 2015</a></span>]

???

- This has to be the first one.
- Means separation plots are some of the most common in scientific publications.
- We have two or more groups, which contains multiple observations; they may have different means, variances, and distributions.
- The task of the visualization is to show the means and the spread (dispersion) of the data.
- In this example, two groups have similar means and standard deviations, but quite different distributions.
- Are they really "the same"?
- Just don't use bar plot for means separation, or at least check a couple things before settling down on a bar plot.
- It's worth mentioning that I was inspired by many researchers who have tweeted on the limitation of bar graphs.

---

## 2. Friends Don't Let Friends Make Violin Plots for Small Sample Sizes

![Beware of Violin Plots for Small Sample Sizes](images/Beware_of_small_n_box_violin_plot.png)

???

- This is quite common in the literature as well, but unfortunately, violin plots (or any sort of smoothed distribution curves) make no sense for small n.
- Distributions and quartiles can vary widely with small n, even if the underlying observations are similar.
- Distribution and quartiles are only meaningful with large n.
- I did an experiment before, where I sampled the same normal distribution several times and computed the quartiles for each sample.
- The quartiles only stablize when n gets larger than 50.

---

## 3. Friends Don't Let Friends Use Bidirectional Color Scales for Unidirectional Data

![Are You Using the Right Color Scale for Your Data?](images/ColorScales.png)

???

- Excuse my language, but this is a truly data visualization sin, and again quite common.
- I can understand why this error is common, because it appears that many of us have not spent a lot of thoughts on this issue.
- Color scales are pretty, but we have to be extra careful.
- When color scales (or color gradients) are used to represent numerical data, the darkest and lightest colors should have special meanings.
- You can decide what those special meanings are: e.g., max, min, mean, zero. But they should represent something meaningful.
- A data visualization sin for heat maps/color gradients is when the lightest or darkers colors are some arbitrary numbers.
- This is as bad as the longest bar in a bar chart not being the largest value. Can you imagine that?

---

## 4. Friends Don't Let Friends Make Bar Plot Meadow

![Horrendous Giant Bar Plot vs. Better Designed Plot](images/AvoidBarPlotMeadow.png)

.footnote[Data from <span class="citation"><a href="https://doi.org/10.1186/s12870-020-2243-7">Matand <i>et al.</i> 2020</a></span>]

???

- We talked about no bar charts for mean separation, but this is a different issue.
- It has to do with presenting results of a multi-factorial experiment.
- Bar plot meadows are very common in scientific publications and unfortunately also ineffective in communicating the results.
- Bar plot meadows are common because multi-factorial experiments are common.
- However, a bar plot meadow is poorly designed for its purpose.
- To communicate results of a multi-factorial experiment, it requires thoughtful designs regarding grouping/faceting by factors of interest.
- In this example, I focus on comparing the effect of `Treatment` & `Explant` on `Response` at the level of each `Variety`.
- However, if the focus is the effect of `Treatment` & `Variety` on `Response` at the level of each `Exaplant`, then it will require a different layout.

---

## 5. Friends Don't Let Friends Make Heatmap without (Considering) Reordering Rows & Columns

![A Heatmap before and after reordering rows and columns](images/Reorder_rows_and_columns_for_heatmap.png)

.footnote[Data from <span class="citation"><a href="https://doi.org/10.1101/2022.07.04.498697">Li <i>et al.</i> 2022</a></span>]

???

- Heatmaps are very common in scientific publications, and very very common in omics papers.
- However, for heatmaps to be effective, we have to consider the ordering of rows and columns.
- In this example, I have cells as columns and features as rows. Grids are showing z scores.
- It is impossible to get anything useful out of the heatmap without reordering rows and columns.
- We can reorder rows and columns using clustering, but that is not the only way.
- Of course, if the rows and columns are mapping to physical entities (rows and columns of a 96-well plate), then you can't reorder them.
- But it is a very good idea to at least consider reordering rows and columns.

---

### Bonus: heatmaps can be very pretty

![aRt with Heatmap](images/Abstract_R_2022_11_24.png)

.footnote[
  [R Code](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/Abstract_aRt.R),
  [Tutorial on reordering](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Heatmap_tutorial.md)
]

???

- Heatmaps can be very pretty...if you are good are reordering rows/columns and choosing color gradients.
- Here is an example "abstract aRt" generated from simulated data.

## 6. Friends Don't Let Friends Make Heatmap without Checking Outliers

![Did you check outliers](images/Check_outliers_for_heatmap.png)

???

- Outliers in heatmap can really change how we perceive and interpret the visualization.
- This generalizes to all sort of visualizations that use colors to represent numeric data.
- Let me show you an example:
- In this example, I have 2 observations. For each observations, I measured 20 features.
- Without checking for outliers, it may appear that the 2 observations are overall similar, except at 2 features.
- However, after maxing out the color scale around 95th percentile of the data, it reveals that the two observations are distinct across all features.

---

## 7. Friends Don't Let Friends Forget to Check Data Range at Each Factor Level

![Did you check data range at each factor level](images/Check_range_at_factor_level.png)

???

- This is a common issue that many of us have encountered.
- In a multifactor experiment, sometimes the range of the response variable changes widely between different factor levels.
- This hypothetical experiment measured 3 compounds across 2 groups (control vs. treatment).
- Without checking data range for each compound, you will likely have missed that the treatment had a strong effect on compound 1.
- This is because the concentration of compound 1 has a much narrower range than the other compounds in this experiment.

---

## 8. Friends Don't Let Friends Make Network Graphs without Trying Different Layouts

![Try different network layouts](images/TryDifferentLayouts.png)

.footnote[Data from <span class="citation"><a href="https://doi.org/10.1101/2022.07.04.498697">Li <i>et al.</i> 2022</a></span>]

???

- Network graphs are common in scientific publications. They are super useful in presenting relationship data.
- However, the apparence (not the topology) of the network can make a huge difference in determing if a network graph is effective.
- Layouts can drastically change the appearance of networks, making them easier or harder to interpret.
- Here are 3 network graphs from the same data. They look very different from each other.

---

## 9. Friends Don't Let Friends Confuse Position-based Visualizations with Length-based Visualizations

![Position vs. length based visualizations](images/Position_and_length_based_visualizations.png)

???

- This is always the elephant in the room and the essence of many misleading visualizations.
- In this example, I measured a response variable across 3 time points.
- Two of the following graphs are fine, but one of them is a data visualization crime. Can you see why?
- In dot and line plots, values are represented by positions along the x and y axis.
- The same idea applies to other position based visualizations, such as box plots.
- In bar plots, values are represented by the distance from the x axis, and thus the length of the bar.
- The 3rd graph is not 0-based, which makes the bar length at time point 2 about 3x longer than that at time point 1.
- In fact, the true difference in means is closer to 1.6x.
- I hope you can see how confusing length and position based visualizations can lead to misleading graphs.

---

### Watch out for bar plots with broken axis

![Broken axis](images/Broken_axis.png)

.footnote[[Example R Code](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/Broken_axis.R)]

???

- Broken axis may be useful for depicting data across a wide range of numeric values.
- (Alternatively, log scaled axis can be used instead.)
- Broken axis are fine for position based graphics, because the data are represented by positions along the axis.
- However, we must be very careful with bar plots that have broken axis. Here is an example.
- In this example, two graphs (left vs. right) are showing the same data.
- However, by changing where the axis is broken, one can make certain bars looks longer or shorter.
- In this example, the length of bar "d" can look really different.
- The illusion of bar "d" being very short on the right graph boils down to bar plot being a length based graphics, not a position based graphics.

---

## 10. Friends Don't Let Friends Make Pie Chart

![Don't make pie charts](images/dont_pie_chart.png)

???

- Pie chart is a common type of visualization for fractional data, where fractions add up to 100%.
- This is achieved by dividing a circle into sectors, and the sectors add up to a full circle.
- Pie charts have been criticized, because human are much worse in reading angles and area than reading lengths.
- In this example, we have two groups, each contains 4 sub-categories.
- In classic pie charts, the angles (and thus arc lengths & sector area) represent the data.
- The problem is that it is very difficult to compare between groups.
- We can visually simplify the pie chart into donut charts, where the data are now represented by arc lengths.
- However, if we want to use lengths to represent the data, why don't we just unwrap the donut and make stacked bars?
- In stacked bar graphs, bars are shown side-by-side and thus easier to compare across groups.
- Fun fact: the scripts underlying stacked bars are much simpler than those underlying the pie charts and donut charts.
- If you want to produce sub-optimal graph types with ggplot, you actually have to work extra hard.

---

## 11. Friends Don't Let Friends Make Concentric Donuts

![Don't make concentric donuts](images/dont_concentric_donuts.png)

???

- In this example, we have 3 groups, each of which contains two sub-categories (Type I or Type II).
- In concentric donuts, you might be tempted to say the data are represented by the arc lengths, which is in fact inaccurate.
- The arc lengths on the outer rings are much longer than those in the inner rings.
- Group 2 and Group 3 have the same exact values, but the arc lengths of Group 3 are much longer.
- In fact the data are represented by the arc angles, which we are bad at reading.
- Since outer rings are longer, the ordering of the groups (which group goes to which ring) has a big impact on the impression of the plot.
- It can lead to the apparent paradox where larger values have shorter arcs.
- The better (and simpler!) alternative is just unwrap the donuts and make a good old stacked bar plot.
- BTW, this is also my main issue with circos plots and other circular plot layouts.

---

## 12. Friends Don't Let Friends Use Red/Green and Rainbow color scales

![are you making a "safe" heatmap?](images/Color_blind_grey_scale_safe_heatmap.png)

???

- Deuteranomaly is the most common type of red/green colorblindness, occurring in 1/16 male and 1/256 female.
- Any color scales that use shades of red and shades of green in the same time would be a problem for a person with red/green colorblindness (third column of the figure).
- In addition, red/green and rainbow do not preserve information well at all when printed on black/white (grey scale, second column in figure).
- Many scientific software still use red/green or rainbow as the default color scales, which drives me crazy.
- More "modern" color scales, such as viridis are both colorblind-friendly and grey scale-safe (third row of figure).
- And they look nice too.

---

## 13. Friends Don't Let Friends Forget to Reorder Stacked Bar Plot

![Reorder your stacked bars](images/Reorder_stacked_bars.png)

.footnote[[Tutorial on optimising](https://github.com/cxli233/FriendsDontLetFriends/blob/main/Scripts/stacked_bars_optimization.Rmd).]

???

- Stacked bar plots are useful for visualizing proportion data.
- Stacked bar plots are commonly used to visualize community structure or population structure or admixture analysis.
- This kind of visualization boils down to a collection of samples, where each sample contains multiple classes of members.
- However, when we have many samples and many classes, stacked bar plots need to be optimized to be effective.
- And by "optimize" I mean the grouping and ordering of samples.
- Here we have an example data with 100 samples and 8 classes of member.
- Due to the number of samples and classes, it is very hard to discern anything from this graph without optimizing the order of bars. What the heck am I looking at?
- After reordering the bars, wow, that really made a difference, don't you think?

---

## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

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  <a href="/training-material/hall-of-fame/cxli233/" class="contributor-badge contributor-cxli233"><img src="https://avatars.githubusercontent.com/cxli233?s=36" alt="Chenxin Li avatar" width="36" class="avatar" />
    Chenxin Li</a>
  
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    Helena Rasche</a>
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