Lollipop Plot R

[Janne Evers]

Thelollipop plot is used exactly in the same situation than abarplot. However it is somewhat more appealing and convey as well theinformation. It is especially useful when you have several bars of thesame height: it avoids to have a cluttered figure and a Moir effect.

The Cleveland dotplot is a handy variation, allowing tocompare the value of 2 numeric values for each group. This kind of datacould also be visualized using a grouped or stack barplot.However, this representation is less cluttered and way easier to read.Use it if you have 2 subgroups per group.

Note: The term cleveland dotplot does not lookto be very well defined as far as I know, and looks to be sometimes usedfor dotplotsor classiclollipop plots as well. The previous chart is also called Dumbbell dotplots. Further investigation is needed on this matter and anyfeedback is more than welcome.

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I'm trying to create a lollipop chart, so I can make simple comparisons between each region (either 'O' or 'E'). I'd make a dumbbell plot, but a lot of the region's two groups are identical so the dumbbell plot ends up looking a lot like a dot plot.

A lollipop chart is an alernative to the more usual barplot. Python allows to build lollipops thanks to the matplotlib library, as shown in the examples below. The strategy here is to use the stem() function or to hack the vline() function depending on your input format.

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A lollipop plot is the combination of a line and a dot. It shows the relationship between a numeric and a categorical variable just like a barplot. A lollipop plot can be used at places where a barplot and scatter plot both are required. This single plot helps us visualize the problem better and takes less ink space than traditional barplots.

To customize this plot we can change the color of the segment and point using the fill property. We can also change the size of the point using the size property. To add color to geom_segment() we add a color property with the desired color and add size property for the thickness of the segment. To add color to geom_point() we add a color property with the desired color and add size property for increasing the size of the point.

Now select the bars and add error bars (select the Error Bars option in the Chart Elements menu). In a bar chart, you are only able to add horizontal error bars, so you get the standard-looking thing with an error bar above and below the end of the bar.

There you go, a lollipop graph. To sum up: (1) build a bar chart; (2) add an error bar; (3) format the error bar with a circle at the end. No need to add another series and edit the series names one at a time. The primary downside of this approach is that the line and the dot are forced to be the same color. But the upside is that this is a much faster approach than creating two scatterplots and editing the series labels by hand.

(Oh, and I should also note that there is an even faster way to make a lollipop using the REPT and & functions, but those are created in the spreadsheet, not as a chart object. Perhaps a separate post? Let me know in the comment box.)

Great post Jon! For the label on the left you could add another series to the chart that is a bar chart. The values of the bars would be the value of the left dot. Change the overlap on the bar to 0%, format the bar to No Fill so it is invisible, and set the label position to Inside End. I think that would work on all versions of Excel.

A hybrid between a bar chart and a Cleveland dot plot is the lollipop chart. A lollipop chart typically contains categorical variables on the y-axis measured against a second (continuous) variable on the x-axis. Similar to the Cleveland dot plot, the emphasis is on the dot to draw the readers attention to the specific x-axis value achieved by each category. The line is meant to be a minimalistic approach to easily tie each category to its relative point without drawing too much attention to the line itself. A lollipop chart is great for comparing multiple categories as it aids the reader in aligning categories to points but minimizes the amount of ink on the graphic.

or as a lollipop chart. In the lollipop chart we use geom_segment to plot the lines and we explicitly state that we want the lines to start at x = 0 and extend to the percollege value with xend = percollege. We simply need to include y = county and yend = county to tell R the lines are horizontally attached to each county.

Consider the case where we want to compare counties in Ohio to see how they differ from the state average. For this the emphasis becomes the state average so we can do a data manipulation to generate the state average value and test if each county above or below that value.

Another comparison approach is if we wanted to compare the top 10 counties for each of the midwest states in our data set. In this case we have to do additional manipulation as each state has a couple county names that are common.

Depending on the number of categories (i.e. counties) you are trying to graphically display, and the range of the x-axis, it can be helpful to add value markers to the points to clarify the difference between the points.

Lollipop charts are a nice alternative to bar charts and dot plots. As previously mentioned, when trying to visualize data across many categories they, much like dot plots, provide a nice minimalistic visualization of the data.

Excel added error bars in all four directions around each dot. We do not need the vertical error bars at all. So in the dialogue box that opened, change the number in the Fixed value box so that it is zero.

In End Style, get rid of the cap. In Error Amount, click the radio button by Percentage and type in 100%. This will fix your x axis so it starts at 0%, like it should, and will extend the lollipop stick from the lollipop head to the y axis.

Right-click on the graph and click Select Data. In the dialogue box that opens, click on Add in the Legend Entries section. Use the cell picker icons in the new box to select Label holders as your series name, all those zeroes at the Series X values, and all the numbers underneath Lollipop spacing as your Series Y values.

Lollipop plot is basically a barplot, where the bar is transformed in a line and a dot. It shows the relationship between a numeric and a categorical variable. A lollipop is built using geom_point() for the circle, and geom_segment() for the stem.

The lollipop chart is great at visualizing differences in variables along a single axis. In this post, we create an elegant lollipop chart, in Matplotlib, to show the differences in model performance.

I was looking to represent model performance after having trained several classification models through k-fold cross-validation. Essentially, I training classical ML models to detect tool wear on a CNC machine. The data set was highly imbalanced, with very few examples of worn tools. This led to a larger divergence in the results across the different k-folds. I wanted to represent this difference, and the lollipop chart was just the tool!

With advances in Cancer Genomics, Mutation Annotation Format (MAF) is being widely accepted and used to store somatic variants detected. The Cancer Genome Atlas Project has sequenced over 30 different cancers with sample size of each cancer type being over 200. Resulting data consisting of somatic variants are stored in the form of Mutation Annotation Format. This package attempts to summarize, analyze, annotate and visualize MAF files in an efficient manner from either TCGA sources or any in-house studies as long as the data is in MAF format.

For VCF files or simple tabular files, easy option is to use vcf2maf utility which will annotate VCFs, prioritize transcripts, and generates an MAF. Recent updates to gatk has also enabled funcotator to genrate MAF files.

maftools functions can be categorized into mainly Visualization and Analysis modules. Each of these functions and a short description is summarized as shown below. Usage is simple, just read your MAF file with read.maf (along with copy-number data if available) and pass the resulting MAF object to the desired function for plotting or analysis.

In the above examples, people may be misled when the x-axis is ignored. It will be better to plot the data into multiple layers. This can be done by setting parameter ranges into a GRangesList object.

other attached packages: [1] httr_1.4.2

[2] VariantAnnotation_1.37.1

[3] Rsamtools_2.7.1

[4] Biostrings_2.59.2

[5] XVector_0.31.1

[6] SummarizedExperiment_1.21.2

[7] MatrixGenerics_1.3.1

[8] matrixStats_0.58.0

[9] org.Hs.eg.db_3.12.0

[10] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 [11] GenomicFeatures_1.43.8

[12] AnnotationDbi_1.53.1

[13] Biobase_2.51.0

[14] rtracklayer_1.51.5

[15] trackViewer_1.27.11

[16] Rcpp_1.0.6

[17] GenomicRanges_1.43.4

[18] GenomeInfoDb_1.27.8

[19] IRanges_2.25.6

[20] S4Vectors_0.29.12

[21] BiocGenerics_0.37.1

Is it possible via GraphBuilder to make a lollipop plot? See example I've posted. I know you can get something akin to this when looking at time series data (under the time series platform). The control chart very nearly does this, too, but it is obviously to be used for other sorts of analyses, and the lines are not drawn directly below the points. What I want is for the point plotted to have a line drawn above or below it that connects it to the mean (to emphasize deviation from the mean). I feel like all the elements for making such a plot are there!

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