Comparison of open-source software for producing directed acyclic graphs

2.1 TikZ

TikZ is a LaTeX library for creating graphical figures, with extensive customization settings to generate a vast array of different images. It relies on portable graphics format (PGF), another LaTeX language as its base layer [30]. Tantau developed and maintains the two libraries. Tantau did not design the library specifically for DAGs, and thus, there is no built-in exposure, outcome, and covariates framework, analysis features, or automatic placement of nodes. Despite these limitations, TikZ serves as one of the leading software to create DAGs, thanks to its flexibility, which allows the user to easily specify the color, size, shape, and arrangement of nodes and edges. In addition, as TikZ is built into the LaTeX environment, one can use mathematical notation to label variables with inline math mode (denoted by $’s), including subscripts, Greek letters, and any other mathematical symbols. There are many tutorials and user-posted question and answer boards online that explain the different possible customization settings. One can also refer to the TikZ and PGF manual for full documentation of all of the package’s capabilities [30]. For the purpose of creating causal diagrams, we recommend beginning with DAG-specific resources as TikZ’s extensive language can be overwhelming, especially for a beginner in LaTeX. To code a DAG using TikZ, we first create the nodes and specify each node’s location, shape, color, and label as desired. We then list the edges and can again customize any stylistic preferences. The code is both intuitive and readable.

As shown in Figure 1, we write the labels using plain text, add circles around the nodes, and use the stealth arrow type. We display the variables chronologically and arrange the exposure, outcome, and confounder such that all edges can be shown using straight lines. As shown in Figure 2, we use a similar design as in Figure 1, but here we write variable labels using inline math mode. This allows us to add subscripts to the confounder nodes, and label them as C 1 , C 2 , and C 3 . Here, we incorporate curved arrows by specifying the angle of the desired curve. For those with less familiarity with LaTeX, there may be a steeper learning curve to create figures with TikZ. However, we believe that frequent users of LaTeX will find it easy to incorporate TikZ into their documents to produce DAGs. As shown Figures 1 and 2, it is obvious why TikZ is a popular resource to draw DAGs. We can easily create a clear and visually appealing DAG, with straightforward changes available to adapt the style to a user’s preferences. In Table 1, we further see that TikZ’s strength comes from its customizability and visual appeal, while its main limitation is that it is not designed for a DAG-specific framework and thus does not have analysis capabilities.

2.2 DAGitty

DAGitty is a browser-based interface, downloadable tool, and R library for creating, editing, and analyzing DAGs. The website interface and downloadable tool are accessible via http://www.dagitty.net/ and the R package is available on CRAN [20]. DAGitty’s browser version provides a graphical user interface that allows users to draw and analyze causal diagrams. Its drag-and-click features make the tool very user-friendly and easy to learn. The website allows the user to select and label nodes, connect nodes via directional edges, and identify the exposure, outcome, and covariates. After one creates a causal diagram, one can explore the conditional independence, ancestor/descendant identification, and minimally sufficient adjustment sets [37,38]. The user can also copy and paste the model code into R after installing and loading the DAGitty library. Similarly, the R library has the functionality to obtain the conditional independencies, ancestor/descendant identification, and adjustment set lists directly in the statistical program. The DAGitty package in R additionally offers functions that can simulate data based on the specified DAG structure [20]. However, the simulation functionality is limited; the creators suggest employing it only for validation purposes and that one use other techniques or software for more complicated simulation studies [20,39]. DAGitty 0.9a, the oldest version of the software available, was released in 2010 with its first announcement via a letter in Epidemiology [40]. The most current version of the R package available is 3.1, which was updated in 2023 (as of August 2023). DAGitty developers also maintain the browser-based website regularly [41].

In Figure 1, we see the simple confounding DAG with DAGitty’s output from R. Notably, there are no circles around the nodes, the lines are very light and thin, and the arrows run very close to the letters. Figure 2 shows the more complicated mediation DAG, with DAGitty’s graph shown second to the left. We can see that DAGitty is not able to incorporate the subscripts on the nodes. It can plot the curved edges, but the placement and execution of the curves do not look as polished as in TikZ. The top curved arrow from A to Y appears condensed due to space limitations imposed by the RStudio plot output box and the R Markdown display region. If the curve had a larger radius from A to Y , then the display region in R would cut off the top part of the curve. This region limitation is not a problem on the DAGitty website.

To create both DAGs, we employ the website to set up the initial placement and then copy and paste code from the website into RStudio, where we use the DAGitty R library. The DAGitty website is user-friendly and intuitive. In summary, DAGitty has extensive analysis capabilities, but is less flexible in visual design. Overall, this is a suitable package for users who want to produce a quick DAG and can accept compromises on visual appearance.

2.3 ggdag

The R package ggdag allows users to plot and analyze causal graphs [21]. It is built on top of DAGitty to utilize DAGitty’s powerful algorithms to analyze DAGs, while allowing users to employ ggplot and tidyverse to create professional, reproducible, and visually appealing DAGs [42]. It also enables the use of DAGitty objects in the context of tidyverse [21,43]. The R functions are flexible in the sense that they allow users to code their DAG structure using DAGitty syntax or ggdag-specific syntax. This feature allows ggdag to have the same analytical capability that DAGitty has, including identification of conditional independencies, ancestor/descendant relationships, and minimally sufficient adjustment sets lists [21]. ggdag additionally offers a visual display of adjustment sets via a colored graph [43,44]. Finally, this package has a wrapper function that allows one to apply DAGitty’s simulating data algorithm to the structural equation model [43]. Thus, ggdag and DAGitty are both able to simulate data but under the same limitations. The initial release was in March 2018, and the current version 0.2.10 was updated in 2023 (as of August 2023) [43].

Figure 1 displays the simple confounding DAG with ggdag’s output shown in the third panel. In ggdag, edges are specified with structural equation model notation in the dagify() function. The ggplot() plotting function then renders the defined dagify object. Figure 2 illustrates the more complicated mediation DAG, with ggdag’s version displayed in the center column. We see that ggdag is able to incorporate curved edges and subscripts on the node labels. The curved edges have a nice bold arc, for which it was easy to control the radius and directionality using the geom_dag_edge_arc() add-on. The incorporation of both the subscript and the professional-looking curved edges makes this graph visually appealing. The creation of this graph took twice the amount of time as DAGitty, suggesting a longer learning curve; however, this might be eased with more frequent use. Incorporating the placement of each node, the location of each curved edge, and the subscripts each took the authors multiple Google searches for examples, vignettes, or user-posted question and answer boards [42–45]. The end product is very appealing, but it did require patience and time. For quicker use of ggdag, one might utilize DAGitty’s web application to specify node and edge locations and then use the corresponding code and DAGitty object in ggdag’s plotting function.

Table 1 highlights ggdag’s performance in terms of analysis capability, and visual appeal. Notably, ggdag can incorporate subscripts and curved arrows, customize node placement, and write Greek symbols to label nodes through unique Unicode values in the ggdag label function [46]. In the utility category, ggdag has many vignettes, is well documented, and has many resources available online [42–45]; however, ggdag falls short compared to other software in ease-of-use due to the relative time needed by the authors to create Figure 2. Overall, ggdag is a valuable tool for users who want to create professional-looking DAGs in R.

2.4 dagR

dagR is an R package developed by Lutz P. Breitling, which was originally released in 2010 and most recently updated in 2022 (as of August 2023) [23,39]. The package allows users to plot DAGs, identify minimal sufficient adjustment sets, list ancestors of a given node, and simulate data from the specified causal diagram. dagR notably provides additional simulation capabilities compared to DAGitty (and ggdag) by allowing for a combination of binary and continuous variables within the same DAG [39]. Unlike DAGitty and ggdag, dagR does not provide functions to identify conditional independencies, and cannot directly identify descendants [23].

While dagR excels at simulating data and provides some analysis functions, it has limitations compared to other available software in terms of visualizations and user-friendliness. The package is lacking in customization settings for DAGs, as it does not allow for curved edges, circles around nodes, subscripts, or math notation. In Figure 1, we see that the visual design is fairly similar to DAGitty, with smaller node labels and thin edges. The dagR default settings print a legend below the DAG, allowing the user to provide longer labels or descriptions for the nodes. However, we found that the legend lines tend to overlay, reducing readability, as seen in Figure 1. In Figure 2, we employ the automatic placement of nodes feature, as without curved arrows, this achieves the clearest arrangement of all edges in the graph. Unfortunately, the DAG still has visual limitations, such as the overlaid arrow heads leading into variables M and Y . With more complex DAGs, it would be challenging to generate a readable graph without curved lines.

In addition, we note that dagR, despite the simple visual results, is one of the hardest to utilize. Compared to DAGitty and ggdag, there are few online resources available with example code and use descriptions. We also find the code itself to be the least intuitive. For example, all edges are specified together in a single vector using pairs of numbers, where each number refers to a different node. With a larger number of variables, it becomes difficult to track which number corresponds to each node, making the system cumbersome. In Table 1, we highlight dagR’s strength in analysis, notably data simulation, as well as its limitations in visual design and utility. The package lags in terms of the readability of its graphs and code. Finally, we note that dagR developers provide a function to translate dagR objects to DAGitty [39]. Thus, if a user desires a more visually appealing graph, but has already written their causal structure using dagR (perhaps to simulate data), then they can convert the object to DAGitty, and use DAGitty or ggdag to plot.

2.5 igraph

Finally, we include igraph, an open-source network analysis tool that emphasizes efficiency and portability [22]. Csárdi and Nepusz began the development of igraph in 2006 [22], but many collaborators have since contributed to its growth. The most recent R update of igraph as of August 2023 is version 1.5.0.1, released in July 2023 [47]. The tool can be utilized in R, Python, Mathematica, and C/C++, but its core is written in C [22]. Here, we focus on igraph’s implementation in R. This package excels at auto-placement of nodes and edges, utilizing its vast array of graph layout algorithms, making it a valuable tool for visualizing complex and large DAGs [22,48]. While igraph provides many functions to calculate various structural properties of graphs and conduct network analysis, it has limited causal analysis capabilities. It can identify ancestors and descendants (i.e., subcomponent()), but does not offer functions to directly compute conditional independencies and adjustment sets [48].

The igraph notation to specify arrows is similar to dagR, where edges are listed in a vector with every pair denoting the origin and destination nodes of the edge [22,48]. However, unlike in dagR, one can name nodes with characters, making the code more readable. While automatic arrangement of the nodes is the default, the user can specify the x and y coordinates for each node via a layout matrix. In Figure 1, we see that the igraph DAG allows for black text surrounded by a circle for each node with black lines connecting each node. Since the default visualization uses navy text, orange circles, and gray arrows, we use several add-on features to achieve the desired styling (i.e., edge.color, vertex.size, and vertex.color are used in the plotting function). In Figure 2, we again specify the color, size, label, and location of all nodes and edges. To draw curved lines, we use the edge.curved specification. To handle subscript notation, we use vertex.label and the expression() feature; one could easily specify Greek letters here using Unicode. A full list of plotting controls is conveniently located in the R Vignette [48]. The DAGs produced are clean, professional, and highly customizable, although fine-tuning all the plotting features can be time-consuming. In summary, igraph excels in its flexibility for the visual design of a DAG; however, it has limited analysis functions to answer causal inference questions and less intuitive code compared to TikZ, DAGitty, and ggdag.