Chart-to-code generation converts a chart image into an executable plotting script, enabling faithful reproduction and editable visualizations. Existing methods are largely Python-centric, limiting practical use and overlooking a critical source of supervision: the same chart can be expressed by semantically equivalent scripts in different plotting languages. To fill this gap, we introduce Chart2NCode, a dataset of 176K charts paired with aligned scripts in Python, R, and LaTeX that render visually equivalent outputs, constructed via a metadata-to-template pipeline with rendering verification and human quality checks. Building on a LLaVA-style architecture, we further propose CharLuMA, a parameter-efficient adaptation module that augments the multimodal projector with a language-conditioned mixture of low-rank subspaces, allowing the model to share core chart understanding while specializing code generation to the target language through lightweight routing. Extensive experiments show consistent gains in executability and visual fidelity across all languages, outperforming strong open-source baselines and remaining competitive with proprietary systems. Further analyses reveal that balanced multi-language supervision benefits all languages and that the adapter allocates a compact shared core plus language-specific capacity. Codes and data are available at https://github.com/Zhihan72/CharLuMA.

翻译：图表到代码生成将图表图像转换为可执行的绘图脚本，从而实现忠实复现和可编辑的可视化。现有方法大多以Python为中心，限制了实际应用，并忽略了一个关键的监督来源：同一图表可由不同绘图语言中语义等效的脚本表达。为填补这一空白，我们提出了Chart2NCode数据集，包含176K个图表及其在Python、R和LaTeX中对齐的脚本，这些脚本生成视觉等效的输出，通过元数据到模板的管道构建，并辅以渲染验证和人工质量检查。基于LLaVA风格的架构，我们进一步提出CharLuMA，这是一种参数高效的适配模块，通过语言条件化的低秩子空间混合增强多模态投影器，使模型能够共享核心图表理解能力，同时通过轻量级路由专精于目标语言的代码生成。大量实验表明，在所有语言中，可执行性和视觉保真度均持续提升，超越了强开源基线，并与专有系统保持竞争力。进一步分析揭示，平衡的多语言监督对所有语言都有益，并且适配器分配了紧凑的共享核心加上语言特定的容量。代码和数据可在https://github.com/Zhihan72/CharLuMA获取。