Sahira Mena
2019-03-19 06:52
因此，我将写出解决方法，以防遇到类似情况的人得到帮助。
我下载了openxlsx软件包。
library(openxlsx) #recall the library
wb <- createWorkbook() # create a workbook
addWorksheet(wb, "Sheet", gridLines = TRUE) #add a worksheet to the workbook
writeData(wb, "Sheet", df) # write my analysis into the worksheet of the workbook,
#where df is the name of my data frame
然后，按照createStyle函数创建样式（请参见文档）。
就我而言，我必须在数据中查找特定字符
warm1Style <- createStyle(fontColour = "#000000", bgFill = "#FFFF00")
# here search for the respective HEX color-code and assign a name to the style
conditionalFormatting(wb, "Sheet", cols = 1:5,
rows = 1:50, rule = "Significantly Warmer", style = warm1Style,
type = "contains")
# account the condition where "Significantly Warmer" is contained in a cell,
# then apply the respective style to it (in this case, warm1Style)
就是这样，就像可以对工作簿中的任何短语或字符进行处理一样。
最后，将工作簿另存为xlsx：
saveWorkbook(wb, file, overwrite = TRUE)
感谢您的回应
热门帖子
相关问题
热门github
1
2
4
The Python Risk Identification Tool for generative AI (PyRIT) is an open access automation framework to empower security professionals and machine learning engineers to proactively find risks in their generative AI systems.
(翻译：用于生成式 AI 的 Python 风险识别工具 (PyRIT) 是一个开放式访问自动化框架，使安全专业人员和机器学习工程师能够主动发现其生成式 AI 系统中的风险。)
5
7
8
9
Mamba is a new state space model architecture showing promising performance on information-dense data such as language modeling, where previous subquadratic models fall short of Transformers. It is based on the line of progress on structured state space models, with an efficient hardware-aware design and implementation in the spirit of FlashAttention.
(翻译：Mamba 是一种新的状态空间模型架构，在信息密集型数据（例如语言建模）上显示出良好的性能，而之前的二次模型在 Transformers 方面存在不足。它基于结构化状态空间模型的进展，并本着FlashAttention的精神进行高效的硬件感知设计和实现。)
10
11
13