1) 熟悉CEL file 从 NCBI GEO (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24460)下载GSE24460. 将得到一个 GSE24460_RAW.tar 文件,解压.产生CEL文件,包含各种信息. if("affy" %in% rownames(installed.packages()) == FALSE) {source("http://bioconductor.org/biocLite.…

6 ways of mean-centering data in R 怎么scale我们的数据? 还是要看我们自己数据的特征. 如何找到我们数据的中心? Cluster analysis with K-means. How to get the cluster representatives? 开发工具时不要先写代码,把算法理顺了再写,就会顺利得多. 1. 随机或者自定义一个点(基因),找到另一个符合条件的点(距离以及overlap数达标): 2. 算出新的group的中心,找下一个符合条件的点…

目录 Importing data in R 学习笔记1 flat files:CSV txt文件 packages:readr read_csv() read_tsv read_delim() data.table() fread readxl excel_sheets() read_excel() gdata read.xls() getSheets() XLConnect loadWorkbook() readWorksheet() createSheet() writeWorksheet…

小伙伴儿们有福啦,2015年4月10日,Hadley Wickham大牛(开发了著名的ggplots包和plyr包等)和RStudio小组又出新作啦,新作品readr包和readxl包分别用于R读取text数据和Excel电子表格数据.事实上,R已经有了一堆读取数据的函数,比如read.table家族以及其巨多的变形,那么为了牛牛们为什么还要开发这两个包呢?原因很简单,这两个包的读取速度比R内置数据读入函数更快!!!记住哦,是快很多哈!不信,我们下来试试就知道啦!哈哈!平时读取小数据的童鞋可能不…

目录 R 中清洗数据 常见三种查看数据的函数 Exploring raw data 使用dplyr包里面的glimpse函数查看数据结构 \(提取指定元素 ```{r} # Histogram of BMIs from 2008 hist(bmi\)Y2008) Scatter plot comparing BMIs from 1980 to those from 2008 Introduction to tidyr gather() spread() spreate() unite() 常见数…

With Power BI Desktop, you can connect to all sorts of different data sources, then combine and shape them in ways that facilitate making interesting, compelling data analysis and visualizations. In this tutorial, you'll learn how to combine data fro…

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Around September of 2016 I wrote two articles on using Python for accessing, visualizing, and evaluating trading strategies (see part 1 and part 2). These have been my most popular posts, up until I published my article on learning programming langua…

factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including: Principal Component Analysis (PCA), which is used to summarize the information contained in a continuous (i.e, quantitati…

When working with big data with R (say, using Spark and sparklyr) we have found it very convenient to keep data handles in a neat list ordata_frame. Please read on for our handy hints on keeping your data handles neat. When using R to work over a big…