H.H. = read.table("bionform/GSE46224_Yang_et_al_human_heart_RNASeq.txt/GSE46224_Yang_et_al_human_heart_RNASeq.txt") colnames(H.H.) = H.H.[1,] rownames(H.H.) = H.H.[ , 1] Human_H = H.H.[-1,-1] class(Human_H) # PREPARATION Human_H Human_Hr =as.matrix(Human_H) is.matrix(Human_Hr) Human_Hrt = apply(X = Human_Hr, MARGIN = 1 , FUN = var) # SELECT THE MOST 100 VARIANCE GENE Human_gene = names(Human_Hrt[order(Human_Hrt , decreasing = T)][1:100]) human_heart = Human_H[Human_gene , ] human_heart_fl = apply(human_heart , 2 ,FUN = as.numeric) pheatmap(mat = human_heart_fl , scale = "row" , cluster_rows = T , cluster_cols = T , cutree_cols = 5 , show_rownames = T , show_colnames = T, main = "Human_Heart data") str(human_heart_fl)

Presentation of my WordPredX app

This article is practical guide to using Git and GitHub with RStudio for version control and collaboration in data science projects. It explains why version control matters, introduces Git (local) and GitHub (cloud), and shows how these tools integrate with RStudio for smooth teamwork

This walkthrough displays how to manipulate data and define factors