Upload log

Below is a list of the most recent file uploads. See the gallery of new files for a more visual overview.

• 11:47, 27 May 2023 Brb talk contribs uploaded File:Vibrant ink rstheme.png (https://github.com/captaincaed/rstudio/blob/main/vibrant_ink_SB_2.rstheme)
• 12:46, 21 May 2023 Brb talk contribs uploaded File:R2.png (<syntaxhighlight lang='rsplus'> x <- seq(0, 2.5, length=20) y <- sin(x) plot(x, y) abline(lsfit(x, y, intercept = F), col = 'red') summary(fit)$r.squared # [1] 0.8554949 </syntaxhighlight>)
• 15:02, 11 May 2023 Brb talk contribs uploaded File:Paletteggplot2.png
• 11:12, 10 May 2023 Brb talk contribs uploaded File:Paletteshowcol.png
• 10:15, 10 May 2023 Brb talk contribs uploaded File:Palettebarplot.png (<syntaxhighlight lang='rsplus'> pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00") # pal <- sample(colors(), 10) # randomly pick 10 colors barplot(rep(1, length(pal)), col = pal, space = 0, axes = FALSE, border = NA) </syntaxhighlight>)
• 10:14, 10 May 2023 Brb talk contribs uploaded File:Paletteheatmap.png (<syntaxhighlight lang='rsplus'> pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00") pal <- matrix(pal, nr=2) # acknowledge a nice warning message pal_matrix <- matrix(seq_along(pal), nr=nrow(pal), nc=ncol(pal)) heatmap(pal_matrix, col = pal, Rowv = NA, Colv = NA, scale = "none", ylab = "", xlab = "", main = "", margins = c(5, 5)) # 2 rows, 3 columns with labeling on two axes </syntaxhighlight>)
• 10:08, 10 May 2023 Brb talk contribs uploaded File:Rpalette.png (<syntaxhighlight lang='rsplus'> pal <- palette() # [1] "black" "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC" "#F5C710" # [8] "gray62" pal_matrix <- matrix(seq_along(pal), nr=1) image(pal_matrix, col = pal, axes = FALSE) # 8 rows, 1 column, but no labeling # Starting from bottom, left. par()$usr # change with the data dim text(0, (par()$usr[4]-par()$usr[3])/8*c(0:7), labels = pal) </syntaxhighlight>)
• 12:52, 9 May 2023 Brb talk contribs uploaded File:Ggplotbarplot.png
• 10:40, 9 May 2023 Brb talk contribs uploaded File:Cbioportal cptac.png
• 20:07, 25 April 2023 Brb talk contribs uploaded File:Losslesscut.png
• 16:47, 23 April 2023 Brb talk contribs uploaded File:Sleepstudy.png (<syntaxhighlight lang='rsplus'> sleepstudy %>% ggplot(aes(x=Days, y = Reaction)) + geom_point() + geom_smooth(method = "lm", se = FALSE) + facet_wrap(~Subject) </syntaxhighlight>)
• 15:58, 17 March 2023 Brb talk contribs uploaded File:Svg4.svg (<pre> svg("svg4.svg", width=4, height=4) plot(1:10, main="width=4, height=4") dev.off() </pre>)
• 09:44, 11 March 2023 Brb talk contribs uploaded File:RStudioVisualMode.png
• 15:01, 8 March 2023 Brb talk contribs uploaded File:Pca directly2.png
• 10:11, 11 February 2023 Brb talk contribs uploaded File:MultipleProbes.PNG
• 10:10, 11 February 2023 Brb talk contribs uploaded File:ClassPredictionOptions.PNG
• 10:08, 11 February 2023 Brb talk contribs uploaded File:ClassPrediction.PNG
• 20:01, 28 January 2023 Brb talk contribs uploaded File:Pca factoextra.png
• 19:34, 28 January 2023 Brb talk contribs uploaded File:Pca autoplot.png
• 19:34, 28 January 2023 Brb talk contribs uploaded File:Pca directly.png
• 09:21, 25 January 2023 Brb talk contribs uploaded File:VscodeEnergy.png
• 21:26, 14 January 2023 Brb talk contribs uploaded File:RbdGeom.png (<pre> require(ggplot2) aggregate( .~ treatment +block,FUN=median, data = data) |> ggplot(aes(treatment, yield)) + geom_line(aes(group = block, color = block), linewidth = 1.2) + geom_point(aes(color = block), shape = 15, size=2.6) </pre>)
• 20:51, 14 January 2023 Brb talk contribs uploaded File:RbdBlock.png (<pre> set.seed(1234) block <- as.factor(rep(1:5, each=6)) treatment <- rep(c("A","B","C"),5) block_shift <- rnorm(5, mean = 0, sd = 2) treatment_shift <- c(A=0, B=4, C=2) random_effect <- rnorm(30, mean = 0, sd = 1) yield <- rnorm(30, mean = 10, sd = 2) + treatment_shift[as.integer(factor(treatment))] + block_shift[as.numeric(block)] + random_effect data <- data.frame(block, treatment, yield) summary(fm1 <- aov(yield ~ treatment + block, data = data)) # Df Sum Sq Mean Sq...)
• 20:50, 14 January 2023 Brb talk contribs uploaded File:RbdTreat.png (<pre> set.seed(1234) block <- as.factor(rep(1:5, each=6)) treatment <- rep(c("A","B","C"),5) block_shift <- rnorm(5, mean = 0, sd = 2) treatment_shift <- c(A=0, B=4, C=2) random_effect <- rnorm(30, mean = 0, sd = 1) yield <- rnorm(30, mean = 10, sd = 2) + treatment_shift[as.integer(factor(treatment))] + block_shift[as.numeric(block)] + random_effect data <- data.frame(block, treatment, yield) summary(fm1 <- aov(yield ~ treatment + block, data = data)) # Df Sum Sq Mean Sq...)
• 11:18, 12 January 2023 Brb talk contribs uploaded File:GseaTable2.png (An example of a plot from 10 non-enriched pathways. <pre> data(examplePathways) data(exampleRanks) fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=1000, minSize=15, maxSize=100) fgseaRes[order(pval, decreasing = T),][1:10, c('NES', 'pval')] # NES pval # 1: -0.4050950 1.0000000 # 2: -0.4050950 1.0000000 # 3: -0.4966664 0.9932584 # 4: 0.4804114 0.9870610 # 5: 0.4804114 0.9870610 # 6: 0.4804114 0.9870610 # 7: 0.4804114 0.9870610 # 8: 0.4955139 0.9854...)
• 11:01, 12 January 2023 Brb talk contribs uploaded File:GseaTable.png (<pre> data(examplePathways) data(exampleRanks) fgseaRes <- fgsea(examplePathways, exampleRanks, nperm=1000, minSize=15, maxSize=100) # I pick 5 pathways with + NES and 5 pathways with - NES. fgseaRes[order(pval), ][62:71, c('pathway', 'NES')] # pathway NES # 1: 5992282_ECM_proteoglycans 1.984081 # 2: 5992219_Regulation_of_cholesterol_biosynthesis_by_SREBP_SREBF_ 1.95...)
• 11:15, 8 January 2023 Brb talk contribs uploaded File:Reorder.dendrogram.png (<pre> set.seed(123) x <- rnorm(20) hc <- hclust(dist(x)) dd <- as.dendrogram(hc) par(mfrow=c(3, 1)) plot(dd, main = "random dendrogram 'dd'") # not the same as reorder(dd, 1:20) plot(reorder(dd, 20:1), main = 'reorder(dd, 20:1, sum)') plot(reorder(dd, 20:1, mean), main = 'reorder(dd, 20:1, mean)') </pre>)
• 20:37, 7 January 2023 Brb talk contribs uploaded File:ComplexHeatmap2.png (<syntaxhighlight lang="rsplus"> # Simulate data library(ComplexHeatmap) ng <- 30; ns <- 20 set.seed(1) mat <- matrix(rnorm(ng*ns), nr=ng, nc=ns) colnames(mat) <- 1:ns rownames(mat) <- 1:ng # color bar on RHS ind_e <- 1:round(ng/3) ind_m <- (1+round(ng/3)):ng epimes <- rep(c("epi", "mes"), c(length(ind_e), length(ind_m))) row_ha <- rowAnnotation(epimes = epimes, col = list(epimes = c("epi" = "orange", "mes" = "darkgreen"))) # color bar on Top tumortype <- rep(c("carcinoma", "sarcoma"...)
• 11:37, 6 January 2023 Brb talk contribs uploaded File:Fgsea3plots.png (<pre> par(mfrow=c(1,3)) with(fgseaRes, plot(abs(ES), pval)) with(fgseaRes, plot(abs(NES), pval)) with(fgseaRes, plot(ES, NES)) </pre>)
• 13:58, 31 December 2022 Brb talk contribs uploaded File:Filebrowser.png
• 19:07, 25 December 2022 Brb talk contribs uploaded File:DisableDropbox4pm.png
• 14:40, 15 December 2022 Brb talk contribs uploaded File:Geom smooth ex.png (<pre> library(dplyr) #group the data by cyl and create the plots mpg %>% group_by(cyl) %>% ggplot(aes(x=displ, y=hwy, color=factor(cyl))) + geom_point() + geom_smooth(method = "lm", se = FALSE) + theme(legend.position="none") </pre>)
• 14:14, 15 December 2022 Brb talk contribs uploaded File:Geom bar4.png (<pre> ggplot(mpg, aes(x = class)) + geom_vline(xintercept = mpg$class, color = "grey", linetype = "dashed", size = 1) + geom_bar() + theme_classic() + coord_flip() </pre>)
• 14:08, 15 December 2022 Brb talk contribs uploaded File:Geom bar3.png (<pre> ggplot(mpg, aes(x=manufacturer)) + geom_bar() + theme(panel.grid.major.x = element_blank(), panel.grid.minor = element_blank()) </pre>)
• 13:41, 15 December 2022 Brb talk contribs uploaded File:Geom bar2.png (<pre> library(ggplot2) library(scales) library(patchwork) dtf <- data.frame(x = c("ETB", "PMA", "PER", "KON", "TRA", "DDR", "BUM", "MAT", "HED", "EXP"), y = c(.02, .11, -.01, -.03, -.03, .02, .1, -.01, -.02, 0.06)) set.seed(1) dtf2 <- data.frame(x = dtf[, 1], y = sample(dtf[, 2])) g1 <- ggplot(dtf, aes(x, y)) + geom_bar(stat = "identity", fill = "#F8767D") + geom_text(aes(label = paste0(y * 100, "%"), hjust = ifelse(y >= 0, 0, 1))) +...)
• 13:39, 15 December 2022 Brb talk contribs uploaded File:Geom bar1.png (<pre> library(ggplot2) library(scales) library(patchwork) dtf <- data.frame(x = c("ETB", "PMA", "PER", "KON", "TRA", "DDR", "BUM", "MAT", "HED", "EXP"), y = c(.02, .11, -.01, -.03, -.03, .02, .1, -.01, -.02, 0.06)) set.seed(1) dtf2 <- data.frame(x = dtf[, 1], y = sample(dtf[, 2])) g1 <- ggplot(dtf, aes(x, y)) + geom_bar(stat = "identity", aes(fill = x)) + geom_text(aes(label = paste0(y * 100, "%"), hjust = ifelse(y >= 0,...)
• 17:00, 5 December 2022 Brb talk contribs uploaded File:GpartedinfoSanDisk.png
• 14:55, 29 October 2022 Brb talk contribs uploaded File:ExampleRanks.png (<pre> plot(exampleRanks) </pre>)
• 14:39, 29 October 2022 Brb talk contribs uploaded File:FgseaPlotSmallm.png
• 14:15, 27 October 2022 Brb talk contribs uploaded File:DHdialog2.png
• 14:15, 27 October 2022 Brb talk contribs uploaded File:DHdialog1.png
• 08:46, 27 October 2022 Brb talk contribs uploaded File:HC single.png
• 09:48, 20 October 2022 Brb talk contribs uploaded File:1NN better NC.png
• 16:26, 19 October 2022 Brb talk contribs uploaded File:NC better kNN.png (The green color is a new observation (Sensitive). By using the kNN method, it will be assigned to Resistant b/c it is closer to the Resistant group. However, using the NC, the distance of it to the Resistant group centroid is 8.42 which is larger than the distance of it to the Sensitive groups centroid 7.31. So NC classified it to Sensitive. Color annotation: green=LOO obs, black=centroid in each class.)
• 10:03, 12 October 2022 Brb talk contribs uploaded File:Foldchangefilter.png (<pre> LFC <- log2(1.5) x <- c(0, 0, 0, 0, 0, 0, 0, 0, 3.22, 0, 0, 0, 8.09, 0, 0.65, 0, 0, 0, 0, 0, 3.38, 0, 5.63, 7.46, 0, 0, 4.38, 0) plot(x, y = 1:28, xlab="log2 intensity", ylab="samples") abline(v=LFC, lty="dashed") axis(side=3,at=LFC, labels="LFC", tick=FALSE, line=0) </pre>)
• 15:39, 11 October 2022 Brb talk contribs uploaded File:Cvglmnetplot.png (<pre> n <- 100 set.seed(1) x1 <- rnorm(n) e <- rnorm(n)*.01 y <- x1 + e x4 <- x fit <- cv.glmnet(x=cbind(x1, x4, matrix(rnorm(n*10), nr=n)), y=y) plot (fit) </pre>)
• 13:12, 6 October 2022 Brb talk contribs uploaded File:Greedypairs.png
• 10:11, 6 October 2022 Brb talk contribs uploaded a new version of File:Barplot base.png
• 10:06, 6 October 2022 Brb talk contribs uploaded File:Barplot ggplot2.png
• 10:06, 6 October 2022 Brb talk contribs uploaded File:Barplot base.png