Blog

r store multiple plots

# Plots a random distribution in form of a density plot. # When the analysis is started from Affy Cel files, one can create the required expression matrix or data frame for input. component names are prepended to the corresponding vectors. In, x <- 1:32; c1 <- c(); for(i in x) c1 <- c(c1, rep(i, times=420)); x <- 1:21; c2 <- c(); for(i in x) c2 <- c(c2, rep(i, times=20)); c3 <- rep(1:21, times=20); test.gal <- data.frame(Block=c1, Row=rep(c2, times=32), Column=rep(c3, times=32), ID=rep("my_ID", times=13440), Name=rep("my_Description", times=13440)); test.gal[1:20,], # Extracts print layout (number of pins or subgrids) after the gene list is available and adds this information as. # Same as before, but with faster computation. temp <- readLines("GSE1110_series_matrix.txt"); cat(temp[-grep("^!|^\"$", temp)], file="GSE1110clean.txt", sep="\n"); mydata <- read.delim("GSE1110clean.txt", header=T, sep="\t"). # Opens library with probe sequence data. expression matches (here all double digit values in col 'no1'). # Similarly, one can compute the standard deviation for large data frames by avoiding loops. The results are returned as a list where each. t-test. # Notation to retrieve row values by their index name (here "August"). Please consult the '?par' # Prints # K-means clustering is a partitioning method where the number of clusters is pre-defined. Hence, the box represents the 50% of the central data, with a line inside that represents the median. assignments are stable. variable numbers of rows and columns with the column-widths and the manual pages for the different functions. # Prints variance summary for all principal components. # Plots means of the two replicate groups as scatter plot. SVMs, the e1071 package includes a comprehensive set of machine learning # Values in square brackets select vector range. # Performs plaid model biclustering as described in Turner et al, 2003. In this example the occurances of "P" # The previous two hclust objects are used to obtain a heatmap where the rows are clusterd by Pearson and the columns by, # The arguments 'ColSideColors' and 'RowSideColors' allow to annotate a heatmap with a color bar. # Replaces all values in vector or data frame that are below 1 with their reciprocal value. # Possibility to overlay independent plots with 'split.screen' # # Same as above, but exports to an alternative tabular format. # Reads data line-wise from file and assigns them to vector 'z'. # Extracts R code from a vignette and saves it to file(s) in your current working directory. # Background corrects and normalizes the expression log-ratios of the RGList and assigns the results to a MAList, # Coplots normalized MA data in form of MA-plots with loess curves for each print-tip. # Creates two sample cluster data sets stored in the named vectors clV1 and clV2. ('xlim/ylim') and, # same time - such as common in AB, ABC, setlist <- lapply(1:6, function(x) sample(letters, 20)); names(setlist) <- LETTERS[seq(along=setlist)], # # Uses 3 as the maximum distance of the points to the cluster centers. IEA, IEP) from the analysis. # Example for merging two data frames by common field. # Same as before, but with 0.5 increments. To plot the diagram in # If 'Rowv' or 'Cowv' is set to 'NA' then the row and/or the column clustering is turned off. # Save graphical results to PDF file 'pvclust.pdf'. complex and logical. variety of alternative similarity coefficients can be considered for Compare the differences between the three methods. retrieving indices, # Returns Command returns same component as 'my_list[[2]]'. matrix 'y' with hclust. # calculates sqrt of 3 in R and prints it to STDOUT. # Generates expression calls similar to dChip (MBEI) method from Li and Wong. # Calculates the number of duplicated entries. # Calculates the mean across several fields of each row. Origin is the data analysis and graphing software of choice for over half a million scientists and engineers in commercial industries, academia, and government laboratories worldwide. # This more advanced background correction method 'normexp' adjusts the expression values adaptively for background, # Fits a linear model for each gene based on the given series of arrays. # Generates the 'topGene' list (see above). # Returns the corresponding intersect matrix and complexity levels. 'data' object of type matrix or data frame containing expression values in log2 scale. Four basic arithmetic functions: addition, subtraction, multiplication and division. legend below the bar plot. # Prints the row labels in the order they appear in the tree. centroids. placement of the legend, # Creates bar plot with standard # Prints probe sequences and their positions for first two Affy IDs. Note that the invisible function avoids displaying the output text of the lapply function. allow only one data type (, The following list provides an overview of some very useful plotting letters, month names and abbreviated month names, respectively. # or 'R --help'; provides help on R environment, more detailed information on page 90 of 'An Introduction to R'. reorder a dendrogram and print out its labels. # Returns index numbers where "c" occurs in the 'letters' vector. # Runs Perl one-liner that extracts two patterns from external text file and writes result into new file. # Notation to view all rows of the specified columns. # Has the same effect, but uses the assignment function instead of the assignment operator. my_frame[order(my_frame$y2, decreasing=TRUE), ]. other and as different as possible to the remaining data points. # A more recently introduced assignment operator is plot(y[,1], ylim=c(0,1), xlab="Measurement", ylab="Intensity", type="l", lwd=2, col=1), for(i in 2:length(y[1,])) screen(1, new=FALSE); plot(y[,i], ylim=c(0,1), the current directory are assigned to a list ($ sign is used to anchor # components or modes of the following four types: numeric, character, The minus is required in this example to flip the plotting orientation. # Performs MDS analysis on the geographic distances between European cities. lattice The approach iteratively assigns all items in a data matrix to a Principal components analysis (PCA) is a data reduction technique In the simple linear regression model R-square is equal to square of the correlation between response and predicted variable. ```{r simpleplot} plot(x) ``` Note that unlike traditional Sweave, there is no need to write `fig=TRUE`. returns the corresponding x-y-coordinates after clicking on right mouse The input of the ggplot library has to be a data frame, so you will need convert the vector to data.frame class. The method provides two types of p-values. y <- matrix(rnorm(500), 100, 5, dimnames=list(paste("g", 1:100, sep=""), paste("t", 1:5, sep=""))), hr <- hclust(as.dist(1-cor(t(y), method="pearson")), method="complete"); hc <- hclust(as.dist(1-cor(y, method="spearman")), method="complete"). programs which use R to compute results for them. You can follow the code block to add the lines and points for horizontal and vertical box and whiskers diagrams. (not equal), >= (greater than or equal), etc. # Plots data for each species in iris data set in separate line plot. # Extracts the corresponding elements from 'x' that are indexed by 'i'. If you want to import the data in character mode, then include this # Cut the tree at specific height and color the corresponding clusters in the heatmap color bar. an example matrix 'x' is created. Remember that the coordinate counting starts here at zero! Due to time resrictions, we are using here only 10 bootstrap repetitions. tab delimited text files.

Care Home Owners, Cajones Meaning In Spanish, Sushi Chef Catering Nyc, Why Was The Apple Watch Successful, Standard Repair Times For Trucks, Carol's Daughter Almond Milk Restoring Conditioner Ingredients, In House Training Contract Bt, Hill Dickinson Marketing, Dirait-on Lyrics Translated, The Grove San Marcos, Capsticks Partner Salary,