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Introduction
Published in Randall L. Eubank, Ana Kupresanin, Statistical Computing in C++ and R, 2011
Randall L. Eubank, Ana Kupresanin
In order to access a previously saved workspace workspacename, one must either supply a full path to load or initialize R from the directory where the saved image resides. If R has been started in a different directory, by entering setwd(“pathToDirectory”) the current working directory can be changed to the one specified by pathToDirectory. The current working directory can be determined via the command getwd (). Somewhat more generally, commands to the underlying Unix system can be executed while in R using the system command. For instance, entering system(“pwd”) at the R command prompt will produce the same information as getwd.
Analyzing data
Published in Victor A. Bloomfield, Using R for Numerical Analysis in Science and Engineering, 2018
To use this function requires consideration of where the data file is stored and in what format. By default, R puts files in the user’s home directory, which—unless instructed otherwise—considers the working directory. To find out the address of the working directory, type getwd() at the R prompt. The working directory can be changed with setwd(). For example, the sequence of commands
Organizing with Unix
Published in Rafael A. Irizarry, Introduction to Data Science, 2019
In Unix, we don’t have the same visual cues, but the concept of a current location is indispensable. We refer to this as the working directory. Each terminal window you have open has a working directory associated with it.
How generative AI models such as ChatGPT can be (mis)used in SPC practice, education, and research? An exploratory study
Published in Quality Engineering, 2023
Fadel M. Megahed, Ying-Ju Chen, Joshua A. Ferris, Sven Knoth, L. Allison Jones-Farmer
First, we asked ChatGPT to write R code that can be used to create a Shewhart X¯ chart based on a ‘data.csv’ file containing 20 subgroups and four observations per subgroup. Figure 4 depicts our exact prompt and the corresponding code. From the output, ChatGPT incorrectly named the code chunk Python even though the code was output in R. ChatGPT selected a suitable R library, qcc, which can be used to construct a X¯ chart. The code assumes that the package is already installed on the user’s machine or that the user has a cursory knowledge of R to know that the library() function call would only work if the package has been installed in the past. Otherwise, the first line of code would return the following: “Error in library(qcc): there is no package called ‘qcc’.” Then, the code assumes that the ‘data.csv’ is in the user’s working directory. In our estimation, both assumptions are reasonable given that the prompt specifically asked for R code. However, running the code results in the following error: “Error in if (chart.all) {: argument is not interpretable as logical.”
Massively Parallel Location and Exchange Tools for Unstructured Meshes
Published in International Journal of Computational Fluid Dynamics, 2020
When running coupled computations with multiple codes, we mostly use the following approach: Assign a different working directory to each code.Start each code in its own working directory using mpiexec (or equivalent) to start the programs together in MPMD (Multiple Program Multiple Data) mode. o Codes that share an MPI_COMM_WORLD communicator.Use MPI_Comm_split to assign each code its ‘main’ communicator.
Improving the energy efficiency of data-intensive applications running on clusters
Published in International Journal of Parallel, Emergent and Distributed Systems, 2020
Weifeng Liu, Jie Zhou, Bin Gong, Hongjun Dai, Meng Guo
Now we explore the energy scheduling algorithm that introduced in the section above. In this section, we completed the measurements on our cluster which consists of 48 Sugon nodes. Each node is with two eight-core Intel Xeon E5-2670 2.60 GHz processors and 32 GB shared memory. Centos6.0 is installed and the kernel version is 2.6.32. The used MPI version is MPICH1.4. All the computing nodes are connected via an gigabit ethernet switch. Lustre [20] is installed for providing I/O services to all nodes. The Lustre parallel file system has 1 meta data servers (MDS) and 8 object storage servers (OSS) for the working directory. Starting with Linux kernel v.2.6.0, processor frequencies can be dynamically scaled through the cpufreq subsystem.