When performing this initialization, a one time "requestId" is used, rather than a more persistent api key. This example shows an integrated Shiny app giving user control of a live visualization of viral load over time.Īn RStudio system administrator can add or overwrite variables and settings during initialization by including an R function in the Rprofile.site file which calls the hidden/named function "". Once you have integrated either RStudio or RStudio Workbench with LabKey Server, you can create Shiny reports using LabKey data. Select a project from the menu circled in this screenshot, then select a folder to see the schema it contains, and a schema to see the queries it contains. The RStudio viewer will now allow you to browse the LabKey schema. On the Configure RStudio page for either type of integration, scroll down and confirm that the box for Initialize Schema Viewer is checked. lubridate::rollback(Sys.Note: To use this feature, you must have the latest version of Rlabkey.įollow the steps for configuring LabKey to access your RStudio or RStudio Workbench instance following the instructions in one of these topics: Subtract months from the current date to get the last 3 months data. df % filter(Date >= Sys.Date() - 7 & Date % filter(Date > lubridate::rollback(Sys.Date())) For example, filtering data from the last 7 days look like this. Take a look at these examples on how to subtract days from the date. You can use dates that are only in the dataset or filter depending on today’s date returned by R function Sys.Date. iris %>%įilter(Species %in% c("setosa", "virginica") & Sepal.Width > 4) %>% In case you have involved multiple columns in filtering, combine them by using or and and operators. iris %>%įilter(Species %in% c("setosa", "virginica")) %>% If you have multiple filter criteria for the content of the same column, then you can also combine them within the function. If you want to organize filter criteria separately, then you can also try this way. head(subset(iris, iris$Species %in% c("setosa", "virginica"))) You can filter multiple values like this. If there are multiple values that you want to use in R to filter, then try in operator. # Sepal.Length Sepal.Width Petal.Length Petal.Width Species Besides the workspace viewer, there is also a data viewer, a plot viewer, and a widget viewer. It is a convenient way to view the R workspace, preview existing R objects, find help topics, and read help pages interactively. head(subset(iris, iris$Species = "virginica")) Select the R icon in the Activity bar and the workspace viewer and help pages viewer will show up. If I want to get the subset of rows that contains the necessary value, it looks like this. Here are a couple of other examples if you want to get a count of something in R. Species column from iris dataset contains 3 different values and 50 records for each of them. You can use function subset to filter the necessary. In that case there will be error: unexpected ‘,’ in “(“data_viewer_max_columns”,”. It might not work if the RStudio version is like. rstudioapi::writeRStudioPreference("data_viewer_max_columns", 500L) If you want to change that, for example, to 500, you can do that like this. In the latest RStudio versions amount of columns that you can see might be limited to 50. Increase amount of columns shown in RStudio viewerīy default, there is a limit of columns that you can see in the RStudio viewer. You can see a filter button like in the picture below. You can test that by viewing the dataset iris. Here are some of the RStudio tips and tricks that show how to open a data viewer by clicking. RStudio has a spreadsheet-style data viewer that you can use mainly by using function View. Depending on your goals solution might differ. Here are more than 5 examples of how to apply a filter in R to take a look or get a subset of your data.
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