To read spreadsheet data and then convert it to an array of JSON objects, pass a schema option when calling readXlsxFile(). In that case, instead of returning an array of rows of cells, it will return an object of shape { rows, errors } where rows is gonna be an array of JSON objects created from the spreadsheet data according to the schema, and errors is gonna be an array of errors encountered while converting spreadsheet data to JSON objects.

Each property of a JSON object should be described by an "entry" in the schema. The key of the entry should be the column's title in the spreadsheet. The value of the entry should be an object with properties:

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When converting cell values to object properties, by default, it skips any missing columns or empty cells, which means that property values for such cells will be undefined. To be more specific, first it interprets any missing columns as if those columns existed but had empty cells, and then it interprets all empty cells as undefineds in the output objects.

For example, spreadsheet data might be used to update an SQL database using Sequelize ORM library, and Sequelize completely ignores any undefined values. In order for Sequelize to set a certain field value to NULL in the database, it must be passed as null rather than undefined.

So for Sequelize use case, property values for any missing columns should stay undefined but property values for any empty cells should be null. That could be achieved by passing two parameters to read-excel-file: schemaPropertyValueForMissingColumn: undefined and schemaPropertyValueForEmptyCell: null.

An additional option that could be passed in that case would be schemaPropertyShouldSkipRequiredValidationForMissingColumn: (column, { object }) => true: it would skip required validation for columns that're missing from the spreadsheet.

If there were any errors while converting spreadsheet data to JSON objects, the errors property returned from the function will be a non-empty array. An element of the errors property contains properties:

Sometimes, a spreadsheet doesn't exactly have the structure required by this library's schema parsing feature: for example, it may be missing a header row, or contain some purely presentational / irrelevant / "garbage" rows that should be removed. To fix that, one could pass an optional transformData(data) function that would modify the spreadsheet contents as required.

Sometimes, a developer might want to use some other (more advanced) solution for schema parsing and validation (like yup). If a developer passes a map option instead of a schema option to readXlsxFile(), then it would just map each data row to a JSON object without doing any parsing or validation. Cell values will remain "as is": as a string, number, date or boolean.

XLSX format originally had no dedicated "date" type, so dates are in almost all cases stored simply as numbers (the count of days since 01/01/1900) along with a "format" description (like "d mmm yyyy") that instructs the spreadsheet viewer software to format the date in the cell using that certain format.

By default, it parses numeric cell values from strings. In some rare cases though, javascript's inherently limited floating-point number precision might become an issue. An example might be finance and banking domain. To work around that, this library supports passing a custom parseNumber(string) function option.

Sometimes, a spreadsheet doesn't exactly have the structure required by this library's schema parsing feature: for example, it may be missing a header row, or contain some purely presentational / empty / "garbage" rows that should be removed. To fix that, one could pass an optional transformData(data) function that would modify the spreadsheet contents as required.

There have been some reports about performance issues when reading very large *.xlsx spreadsheets using this library. It's true that this library's main point have been usability and convenience, and not performance when handling huge datasets. For example, the time of parsing a file with 2000 rows / 20 columns is about 3 seconds. So, for reading huge datasets, perhaps use something like xlsx package instead. There're no comparative benchmarks between the two, so if you'll be making one, share it in the Issues.

On March 9th, 2020, GitHub, Inc. silently banned my account (erasing all my repos, issues and comments, even in my employer's private repos) without any notice or explanation. Because of that, all source codes had to be promptly moved to GitLab. The GitHub repo is now only used as a backup (you can star the repo there too), and the primary repo is now the GitLab one. Issues can be reported in any repo.

Previously, we described the essentials of R programming and some best practices for preparing your data. We also provided quick start guides for reading and writing txt and csv files using R base functions as well as using a most modern R package named readr, which is faster (X10) than R base functions.

Now I know that the step got executed successfully, but I want to know how i can parse the excel file that has been read so that I can understand how the data in the excel maps to the data in the variable data. 

I learnt that data is a Dataframe object if I'm not wrong. So How do i parse this dataframe object to extract each line row by row.

I am trying to create a flow that will read excel files and based on a column (SellerCode) it will send an email to the account that corresponds to this Code with data from the excel row. The accounts and codes are stored in a SharePoint List since i dont have the email stored in the excel file.

my actual issue is that if there are more than one excel files uploaded in the Library i can not get all files with the List rows option. I am also trying a different approach using 'when a file is created in a folder' as a trigger and a 'get tables' as a second step.

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Note that when you define the libref using the LIBNAME statement SAS does not know whether your intention is to READ from that file or write to that file. So if the file does not exist the LIBNAME statement will work fine. But then trying to get the contents of the file you haven't written anything into yet will fail.

Make sure the file you want to access actually is in that directory on the machine where SAS is running (NOT on the machine where the browser you are using to access the SAS/Studio.) Note that the S drive on the SAS server might not be the same location as the S drive on the machine that is accessing SAS/Studio. So if S: is mapped to some network share (like \\servername\sharename\) you might want to use that instead of the S: in the path.

Neither response is useful. I am facing the same problem literally in similar circumstances. Trying to run an activity in the course work to replicate results displayed in the video. After running the code, the library is actually assigned, but not a single worksheet from the workbook was distributed into it. The message error came from the proc contents part of the code, because the worksheet it is trying to assess actually does not exist in the concerned library. The question is: how can the library be properly assigned and the worksheets not distributed, when the Excel spreadsheet actually exists and the log did not return a "File not found" or "File does not exist" error message? That's the crux of the question that needs to be addressed.

Since you are using SAS/Studio you can use the Server Files and Folder tab in the SAS/Studio interface to find where the file you want to read is. Right click on the filename there and select Properties and you will see the filename you need to use in your SAS code to reference that file in your SAS code.

Hi:

 S:\workshop\data is the path we use in our classroom lab machines or in our Live Web labs. Some of the course videos in Programming 1 do show this path. However, the intention is for the student to replace the s:\workshop\data portion of the path with THEIR location for the class data. Typically this will be something like one of the following examples (depending on how you are using SAS and what set of directions you followed to make the data):

If you are using the e-learning class and skipped over the Course Overview and Data Setup section of the course, then please go back to that section (above Lesson 1 in the Table of Contents -- with the word "REQUIRED" next to it) and follow the instructions to make the data for your method of using SAS. 152ee80cbc

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