Data wrangling

Our most common data format is table data – a single worksheet in Microsoft excel or a CSV file. This is a table of rows and columns of data.

Here is a list of the current data wrangling functions :

Merging files

The Merge Files functionality in Truii allows you to combine two files.

You can merge the files when you have:
1) extra rows – you have some new data to add to the original data such as additional rows due to extra data that has been collected.
2) extra columns – you have some additional parameters/fields for the data represented as new columns.
3) Updated cells – you have found some errors in the original file and have updated various cells in the original data set.
4) data custard – your colleague has updated some cells in one column, added some new rows and maybe an extra column or two. You have also been working on the file and modified different parts of the table.

Tidy Data

Delete Empty Rows: When you have a sparse file with lots of empty cells you may want to remove rows with no values in you target columns.

Delete Duplicate Rows: After many hands have been helpfully modifying the data file, it is often handy to clean the file by removing duplicate entries that may have inadvertently made their way into the table.

Search and Replace: The search and replace function allows you to correct the consistent spelling errors, making sure you always capitalise the site names or even correct a consistent data error.

Remove Outliers: The remove outlier function allows you to remove the inconsistent entries from your data.

Remove Linear Infill: Use the Remove Liner Infill functionality to return to raw data that has previously been infilled. You may wish to Remove Linear Infill before applying statistical tests to the data.

Fill Gaps: The fill gaps function helps to make the incomplete data gap free and after filling gaps it can be useful for the subsequent analysis of the data set.

Manage time step: The Manage time step allows you to change the time step of time series data, or to restructure and irregular time step to a regular one by adding rows to the data.

Manipulate data files – subset and convert formats

Long to wide data format conversion: The two most common data structures in use are wide format data tables and long format data tables. The wide format data table is used where every parameter or variable is stored in its own column. The long format is generally used where data is contained in two columns, the first for the data label and the second for the data value.

Wide to long data format conversion: The two most common data structures in use are wide format data tables and long format data tables. The wide to long data format allows you to transform data into long format in order to use your data in other applications.