¿Cómo se utiliza la manipulación de datos en R?

How do you use data manipulation in R?

Main data manipulation functions

1. filter() : Pick rows (observations/samples) based on their values.
2. distinct() : Remove duplicate rows.
3. arrange() : Reorder the rows.
4. select() : Select columns (variables) by their names.
5. rename() : Rename columns.
6. mutate() and transmutate() : Add/create new variables.

Arrange rows The dplyr function arrange() can be used to reorder (or sort) rows by one or more variables. Instead of using the function desc(), you can prepend the sorting variable by a minus sign to indicate descending order, as follow. If the data contain missing values, they will always come at the end.

Data manipulation involves modifying data to make it easier to read and to be more organized. We manipulate data for analysis and visualization. It is also used with the term ‘data exploration’ which involves organizing data using available sets of variables.

Aggregate is a function in base R which can, as the name suggests, aggregate the inputted data. frame d.f by applying a function specified by the FUN parameter to each column of sub-data. frames defined by the by input parameter. The by parameter has to be a list .

R programming is better suited for statistical learning, with unmatched libraries for data exploration and experimentation. Python is a better choice for machine learning and large-scale applications, especially for data analysis within web applications.

DML is a computer programming language that is used for inserting, omitting, and altering data in a database. It makes the data easy to cleanse and map for further analysis. A commonly used data manipulation language is Structured Query Language (SQL). SQL is used to update and retrieve data in a relational database.

In this tutorial, we introduce how to filter a data frame rows using the dplyr package:

1. Filter rows by logical criteria: my_data %>% filter(Sepal.
2. Select n random rows: my_data %>% sample_n(10)
3. Select a random fraction of rows: my_data %>% sample_frac(10)
4. Select top n rows by values: my_data %>% top_n(10, Sepal.

The compound assignment %<>% operator is used to update a value by first piping it into one or more expressions, and then assigning the result. For instance, let’s say you want to transform the mpg variable in the mtcars data frame to a square root measurement.

Aggregate() Function in R Splits the data into subsets, computes summary statistics for each subsets and returns the result in a group by form. Aggregate function in R is similar to group by in SQL. Aggregate() function is useful in performing all the aggregate operations like sum,count,mean, minimum and Maximum.

In order to aggregate data, you can simply use Pivot table or other charts, which aggregate the data by the column assigned Row/Column (Pivot) or X-Axis (Bar/Line/other charts). But sometimes, you want to aggregate the data itself, not as how it’s presented.

To combine a number of vectors into a data frame, you simple add all vectors as arguments to the data.frame() function, separated by commas. R will create a data frame with the variables that are named the same as the vectors used.

R Data Frame. A “data frame” is basically a quasi-builtin type of data in R. It’s not a primitive; that is, the language definition mentions “Data frame objects” but only in passing. “Data frame is a list of factors, vectors, and matrices with all of these having the same length (equal number of rows in matrices).

Use a static approach – copy and paste as transpose

In order to transpose rows or columns, select the whole column or row and copy it by pressing Ctrl+C on the keyboard. Now, select the location where you want to transpose the copied row. Now, all you need is to use the Ctrl+V hotkey combination to paste the copied row/column.