**Learn Data Manipulation In R:** Data manipulation in R is crucial to data analysis and statistical modeling. It involves transforming raw data into a format that can be used for further analysis. Various techniques are used for data manipulation in R, including **filtering**, **aggregating**, **reshaping**, and **merging data**.

Filtering is a common data manipulation technique used to extract a subset of data based on specific criteria. For instance, you might want to extract only the observations that meet certain conditions, such as age greater than 18 or salary greater than $50,000. This can be achieved using the subset() function in R.

Aggregation involves summarizing data by computing descriptive statistics such as mean, median, and standard deviation. R provides a number of functions for aggregating data, including aggregate(), tapply(), and by(). These functions can be used to summarize data by grouping variables, such as calculating the mean salary by the department.

Reshaping data involves changing the structure of the data, such as converting a wide format data frame to a long format data frame. This is useful when you work with data in a different format for analysis or visualization. R provides several functions for reshaping data, including melt(), cast(), and reshape().

Merging data involves combining two or more data sets into a single data set. This can be useful when you have data that is collected from different sources and needs to be combined for analysis. R provides several functions for merging data, including merge(), join(), and cbind().

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