Introduction

Data manipulation is a crucial aspect of data analysis in R. It involves restructuring, filtering, and transforming data to derive meaningful insights. In this post, we will explore various techniques for data manipulation using R.

Subsetting and Filtering Data

Subsetting is the process of extracting specific subsets of data based on certain conditions. In R, you can subset data using logical operators such as '==', '>', '<', etc. For instance, if you have a data frame 'df' with a column 'age', you can subset it to only include individuals above 30 years old with the following code:

subset_df <- df[df$age >= 30, ]

Merging and Joining Data

Merging and joining allow you to combine data sets based on a common key. The 'merge()' function is commonly used for combining data frames, while 'join()' functions from the 'dplyr' package provide a more flexible and intuitive approach. For example, you can merge two data frames based on a common column 'id' using the following code:

merged_df <- merge(df1, df2, by = 'id')

Aggregating and Summarizing Data

Aggregating data involves summarizing the values within a group. R provides useful functions like 'aggregate()' and 'summarize()' to compute various summary statistics. For instance, to calculate the mean and standard deviation of a column 'score' in a data frame 'df', grouped by a variable 'group', you can use the following code:

summary_df <- aggregate(score ~ group, data = df, FUN = function(x) c(mean = mean(x), sd = sd(x)))

Remember, data manipulation is a powerful tool in data analysis, enabling you to extract, combine, and summarize relevant information from your data sets. Happy manipulating!