Master R Data Preprocessing: Sorting, Merging, and Handling Missing Values

Before performing statistical analysis, data usually requires preprocessing; R provides functions for data management.

Data Sorting

In some cases a dataset must be sorted to extract more information. R offers sort() , rank() , and order() for sorting vectors, where sort() arranges values in ascending order, rank() returns the rank of each element, and order() returns the positions of sorted values. The dplyr package’s arrange() function sorts data frames based on one or more columns.

Data Merging

When data is scattered, it can be combined horizontally (adding columns) or vertically (adding rows).

(1) Adding columns: use merge() or cbind() to merge two datasets side‑by‑side. cbind() requires the objects to have the same number of rows in the same order. The basic syntax of merge() is merge(x, y, by = intersect(names(x), names(y)), by.x = by, by.y = by, all = FALSE) , where x and y are the datasets, by , by.x , by.y specify the joining variables, and all (default FALSE) determines whether to keep only matching rows or the full outer join.

(2) Adding rows: use rbind() to vertically merge datasets. The data frames must share the same variables, though their order may differ. rbind() is typically used to append observations to a data frame.

Missing Value Handling

Missing data inevitably occurs. In R, missing values are represented by NA (Not Available), while impossible numeric results are NaN . Functions often include the argument na.rm = TRUE to remove missing values, and na.omit() can drop all rows containing missing values.

Source: Liu Hongde, Sun Xiao, Xie Jianming, “Bioinformatics Data Analysis and Practice”.