Data manipulation and analysis are core components of many fields, including data science, statistics, and research. In R, the “dplyr” package is a powerful tool for data transformation, offering a coherent set of verbs that help in data exploration and transformation tasks. Today, we’re diving into a practical example of how “dplyr” can simplify data aggregation tasks, focusing on counting unique samples per patient in a dataset.
Imagine you’re working with a dataset from a medical study. This dataset, named “MYData”, contains records of urine sample collections from various patients over time. Each record includes a “patientNumber”, the “DateOfCollection” date, and a unique “SampleID”. Your goal is to count how many unique samples have been collected for each patient.
First, let’s create some simulated data to illustrate our task:
# Load necessary library
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union# Simulate some data
set.seed(123) # Ensure reproducibility
MYData <- data.frame(
patientNumber = sample(1:5, 20, replace = TRUE), # Simulate patient IDs
DateOfCollection = sample(seq(as.Date('2021-01-01'), as.Date('2021-12-31'), by="day"), 20),
SampleID = paste0("S", sample(10000:99999, 20, replace = FALSE))
)
MYData <- MYData %>% arrange(patientNumber, DateOfCollection)
head(MYData)## patientNumber DateOfCollection SampleID
## 1 1 2021-01-23 S16133
## 2 1 2021-05-15 S31811
## 3 1 2021-11-28 S82819
## 4 1 2021-12-17 S49894
## 5 2 2021-05-23 S61655
## 6 2 2021-07-30 S63240This simulated dataset represents 20 sample collections from 5 patients over the year 2021, with a unique barcode assigned to each sample.
Our objective is to count the number of unique samples per patient. To ensure accuracy, we must consider each “SampleID” only once per “patientNumber”. Here’s how we can achieve this with “dplyr”:
samples_per_patient <- MYData %>%
distinct(patientNumber, DateOfCollection, SampleID) %>% # Ensure uniqueness
group_by(patientNumber) %>%
summarise(NumberOfSamples = n(), .groups = 'drop') # Count samples and drop grouping
# Display the results
print(samples_per_patient)## # A tibble: 5 × 2
## patientNumber NumberOfSamples
## <int> <int>
## 1 1 4
## 2 2 4
## 3 3 7
## 4 4 2
## 5 5 3In this snippet, “distinct()” ensures that each sample is counted only once, even if there were multiple entries for the same “SampleID”. By grouping the data by “patientNumber” and then summarising with “n()”, we count the unique samples for each patient. Finally, “.groups = ‘drop’” tells “dplyr” to return an ungrouped tibble, making the data easier to work with afterward.
When we use “group_by()” in “dplyr”, the resulting tibble retains a grouping structure based on the specified variables. This is incredibly useful for performing operations within each group. However, once we’re done with these grouped operations, it’s often practical to remove this grouping structure, especially if we’re moving on to analyses that don’t require it. By setting “.groups = ‘drop’” in our “summarise()” call, we’re instructing “dplyr” to remove all grouping metadata from the resulting tibble, treating it as a standard, ungrouped dataset.
“dplyr” streamlines data manipulation tasks in R, making it easier to perform complex data transformations with simple, readable code. In our example, counting unique samples per patient was accomplished with just a few lines of code, showcasing the power and efficiency of “dplyr”. Whether you’re working with medical data, business data, or any other type of dataset, mastering “dplyr” can significantly enhance your data analysis workflows.
As with any tool, practice is key to mastery. Experiment with “dplyr” on your datasets, and explore its extensive documentation and community resources to discover more advanced features and techniques. Happy data wrangling!