Analyzing Patient Data

Last updated on 2026-05-21 | Edit this page

Overview

Questions

  • How can I process tabular data files in Julia?

Objectives

  • Explain what a package is and what libraries are used for.
  • Import a package and use the functions it contains.
  • Read tabular data from a file into a program.
  • Select individual values and subsections from data.
  • Perform operations on arrays of data.

Loading data

To begin processing the clinical trial inflammation data, we need to load it into Julia. Depending on the file format we have to use different packages. Some examples are XLSX.jl or JSON.jl. In this example we work with a CSV File. That means we use the package CSV.jl

Before we can use a package in Julia, we need to install it. This can be done either by entering the package mode in the Julia REPL or by using Pkg.add("Example"), for example inside a script.

To enter the package manager mode, press ] in the Julia REPL:

JULIA 

]

Then you can add a package

JULIA 

pkg> add CSV

Alternatively, to add a package inside a script use

JULIA 

using Pkg
Pkg.add("CSV")

After installing the package, you still need to load it before using its functionality:

JULIA 

using CSV

After installing the package, we can read the data file like this:

JULIA 

data = CSV.read("inflammation-01.csv", CSV.Tables.matrix, header = false)

OUTPUT 

60×40 Matrix{Int64}:
 0  0  1  3  1  2  4  7  8  3   3   3  10   5
 0  1  2  1  2  1  3  2  2  6  10  11   5   9
 0  1  1  3  3  2  6  2  5  9   5   7   4   5
 0  0  2  0  4  2  2  1  6  7  10   7   9  13
 0  1  1  3  3  1  3  5  2  4   4   7   6   5
 0  0  1  2  2  4  2  1  6  4   7   6   6   9
 ⋮              ⋮               ⋮
 0  1  2  1  1  4  5  4  4  5   9   7  10   3
 0  0  1  3  2  3  6  4  5  7   2   4  11  11
 0  1  1  2  2  5  1  7  4  2   5   5   4   6
 0  1  1  1  4  1  6  4  6  3   6   5   6   4
 0  0  0  1  4  5  6  3  8  7   9  10   8   6
 0  0  1  0  3  2  5  4  8  2   9   3   3  10

If we want to check that the data loaded correctly, we can just print it:

JULIA 

print(data)

We can check the type of object we’ve created:

JULIA 

typeof(data)

OUTPUT 

Matrix{Int64} (alias for Array{Int64, 2})

To see how many rows and columns the data contains, we can use:

JULIA 

size(data)

OUTPUT 

(60,40)

Analyzing Data

Julia provides powerful tools for analyzing data. To calculate the average inflammation for all patients on all days, we can use the mean function from the Statistics standard library.

Tip: Make sure to install the required packages first

Then load the packages:

JULIA 

using Statistics

JULIA 

mean(data)

OUTPUT 

6.160593220338983

Descriptive Statistics

Let’s use three Julia functions to get some basic descriptive statistics from our dataset: maximum, minimum, and standard deviation.

We can use multiple assignment to store all the results in one line.

JULIA 


maxval, minval, stdval = maximum(data), minimum(data), std(data)

println("maximum inflammation: ", maxval)
println("minimum inflammation: ", minval)
println("standard deviation: ", stdval)
maximum inflammation: 20
minimum inflammation: 0
standard deviation: 4.625075651890539
Callout

Exploring Functions

How can you find out what functions are available in a Julia module and how to use them?

Julia provides several ways to explore functions and get help:

  • To list functions and names in a module, use the names() function. For example:

    JULIA 

    using Statistics
names(Statistics)

  • To get detailed documentation on a function, use the help mode by typing a question mark ? before the function name in the REPL or Jupyter notebook:

    JULIA 

    ?mean

    This will show you the official help text for mean.

  • To see all methods and signatures of a function, use:

    JULIA 

    methods(mean)

When analyzing data, we often want to find values like the maximum inflammation per patient or the average inflammation per day.

One way is to first select the data for a single patient (row), then apply a function to that data:

JULIA 

# Select data for patient 1
patient_1 = data[1, :]  

println("maximum inflammation for patient 1: ", maximum(patient_1))

OUTPUT 

maximum inflammation for patient 1: 18

We don’t need to store the row separately — we can combine selecting the data and applying the function in one step:

JULIA 

println("maximum inflammation for patient 3: ", maximum(data[3, :]))

OUTPUT 

maximum inflammation for patient 3: 17

What if we want the maximum inflammation for each patient across all days (i.e., row-wise maximum), or the average inflammation for each day across all patients (i.e., column-wise average)?

In Julia, many functions accept a dims keyword argument that specifies the dimension (axis) to operate on:

  • dims=1 means operate across rows, producing one result per column.
  • dims=2 means operate across columns, producing one result per row.

For example, to find the average inflammation per day (i.e., average across all patients for each day — column-wise):

JULIA 

day_avg = mean(data, dims=1)
println(day_avg)

OUTPUT 

[0.0 0.4576271186440678 1.11864406779661 1.728813559322034 ...]

To check the shape:

JULIA 

println(size(day_avg))

To get the average inflammation per patient:

JULIA 

patient_avg = mean(data, dims=2)
println(patient_avg)

OUTPUT 

[5.45; 5.425; 6.1; 5.9; 5.55; ...]
Challenge

Change in Inflammation

The patient data is longitudinal — each row represents a series of measurements for one patient over time. That means calculating the change in inflammation over consecutive days is meaningful.

Here’s how you can explore those changes:

JULIA 

patient3_week1 = data[4, 1:7]  
 # the data for patient 4 over days 1 to 7

OUTPUT 

7-element Vector{Int64}:
 0
 1
 1
 3
 3
 1
 3

To compute changes day by day, we use diff:

JULIA 

println(diff(patient3_week1))

OUTPUT 

[1, 0, 2, 0, -2, 2]

Questions

  1. If you use diff(data; dims=?), which dims value computes daily changes for each patient?
  2. If your data array has shape (60, 40), what will the shape be after calling diff(data; dims=?), and why?
  3. How can you compute the largest absolute change for each patient across all days?
  • Set dims=2 in diff(data; dims=2) to compute differences along each row (across days for each patient).
  • If your data is shaped (60, 40), the result of diff will be (60, 39) — one fewer column, because differences are between pairs of adjacent days.
  • To get the largest magnitude change for each patient, combine diff, abs., and maximum, again using dims=2:

JULIA 

maximum(abs.(diff(data; dims=2)), dims=2)

This returns a 60×1 array, where each entry is the maximum absolute change in inflammation for that patient.

Key Points
  • Use Pkg.add("PackageName") to install and using PackageName to load packages in Julia.
  • Load CSV data into a Matrix with CSV.read("file.csv", CSV.Tables.matrix).
  • Use size(df) to inspect Matrix dimensions.
  • Use mean, maximum, minimum, and std to compute statistics on data arrays.
  • Use mean(data, dims=1) for column-wise and dims=2 for row-wise operations.
  • Use diff(data; dims=2) to calculate daily changes per patient.