In this section, we’ll explore the critical distinction between data exploration and data presentation. Both are essential components of the data analysis process, but they have different goals, methods, and considerations. Knowing the difference between them will help you tailor your analysis and communication to the appropriate context and audience.
Data exploration is an iterative process of discovering patterns, relationships, and anomalies in a dataset. It’s a detective-like activity, guided by curiosity and a desire to understand the underlying data.
Data presentation focuses on communicating findings and insights to a specific audience. It’s about telling a compelling story with the data in a clear, concise, and visually appealing manner.
| Feature | Data Exploration | Data Presentation |
|---|---|---|
| Goal | Discovery, understanding | Communication, persuasion |
| Audience | Analyst, small team | Stakeholders, decision-makers, wider audience |
| Focus | Flexibility, experimentation | Clarity, accuracy, visual appeal |
| Visualizations | Quick, diverse, exploratory | Polished, focused, impactful |
| Narrative | Minimal, internal notes | Clear, compelling, tailored to audience |
| Level of Detail | High (all aspects of the data) | Selective (highlights key insights) |
| “Aha!” moments | New insights | Confirmation of insights |
| Polished Level | Quick, functional | Professional, polished |
The choice of visualization method depends on whether you are in the exploration or presentation phase.
For data presentation, it’s crucial to create visuals that are clear, accurate, and engaging. Here are some tips:
Let’s say you want to present the average exam score for each grade. Here’s how you might create a more polished version for presentation:
Initial Exploration Chart:
library(ggplot2)Warning: package 'ggplot2' was built under R version 4.2.3#Basic graph, for exploration
exam_scores <- read.csv("https://raw.githubusercontent.com/sijuswamyresearch/R-for-Data-Analytics/refs/heads/main/data/exam_scores.csv")
ggplot(exam_scores, aes(x = grade, y = score)) +
geom_bar(stat = "summary", fun = "mean")
library(tidyverse)Warning: package 'tidyverse' was built under R version 4.2.3Warning: package 'tibble' was built under R version 4.2.3Warning: package 'tidyr' was built under R version 4.2.3Warning: package 'readr' was built under R version 4.2.3Warning: package 'purrr' was built under R version 4.2.3Warning: package 'dplyr' was built under R version 4.2.3Warning: package 'stringr' was built under R version 4.2.3Warning: package 'forcats' was built under R version 4.2.3Warning: package 'lubridate' was built under R version 4.2.3── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ lubridate 1.9.2 ✔ tibble 3.2.1
✔ purrr 1.0.2 ✔ tidyr 1.3.0
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors#Assuming the grade is the main column with the average score.
ggplot(exam_scores, aes(x = grade, y = score)) +
geom_bar(stat = "summary", fun = "mean", fill = "steelblue") +
labs(title = "Average Exam Score by Grade",
x = "Grade",
y = "Average Score") +
theme_minimal() +
ylim(0, 100) # Force score values between 0 and 100.
Data visualization can be very powerful for communicating the information with your stakeholders. But it can also give the audience an incorrect understanding of your data
Incorrect data scale
Hidden and misrepresented data
Skewed charts
When communicating results make sure that the Y scale correctly represents the values for an accurate picture. Here is an example of an incorrect visualization of the data.
library(tidyverse)
set.seed(123)
data <- data.frame(Category = c("A", "B"),
Value = c(60, 55))
ggplot(data, aes(x = Category, y = Value)) +
geom_bar(stat = "identity") +
ylim(0, 60) +
labs(title = "Misleading: Y-axis starts at 50",
x = "Category", y = "Value")
One common visualziation mistake is to have incorrect scales and to remove data. If you had one set of data, and only communicated a small part of the data set the audience can get a very wrong idea about the quality and the overall trend.
This also applies to things such as selecting the data to use, or cleaning data that may be relevant.
Make sure you’re choosing the best methods to display the data. Charts like a pie chart can often be more confusing than helpful.
Data exploration and data presentation are two distinct but complementary phases of the data analysis process. Data exploration is about discovering patterns and generating hypotheses. Data presentation is about communicating findings and insights to a specific audience. By understanding the key differences between these phases, you can tailor your analysis and communication to the appropriate context and audience. Choose the right visualization and format for the given situation.
Practice
Take a dataset you’ve used previously.
Create exploratory visualizations to understand the data.
Select a key insight you want to communicate.
Design a polished visualization to present that insight to a specific audience (e.g., a non-technical stakeholder).