sales_k_eur <- 125
employees_count <- 40
sales_per_employee <- sales_k_eur / employees_count
sales_per_employee[1] 3.1251. R, RStudio, and management data
Learning goals
After this lesson, you should be able to:
- explain what R is and how RStudio differs from it;
- explain why quantitative analysis matters in management research and practice;
- recognize objects, functions, arguments, packages, scripts, data frames, variables, and observations;
- import a CSV file and inspect its structure;
- distinguish statistical variable types from R storage types;
- explain why statistical reasoning remains necessary when using generative AI.
Management question: A company has an employee survey. Before calculating anything, what information is actually in the dataset, how is it represented, and what kinds of questions could it support?
Where quantitative analysis fits in management
Quantitative evidence is used in academic management research and in professional work:
People & organizations
Engagement, turnover, leadership, teams, performance.
Marketing
Experiments, segmentation, pricing, campaigns, customer behavior.
Strategy & operations
Forecasts, productivity, capacity, process performance, risk.
Finance & decision support
Performance, uncertainty, scenarios, resource allocation.
The basic logic is similar:
Question→Measurement→Data→Statistical model→Interpretation→Decision
What is R?
R is a programming language and environment for statistical computing and graphics. It is free, open-source, extensible through packages, and widely used for data analysis and research.
R is not a point-and-click application. We give it explicit instructions in code.
R versus RStudio
R performs the computing. RStudio organizes the work.
RStudio gives us a source editor, Console, Environment, file browser, plot pane, package manager, and help system.
The programming vocabulary you need first
| Term | Plain-language meaning | Example |
|---|---|---|
| object | a named thing stored in memory | employees |
| function | an action R can perform | mean() |
| argument | information supplied to a function | na.rm = TRUE |
| package | a collection of functions/data | tidyverse |
| script | a saved file of instructions | 01-first-steps.R |
| data frame | a rectangular dataset | rows × columns |
| variable | a column | performance |
| observation | a row | one employee |
Start with a tiny object
After running the code, inspect the Environment pane.
Load a package
library() makes a package available in the current R session. The tidyverse provides tools for importing, transforming, and visualizing data.
Import the employee survey
employees <- read_csv(
"data/employee_survey.csv",
show_col_types = FALSE
)Read this as:
-
read_csv()reads the file; - the result is assigned to
employees; - later commands can reuse that object.
Inspect before analysing
head(employees)# A tibble: 6 × 26
employee_id age gender department remote_days tenure_years manager
<chr> <dbl> <chr> <chr> <dbl> <dbl> <chr>
1 E000001 37 Man Operations 3 9.2 No
2 E000002 32 Man Operations 3 7 No
3 E000003 22 Man Operations 1 0.7 No
4 E000004 44 Woman Technology 1 4.2 No
5 E000005 42 Woman Operations 3 7.4 No
6 E000006 41 Woman Technology 0 1.6 Yes
# ℹ 19 more variables: training_group <chr>, leadership_1 <dbl>,
# leadership_2 <dbl>, leadership_3 <dbl>, leadership_4_reverse <dbl>,
# engagement_1 <dbl>, engagement_2 <dbl>, engagement_3 <dbl>,
# engagement_4_reverse <dbl>, role_clarity_1 <dbl>, role_clarity_2 <dbl>,
# role_clarity_3 <dbl>, role_clarity_4 <dbl>, workload <dbl>,
# organizational_support <dbl>, job_satisfaction <dbl>, performance <dbl>,
# burnout <dbl>, turnover_intention <dbl>glimpse(employees)Rows: 480
Columns: 26
$ employee_id <chr> "E000001", "E000002", "E000003", "E000004", "E0…
$ age <dbl> 37, 32, 22, 44, 42, 41, 48, 23, 32, 25, 51, 39,…
$ gender <chr> "Man", "Man", "Man", "Woman", "Woman", "Woman",…
$ department <chr> "Operations", "Operations", "Operations", "Tech…
$ remote_days <dbl> 3, 3, 1, 1, 3, 0, 3, 0, 3, 4, 2, 0, 4, 1, 2, 2,…
$ tenure_years <dbl> 9.2, 7.0, 0.7, 4.2, 7.4, 1.6, 1.6, 2.9, 10.0, 6…
$ manager <chr> "No", "No", "No", "No", "No", "Yes", "No", "Yes…
$ training_group <chr> "Training", "Training", "Control", "Training", …
$ leadership_1 <dbl> 1, 3, 5, 3, 3, 2, 3, 3, 5, 2, 4, 5, 1, 2, 1, 2,…
$ leadership_2 <dbl> 1, 1, 5, 2, 4, 3, 3, 4, 5, 1, 5, 5, 1, 1, 1, 2,…
$ leadership_3 <dbl> 1, 1, 5, 3, 5, 4, 4, 2, 5, 2, 5, 5, 1, 1, 2, 2,…
$ leadership_4_reverse <dbl> 5, 5, 1, 4, 1, 3, 3, 4, 1, 5, 2, 1, 3, 4, 4, 3,…
$ engagement_1 <dbl> 2, 3, 5, 4, 5, 3, 5, 2, 5, 3, 4, 5, 3, 2, 4, 5,…
$ engagement_2 <dbl> 2, 1, 3, 5, 4, 4, 5, 1, 5, 2, 4, 5, 4, 2, 3, 1,…
$ engagement_3 <dbl> 1, 1, 4, 4, 4, 4, 5, 2, 5, 2, 5, 5, 4, 1, 3, 3,…
$ engagement_4_reverse <dbl> 3, 4, 1, 2, 2, 4, 1, 3, 1, 5, 1, 1, 3, 5, 2, 2,…
$ role_clarity_1 <dbl> 1, 1, 4, 4, 5, 4, 3, 2, 3, 3, 5, 3, 2, 1, 1, 2,…
$ role_clarity_2 <dbl> 1, 3, 3, 5, 4, 4, 5, 3, 4, 2, 5, 5, 1, 1, 1, 1,…
$ role_clarity_3 <dbl> 1, 2, 3, 4, 4, 3, 4, 3, 5, 4, 5, 2, 2, 1, 1, 2,…
$ role_clarity_4 <dbl> 1, 4, 3, 3, 3, 2, 5, 5, 5, 3, 4, 4, 2, 1, 1, 3,…
$ workload <dbl> 3.44, 4.68, 4.68, 4.09, 4.26, 2.25, 2.79, 4.16,…
$ organizational_support <dbl> 2.50, 3.92, 5.09, 3.72, 3.63, 3.97, 5.58, 4.25,…
$ job_satisfaction <dbl> 2.52, 4.17, NA, 4.87, 3.17, 5.68, 4.86, 3.77, 5…
$ performance <dbl> 45.1, 43.8, 52.2, 60.3, 59.9, NA, 64.3, 48.1, 6…
$ burnout <dbl> 49.8, 48.5, 47.4, 47.1, 51.8, 36.3, 32.1, 46.6,…
$ turnover_intention <dbl> 4.84, 4.18, 3.66, 4.12, 4.62, 2.83, 3.16, 5.26,…names(employees) [1] "employee_id" "age" "gender"
[4] "department" "remote_days" "tenure_years"
[7] "manager" "training_group" "leadership_1"
[10] "leadership_2" "leadership_3" "leadership_4_reverse"
[13] "engagement_1" "engagement_2" "engagement_3"
[16] "engagement_4_reverse" "role_clarity_1" "role_clarity_2"
[19] "role_clarity_3" "role_clarity_4" "workload"
[22] "organizational_support" "job_satisfaction" "performance"
[25] "burnout" "turnover_intention" dim(employees)[1] 480 26-
head()shows the first rows; -
glimpse()shows variables and storage types; -
names()lists columns; -
dim()returns rows and columns.
In RStudio, try:
View(employees)The spreadsheet-like viewer is useful for orientation, but your actual analysis should remain in code.
Statistical type versus R type
These are related but not identical.
Statistical meaning
- nominal categorical: department, country, treatment group;
- ordinal categorical: satisfaction category, rating;
- discrete numeric: number of employees;
- continuous numeric: sales, age, performance.
Common R storage classes
| R display | Meaning |
|---|---|
chr |
character/text |
dbl |
numeric double |
int |
integer |
lgl |
TRUE/FALSE |
fct |
factor/category |
Date |
calendar date |
A variable coded as 1, 2, 3 is not automatically a continuous quantity. You need to understand what those numbers mean before choosing an analysis.
Script versus Console
Console
- immediate;
- useful for trying a command;
- easy to lose;
- poor record of a full analysis.
Script
- saved;
- editable;
- rerunnable;
- shareable;
- basis of reproducible analysis.
Course habit: experiment in the Console; keep meaningful work in the script.
Why learn this when GenAI can write R?
AI can help:
- explain code;
- draft syntax;
- debug;
- suggest a plot or function.
But you still decide whether:
- the data measure the intended concept;
- the test matches the design;
- assumptions are reasonable;
- the direction of a hypothesis was defined in advance;
- repeated/nested observations require a different model;
- the output supports a descriptive, associational, predictive, or causal claim.
AI can generate code. It cannot make an invalid research design valid.
Practice
- Open
code/01-first-steps.R. - Run it one block at a time.
- Find the number of rows and columns.
- Select
performancewithemployees$performance. - Ask R for help with
?summary. - In one sentence, describe one research question this dataset could support.
Takeaway
The first step in R is not a statistical test. It is understanding the question, data structure, variable meaning, and analysis environment.