SDS 1201 — Data Analytics (Intro Notebook)
Notebook 0: Getting Started with RStudio + R Markdown
Instructor: RK
26 February 2026
- 1 0. What you should have before we start
- 2 1. RStudio tour (the 4 main panes)
- 3 2. Working directory and files (very important)
- 4 3. Packages (install once, load every time)
- 5 4. R Markdown (.Rmd): what it is and how to run it
- 6 5. Core R objects you must know (fast, practical)
- 7 6. Reading data (CSV) and checking it
- 8 7. The basic analytics workflow (the one you should master)
- 9 8. Example: Cleaning + summarizing + plotting (step-by-step)
- 10 9. Saving your work
- 11 10. Getting help (quickly)
- 12 11. Common mistakes (and how to fix them)
- 13 12. Student tasks (submit in this same .Rmd)
- 14 13. Checklist before you submit
How to use this notebook in class
- Read the text.
- Run code chunks one-by-one (click the green ▶ button).
- Answer the quick tasks where you see Task boxes.
- At the end, click Knit to produce a clean HTML report.
1 0. What you should have before we start
1.1 Required software
You need:
- R (the language)
- RStudio (the editor where we work)
If you are already in the computer lab and can open RStudio, you’re good.
1.2 First-time check
Run the next chunk. If it prints your R version, you are ready.
R.version.string## [1] "R version 4.5.2 (2025-10-31 ucrt)"2 1. RStudio tour (the 4 main panes)
When you open RStudio, you usually see these panes:
- Source (top-left): where you write scripts and
notebooks
- Console (bottom-left): where R runs commands
- Environment/History (top-right): your objects and
past commands
- Files/Plots/Packages/Help (bottom-right): where outputs and help show
2.1 What to avoid
- Do not write long code in the Console.
- Write code in the Source pane (scripts or R Markdown) and run from there.
3 2. Working directory and files (very important)
3.1 What is a working directory?
It is the folder where R looks for files by default (CSV, images, saved outputs).
Check your current working directory:
getwd()## [1] "C:/Users/uSer/OneDrive/Documents/SEMESTER 1.2"3.2 Best practice (recommended)
Use an RStudio Project so your work is organized.
3.2.1 Create a project (once)
- File → New Project → New Directory → New Project
- Choose a folder name like
SDS1201 - Click Create Project
After that: - Put your data files inside that project folder - Your working directory becomes stable
3.3 Setting the directory (only when needed)
If you must set it manually:
setwd("C:/Users/YourName/Documents/SDS1201")Note: In class, we prefer Projects over
setwd().
4 3. Packages (install once, load every time)
4.1 Install packages (once per computer)
You only install a package once (like installing an app).
install.packages("tidyverse")
install.packages("readr")4.2 Load packages (every session)
Each time you open RStudio, you load packages using
library().
# If tidyverse is installed, this should load without errors:
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.2.0
## ✔ forcats 1.0.1 ✔ stringr 1.6.0
## ✔ ggplot2 4.0.0 ✔ tibble 3.3.0
## ✔ lubridate 1.9.5 ✔ tidyr 1.3.2
## ✔ purrr 1.2.1
## ── 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 errorsIf you see an error like “there is no package called
…”
→ you must install it first.
5 4. R Markdown (.Rmd): what it is and how to run it
An R Markdown file is a document that combines: - text explanations - code chunks - output (tables, plots)
5.1 Running code chunks
A code chunk looks like this:
``` r
2 + 2
```
```
## [1] 4
```Try it:
2 + 2## [1] 45.2 Knitting (exporting)
Click Knit (top of RStudio) to generate an HTML report.
- If it fails, read the error message carefully.
- Most common issues are missing packages or file paths.
6 5. Core R objects you must know (fast, practical)
6.1 5.1 Variables
A variable stores a value.
x <- 10
y <- 3
x + y## [1] 136.2 5.2 Vectors (most common)
A vector is a 1D collection of values of the same type.
scores <- c(65, 70, 80, 55)
scores## [1] 65 70 80 55Common operations:
length(scores)## [1] 4mean(scores)## [1] 67.5max(scores)## [1] 80Indexing:
scores[1] # first item## [1] 65scores[2:4] # items 2 to 4## [1] 70 80 55scores[scores >= 70] # conditional selection## [1] 70 806.3 5.3 Data frames (most common in analytics)
A data frame is a table (rows and columns).
students <- data.frame(
name = c("Amina", "Brian", "Carol"),
age = c(20, 21, 19),
score = c(78, 62, 85)
)
studentsAccess columns:
students$score## [1] 78 62 85mean(students$score)## [1] 757 6. Reading data (CSV) and checking it
7.1 6.1 Recommended:
readr::read_csv()
It is fast and gives clean output.
# We'll create a small CSV in memory for practice
tmp_file <- tempfile(fileext = ".csv")
writeLines(
c("name,age,score",
"Amina,20,78",
"Brian,21,62",
"Carol,19,85"),
tmp_file
)
df <- readr::read_csv(tmp_file, show_col_types = FALSE)
df7.2 6.2 Basic checks you should always do
glimpse(df)## Rows: 3
## Columns: 3
## $ name <chr> "Amina", "Brian", "Carol"
## $ age <dbl> 20, 21, 19
## $ score <dbl> 78, 62, 85summary(df)## name age score
## Length:3 Min. :19.0 Min. :62.0
## Class :character 1st Qu.:19.5 1st Qu.:70.0
## Mode :character Median :20.0 Median :78.0
## Mean :20.0 Mean :75.0
## 3rd Qu.:20.5 3rd Qu.:81.5
## Max. :21.0 Max. :85.0Missing values:
colSums(is.na(df))## name age score
## 0 0 08 7. The basic analytics workflow (the one you should master)
We will use a simple structure in this course:
- Load data
- Clean data
- Explore data
- Summarize results
- Visualize key patterns
- Report cleanly (Knit)
Let’s do a small example.
9 8. Example: Cleaning + summarizing + plotting (step-by-step)
9.1 8.1 Create a slightly messy dataset
set.seed(1)
demo <- tibble(
student_id = 1:12,
gender = sample(c("F", "M"), 12, replace = TRUE),
math = sample(c(NA, 40:95), 12, replace = TRUE),
english = sample(c(NA, 40:95), 12, replace = TRUE)
)
demo9.2 8.2 Check missingness
colSums(is.na(demo))## student_id gender math english
## 0 0 0 09.3 8.3 Simple cleaning rule (example)
Here we fill missing marks using the subject mean (basic imputation).
demo_clean <- demo %>%
mutate(
math = ifelse(is.na(math), mean(math, na.rm = TRUE), math),
english = ifelse(is.na(english), mean(english, na.rm = TRUE), english),
average = (math + english) / 2
)
demo_clean9.4 8.4 Summaries
Mean by gender:
demo_clean %>%
group_by(gender) %>%
summarise(
n = n(),
mean_math = mean(math),
mean_english = mean(english),
mean_average = mean(average),
.groups = "drop"
)9.5 8.5 Visualization (ggplot2)
ggplot(demo_clean, aes(x = math, y = english)) +
geom_point() +
labs(
title = "Math vs English (Demo Data)",
x = "Math score",
y = "English score"
)
A distribution plot:
ggplot(demo_clean, aes(x = average)) +
geom_histogram(bins = 8) +
labs(
title = "Distribution of Average Score",
x = "Average",
y = "Count"
)
10 9. Saving your work
10.1 9.1 Save objects (RDS)
If you want to save an R object to re-use later:
saveRDS(demo_clean, "demo_clean.rds")To load it:
loaded <- readRDS("demo_clean.rds")
head(loaded)10.2 9.2 Export a clean CSV
readr::write_csv(demo_clean, "demo_clean.csv")11 10. Getting help (quickly)
11.1 10.1 Use
? help
?mean
?read_csv11.2 10.2 Search examples
help.search("histogram")11.3 10.3 Inspect objects
str(demo_clean)## tibble [12 × 5] (S3: tbl_df/tbl/data.frame)
## $ student_id: int [1:12] 1 2 3 4 5 6 7 8 9 10 ...
## $ gender : chr [1:12] "F" "M" "F" "F" ...
## $ math : int [1:12] 71 59 59 80 92 84 48 45 47 53 ...
## $ english : int [1:12] 79 63 84 75 75 72 80 63 82 53 ...
## $ average : num [1:12] 75 61 71.5 77.5 83.5 78 64 54 64.5 53 ...names(demo_clean)## [1] "student_id" "gender" "math" "english" "average"12 11. Common mistakes (and how to fix them)
12.1 Mistake 1: Package not found
Error:
there is no package called 'tidyverse'
Fix: install once, then load.
install.packages("tidyverse")
library(tidyverse)12.2 Mistake 2: File not found
Error: cannot open the connection
Fix: check working directory + file name.
getwd()## [1] "C:/Users/uSer/OneDrive/Documents/SEMESTER 1.2"list.files()## [1] "00_Introduction.html"
## [2] "01_Foundations_Data_Analytics_R.Rmd"
## [3] "02_Data_Cleaning_Preparation_R.Rmd"
## [4] "03_Exploratory_Data_Analysis_R (2).Rmd"
## [5] "04_Introduction_2_Analytical_Models_R.Rmd"
## [6] "05_Model_Validation_Responsible_Analysis_R.Rmd"
## [7] "06_Applied_Project_R.Rmd"
## [8] "2025_A_KSD_1466_F.html"
## [9] "2025_A_KSD_1466_F.Rmd"
## [10] "2025_A_KSD_1466_F_files"
## [11] "airquality.csv"
## [12] "clean_student_grades.csv"
## [13] "demo_clean.csv"
## [14] "demo_clean.rds"
## [15] "dice_game_results.csv"
## [16] "dice_rolls.csv"
## [17] "dice_sum_counts.csv"
## [18] "DOC-20250406-WA0005.pdf"
## [19] "Macro economics - introductory.pdf"
## [20] "messy_data.csv"
## [21] "Naboth_Harris.html"
## [22] "processed_mtcars.csv"
## [23] "progam.csv"
## [24] "progam.rds"
## [25] "Riemann Integration.pdf"
## [26] "rsconnect"
## [27] "Share with CamScanner.zip"
## [28] "STA1204_APT_COURSE CONTENT APPLIED PROBABILITY.pdf"
## [29] "STA1205_COURSE CONTENT MATHEMATICAL STATISTICS.pdf"
## [30] "student.csv"
## [31] "students.csv"
## [32] "students.rds"
## [33] "students_clean.csv"
## [34] "The-Academic-Policy-and-Examination-Regulations-Kabale-University.pdf"12.3 Mistake 3: Using commas instead of dots
In R: - decimal is 3.14 not 3,14
13 12. Student tasks (submit in this same .Rmd)
13.1 Task A: Your profile dataset
Create a small data frame named profile with: - your
name - your program - your home district - your favorite number
Then print it and show its structure.
# TODO: write your code here
profile <- data.frame(
name = c("AHABWE NABOTH KAKURU"),
program = c("STATISTICS AND DATA SCIENCE"),
home_district = c("KABALE"),
fav_number = 0040910635672174.74
)
profile13.2 Task B: Quick summary
Create a vector of 10 numbers (any numbers), then compute: - mean - median - standard deviation - min and max
# TODO: write your code here
v1 <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
mean(v1)## [1] 5.5median(v1)## [1] 5.5sd(v1)## [1] 3.02765min(v1)## [1] 1max(v1)## [1] 1013.3 Task C: Mini-plot
Create a data frame with two columns x and
y (10 rows), then make a scatter plot.
# TODO: write your code here
dading <- data.frame(
x = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
y = c(10, 20, 30, 40, 50, 60, 70, 80, 90, 100)
)
dadingplot(dading$x, dading$y,
main = "X valiations against Y",
xlab = "X values",
ylab = "Y values",
pch = 21,
col = "red")
13.4 Task D: Import practice
- Create a small CSV file with 5 rows and 3 columns.
- Read it using
read_csv(). - Print
glimpse()andsummary().
# TODO: write your code here
progam <- data.frame(
name = c("Naboth", "Nebart", "Lynn", "Ian", "Collins"),
scores = c(90, 80, 70, 60, 50),
grades = c("A", "B", "C", "D", "E")
)
progamwrite.csv(progam, "progam.csv", row.names = FALSE)
read.csv("progam.csv")glimpse(progam)## Rows: 5
## Columns: 3
## $ name <chr> "Naboth", "Nebart", "Lynn", "Ian", "Collins"
## $ scores <dbl> 90, 80, 70, 60, 50
## $ grades <chr> "A", "B", "C", "D", "E"summary(progam)## name scores grades
## Length:5 Min. :50 Length:5
## Class :character 1st Qu.:60 Class :character
## Mode :character Median :70 Mode :character
## Mean :70
## 3rd Qu.:80
## Max. :9014 13. Checklist before you submit
14.1 End
If you can run this notebook and complete Tasks A–D, you are ready for SDS 1201.