——————————————————————————————–

Facilitator: D. K. Muriithi

——————————————————————————————–

Session 4: Data Visualization with ggplot2

By the end of this session, you will be able to:

Understand the grammar of graphics and how ggplot2 works.
Create basic visualizations including scatter plots, bar charts, histograms, and boxplots.
Customize visualizations using titles, axis labels, colors, and themes.
Use ggplot2 in your exploratory data analysis (EDA) workflow.

Data Visualization

Data visualization helps in understanding patterns, trends, and relationships in data.

It is a crucial element in scientific research, enabling researchers to interpret and communicate their results effectively

Types of Data Visualization

1. Univariate Data Visualizations (Single Variable)

✔️ Histogram: Used for understanding the distribution of a single variable.

✔️ Box Plot: Used for Detecting outliers and understanding the spread of data.

2. Bivariate Data Visualizations (Two Variables)

✔️ Scatter Plot: Used for understanding relationships between two numerical variables.

✔️ Line Plot: Used for showing trends over time or continuous data.

✔️ Bar Chart: Used for comparing categorical data.

3. Multivariate Data Visualizations (More than Two Variables)

✔️ Heatmap: Used for visualizing correlations between multiple numerical variables.

✔️ Pair Plot: Used for visualizing pairwise relationships in the dataset.

✔️ Violin Plot: Used for understanding the distribution of a variable across categories.

4. Specialized Data Visualizations

✔️ Pie Chart: Used for representing proportions.

✔️ Bubble Chart: Used for adding a third variable to a scatter plot(Comparing three numerical variables)

✔️ Word Cloud: used to highlight keywords, trends, or themes in textual data (Text Analysis : Highlighting key terms in articles, reviews, or social media posts.)

5. Time Series Visualizations

✔️ Time Series Line plot: Used to analyze trends, patterns, or changes in data over a continuous period (e.g., days, months, years).

✔️ Autocorrelation Plot: Used for finding patterns in time series data.

Best Practices

✅ Choose the Right Chart Type (e.g., bar charts for categories, line charts for trends).

✅ Follow Design Principles (simplicity, consistency, accessibility).

✅ Use Storytelling to highlight key insights and structure visuals logically.

✅ Avoid Common Pitfalls (misleading scales, cluttered visuals, unnecessary 3D charts).

1. Introduction to ggplot2

What is ggplot2?

A powerful plotting system for R, based on the Grammar of Graphics .
Developed by Hadley Wickham .
Allows building complex, publication-quality graphics in layers.

Why Use ggplot2?

Consistent syntax across plot types
Highly customizable
Supports faceting, grouping, and mapping aesthetics
Produces professional-quality visuals

Set a working Directory

setwd("~/2025_R_TRAINING")

1. Install and Load necessary libraries

library(ggplot2)       ## for data visualization
library(tidyverse)     ## for data manipulation
library(psych)         ## for description of  data
library(ggdist)        ## Enhance visual representation of distribution & uncertainty
library(gganimate)     ## adds dynamic animations to your visualizations
library(patchwork)     ## Easily combines multiple plots into cohesive layouts
library(rio)           ## For easy data import, export(saving) and conversion

2. Import the dataset

#gss = read.csv("GSSsubset.csv")     # load csv dataset

gss = import("GSSsubset.csv")        # alternative way to lad csv dataset

#View(gss)        # for viewing the dataset

Exploratory data analysis (EDA)

Before we start visualizing our data, we need to understand the characteristics of our data. The goal is to get an idea of the data structure and to understand the relationships between variables.

Here are some functions that can help us understand the structure of our data:

head(gss)         # for the 1st few rows in the dataset

names(gss)        # for features/ variable names in the dataset

[1] "id"      "sex"     "degree"  "income"  "marital" "age"     "height"  "weight"  "hrswrk"

dim(gss)          # for dimensions of dataset

[1] 994   9

str(gss)          # for structure of dataset

'data.frame':   994 obs. of  9 variables:
 $ id     : int  1 2 4 14 16 19 21 27 28 30 ...
 $ sex    : chr  "MALE" "FEMALE" "FEMALE" "FEMALE" ...
 $ degree : chr  "BACHELOR" "BACHELOR" "BACHELOR" "HIGH SCHOOL" ...
 $ income : num  60968 60968 10161 17551 17551 ...
 $ marital: chr  "DIVORCED" "MARRIED" "MARRIED" "MARRIED" ...
 $ age    : int  53 26 56 40 56 51 30 35 57 54 ...
 $ height : int  72 60 68 65 66 68 62 70 71 71 ...
 $ weight : int  190 97 160 156 210 170 115 180 225 165 ...
 $ hrswrk : int  60 40 20 37 6 50 38 40 40 40 ...

summary(gss)      # for summary of descriptive statistics

       id             sex               degree              income           marital         
 Min.   :   1.0   Length:994         Length:994         Min.   :   369.5   Length:994        
 1st Qu.: 648.2   Class :character   Class :character   1st Qu.: 15703.8   Class :character  
 Median :1254.5   Mode  :character   Mode  :character   Median : 27712.5   Mode  :character  
 Mean   :1271.2                                         Mean   : 36887.2                     
 3rd Qu.:1915.8                                         3rd Qu.: 49882.5                     
 Max.   :2538.0                                         Max.   :158657.0                     
      age            height          weight          hrswrk     
 Min.   :19.00   Min.   :57.00   Min.   : 90.0   Min.   : 1.00  
 1st Qu.:33.00   1st Qu.:64.00   1st Qu.:150.0   1st Qu.:38.00  
 Median :44.00   Median :67.00   Median :175.0   Median :40.00  
 Mean   :44.49   Mean   :67.41   Mean   :181.3   Mean   :42.64  
 3rd Qu.:55.00   3rd Qu.:70.00   3rd Qu.:205.0   3rd Qu.:50.00  
 Max.   :79.00   Max.   :79.00   Max.   :410.0   Max.   :89.00

describe(gss)     # for descriptive statistics

3. Data Visualization with ggplot2

The “Grammar of Graphics” is a powerful concept that ggplot2 in R is built on.

It breaks down the process of data visualization into layers, making it easier to customize and understand how to build effective charts.

Layers are added using the ‘+’ operator.

Essential layers(Components of a ggplot) used to create a plot:

1️ Data: The foundation, where you start by defining the data set.

2️ Aesthetics: Map variables to visual aspects like color, size, and position.

3️⃣ Geometries: Specify the type of plot you want, such as bar, line, or scatter.

4️⃣ Facets: Create subplots for different subsets of your data.

5️⃣ Statistics: Add statistical transformations, like mean lines or trend lines.

6️⃣ Coordinates: Control the plot’s coordinate system, such as flipping axes.

7️⃣ Theme: Adjust the overall appearance, like grid lines, font styles, and background.

The data

All ggplot2 plots require a data frame as input. Just running this line will produce a blank plot because we have not stated which elements from the data we want to visualize or how we want to visualize them.

ggplot(data = gss)

The aesthetics

Next, we need to specify the visual properties of the plot that are determined by the data. The aesthetics are specified using the aes() function. The output should now produce a blank plot but with determined visual properties (e.g., axes labels).

ggplot(data = gss, aes(x = income))

The geometries

Finally, we need to specify the visual representation of the data. The geometries are specified using the geom_*() function. There are many different types of geometries that can be used in ggplot2. We will use geom_point() in this example and we will append it to the previous plot using the + operator. The output should now produce a plot with the specified visual representation of the data.

Histogram: Show the distribution of a single variable.

Example: Histogram of ‘income’

use geom_histogram() ✔️ Histogram: Used for understanding the distribution of a single variable.

ggplot(gss, aes(x = income)) +
  geom_histogram(binwidth = 10000, fill = "orange", color = "black") +
  labs(title = "Income Distribution", 
       x = "Income", 
       y = "Frequency") +
  theme_light()

ggplot(gss, aes(x = income)) +
  geom_histogram(bins = 20, fill = "orange", color = "black") +
  labs(title = "Income Distribution", 
       x = "Income", 
       y = "Frequency") +
  theme_classic()

Boxplot: Summarize the distribution of a continuous variable across categories.

Example: Boxplot of ‘income’ by ‘gender’

ggplot(data = gss, aes(x = sex, y = income)) +
  geom_boxplot(fill = "purple") +
  labs(title = "Income by Gender", 
       x = "Gender", 
       y = "Income") +
  theme_bw()

Scatter Plot: Visualize the relationship between two continuous variables.

Example: Scatter plot of ‘income’ vs ‘age’

ggplot(data = gss, aes(x = age, y =log(income))) +
  geom_point()

change the color of point to my choice

ggplot(data = gss, aes(x = age, y =income)) +
  geom_point(color = "tomato")

color point in the plot by sex

ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point()

set the theme at the end of your plot

ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  theme_gray()

Labels Labels can be added to various components of a plot using the labs() function

ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender distribution",
       x = "Gender",
       y = "Number of respondent",
       caption = "CDAM expert, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

Add caption

ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent",
       caption = "Source: CDAM Experts, 2025") +
  theme_classic()

Align the title to the center

ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent",
       caption = "Soure: CDAM Experts, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

Facets ** make use of the ~ operator

ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  facet_wrap(~ sex) +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent",
       caption = "Source: CDAM Experts, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

Bar Plot: Display the count or summary of categorical data.

Example: Bar plot of ‘gender’ counts

ggplot(data= gss, aes(x = sex)) +
  geom_bar(fill = "red") +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent") +
  theme_classic()

3. Customizing Plots

Adding Titles and Labels***Customize the title, axis labels, and legend.

———————————————–

Example: Scatter plot with customizations

p1 = ggplot(gss, aes(x = age, y = income, color = sex)) +
  geom_point(size = 3) +
  labs(title = "Income vs Age by Gender",
       x = "Age (years)",
       y = "Income ($)",
       color = "Gender") +
  theme_classic()

Changing Themes***Use different themes for better aesthetics.

Example: Scatter plot with a dark theme

p2 = ggplot(gss, aes(x = age, y = income)) +
  geom_point(color = "tomato") +
  labs(title = "Income vs Age", 
       x = "Age", 
       y = "Income") +
  theme_classic()

Modifying Colors, Shapes, and Sizes

Enhance visualizations by modifying aesthetics.

Example 1: Scatter plot with customized colors and sizes

p3 = ggplot(gss, aes(x = age, y = income, color = sex, size = income)) +
  geom_point(alpha = 0.6) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "Income vs Age by Gender", 
       x = "Age", 
       y = "Income") +
  theme_classic()

Easily combines multiple plots into cohesive layouts

# Load the library
library(patchwork)

combine_1 <- (p1| p2 | p3) +
  plot_annotation(title = "Combined Plots Example")


# Display the combined plot
print(combine_1)

# Exporting plots

plot_1 <-ggsave("Plot_1.png", width = 10, height = 6, dpi = 300)

# combine the plots
library(patchwork)

p1 = ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender distribution",
       x = "Gender",
       y = "Number of respondent",
       caption = "CDAM expert, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

p2 = ggplot(data= gss, aes(x = sex)) +
  geom_bar(fill = "red") +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent") +
  theme_classic()

p3 = ggplot(data = gss, aes(x = sex, y = income)) +
  geom_boxplot(fill = "purple") +
  labs(title = "Income by Gender", 
       x = "Gender", 
       y = "Income") +
  theme_bw()

combine <- (p1| p2 | p3) +
  plot_annotation(title = "Combined Plots Example")

print(combine)

plot <- ggsave("Plot.png", width = 10, height = 6, dpi = 300)

Adds dynamic animations to your visualizations

# Load required libraries
library(gganimate) 

p4 = ggplot(gss, aes(x = degree)) +
  geom_bar(fill = "red") +
  labs(title = "Gender Distribution", 
       x = "degree", 
       y = "No. of respondent") +
  theme_classic()

# Add animation with gganimate
animated_plot <- p4 +
  transition_time (age) +  # Animate over 'time' variable
  ease_aes('linear')       # Smooth linear transitions

# Save or display animation
anim_save("Bar_chart.gif", animation = animated_plot) # Save as GIF


Inserting image 1 at 0.00s (1%)...
Inserting image 2 at 0.10s (2%)...
Inserting image 3 at 0.20s (3%)...
Inserting image 4 at 0.30s (4%)...
Inserting image 5 at 0.40s (5%)...
Inserting image 6 at 0.50s (6%)...
Inserting image 7 at 0.60s (7%)...
Inserting image 8 at 0.70s (8%)...
Inserting image 9 at 0.80s (9%)...
Inserting image 10 at 0.90s (10%)...
Inserting image 11 at 1.00s (11%)...
Inserting image 12 at 1.10s (12%)...
Inserting image 13 at 1.20s (13%)...
Inserting image 14 at 1.30s (14%)...
Inserting image 15 at 1.40s (15%)...
Inserting image 16 at 1.50s (16%)...
Inserting image 17 at 1.60s (17%)...
Inserting image 18 at 1.70s (18%)...
Inserting image 19 at 1.80s (19%)...
Inserting image 20 at 1.90s (20%)...
Inserting image 21 at 2.00s (21%)...
Inserting image 22 at 2.10s (22%)...
Inserting image 23 at 2.20s (23%)...
Inserting image 24 at 2.30s (24%)...
Inserting image 25 at 2.40s (25%)...
Inserting image 26 at 2.50s (26%)...
Inserting image 27 at 2.60s (27%)...
Inserting image 28 at 2.70s (28%)...
Inserting image 29 at 2.80s (29%)...
Inserting image 30 at 2.90s (30%)...
Inserting image 31 at 3.00s (31%)...
Inserting image 32 at 3.10s (32%)...
Inserting image 33 at 3.20s (33%)...
Inserting image 34 at 3.30s (34%)...
Inserting image 35 at 3.40s (35%)...
Inserting image 36 at 3.50s (36%)...
Inserting image 37 at 3.60s (37%)...
Inserting image 38 at 3.70s (38%)...
Inserting image 39 at 3.80s (39%)...
Inserting image 40 at 3.90s (40%)...
Inserting image 41 at 4.00s (41%)...
Inserting image 42 at 4.10s (42%)...
Inserting image 43 at 4.20s (43%)...
Inserting image 44 at 4.30s (44%)...
Inserting image 45 at 4.40s (45%)...
Inserting image 46 at 4.50s (46%)...
Inserting image 47 at 4.60s (47%)...
Inserting image 48 at 4.70s (48%)...
Inserting image 49 at 4.80s (49%)...
Inserting image 50 at 4.90s (50%)...
Inserting image 51 at 5.00s (51%)...
Inserting image 52 at 5.10s (52%)...
Inserting image 53 at 5.20s (53%)...
Inserting image 54 at 5.30s (54%)...
Inserting image 55 at 5.40s (55%)...
Inserting image 56 at 5.50s (56%)...
Inserting image 57 at 5.60s (57%)...
Inserting image 58 at 5.70s (58%)...
Inserting image 59 at 5.80s (59%)...
Inserting image 60 at 5.90s (60%)...
Inserting image 61 at 6.00s (61%)...
Inserting image 62 at 6.10s (62%)...
Inserting image 63 at 6.20s (63%)...
Inserting image 64 at 6.30s (64%)...
Inserting image 65 at 6.40s (65%)...
Inserting image 66 at 6.50s (66%)...
Inserting image 67 at 6.60s (67%)...
Inserting image 68 at 6.70s (68%)...
Inserting image 69 at 6.80s (69%)...
Inserting image 70 at 6.90s (70%)...
Inserting image 71 at 7.00s (71%)...
Inserting image 72 at 7.10s (72%)...
Inserting image 73 at 7.20s (73%)...
Inserting image 74 at 7.30s (74%)...
Inserting image 75 at 7.40s (75%)...
Inserting image 76 at 7.50s (76%)...
Inserting image 77 at 7.60s (77%)...
Inserting image 78 at 7.70s (78%)...
Inserting image 79 at 7.80s (79%)...
Inserting image 80 at 7.90s (80%)...
Inserting image 81 at 8.00s (81%)...
Inserting image 82 at 8.10s (82%)...
Inserting image 83 at 8.20s (83%)...
Inserting image 84 at 8.30s (84%)...
Inserting image 85 at 8.40s (85%)...
Inserting image 86 at 8.50s (86%)...
Inserting image 87 at 8.60s (87%)...
Inserting image 88 at 8.70s (88%)...
Inserting image 89 at 8.80s (89%)...
Inserting image 90 at 8.90s (90%)...
Inserting image 91 at 9.00s (91%)...
Inserting image 92 at 9.10s (92%)...
Inserting image 93 at 9.20s (93%)...
Inserting image 94 at 9.30s (94%)...
Inserting image 95 at 9.40s (95%)...
Inserting image 96 at 9.50s (96%)...
Inserting image 97 at 9.60s (97%)...
Inserting image 98 at 9.70s (98%)...
Inserting image 99 at 9.80s (99%)...
Inserting image 100 at 9.90s (100%)...
Encoding to gif... done!

# Load the libraries
library(tm)
library(wordcloud)
library(ggplot2)

# Sample text data
docs <- Corpus(VectorSource(c("Data visualization is an essential skill for analysts.",
                              "Text mining techniques help derive insights from text data.",
                              "Word clouds and bar charts are popular text visualization methods.")))

#Text preprocessing

docs <- tm_map(docs, content_transformer(tolower))  # Convert to lowercase
docs <- tm_map(docs, removePunctuation)             # Remove punctuation
docs <- tm_map(docs, removeNumbers)                 # Remove numbers
docs <- tm_map(docs, removeWords, stopwords("en"))  # Remove stopwords
docs <- tm_map(corpus, stripWhitespace)              # Remove extra whitespace

Error: object 'corpus' not found

Word cloud visualization


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuY3VzdG9tX2NvbG9ycyA8LSBjKFwiI0ZGNTczM1wiLCBcIiMzM0ZGNTdcIiwgXCIjMzM1N0ZGXCIsIFwiI0YzRkYzM1wiLCBcIiNGRjMzRjNcIiwgXCIjMzNGRkY2XCIsIFwiI0ZGQTUwMFwiLCBcIiM4MDAwODBcIilcbndvcmRjbG91ZCh3b3JkcyA9IGRmJHdvcmQsIFxuICAgICAgICAgIGZyZXEgPSBkZiRmcmVxLCBcbiAgICAgICAgICBtaW4uZnJlcSA9IDEsXG4gICAgICAgICAgbWF4LndvcmRzPTEwMCwgXG4gICAgICAgICAgcmFuZG9tLm9yZGVyPUZBTFNFLCBcbiAgICAgICAgICBjb2xvcnMgPSBjdXN0b21fY29sb3JzKVxuYGBgIn0= -->

```r
custom_colors <- c(\#FF5733\, \#33FF57\, \#3357FF\, \#F3FF33\, \#FF33F3\, \#33FFF6\, \#FFA500\, \#800080\)
wordcloud(words = df$word, 
          freq = df$freq, 
          min.freq = 1,
          max.words=100, 
          random.order=FALSE, 
          colors = custom_colors)
```

<!-- rnb-source-end -->

```r
custom_colors <- c(\#FF5733\, \#33FF57\, \#3357FF\, \#F3FF33\, \#FF33F3\, \#33FFF6\, \#FFA500\, \#800080\)
wordcloud(words = df$word, 
          freq = df$freq, 
          min.freq = 1,
          max.words=100, 
          random.order=FALSE, 
          colors = custom_colors)


<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1wbG90LWJlZ2luIGV5Sm9aV2xuYUhRaU9qUXpNaTQyTXpJNUxDSjNhV1IwYUNJNk56QXdMQ0prY0draU9pMHhMQ0p6YVhwbFgySmxhR0YyYVc5eUlqb3dMQ0pqYjI1a2FYUnBiMjV6SWpwYlhYMD0gLS0+XG5cbjxpbWcgc3JjPVxcZGF0YTppbWFnZS9wbmc7YmFzZTY0XG4ifQ== -->

<img src=:image/png;base64




<!-- rnb-output-end -->

<!-- rnb-output-begin {"data":"\n<!-- rnb-plot-begin -->\n\n<img src=\"data:image/png;base64,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\" />\n\n<!-- rnb-plot-end -->\n"} -->


<!-- rnb-plot-begin -->

<img src="data:image/png;base64,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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


# Bar chart visualization

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

ggplot(df[1:10,], aes(x = reorder(word, freq), y = freq, fill = word)) +
  geom_bar(stat = \identity\) +
  coord_flip() +
  theme_minimal() +
  labs(title = \Top Words in Text Data\, x = \Words\, y = \Frequency\)




<!-- rnb-output-end -->

<!-- rnb-output-begin 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 -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxuZ2dwbG90KGRmWzE6MTAsXSwgYWVzKHggPSByZW9yZGVyKHdvcmQsIGZyZXEpLCB5ID0gZnJlcSwgZmlsbCA9IHdvcmQpKSArXG4gIGdlb21fYmFyKHN0YXQgPSBcXGlkZW50aXR5XFwpICtcbiAgY29vcmRfZmxpcCgpICtcbiAgdGhlbWVfbWluaW1hbCgpICtcbiAgbGFicyh0aXRsZSA9IFxcVG9wIFdvcmRzIGluIFRleHQgRGF0YVxcLCB4ID0gXFxXb3Jkc1xcLCB5ID0gXFxGcmVxdWVuY3lcXClcbmBgYFxuYGBgIn0= -->

```r
```r
ggplot(df[1:10,], aes(x = reorder(word, freq), y = freq, fill = word)) +
  geom_bar(stat = \identity\) +
  coord_flip() +
  theme_minimal() +
  labs(title = \Top Words in Text Data\, x = \Words\, y = \Frequency\)
```
```

<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1wbG90LWJlZ2luIGV5Sm9aV2xuYUhRaU9qUXpNaTQyTXpJNUxDSjNhV1IwYUNJNk56QXdMQ0prY0draU9pMHhMQ0p6YVhwbFgySmxhR0YyYVc5eUlqb3dMQ0pqYjI1a2FYUnBiMjV6SWpwYlhYMD0gLS0+XG5cbjxpbWcgc3JjPVxcZGF0YTppbWFnZS9wbmc7YmFzZTY0XG4ifQ== -->

```

<!-- rnb-plot-begin eyJoZWlnaHQiOjQzMi42MzI5LCJ3aWR0aCI6NzAwLCJkcGkiOi0xLCJzaXplX2JlaGF2aW9yIjowLCJjb25kaXRpb25zIjpbXX0= -->

<img src=\data:image/png;base64
```



<!-- rnb-output-end -->

<!-- rnb-output-begin {"data":"\n<!-- rnb-plot-begin -->\n\n<img src=\"data:image/png;base64,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\" />\n\n<!-- rnb-plot-end -->\n"} -->


<!-- rnb-plot-begin -->

<img src="data:image/png;base64,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" />

<!-- rnb-plot-end -->


<!-- rnb-output-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


# Cumulative Frequency Plot

<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-output-begin 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 -->

word_df <- word_df %>% 
  mutate(cum_freq = cumsum(freq)/sum(freq))

ggplot(word_df, aes(x = reorder(word, -freq), y = cum_freq)) +
  geom_line(group = 1, color = \blue\, size = 1) +
  geom_point(size = 3) +
  labs(title = \Cumulative Word Frequency\,
       x = \Words\,
       y = \Cumulative Frequency\) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

````

```r
word_df <- word_df %>% 
  mutate(cum_freq = cumsum(freq)/sum(freq))

ggplot(word_df, aes(x = reorder(word, -freq), y = cum_freq)) +
  geom_line(group = 1, color = \blue\, size = 1) +
  geom_point(size = 3) +
  labs(title = \Cumulative Word Frequency\,
       x = \Words\,
       y = \Cumulative Frequency\) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))


<!-- rnb-source-end -->


<!-- rnb-output-end -->

<!-- rnb-output-begin eyJkYXRhIjoiXG48IS0tIHJuYi1wbG90LWJlZ2luIGV5Sm9aV2xuYUhRaU9qUXpNaTQyTXpJNUxDSjNhV1IwYUNJNk56QXdMQ0prY0draU9pMHhMQ0p6YVhwbFgySmxhR0YyYVc5eUlqb3dMQ0pqYjI1a2FYUnBiMjV6SWpwYlhYMD0gLS0+XG5cbjxpbWcgc3JjPVxcZGF0YTppbWFnZS9wbmc7YmFzZTY0XG4ifQ== -->

<img src=:image/png;base64 ```

———————————————–

Homework

———————————————–

Create Visualizations for “German credit”Dataset :

Use the ggplot2 package to create visualizations for the dataset

Perform the following tasks:

Create a scatter plot to visualize the relationship between two continuous variables.

Create a bar plot to display the count or summary of a categorical variable.

Create a histogram to show the distribution of a single variable.

Create a boxplot to summarize the distribution of a continuous variable across categories.

Customize your plots with titles, labels, themes, and aesthetic modifications.

---
title: "R Notebook"
output: 
  html_notebook: 
    highlight: espresso
    theme: readable
---


 <div style='text-align:center;margin:auto;display:block:'><IMG  src=  "C:\Users\Admin\Documents\2025_R_TRAINING\inrod_R4DS.PNG"></div>
 
## -------------------------------------------------------------------------------------------- 
<div style='text-align:center;font-weight:bold;font-size:25px;color:blue;'>Facilitator: D. K. Muriithi</div>

 <div style='text-align:center;margin:auto;display:block:'><IMG  src=  "C:\Users\Admin\Documents\2025_R_TRAINING\QR_3R.PNG"></div>

## -------------------------------------------------------------------------------------------- 

# Session 4: Data Visualization with ggplot2

By the end of this session, you will be able to:

* Understand the grammar of graphics and how ggplot2 works.

* Create basic visualizations including scatter plots, bar charts, histograms, and boxplots.

* Customize visualizations using titles, axis labels, colors, and themes.

* Use ggplot2 in your exploratory data analysis (EDA) workflow.

## Data Visualization

Data visualization helps in understanding patterns, trends, and relationships in data.

It is a crucial element in scientific research, enabling researchers to interpret and communicate their results effectively

## Types of Data Visualization

## 1. Univariate Data Visualizations (Single Variable)

✔️ Histogram: Used for understanding the distribution of a single variable.

✔️ Box Plot: Used for Detecting outliers and understanding the spread of data.

## 2. Bivariate Data Visualizations (Two Variables)

✔️ Scatter Plot: Used for understanding relationships between two numerical variables.

✔️ Line Plot: Used for showing trends over time or continuous data.

✔️ Bar Chart: Used for comparing categorical data.

## 3. Multivariate Data Visualizations (More than Two Variables)

✔️ Heatmap: Used for visualizing correlations between multiple numerical variables.

✔️ Pair Plot: Used for visualizing pairwise relationships in the dataset.

✔️ Violin Plot: Used for understanding the distribution of a variable across categories.

## 4. Specialized Data Visualizations

✔️ Pie Chart: Used for representing proportions.

✔️ Bubble Chart: Used for adding a third variable to a scatter plot(Comparing three numerical variables)

✔️ Word Cloud: used to highlight keywords, trends, or themes in textual data (Text Analysis : Highlighting key terms in articles, reviews, or social media posts.)

## 5. Time Series Visualizations

✔️ Time Series Line plot: Used to analyze trends, patterns, or changes in data over a continuous period (e.g., days, months, years).

✔️ Autocorrelation Plot: Used for finding patterns in time series data.

## Best Practices

✅ Choose the Right Chart Type (e.g., bar charts for categories, line charts for trends).

✅ Follow Design Principles (simplicity, consistency, accessibility).

✅ Use Storytelling to highlight key insights and structure visuals logically.

✅ Avoid Common Pitfalls (misleading scales, cluttered visuals, unnecessary 3D charts).

## 1. Introduction to ggplot2

**What is ggplot2?**

* A powerful plotting system for R, based on the Grammar of Graphics .

* Developed by Hadley Wickham .

* Allows building complex, publication-quality graphics in layers.

**Why Use ggplot2?**

* Consistent syntax across plot types

* Highly customizable

* Supports faceting, grouping, and mapping aesthetics

* Produces professional-quality visuals

## Set a working Directory
```{r}
setwd("~/2025_R_TRAINING")

```

## 1. Install and Load necessary libraries
```{r}
library(ggplot2)       ## for data visualization
library(tidyverse)     ## for data manipulation
library(psych)         ## for description of  data
library(ggdist)        ## Enhance visual representation of distribution & uncertainty
library(gganimate)     ## adds dynamic animations to your visualizations
library(patchwork)     ## Easily combines multiple plots into cohesive layouts
library(rio)           ## For easy data import, export(saving) and conversion
```


## 2. Import the dataset
```{r}
#gss = read.csv("GSSsubset.csv")     # load csv dataset

gss = import("GSSsubset.csv")        # alternative way to lad csv dataset

#View(gss)        # for viewing the dataset
```


## Exploratory data analysis (EDA)

Before we start visualizing our data, we need to understand the characteristics of our data. The goal is to get an idea of the data structure and to understand the relationships between variables.

Here are some functions that can help us understand the structure of our data:

```{r}
head(gss)         # for the 1st few rows in the dataset
names(gss)        # for features/ variable names in the dataset
dim(gss)          # for dimensions of dataset
str(gss)          # for structure of dataset
summary(gss)      # for summary of descriptive statistics
describe(gss)     # for descriptive statistics
```

## 3. Data Visualization with ggplot2

The "Grammar of Graphics" is a powerful concept that ggplot2 in R is built on. 

It breaks down the process of data visualization into layers, making it easier to customize and understand how to build effective charts. 

Layers are added using the ‘+’ operator.


## Essential layers(Components of a ggplot) used to create a plot:

1️ **Data:** The foundation, where you start by defining the data set.

2️ **Aesthetics:** Map variables to visual aspects like color, size, and position.

3️⃣ **Geometries:** Specify the type of plot you want, such as bar, line, or scatter.

4️⃣ **Facets:** Create subplots for different subsets of your data.

5️⃣ **Statistics:** Add statistical transformations, like mean lines or trend lines.

6️⃣ **Coordinates:** Control the plot’s coordinate system, such as flipping axes.

7️⃣ **Theme:** Adjust the overall appearance, like grid lines, font styles, and background.

 <div style='text-align:center;margin:auto;display:block:'><IMG  src=  "C:\Users\Admin\Documents\2025_R_TRAINING\ggplot2_chart.PNG"></div>
 

## The data
All ggplot2 plots require a data frame as input. Just running this line will produce a blank plot because we have not stated which elements from the data we want to visualize or how we want to visualize them.

```{r}
ggplot(data = gss)

```
## The aesthetics
Next, we need to specify the visual properties of the plot that are determined by the data. The aesthetics are specified using the aes() function. The output should now produce a blank plot but with determined visual properties (e.g., axes labels).
```{r}
ggplot(data = gss, aes(x = income))
```

## The geometries
Finally, we need to specify the visual representation of the data. The geometries are specified using the geom_*() function. There are many different types of geometries that can be used in ggplot2. We will use geom_point() in this example and we will append it to the previous plot using the + operator. The output should now produce a plot with the specified visual representation of the data.

## Histogram: Show the distribution of a single variable.

## Example: Histogram of 'income'

## ***use geom_histogram()***  ✔️  Histogram: Used for understanding the distribution of a single variable.

```{r}
ggplot(gss, aes(x = income)) +
  geom_histogram(binwidth = 10000, fill = "orange", color = "black") +
  labs(title = "Income Distribution", 
       x = "Income", 
       y = "Frequency") +
  theme_light()

```

```{r}
ggplot(gss, aes(x = income)) +
  geom_histogram(bins = 20, fill = "orange", color = "black") +
  labs(title = "Income Distribution", 
       x = "Income", 
       y = "Frequency") +
  theme_classic()
```


# Boxplot: Summarize the distribution of a continuous variable across categories.

# Example: Boxplot of 'income' by 'gender'
```{r}
ggplot(data = gss, aes(x = sex, y = income)) +
  geom_boxplot(fill = "purple") +
  labs(title = "Income by Gender", 
       x = "Gender", 
       y = "Income") +
  theme_bw()
```

## Scatter Plot: Visualize the relationship between two continuous variables.

## Example: Scatter plot of 'income' vs 'age'

```{r}
ggplot(data = gss, aes(x = age, y =log(income))) +
  geom_point()
```

# change the color of point to my choice
```{r}
ggplot(data = gss, aes(x = age, y =income)) +
  geom_point(color = "tomato")
```


# color point in the plot by sex
```{r}
ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point()
```

# set the theme at the end of your plot
```{r}
ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  theme_gray()
```

# Labels **Labels can be added to various components of a plot using the labs() function**
```{r}
ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender distribution",
       x = "Gender",
       y = "Number of respondent",
       caption = "CDAM expert, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))
```

# Add caption
```{r}
ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent",
       caption = "Source: CDAM Experts, 2025") +
  theme_classic()

```

# Align the title to the center
```{r}
ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent",
       caption = "Soure: CDAM Experts, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

```

# Facets ** make use of the ~ operator
```{r}
ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  facet_wrap(~ sex) +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent",
       caption = "Source: CDAM Experts, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))
```


## Bar Plot: Display the count or summary of categorical data.

# Example: Bar plot of 'gender' counts
```{r}
ggplot(data= gss, aes(x = sex)) +
  geom_bar(fill = "red") +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent") +
  theme_classic()
```


# 3. Customizing Plots

# Adding Titles and Labels***Customize the title, axis labels, and legend.
# -----------------------------------------------
# Example: Scatter plot with customizations
```{r}
p1 = ggplot(gss, aes(x = age, y = income, color = sex)) +
  geom_point(size = 3) +
  labs(title = "Income vs Age by Gender",
       x = "Age (years)",
       y = "Income ($)",
       color = "Gender") +
  theme_classic()
```

# Changing Themes***Use different themes for better aesthetics.

# Example: Scatter plot with a dark theme
```{r}
p2 = ggplot(gss, aes(x = age, y = income)) +
  geom_point(color = "tomato") +
  labs(title = "Income vs Age", 
       x = "Age", 
       y = "Income") +
  theme_classic()
```

# Modifying Colors, Shapes, and Sizes
# Enhance visualizations by modifying aesthetics.

# Example 1: Scatter plot with customized colors and sizes
```{r}
p3 = ggplot(gss, aes(x = age, y = income, color = sex, size = income)) +
  geom_point(alpha = 0.6) +
  scale_color_manual(values = c("red", "blue")) +
  labs(title = "Income vs Age by Gender", 
       x = "Age", 
       y = "Income") +
  theme_classic()
```

# Easily combines multiple plots into cohesive layouts
```{r}
# Load the library
library(patchwork)

combine_1 <- (p1| p2 | p3) +
  plot_annotation(title = "Combined Plots Example")

# Display the combined plot
print(combine_1)

# Exporting plots

plot_1 <-ggsave("Plot_1.png", width = 10, height = 6, dpi = 300)

```

```{r}
# combine the plots
library(patchwork)

p1 = ggplot(data = gss, aes(x = age, y =income, color = sex)) +
  geom_point() +
  labs(title = "Gender distribution",
       x = "Gender",
       y = "Number of respondent",
       caption = "CDAM expert, 2025") +
  theme_classic() +
  theme(plot.title = element_text(hjust = 0.5))

p2 = ggplot(data= gss, aes(x = sex)) +
  geom_bar(fill = "red") +
  labs(title = "Gender Distribution", 
       x = "Gender", 
       y = "No. of respondent") +
  theme_classic()

p3 = ggplot(data = gss, aes(x = sex, y = income)) +
  geom_boxplot(fill = "purple") +
  labs(title = "Income by Gender", 
       x = "Gender", 
       y = "Income") +
  theme_bw()

combine <- (p1| p2 | p3) +
  plot_annotation(title = "Combined Plots Example")
print(combine)

plot <- ggsave("Plot.png", width = 10, height = 6, dpi = 300)

```

## Adds dynamic animations to your visualizations
```{r}
# Load required libraries
library(gganimate) 

p4 = ggplot(gss, aes(x = degree)) +
  geom_bar(fill = "red") +
  labs(title = "Gender Distribution", 
       x = "degree", 
       y = "No. of respondent") +
  theme_classic()

# Add animation with gganimate
animated_plot <- p4 +
  transition_time (age) +  # Animate over 'time' variable
  ease_aes('linear')       # Smooth linear transitions

# Save or display animation
anim_save("Bar_chart.gif", animation = animated_plot) # Save as GIF
```


```{r}
# Load the libraries
library(tm)
library(wordcloud)
library(ggplot2)

# Sample text data
docs <- Corpus(VectorSource(c("Data visualization is an essential skill for analysts.",
                              "Text mining techniques help derive insights from text data.",
                              "Word clouds and bar charts are popular text visualization methods.")))
```

#Text preprocessing
```{r}
docs <- tm_map(docs, content_transformer(tolower))  # Convert to lowercase
docs <- tm_map(docs, removePunctuation)             # Remove punctuation
docs <- tm_map(docs, removeNumbers)                 # Remove numbers
docs <- tm_map(docs, removeWords, stopwords("en"))  # Remove stopwords
docs <- tm_map(corpus, stripWhitespace)              # Remove extra whitespace

dtm <- TermDocumentMatrix(docs)
matrix <- as.matrix(dtm)
word_freqs <- sort(rowSums(matrix), decreasing=TRUE)
df <- data.frame(word = names(word_freqs), freq = word_freqs)
```

# Word cloud visualization
```{r}
custom_colors <- c("#FF5733", "#33FF57", "#3357FF", "#F3FF33", "#FF33F3", "#33FFF6", "#FFA500", "#800080")
wordcloud(words = df$word, 
          freq = df$freq, 
          min.freq = 1,
          max.words=100, 
          random.order=FALSE, 
          colors = custom_colors)
```

# Bar chart visualization
```{r}
ggplot(df[1:10,], aes(x = reorder(word, freq), y = freq, fill = word)) +
  geom_bar(stat = "identity") +
  coord_flip() +
  theme_minimal() +
  labs(title = "Top Words in Text Data", x = "Words", y = "Frequency")
```

# Cumulative Frequency Plot
```{r}
word_df <- word_df %>% 
  mutate(cum_freq = cumsum(freq)/sum(freq))

ggplot(word_df, aes(x = reorder(word, -freq), y = cum_freq)) +
  geom_line(group = 1, color = "blue", size = 1) +
  geom_point(size = 3) +
  labs(title = "Cumulative Word Frequency",
       x = "Words",
       y = "Cumulative Frequency") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))
```

# -----------------------------------------------
# Homework
# -----------------------------------------------
# Create Visualizations for "German credit"Dataset :
# Use the ggplot2 package to create visualizations for the dataset 
# Perform the following tasks:
# Create a scatter plot to visualize the relationship between two continuous variables.
# Create a bar plot to display the count or summary of a categorical variable.
# Create a histogram to show the distribution of a single variable.
# Create a boxplot to summarize the distribution of a continuous variable across categories.
# Customize your plots with titles, labels, themes, and aesthetic modifications.




```{r}

```


























R Notebook

——————————————————————————————–

——————————————————————————————–

Session 4: Data Visualization with ggplot2

Data Visualization

Types of Data Visualization

1. Univariate Data Visualizations (Single Variable)

2. Bivariate Data Visualizations (Two Variables)

3. Multivariate Data Visualizations (More than Two Variables)

4. Specialized Data Visualizations

5. Time Series Visualizations

Best Practices

1. Introduction to ggplot2

Set a working Directory

1. Install and Load necessary libraries

2. Import the dataset

Exploratory data analysis (EDA)

3. Data Visualization with ggplot2

Essential layers(Components of a ggplot) used to create a plot:

The data

The aesthetics

The geometries

Histogram: Show the distribution of a single variable.

Example: Histogram of ‘income’

use geom_histogram() ✔️ Histogram: Used for understanding the distribution of a single variable.

Boxplot: Summarize the distribution of a continuous variable across categories.

Example: Boxplot of ‘income’ by ‘gender’

Scatter Plot: Visualize the relationship between two continuous variables.

Example: Scatter plot of ‘income’ vs ‘age’

change the color of point to my choice

color point in the plot by sex

set the theme at the end of your plot

Labels Labels can be added to various components of a plot using the labs() function

Add caption

Align the title to the center

Facets ** make use of the ~ operator

Bar Plot: Display the count or summary of categorical data.

Example: Bar plot of ‘gender’ counts

3. Customizing Plots

Adding Titles and Labels***Customize the title, axis labels, and legend.

———————————————–

Example: Scatter plot with customizations

Changing Themes***Use different themes for better aesthetics.

Example: Scatter plot with a dark theme

Modifying Colors, Shapes, and Sizes

Enhance visualizations by modifying aesthetics.

Example 1: Scatter plot with customized colors and sizes

Easily combines multiple plots into cohesive layouts

Adds dynamic animations to your visualizations

Word cloud visualization

———————————————–

Homework

———————————————–

Create Visualizations for “German credit”Dataset :

Use the ggplot2 package to create visualizations for the dataset

Perform the following tasks:

Create a scatter plot to visualize the relationship between two continuous variables.

Create a bar plot to display the count or summary of a categorical variable.

Create a histogram to show the distribution of a single variable.

Create a boxplot to summarize the distribution of a continuous variable across categories.

Customize your plots with titles, labels, themes, and aesthetic modifications.