R Programming Basic

R programming

Author

Prof CKDash

Published

September 10, 2025

Introduction

This eBook demonstrates how to include R code, plots, tables, and real images.

Example Dataset

We will analyze pest infestation levels across crops.

library(reshape2)

library(ggplot2)

Warning: package 'ggplot2' was built under R version 4.4.3

library(knitr)

Warning: package 'knitr' was built under R version 4.4.3

pest_data <- data.frame(

  Crop = c("Tomato","Brinjal","Chili"),

  Low = c(5, 3, 6),

  Medium = c(7, 6, 4),

  High = c(2, 4, 5)

)

pest_data

     Crop Low Medium High

1  Tomato   5      7    2

2 Brinjal   3      6    4

3   Chili   6      4    5

Pest Infestation Bar Plot

df <- melt(pest_data, id.vars="Crop")

colnames(df) <- c("Crop","Pest_Level","Count")

ggplot(df, aes(x=Crop, y=Count, fill=Pest_Level)) +

  geom_bar(stat="identity", position="dodge") +

  labs(title="Pest Infestation by Crop Type",

       x="Crop Type", y="Number of Plots") +

  theme_minimal()

# Adding an Image (Fertilizer Example)

Sometimes we want to **visually explain concepts**.

Here we include an image of fertilizer and crop yield.

{width=50%}

And here is a picture of healthy crop yield:

{width=50%}

> 📌 Place your images in a folder called `images/` inside your project directory.

Correlation Example (Fertilizer vs Yield)

set.seed(123)

fertilizer <- seq(50, 250, 10)

yield <- 0.4 * fertilizer + rnorm(length(fertilizer), mean=10, sd=5)

data <- data.frame(fertilizer, yield)

ggplot(data, aes(x=fertilizer, y=yield)) +

  geom_point(color="darkgreen", size=3) +

  geom_smooth(method="lm", se=FALSE, color="blue") +

  labs(title="Correlation between Fertilizer and Crop Yield",

       x="Fertilizer (kg/ha)", y="Yield (t/ha)") +

  theme_minimal()

`geom_smooth()` using formula = 'y ~ x'

Data Table (Formatted)

knitr::kable(

  pest_data, 

  caption = "Pest Infestation Summary",

  align = "c"

)

Pest Infestation Summary

CropLowMediumHigh

Tomato572

Brinjal364

Chili645

Conclusion

This eBook combines R code, outputs, tables, plots, and real images.

You can use it for agricultural research, training, or extension materials.