Main Components of RStudio
- Source Editor
- Console
- Environment/History
- Files/Plots/Packages/Help
Correlation Analysis in R Studio Using Agricultural Dataset
Abhishek Thakur
2026-05-20
Load Required Packages
library(ggplot2)
library(dplyr)
library(corrplot)
library(GGally)
library(psych)
library(knitr)Introduction
Correlation analysis is one of the most important statistical techniques used in agricultural research. It helps measure the strength and direction of relationship among variables such as fertilizer application, rainfall, irrigation, temperature, and crop yield.
Correlation analysis helps researchers understand how agricultural variables are associated with one another and supports scientific decision-making in crop management and agricultural planning.
R Studio provides a powerful and user-friendly platform for performing correlation analysis, graphical visualization, and interpretation of agricultural datasets.
Objectives
The objectives of this tutorial are:
- To understand correlation analysis
- To create agricultural datasets in R Studio
- To compute correlation coefficients
- To visualize correlation matrices
- To perform significance testing of correlation
- To generate graphical representations of relationships among variables
Software Requirements
| Software | Purpose |
|---|---|
| R Software | Statistical Computing |
| RStudio | Integrated Development Environment |
Recommended Versions
- R version 4.0 or above
- Latest version of RStudio
Introduction to R and RStudio
R is an open-source programming language widely used for statistical analysis, data visualization, and predictive modelling.
RStudio is an Integrated Development Environment (IDE) for R.
Agricultural Dataset Description
In this tutorial, a hypothetical agricultural dataset is used to study the relationship among:
- Fertilizer application
- Rainfall
- Irrigation
- Crop yield
Variables Used
| Variable | Description | Unit |
|---|---|---|
| fertilizer | Amount of fertilizer applied | kg/ha |
| rainfall | Seasonal rainfall received | mm |
| irrigation | Irrigation hours supplied | hours |
| yield | Crop yield | quintal/ha |
Creating Agricultural Dataset
cropdata <- data.frame(
fertilizer = c(40,42,38,50,45,47,43,39,41,44,
46,48,37,36,49,51,52,53,35,34,
55,56,57,58,59,60,61,62,63,64,
65,66,67,68,69,70,71,72,73,74),
rainfall = c(820,790,760,880,850,840,810,770,800,830,
860,870,750,740,890,900,910,920,730,720,
930,940,950,960,970,980,990,1000,1010,1020,
1030,1040,1050,1060,1070,1080,1090,1100,1110,1120),
irrigation = c(12,13,11,15,14,14,13,12,13,14,
15,15,11,10,16,16,17,17,10,9,
18,18,19,19,20,20,21,21,22,22,
23,23,24,24,25,25,26,26,27,27),
yield = c(28,30,26,36,33,34,31,27,29,32,
35,36,25,24,38,39,40,41,23,22,
42,43,44,45,46,47,48,49,50,51,
52,53,54,55,56,57,58,59,60,61)
)
cropdata## fertilizer rainfall irrigation yield
## 1 40 820 12 28
## 2 42 790 13 30
## 3 38 760 11 26
## 4 50 880 15 36
## 5 45 850 14 33
## 6 47 840 14 34
## 7 43 810 13 31
## 8 39 770 12 27
## 9 41 800 13 29
## 10 44 830 14 32
## 11 46 860 15 35
## 12 48 870 15 36
## 13 37 750 11 25
## 14 36 740 10 24
## 15 49 890 16 38
## 16 51 900 16 39
## 17 52 910 17 40
## 18 53 920 17 41
## 19 35 730 10 23
## 20 34 720 9 22
## 21 55 930 18 42
## 22 56 940 18 43
## 23 57 950 19 44
## 24 58 960 19 45
## 25 59 970 20 46
## 26 60 980 20 47
## 27 61 990 21 48
## 28 62 1000 21 49
## 29 63 1010 22 50
## 30 64 1020 22 51
## 31 65 1030 23 52
## 32 66 1040 23 53
## 33 67 1050 24 54
## 34 68 1060 24 55
## 35 69 1070 25 56
## 36 70 1080 25 57
## 37 71 1090 26 58
## 38 72 1100 26 59
## 39 73 1110 27 60
## 40 74 1120 27 61Structure of Dataset
str(cropdata)## 'data.frame': 40 obs. of 4 variables:
## $ fertilizer: num 40 42 38 50 45 47 43 39 41 44 ...
## $ rainfall : num 820 790 760 880 850 840 810 770 800 830 ...
## $ irrigation: num 12 13 11 15 14 14 13 12 13 14 ...
## $ yield : num 28 30 26 36 33 34 31 27 29 32 ...Summary Statistics
summary(cropdata)## fertilizer rainfall irrigation yield
## Min. :34.00 Min. : 720.0 Min. : 9.00 Min. :22.00
## 1st Qu.:43.75 1st Qu.: 827.5 1st Qu.:13.75 1st Qu.:31.75
## Median :54.00 Median : 925.0 Median :17.50 Median :41.50
## Mean :54.00 Mean : 923.5 Mean :17.93 Mean :41.48
## 3rd Qu.:64.25 3rd Qu.:1022.5 3rd Qu.:22.25 3rd Qu.:51.25
## Max. :74.00 Max. :1120.0 Max. :27.00 Max. :61.00Correlation Analysis
Correlation analysis measures the strength and direction of relationship among variables.
The correlation coefficient ranges from:
| Correlation Value | Interpretation |
|---|---|
| +1 | Perfect Positive Correlation |
| 0 | No Correlation |
| -1 | Perfect Negative Correlation |
Computing Correlation Matrix
correlation_matrix <- cor(cropdata)
correlation_matrix## fertilizer rainfall irrigation yield
## fertilizer 1.0000000 0.9974009 0.9961273 0.9992096
## rainfall 0.9974009 1.0000000 0.9946824 0.9981105
## irrigation 0.9961273 0.9946824 1.0000000 0.9971846
## yield 0.9992096 0.9981105 0.9971846 1.0000000Interpretation of Correlation Matrix
- Positive values indicate positive association
- Negative values indicate negative association
- Values close to +1 indicate strong positive correlation
- Values close to -1 indicate strong negative correlation
Visualizing Correlation Matrix
corrplot(correlation_matrix,
method = "circle",
type = "upper",
tl.col = "black",
tl.srt = 45)
Pairwise Scatter Plot Matrix
ggpairs(cropdata)
Correlation Test Between Fertilizer and Yield
cor.test(cropdata$fertilizer,
cropdata$yield)##
## Pearson's product-moment correlation
##
## data: cropdata$fertilizer and cropdata$yield
## t = 154.95, df = 38, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.998495 0.999585
## sample estimates:
## cor
## 0.9992096Interpretation of Correlation Test
The correlation test provides:
- Correlation coefficient (r)
- p-value
- Confidence interval
Decision Rule
| p-value | Interpretation |
|---|---|
| p < 0.05 | Significant Correlation |
| p > 0.05 | Non-significant Correlation |
A significant positive correlation indicates that increase in fertilizer application is associated with increase in crop yield.
Scatter Plot of Fertilizer and Yield
plot(cropdata$fertilizer,
cropdata$yield,
main = "Relationship Between Fertilizer and Crop Yield",
xlab = "Fertilizer (kg/ha)",
ylab = "Crop Yield (quintal/ha)",
pch = 19,
col = "blue")
Applications in Agriculture
Correlation analysis has wide applications in agricultural sciences.
Major Applications
- Crop yield relationship studies
- Fertilizer response analysis
- Rainfall impact assessment
- Soil nutrient relationship studies
- Irrigation management
- Climate impact studies
- Pest and disease association studies
- Agricultural forecasting
Saving Graphs
Graphs can be exported from the Plots window using:
- Export
- Save as Image
- Choose format:
- PNG
- JPEG
Conclusion
Correlation analysis is an important statistical tool for understanding relationships among agricultural variables.
R Studio provides a powerful environment for:
- Statistical analysis
- Data visualization
- Correlation studies
- Graphical interpretation
- Agricultural data analysis
The methods explained in this tutorial can be extended to advanced statistical and predictive agricultural studies.
References
- R Core Team. R: A Language and Environment for Statistical Computing.
- https://cran.r-project.org/
- https://posit.co/download/rstudio-desktop/