ICAR Research Complex for NEH Region
Umiam, Meghalaya
Library Required Packages
library(readxl)
## Warning: package 'readxl' was built under R version 4.5.3
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.3
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.5.3
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(corrplot)
## Warning: package 'corrplot' was built under R version 4.5.3
## corrplot 0.95 loaded
library(GGally)
## Warning: package 'GGally' was built under R version 4.5.3
library(psych)
## Warning: package 'psych' was built under R version 4.5.3
##
## Attaching package: 'psych'
## The following objects are masked from 'package:ggplot2':
##
## %+%, alpha
library(knitr)
## Warning: package 'knitr' was built under R version 4.5.3
Import Data
Datanew <- read_excel("cropdata.xlsx")
head(Datanew)
## # A tibble: 6 × 4
## fertilizer rainfall irrigation yield
## <dbl> <dbl> <dbl> <dbl>
## 1 40 820 12 28
## 2 42 790 13 30
## 3 38 760 11 26
## 4 50 880 15 36
## 5 55 910 16 40
## 6 48 850 14 34
Structure of Dataset
str(Datanew)
## tibble [40 × 4] (S3: tbl_df/tbl/data.frame)
## $ fertilizer: num [1:40] 40 42 38 50 55 48 60 62 45 52 ...
## $ rainfall : num [1:40] 820 790 760 880 910 850 940 960 810 900 ...
## $ irrigation: num [1:40] 12 13 11 15 16 14 17 18 13 15 ...
## $ yield : num [1:40] 28 30 26 36 40 34 44 46 31 38 ...
Summary Statistics
summary(Datanew)
## fertilizer rainfall irrigation yield
## Min. :38.00 Min. : 760.0 Min. :11.00 Min. :26.00
## 1st Qu.:45.75 1st Qu.: 827.5 1st Qu.:13.00 1st Qu.:31.75
## Median :53.50 Median : 897.5 Median :15.00 Median :38.50
## Mean :53.35 Mean : 889.6 Mean :15.40 Mean :38.40
## 3rd Qu.:60.25 3rd Qu.: 942.5 3rd Qu.:17.25 3rd Qu.:44.25
## Max. :69.00 Max. :1010.0 Max. :20.00 Max. :52.00
Correlation Analysis
correlation_matrix <- cor(Datanew)
correlation_matrix
## fertilizer rainfall irrigation yield
## fertilizer 1.0000000 0.9923284 0.9904526 0.9979198
## rainfall 0.9923284 1.0000000 0.9862665 0.9941276
## irrigation 0.9904526 0.9862665 1.0000000 0.9929366
## yield 0.9979198 0.9941276 0.9929366 1.0000000
Correlation Matrix Plot
corrplot(correlation_matrix,
method = "circle",
type = "upper",
tl.col = "blue",
tl.srt = 45)
Numeric Correlation Matrix
corrplot(correlation_matrix,
method = "number",
type = "lower")
Correlation Test
cor.test(Datanew$fertilizer,
Datanew$yield)
##
## Pearson's product-moment correlation
##
## data: Datanew$fertilizer and Datanew$yield
## t = 95.421, df = 38, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9960411 0.9989074
## sample estimates:
## cor
## 0.9979198
Scatter Plot
plot(Datanew$fertilizer,
Datanew$yield,
main = "Relationship Between Fertilizer and Crop Yield",
xlab = "Fertilizer (kg/ha)",
ylab = "Crop Yield (quintal/ha)",
pch = 19,
col = "blue")
Detailed Correlation Statistics
psych::corr.test(Datanew)
## Call:psych::corr.test(x = Datanew)
## Correlation matrix
## fertilizer rainfall irrigation yield
## fertilizer 1.00 0.99 0.99 1.00
## rainfall 0.99 1.00 0.99 0.99
## irrigation 0.99 0.99 1.00 0.99
## yield 1.00 0.99 0.99 1.00
## Sample Size
## [1] 40
## Probability values (Entries above the diagonal are adjusted for multiple tests.)
## fertilizer rainfall irrigation yield
## fertilizer 0 0 0 0
## rainfall 0 0 0 0
## irrigation 0 0 0 0
## yield 0 0 0 0
##
## To see confidence intervals of the correlations, print with the short=FALSE option