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# Generate some random data
set.seed(123)
x <- rnorm(150) # changed from 100 to 150
# Summary statistics
summary(x)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -2.30917 -0.63751 -0.05874 -0.02436 0.57694 2.18733
hist(x, main = "Histogram of Random Data", xlab = "Value")
# Load a sample dataset
data(iris)
# View the first few rows of the dataset
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
# Scatter plot of iris dataset
plot(iris$Sepal.Length, iris$Sepal.Width,
main = "Sepal Length vs. Sepal Width",
xlab = "Sepal Length", ylab = "Sepal Width",
col = iris$Species)
legend("topright", legend = levels(iris$Species), col = 1:3, pch = 1)
# Fit a linear regression model
lm_model <- lm(Petal.Width ~ Petal.Length, data = iris)
# Summary of the model
summary(lm_model)
##
## Call:
## lm(formula = Petal.Width ~ Petal.Length, data = iris)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.56515 -0.12358 -0.01898 0.13288 0.64272
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.363076 0.039762 -9.131 4.7e-16 ***
## Petal.Length 0.415755 0.009582 43.387 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.2065 on 148 degrees of freedom
## Multiple R-squared: 0.9271, Adjusted R-squared: 0.9266
## F-statistic: 1882 on 1 and 148 DF, p-value: < 2.2e-16
# Plot the regression line
plot(iris$Petal.Length, iris$Petal.Width,
main = "Petal Width vs. Petal Length with Regression Line",
xlab = "Petal Length", ylab = "Petal Width")
abline(lm_model, col = "red")
# Load the plotly library
library(plotly)
## Loading required package: ggplot2
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
##
## last_plot
## The following object is masked from 'package:stats':
##
## filter
## The following object is masked from 'package:graphics':
##
## layout
# Scatter plot using plotly
plot_ly(iris, x = ~Petal.Length, y = ~Petal.Width, color = ~Species,
type = 'scatter', mode = 'markers',
marker = list(size = 10)) %>%
layout(title = "Interactive Scatter Plot: Petal Width vs. Petal Length",
xaxis = list(title = "Petal Length"),
yaxis = list(title = "Petal Width"))
sessionInfo()
## R version 4.3.3 (2024-02-29)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Sonoma 14.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Warsaw
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] plotly_4.10.4 ggplot2_3.5.1
##
## loaded via a namespace (and not attached):
## [1] gtable_0.3.5 jsonlite_1.8.8 dplyr_1.1.4 compiler_4.3.3
## [5] highr_0.10 tidyselect_1.2.1 tidyr_1.3.1 jquerylib_0.1.4
## [9] scales_1.3.0 yaml_2.3.8 fastmap_1.1.1 R6_2.5.1
## [13] generics_0.1.3 knitr_1.45 htmlwidgets_1.6.4 tibble_3.2.1
## [17] munsell_0.5.1 RColorBrewer_1.1-3 bslib_0.7.0 pillar_1.9.0
## [21] rlang_1.1.4 utf8_1.2.4 cachem_1.0.8 xfun_0.49
## [25] sass_0.4.9 lazyeval_0.2.2 viridisLite_0.4.2 cli_3.6.2
## [29] withr_3.0.2 magrittr_2.0.3 crosstalk_1.2.1 digest_0.6.35
## [33] grid_4.3.3 rstudioapi_0.16.0 lifecycle_1.0.4 vctrs_0.6.5
## [37] evaluate_0.23 glue_1.8.0 data.table_1.15.4 farver_2.1.2
## [41] fansi_1.0.6 colorspace_2.1-0 rmarkdown_2.29 purrr_1.0.2
## [45] httr_1.4.7 tools_4.3.3 pkgconfig_2.0.3 htmltools_0.5.8.1