library(mosaicCalc)
## Loading required package: mosaic
## Registered S3 method overwritten by 'mosaic':
## method from
## fortify.SpatialPolygonsDataFrame ggplot2
##
## The 'mosaic' package masks several functions from core packages in order to add
## additional features. The original behavior of these functions should not be affected by this.
##
## Attaching package: 'mosaic'
## The following objects are masked from 'package:dplyr':
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## count, do, tally
## The following object is masked from 'package:Matrix':
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## mean
## The following object is masked from 'package:ggplot2':
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## stat
## The following objects are masked from 'package:stats':
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## binom.test, cor, cor.test, cov, fivenum, IQR, median, prop.test,
## quantile, sd, t.test, var
## The following objects are masked from 'package:base':
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## max, mean, min, prod, range, sample, sum
## Loading required package: mosaicCore
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## Attaching package: 'mosaicCore'
## The following objects are masked from 'package:dplyr':
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## count, tally
## The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
## which was just loaded, will retire in October 2023.
## Please refer to R-spatial evolution reports for details, especially
## https://r-spatial.org/r/2023/05/15/evolution4.html.
## It may be desirable to make the sf package available;
## package maintainers should consider adding sf to Suggests:.
## The sp package is now running under evolution status 2
## (status 2 uses the sf package in place of rgdal)
##
## Attaching package: 'mosaicCalc'
## The following object is masked from 'package:stats':
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## D
Diferensiasi dalam ilmu kalkulus adalah konsep turunan (derivative) dari suatu fungsi. Berikut penjelasan lengkapnya:
Simbol untuk turunan adalah f’(x) atau df/dx. Jadi f’(x) = limit h->0 (f(x + h) - f(x))/h
Turunan juga dapat diinterpretasi sebagai laju perubahan. Misal dalam fungsi posisi terhadap waktu s(t), turunannya adalah kecepatan v(t) = s’(t).
Itulah penjelasan lengkap mengenai deferensiasi atau konsep turunan dalam kalkulus. Turunan merupakan salah satu konsep paling penting dalam ilmu matematika. ilustrasi diferensiasi :
Memasukkan paket ggplot2
library(ggplot2)
Membuat data frame untuk Slice Plot
data <- data.frame(
x = seq(-3, 7, by = 0.04), # Range nilai x
y = sapply(seq(-3, 7, by = 0.04), function(x) x^2), # Fungsi f(x) = x^2
dy = sapply(seq(-3, 7, by = 0.04), function(x) 2 * x) # Turunan f'(x) = 2x
)
Membuat Slice Plot
plot <- ggplot(data, aes(x, y)) +
geom_line(aes(color = "f(x)")) +
geom_line(aes(x, dy, color = "f'(x)")) +
labs(title = "Slice Plot: f(x) = x^2 and f'(x) = 2x", x = "x", y = "y") +
scale_color_manual(values = c("f(x)" = "maroon", "f'(x)" = "navy"))
Menampilkan grafik Slice Plot
print(plot)
dua kurva dalam satu grafik. Kurva orange mewakili fungsi f(x)=x2 ,
sementara kurva kuning mewakili turunannya, f′(x)=2x . Grafik diatas
memperlihatkan bagaimana turunan f(x) menggambarkan laju perubahan y
terhadap x pada setiap titik dalam domain fungsi.