——————————————-
# Import data CSV
data <- read.csv(file.choose("C:/Users/Asus/Downloads/archive"))
Y <- data[c("Shares","LastPrice")]
head(Y)
## Shares LastPrice
## 1 1924688333 8000
## 2 3935892857 142
## 3 620806680 6700
## 4 2753165000 3050
## 5 17150000000 490
## 6 12675160000 156
# Statistik deskriptif sederhana
summary(Y)
## Shares LastPrice
## Min. :3.600e+06 Min. : 25.0
## 1st Qu.:1.231e+09 1st Qu.: 101.8
## Median :3.128e+09 Median : 287.0
## Mean :1.094e+10 Mean : 1363.5
## 3rd Qu.:9.327e+09 3rd Qu.: 975.0
## Max. :1.180e+12 Max. :38000.0
## NA's :5
# Standar deviasi
sd(Y$Shares)
## [1] 45257987781
sd(Y$LastPrice)
## [1] NA
# Uji korelasi Pearson
hasil_korelasi <- cor.test(Y$Shares,
Y$LastPrice,
method = "pearson")
# Menampilkan hasil
print(hasil_korelasi)
##
## Pearson's product-moment correlation
##
## data: Y$Shares and Y$LastPrice
## t = -0.82001, df = 822, p-value = 0.4124
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09669745 0.03978488
## sample estimates:
## cor
## -0.02858953
# Membuat scatter plot
plot(Y$Shares, Y$LastPrice,
main = "Scatter Plot Shares vs Last Price",
xlab = "Shares",
ylab = "Last Price",
pch = 19,
col = "blue")
# Menambahkan garis regresi
abline(lm(LastPrice ~ Shares, data = Y),
col = "red",
lwd = 2)
# Uji korelasi Spearman Data Tdk Normal
hasil_spearman <- cor.test(Y$Shares,
Y$LastPrice,
method = "spearman",
exact = FALSE)
print(hasil_spearman)
##
## Spearman's rank correlation rho
##
## data: Y$Shares and Y$LastPrice
## S = 104483847, p-value = 0.0005264
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## -0.1205195
# -------------------------------------------
# DATA SAHAM
# -------------------------------------------
set.seed(123)
Shares <- Y$Shares
LastPrice <- Y$LastPrice
data_saham <- data.frame(Shares, LastPrice)
plot(Shares, LastPrice,
main = "Scatter Plot Shares vs Last Price",
xlab = "Shares",
ylab = "Last Price",
pch = 19)
abline(lm(LastPrice ~ Shares), lwd = 2)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.2
ggplot(data_saham, aes(x = Shares, y = LastPrice)) +
geom_point(size = 3) +
geom_smooth(method = "lm", formula = y ~ x, se = TRUE)
## Warning: Removed 5 rows containing non-finite outside the scale range
## (`stat_smooth()`).
## Warning: Removed 5 rows containing missing values or values outside the scale range
## (`geom_point()`).
labs(title = "Hubungan Shares dan Last Price",
x = "Shares",
y = "Last Price") +
theme_minimal()
## NULL
# Menghitung matriks korelasi
matriks_korelasi <- cor(data_saham, use = "complete.obs")
# Heatmap
heatmap(matriks_korelasi)
# Uji korelasi Kendall
cor.test(Shares, LastPrice, method = "kendall")
##
## Kendall's rank correlation tau
##
## data: Shares and LastPrice
## z = -3.4799, p-value = 0.0005016
## alternative hypothesis: true tau is not equal to 0
## sample estimates:
## tau
## -0.08147288