Visdat Individu
Muhammad Fahrezi Maulana
2024-05-14
##1. Visualisasi untuk antar peubah numerik #Hubungan Antar Peubah Numerik
library(readxl)
data <- read_xlsx("C:/Users/Asus/Downloads/TA Anreg 8.xlsx", sheet = 5)
data## # A tibble: 25 × 9
## `Kabupaten/Kota` `Luas Tanam` Persentase Jumlah Peta…¹ Jumlah Pupuk Urea (T…²
## <chr> <dbl> <dbl> <dbl>
## 1 Bogor 81843 5.94 0.27
## 2 Sukabumi 156947 11.3 0.32
## 3 Cianjur 156882 9.46 0.28
## 4 Bandung 98215 5.13 0.42
## 5 Garut 135295 10.2 0.72
## 6 Tasikmalaya 112094 9.52 0.55
## 7 Ciamis 77302 6.16 0.19
## 8 Kuningan 59490 3.68 0.31
## 9 Cirebon 91383 2.55 0.28
## 10 Majalengka 104346 4.53 0.32
## # ℹ 15 more rows
## # ℹ abbreviated names: ¹​`Persentase Jumlah Petani`,
## # ²​`Jumlah Pupuk Urea (Ton/Ha)`
## # ℹ 5 more variables: `Jumlah Pupuk NPK (Ton/Ha)` <dbl>,
## # `Jumlah Usaha Tani Padi (Unit)` <dbl>, `Jumlah Teknologi (Unit)` <dbl>,
## # `Harga Beras (Rp/Kg)` <dbl>, `Produksi Padi 2023 (Ton)` <dbl>library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(ggplot2)
X1 <- data$`Luas Tanam`
X2 <- data$`Persentase Jumlah Petani`
X3 <- data$`Jumlah Pupuk Urea (Ton/Ha)`
X4 <- data$`Jumlah Pupuk NPK (Ton/Ha)`
X5 <- data$`Jumlah Usaha Tani Padi (Unit)`
X6 <- data$`Jumlah Teknologi (Unit)`
X7 <- data$`Harga Beras (Rp/Kg)`
X8 <- data$`Produksi Padi 2023 (Ton)`
plot(X2,X1,col = "darkgreen",main = "Hubungan antara Jumlah Petani dan Luas Tanam",xlab = "Persentase Jumlah Petani",ylab = "Luas Tanam")
abline(lm(X1 ~ X2),col = "black")
## Interpretasi Pada plot ini, sumbu X adalah Persentase jumlah petani
dan sumbu Y adalah Luas Tanah. Plot ini menunjukkan bahwa hubungan antar
dua peubah numerik ini relatif positif yang artinya semakin banyak
jumlah petani maka semakin luas tanah yang dipunyai.
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithslibrary(ggplot2)
datanum <- data[,-1]
kor <- cor(datanum)
meltnum <- melt(kor)
ggplot(meltnum, aes(Var1, Var2, fill = value)) +
geom_tile(color = "white") +
scale_fill_gradient2(low = "darkslategrey", mid = "white", high = "darkorange", midpoint = 0, limits = c(-1,1), name="Nilai Korelasi") +
labs(title = "Korelasi antar Peubah") +
theme_minimal() +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
## Interpretasi Dari plot korelasi ini dapat dilihat korelasi korelasi
antar peubah yang mempengaruhi Produktivitas Padi.
library(readr)
databca <- read.csv("C:/Users/Asus/Downloads/BBCA.JK (1).csv")
databca## Date Open High Low Close Adj.Close Volume
## 1 2019-05-13 5560 5630 5180 5180 4654.208 349011500
## 2 2019-05-20 5180 5630 5140 5610 5040.561 282982500
## 3 2019-05-27 5535 5895 5530 5820 5229.244 508406500
## 4 2019-06-03 5820 5820 5820 5820 5229.244 0
## 5 2019-06-10 6000 6190 5790 5800 5211.275 457417500
## 6 2019-06-17 5810 5960 5790 5880 5283.154 433010000
## 7 2019-06-24 5895 5995 5805 5995 5386.481 288424000
## 8 2019-07-01 5995 6025 5940 5970 5364.020 209763000
## 9 2019-07-08 5935 6040 5865 6010 5399.959 202402500
## 10 2019-07-15 6060 6210 6035 6200 5570.673 232614500
## 11 2019-07-22 6260 6290 6165 6195 5566.180 233062500
## 12 2019-07-29 6220 6270 6160 6165 5539.226 312081500
## 13 2019-08-05 6215 6215 5765 6065 5449.376 472736000
## 14 2019-08-12 6070 6080 5860 5960 5355.034 321436000
## 15 2019-08-19 6030 6035 5905 5995 5386.481 243911500
## 16 2019-08-26 5940 6100 5860 6100 5480.824 346032000
## 17 2019-09-02 6100 6100 6000 6025 5413.436 235151000
## 18 2019-09-09 6050 6105 5980 6030 5417.929 179900000
## 19 2019-09-16 6000 6050 5985 5990 5381.989 393415000
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## 23 2019-10-14 6130 6215 6105 6160 5534.733 230901000
## 24 2019-10-21 6200 6325 6175 6200 5570.673 324480500
## 25 2019-10-28 6200 6325 6150 6325 5682.986 284350000
## 26 2019-11-04 6350 6380 6250 6280 5642.553 325361500
## 27 2019-11-11 6335 6340 6210 6275 5638.060 242926000
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## 34 2019-12-30 6680 6800 6670 6800 6129.105 158368500
## 35 2020-01-06 6720 6825 6670 6725 6061.505 239740500
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## 51 2020-04-27 4940 5250 4800 5170 4740.479 626833500
## 52 2020-05-04 5150 5400 5000 5245 4809.249 442564500
## 53 2020-05-11 5245 5340 4680 4785 4387.465 591306500
## 54 2020-05-18 4800 4960 4680 4765 4369.127 387660000
## 55 2020-05-25 4765 5320 4765 5190 4758.818 956477000
## 56 2020-06-01 5190 5990 5190 5725 5249.371 785359000
## 57 2020-06-08 5755 5995 5480 5670 5198.940 570281500
## 58 2020-06-15 5680 5800 5500 5575 5111.833 450266000
## 59 2020-06-22 5595 5760 5520 5645 5176.017 287760000
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## 62 2020-07-13 6230 6240 6040 6120 5611.554 332959000
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## 64 2020-07-27 6100 6240 6000 6240 5721.585 238047500
## 65 2020-08-03 6220 6295 5825 6180 5666.570 364708500
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## 69 2020-08-31 6500 6560 6240 6380 5849.954 379365000
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## 72 2020-09-21 5640 5735 5390 5610 5143.925 565726000
## 73 2020-09-28 5650 5695 5420 5505 5047.647 442418500
## 74 2020-10-05 5515 5800 5460 5775 5295.217 421819000
## 75 2020-10-12 5840 5900 5730 5760 5281.463 385405000
## 76 2020-10-19 5750 5900 5705 5770 5290.632 373011500
## 77 2020-10-26 5725 5835 5725 5790 5308.971 159934500
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## 79 2020-11-09 6300 6600 6215 6390 5859.124 570653500
## 80 2020-11-16 6485 6650 6385 6600 6051.676 490293000
## 81 2020-11-23 6600 6610 6300 6385 5854.538 469456000
## 82 2020-11-30 6385 6525 6135 6390 5859.124 764758000
## 83 2020-12-07 6500 6780 6425 6735 6175.460 532778000
## 84 2020-12-14 6800 7000 6605 6800 6253.948 721642500
## 85 2020-12-21 6890 6905 6620 6725 6184.971 232431000
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## 216 2023-06-26 9025 9150 9025 9150 8901.428 72615000
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## 219 2023-07-17 9200 9250 9100 9150 8901.428 241619100
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## 243 2024-01-01 9400 9600 9325 9575 9359.325 206940400
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## 248 2024-02-05 9675 9750 9525 9700 9481.510 238587800
## 249 2024-02-12 9750 10000 9700 9950 9725.879 461047900
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## 251 2024-02-26 9750 10000 9725 9825 9603.694 317326700
## 252 2024-03-04 9800 10300 9750 10150 9921.374 395334500
## 253 2024-03-11 10400 10400 10000 10150 9921.374 406480500
## 254 2024-03-18 10175 10275 9950 10100 9872.500 365966200
## 255 2024-03-25 10075 10100 9925 10075 9848.062 251457500
## 256 2024-04-01 10075 10100 9525 9825 9825.000 479852300
## 257 2024-04-08 9825 9825 9825 9825 9825.000 0
## 258 2024-04-15 9350 9675 9250 9475 9475.000 653804900
## 259 2024-04-22 9400 10000 9350 9625 9625.000 646380700
## 260 2024-04-29 9525 10050 9500 9850 9850.000 377966300
## 261 2024-05-06 9850 9875 9375 9375 9375.000 232631900
## 262 2024-05-13 9200 9525 9200 9525 9525.000 145469900
## 263 2024-05-14 9625 9675 9525 9550 9550.000 95363500library(ggplot2)
databca$Date <- as.Date(databca$Date)
ggplot(databca, aes(x = Date, y = Adj.Close)) +
geom_line(color = "blue") +
labs(
title = "Time Series Plot",
x = "Date",
y = "Value"
) +
theme_minimal() +
theme(
plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45, hjust = 1)
)
## Interpretasi Data time series yang digunakan diatas adalah data saham
bank BCA dari tahun 2019-2024, dimana setiap tahun selalu mengalaming
peningkatan dan yang paling tinggi adalah pada tahun 2024.