library(DBI)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.5.2
library(scales)
## Warning: package 'scales' was built under R version 4.5.2
library(odbc)
## Warning: package 'odbc' was built under R version 4.5.2

R Markdown

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summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

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con <- DBI::dbConnect(odbc::odbc(),
Driver = "MySQL ODBC 8.0 ANSI Driver",
Server = "127.0.0.1",
Database = "classicmodels",
UID = "root",
PWD = "27112006", 
Port = 3306)
data1 <- dbGetQuery(con, "SELECT o.ordernumber, country, SUM(quantityordered*priceeach) as TOTAL_VALUE
FROM orders o
JOIN orderdetails od ON o.ordernumber = od.ordernumber 
JOIN customers c ON o.customernumber = c.customernumber
WHERE c.country IN ('Denmark', 'Finland', 'Norway', 'Sweden')
GROUP BY o.ordernumber")
data1
##    ordernumber  country TOTAL_VALUE
## 1        10103   Norway    50218.95
## 2        10105  Denmark    53959.21
## 3        10112   Sweden     7674.94
## 4        10141  Finland    29716.86
## 5        10151  Finland    32723.04
## 6        10155  Finland    37602.48
## 7        10158   Norway     1491.38
## 8        10161  Denmark    36164.46
## 9        10167   Sweden    44167.09
## 10       10181 Norway      55069.55
## 11       10188 Norway      29954.91
## 12       10238  Denmark    28211.70
## 13       10239  Finland    16212.59
## 14       10247  Finland    28394.54
## 15       10256  Denmark     4710.73
## 16       10284 Norway      32260.16
## 17       10289 Norway      12538.01
## 18       10291   Sweden    48809.90
## 19       10299  Finland    34341.08
## 20       10301 Norway      36798.88
## 21       10309   Norway    17876.32
## 22       10314  Denmark    53745.34
## 23       10320   Sweden    16799.03
## 24       10325   Norway    34638.14
## 25       10326   Sweden    19206.68
## 26       10327  Denmark    20564.86
## 27       10334   Sweden    23014.17
## 28       10363  Finland    45785.34
## 29       10373  Finland    46770.52
## 30       10377  Finland    23602.90
## 31       10389   Sweden    27966.54
## 32       10406  Denmark    21638.62

Visualisasi banyaknya item tiap product scale

Kita menggunakan library ggplot2 untuk membuat histogram. Fungsi geom_histogram() digunakan untuk melihat banyaknya item tiap product scale.

ggplot(data1, aes(x = TOTAL_VALUE)) +
  geom_histogram(aes(fill=after_stat(count)), bin = 15, color = "white") +
  stat_bin(bins=15, geom = "text", aes(label = after_stat(count), vjust = -0.5, size = 3)) +
  theme_minimal() +
  labs(title = "DISTRIBUSI NILAI PESANAN DI NEGARA NORDIK", 
       subtitle = "Negara ; Norwegia, Denmark, Finland, Swedia",
       x = "Nilai Pesanan (USD)",
       y = "Frekuensi (Jumlah Pesanan)") +
  scale_fill_gradient(low = "lightgreen", high = "darkgreen") +
  scale_x_continuous(labels=label_dollar()) +
    scale_y_continuous(expand=expansion(mult = c(0, 0.15))) +
    theme(legend.position = "none")
## Warning in geom_histogram(aes(fill = after_stat(count)), bin = 15, color =
## "white"): Ignoring unknown parameters: `bin`
## `stat_bin()` using `bins = 30`. Pick better value `binwidth`.