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[1] "This is how many different industries are in this dataset:"[1] 7[1] "This is how many product categories in the dataset:"[1] 7[1] "This is how many different countries suppiers are from:"[1] 8[1] "These are the different supplier certifactions:"[1] "None" "ISO 9001" "ISO 14001" "ISO 45001"[1] "This is the number of shipping modes for sales:"[1] 4[1] "This is the number of carriers for sales:"[1] 5 variable_name dataset
1 On_Time_Delivery_Rate supplier_master
2 Certification_Level supplier_master
3 Category product_master
4 Unit_Cost product_master
5 Order_Quantity sales_orders
6 Unit_Price sales_orders
7 Discount sales_orders
8 Order_Total sales_orders
9 Profit_Per_Order sales_orders
description general_type
1 Historical on-time delivery percentage quantitative - continous
2 Supplier certification level (ISO standards etc.) categorical - nominal
3 Product Category categorical - nominal
4 Cost per unit (raw manufacturing cost) quantitative - continous
5 Amount of Product ordered quantitative - discrete
6 Standard price per unit at time of order quantitative - continous
7 Discount applied as a fraction of price quantitative - continous
8 Total revenue after discount, excluding VAT quantitative - continous
9 Profit for the order after subtracting COGS quantitative - continous
units
1 Percentage
2 N/A
3 N/A
4 USD
5 Pieces
6 USD
7 Proportion
8 USD
9 USD# A tibble: 1 × 4
variable mean median sd
<chr> <dbl> <dbl> <dbl>
1 Delivery Rate: 80.1 81 11.8# A tibble: 1 × 4
variable mean median sd
<chr> <dbl> <dbl> <dbl>
1 Unit Cost: 100. 102. 59.5# A tibble: 1 × 4
variable mean median sd
<chr> <dbl> <dbl> <dbl>
1 Unit Price: 177. 176. 70.7# A tibble: 1 × 4
variable mean median sd
<chr> <dbl> <dbl> <dbl>
1 Discount: 0.125 0.13 0.0722# A tibble: 1 × 4
variable mean median sd
<chr> <dbl> <dbl> <dbl>
1 Order Total: 776. 703. 459.# A tibble: 1 × 4
variable mean median sd
<chr> <dbl> <dbl> <dbl>
1 Profit Per Order: 280. 240. 243.The average on-time delivery rate was 80.07, the median was 81 and the standard deviation was 11.77. This indicates that on average the suppliers had on time deliveries around 80% of the time. The average Unit Cost is 100 dollars with a standard deviation of 59.45. This shows that there is a large variation in Unit Cost. The average Unit Price is 177.0 dollars with a standard deviation of 70.75. This
cat_summary <- function(df, var) {
freq_tbl <- table(df[[var]])
list(
frequency_table = as.data.frame(freq_tbl),
mode = names(freq_tbl)[which.max(freq_tbl)]
)
}
cat_summary(supplier_master, "Certification_Level")$frequency_table
Var1 Freq
1 ISO 14001 25
2 ISO 45001 20
3 ISO 9001 34
4 None 21
$mode
[1] "ISO 9001"cat_summary(product_master, "Category")$frequency_table
Var1 Freq
1 Apparel 27
2 Beauty 34
3 Electronics 24
4 Food 37
5 Home 26
6 Industrial 23
7 Sport 29
$mode
[1] "Food"supplier_master$otd_category <- cut(
supplier_master$On_Time_Delivery_Rate,
breaks = c(0, 60, 70, 80, 90, 100),
include.lowest = TRUE,
right = FALSE,
labels = c("Very Poor", "Poor", "Medium", "Good", "Great")
)table(supplier_master$Certification_Level, supplier_master$otd_category)
Very Poor Poor Medium Good Great
ISO 14001 0 9 4 9 3
ISO 45001 0 5 2 4 9
ISO 9001 0 6 8 11 9
None 0 3 10 3 5round(prop.table(table(supplier_master$Certification_Level, supplier_master$otd_category), margin = 1) * 100, 2)
Very Poor Poor Medium Good Great
ISO 14001 0.00 36.00 16.00 36.00 12.00
ISO 45001 0.00 25.00 10.00 20.00 45.00
ISO 9001 0.00 17.65 23.53 32.35 26.47
None 0.00 14.29 47.62 14.29 23.81round(prop.table(table(supplier_master$Certification_Level, supplier_master$otd_category), margin = 2) * 100, 2)
Very Poor Poor Medium Good Great
ISO 14001 39.13 16.67 33.33 11.54
ISO 45001 21.74 8.33 14.81 34.62
ISO 9001 26.09 33.33 40.74 34.62
None 13.04 41.67 11.11 19.23
# A tibble: 7 × 8
Category mean meadian sd min q1 q3 max
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Apparel 91.1 94.4 66.4 8.55 24.4 147. 199.
2 Beauty 110. 128. 60.6 7.21 63.6 156. 198.
3 Electronics 110. 123. 57.0 12.9 75.1 154. 190.
4 Food 116. 129. 62.1 7.81 67.2 170. 197.
5 Home 93.5 89.2 56.1 7.11 62.5 131. 194.
6 Industrial 81.3 75.2 57.7 9.54 31.8 128. 195.
7 Sport 88.9 80.3 50.6 11.0 48.6 119. 190.# A tibble: 7 × 8
Category n mean sd se t_star lower upper
<chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Apparel 27 91.1 66.4 12.8 2.06 64.8 117.
2 Beauty 30 108. 63.3 11.6 2.05 84.2 131.
3 Electronics 24 110. 57.0 11.6 2.07 86.2 134.
4 Food 30 123. 58.9 10.8 2.05 101. 145.
5 Home 26 93.5 56.1 11.0 2.06 70.9 116.
6 Industrial 23 81.3 57.7 12.0 2.07 56.3 106.
7 Sport 29 88.9 50.6 9.40 2.05 69.7 108.
Call:
lm(formula = Discount ~ Order_Quantity, data = sales_orders)
Residuals:
Min 1Q Median 3Q Max
-0.12787 -0.06448 0.00251 0.06411 0.12627
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.1233518 0.0013774 89.554 <2e-16 ***
Order_Quantity 0.0003762 0.0002553 1.473 0.141
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.07216 on 19998 degrees of freedom
Multiple R-squared: 0.0001086, Adjusted R-squared: 5.855e-05
F-statistic: 2.171 on 1 and 19998 DF, p-value: 0.1406`geom_smooth()` using formula = 'y ~ x'