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Exploratory-Data-Analysis.R
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Exploratory-Data-Analysis.R
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library(stringr) # Wrappers for Common String Operations
library(DT) # DataTables library
library(scales) # Functions for Visualization
library(readr) # Read rectangular data like 'csv'
library(data.table) # Fast aggregation of large data
library(ggplot2) # Create Elegant Data Visualisations
library(gridExtra) # To arrange ggplots in grids
library(scales) # To adjust numeric scales in ggplots
library(dplyr) # For Data manupulation
getwd()
setwd("C:/Users/Saurabh/Desktop/Instacart-Data-Mining/")
# Updating all the available packages!
#install.packages(
# pkgs = as.data.frame(installed.packages(.libPaths()[1]), stringsAsFactors=FALSE)$Package,
# type = 'source'
#)
# Load Data
setwd('C:/Users/Saurabh/Desktop/InstacartDataMining/')
orders <- fread('../Input/orders.csv')
products <- fread('../Input/products.csv')
head(products)
products <- subset(products, select = -c(V5,V6,V7,V8))
aisles <- fread('../Input/aisles.csv')
departments <- fread('../Input/departments.csv')
order_products_train <- fread('../Input/order_products__train.csv')
order_products_prior <- fread('../Input/order_products__prior.csv')
# Little Preprocessing
orders <- orders %>% mutate(order_hour_of_day = as.numeric(order_hour_of_day), eval_set = as.factor(eval_set))
products <- products %>% mutate(product_name = as.factor(product_name))
aisles <- aisles %>% mutate(aisle = as.factor(aisle))
departments <- departments %>% mutate(department = as.factor(department))
# View the available Data
head(orders)
summary(orders$eval_set)
head(products)
head(aisles)
head(departments)
head(order_products_train)
head(order_products_prior)
## 1. Orders by hour of the day
x11()
ggplot(orders, aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 10), "gold", rep("grey25", 4), "cyan3",
rep("grey25", 8))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(x = "Hour of the Day",
y = "Number of Orders",
title = "Distribution of Orders by Hour of the Day") +
scale_y_continuous(labels = comma)
## 2. Orders by Hour and Day of Week
x11()
p0 <- ggplot(orders[orders$order_dow == 0, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 14), "gold", rep("grey25", 9))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 0")
p1 <- ggplot(orders[orders$order_dow == 1, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 10), "gold", rep("grey25", 13))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 1")
p2 <- ggplot(orders[orders$order_dow == 2, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 10), "gold", rep("grey25", 13))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 2")
p3 <- ggplot(orders[orders$order_dow == 3, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 10), "gold", rep("grey25", 13))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 3")
p4 <- ggplot(orders[orders$order_dow == 4, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 10), "gold", rep("grey25", 13))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 4")
p5 <- ggplot(orders[orders$order_dow == 5, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 10), "gold", rep("grey25", 13))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
axis.text.x = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 5")
p6 <- ggplot(orders[orders$order_dow == 6, ], aes(x = order_hour_of_day)) +
geom_bar(fill = c(rep("grey25", 14), "gold", rep("grey25", 9))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(y = "Day 6",
x = "Hour of the Day")
grid.arrange(p0, p1, p2, p3, p4, p5, p6, ncol = 1)
## 3. Orders by Day of the Week
x11()
ggplot(orders, aes(x = order_dow)) +
geom_bar(fill = c(rep("gold", 2), rep("grey25", 5))) +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(x = "Day of Week (unknown)",
y = "Count",
title = "Distribution of Orders by Day of Week") +
scale_y_continuous(labels = comma)
## 4. How many items do People buy
x11()
order_products_train %>%
group_by(order_id) %>%
summarize(n_items = last(add_to_cart_order)) %>%
ggplot(aes(x = n_items)) +
geom_bar(fill = c(rep("cyan4", 4),"gold",rep("cyan4",4),rep("grey25",66)))+
geom_rug()+
coord_cartesian(xlim = c(0,80))+
scale_x_continuous(name = "Items per Order",labels = comma)+
scale_y_continuous(name = "Count", labels = comma)+
theme_classic()
?summarize
## 5. Orders by Days since Prior Orders
x11()
ggplot(orders, aes(x = days_since_prior_order)) +
geom_bar(fill = c("cyan3", rep("grey25", 6),
"cyan3", rep("grey25", 6),
"cyan3", rep("grey25", 6),
"cyan3", rep("grey25", 6),
"cyan3", "grey50", "cyan3")) +
theme_minimal() +
labs(x = "Days Since Prior Order",
y = "Count",
title = "Distribution of Orders by Days Since Prior Order") +
scale_y_continuous(labels = comma)
## 6. Best Selling Products
x11()
head(order_products_train)
order_products_train %>%
group_by(product_id)%>%
summarize(count = n())%>%
top_n(10, wt = count)%>%
left_join(select(products,product_id,product_name),by="product_id")%>%
arrange(desc(count))%>%
ggplot(aes(x=reorder(product_name,-count), y=count))+
geom_bar(stat="identity",fill=c("cyan3",rep("grey25",9)))+
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.x=element_text(angle=45, hjust=1, colour = "grey0"),
legend.position = "none",
panel.grid.major = element_blank()) +
labs(x = "Product Name",
y = "Number of times Ordered",
title = "Distribution of Best Selling Products") +
scale_y_continuous(labels = comma)
## 7. Top 10 aisles
aisles$aisle_id = as.character(aisles$aisle_id)
departments$department_id = as.integer(departments$department_id)
x11()
order_products_train %>%
left_join(products,by="product_id") %>%
left_join(aisles,by="aisle_id") %>%
left_join(departments,by="department_id")%>%
group_by(aisle,department)%>%
tally(sort=TRUE)%>%
mutate(perc = round(100*n/nrow(order_products_train),2))%>%
ungroup() %>%
top_n(10,n) %>%
ggplot(aes(x=reorder(aisle, -n), y=n, fill=department)) +
geom_bar(stat="identity") +
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
axis.text.x=element_text(angle=45, hjust=1, size = 12),
panel.grid.major = element_blank()) +
labs(x = "Aisle Names",
y = "Number of Orders",
title = "Distribution of Number of Orders per Aisle") +
scale_y_continuous(labels = comma)
## 8. Look on the customer behavior and find if we can cluster them by how often they buy.
summary(orders$order_number)
head(orders)
#Group the Data by Users
user_freq <- orders %>%
select(user_id, order_number, days_since_prior_order)%>%
group_by(user_id) %>%
summarise(total = max(order_number),
frequency = mean(days_since_prior_order, na.rm = TRUE))
glimpse(user_freq)
glimpse(user_freq$order_number)
#How many users haven't bought again ?
user_freq %>%
filter(total == 1) %>%
glimpse
# Make the Cluster
?set.seed
set.seed(42)
clus <- kmeans(user_freq[,2:3], 4)
clus
clus$cluster <- as.factor(clus$cluster)
# See the Cluster
Clusters <- clus$cluster
Clusters
x11()
ggplot(user_freq,
aes(total, frequency, shape = Clusters, color = Clusters)) +
geom_point()+
labs(x = "Order Sequence for User",
y = "Frequency of Orders(Mean of Days since Prior Order)",
title = "Distribution of People rebuying items")+
scale_colour_discrete(name="Clusters",
labels = c("Buys twice a week",
"Buys monthly",
"Buys weekly",
"Buys every two weeks"))+
theme_minimal() +
theme(axis.ticks.x = element_blank(),
axis.ticks.y = element_blank(),
panel.grid.major = element_blank())