install.packages("readr")
## Installing package into '/home/Saad/R/x86_64-pc-linux-gnu-library/4.1'
## (as 'lib' is unspecified)
# Install and load required packages
library(lubridate)
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(ggplot2)
library(caret)
## Loading required package: lattice
library(e1071)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(vcd)
## Loading required package: grid
library(dplyr)
library(stringr)
library(readr)
# Import data using read_csv
bg <- read_csv("/home/Saad/Documents/fid_files/archive/BG_with_webscan.csv")
## Rows: 4469 Columns: 49
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (25): id, country_region, rest_name, is_claimed, last_review, address, p...
## dbl (19): rating, latitude, longitude, n_reviews, travellers_choice, food_ra...
## num (2): traveller_rating_5, fb_reviews
## lgl (3): michelin, working_hours, telephone
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
fi <- read_csv("/home/Saad/Documents/fid_files/archive/FI_with_webscan.csv")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 7372 Columns: 49
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (25): id, country_region, rest_name, is_claimed, last_review, address, p...
## dbl (21): rating, latitude, longitude, n_reviews, travellers_choice, micheli...
## num (1): fb_reviews
## lgl (2): working_hours, telephone
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
ni <- read_csv("/home/Saad/Documents/fid_files/archive/NI_with_webscan.csv")
## Rows: 3634 Columns: 49
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (25): id, country_region, rest_name, is_claimed, last_review, address, p...
## dbl (19): rating, latitude, longitude, n_reviews, travellers_choice, micheli...
## num (3): traveller_rating_5, traveller_rating_4, fb_reviews
## lgl (2): working_hours, telephone
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
sk <- read_csv("/home/Saad/Documents/fid_files/archive/SK_with_webscan.csv")
## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)
## Rows: 4251 Columns: 49
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (25): id, country_region, rest_name, is_claimed, last_review, address, p...
## dbl (21): rating, latitude, longitude, n_reviews, travellers_choice, food_ra...
## lgl (3): michelin, working_hours, telephone
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Convert traveller_rating_4 column to character
bg$traveller_rating_4 <- as.character(bg$traveller_rating_4)
fi$traveller_rating_4 <- as.character(fi$traveller_rating_4)
ni$traveller_rating_4 <- as.character(ni$traveller_rating_4)
sk$traveller_rating_4 <- as.character(sk$traveller_rating_4)
# Combine datasets
restaurant_data <- bind_rows(
mutate(bg, traveller_rating_4 = as.character(traveller_rating_4)),
mutate(fi, traveller_rating_4 = as.character(traveller_rating_4)),
mutate(ni, traveller_rating_4 = as.character(traveller_rating_4)),
mutate(sk, traveller_rating_4 = as.character(traveller_rating_4)),
.id = "location"
)
# Extract data collection date from metadata (if available)
data_collection_date <- attr(bg, "data_collection_date")
# Count the number of variables
num_vars <- ncol(bg)
cat("Data collection date:", data_collection_date, "\n")
## Data collection date:
cat("Number of variables:", num_vars, "\n")
## Number of variables: 49
# Data cleaning
restaurant_data_with_status <- restaurant_data %>%
filter(!is.na(service_rating))
# Display the first few rows of the cleaned dataset
head(restaurant_data_with_status, n = 10)
## # A tibble: 10 × 50
## location id country_region rest_name is_claimed rating last_review address
## <chr> <chr> <chr> <chr> <chr> <dbl> <chr> <chr>
## 1 1 g102… "['Europe', '… Enjoy Te… Unclaimed 4.5 March 2, 2… Chepel…
## 2 1 g102… "['Europe', '… Restaura… Claimed 4 March 25, … The Ca…
## 3 1 g102… "['Europe', '… Golden E… Claimed 4 February 1… Snezha…
## 4 1 g102… "['Europe', '… The Whit… Unclaimed 2.5 July 1, 20… 4870 P…
## 5 1 g102… "['Europe', '… Neptun W… Claimed 4.5 March 14, … H-L Pe…
## 6 1 g102… "['Europe', '… The Whit… Claimed 4 March 1, 2… 4870 P…
## 7 1 g102… "['Europe', '… Gloria M… Claimed 4 August 10,… Ul. Sh…
## 8 1 g102… "['Europe', '… The Whit… Claimed 4.5 February 2… Malina…
## 9 1 g102… "['Europe', '… Alex's P… Claimed 4.5 December 2… Malina…
## 10 1 g102… "['Europe', '… Black Ho… Claimed 4 July 14, 2… Pampor…
## # ℹ 42 more variables: latitude <dbl>, longitude <dbl>, popularity_top <chr>,
## # popularity_detailed <chr>, n_reviews <dbl>, travellers_choice <dbl>,
## # michelin <dbl>, price_range <chr>, cuisines <chr>, meal_types <chr>,
## # special_diets <chr>, features <chr>, working_hours <lgl>, telephone <lgl>,
## # food_rating <dbl>, service_rating <dbl>, value_rating <dbl>,
## # atmosphere_rating <dbl>, traveller_rating_5 <dbl>,
## # traveller_rating_4 <chr>, traveller_rating_3 <dbl>, …
# Define the key variables
key_vars <- c("rating", "location", "traveller_rating_4", "service_rating", "status")
# Separate numeric and categorical variables
numeric_vars <- key_vars[sapply(restaurant_data_with_status[key_vars], is.numeric)]
categorical_vars <- key_vars[!sapply(restaurant_data_with_status[key_vars], is.numeric)]
# Create a summary table
summary_table <- restaurant_data_with_status %>%
select(all_of(key_vars)) %>%
summarise(across(
.cols = numeric_vars,
.fns = list(
min = ~ min(., na.rm = TRUE),
max = ~ max(., na.rm = TRUE),
mean = ~ mean(., na.rm = TRUE)
),
.names = "{.col}_{.fn}"
),
across(
.cols = categorical_vars,
.fns = list(
unique_values = ~ n_distinct(.)
),
.names = "{.col}_{.fn}"
))
## Warning: There were 2 warnings in `summarise()`.
## The first warning was:
## ℹ In argument: `across(...)`.
## Caused by warning:
## ! Using an external vector in selections was deprecated in tidyselect 1.1.0.
## ℹ Please use `all_of()` or `any_of()` instead.
## # Was:
## data %>% select(numeric_vars)
##
## # Now:
## data %>% select(all_of(numeric_vars))
##
## See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.
## ℹ Run `dplyr::last_dplyr_warnings()` to see the 1 remaining warning.
# Print the summary table
print(summary_table)
## # A tibble: 1 × 9
## rating_min rating_max rating_mean service_rating_min service_rating_max
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1.5 5 4.01 10 50
## # ℹ 4 more variables: service_rating_mean <dbl>, location_unique_values <int>,
## # traveller_rating_4_unique_values <int>, status_unique_values <int>
survival_by_rating <- restaurant_data_with_status %>%
group_by(rating, status) %>%
summarize(count = n(), .groups = "drop") %>%
mutate(percentage = count / sum(count) * 100)
# Plot restaurant ratings distribution
# Creating rating categories
restaurant_data$category <- cut(restaurant_data$rating, breaks = c(0, 2, 3, 4, 5),
labels = c("Low", "Medium-Low", "Medium-High", "High"))
# Plotting with colored histogram
ggplot(restaurant_data, aes(x = rating, fill = category)) +
geom_histogram(bins = 10, color = "black") +
xlab("Rating") +
ylab("Count") +
ggtitle("Distribution of Restaurant Ratings") +
scale_fill_manual(values = c("red", "orange", "yellow", "green")) + # Specify colors here
theme_minimal()
## Warning: Removed 2964 rows containing non-finite outside the scale range
## (`stat_bin()`).
# Convert 'status' column to a factor
restaurant_data_with_status <- restaurant_data %>%
filter(!is.na(status))
survival_by_rating <- restaurant_data_with_status %>%
group_by(rating, status) %>%
summarize(count = n(), .groups = "drop") %>%
mutate(percentage = count / sum(count) * 100)
# Box plot for rating distribution by location
ggplot(restaurant_data, aes(x = location, y = rating, na.rm = TRUE)) +
geom_boxplot(fill = "steelblue", alpha = 0.6) +
xlab("Location") +
ylab("Rating") +
ggtitle("Rating Distribution by Location") +
theme_minimal()
## Warning: Removed 2964 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
# Check if there are any non-missing values in the `location` column
sum(!is.na(restaurant_data_with_status$location))
## [1] 19726
# Check the unique values in the `location` column
unique(restaurant_data_with_status$location)
## [1] "1" "2" "3" "4"
table_status_location <- table(restaurant_data_with_status$status, restaurant_data_with_status$location)
assocplot(table_status_location,
xlab = "Status", ylab = "Location",
main = "Association between Restaurant Survival and Location")
# User ratings for different restaurants
user_ratings <- restaurant_data %>%
group_by(rest_name) %>%
summarize(avg_rating = mean(rating, na.rm = TRUE))
# Bar plot for user ratings of different restaurants
top_restaurants <- user_ratings %>%
filter(!is.na(avg_rating)) %>% # Remove NAs
arrange(desc(avg_rating)) %>%
head(10)
ggplot(top_restaurants, aes(x = reorder(rest_name, -avg_rating), y = avg_rating)) +
geom_bar(stat = "identity", fill = "steelblue") +
xlab("Restaurant") +
ylab("Average Rating") +
ggtitle("Top 10 Restaurants by User Rating") +
theme(axis.text.x = element_text(angle = 45, hjust = 1, size = 8)) + # Adjust the font size
theme_minimal() +
scale_x_discrete(labels = function(x) str_wrap(x, width = 10)) # Wrap long labels
# Mosaic plot for restaurant survival and location
table_status_location <- na.omit(table_status_location) # Remove NAs
mosaicplot(table_status_location,
main = "Mosaic Plot of Restaurant Survival and Location",
xlab = "Status", ylab = "Location",
shade = TRUE)
# Locations where users traveled
user_locations <- restaurant_data %>%
group_by(location) %>%
summarize(count = n())
# Dot chart for top locations visited by users
top_locations <- user_locations %>%
filter(!is.na(count)) %>% # Remove NAs
arrange(desc(count)) %>%
head(10)
dotchart(top_locations$count, labels = top_locations$location,
main = "Top Locations Visited by Users",
xlab = "Number of Visits",
color = "steelblue")
# Build a decision tree model to predict restaurant survival
set.seed(123)
train_index <- createDataPartition(restaurant_data$status, p = 0.8, list = FALSE)
train_data <- restaurant_data[train_index, ]
test_data <- restaurant_data[-train_index, ]
test_data$status
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## [2329] "in rating" "in rating" "in rating" "in rating" "redirect" "closed"
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## [2347] "in rating" "in rating" "in rating" "redirect" "in rating" "redirect"
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## [2371] "in rating" "in rating" "closed" "in rating" "in rating" "redirect"
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## [2383] "closed" "in rating" "redirect" "in rating" "in rating" "in rating"
## [2389] "in rating" "closed" "in rating" "in rating" "in rating" "in rating"
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## [2407] "in rating" "closed" "in rating" "in rating" "in rating" "in rating"
## [2413] "in rating" "in rating" "in rating" "redirect" "closed" "closed"
## [2419] "closed" "in rating" "closed" "in rating" "in rating" "closed"
## [2425] "in rating" "redirect" "in rating" "in rating" "closed" "closed"
## [2431] "in rating" "in rating" "in rating" "in rating" "in rating" "in rating"
## [2437] "in rating" "redirect" "in rating" "in rating" "closed" "in rating"
## [2443] "in rating" "in rating" "in rating" "in rating" "in rating" "in rating"
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# Convert the 'status' column to a factor
restaurant_data_with_status$status <- as.factor(restaurant_data_with_status$status)
# Select the relevant features and the target variable
features <- c("rating", "location", "traveller_rating_4", "service_rating")
target <- "status"
train_data <- train_data[, c(features, target)]
test_data <- test_data[, c(features, target)]
# Remove rows with missing values
train_data <- na.omit(train_data)
# Set the number of folds for cross-validation
num_folds <- 5
# Create a train control object
train_control <- trainControl(method = "cv", number = num_folds)
library(rpart)
# Train the decision tree model
tree_model <- train(status ~ ., data = train_data,
method = "rpart",
trControl = train_control,
tuneLength = 10,
na.action = na.omit)
# Print the best model parameters
print(tree_model)
## CART
##
## 6265 samples
## 4 predictor
## 4 classes: 'closed', 'duplicate', 'fb_closed', 'in rating'
##
## No pre-processing
## Resampling: Cross-Validated (5 fold)
## Summary of sample sizes: 5012, 5013, 5011, 5012, 5012
## Resampling results:
##
## Accuracy Kappa
## 0.950519 0
##
## Tuning parameter 'cp' was held constant at a value of 0