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movies <- read.csv("C:/Users/Prasad/Downloads/imdb.csv")
# Load library
library(lubridate)
## Warning: package 'lubridate' was built under R version 4.3.2
## 
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union
library(tsibble)
## Warning: package 'tsibble' was built under R version 4.3.2
## 
## Attaching package: 'tsibble'
## The following object is masked from 'package:lubridate':
## 
##     interval
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, union
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.3.2
df <- data.frame(
  date = c("2020-01-15", "2020-02-28", "2020-03-31"), 
  revenue = c(100, 150, 200)
) 

# Convert to Date format
df$date <- as.Date(df$date)

# Or, convert from separate year/month/day columns 
year <- c(2020, 2020, 2020)
month <- c(1, 2, 3) 
day <- c(15, 28, 31)
df$date <- as.Date(paste(year, month, day, sep = "-"))

# Print excerpt to show Date format          
head(df)
##         date revenue
## 1 2020-01-15     100
## 2 2020-02-28     150
## 3 2020-03-31     200
#The key steps are using as.Date() to convert a character column, or pasting together separate pieces like year/month/day. This preserves the date information in a standardized Date format

#the focus is on temporal data preparation. If a time-based column exists, it should be converted into the Date format using functions like as.Date() or to_datetime(). It's crucial to ensure the correct date format is specified for accurate analysis. In cases where a time column is absent, an alternative involves selecting a Wikipedia page related to the dataset and extracting a time series of page views using the Wikipedia page views website or relevant R packages.
# Assuming you have a dataset named 'your_data' with a column 'SalesRevenue'
# Make sure to replace 'your_data' and 'SalesRevenue' with your actual data and column name.

# Load necessary libraries
library(ggplot2)

# Assuming your data has a column for dates named 'Date'
# If not, replace 'Date' with the actual column name representing time.

# Assuming your_data is a data frame with a column named 'Date' and 'SalesRevenue'
# Convert 'Date' to a Date object if it's not already
df$date <- as.Date(df$date)

# Plotting sales revenue over time
# Assuming you have a dataset named 'your_data' with a column 'SalesRevenue'
# Make sure to replace 'your_data' and 'SalesRevenue' with your actual data and column name.

# Load necessary libraries
library(ggplot2)

# Assuming your data has a column for dates named 'Date'
# If not, replace 'Date' with the actual column name representing time.

# Assuming your_data is a data frame with a column named 'Date' and 'SalesRevenue'
# Convert 'Date' to a Date object if it's not already
df$date <- as.Date(df$date)

# Plotting sales revenue over time
# Assuming you have a dataset named 'your_data' with a column 'revenue'
# Make sure to replace 'your_data' and 'revenue' with your actual data and column name.

# Load necessary libraries
library(ggplot2)
library(lubridate)

# Assuming your_data is a data frame with a column named 'date' and 'revenue'
# Convert 'date' to a Date object if it's not already
df$date  <- as.Date(df$date )

# Assuming you have a dataset named 'your_data' with a column 'revenue'
# Make sure to replace 'your_data' and 'revenue' with your actual data and column names.

# Load necessary libraries
library(ggplot2)
library(lubridate)

# Assuming your_data is a data frame with a column named 'date' and 'revenue'
# Convert 'date' to a Date object if it's not already
df$date <- as.Date(df$date)
#After establishing a temporal foundation, attention shifts to choosing a response variable for analysis. This variable should exhibit patterns or trends over time. Following this, a tsibble object is created, allowing for efficient time series analysis. The ggplot2 package aids in visualizing the response variable over time, providing insights into immediate trends, patterns, or anomalies.
# Install and load necessary packages
# Assuming your_data is a data frame with columns named 'date' and 'revenue'
# Convert 'date' to a Date object if it's not already
df$date <- as.Date(df$date)
# Create a time series data frame
class(frequency)
## [1] "function"
# Correct assignment
frequency <- 12  # Replace 12 with your actual frequency value
frequency <- as.numeric(frequency)
frequency <- function(x) { ... }
# Assuming df$date is a vector of date values
start_date <- as.Date("2023-01-01")
end_date <- as.Date("2023-12-31")
frequency <- 12

# Create a time series object
ts_data <- ts(data = df$date, start = start_date, end = end_date, frequency = frequency)


# Now you can use 'ts_data' in your analysis

# Create a tsibble object
tsib <- as_tsibble(df, key = NULL)
## Using `date` as index variable.
# Fit linear model
fit <- lm(revenue ~ budget_x + score, data = movies)
plot(fit)

summary(fit)
## 
## Call:
## lm(formula = revenue ~ budget_x + score, data = movies)
## 
## Residuals:
##        Min         1Q     Median         3Q        Max 
## -849724115 -149235154  -37765915  122488565 1906220079 
## 
## Coefficients:
##               Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -5.473e+08  8.601e+07  -6.363 5.79e-10 ***
## budget_x     3.878e+00  2.011e-01  19.285  < 2e-16 ***
## score        8.495e+06  1.186e+06   7.164 4.21e-12 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 280800000 on 374 degrees of freedom
## Multiple R-squared:  0.5189, Adjusted R-squared:  0.5163 
## F-statistic: 201.7 on 2 and 374 DF,  p-value: < 2.2e-16
#Linear regression comes into play for detecting upward or downward trends in the time series data. The lm() function is employed, and the resulting coefficients and p-values offer valuable information about the direction and significance of identified trends. If multiple trends exist within the data, subsets may be created to explore variations in different time periods.
# Check assumptions and interpret the coefficients:
ggplot(fit, aes(x = .fitted, y = .resid)) + 
  geom_point() +
  geom_smooth()
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

 # Create a ggplot object and specify the data
p <- ggplot(data = movies, aes(x = budget_x))
##This comprehensive approach involves data preparation, exploratory visualization, statistical analysis, and detection of patterns in time series data. It allows for a nuanced understanding of trends, seasonality, and potential areas for further investigation. Adjustments and iterations can be made based on the specific characteristics and goals of the dataset.