R-Codes Data Analysis GPT Prompt

Data Loading and Preprocessing:
Import the dataset using read_csv method
tweets <- read.csv("tweets_data.csv")
Check for missing values and handle them appropriately
Check for missing values
missingvalues <- colSums(is.na(tweets))
print(missingvalues)
Handle missing values by imputation or removal
tweets <- na.omit(tweets)
Clean and preprocess the data
Convert date column to proper date format
tweets$date <- as.Date(tweets$date)
Convert text to lowercase for consistency
tweets$text <- tolower(tweets$text)
Exploratory Data Analysis (EDA):
Generate summary statistics for key variables
summary(tweets)
Create visualizations to understand data distribution and relationships
Histogram of tweet lengths
hist(nchar(tweets$text), xlab = "Tweet Length", main = "Distribution of Tweet Lengths")
Relationship between retweets and favorites
plot(tweets$retweets, tweets$favorites, xlab = "Retweets", ylab = "Favorites", main = "Retweets vs Favorites")
Data Manipulation:
Perform necessary transformations on the data if required
Log transformation on retweets
tweets$log_retweets <- log(tweets$retweets + 1)
Filter data based on specified conditions
Filter tweets with more than 100 characters
tweets_long <- tweets[nchar(tweets$text) > 100, ]
Group data based on date
tweetsbydate <- aggregate(retweets ~ date, data = tweets, FUN = sum)
Statistical Analysis:
Apply statistical tests as needed
Conduct correlation analysis
correlation <- cor(tweets$retweets, tweets$favorites)
print(correlation)
Conduct regression analysis
model <- lm(favorites ~ retweets, data = tweets)
summary(model)
Visualization and Reporting:
Generate informative visualizations for insights
Scatter plot of retweets vs favorites
plot(tweets$retweets, tweets$favorites, xlab = "Retweets", ylab = "Favorites", main = "Retweets vs Favorites")
Summarize findings
cat("The correlation between retweets and favorites is:", correlation)
cat("\nRegression Analysis Summary:")
print(summary(model))