Complete Data Project in R (Example) | Import, Manipulation, Graphics, Statistical Analysis & PCA скачать в хорошем качестве

Complete Data Project in R (Example) | Import, Manipulation, Graphics, Statistical Analysis & PCA

How to conduct a complete data project in the R programming language. Overview of the courses offered by Statistics Globe: https://statisticsglobe.com/courses Link to data: https://www.kaggle.com/datasets/nelgi... R Code: install.packages("dplyr") # Install & load dplyr library(dplyr) install.packages("ggplot2") # Install & load ggplot2 library(ggplot2) my_path ＜- "C:/Users/Joachim Schork/Dropbox/Jock/Data Sets/" # Path my_spotify ＜- read.csv(paste0(my_path, # Import data "Most_Streamed_Spotify_Songs_2024.csv")) my_spotify ＜- my_spotify %＞% # Modify data select("Spotify.Streams", "Spotify.Playlist.Count", # Select relevant columns "Spotify.Playlist.Reach", "YouTube.Views", "YouTube.Likes", "TikTok.Posts", "TikTok.Likes", "TikTok.Views", "YouTube.Playlist.Reach", "AirPlay.Spins", "Deezer.Playlist.Reach", "Pandora.Streams", "Shazam.Counts", "Explicit.Track") %＞% select(sort(names(.))) %＞% # Sort columns mutate(across(where(is.character), ~na_if(.x, ""))) %＞% # Replace empty cells with NA mutate(across(where(is.character), # Modify commas & class ~as.numeric(gsub(",", "", .x)))) %＞% mutate(across(-Explicit.Track, ~ifelse(. ＜ 10, NA, .))) %＞% # Replace below 10 with NA na.omit() # Remove rows with NA ggplot(my_spotify, # Draw density plot of Streams aes(x = Spotify.Streams)) + geom_density() ggplot(my_spotify, # Grouped densities aes(x = Spotify.Streams, col = factor(Explicit.Track))) + geom_density() my_spotify %＞% # Improve layout of plot mutate(Explicit.Track = ifelse(Explicit.Track == 1, # Change legend items "Yes", "No")) %＞% ggplot(aes(x = Spotify.Streams, # Specify plot variables color = Explicit.Track)) + geom_density(linewidth = 1) + # Draw density labs(title = "Density Plot of Spotify Streams by Explicit Track", # Plot labels x = "Spotify Streams", y = "Density", color = "") + theme_minimal(base_size = 14) + # Specify plot theme theme(plot.title = element_text(face = "bold", hjust = 0.5), legend.position = "top") my_mod ＜- lm(Spotify.Streams ~ ., my_spotify) # Estimate linear regression summary(my_mod) # Print summary statistics my_spotify_x ＜- my_spotify %＞% # Create subset of predictors select(-Spotify.Streams) pca_result ＜- prcomp(my_spotify_x, scale. = TRUE) # Apply PCA summary(pca_result) # Summary of PCA results my_spotify_pca ＜- data.frame(Spotify.Streams = my_spotify$Spotify.Streams, # Combine data pca_result$x[ , 1:5]) my_mod_pca ＜- lm(Spotify.Streams ~ ., data = my_spotify_pca) # Apply PCR summary(my_mod_pca) # Summary of PCR results Follow me on Social Media: LinkedIn – Joachim Schork Profile:   / linkedin   – Statistics Globe Page:   / statisticsglobe   LinkedIn – R Programming Group for Discussions & Questions:   / 12555223   LinkedIn – Python Programming Group for Discussions & Questions:   / 12673534   X (Formerly Twitter): https://x.com/JoachimSchork Facebook – Joachim Schork Profile:   / facebook   – Statistics Globe Page:   / statisticsglobecom   Facebook – R Programming Group for Discussions & Questions:   / statisticsglobe   Facebook – Python Programming Group for Discussions & Questions:   / statisticsglobepython   Instagram:   / statisticsglobecom   TikTok:   / statisticsglobe