Shiny-Data-Loader.Rmd

---
title: "Shiny Data Loader"
author: "James Topor"
date: "July 11, 2017"
output: html_document
---

Make sure to load the 'readr' and 'dplyr' libraries
```{r, warning = FALSE, message = FALSE}
library(knitr)
library(readr)
library(dplyr)

set.seed(1)
```

# Load Artist + Genre Tag Info

Each of these files needs to be loaded into a data frame so that we can 
pull the required artist names or genre names when needed

```{r, warning = FALSE, message = FALSE}
# load list of last.fm artists: drop columns containing URL's since they aren't needed
lfm_art <- read_delim("https://raw.githubusercontent.com/jtopor/CUNY-MSDA-643/master/FP/artists.dat", delim = "\t") %>% select(id, name)

# cleanup foreign characters in artist names: most will be converted to '?'
lfm_art$name <- iconv(lfm_art$name, from = "UTF-8", to = "ASCII//TRANSLIT")

# ----------------------------

# load last.fm tags.dat file
lfm_tags <- read_delim("https://raw.githubusercontent.com/jtopor/CUNY-MSDA-643/master/FP/tags.dat", delim = "\t")
```




# Load + Use UBCF recommendations

Given a user, display a list of 10 recommended artists based on output of UBCF

```{r}
# load UBCF recs for each user (10 artists recommendations per user)
user_tenrecs <- read.csv("https://raw.githubusercontent.com/RobertSellers/R_Shiny_Recommender/master/R-Obj-CSVs/user_tenrecs.csv", 
                  header=TRUE, sep = ",", stringsAsFactors = FALSE)

###############################################
# now randomly select a user and display their 10 UBCF recommended artists

# randomly select a user
user <- sample(user_tenrecs$userID, 1)

# fetch their recommendations
urecs <- sort(as.vector(subset(user_tenrecs, userID == user)[2:11]) )

# create list of artist names from artist ID's in list
rec_names <- subset(lfm_art, id %in% urecs)$name

kable(rec_names, col.names = "Artists You Might Enjoy")

```

_____

# Load + Use Artist-Genre Matrix 

Given a specific genre, generate list of top 5 artists from that genre

```{r}
# load artist-genre matrix
ag_mat <- as.matrix(read.csv("https://raw.githubusercontent.com/RobertSellers/R_Shiny_Recommender/master/R-Obj-CSVs/ag_mat.csv", check.names = FALSE,
                  header=TRUE, sep = ",", stringsAsFactors = FALSE) )

# set rownames to values in V1
row.names(ag_mat) <- as.numeric(ag_mat[,1])

# now truncate matrix to eliminate col 1
ag_mat <- ag_mat[,2:ncol(ag_mat)]


##############################################
# now simulate finding top 5 artists for a genre

# this is only here for random number generation: delete in production mode
set.seed(42)

# extract the genre tagIDs from matrix and convert to numeric
tagIDs <- as.numeric(colnames(ag_mat))

# set number of artists to recommend
n_recommended <- 5

# randomly select a genre
tagID <- sample(tagIDs, 1)

# get name of genre from tagID list
g_name <- lfm_tags[lfm_tags$tagID == tagID,]$tagValue

# fetch the top N artists:
# the names of the items are the artist IDs
g_arecs <- sort(ag_mat[,as.character(tagID)], decreasing = TRUE)[1:n_recommended]

# extract the artist IDs and convert to numeric
g_arecs_IDs <- as.numeric(names(g_arecs))

# create list of artist names from artist ID's in list
g_arec_names <- lfm_art[lfm_art$id %in% g_arecs_IDs,]$name

# create a heading for the list of similar artists
table_head <- sprintf("Top Artists in %s genre:", g_name)

# display the list of similar artists
kable(g_arec_names, col.names = table_head)

```

_____

# Load + Use Artist Similarity Matrix:

Given a specific artist, generate list of top 5 similar artists

```{r}
# load artist similarity matrix
art_sim <- as.matrix(read.csv("https://raw.githubusercontent.com/RobertSellers/R_Shiny_Recommender/master/R-Obj-CSVs/art_sim.csv", check.names = FALSE,
                  header=TRUE, sep = ",", stringsAsFactors = FALSE) )

# set rownames to values in V1
row.names(art_sim) <- as.numeric(art_sim[,1])

# now truncate matrix to eliminate col 1
art_sim <- art_sim[,2:ncol(art_sim)]


####################################
# now simulate finding top 5 artists similar to a specific artist

# set number of similar artists to recommend
n_recommended <- 5

# randomly select a user
artist <- as.numeric(sample(rownames(art_sim), 1) )

# get name of artist from artist list
a_name <- lfm_art[lfm_art$id == artist,]$name

# fetch their recommendations: this returns a named vector sorted by similarity
# the names of the items are the artist IDs
arecs <- sort(art_sim[as.character(artist),], decreasing = TRUE)[1:n_recommended]

# extract the artist IDs and convert to numeric
arecs_IDs <- as.numeric(names(arecs))

# create list of artist names from artist ID's in list
arec_names <- lfm_art[lfm_art$id %in% arecs_IDs,]$name

# create a heading for the list of similar artists
table_head <- sprintf("Artists Similar to %s", a_name)

# display the list of similar artists
kable(arec_names, col.names = table_head)

```


_____

# Load + Use User-Artist Dataframe

Use this object to produce a list of artists a user has listened to

```{r}
# load user-artist data frame: to be used for displaying list of artists
# a user has listened to.
last_sm <- read.csv("https://raw.githubusercontent.com/RobertSellers/R_Shiny_Recommender/master/R-Obj-CSVs/last_sm.csv", 
                  header=TRUE, sep = ",", stringsAsFactors = FALSE)

################################################
# display list of artists previously listened to for a given user

# randomly select a user
user <- sample(last_sm$userID, 1)

# get distinct artist ID's they've listened to 
user_arts <- last_sm$artistID[last_sm$userID == user]

# create list of artist names from artist ID's in list
ul_names <- lfm_art[lfm_art$id %in% user_arts,]$name

# create a heading for the list of artists
table_head <- "Artists You Have Listened To"

# display the list of artists
kable(ul_names, col.names = table_head)
```