# Look at data
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library(tidyverse)
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library(DBI)
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library(RSQLite)
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gpsData <- read.csv("data/gps.csv")
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gpsDataTibble <- as_tibble(gpsData)
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#workingTibble <- head(gpsDataTibble, 500000)
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workingTibble <- gpsDataTibble
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playerIds <-unique(workingTibble$PlayerID)
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cat("Number of Players: ", length(playerIds), sep="")
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gameIds <- unique(workingTibble$GameID)
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cat("Number of Games: ", length(gameIds), sep="")
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playerIDMetrics <- c()
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gameIDMetrics <- c()
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averageSpeed <- c()
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accelDistance <- c()
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for(playerID in playerIds)
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{
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for(gameID in gameIds)
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{
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cat(playerID, gameID , '\n', sep=" ")
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speedTibble <- subset(workingTibble, GameID == gameID & PlayerID == playerID)
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# crunch average speed
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averageSpeed <- c(averageSpeed, mean(speedTibble$Speed, na.rm = 0))
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# average for accel value
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accelDistance <- c(accelDistance, mean(sqrt(speedTibble$AccelX^2 + speedTibble$AccelY^2 + speedTibble$AccelZ^ 2), na.rm = 0))
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# game and player id to vector
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playerIDMetrics <- c(playerIDMetrics, playerID)
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gameIDMetrics <- c(gameIDMetrics, gameID)
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}
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}
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plot(accelDistance, averageSpeed)
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compressedMetrics <- tibble(gameID = gameIDMetrics, playerID = playerIDMetrics, averageSpeed = averageSpeed, accelerationVector = accelDistance)
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length(compressedMetrics$averageSpeed)
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length(compressedMetrics$accelerationVector)
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write.csv(compressedMetrics, "data/speedData.csv")
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#putSQLiteHere <- "gpsData.sqlite" # could also be ":memory:"
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#mySQLiteDB <- dbConnect(RSQLite::SQLite(),putSQLiteHere)
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#dbWriteTable(mySQLiteDB, "gpsData", compressedMetrics, overwrite=TRUE)
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#dbDisconnect(mySQLiteDB)
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rpeData <- read.csv("./data/rpe.csv")
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rpeDataTibble <- as_tibble(rpeData)
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gameData <- read.csv("./data/game.csv")
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gameDataTibble <- as_tibble(gameData)
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wellnessData <- read.csv("./data/wellness.csv")
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wellnessDataTibble <- as_tibble(wellnessData)
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head(gpsData)
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