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146 lines
3.7 KiB
146 lines
3.7 KiB
# 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/games.csv")
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gameDataTibble <- as_tibble(gameData)
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wellnessData <- read.csv("./data/wellness_na.csv")
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wellnessDataTibble <- as_tibble(wellnessData)
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wellnesPlayer1 <- subset(wellnessDataTibble, PlayerID == 1)
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plot(wellnesPlayer1$Fatigue * wellnesPlayer1$Soreness * wellnesPlayer1$Irritability, wellnesPlayer1$SleepHours * wellnesPlayer1$SleepQuality)
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wellnessCleaned <- as_tibble(read.csv("./cleaned/dirty_wellness.csv"))
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ggplot(data = wellnessCleaned) +
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theme(plot.title = element_text(hjust = 0.5)) +
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ggtitle("Hours of Sleep Box Plot") +
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geom_boxplot(na.rm = T, mapping = aes(y=SleepHours, group = PlayerID), outlier.colour = "red", outlier.shape = 1) +
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labs(group = "Player ID", y = "Hours of Sleep") +
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coord_flip() +
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theme_bw()
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ggplot(data = wellnessCleaned) +
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theme(plot.title = element_text(hjust = 0.5)) +
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ggtitle("Hours of Sleep Box Plot") +
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geom_boxplot(na.rm = T, mapping = aes(y=Fatigue, group = PlayerID), outlier.colour = "red", outlier.shape = 1) +
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labs(group = "Player ID", y = "Fatigue Score") +
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coord_flip() +
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theme_bw()
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ggplot(data = wellnessCleaned) +
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theme(plot.title = element_text(hjust = 0.5)) +
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ggtitle("Sleep Quality Box Plot") +
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geom_boxplot(na.rm = T, mapping = aes(y=SleepQuality, group = PlayerID), outlier.colour = "red", outlier.shape = 1) +
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labs(group = "Player ID", y = "Sleep Quality") +
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coord_flip() +
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theme_bw()
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ggplot(data = wellnessCleaned) +
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theme(plot.title = element_text(hjust = 0.5)) +
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ggtitle("Training Readiness Box Plot") +
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geom_boxplot(na.rm = T, mapping = aes(y=TrainingReadinessNum, group = PlayerID), outlier.colour = "red", outlier.shape = 1) +
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labs(group = "Player ID", y = "Training Readiness") +
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coord_flip() +
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theme_bw()
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max(wellnessCleaned$SleepHours, na.rm = T)
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min(wellnessCleaned$SleepHours, na.rm = T)
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playerIdsWellness <-unique(wellnessCleaned$PlayerID)
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cat("Number of Players: ", length(playerIdsWellness), sep="")
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head(gpsData)
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