Merge branch 'master' of https://github.com/PerryXDeng/wheatyeeters

data_preparation/cleaned/fatigue_total_sum.csv

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data_preparation/cleaned/time_series_days_ranked.csv

@@ -0,0 +1,18 @@
+,Date,DailyElo
+0,121,0.0
+1,122,-3.714599999999998
+2,178,0.04346000000000028
+3,179,2.1916710000000013
+4,180,0.0
+5,255,0.0
+6,256,0.0
+7,257,-2.520374784999996
+8,263,-2.0880156214999985
+9,264,-1.7032140593500005
+10,284,-0.6130256153877235
+11,285,2.620463284090865
+12,311,-2.076954630427971
+13,312,1.0960590427574828
+14,313,1.8954531384817344
+15,353,-0.2940921753664384
+16,354,-1.8646829578297937

data_preparation/createWorkSequenceData.R

@@ -0,0 +1,50 @@
+source("readData.R")
+
+library(tidyverse)
+
+
+RPEData <-readNArpeData()
+
+
+numDays <- max(RPEData$TimeSinceAugFirst)
+
+
+dayList <- 0:numDays
+workLoad <- c()
+averageWorkLoad <- c()
+
+
+for(day in dayList)
+{
+  total <- 0
+  
+  daylyActivities <- subset(RPEData, TimeSinceAugFirst == day)
+  cat("day: ", day, "\n",sep="")
+  cat("Activity count:", length(daylyActivities$DailyLoad), "\n", sep="")
+  
+  averageWorkLoad <- c(averageWorkLoad, mean(daylyActivities$SessionLoad, na.rm = T))
+  workLoad <- c(workLoad, sum(daylyActivities$SessionLoad, na.rm = T))
+}
+plot(dayList, averageWorkLoad, main="Average Work Load")
+plot(dayList, workLoad, main="Daily Total Work Load")
+
+
+slidingAverage <- c()
+
+window <- 21 - 1
+for(day in window:numDays)
+{
+  print(length(workLoad[c((day-window):day)]))
+  windowAverage <- mean(workLoad[c((day-window):day)])
+  
+  slidingAverage <- c(slidingAverage, windowAverage)
+}
+
+plot(window:numDays, slidingAverage, main="Sliding Average")
+plot(density(slidingAverage), main="Sliding Average Density")
+plot(density(workLoad), main="Total Work Load Average")
+
+
+dataTibble <- tibble(TimeSinceAugFirst = window:numDays, slidingWorkAverage = slidingAverage)
+
+write.csv(dataTibble, "cleaned/slidingWorkAverage.csv")

data_preparation/dataPrep.R

@@ -7,7 +7,7 @@ library(tidyverse)
 library(DBI)
 library(RSQLite)
 
-gpsData <- read.csv("data/gps.csv")
+gpsData <- read.csv("data/gps.csv")c
 
 
 gpsDataTibble <- as_tibble(gpsData)

data_preparation/normalizeData.R

@@ -83,11 +83,11 @@ for(id in playerIds)
 }
 
 
-normalWellnessData <- tibble(date = normDate, playerID = normPlayerIDs, normSoreness = normSoreness, 
+normalWellnessData <- tibble(TimeSinceAugFirst = normDate, playerID = normPlayerIDs, normSoreness = normSoreness, 
                             normFatigue = normFatigue, normDesire = normDesire, normIrritability = normIrritability,
                             normSleepHours = normSleepHours, normSleepQuality = normSleepQuality)
 
-write.csv(normalWellnessData, "cleaned/normalizedWellness.csv")
+write.csv(normalWellnessData, "cleaned/time_series_normalized_wellness.csv")
 
 
 plot()

data_preparation/readData.R

@@ -13,5 +13,11 @@ readWellnessData <- function()
 
 readRPEData <- function()
 {
-  as_tibble(read.csv("./cleaned/notnormalized_with_0Nan_rpe.csv"))
+  as_tibble(read.csv("./cleaned/time_series_notnormalized_with_continuousNan_rpe.csv"))
+}
+
+
+readNArpeData <- function()
+{
+  as_tibble(read.csv("./cleaned/time_series_notnormalized_with_continuousNan_rpe.csv"))
 }

data_preparation/replaceNanWithMedian.R

@@ -0,0 +1,56 @@
+source("readData.R")
+
+library(tidyverse)
+
+
+# file to replace NA values with the median for thet column
+
+
+trainingData <- readRPEData()
+
+#duration
+trainingData$Duration[is.na(trainingData$Duration)] <- median(trainingData$Duration, na.rm=TRUE)
+
+
+print(trainingData$Duration)
+
+
+
+#RPE
+trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
+
+# acute load
+trainingData$AcuteLoad[is.na(trainingData$AcuteLoad)] <- median(trainingData$AcuteLoad, na.rm=TRUE)
+
+# chronic load
+trainingData$ChronicLoad[is.na(trainingData$ChronicLoad)] <- median(trainingData$ChronicLoad, na.rm=TRUE)
+
+# ratio
+trainingData$AcuteChronicRatio[is.na(trainingData$AcuteChronicRatio)] <- median(trainingData$AcuteChronicRatio, na.rm=TRUE)
+
+# objective rating
+trainingData$ObjectiveRating[is.na(trainingData$ObjectiveRating)] <- median(trainingData$ObjectiveRating, na.rm=TRUE)
+
+
+# focus rating
+trainingData$FocusRating[is.na(trainingData$FocusRating)] <- median(trainingData$FocusRating, na.rm=TRUE)
+
+
+
+# session load
+trainingData$SessionLoad[is.na(trainingData$SessionLoad)] <- median(trainingData$SessionLoad, na.rm=TRUE)
+
+# daily load
+trainingData$DailyLoad[is.na(trainingData$DailyLoad)] <- median(trainingData$DailyLoad, na.rm=TRUE)
+
+
+trainingData$RPE[is.na(trainingData$RPE)] <- median(trainingData$RPE, na.rm=TRUE)
+
+write.csv(as.data.frame(trainingData), "cleaned/time_series_rpe_NA_ReplacedWithMedian.csv")
+
+
+
+head(as.data.frame(trainingData), 100)
+
+
+trainingData

data_preparation/vectorization_ex.py

@@ -47,5 +47,34 @@ class WellnessCSV:
         df.to_csv(self.end)
 
 
-cls = WellnessCSV()
-cls.vectorize()
+class FatigueSum:
+    def __init__(self):
+        self.file = "cleaned/time_series_normalized_wellness.csv"
+        self.end = "cleaned/fatigue_total_sum.csv"
+
+    def calculate(self):
+        df = pd.read_csv(self.file)
+
+        # get some of the fatigue for a particular date
+        diction = dict()
+        dates = df["TimeSinceAugFirst"].unique()
+        dates = set(dates)
+        dates = list(dates)
+
+        # for each date, get unique data and get calculation
+        for date in dates:
+            pdf = df[df["TimeSinceAugFirst"] == date]
+            num_players = len(pdf["playerID"].unique())
+            fatigue_sum = pdf["normFatigue"].sum()
+            result = fatigue_sum / num_players
+            diction[date] = result
+
+        # Converting
+        dates = diction.keys()
+        values = diction.values()
+
+        final_df = pd.DataFrame()
+        final_df["TimeSinceAugFirst"] = dates
+        final_df["fatigueSum"] = values
+        final_df.to_csv(self.end)
+

elo_per_day.py

@@ -0,0 +1,33 @@
+import numpy as np
+import pandas as pd
+
+
+def join_cols():
+
+    # Reads in csv files to be manipulated
+    dfg = pd.read_csv('data_preparation/data/games_ranked.csv')
+
+    # Creates the new dataframe where each date is a unique column, and gets the number of dates
+    unique_dates = pd.DataFrame(dfg["Date"].unique()).to_numpy()
+    unique_rows = unique_dates.shape[0]
+    daily_elos = np.array(unique_rows).astype(float)
+    print(unique_rows)
+
+    # Creates two numpy arrays to perform some operations on
+    dates = dfg["Date"].to_numpy()
+    e_change = dfg["eloChangeAdjusted"].to_numpy()
+    rows = dates.shape()[0]
+
+    # sums up the elo change on a given day and then exports it to a unique .csv file
+    x = 0
+    for i in range(0, rows):
+        if not (dates[i] == unique_dates[x]):
+            x = x + 1
+        daily_elos[x] = daily_elos[x] + e_change[i]
+
+    # Creates a new dataframe from the two unique date array and the daily elo change array
+    df_dec = pd.DataFrame()
+    df_dec["Date"] = unique_dates
+    df_dec["DailyElo"] = daily_elos
+    print(df_dec)
+