Title: Cleaning and Exploratory Analysis of New Haven Runs Data
Synopsis: In this project I clean and merge two data sets of the New Haven marathon in order to determine the change in performance of repeat runners and to explore changes in run time for both male and female runners.
Skills: Data cleaning, bootstrapping, lubridate, function writing, permutation testing
Data
https://www.dropbox.com/s/n5abps8hvnnpgae/NHRR2017.csv?dl=0 https://www.dropbox.com/s/zu1ypbqovlfv056/NHRR2016.csv?dl=0
- Data Cleaning.
To clean the data I wrote an R function that takes in the raw data frame and returns the cleaned data frame (Div parsed into columns Gender and AgeGrp; time columns parsed into decimal minutes).
Hint: You should be able to just copy sections of code from Tuesday’s or Thursday’s lecture, changing up the variable names to be more generic. Do not include any unnecessary lines of code that simply print frequency tables or make plots inside of the function.
Cleaning my global environment and loading the data
rm(list = ls())
setwd("/Users/Roberta/Desktop/Data Analysis & Exploration/HW4")
x2016 <- read.csv("NHRR2016.csv", as.is = TRUE)
x2017 <- read.csv("NHRR2017.csv", as.is = TRUE)library(lubridate) #to convert time variable from a character to a time variable##
## Attaching package: 'lubridate'## The following object is masked from 'package:base':
##
## dateconvertTimes <- function(v) {
hourplus <- nchar(v) == 7
outtimes <- ms(v)
if (sum(hourplus) > 0) { # if there is at least 1 time that exceeds 1 hr
outtimes[hourplus] <- hms(v[hourplus])
}
outtimes <- as.numeric(outtimes)/60
return(outtimes)
}Your function below:
cleanNHData <- function(data) {
data <- data
data$Div[data$Div == ""] <- NA
#Extracting gender variable
data$Gender <- substr(data$Div, 1, 1)
data$AgeGrp <- substr(data$Div, 2, nchar(data$Div))
#Converting our various time variables into
data$Nettime_min <- convertTimes(data$Nettime)
data$Time_min <- convertTimes(data$Time)
data$Pace_min <- convertTimes(data$Pace)
return(data)
}Cleaning New Haven Run 2016 Data
nh2016 <- cleanNHData(x2016)
head(nh2016)## No. Name City Div Nettime Time Pace Gender
## 1 4606 Jake Gurzler Manchester M30-39 15:49 15:49 5:06 M
## 2 4384 Patrick Galvin Stamford M20-29 15:51 15:52 5:07 M
## 3 4598 Aidan Pillard Washington M20-29 15:55 15:56 5:08 M
## 4 3745 Omar Perez Poughkeepsie M20-29 15:59 16:00 5:09 M
## 5 5807 Timothy Milenkevich Ansonia M30-39 16:12 16:14 5:14 M
## 6 3954 Tim Foldy-Porto Northampton M13-19 16:22 16:23 5:17 M
## AgeGrp Nettime_min Time_min Pace_min
## 1 30-39 15.81667 15.81667 5.100000
## 2 20-29 15.85000 15.86667 5.116667
## 3 20-29 15.91667 15.93333 5.133333
## 4 20-29 15.98333 16.00000 5.150000
## 5 30-39 16.20000 16.23333 5.233333
## 6 13-19 16.36667 16.38333 5.283333summary(nh2016)## No. Name City Div
## Min. : 233 Length:2655 Length:2655 Length:2655
## 1st Qu.:3460 Class :character Class :character Class :character
## Median :4178 Mode :character Mode :character Mode :character
## Mean :4174
## 3rd Qu.:4908
## Max. :6764
## Nettime Time Pace
## Length:2655 Length:2655 Length:2655
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Gender AgeGrp Nettime_min Time_min
## Length:2655 Length:2655 Min. : 15.82 Min. : 15.82
## Class :character Class :character 1st Qu.: 25.23 1st Qu.: 25.64
## Mode :character Mode :character Median : 29.20 Median : 29.70
## Mean : 30.28 Mean : 30.76
## 3rd Qu.: 33.85 3rd Qu.: 34.48
## Max. :109.70 Max. :110.28
## Pace_min
## Min. : 5.100
## 1st Qu.: 8.258
## Median : 9.567
## Mean : 9.906
## 3rd Qu.:11.100
## Max. :35.500Cleaning New Haven Run 2017 Data
nh2017 <- cleanNHData(x2017)
head(nh2017)## No. Name City Div Time Pace Nettime Gender AgeGrp
## 1 3376 Patrick Dooley Brooklyn M30-39 15:17 4:56 15:16 M 30-39
## 2 2884 Calvin Park Trumbull M20-29 15:19 4:56 15:18 M 20-29
## 3 2839 Jake Duckworth Monroe M20-29 15:29 4:59 15:28 M 20-29
## 4 1150 Scott Rodilitz New Haven M20-29 15:37 5:02 15:36 M 20-29
## 5 1567 Robert Dillon Shelton M13-19 15:47 5:05 15:46 M 13-19
## 6 4256 Nicholas Migani Higganum M20-29 16:00 5:09 15:59 M 20-29
## Nettime_min Time_min Pace_min
## 1 15.26667 15.28333 4.933333
## 2 15.30000 15.31667 4.933333
## 3 15.46667 15.48333 4.983333
## 4 15.60000 15.61667 5.033333
## 5 15.76667 15.78333 5.083333
## 6 15.98333 16.00000 5.150000summary(nh2017)## No. Name City Div
## Min. : 814 Length:2736 Length:2736 Length:2736
## 1st Qu.:3023 Class :character Class :character Class :character
## Median :3762 Mode :character Mode :character Mode :character
## Mean :3613
## 3rd Qu.:4502
## Max. :6627
## Time Pace Nettime
## Length:2736 Length:2736 Length:2736
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Gender AgeGrp Nettime_min Time_min
## Length:2736 Length:2736 Min. : 15.27 Min. : 15.28
## Class :character Class :character 1st Qu.: 25.45 1st Qu.: 25.90
## Mode :character Mode :character Median : 29.37 Median : 29.98
## Mean : 30.89 Mean : 31.48
## 3rd Qu.: 34.60 3rd Qu.: 35.22
## Max. :188.08 Max. :188.08
## Pace_min
## Min. : 4.933
## 1st Qu.: 8.346
## Median : 9.650
## Mean :10.137
## 3rd Qu.:11.333
## Max. :60.550- Exploratory Data Analysis. I assumed that the 2016 dataset had exactly the same format as the 2017 dataset, and that there were no unusual quirks specific to the 2016 dataset. Here I check to ensure that I’ve cleaned the 2016 dataset.
#General overview, the datasets were similar
summary(nh2016)
summary(nh2017)
dim(nh2016)
dim(nh2017)
#Identifying NAs, neither method identified any NAs in either dataset
sum(is.na(nh2016))
sum(is.na(nh2017))
anyNA(nh2016)
anyNA(nh2017)
#Gender
boxplot(Nettime_min ~ Gender, data=nh2016)
boxplot(Nettime_min ~ Gender, data=nh2017)
table(nh2016$Gender)
table(nh2017$Gender)
#Age Group
boxplot(Nettime_min ~ AgeGrp, data=nh2016)
boxplot(Nettime_min ~ AgeGrp, data=nh2017)
table(nh2016$AgeGrp)
table(nh2017$AgeGrp)
#Nettime_min
anyNA(nh2016$Nettime_min)
sum(is.na(nh2016$Nettime_min))
anyNA(nh2017$Nettime_min)
sum(is.na(nh2017$Nettime_min))
#Time_min
sum(is.na(nh2016$Time_min))
sum(is.na(nh2017$Time_min))
#Pace_min
anyNA(nh2016$Pace_min)
anyNA(nh2017$Pace_min)In searching the entirety of each dataset for NAs, I returned no NAs in either dataset. However, upon visually exploring my datasets I discovered a third gender category, the equivalent of an NA. 13 individuals in the 2016 dataset and 20 individuals in the 2017 dataset were missing Gender data. In repeating this I discovered similarly missing data values in my Age Group column (13 in 2016 and 20 in 2017). It is likely that this was a result of missing values in my Div columns of my x2016 and x2017 data frames and as a result I returned to my original CleanData function and removed these values. In running through my data a second time these 3rd gender values had be removed.
- Creating a Repeat Runners Dataset. I would like to explored improvements in participants who competed in both 2016 and 2017. To do so, I created a combined dataset where we have both 2016 and 2017 net runtimes for repeat runners exclusively.
# get rid of duplicates
nh2016[nh2016$Name == "Brian Miller",]## No. Name City Div Nettime Time Pace Gender AgeGrp
## 1751 5009 Brian Miller Alexandria M50-54 31:54 32:20 10:25 M 50-54
## 1991 5013 Brian Miller Alexandria M01-12 34:02 34:29 11:06 M 01-12
## Nettime_min Time_min Pace_min
## 1751 31.90000 32.33333 10.41667
## 1991 34.03333 34.48333 11.10000dupenames16 <- nh2016$Name[duplicated(nh2016$Name)]
dupenames17 <- nh2017$Name[duplicated(nh2017$Name)]
nh2016 <- nh2016[!(nh2016$Name %in% dupenames16),]
nh2017 <- nh2017[!(nh2017$Name %in% dupenames17),]
# create a vector of individuals who raced in both years' 5Ks
repeatrunners <- intersect(nh2016$Name, nh2017$Name)
length(repeatrunners) # 1029 runners took part in both the 2016 and the 2017 race## [1] 1029head(repeatrunners) ## [1] "Omar Perez" "Tim Foldy-Porto" "Scott Rodilitz" "Wade Davis"
## [5] "Robert Dillon" "Matt Chaston"# create the combined data frame with just the 2016 runtimes first
# we'll also just use the 2016 Gender variable (for convenience)
w <- nh2016$Name %in% repeatrunners # indices corresponding to the repeat runners
nhcombined <- data.frame(Name = nh2016$Name[w],
Gender = nh2016$Gender[w],
Nettime_2016 = nh2016$Nettime_min[w])
# Merge in 2017 dataset
nhcombined <- merge(nhcombined, nh2017[, c("Name", "Nettime_min")], all.x=TRUE)
colnames(nhcombined)[4] <- "Nettime_2017"Create a new variable in the data frame that is the improvement in run time from 2016 to 2017 (a positive number here should indicate an improvement, a negative number means they did worse in 2017). Then make a histogram of this variable. If there are extreme points, identify who they are (provide the names of the runners) and remove them from the dataset before redrawing the histogram.
nhcombined## Name Gender Nettime_2016 Nettime_2017
## 1 Abbey Shaw F 39.56667 40.25000
## 2 Abby Dziura F 30.43333 35.63333
## 3 Abby Ganzle F 31.53333 35.66667
## 4 Abigail Murphy F 31.43333 34.06667
## 5 Abraham Cordero M 36.83333 31.83333
## 6 Adam Hagens M 20.88333 21.01667
## 7 Adam Williams M 31.73333 35.01667
## 8 Adrianna Perez F 29.35000 34.08333
## 9 Adrien Edwards M 28.18333 27.85000
## 10 Aeryn Boyer F 29.58333 27.78333
## 11 Ahmet Kurdoglu M 23.76667 23.08333
## 12 Aiden Suski M 32.23333 30.26667
## 13 Ainsley Dziura F 35.91667 36.78333
## 14 Aksel Sather M 21.25000 21.73333
## 15 Alan Graham M 28.73333 29.81667
## 16 Alan Liu M 23.91667 25.45000
## 17 Albert Defilippo M 25.28333 25.55000
## 18 Albert Pallotto M 26.41667 27.05000
## 19 Alecia Walkuski F 31.53333 27.76667
## 20 Alejandro Galarza M 25.23333 25.26667
## 21 Alessandra Stankewich F 23.63333 24.35000
## 22 Alex Dalene M 26.75000 28.43333
## 23 Alex English M 28.10000 28.46667
## 24 Alex Walker M 23.76667 29.03333
## 25 Alexa Purcell F 30.35000 32.38333
## 26 Alexander Orcutt M 20.86667 20.68333
## 27 Alexis Liptrot F 34.65000 30.93333
## 28 Ali Barrett F 27.26667 23.80000
## 29 Ali Ciaburro F 31.33333 31.06667
## 30 Aliana Glatt F 41.46667 32.08333
## 31 Alin Spearman F 48.90000 44.85000
## 32 Alison Dorrough F 35.46667 39.26667
## 33 Alison Ho F 26.95000 29.40000
## 34 Alison Thurber F 25.83333 26.53333
## 35 Aliza Taylor F 23.68333 23.21667
## 36 Allan Chernak M 28.96667 28.56667
## 37 Almudena Villanueva F 32.10000 38.98333
## 38 Alyssa Amick F 26.56667 24.98333
## 39 Alyssa Waligroski F 29.11667 27.16667
## 40 Amanda Addesso F 22.58333 23.53333
## 41 Amanda Robinson F 37.23333 40.43333
## 42 Amanda Royka F 25.30000 28.30000
## 43 Amelia Cox F 37.68333 32.43333
## 44 Amelia Raucci F 35.95000 33.60000
## 45 Amy Caplan F 30.08333 31.56667
## 46 Amy Marx F 40.96667 46.30000
## 47 Amy Scholz F 36.11667 36.83333
## 48 Andrea Benjamin F 31.56667 33.61667
## 49 Andrew Benner M 23.68333 26.26667
## 50 Andrew Hoffman M 36.91667 31.08333
## 51 Andrew Lingenfelter M 19.91667 21.73333
## 52 Andrew Parkany M 22.63333 24.05000
## 53 Andy Spagnoletti M 31.40000 32.41667
## 54 Angelina Ramada F 30.86667 33.63333
## 55 Ania Washington F 39.51667 33.16667
## 56 Ann Hurlbut F 29.31667 29.96667
## 57 Ann Louise Onton F 63.68333 64.71667
## 58 Ann Tramontana-Veno F 32.46667 29.45000
## 59 Anna Deming F 42.85000 30.31667
## 60 Anna Simon F 31.56667 29.25000
## 61 Anna Thompson F 33.41667 37.71667
## 62 Annalise Buckert F 48.95000 36.68333
## 63 Anne Eddy F 28.76667 27.43333
## 64 Anne Whitman F 37.90000 36.43333
## 65 Annemarie Sliby F 27.81667 27.85000
## 66 Annie Hartigan F 38.26667 40.21667
## 67 Annie Wang F 27.76667 33.38333
## 68 Annique Wong F 26.95000 31.58333
## 69 Anthony Berruecos M 27.83333 25.66667
## 70 Anthony Famiglietti M 21.50000 20.18333
## 71 Anthony Garcia M 38.65000 48.23333
## 72 Anthony Law M 36.78333 35.53333
## 73 Anthony Miranda M 35.51667 36.25000
## 74 Anthony Shonti M 21.43333 21.36667
## 75 Antonieta Ortiz F 26.65000 25.30000
## 76 Anua Dawson F 30.40000 28.46667
## 77 Arjun Katechia M 19.91667 20.25000
## 78 Arushi Srivastava F 26.73333 26.76667
## 79 Ashley Guyon F 29.78333 32.18333
## 80 Assata Dawson F 26.06667 28.73333
## 81 Audrey Kamen F 29.15000 29.15000
## 82 Audrey Kiesling F 24.30000 24.18333
## 83 August Taylor M 21.83333 21.88333
## 84 Aurora Fonseca F 32.28333 34.01667
## 85 Ava Bucci F 30.51667 29.83333
## 86 Ava Galdenzi F 27.30000 26.48333
## 87 Ava Gladwin F 29.56667 29.38333
## 88 Avonlea Lebeau F 25.01667 23.46667
## 89 Barbara Hedden F 26.66667 27.31667
## 90 Barbie Albert F 37.08333 36.65000
## 91 Barry Panensky M 45.91667 46.71667
## 92 Benjamin Danehy M 33.78333 34.18333
## 93 Benjamin Delmonico M 24.75000 25.91667
## 94 Benjamin Johnson M 31.33333 33.95000
## 95 Benjamin Simon M 30.36667 28.81667
## 96 Bernadette Karpel F 27.35000 28.81667
## 97 Beth Peterson F 28.13333 27.48333
## 98 Beth Pierpont F 25.75000 25.90000
## 99 Betsy Schulman F 40.98333 46.31667
## 100 Betty Holroyd F 51.85000 53.35000
## 101 Beverly Pajer F 27.25000 25.86667
## 102 Bianca D'Onofrio F 33.73333 34.00000
## 103 Bill Damsky M 28.11667 29.10000
## 104 Bill Galdenzi M 22.76667 24.18333
## 105 Bill Goettler M 33.01667 37.35000
## 106 Bill Nelson M 21.93333 21.45000
## 107 Billy Liebler M 24.68333 23.63333
## 108 Blaine Boxwell III M 23.53333 26.86667
## 109 Bob Laudati M 24.96667 29.28333
## 110 Bob Wardwell M 23.08333 23.16667
## 111 Bobby Vander Woude M 22.30000 22.28333
## 112 Bonnie Pepper F 37.46667 38.70000
## 113 Borys Kuca M 22.05000 22.05000
## 114 Brady Connolly M 33.28333 35.20000
## 115 Brandon Licke M 24.88333 22.71667
## 116 Brandon Ortiz M 23.73333 28.26667
## 117 Brendan Mellitt M 18.76667 17.53333
## 118 Brent Mertz M 30.00000 36.75000
## 119 Brett Spearman M 48.93333 44.86667
## 120 Brian Cashin M 20.66667 21.65000
## 121 Brian Dore M 24.45000 26.30000
## 122 Brian Hannan M 28.85000 27.30000
## 123 Brian Keane M 27.40000 27.43333
## 124 Brian McConnell M 24.13333 23.45000
## 125 Brian Smith M 38.36667 37.28333
## 126 Brian Toole M 22.65000 20.06667
## 127 Briana Amodio F 30.76667 31.66667
## 128 Briana Lennon F 24.91667 24.08333
## 129 Brianna Floyd F 26.71667 24.83333
## 130 Brielle Kendrioski F 32.05000 31.06667
## 131 Brittany Bailey F 31.26667 40.86667
## 132 Brooke Luokkala F 27.03333 24.53333
## 133 Bruce Delmonico M 27.90000 53.41667
## 134 Bruce Miner M 35.66667 38.26667
## 135 Bryce Crawford M 28.20000 26.41667
## 136 Bryce Makula F 26.65000 30.00000
## 137 Bunmi Mfuko F 36.73333 31.16667
## 138 Caitlin Chatterton F 29.41667 27.51667
## 139 Caitlyn Guerrera F 25.56667 25.28333
## 140 Caleb Nyhart M 23.66667 23.96667
## 141 Cameron Kaplan M 25.76667 35.20000
## 142 Candace Walter F 39.30000 37.80000
## 143 Carla MacMullen F 44.73333 41.53333
## 144 Carlos Rojas M 24.48333 22.95000
## 145 Carol Chatterton F 29.68333 27.55000
## 146 Carol Greeley F 27.61667 30.06667
## 147 Carolina Guiral F 27.81667 27.10000
## 148 Carolino Monteiro M 23.36667 23.33333
## 149 Carolyn Marks F 28.43333 30.98333
## 150 Carter Richards M 23.06667 19.11667
## 151 Casey Dorrough M 30.38333 39.10000
## 152 Cash Carocci F 30.81667 39.93333
## 153 Cassie Clayton F 31.53333 29.65000
## 154 Cathy Swift F 30.50000 30.20000
## 155 Cayden Drost M 22.23333 23.15000
## 156 Cayleen Defaranos F 37.13333 33.65000
## 157 Celia Mariconda F 35.05000 33.83333
## 158 Cesar Tlecuitl M 25.30000 25.30000
## 159 Charles Hornak M 20.78333 21.35000
## 160 Charlotte Dziura F 39.56667 40.23333
## 161 Chase Ireland F 23.93333 31.23333
## 162 Chentian Jin M 22.56667 22.63333
## 163 Chloe Kurtz F 25.83333 28.76667
## 164 Chloe Stancarone F 33.86667 29.03333
## 165 Chris Kenney M 29.46667 32.26667
## 166 Chris Lemay M 19.58333 18.65000
## 167 Chris Ryan M 31.11667 39.55000
## 168 Chris Tyler M 29.58333 31.05000
## 169 Chris Walsh M 24.18333 24.51667
## 170 Christian Bjork M 24.06667 22.73333
## 171 Christian Quadrino M 34.61667 34.11667
## 172 Christina Garcia F 46.36667 48.25000
## 173 Christine Bjork F 28.21667 28.05000
## 174 Christine Ly F 28.13333 27.95000
## 175 Christine Manzi F 35.45000 36.60000
## 176 Christine Tine F 32.25000 31.95000
## 177 Christine Williams F 49.36667 49.13333
## 178 Christopher Goggin M 30.90000 27.05000
## 179 Christopher Illick M 26.51667 27.00000
## 180 Christopher Prato M 25.96667 25.51667
## 181 Christopher Reese M 23.31667 22.46667
## 182 Christopher Takoudes M 28.81667 25.65000
## 183 Claire Donelan F 34.31667 28.96667
## 184 Claire MacKenzie F 33.41667 25.73333
## 185 Claud Chong M 22.65000 22.70000
## 186 Claudia O'Connell F 30.51667 30.23333
## 187 Claudia Reynolds F 28.98333 30.40000
## 188 Clayton Johnson M 41.56667 38.93333
## 189 Clifton Holloway M 39.36667 38.35000
## 190 Cole Bova M 23.15000 23.96667
## 191 Cole Purcell M 27.60000 27.75000
## 192 Colleen Lawler F 31.68333 35.96667
## 193 Colleen Stankewich F 32.91667 29.88333
## 194 Connor Hartigan M 20.80000 19.01667
## 195 Constance Catrone F 35.86667 36.10000
## 196 Corbin Forselius M 24.18333 26.15000
## 197 Corey Duncanson M 25.03333 20.66667
## 198 Corey Wadlinger M 21.86667 22.10000
## 199 Cullen Cox M 25.43333 27.56667
## 200 Curtiss Griesing M 29.73333 30.20000
## 201 Cynara McPhail F 34.76667 34.70000
## 202 Daisy Johnson F 45.01667 48.43333
## 203 Daisy Ramos F 28.76667 34.03333
## 204 Dale Canale M 40.66667 40.58333
## 205 Dan Kinton M 28.91667 30.73333
## 206 Dan Reilly M 31.48333 36.61667
## 207 Daniel Beach M 27.60000 28.23333
## 208 Daniel Bondeson M 19.91667 23.43333
## 209 Daniel Kaylor M 26.31667 25.83333
## 210 Daniela Hervella F 27.85000 28.48333
## 211 Dara Kovel F 25.43333 27.55000
## 212 Darcie Roy F 31.68333 36.00000
## 213 Darron Vigliotti M 29.55000 35.56667
## 214 David Adams M 25.60000 36.78333
## 215 David Bach M 29.41667 27.95000
## 216 David Byron-Brown M 27.88333 29.31667
## 217 David Carlson M 26.18333 24.95000
## 218 David Durham M 24.61667 25.96667
## 219 David Judd M 20.66667 22.81667
## 220 David Manning M 23.63333 25.03333
## 221 David Musto M 28.05000 27.85000
## 222 David Purcell M 27.75000 27.91667
## 223 David Simon M 29.76667 26.06667
## 224 David Toole M 29.46667 28.01667
## 225 Davina Ismail F 32.08333 37.46667
## 226 Dawn Demeo F 33.70000 33.90000
## 227 Dawn Jacobson F 29.20000 28.83333
## 228 Dawn Kaplan F 29.16667 32.50000
## 229 De Janey Davis F 19.51667 22.56667
## 230 Deb Risisky F 32.01667 32.26667
## 231 Deb Willis F 25.71667 28.38333
## 232 Debbie Swirsky F 30.86667 31.60000
## 233 Debra Johns F 49.13333 45.43333
## 234 Deirdre Oneill F 32.08333 29.53333
## 235 Del Toreno M 22.51667 21.73333
## 236 Denise Smith F 33.86667 31.83333
## 237 Dennis Gelinas M 23.51667 22.25000
## 238 Dennis Vu M 27.18333 26.93333
## 239 Diane Cain F 38.05000 36.20000
## 240 Diane Puglia F 37.66667 43.45000
## 241 Dick Walsh M 29.00000 29.25000
## 242 Dominic Scarano Jr M 28.85000 29.41667
## 243 Dominick Alberino M 29.01667 31.25000
## 244 Donald Acker M 26.58333 24.50000
## 245 Donald Filer M 30.16667 28.51667
## 246 Doug Levens M 18.40000 18.48333
## 247 Doug Wipfler M 40.66667 40.58333
## 248 Douglas Skalka M 33.38333 34.53333
## 249 Drew Manzi F 35.08333 36.48333
## 250 Dwyer Illick M 26.51667 26.98333
## 251 Dylan Johnson M 47.80000 43.48333
## 252 Dylan McDonnell M 19.16667 18.43333
## 253 Earle Giovanniello M 32.30000 35.40000
## 254 Ed Keating M 28.25000 28.35000
## 255 Eduardo Torrealba M 42.11667 37.00000
## 256 Edward Marczyszak M 30.81667 32.28333
## 257 Edward Regner M 55.35000 55.96667
## 258 Edward Whitman M 28.91667 29.08333
## 259 Ej Edelman F 27.93333 28.40000
## 260 Elannah Devin F 32.65000 29.56667
## 261 Eli Marcus M 27.03333 28.46667
## 262 Eli Rami M 25.75000 23.65000
## 263 Eliana Ross F 28.33333 30.01667
## 264 Elizabeth Addesso F 25.70000 29.03333
## 265 Elizabeth Alexander F 33.30000 37.05000
## 266 Elizabeth Derose F 21.40000 22.16667
## 267 Elizabeth Holman F 43.30000 43.68333
## 268 Elle Shaw F 28.50000 28.61667
## 269 Ellen Su F 30.03333 31.46667
## 270 Elliot Rosa M 23.50000 26.91667
## 271 Elsa Holahan F 33.71667 34.20000
## 272 Emerson Suski M 26.41667 27.66667
## 273 Emily Erb F 30.50000 33.00000
## 274 Emily Olsen F 33.71667 27.61667
## 275 Emily Puglielli F 38.20000 24.10000
## 276 Emily Ross F 28.80000 30.08333
## 277 Emily Ruan F 29.48333 27.31667
## 278 Emily Torre F 29.58333 29.45000
## 279 Emma Kennedy F 43.75000 43.93333
## 280 Emma Kirck F 25.43333 22.10000
## 281 Endre Sarkany M 36.83333 37.98333
## 282 Eric Annes M 36.33333 31.06667
## 283 Eric Goulet M 23.38333 23.71667
## 284 Eric Tuttle M 27.96667 26.55000
## 285 Erica Jermacans F 29.18333 36.60000
## 286 Erik Aguilar M 23.55000 26.91667
## 287 Erin Brady F 50.73333 50.18333
## 288 Erin Cassidy Donohue F 35.55000 26.48333
## 289 Ernie Asprelli M 30.00000 30.41667
## 290 Errol Saunders M 34.46667 37.16667
## 291 Ethan Berman M 26.26667 27.68333
## 292 Ethan Laudermilch M 17.93333 16.58333
## 293 Ethan Puc M 19.21667 18.33333
## 294 Ethan Woolbert M 20.63333 20.71667
## 295 Eugine Szeto M 23.76667 24.16667
## 296 Evan Harte M 23.36667 23.06667
## 297 Eve Sneider F 29.70000 30.90000
## 298 Evelyn Quadrino F 34.58333 34.11667
## 299 Fabian Villacis M 27.13333 25.61667
## 300 Felicia Liebler F 31.26667 30.50000
## 301 Felicia Williams F 46.00000 47.30000
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## 786 Parth Mody M 19.40000 19.00000
## 787 Parthib Samadder M 25.33333 24.81667
## 788 Pat Anthony M 34.40000 35.30000
## 789 Pat Summa M 19.53333 18.70000
## 790 Patricia Gallagher-Cr F 27.03333 28.66667
## 791 Patrick O'Connor M 43.98333 41.86667
## 792 Patty Carton F 34.90000 36.96667
## 793 Paul El-Fishawy M 19.91667 21.25000
## 794 Paul Flinter M 28.95000 29.43333
## 795 Paul Garlinghouse M 29.36667 31.06667
## 796 Paul Gilfillan M 27.51667 26.08333
## 797 Paul Kish M 31.35000 32.61667
## 798 Paul Miller M 24.16667 22.80000
## 799 Paul Scholz M 36.10000 36.83333
## 800 Paul Szollosi M 27.13333 28.60000
## 801 Pedro Hervella M 22.73333 26.61667
## 802 Pete Chometa M 41.05000 31.36667
## 803 Peter Butler M 26.40000 26.38333
## 804 Peter Ellis M 22.68333 21.96667
## 805 Peter Kihara M 28.75000 27.81667
## 806 Peter Remillard M 21.28333 21.61667
## 807 Peter Wang M 26.65000 28.33333
## 808 Philip Guay M 22.98333 22.85000
## 809 Phoebe Carias F 27.93333 24.60000
## 810 Phyllis Tatro F 30.45000 30.58333
## 811 Pia Gero F 32.95000 38.70000
## 812 Polly Sather F 35.63333 35.43333
## 813 Prem Patel M 26.93333 29.36667
## 814 Rachael Amatruda F 27.80000 29.23333
## 815 Rachel Diglio F 30.15000 31.33333
## 816 Rachel Furey F 21.65000 21.86667
## 817 Rachel Hagani F 24.90000 26.48333
## 818 Rachel Joyce F 22.40000 21.93333
## 819 Rachel Kaplan F 21.71667 22.16667
## 820 Rachel Skalka F 34.23333 33.43333
## 821 Rafael Torres M 24.98333 25.23333
## 822 Rajani Subramanian F 29.33333 31.86667
## 823 Ralph Russo M 28.21667 41.31667
## 824 Ramya Subramanian F 34.78333 35.61667
## 825 Randall Rode M 36.91667 31.06667
## 826 Randy Ruotolo M 28.58333 29.16667
## 827 Ray Bendici M 24.83333 25.03333
## 828 Raymond Pajer M 30.70000 32.83333
## 829 Rebecca Alberino F 43.28333 41.58333
## 830 Rebecca Rose F 27.93333 28.88333
## 831 Rebecca Samuels F 32.60000 30.30000
## 832 Rebecca Yan F 29.01667 26.76667
## 833 Reed Bobroff M 27.40000 29.81667
## 834 Rene Moreno M 22.93333 25.01667
## 835 Renee Bauer F 32.48333 34.76667
## 836 Renee Landers F 43.91667 47.53333
## 837 Ricardo Chavira M 39.23333 23.45000
## 838 Richard Ford M 28.60000 34.43333
## 839 Richard Kaplan M 34.66667 37.08333
## 840 Richard Spears M 30.80000 30.88333
## 841 Rick Shannon M 24.86667 26.45000
## 842 Rick Smilow M 29.96667 29.81667
## 843 Riley Teagan Donohue F 37.71667 37.80000
## 844 Rob Bonitz M 23.03333 24.75000
## 845 Robert Backhaus M 21.23333 20.75000
## 846 Robert Cox M 37.93333 30.96667
## 847 Robert Dillon M 16.68333 15.76667
## 848 Robert Gagnon M 21.86667 22.28333
## 849 Robert Hawke M 31.40000 33.46667
## 850 Robert Henry F 34.11667 33.76667
## 851 Robert Schwaber M 22.88333 22.91667
## 852 Robert Testo M 29.61667 27.41667
## 853 Robin Pellish F 38.11667 36.80000
## 854 Robyn Martin F 32.75000 32.15000
## 855 Rochelle Stackhouse F 29.55000 29.81667
## 856 Roger Levine M 40.91667 41.65000
## 857 Romeo Voira M 33.28333 31.61667
## 858 Ronald Pitcher M 28.08333 40.16667
## 859 Rory McCormack M 26.83333 25.41667
## 860 Rory O'Connor M 20.95000 21.96667
## 861 Rosalie Miller F 31.90000 32.18333
## 862 Rose Monaco F 45.21667 47.68333
## 863 Rosemary Wuestefeld F 28.56667 28.28333
## 864 Rosie Marquez F 26.50000 24.35000
## 865 Ryan Bauman M 21.18333 21.13333
## 866 Ryan Haug M 19.71667 19.93333
## 867 Ryan Philipp M 31.86667 30.20000
## 868 Ryan Podann M 20.83333 21.31667
## 869 Ryder Holloway M 30.18333 26.81667
## 870 Sadie Smith F 37.70000 33.81667
## 871 Sahar Usmani-Brown F 33.73333 33.73333
## 872 Saki Tomita F 27.30000 26.91667
## 873 Sally Hellerman F 27.28333 27.83333
## 874 Sally Leahy F 25.50000 23.51667
## 875 Salvatore Lanzofano M 29.18333 29.63333
## 876 Samantha Ajello F 34.30000 33.00000
## 877 Samantha Alexander F 33.26667 26.80000
## 878 Samantha Angle F 29.88333 29.03333
## 879 Samantha Lee F 31.10000 30.10000
## 880 Samantha Nowak F 27.48333 27.00000
## 881 Samantha Smith F 29.23333 32.48333
## 882 Samuel Heenan M 33.41667 27.56667
## 883 Samuel Kaplan M 21.48333 21.16667
## 884 Sandra Geiger F 29.75000 30.48333
## 885 Sandy Resnick F 37.53333 50.36667
## 886 Sandy Shaner F 39.41667 40.05000
## 887 Sandy Spector F 50.98333 52.05000
## 888 Sanelma Heinonen F 23.41667 26.08333
## 889 Saniyah Boone F 39.60000 40.10000
## 890 Sarah Mary-Ann Geach F 31.30000 24.15000
## 891 Sarah Minuit F 33.16667 29.31667
## 892 Saul Fussiner M 28.93333 32.85000
## 893 Scott Prudhomme M 36.40000 34.26667
## 894 Scott Rodilitz M 16.38333 15.60000
## 895 Sean Barkasy M 18.90000 18.25000
## 896 Sean Kelleher M 32.38333 35.01667
## 897 Sean McGarry M 27.20000 24.45000
## 898 Sean Wadlinger M 27.60000 24.56667
## 899 Shaelees Bell F 39.38333 41.61667
## 900 Shari Millen F 31.53333 44.86667
## 901 Sharon Belfonti F 25.58333 25.01667
## 902 Shawn Limata M 25.03333 24.91667
## 903 Shaylin Marshall F 31.66667 30.90000
## 904 Sheila Schreck F 33.58333 33.16667
## 905 Shel Swanson F 26.20000 36.73333
## 906 Shelley Goodstine F 26.00000 25.71667
## 907 Sheri Steinmetz F 31.56667 33.61667
## 908 Shikha Sharma F 40.21667 35.81667
## 909 Shirin Bahmanyar F 30.98333 27.48333
## 910 Shiyue Cheng M 25.21667 24.96667
## 911 Shunhe Wang M 20.65000 22.90000
## 912 Sid Levine M 37.90000 36.86667
## 913 Simon Queenborough M 26.21667 25.53333
## 914 Skylar Takacs F 33.71667 34.21667
## 915 Sofia Menemenlis F 22.95000 22.25000
## 916 Sofia Verna F 29.28333 26.85000
## 917 Sophia Baldino F 44.35000 39.76667
## 918 Sophie Cremo F 21.98333 20.88333
## 919 Sophie Freeman F 29.11667 27.33333
## 920 Sophie Gangloff F 29.95000 31.10000
## 921 Sophie Kurtz F 30.63333 40.58333
## 922 Stacey Sullivan F 25.71667 26.35000
## 923 Stacey Thompson F 26.88333 25.95000
## 924 Stephanie Aragon F 26.36667 29.06667
## 925 Stephanie Critch F 31.81667 31.33333
## 926 Stephanie Eisenbarth F 31.73333 36.73333
## 927 Stephanie Grilo F 35.20000 34.93333
## 928 Stephanie Loeb F 24.45000 24.71667
## 929 Stephanie Luekens F 38.95000 43.73333
## 930 Stephen Corbett M 33.45000 28.03333
## 931 Stephen Dorrough M 30.38333 24.85000
## 932 Stephen Gamboa M 30.96667 37.13333
## 933 Stephen Gates M 27.16667 20.66667
## 934 Stephen Lynaugh M 30.08333 32.88333
## 935 Stephen McGovern M 29.93333 27.93333
## 936 Stephen Sheiffele M 26.05000 24.48333
## 937 Steve Arnold M 33.11667 32.81667
## 938 Steve Cassella M 30.95000 31.31667
## 939 Steve Thompson M 26.00000 28.56667
## 940 Steven Della-Giustina M 29.95000 30.55000
## 941 Steven Germaine M 28.38333 26.06667
## 942 Steven Jacques M 26.05000 27.25000
## 943 Steven Judd M 30.26667 31.43333
## 944 Steven Niccolai M 29.85000 28.21667
## 945 Summer Wu F 24.06667 22.90000
## 946 Sunnie Scarpa F 31.16667 32.78333
## 947 Susan Kerley F 32.01667 33.96667
## 948 Susan Mayer F 34.90000 35.43333
## 949 Suzanne Poudrier F 21.80000 20.86667
## 950 Talia Remnick F 40.36667 39.08333
## 951 Tamara Malm F 25.76667 27.26667
## 952 Tanner Festa M 27.55000 29.85000
## 953 Tanya Bell F 36.80000 36.20000
## 954 Tanya Wiedeking F 27.80000 27.63333
## 955 Tarana Eldredge F 29.78333 25.10000
## 956 Taryn Case F 54.48333 40.20000
## 957 Taryn Disorbo F 32.11667 48.96667
## 958 Taylor Dostie F 34.28333 32.96667
## 959 Teresa Massimino F 28.98333 30.55000
## 960 Terri Addesso F 24.56667 23.85000
## 961 Terri Buganski F 29.68333 29.91667
## 962 Terri Lachance F 27.91667 27.73333
## 963 Terry Williams M 49.38333 49.11667
## 964 Theo Baron M 42.20000 46.11667
## 965 Theresa Voss F 45.01667 48.16667
## 966 Thomas Barrette M 26.28333 28.46667
## 967 Thomas Holman M 43.88333 44.35000
## 968 Thomas Maynard M 31.00000 24.28333
## 969 Thomas Paterno M 29.26667 28.03333
## 970 Thomas Redding M 20.63333 21.28333
## 971 Thomas Takoudes M 28.81667 25.63333
## 972 Thomas Trumbley M 36.21667 35.26667
## 973 Thomas Ude, Jr. M 28.76667 28.75000
## 974 Thomas Ullmann M 42.56667 42.55000
## 975 Tiffany Katz F 53.66667 42.63333
## 976 Tim Foldy-Porto M 16.36667 16.90000
## 977 Timmy Case M 25.95000 24.78333
## 978 Tina Chan F 45.61667 50.93333
## 979 Tina Newman F 35.93333 37.08333
## 980 Todd Kennedy M 30.58333 38.30000
## 981 Todd Liu M 24.33333 29.73333
## 982 Todd Petronio M 29.66667 26.63333
## 983 Tom Missett M 34.38333 36.43333
## 984 Tom Sullivan M 33.53333 35.05000
## 985 Tracy Bellucci F 28.58333 28.16667
## 986 Tracy Weiss F 33.88333 33.85000
## 987 Trey Chometa M 22.26667 23.13333
## 988 Tricia Guyon F 29.78333 32.18333
## 989 Ty Holloway M 31.93333 28.80000
## 990 Tyler Connelly M 26.50000 25.96667
## 991 Tyler Pineau M 18.01667 17.28333
## 992 Tyler Smith M 33.18333 30.85000
## 993 Tyler Whiting M 21.63333 25.00000
## 994 Unedra Muley F 40.08333 37.03333
## 995 Varman Samuel M 28.88333 28.86667
## 996 Verica Milivojevic F 34.73333 33.60000
## 997 Veronica Chase F 33.51667 33.18333
## 998 Veronica Marion F 47.25000 48.41667
## 999 Victor Figueroa M 22.15000 21.80000
## 1000 Victor Toal M 38.15000 33.63333
## 1001 Victoria Murphy F 31.46667 27.98333
## 1002 Victoria Zigmont F 38.98333 33.66667
## 1003 Vikram Dalal M 31.78333 30.20000
## 1004 Vincent Balbarin M 33.43333 28.96667
## 1005 Vincenzo Dinatale M 19.48333 19.26667
## 1006 Wade Davis M 16.61667 16.56667
## 1007 Walter Harding M 24.01667 24.86667
## 1008 Walter Pieper M 35.40000 35.46667
## 1009 Ward Naylor M 24.93333 24.65000
## 1010 Wendy Feldman F 28.91667 32.10000
## 1011 Will Culligan M 24.98333 24.96667
## 1012 William Connolly M 33.26667 35.21667
## 1013 William Farrell M 43.16667 43.30000
## 1014 William Garfinkel M 42.00000 46.30000
## 1015 William Hammond M 27.38333 30.06667
## 1016 William Hanley M 26.36667 27.26667
## 1017 William Long M 41.93333 43.31667
## 1018 William Meaney M 22.33333 22.80000
## 1019 William Thompson M 26.88333 33.65000
## 1020 Wolf Boone M 21.55000 22.08333
## 1021 Wyatt Sloan M 33.43333 31.38333
## 1022 Xuan Chau M 25.65000 25.10000
## 1023 Yabdriel Gonzalez M 26.33333 30.08333
## 1024 Yasmin Bergemann F 23.76667 23.91667
## 1025 Yue Yin F 29.93333 29.31667
## 1026 Zach Wright M 22.81667 23.71667
## 1027 Zackary Schnitzer M 30.76667 32.30000
## 1028 Zelda Galdenzi F 27.30000 26.75000
## 1029 Zoe Smith F 34.25000 30.48333nhcombined$diff20162017 <- nhcombined$Nettime_2016 - nhcombined$Nettime_2017
head(nhcombined)## Name Gender Nettime_2016 Nettime_2017 diff20162017
## 1 Abbey Shaw F 39.56667 40.25000 -0.6833333
## 2 Abby Dziura F 30.43333 35.63333 -5.2000000
## 3 Abby Ganzle F 31.53333 35.66667 -4.1333333
## 4 Abigail Murphy F 31.43333 34.06667 -2.6333333
## 5 Abraham Cordero M 36.83333 31.83333 5.0000000
## 6 Adam Hagens M 20.88333 21.01667 -0.1333333hist(nhcombined$diff20162017)
boxplot(nhcombined$diff20162017) #Boxplot identified 3 individuals who are squewing the data to the left, with less than -25.
table(nhcombined$diff20162017 < -25)##
## FALSE TRUE
## 1026 3nhcombined[nhcombined$diff20162017 < -25, ]## Name Gender Nettime_2016 Nettime_2017 diff20162017
## 133 Bruce Delmonico M 27.90000 53.41667 -25.51667
## 529 Ken Liebeskind M 33.93333 130.75000 -96.81667
## 729 Mike Trumbley M 29.15000 188.08333 -158.93333There were 3 individuals squewing the data, Bruce Delmonico, Ken Liebeskind, and Mike Trembley. They were removed.
nhcombined[-c(133, 529, 729), ]## Name Gender Nettime_2016 Nettime_2017 diff20162017
## 1 Abbey Shaw F 39.56667 40.25000 -0.68333333
## 2 Abby Dziura F 30.43333 35.63333 -5.20000000
## 3 Abby Ganzle F 31.53333 35.66667 -4.13333333
## 4 Abigail Murphy F 31.43333 34.06667 -2.63333333
## 5 Abraham Cordero M 36.83333 31.83333 5.00000000
## 6 Adam Hagens M 20.88333 21.01667 -0.13333333
## 7 Adam Williams M 31.73333 35.01667 -3.28333333
## 8 Adrianna Perez F 29.35000 34.08333 -4.73333333
## 9 Adrien Edwards M 28.18333 27.85000 0.33333333
## 10 Aeryn Boyer F 29.58333 27.78333 1.80000000
## 11 Ahmet Kurdoglu M 23.76667 23.08333 0.68333333
## 12 Aiden Suski M 32.23333 30.26667 1.96666667
## 13 Ainsley Dziura F 35.91667 36.78333 -0.86666667
## 14 Aksel Sather M 21.25000 21.73333 -0.48333333
## 15 Alan Graham M 28.73333 29.81667 -1.08333333
## 16 Alan Liu M 23.91667 25.45000 -1.53333333
## 17 Albert Defilippo M 25.28333 25.55000 -0.26666667
## 18 Albert Pallotto M 26.41667 27.05000 -0.63333333
## 19 Alecia Walkuski F 31.53333 27.76667 3.76666667
## 20 Alejandro Galarza M 25.23333 25.26667 -0.03333333
## 21 Alessandra Stankewich F 23.63333 24.35000 -0.71666667
## 22 Alex Dalene M 26.75000 28.43333 -1.68333333
## 23 Alex English M 28.10000 28.46667 -0.36666667
## 24 Alex Walker M 23.76667 29.03333 -5.26666667
## 25 Alexa Purcell F 30.35000 32.38333 -2.03333333
## 26 Alexander Orcutt M 20.86667 20.68333 0.18333333
## 27 Alexis Liptrot F 34.65000 30.93333 3.71666667
## 28 Ali Barrett F 27.26667 23.80000 3.46666667
## 29 Ali Ciaburro F 31.33333 31.06667 0.26666667
## 30 Aliana Glatt F 41.46667 32.08333 9.38333333
## 31 Alin Spearman F 48.90000 44.85000 4.05000000
## 32 Alison Dorrough F 35.46667 39.26667 -3.80000000
## 33 Alison Ho F 26.95000 29.40000 -2.45000000
## 34 Alison Thurber F 25.83333 26.53333 -0.70000000
## 35 Aliza Taylor F 23.68333 23.21667 0.46666667
## 36 Allan Chernak M 28.96667 28.56667 0.40000000
## 37 Almudena Villanueva F 32.10000 38.98333 -6.88333333
## 38 Alyssa Amick F 26.56667 24.98333 1.58333333
## 39 Alyssa Waligroski F 29.11667 27.16667 1.95000000
## 40 Amanda Addesso F 22.58333 23.53333 -0.95000000
## 41 Amanda Robinson F 37.23333 40.43333 -3.20000000
## 42 Amanda Royka F 25.30000 28.30000 -3.00000000
## 43 Amelia Cox F 37.68333 32.43333 5.25000000
## 44 Amelia Raucci F 35.95000 33.60000 2.35000000
## 45 Amy Caplan F 30.08333 31.56667 -1.48333333
## 46 Amy Marx F 40.96667 46.30000 -5.33333333
## 47 Amy Scholz F 36.11667 36.83333 -0.71666667
## 48 Andrea Benjamin F 31.56667 33.61667 -2.05000000
## 49 Andrew Benner M 23.68333 26.26667 -2.58333333
## 50 Andrew Hoffman M 36.91667 31.08333 5.83333333
## 51 Andrew Lingenfelter M 19.91667 21.73333 -1.81666667
## 52 Andrew Parkany M 22.63333 24.05000 -1.41666667
## 53 Andy Spagnoletti M 31.40000 32.41667 -1.01666667
## 54 Angelina Ramada F 30.86667 33.63333 -2.76666667
## 55 Ania Washington F 39.51667 33.16667 6.35000000
## 56 Ann Hurlbut F 29.31667 29.96667 -0.65000000
## 57 Ann Louise Onton F 63.68333 64.71667 -1.03333333
## 58 Ann Tramontana-Veno F 32.46667 29.45000 3.01666667
## 59 Anna Deming F 42.85000 30.31667 12.53333333
## 60 Anna Simon F 31.56667 29.25000 2.31666667
## 61 Anna Thompson F 33.41667 37.71667 -4.30000000
## 62 Annalise Buckert F 48.95000 36.68333 12.26666667
## 63 Anne Eddy F 28.76667 27.43333 1.33333333
## 64 Anne Whitman F 37.90000 36.43333 1.46666667
## 65 Annemarie Sliby F 27.81667 27.85000 -0.03333333
## 66 Annie Hartigan F 38.26667 40.21667 -1.95000000
## 67 Annie Wang F 27.76667 33.38333 -5.61666667
## 68 Annique Wong F 26.95000 31.58333 -4.63333333
## 69 Anthony Berruecos M 27.83333 25.66667 2.16666667
## 70 Anthony Famiglietti M 21.50000 20.18333 1.31666667
## 71 Anthony Garcia M 38.65000 48.23333 -9.58333333
## 72 Anthony Law M 36.78333 35.53333 1.25000000
## 73 Anthony Miranda M 35.51667 36.25000 -0.73333333
## 74 Anthony Shonti M 21.43333 21.36667 0.06666667
## 75 Antonieta Ortiz F 26.65000 25.30000 1.35000000
## 76 Anua Dawson F 30.40000 28.46667 1.93333333
## 77 Arjun Katechia M 19.91667 20.25000 -0.33333333
## 78 Arushi Srivastava F 26.73333 26.76667 -0.03333333
## 79 Ashley Guyon F 29.78333 32.18333 -2.40000000
## 80 Assata Dawson F 26.06667 28.73333 -2.66666667
## 81 Audrey Kamen F 29.15000 29.15000 0.00000000
## 82 Audrey Kiesling F 24.30000 24.18333 0.11666667
## 83 August Taylor M 21.83333 21.88333 -0.05000000
## 84 Aurora Fonseca F 32.28333 34.01667 -1.73333333
## 85 Ava Bucci F 30.51667 29.83333 0.68333333
## 86 Ava Galdenzi F 27.30000 26.48333 0.81666667
## 87 Ava Gladwin F 29.56667 29.38333 0.18333333
## 88 Avonlea Lebeau F 25.01667 23.46667 1.55000000
## 89 Barbara Hedden F 26.66667 27.31667 -0.65000000
## 90 Barbie Albert F 37.08333 36.65000 0.43333333
## 91 Barry Panensky M 45.91667 46.71667 -0.80000000
## 92 Benjamin Danehy M 33.78333 34.18333 -0.40000000
## 93 Benjamin Delmonico M 24.75000 25.91667 -1.16666667
## 94 Benjamin Johnson M 31.33333 33.95000 -2.61666667
## 95 Benjamin Simon M 30.36667 28.81667 1.55000000
## 96 Bernadette Karpel F 27.35000 28.81667 -1.46666667
## 97 Beth Peterson F 28.13333 27.48333 0.65000000
## 98 Beth Pierpont F 25.75000 25.90000 -0.15000000
## 99 Betsy Schulman F 40.98333 46.31667 -5.33333333
## 100 Betty Holroyd F 51.85000 53.35000 -1.50000000
## 101 Beverly Pajer F 27.25000 25.86667 1.38333333
## 102 Bianca D'Onofrio F 33.73333 34.00000 -0.26666667
## 103 Bill Damsky M 28.11667 29.10000 -0.98333333
## 104 Bill Galdenzi M 22.76667 24.18333 -1.41666667
## 105 Bill Goettler M 33.01667 37.35000 -4.33333333
## 106 Bill Nelson M 21.93333 21.45000 0.48333333
## 107 Billy Liebler M 24.68333 23.63333 1.05000000
## 108 Blaine Boxwell III M 23.53333 26.86667 -3.33333333
## 109 Bob Laudati M 24.96667 29.28333 -4.31666667
## 110 Bob Wardwell M 23.08333 23.16667 -0.08333333
## 111 Bobby Vander Woude M 22.30000 22.28333 0.01666667
## 112 Bonnie Pepper F 37.46667 38.70000 -1.23333333
## 113 Borys Kuca M 22.05000 22.05000 0.00000000
## 114 Brady Connolly M 33.28333 35.20000 -1.91666667
## 115 Brandon Licke M 24.88333 22.71667 2.16666667
## 116 Brandon Ortiz M 23.73333 28.26667 -4.53333333
## 117 Brendan Mellitt M 18.76667 17.53333 1.23333333
## 118 Brent Mertz M 30.00000 36.75000 -6.75000000
## 119 Brett Spearman M 48.93333 44.86667 4.06666667
## 120 Brian Cashin M 20.66667 21.65000 -0.98333333
## 121 Brian Dore M 24.45000 26.30000 -1.85000000
## 122 Brian Hannan M 28.85000 27.30000 1.55000000
## 123 Brian Keane M 27.40000 27.43333 -0.03333333
## 124 Brian McConnell M 24.13333 23.45000 0.68333333
## 125 Brian Smith M 38.36667 37.28333 1.08333333
## 126 Brian Toole M 22.65000 20.06667 2.58333333
## 127 Briana Amodio F 30.76667 31.66667 -0.90000000
## 128 Briana Lennon F 24.91667 24.08333 0.83333333
## 129 Brianna Floyd F 26.71667 24.83333 1.88333333
## 130 Brielle Kendrioski F 32.05000 31.06667 0.98333333
## 131 Brittany Bailey F 31.26667 40.86667 -9.60000000
## 132 Brooke Luokkala F 27.03333 24.53333 2.50000000
## 134 Bruce Miner M 35.66667 38.26667 -2.60000000
## 135 Bryce Crawford M 28.20000 26.41667 1.78333333
## 136 Bryce Makula F 26.65000 30.00000 -3.35000000
## 137 Bunmi Mfuko F 36.73333 31.16667 5.56666667
## 138 Caitlin Chatterton F 29.41667 27.51667 1.90000000
## 139 Caitlyn Guerrera F 25.56667 25.28333 0.28333333
## 140 Caleb Nyhart M 23.66667 23.96667 -0.30000000
## 141 Cameron Kaplan M 25.76667 35.20000 -9.43333333
## 142 Candace Walter F 39.30000 37.80000 1.50000000
## 143 Carla MacMullen F 44.73333 41.53333 3.20000000
## 144 Carlos Rojas M 24.48333 22.95000 1.53333333
## 145 Carol Chatterton F 29.68333 27.55000 2.13333333
## 146 Carol Greeley F 27.61667 30.06667 -2.45000000
## 147 Carolina Guiral F 27.81667 27.10000 0.71666667
## 148 Carolino Monteiro M 23.36667 23.33333 0.03333333
## 149 Carolyn Marks F 28.43333 30.98333 -2.55000000
## 150 Carter Richards M 23.06667 19.11667 3.95000000
## 151 Casey Dorrough M 30.38333 39.10000 -8.71666667
## 152 Cash Carocci F 30.81667 39.93333 -9.11666667
## 153 Cassie Clayton F 31.53333 29.65000 1.88333333
## 154 Cathy Swift F 30.50000 30.20000 0.30000000
## 155 Cayden Drost M 22.23333 23.15000 -0.91666667
## 156 Cayleen Defaranos F 37.13333 33.65000 3.48333333
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## 892 Saul Fussiner M 28.93333 32.85000 -3.91666667
## 893 Scott Prudhomme M 36.40000 34.26667 2.13333333
## 894 Scott Rodilitz M 16.38333 15.60000 0.78333333
## 895 Sean Barkasy M 18.90000 18.25000 0.65000000
## 896 Sean Kelleher M 32.38333 35.01667 -2.63333333
## 897 Sean McGarry M 27.20000 24.45000 2.75000000
## 898 Sean Wadlinger M 27.60000 24.56667 3.03333333
## 899 Shaelees Bell F 39.38333 41.61667 -2.23333333
## 900 Shari Millen F 31.53333 44.86667 -13.33333333
## 901 Sharon Belfonti F 25.58333 25.01667 0.56666667
## 902 Shawn Limata M 25.03333 24.91667 0.11666667
## 903 Shaylin Marshall F 31.66667 30.90000 0.76666667
## 904 Sheila Schreck F 33.58333 33.16667 0.41666667
## 905 Shel Swanson F 26.20000 36.73333 -10.53333333
## 906 Shelley Goodstine F 26.00000 25.71667 0.28333333
## 907 Sheri Steinmetz F 31.56667 33.61667 -2.05000000
## 908 Shikha Sharma F 40.21667 35.81667 4.40000000
## 909 Shirin Bahmanyar F 30.98333 27.48333 3.50000000
## 910 Shiyue Cheng M 25.21667 24.96667 0.25000000
## 911 Shunhe Wang M 20.65000 22.90000 -2.25000000
## 912 Sid Levine M 37.90000 36.86667 1.03333333
## 913 Simon Queenborough M 26.21667 25.53333 0.68333333
## 914 Skylar Takacs F 33.71667 34.21667 -0.50000000
## 915 Sofia Menemenlis F 22.95000 22.25000 0.70000000
## 916 Sofia Verna F 29.28333 26.85000 2.43333333
## 917 Sophia Baldino F 44.35000 39.76667 4.58333333
## 918 Sophie Cremo F 21.98333 20.88333 1.10000000
## 919 Sophie Freeman F 29.11667 27.33333 1.78333333
## 920 Sophie Gangloff F 29.95000 31.10000 -1.15000000
## 921 Sophie Kurtz F 30.63333 40.58333 -9.95000000
## 922 Stacey Sullivan F 25.71667 26.35000 -0.63333333
## 923 Stacey Thompson F 26.88333 25.95000 0.93333333
## 924 Stephanie Aragon F 26.36667 29.06667 -2.70000000
## 925 Stephanie Critch F 31.81667 31.33333 0.48333333
## 926 Stephanie Eisenbarth F 31.73333 36.73333 -5.00000000
## 927 Stephanie Grilo F 35.20000 34.93333 0.26666667
## 928 Stephanie Loeb F 24.45000 24.71667 -0.26666667
## 929 Stephanie Luekens F 38.95000 43.73333 -4.78333333
## 930 Stephen Corbett M 33.45000 28.03333 5.41666667
## 931 Stephen Dorrough M 30.38333 24.85000 5.53333333
## 932 Stephen Gamboa M 30.96667 37.13333 -6.16666667
## 933 Stephen Gates M 27.16667 20.66667 6.50000000
## 934 Stephen Lynaugh M 30.08333 32.88333 -2.80000000
## 935 Stephen McGovern M 29.93333 27.93333 2.00000000
## 936 Stephen Sheiffele M 26.05000 24.48333 1.56666667
## 937 Steve Arnold M 33.11667 32.81667 0.30000000
## 938 Steve Cassella M 30.95000 31.31667 -0.36666667
## 939 Steve Thompson M 26.00000 28.56667 -2.56666667
## 940 Steven Della-Giustina M 29.95000 30.55000 -0.60000000
## 941 Steven Germaine M 28.38333 26.06667 2.31666667
## 942 Steven Jacques M 26.05000 27.25000 -1.20000000
## 943 Steven Judd M 30.26667 31.43333 -1.16666667
## 944 Steven Niccolai M 29.85000 28.21667 1.63333333
## 945 Summer Wu F 24.06667 22.90000 1.16666667
## 946 Sunnie Scarpa F 31.16667 32.78333 -1.61666667
## 947 Susan Kerley F 32.01667 33.96667 -1.95000000
## 948 Susan Mayer F 34.90000 35.43333 -0.53333333
## 949 Suzanne Poudrier F 21.80000 20.86667 0.93333333
## 950 Talia Remnick F 40.36667 39.08333 1.28333333
## 951 Tamara Malm F 25.76667 27.26667 -1.50000000
## 952 Tanner Festa M 27.55000 29.85000 -2.30000000
## 953 Tanya Bell F 36.80000 36.20000 0.60000000
## 954 Tanya Wiedeking F 27.80000 27.63333 0.16666667
## 955 Tarana Eldredge F 29.78333 25.10000 4.68333333
## 956 Taryn Case F 54.48333 40.20000 14.28333333
## 957 Taryn Disorbo F 32.11667 48.96667 -16.85000000
## 958 Taylor Dostie F 34.28333 32.96667 1.31666667
## 959 Teresa Massimino F 28.98333 30.55000 -1.56666667
## 960 Terri Addesso F 24.56667 23.85000 0.71666667
## 961 Terri Buganski F 29.68333 29.91667 -0.23333333
## 962 Terri Lachance F 27.91667 27.73333 0.18333333
## 963 Terry Williams M 49.38333 49.11667 0.26666667
## 964 Theo Baron M 42.20000 46.11667 -3.91666667
## 965 Theresa Voss F 45.01667 48.16667 -3.15000000
## 966 Thomas Barrette M 26.28333 28.46667 -2.18333333
## 967 Thomas Holman M 43.88333 44.35000 -0.46666667
## 968 Thomas Maynard M 31.00000 24.28333 6.71666667
## 969 Thomas Paterno M 29.26667 28.03333 1.23333333
## 970 Thomas Redding M 20.63333 21.28333 -0.65000000
## 971 Thomas Takoudes M 28.81667 25.63333 3.18333333
## 972 Thomas Trumbley M 36.21667 35.26667 0.95000000
## 973 Thomas Ude, Jr. M 28.76667 28.75000 0.01666667
## 974 Thomas Ullmann M 42.56667 42.55000 0.01666667
## 975 Tiffany Katz F 53.66667 42.63333 11.03333333
## 976 Tim Foldy-Porto M 16.36667 16.90000 -0.53333333
## 977 Timmy Case M 25.95000 24.78333 1.16666667
## 978 Tina Chan F 45.61667 50.93333 -5.31666667
## 979 Tina Newman F 35.93333 37.08333 -1.15000000
## 980 Todd Kennedy M 30.58333 38.30000 -7.71666667
## 981 Todd Liu M 24.33333 29.73333 -5.40000000
## 982 Todd Petronio M 29.66667 26.63333 3.03333333
## 983 Tom Missett M 34.38333 36.43333 -2.05000000
## 984 Tom Sullivan M 33.53333 35.05000 -1.51666667
## 985 Tracy Bellucci F 28.58333 28.16667 0.41666667
## 986 Tracy Weiss F 33.88333 33.85000 0.03333333
## 987 Trey Chometa M 22.26667 23.13333 -0.86666667
## 988 Tricia Guyon F 29.78333 32.18333 -2.40000000
## 989 Ty Holloway M 31.93333 28.80000 3.13333333
## 990 Tyler Connelly M 26.50000 25.96667 0.53333333
## 991 Tyler Pineau M 18.01667 17.28333 0.73333333
## 992 Tyler Smith M 33.18333 30.85000 2.33333333
## 993 Tyler Whiting M 21.63333 25.00000 -3.36666667
## 994 Unedra Muley F 40.08333 37.03333 3.05000000
## 995 Varman Samuel M 28.88333 28.86667 0.01666667
## 996 Verica Milivojevic F 34.73333 33.60000 1.13333333
## 997 Veronica Chase F 33.51667 33.18333 0.33333333
## 998 Veronica Marion F 47.25000 48.41667 -1.16666667
## 999 Victor Figueroa M 22.15000 21.80000 0.35000000
## 1000 Victor Toal M 38.15000 33.63333 4.51666667
## 1001 Victoria Murphy F 31.46667 27.98333 3.48333333
## 1002 Victoria Zigmont F 38.98333 33.66667 5.31666667
## 1003 Vikram Dalal M 31.78333 30.20000 1.58333333
## 1004 Vincent Balbarin M 33.43333 28.96667 4.46666667
## 1005 Vincenzo Dinatale M 19.48333 19.26667 0.21666667
## 1006 Wade Davis M 16.61667 16.56667 0.05000000
## 1007 Walter Harding M 24.01667 24.86667 -0.85000000
## 1008 Walter Pieper M 35.40000 35.46667 -0.06666667
## 1009 Ward Naylor M 24.93333 24.65000 0.28333333
## 1010 Wendy Feldman F 28.91667 32.10000 -3.18333333
## 1011 Will Culligan M 24.98333 24.96667 0.01666667
## 1012 William Connolly M 33.26667 35.21667 -1.95000000
## 1013 William Farrell M 43.16667 43.30000 -0.13333333
## 1014 William Garfinkel M 42.00000 46.30000 -4.30000000
## 1015 William Hammond M 27.38333 30.06667 -2.68333333
## 1016 William Hanley M 26.36667 27.26667 -0.90000000
## 1017 William Long M 41.93333 43.31667 -1.38333333
## 1018 William Meaney M 22.33333 22.80000 -0.46666667
## 1019 William Thompson M 26.88333 33.65000 -6.76666667
## 1020 Wolf Boone M 21.55000 22.08333 -0.53333333
## 1021 Wyatt Sloan M 33.43333 31.38333 2.05000000
## 1022 Xuan Chau M 25.65000 25.10000 0.55000000
## 1023 Yabdriel Gonzalez M 26.33333 30.08333 -3.75000000
## 1024 Yasmin Bergemann F 23.76667 23.91667 -0.15000000
## 1025 Yue Yin F 29.93333 29.31667 0.61666667
## 1026 Zach Wright M 22.81667 23.71667 -0.90000000
## 1027 Zackary Schnitzer M 30.76667 32.30000 -1.53333333
## 1028 Zelda Galdenzi F 27.30000 26.75000 0.55000000
## 1029 Zoe Smith F 34.25000 30.48333 3.76666667nhcombined <- nhcombined[-c(133, 529, 729), ]
hist(nhcombined$diff20162017)
- Studying Improvements.
- I used a 95% bootstrap confidence interval to explore the average improvement among the population of all female repeat 5K runners and of all male repeat 5K runners.
Female repeat 5k runners
femalerr <- nhcombined[nhcombined$Gender == "F", ]
N <- 1000
avgimpfem <- rep(NA, N) #averageimprovementfemale
set.seed(1)
for (i in 1:N) {
s <- sample(na.omit(femalerr$diff20162017), nrow(femalerr), replace=TRUE)
test <- mean(s)
avgimpfem[i] <- mean(s)
}
hist(avgimpfem)
quantile(avgimpfem, c(0.025, 0.975))## 2.5% 97.5%
## -0.4553421 0.1769525q <- quantile(avgimpfem, c(0.025, 0.975))
abline(v=q, lwd=2, col="blue")
#There is a single NA in femalerr, at position 283. I wrapped my femalerr in na.omit to remove the NA while preserving the data. An alternative would have been to include na.rm = TRUE in my quantile calculation e.g. quantile(avgimpfem, c(0.025, 0.975), na.rm = TRUE). I likewise repeated this process for my Male repeat runners below.For female runners, the confidence internal is between -0.4553421 and 0.1769525. I would conclude that 95% of women experienced a change in run time between -0.46 and +0.18 minutes from 2016 to 2017.
Male repeat 5k runners
malerr <- nhcombined[nhcombined$Gender == "M", ]
N <- 1000
avgimpmale <- rep(NA, N) #averageimprovementmale
set.seed(1)
for (i in 1:N) {
s <- sample(na.omit(malerr$diff20162017), nrow(malerr), replace=TRUE)
avgimpmale[i] <- mean(s)
}
hist(avgimpmale)
quantile(avgimpmale, c(0.025, 0.975))## 2.5% 97.5%
## -0.48013915 0.01587548q <- quantile(avgimpmale, c(0.025, 0.975))
abline(v=q, lwd=2, col="blue")
For male runners, the confidence internal is between -0.48013915 to 0.01587548 . I would conclude that 95% of men experienced a change in run time between -0.48 and +0.02 minutes from 2016 to 2017.
- I then used a permutation test to explore if there is a significant difference in the average improvement between males and females? I used a significance level of 0.05.
realmeandiff <- mean(na.omit(nhcombined$diff20162017[nhcombined$Gender == "F"])) - mean(na.omit(nhcombined$diff20162017[nhcombined$Gender == "M"]))
set.seed(1)
N <- 1000
diffH0vals <- rep(NA, N)
for (i in 1:N) {
fakegender <- sample(nhcombined$Gender)
diffH0vals[i] <- mean(na.omit(nhcombined$diff20162017[fakegender == "F"])) - mean(na.omit(nhcombined$diff20162017[fakegender == "M"])) #I was unable to calculate the median for female, and so wrapped my value in na.omit. Going forward, delete NAs from the data frame.
}
hist(diffH0vals)
abline(v=realmeandiff, col="red", lwd=2)
- Hypothesis testing and Conclusion
mean(diffH0vals >= realmeandiff)## [1] 0.328mean(diffH0vals <= realmeandiff)## [1] 0.672mean(abs(diffH0vals) >= abs(realmeandiff))## [1] 0.666The null hypothesis is that there is no difference in improvement between male and female times, this is represented in the above histogram with the abline representing the actual difference in sample means of this distribution. As such our alternative hypotheses are (1) male times have improved more than female times (2) female times have improved more than male times and (3) that there is any difference at all between our male and female improvement times.
In the first case our p-value is 0.328 suggesting that 32.8% of our 1000 simulations produced a difference more extreme than our actual difference in means. In our second case 67.2% of our simulations produced a difference more extreme than our actual difference in means and in our final case 66.6% of our test cases produced a fake test statistic more extreme than our actual difference in means. All the p-values are greater than 0.05 and as a result we fail to reject the null hypothesis.