analysis.R

library(cluster)
library(moments)
# # # if(!require(data.table)){install.packages("data.table")}
library(data.table)

school_data_set <- read.csv("school_data_test.csv")
# str(school_data_set)
print("analysis.R loaded successfully")
# This returns the unscaled data_set with the cluster assignment and cluster distance
# ?? What other values will we need to return and display to the user?
# run_cluster_analysis <- function(original_data_set, cluster_count=6){
#   seed_value <- 52579
#   # TODO: Need a better way to initialize this so it's not hard coded in the R file
#   boolean_column_names <- c("bureau_of_indian_education", "reconstituted_flag", "title_1_eligible", "school_wide_title_1", "magnet", "charter", "shared_time", "urban_locale", "suburban_locale", "town_locale", "rural_locale", "northeast_region", "midwest_region", "south_region", "west_region")

#   # Allows me to easily remove columns from the data set when doing analysis
#   get_column_exclusion_vector <- function(data_set, column_names=c("id")){
#     -which(names(data_set) %in% column_names)
#   }

#   # Scale the values in the data set
#   scale_data <- function(data_set){
#     # scale columns except boolean, id and cluster assignments
#     column_exclusions <- get_column_exclusion_vector(data_set, c("id", "cluster", boolean_column_names))
#     data_set[,column_exclusions] <- scale(data_set[,column_exclusions])

#     data_set
#   }
#   dt_scaled_data <- function(data_set){
#     cluster_column_exclusions <- get_column_exclusion_vector(data_set, c("id", "cluster", boolean_column_names))
#     included_columns <- colnames(data_set)[cluster_column_exclusions]
#     data_set[, (included_columns):=lapply(.SD,scale), .SDcols=included_columns]
#   }

#   #
#   set_cluster_assignment <- function(data_set){
#     # seed_value comes from the wrapper function init
#     set.seed(seed_value)

#     # cluster_count comes from the wrapper function params
#     data_set_kmeans <- kmeans(data_set[,get_column_exclusion_vector(data_set)], cluster_count)
#     cbind(data_set, cluster=data_set_kmeans$cluster)
#   }

#   set_cluster_distance <- function(data_set){
#     # Nested this function bc it requires that we only hand it the columns of data we wish to
#     # process, thus it's dangerous to outside scopes
#     find_cluster_distance <- function(cluster_data) {
#       # seed_value comes from the wrapper function init
#       set.seed(seed_value)
#       cluster_mean <- moment(cluster_data, order=1, central=FALSE)
#       cluster_variance <- moment(cluster_data, order=2, central=TRUE)
#       inverse_variance <- diag(1/cluster_variance)
#       mahalanobis(cluster_data, cluster_mean, inverse_variance)
#     }
#     cluster_column_exclusions <- get_column_exclusion_vector(data_set, c("id", "cluster"))
#     included_columns <- colnames(data_set)[cluster_column_exclusions]

#     dt <- data.table(data_set)
#     setkey(dt, "cluster")

#     # add the cluster_distance column to the data table by cluster
#     dt[, cluster_distance:=find_cluster_distance(.SD), by=cluster, .SDcols=included_columns]
#   }

#   scaled_data_set <- na.omit(scale_data(original_data_set))
#   scaled_data_set <- set_cluster_assignment(scaled_data_set)
#   scaled_data_set <- set_cluster_distance(scaled_data_set)
#   scaled_columns <- colnames(scaled_data_set)[-which(colnames(scaled_data_set) %in% c("id", "cluster", "cluster_distance"))]
#   scaled_data_set[, (scaled_columns):=NULL]
#   # scaled_data_set[,c("id", "cluster", "cluster_distance"), with = FALSE]

#   # The code below is needed if we want to join the results of analysis to the original data set
#   # Convert our data_set to a data.table for efficient join and ordering
#   dt_data_set <- data.table(original_data_set)
#   setkey(scaled_data_set, "id")
#   setkey(dt_data_set, "id")
#   # # data.table join which uses id as the key
#   dt_data_set[scaled_data_set]
#   # setorder(dt_data_set, cluster, cluster_distance)
#   # # Returns an unscaled data_set with cluster assignment and cluster distance
#   # dt_data_set
# }

run_cluster_analysis <- function(original_data_set, cluster_count=6){
  seed_value <- 52579
  # TODO: Need a better way to initialize this so it's not hard coded in the R file
  boolean_column_names <- c("bureau_of_indian_education", "reconstituted_flag", "title_1_eligible", "school_wide_title_1", "magnet", "charter", "shared_time", "urban_locale", "suburban_locale", "town_locale", "rural_locale", "northeast_region", "midwest_region", "south_region", "west_region")
  # Remove NAs and create data.table
  original_data_set <- na.omit(original_data_set)
  calculated_data_set <- data.table(original_data_set)
  # scale the data
  scaleable_columns <- colnames(calculated_data_set)[which(!colnames(calculated_data_set) %in% c("id", boolean_column_names))]
  calculated_data_set[, (scaleable_columns):=lapply(.SD,scale), .SDcols=scaleable_columns]
  # set cluster assignment
  data_columns <- colnames(calculated_data_set)[which(!colnames(calculated_data_set) %in% c("id"))]
  calculated_data_set[, cluster:=kmeans(.SD, cluster_count)$cluster, .SDcols=data_columns]

  # set cluster distance
  find_cluster_distance <- function(cluster_data) {
    # seed_value comes from the wrapper function init
    set.seed(seed_value)
    cluster_mean <- moment(cluster_data, order=1, central=FALSE)
    cluster_variance <- moment(cluster_data, order=2, central=TRUE)
    inverse_variance <- diag(1/cluster_variance)
    mahalanobis(cluster_data, cluster_mean, inverse_variance)
  }
  setkey(calculated_data_set, "cluster")
  data_columns <- colnames(calculated_data_set)[which(!colnames(calculated_data_set) %in% c("id", "cluster"))]
  calculated_data_set[, cluster_distance:=find_cluster_distance(.SD), by=cluster, .SDcols=data_columns]

  # get the cluster means
  data_columns <- colnames(calculated_data_set)[which(!colnames(calculated_data_set) %in% c("id", "cluster"))]
  scaled_cluster_means <- t(calculated_data_set[, lapply(.SD, mean), by=cluster, .SDcols=data_columns])

  # remove the scaled columns
  scaled_columns <- colnames(calculated_data_set)[-which(colnames(calculated_data_set) %in% c("id", "cluster", "cluster_distance"))]
  calculated_data_set[, (scaled_columns):=NULL]

  # Join the cluster calculations to the original data
  original_data_set <- data.table(original_data_set)
  setkey(calculated_data_set, "id")
  setkey(original_data_set, "id")

  list(cluster_data=original_data_set[calculated_data_set], cluster_means=scaled_cluster_means)

  # =============== end data.table implementation ==============
}

histogram_with_proportions <- function(data_set, ...) {
  h <- hist(na.omit(data_set), plot=FALSE)
  h$counts <- h$counts / sum(h$counts)
  h
}

school_data_test.csv

id,proportion_female,district_size,proportion_white,proportion_black,urban_locale,suburban_locale,town_locale,rural_locale,proportion_district_ell,title_1_eligible
12,0.455607476635514,12,0.441588785046729,0.00700934579439252,0,0,1,0,0.143719806763285,1
14,0.477406679764244,30,0.980353634577603,0.00589390962671906,0,0,0,1,0.0557728277503826,1
15,0.466981132075472,30,0.768867924528302,0.00471698113207547,0,0,0,1,0.0557728277503826,1
17,0.452431289640592,30,0.682875264270613,0.00845665961945032,0,0,0,1,0.0557728277503826,1
18,0.465779467680608,30,0.790874524714829,0.0152091254752852,0,0,0,1,0.0557728277503826,1
19,0.505747126436782,30,0.92816091954023,0.014367816091954,0,0,0,1,0.0557728277503826,1
20,0.435064935064935,30,0.96969696969697,0.00649350649350649,0,0,0,1,0.0557728277503826,1
21,0.453488372093023,30,0.961240310077519,0.00775193798449612,0,0,0,1,0.0557728277503826,1
26,0.484293193717277,30,0.979057591623037,0.00523560209424084,0,0,0,1,0.0557728277503826,1
28,0.50635593220339,30,0.739406779661017,0.0169491525423729,0,0,0,1,0.0557728277503826,1
31,0.499228395061728,34,0.627700617283951,0.251157407407407,0,0,0,1,0.0363622843545509,0
32,0.484297520661157,34,0.670247933884297,0.234710743801653,1,0,0,0,0.0363622843545509,0
35,0.49771986970684,34,0.700325732899023,0.224755700325733,1,0,0,0,0.0363622843545509,0
36,0.47680412371134,34,0.617268041237113,0.220360824742268,1,0,0,0,0.0363622843545509,0
39,0.498188405797101,34,0.710144927536232,0.168478260869565,1,0,0,0,0.0363622843545509,1
44,0.4752,34,0.6272,0.216,1,0,0,0,0.0363622843545509,0
46,0.512727272727273,20,0.689090909090909,0.183636363636364,0,1,0,0,0.0262110152621102,1
50,0.463350785340314,20,0.667539267015707,0.193717277486911,0,1,0,0,0.0262110152621102,0
54,0.474009900990099,20,0.618811881188119,0.181930693069307,0,1,0,0,0.0262110152621102,1
61,0.467908902691511,6,0.594202898550725,0.260869565217391,0,0,1,0,0.0574850299401198,1
62,0.445255474452555,6,0.571776155717762,0.326034063260341,0,0,1,0,0.0574850299401198,0
65,0.505681818181818,10,0.690340909090909,0.0255681818181818,0,0,1,0,0.0694945848375451,1
67,0.497685185185185,10,0.675925925925926,0.0115740740740741,0,0,1,0,0.0694945848375451,1
68,0.483124397299904,8,0.85053037608486,0.105110896817743,0,1,0,0,0.0138457865839102,0
69,0.509052183173589,8,0.873269435569755,0.0873269435569755,0,0,0,1,0.0138457865839102,0
71,0.493975903614458,8,0.859939759036145,0.0978915662650602,0,0,0,1,0.0138457865839102,0
81,0.479371316306483,10,0.620825147347741,0.357563850687623,0,0,1,0,0.0192429022082019,0
82,0.490079365079365,10,0.583333333333333,0.391865079365079,0,0,1,0,0.0192429022082019,0
83,0.475644699140401,10,0.518624641833811,0.404011461318052,0,0,0,1,0.0192429022082019,1
84,0.462203023758099,10,0.570194384449244,0.349892008639309,0,0,1,0,0.0192429022082019,1
96,0.518828451882845,6,0.579497907949791,0.376569037656904,0,0,1,0,0.00818713450292398,1
97,0.49009900990099,6,0.586633663366337,0.383663366336634,0,0,0,1,0.00818713450292398,1
99,0.529203539823009,18,0.0212389380530973,0.964601769911504,1,0,0,0,0.00594693504117109,1
101,0.545454545454545,18,0.200956937799043,0.688995215311005,1,0,0,0,0.00594693504117109,1
102,0.527896995708155,18,0.0257510729613734,0.96137339055794,1,0,0,0,0.00594693504117109,1
103,0.504178272980501,18,0.103064066852368,0.863509749303621,1,0,0,0,0.00594693504117109,1
108,0.48785046728972,8,0.957009345794393,0.00186915887850467,0,0,0,1,0.00554235946159937,1
110,0.476650563607085,8,0.966183574879227,0.00322061191626409,0,0,0,1,0.00554235946159937,0
111,0.48944099378882,8,0.967701863354037,0.00496894409937888,0,0,0,1,0.00554235946159937,0
114,0.486394557823129,14,0.623582766439909,0.260770975056689,0,0,0,1,0.063417681974383,0
115,0.4957805907173,14,0.573839662447257,0.293248945147679,0,0,1,0,0.063417681974383,0
116,0.477508650519031,14,0.591695501730104,0.231833910034602,0,0,1,0,0.063417681974383,1
118,0.527586206896552,14,0.627586206896552,0.213793103448276,0,0,1,0,0.063417681974383,1
130,0.526119402985075,6,0.673507462686567,0.21455223880597,0,1,0,0,0.025028441410694,1
139,0.504847645429363,22,0.599722991689751,0.28185595567867,1,0,0,0,0.0218253968253968,0
140,0.492018779342723,22,0.603755868544601,0.302347417840376,1,0,0,0,0.0218253968253968,0
142,0.523474178403756,22,0.732394366197183,0.143192488262911,1,0,0,0,0.0218253968253968,1
143,0.494159928122192,22,0.634321653189578,0.248876909254268,1,0,0,0,0.0218253968253968,0
144,0.495575221238938,22,0.646017699115044,0.280973451327434,1,0,0,0,0.0218253968253968,1
146,0.493230174081238,22,0.682785299806576,0.205029013539652,0,0,0,1,0.0218253968253968,0
147,0.510948905109489,22,0.536496350364963,0.291970802919708,0,0,0,1,0.0218253968253968,1
150,0.511111111111111,30,0.74962962962963,0.234074074074074,0,0,0,1,0.00865139949109415,1
155,0.520408163265306,30,0.729591836734694,0.236394557823129,0,0,1,0,0.00865139949109415,1
164,0.488142292490119,30,0.849802371541502,0.130434782608696,0,0,0,1,0.00865139949109415,0
165,0.493297587131367,94,0.600536193029491,0.375335120643432,0,0,1,0,0.0225783972125436,1
166,0.493761140819964,94,0.700534759358289,0.228163992869875,0,0,0,1,0.0225783972125436,0
167,0.540792540792541,94,0.913752913752914,0.027972027972028,0,0,0,1,0.0225783972125436,0
168,0.485959438377535,94,0.845553822152886,0.0975039001560062,0,0,1,0,0.0225783972125436,0
169,0.486425339366516,94,0.719457013574661,0.199095022624434,0,0,1,0,0.0225783972125436,0
171,0.563106796116505,94,0.922330097087379,0.0194174757281553,0,0,0,1,0.0225783972125436,1
173,0.48961937716263,94,0.64878892733564,0.22318339100346,0,0,1,0,0.0225783972125436,0
174,0.460764587525151,94,0.899396378269618,0.0241448692152917,0,0,0,1,0.0225783972125436,1
177,0.501355013550135,94,0.87940379403794,0.0826558265582656,0,0,1,0,0.0225783972125436,0
178,0.477064220183486,94,0.954128440366973,0.0275229357798165,0,0,0,1,0.0225783972125436,1
179,0.465863453815261,94,0.799196787148594,0.0863453815261044,0,0,0,1,0.0225783972125436,1
181,0.494600431965443,94,0.868250539956803,0.105111591072714,0,0,0,1,0.0225783972125436,0
195,0.479674796747967,94,0.705691056910569,0.195121951219512,0,0,1,0,0.0225783972125436,0
197,0.480519480519481,94,0.807192807192807,0.125874125874126,0,0,1,0,0.0225783972125436,0
199,0.470108695652174,94,0.573369565217391,0.214673913043478,0,0,0,1,0.0225783972125436,1
201,0.46972860125261,94,0.475991649269311,0.496868475991649,0,0,0,1,0.0225783972125436,1
203,0.492678725236865,94,0.713178294573643,0.218776916451335,0,0,1,0,0.0225783972125436,0
205,0.462252475247525,94,0.753094059405941,0.153465346534653,0,0,0,1,0.0225783972125436,0
206,0.493881118881119,94,0.668706293706294,0.298076923076923,0,0,0,1,0.0225783972125436,0
207,0.49009009009009,94,0.612612612612613,0.354954954954955,0,0,1,0,0.0225783972125436,0
209,0.48302300109529,94,0.771084337349398,0.16210295728368,0,0,0,1,0.0225783972125436,0
213,0.488372093023256,8,0.12015503875969,0.720930232558139,0,0,0,1,0.0528301886792453,1
217,0.484419263456091,18,0.0,0.971671388101983,0,1,0,0,0.0155393053016453,1
219,0.492281303602058,18,0.0145797598627787,0.945111492281304,0,1,0,0,0.0155393053016453,1
225,0.471326164874552,20,0.602150537634409,0.378136200716846,0,0,0,1,0.000847697089573326,1
227,0.46353322528363,20,0.636952998379254,0.3354943273906,0,0,1,0,0.000847697089573326,1
229,0.449275362318841,20,0.83695652173913,0.130434782608696,0,0,0,1,0.000847697089573326,1
234,0.455927051671733,20,0.933130699088146,0.027355623100304,0,0,0,1,0.000847697089573326,1
236,1.0,162,0.0,1.0,1,0,0,0,0.0208441273763501,0
239,0.47787610619469,162,0.00442477876106195,0.960176991150443,1,0,0,0,0.0208441273763501,1
240,0.438202247191011,162,0.0,0.99250936329588,1,0,0,0,0.0208441273763501,1
242,0.474903474903475,162,0.027027027027027,0.868725868725869,1,0,0,0,0.0208441273763501,1
243,0.476489028213166,162,0.00470219435736677,0.95141065830721,1,0,0,0,0.0208441273763501,1
245,0.496815286624204,162,0.0,0.996815286624204,1,0,0,0,0.0208441273763501,1
248,0.51380042462845,162,0.0127388535031847,0.968152866242038,1,0,0,0,0.0208441273763501,1
249,0.468253968253968,162,0.0238095238095238,0.619047619047619,1,0,0,0,0.0208441273763501,1
251,0.447432762836186,162,0.0,0.987775061124694,1,0,0,0,0.0208441273763501,1
253,0.418918918918919,162,0.00337837837837838,0.986486486486487,1,0,0,0,0.0208441273763501,1
254,0.442307692307692,162,0.00480769230769231,0.838942307692308,1,0,0,0,0.0208441273763501,1
259,0.465968586387435,162,0.0261780104712042,0.950261780104712,1,0,0,0,0.0208441273763501,1
265,0.519047619047619,162,0.00238095238095238,0.966666666666667,1,0,0,0,0.0208441273763501,1
267,0.487229862475442,162,0.0157170923379175,0.931237721021611,1,0,0,0,0.0208441273763501,1
268,0.472602739726027,162,0.0,0.972602739726027,1,0,0,0,0.0208441273763501,1
274,0.59825327510917,162,0.0,1.0,1,0,0,0,0.0208441273763501,0
279,0.507425742574257,162,0.0,0.992574257425743,1,0,0,0,0.0208441273763501,1
280,0.484098939929329,162,0.00235571260306243,0.997644287396938,1,0,0,0,0.0208441273763501,1
283,0.491189427312775,162,0.00220264317180617,0.94273127753304,1,0,0,0,0.0208441273763501,1
284,0.482939632545932,162,0.0078740157480315,0.910761154855643,1,0,0,0,0.0208441273763501,1
296,0.483709273182957,162,0.0,0.992481203007519,1,0,0,0,0.0208441273763501,1
297,0.49859943977591,162,0.0252100840336134,0.929971988795518,1,0,0,0,0.0208441273763501,1
299,0.492044063647491,162,0.00856793145654835,0.746634026927785,1,0,0,0,0.0208441273763501,1
313,0.575842696629214,162,0.00842696629213483,0.980337078651685,1,0,0,0,0.0208441273763501,0
316,0.48169014084507,34,0.971830985915493,0.0169014084507042,0,0,0,1,0.0318198069360029,1
318,0.507267441860465,34,0.819767441860465,0.0101744186046512,0,0,0,1,0.0318198069360029,1
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1903,0.494535519125683,88,0.770491803278688,0.0,0,0,0,1,0.0210537918871252,1
1904,0.481132075471698,88,1.0,0.0,0,0,0,1,0.0210537918871252,1
1905,0.49,88,0.7025,0.005,0,0,0,1,0.0210537918871252,0
1908,0.479452054794521,88,0.821917808219178,0.0,0,0,0,1,0.0210537918871252,0
1910,0.490740740740741,32,0.444444444444444,0.0138888888888889,0,0,1,0,0.127625842251288,1
1911,0.529411764705882,32,0.0,0.0,0,0,0,1,0.127625842251288,1
1913,0.459876543209877,32,0.401234567901235,0.00308641975308642,0,0,1,0,0.127625842251288,1
1914,0.483487450462351,32,0.40554821664465,0.00396301188903567,0,0,1,0,0.127625842251288,1
1917,0.462222222222222,32,0.0933333333333333,0.0133333333333333,0,0,1,0,0.127625842251288,1
1919,0.452914798206278,32,0.780269058295964,0.0,0,0,1,0,0.127625842251288,0
1920,0.5,32,0.307692307692308,0.0,0,0,0,1,0.127625842251288,1
1921,0.558823529411765,32,0.0294117647058824,0.0,0,0,0,1,0.127625842251288,1
1922,0.692307692307692,32,0.769230769230769,0.0,0,0,1,0,0.127625842251288,0
1923,0.25,32,0.0,0.0,0,0,0,1,0.127625842251288,1
1926,0.588235294117647,30,0.352941176470588,0.0,0,0,0,1,0.0,1
1927,0.666666666666667,30,0.0833333333333333,0.0,0,0,0,1,0.0,0
1929,0.3,30,0.3,0.0,0,0,0,1,0.0,1
1932,0.525641025641026,30,0.0256410256410256,0.0,0,0,0,1,0.0,1
1935,0.464285714285714,30,0.0357142857142857,0.0,0,0,0,1,0.0,1
1936,0.333333333333333,30,0.0,0.0,0,0,0,1,0.0,1
1937,0.475,30,0.45,0.15,0,0,0,1,0.0,1
1943,0.394736842105263,88,0.842105263157895,0.0,0,0,0,1,0.0248855835240275,1
1944,0.484593837535014,88,0.69187675070028,0.0476190476190476,0,0,1,0,0.0248855835240275,1
1945,0.477018633540373,88,0.782608695652174,0.0186335403726708,0,0,1,0,0.0248855835240275,0
1946,0.537777777777778,88,0.735555555555556,0.0377777777777778,0,0,1,0,0.0248855835240275,0
1947,0.505681818181818,88,0.772727272727273,0.0,0,0,0,1,0.0248855835240275,1
1949,0.461538461538462,88,0.725718725718726,0.0248640248640249,0,0,1,0,0.0248855835240275,0
1953,0.515706806282722,88,0.774869109947644,0.0183246073298429,0,0,0,1,0.0248855835240275,1
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1962,0.474934036939314,88,0.754617414248021,0.0131926121372032,0,0,1,0,0.0248855835240275,0
1963,0.470957613814757,88,0.748822605965463,0.0156985871271586,0,0,0,1,0.0248855835240275,1
1965,0.476190476190476,88,0.736842105263158,0.0225563909774436,0,0,1,0,0.0248855835240275,1
1966,0.439226519337017,88,0.773480662983425,0.0414364640883978,0,0,0,1,0.0248855835240275,0
1968,0.427536231884058,88,0.751207729468599,0.0241545893719807,0,0,0,1,0.0248855835240275,1
1969,0.48441449031171,88,0.741364785172704,0.025273799494524,0,0,0,1,0.0248855835240275,0
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1974,0.479166666666667,88,0.854166666666667,0.0,0,0,0,1,0.0248855835240275,1
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1978,0.465116279069767,88,0.704651162790698,0.00930232558139535,0,0,0,1,0.0248855835240275,1
1979,0.435294117647059,88,0.865882352941176,0.00705882352941176,0,0,0,1,0.0248855835240275,0
1981,0.280701754385965,88,0.719298245614035,0.0526315789473684,0,0,1,0,0.0248855835240275,1
1986,0.511111111111111,18,0.0444444444444444,0.0,0,0,0,1,0.0984615384615385,1
1994,0.484375,6,0.015625,0.0,0,0,0,1,0.0,1
1999,0.50765306122449,10,0.114795918367347,0.00255102040816327,0,0,1,0,0.0277372262773723,1
2001,0.0,10,0.0,0.0,0,0,1,0,0.0277372262773723,1
2002,0.5625,10,0.5625,0.0,0,0,0,1,0.0277372262773723,0
2005,0.476833976833977,70,0.613899613899614,0.0250965250965251,0,1,0,0,0.0258356545961003,0
2010,0.456666666666667,70,0.71,0.03,0,0,1,0,0.0258356545961003,0
2011,0.501718213058419,70,0.742268041237113,0.0274914089347079,0,0,1,0,0.0258356545961003,1
2012,0.469230769230769,70,0.755769230769231,0.00961538461538462,0,0,0,1,0.0258356545961003,0
2019,0.458689458689459,70,0.487179487179487,0.108262108262108,1,0,0,0,0.0258356545961003,1
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19564,0.0,4,0.0,1.0,0,0,0,1,0.0,0
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19655,0.483651226158038,100,0.852861035422343,0.0245231607629428,1,0,0,0,0.0160935246337205,1
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19670,0.473404255319149,100,0.912234042553192,0.0412234042553191,0,0,0,1,0.0160935246337205,1
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19675,0.506841505131129,100,0.859179019384265,0.0655644241733181,1,0,0,0,0.0160935246337205,0
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19747,0.475490196078431,258,0.520833333333333,0.270833333333333,1,0,0,0,0.0272035881435257,1
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19756,0.48209549071618,258,0.771883289124668,0.0490716180371353,0,1,0,0,0.0272035881435257,0
19757,0.537154989384289,258,0.786624203821656,0.0191082802547771,0,1,0,0,0.0272035881435257,0
19758,0.463178294573643,258,0.614341085271318,0.164728682170543,0,1,0,0,0.0272035881435257,1
19760,0.415413533834586,258,0.766917293233083,0.0432330827067669,0,1,0,0,0.0272035881435257,0
19762,0.468118195956454,258,0.757387247278383,0.0482115085536547,0,1,0,0,0.0272035881435257,0
19767,0.508126603934987,258,0.837467921300257,0.0213857998289136,0,1,0,0,0.0272035881435257,0
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19770,0.457627118644068,258,0.768361581920904,0.0423728813559322,0,1,0,0,0.0272035881435257,0
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19773,0.507586206896552,258,0.884137931034483,0.0110344827586207,0,1,0,0,0.0272035881435257,0
19774,0.500920810313076,258,0.837937384898711,0.0165745856353591,0,1,0,0,0.0272035881435257,0
19775,0.495559502664298,258,0.909413854351687,0.00355239786856128,0,1,0,0,0.0272035881435257,0
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19777,0.481481481481481,258,0.74962962962963,0.0459259259259259,0,1,0,0,0.0272035881435257,1
19778,0.497816593886463,258,0.605531295487627,0.141193595342067,1,0,0,0,0.0272035881435257,1
19780,0.205882352941176,258,0.588235294117647,0.294117647058824,0,1,0,0,0.0272035881435257,1
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19782,0.489100817438692,258,0.618528610354223,0.110354223433243,1,0,0,0,0.0272035881435257,1
19783,0.483130904183536,258,0.446693657219973,0.241565452091768,1,0,0,0,0.0272035881435257,1
19785,0.463829787234043,258,0.53758865248227,0.211347517730496,1,0,0,0,0.0272035881435257,1
19793,0.47030303030303,258,0.764848484848485,0.0612121212121212,0,1,0,0,0.0272035881435257,0
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19798,0.0,258,0.296296296296296,0.518518518518518,0,1,0,0,0.0272035881435257,0
19803,0.449152542372881,258,0.73728813559322,0.0480225988700565,1,0,0,0,0.0272035881435257,0
19804,0.534232365145228,258,0.761410788381743,0.0311203319502075,1,0,0,0,0.0272035881435257,0
19806,0.490033222591362,258,0.659468438538206,0.0946843853820598,0,1,0,0,0.0272035881435257,0
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