Hallo
library("DBI")
library("RSQLite")
drv <- dbDriver("SQLite")
tfile <- "/Users/tobias/Documents/riotStat/sqlite.db"
con <- dbConnect(drv, dbname = tfile)
smaller <- dbGetQuery(con, "SELECT GameID FROM GameStatForR WHERE SubType ='RANKED_SOLO_5x5' GROUP BY GameID HAVING COUNT(*)==10")
df <- dbGetQuery(con, "select SummonerID,GameID,Win,WardKilled,WardPlaced,ChampionsKilled from GameStatForR WHERE SubType = 'RANKED_SOLO_5x5' ")
df <- df[which(df$GameID %in% smaller$GameID), ]
df.1 <- aggregate(df[, 4:5], list(df$Win, df$GameID), mean)
grpA <- df.1[which(df.1$Group.1 == 1 & df.1$WardPlaced < 30), ]$WardPlaced
grpB <- df.1[which(df.1$Group.1 == 0 & df.1$WardPlaced < 30), ]$WardPlaced
boxplot(df.1$WardPlaced ~ df.1$Group.1, main = "WardPlaced")
t.test(grpA, grpB)
##
## Welch Two Sample t-test
##
## data: grpA and grpB
## t = 2.126, df = 347.5, p-value = 0.03422
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.06326 1.62817
## sample estimates:
## mean of x mean of y
## 8.943 8.097
t.test(grpA, grpB, alternative = "greater")
##
## Welch Two Sample t-test
##
## data: grpA and grpB
## t = 2.126, df = 347.5, p-value = 0.01711
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 0.1896 Inf
## sample estimates:
## mean of x mean of y
## 8.943 8.097
boxplot(df.1$WardKilled ~ df.1$Group.1, main = "WardKilled")
grpA <- df.1[which(df.1$Group.1 == 1 & df.1$WardPlaced < 30), ]$WardKilled
grpB <- df.1[which(df.1$Group.1 == 0 & df.1$WardPlaced < 30), ]$WardKilled
t.test(grpA, grpB)
##
## Welch Two Sample t-test
##
## data: grpA and grpB
## t = 0.1875, df = 339.9, p-value = 0.8513
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.0759 0.0919
## sample estimates:
## mean of x mean of y
## 0.3086 0.3006
t.test(grpA, grpB, alternative = "greater")
##
## Welch Two Sample t-test
##
## data: grpA and grpB
## t = 0.1875, df = 339.9, p-value = 0.4257
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## -0.06236 Inf
## sample estimates:
## mean of x mean of y
## 0.3086 0.3006
df.2 <- aggregate(df[, 6], list(df$Win, df$GameID), sum)
boxplot(df.2$x ~ df.2$Group.1)
grpA <- df.2[which(df.2$Group.1 == 1), ]$x
grpB <- df.2[which(df.2$Group.1 == 0), ]$x
t.test(grpA, grpB)
##
## Welch Two Sample t-test
##
## data: grpA and grpB
## t = 12.14, df = 341.4, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 12.13 16.82
## sample estimates:
## mean of x mean of y
## 39.17 24.70
t.test(grpA, grpB, alternative = "greater")
##
## Welch Two Sample t-test
##
## data: grpA and grpB
## t = 12.14, df = 341.4, p-value < 2.2e-16
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 12.51 Inf
## sample estimates:
## mean of x mean of y
## 39.17 24.70
d #which(df$WardPlaced>100)
## Error: object 'd' not found
# SELECT ChampionID FROM GameStatForR WHERE SummonerID= 20966655 AND GameID
# = 1381187479; Teemo ist 17
# grpA2 <- df.1[which(df.1$Group.1==1),]$WardKilled grpB2 <-
# df.1[which(df.1$Group.1==0),]$WardKilled t.test(grpA2,grpB2)
ddd
##########################
require(vegan)
## Loading required package: vegan
## Loading required package: permute
## Loading required package: lattice
## This is vegan 2.0-10
df.All <- dbGetQuery(con, "SELECT * FROM GameStatForR")
df.interest <- df.All[which(df.All$GameID %in% smaller$GameID), ]
df.interest[1:2, c(2, 3, 81)]
## GameID ChampionID Win
## 2 1388101846 412 1
## 3 1388085725 412 1
ttt <- function(x) {
t <- c()
for (i in 1:length(x)) {
t <- c(t, x[i])
}
return(t)
}
df.3 <- aggregate(df.interest[, 3], list(df.interest$Win, df.interest$GameID),
ttt)
df.3.char <- df.3[, -c(1, 2)]
d <- vegdist(t(df.3[, -c(1, 2)]), methods = "jaccard")
fit <- hclust(d, method = "ward")
plot(fit)
data <- as.data.frame(t(matrix(test, nrow = 3)))
## Error: object 'test' not found
data <- data[-c(2, 7), ]
## Error: object of type 'closure' is not subsettable
d <- vegdist(data, method = "jaccard")
## Error: 'x' must be an array of at least two dimensions
fit <- hclust(d, method = "ward")
plot(fit)
####
helpfac <- function(x, y) {
return(table(factor(x, unique(c(y)))))
}
works <- apply(df.3.char, 1, helpfac, y = df.3.char)
d <- vegdist(works, method = "jaccard")
fit <- hclust(d, method = "complete")
plot(fit)
############## häufigstes item
t <- c(as.factor(df.interest$Item0), (df.interest$Item1), df.interest$Item2,
df.interest$Item3, df.interest$Item4, df.interest$Item5, df.interest$Item6)
sort(table(t))
## t
## 3 13 15 32 33 38 42 50 64 73 83 108 111 116 118
## 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## 122 124 3140 3141 3253 3261 3266 3273 3281 3303 9 14 25 31 46
## 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2
## 47 60 74 76 87 91 94 101 120 123 127 1080 2039 2041 3056
## 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## 3106 3131 3167 3168 3263 3271 3276 3279 3284 16 27 37 52 65 75
## 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3
## 79 86 112 114 1039 3005 3166 3171 3257 3274 3302 6 7 20 22
## 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4
## 55 62 85 97 99 1043 3050 3154 3264 24 28 35 77 90 126
## 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5
## 3060 3097 3124 3206 3254 3259 11 70 71 100 109 125 3004 3023 3042
## 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6
## 3091 3101 12 21 39 59 104 121 1027 3098 3301 3342 4 10 26
## 6 6 7 7 7 7 7 7 7 7 7 7 8 8 8
## 51 1004 3041 3152 3269 5 8 93 96 103 119 1051 3142 2 72
## 8 8 8 8 8 9 9 9 9 9 9 9 9 10 10
## 82 3003 3145 3401 63 3028 3172 3252 40 48 80 84 88 3009 3010
## 10 10 10 10 11 11 11 11 12 12 12 12 12 12 12
## 3144 3361 41 56 92 2010 3093 3105 3280 2043 3085 3222 61 66 115
## 12 12 13 13 13 13 13 13 13 14 14 14 15 15 15
## 117 3146 3364 45 81 3115 36 105 1 29 44 49 67 3077 23
## 15 15 15 16 16 16 17 17 18 18 18 18 18 18 19
## 53 3040 68 3092 102 1033 3096 1006 69 110 3108 3207 3362 3024 30
## 19 19 20 20 21 21 21 22 24 24 24 24 24 25 26
## 78 3136 3155 128 113 3044 3070 34 3067 3156 3275 1042 3165 43 3001
## 26 26 26 27 28 29 29 30 32 32 33 34 34 35 36
## 3025 3057 3022 2004 1018 3128 3075 3158 2044 3086 54 3134 3190 1054 3191
## 36 36 37 38 39 39 40 41 42 44 46 46 46 47 48
## 3100 89 98 3110 3102 3211 58 3069 3209 17 3116 3026 3151 106 3027
## 49 50 50 50 51 52 54 54 56 57 57 58 59 60 61
## 57 3083 1029 1038 1057 1053 1052 3082 1036 3260 1056 95 1058 3265 3135
## 62 62 63 64 64 65 67 67 71 72 74 78 78 87 88
## 19 3270 1055 2045 3111 3174 107 1001 3157 1031 3071 3078 2049 3117 3153
## 89 90 94 97 98 99 101 103 104 105 106 108 112 112 114
## 3065 1028 1037 3046 3250 3047 3074 3087 1026 2003 18 3255 1011 3031 3020
## 115 118 118 120 123 127 127 128 131 135 139 146 148 157 167
## 3143 3089 3035 3006 3341 3068 3072 0 3340
## 172 177 195 200 220 246 273 1310 1468