class: title-slide <br> <br> .right-panel[ # Data Wrangling in R ## Dr. Mine Dogucu ] --- ``` r glimpse(alzheimer_data) ``` ``` ## Rows: 2,700 ## Columns: 57 ## $ id <chr> "S060833", "S932623", "S755478", "S852291", "S011143", "S069… ## $ diagnosis <int> 0, 0, 0, 0, 1, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 1, 2, 2, 2, 1, … ## $ age <int> 74, 56, 77, 74, 75, 72, 64, 78, 73, 81, 66, 65, 66, 73, 78, … ## $ educ <int> 12, 16, 18, 20, 14, 16, 16, 17, 18, 13, 16, 16, 17, 20, 13, … ## $ female <int> 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, … ## $ height <dbl> 65.0, 62.0, 65.0, 62.0, 62.0, 61.8, 60.0, 69.0, 65.0, 71.0, … ## $ weight <int> 233, 110, 137, 112, 127, 141, 124, 152, 131, 197, 134, 144, … ## $ bpsys <int> 148, 110, 144, 120, 145, 107, 112, 134, 122, 120, 150, 126, … ## $ bpdias <int> 100, 75, 60, 60, 61, 65, 70, 74, 60, 70, 85, 78, 60, 72, 80,… ## $ hrate <int> 72, 60, 64, 72, 58, 83, 76, 70, 60, 76, 60, 60, 76, 60, 68, … ## $ cdrglob <dbl> 0.5, 0.0, 0.0, 0.0, 0.5, 0.0, 0.0, 0.5, 0.0, 1.0, 0.0, 0.0, … ## $ naccgds <int> 5, 1, 0, 0, 4, 1, 2, 0, 0, 5, 0, 1, 0, 0, 0, 6, 3, 1, 3, 4, … ## $ delsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ hallsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ agitsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 1, 0, 0, 0, 0, … ## $ depdsev <int> 2, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 3, 0, 0, 0, 0, … ## $ anxsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 2, 0, … ## $ elatsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ apasev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 1, 0, 0, 0, 2, 1, 0, 0, 0, … ## $ disnsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, … ## $ irrsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 2, 0, 0, 0, 0, … ## $ motsev <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ nitesev <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ appsev <int> 0, 0, 0, 0, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, … ## $ bills <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 8, 1, 3, 2, … ## $ taxes <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 8, 2, 3, 3, … ## $ shopping <int> 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 2, 1, … ## $ games <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 8, 0, 0, 8, 0, 0, 8, 3, 0, 1, 0, … ## $ stove <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, … ## $ mealprep <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 8, 0, 0, 1, 3, 8, 0, 0, … ## $ events <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 3, 0, 0, 2, … ## $ payattn <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 2, 1, 1, 1, … ## $ remdates <int> 1, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 1, 2, … ## $ travel <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 3, 0, 3, 1, … ## $ naccmmse <int> 30, 29, 30, 30, 27, 30, 30, 26, 29, 28, 30, 30, 29, 30, 29, … ## $ memunits <int> 8, 17, 19, 11, 6, 16, 11, 6, 4, 3, 14, 9, 13, 17, 14, 12, 9,… ## $ digif <int> 7, 11, 7, 6, 8, 7, 8, 9, 4, 7, 12, 9, 6, 10, 7, 6, 1, 8, 5, … ## $ animals <int> 17, 25, 19, 23, 19, 14, 28, 14, 14, 16, 16, 21, 11, 14, 21, … ## $ traila <int> 49, 16, 38, 54, 40, 44, 31, 69, 53, 34, 46, 27, 22, 38, 38, … ## $ trailb <int> 130, 47, 83, 100, 67, 100, 55, 168, 123, 90, 118, 72, 47, 85… ## $ naccicv <dbl> 1389.520, 1366.945, 1367.420, 1359.850, 1367.420, 1240.390, … ## $ csfvol <dbl> 381.840, 366.622, 343.176, 332.880, 390.415, 345.600, 310.43… ## $ lhippo <dbl> 2.2900, 3.2606, 2.6990, 3.0600, 2.9342, 3.2100, 3.6800, 1.73… ## $ rhippo <dbl> 2.9200, 3.3321, 2.5028, 3.0000, 3.2890, 3.0600, 3.8200, 2.30… ## $ frcort <dbl> 160.570, 187.874, 163.214, 165.120, 149.138, 140.220, 195.24… ## $ lparcort <dbl> 46.7100, 43.1023, 40.1172, 46.0700, 40.2664, 42.9700, 48.740… ## $ rparcort <dbl> 47.7300, 43.8414, 38.2377, 46.3700, 42.1025, 43.1300, 48.240… ## $ ltempcor <dbl> 57.9700, 60.3437, 58.8357, 54.2100, 57.3215, 55.2600, 66.770… ## $ rtempcor <dbl> 58.5800, 58.7091, 51.5753, 56.1600, 54.4138, 51.6100, 60.530… ## $ lcac <dbl> 3.3200, 3.7060, 3.2748, 2.6300, 3.8628, 2.2600, 2.7800, 2.98… ## $ rcac <dbl> 1.9800, 2.1906, 1.7054, 1.4400, 1.5277, 1.9300, 1.8600, 1.36… ## $ lent <dbl> 3.2000, 3.6755, 3.6207, 4.3300, 4.2328, 3.8200, 4.5000, 2.73… ## $ rent <dbl> 3.7300, 4.6463, 2.5787, 4.1000, 4.4572, 3.4900, 4.3700, 2.58… ## $ lparhip <dbl> 3.5800, 3.5534, 3.7515, 3.6000, 3.7079, 4.0700, 5.1100, 3.63… ## $ rparhip <dbl> 3.6800, 4.1952, 3.6703, 3.9200, 3.4988, 4.0100, 5.1300, 3.12… ## $ lposcin <dbl> 3.7500, 3.9091, 3.8686, 3.4500, 3.1321, 3.4500, 4.3700, 4.38… ## $ rposcin <dbl> 3.4400, 4.2362, 3.7062, 3.5300, 2.9051, 2.9200, 4.1800, 3.85… ``` --- ``` r colnames(alzheimer_data) ``` ``` ## [1] "id" "diagnosis" "age" "educ" "female" "height" ## [7] "weight" "bpsys" "bpdias" "hrate" "cdrglob" "naccgds" ## [13] "delsev" "hallsev" "agitsev" "depdsev" "anxsev" "elatsev" ## [19] "apasev" "disnsev" "irrsev" "motsev" "nitesev" "appsev" ## [25] "bills" "taxes" "shopping" "games" "stove" "mealprep" ## [31] "events" "payattn" "remdates" "travel" "naccmmse" "memunits" ## [37] "digif" "animals" "traila" "trailb" "naccicv" "csfvol" ## [43] "lhippo" "rhippo" "frcort" "lparcort" "rparcort" "ltempcor" ## [49] "rtempcor" "lcac" "rcac" "lent" "rent" "lparhip" ## [55] "rparhip" "lposcin" "rposcin" ``` --- ## subsetting variables/columns <img src="img/data-wrangle.001.jpeg" width="80%" /> -- `select()` --- ## subsetting observations/rows <img src="img/data-wrangle.002.jpeg" width="80%" /> `slice()` and `filter()` --- `select` is used to select certain variables in the data frame. .left-panel[ ``` r select(alzheimer_data, height, weight) %>% head() ``` ``` ## height weight ## 1 65.0 233 ## 2 62.0 110 ## 3 65.0 137 ## 4 62.0 112 ## 5 62.0 127 ## 6 61.8 141 ``` ] -- .right-panel[ ``` r alzheimer_data %>% select(height, weight) %>% head() ``` ``` ## height weight ## 1 65.0 233 ## 2 62.0 110 ## 3 65.0 137 ## 4 62.0 112 ## 5 62.0 127 ## 6 61.8 141 ``` ] --- `select` can also be used to drop certain variables if used with a negative sign. ``` r select(alzheimer_data, -id, -educ) %>% head() ``` ``` ## diagnosis age female height weight bpsys bpdias hrate cdrglob naccgds delsev ## 1 0 74 0 65.0 233 148 100 72 0.5 5 0 ## 2 0 56 1 62.0 110 110 75 60 0.0 1 0 ## 3 0 77 1 65.0 137 144 60 64 0.0 0 0 ## 4 0 74 1 62.0 112 120 60 72 0.0 0 0 ## 5 1 75 0 62.0 127 145 61 58 0.5 4 0 ## 6 0 72 1 61.8 141 107 65 83 0.0 1 0 ## hallsev agitsev depdsev anxsev elatsev apasev disnsev irrsev motsev nitesev ## 1 0 0 2 0 0 0 0 0 0 1 ## 2 0 0 1 0 0 0 0 0 0 0 ## 3 0 0 0 0 0 0 0 0 0 0 ## 4 0 0 0 0 0 0 0 0 0 0 ## 5 0 0 0 0 0 0 0 0 0 0 ## 6 0 0 0 0 0 0 0 0 0 0 ## appsev bills taxes shopping games stove mealprep events payattn remdates ## 1 0 0 0 0 0 0 0 0 0 1 ## 2 0 0 0 0 0 0 0 0 0 0 ## 3 0 0 0 0 0 0 0 0 0 0 ## 4 0 0 0 0 0 0 0 0 0 0 ## 5 3 0 0 1 0 0 0 0 0 0 ## 6 0 0 0 0 0 0 0 0 0 0 ## travel naccmmse memunits digif animals traila trailb naccicv csfvol lhippo ## 1 0 30 8 7 17 49 130 1389.520 381.840 2.2900 ## 2 0 29 17 11 25 16 47 1366.945 366.622 3.2606 ## 3 0 30 19 7 19 38 83 1367.420 343.176 2.6990 ## 4 0 30 11 6 23 54 100 1359.850 332.880 3.0600 ## 5 0 27 6 8 19 40 67 1367.420 390.415 2.9342 ## 6 0 30 16 7 14 44 100 1240.390 345.600 3.2100 ## rhippo frcort lparcort rparcort ltempcor rtempcor lcac rcac lent ## 1 2.9200 160.570 46.7100 47.7300 57.9700 58.5800 3.3200 1.9800 3.2000 ## 2 3.3321 187.874 43.1023 43.8414 60.3437 58.7091 3.7060 2.1906 3.6755 ## 3 2.5028 163.214 40.1172 38.2377 58.8357 51.5753 3.2748 1.7054 3.6207 ## 4 3.0000 165.120 46.0700 46.3700 54.2100 56.1600 2.6300 1.4400 4.3300 ## 5 3.2890 149.138 40.2664 42.1025 57.3215 54.4138 3.8628 1.5277 4.2328 ## 6 3.0600 140.220 42.9700 43.1300 55.2600 51.6100 2.2600 1.9300 3.8200 ## rent lparhip rparhip lposcin rposcin ## 1 3.7300 3.5800 3.6800 3.7500 3.4400 ## 2 4.6463 3.5534 4.1952 3.9091 4.2362 ## 3 2.5787 3.7515 3.6703 3.8686 3.7062 ## 4 4.1000 3.6000 3.9200 3.4500 3.5300 ## 5 4.4572 3.7079 3.4988 3.1321 2.9051 ## 6 3.4900 4.0700 4.0100 3.4500 2.9200 ``` --- ## Selection helpers `starts_with()` `ends_with()` `contains()` --- ``` r select(alzheimer_data, starts_with("lp")) %>% head() ``` ``` ## lparcort lparhip lposcin ## 1 46.7100 3.5800 3.7500 ## 2 43.1023 3.5534 3.9091 ## 3 40.1172 3.7515 3.8686 ## 4 46.0700 3.6000 3.4500 ## 5 40.2664 3.7079 3.1321 ## 6 42.9700 4.0700 3.4500 ``` --- ``` r select(alzheimer_data, contains("arhip")) %>% head() ``` ``` ## lparhip rparhip ## 1 3.5800 3.6800 ## 2 3.5534 4.1952 ## 3 3.7515 3.6703 ## 4 3.6000 3.9200 ## 5 3.7079 3.4988 ## 6 4.0700 4.0100 ``` --- ## subsetting variables/columns <img src="img/data-wrangle.001.jpeg" width="80%" /> -- `select()` --- ## subsetting observations/rows <img src="img/data-wrangle.002.jpeg" width="80%" /> `slice()` and `filter()` --- .pull-left[ `slice()` subsetting rows based on a row number. The data below include first six rows from third to seventh. Including third and seventh. ``` r slice(alzheimer_data, 3:5) %>% head() ``` ``` ## id diagnosis age educ female height weight bpsys bpdias hrate cdrglob ## 1 S755478 0 77 18 1 65 137 144 60 64 0.0 ## 2 S852291 0 74 20 1 62 112 120 60 72 0.0 ## 3 S011143 1 75 14 0 62 127 145 61 58 0.5 ## naccgds delsev hallsev agitsev depdsev anxsev elatsev apasev disnsev irrsev ## 1 0 0 0 0 0 0 0 0 0 0 ## 2 0 0 0 0 0 0 0 0 0 0 ## 3 4 0 0 0 0 0 0 0 0 0 ## motsev nitesev appsev bills taxes shopping games stove mealprep events ## 1 0 0 0 0 0 0 0 0 0 0 ## 2 0 0 0 0 0 0 0 0 0 0 ## 3 0 0 3 0 0 1 0 0 0 0 ## payattn remdates travel naccmmse memunits digif animals traila trailb naccicv ## 1 0 0 0 30 19 7 19 38 83 1367.42 ## 2 0 0 0 30 11 6 23 54 100 1359.85 ## 3 0 0 0 27 6 8 19 40 67 1367.42 ## csfvol lhippo rhippo frcort lparcort rparcort ltempcor rtempcor lcac ## 1 343.176 2.6990 2.5028 163.214 40.1172 38.2377 58.8357 51.5753 3.2748 ## 2 332.880 3.0600 3.0000 165.120 46.0700 46.3700 54.2100 56.1600 2.6300 ## 3 390.415 2.9342 3.2890 149.138 40.2664 42.1025 57.3215 54.4138 3.8628 ## rcac lent rent lparhip rparhip lposcin rposcin ## 1 1.7054 3.6207 2.5787 3.7515 3.6703 3.8686 3.7062 ## 2 1.4400 4.3300 4.1000 3.6000 3.9200 3.4500 3.5300 ## 3 1.5277 4.2328 4.4572 3.7079 3.4988 3.1321 2.9051 ``` ] -- .pull-right[ `filter()` subsetting rows based on a condition. The data below includes rows when the age is 80. ``` r filter(alzheimer_data, age == 80) %>% head() ``` ``` ## id diagnosis age educ female height weight bpsys bpdias hrate cdrglob ## 1 S516961 0 80 7 1 66 144 140 85 80 0.0 ## 2 S265934 0 80 18 1 59 139 110 70 64 0.0 ## 3 S295598 2 80 16 1 61 131 136 88 72 1.0 ## 4 S598453 2 80 18 1 59 182 120 70 80 1.0 ## 5 S461735 2 80 12 0 67 166 116 60 80 1.0 ## 6 S350730 2 80 16 0 71 168 108 58 68 0.5 ## naccgds delsev hallsev agitsev depdsev anxsev elatsev apasev disnsev irrsev ## 1 0 0 0 0 0 0 0 0 0 0 ## 2 0 0 0 0 0 0 0 0 0 0 ## 3 1 0 0 0 0 1 0 1 0 0 ## 4 5 0 0 0 1 2 0 2 0 0 ## 5 7 0 0 0 1 0 0 0 0 0 ## 6 0 0 0 2 0 0 0 0 1 2 ## motsev nitesev appsev bills taxes shopping games stove mealprep events ## 1 0 0 0 0 0 0 0 0 0 0 ## 2 0 0 0 0 0 0 0 0 0 0 ## 3 0 0 0 2 3 1 0 0 1 2 ## 4 0 3 0 2 2 2 3 1 3 3 ## 5 0 0 2 3 3 3 0 8 3 3 ## 6 2 0 0 3 3 1 0 0 2 0 ## payattn remdates travel naccmmse memunits digif animals traila trailb ## 1 0 0 0 30 9 3 18 93 295 ## 2 0 0 0 29 10 10 19 50 99 ## 3 1 2 2 23 0 9 9 42 111 ## 4 3 3 3 28 1 9 5 45 300 ## 5 0 3 3 20 0 9 10 77 300 ## 6 0 2 1 23 0 9 13 29 300 ## naccicv csfvol lhippo rhippo frcort lparcort rparcort ltempcor rtempcor ## 1 1179.590 340.360 2.7600 2.8100 145.080 40.0400 42.9600 54.2900 44.7200 ## 2 1255.231 245.663 2.9873 3.0703 154.904 46.8076 42.7822 57.5039 55.2773 ## 3 1367.420 368.360 2.4900 2.2600 158.510 40.0300 42.4200 51.7300 45.3000 ## 4 1338.210 358.970 2.8800 3.0400 159.070 43.8200 42.9200 54.2300 54.6800 ## 5 1538.794 353.821 2.2588 2.4133 175.033 53.2583 53.8019 55.1880 51.5588 ## 6 1458.370 374.740 2.5600 2.4200 183.200 48.6800 46.9000 62.4600 55.7800 ## lcac rcac lent rent lparhip rparhip lposcin rposcin ## 1 3.6900 2.4100 3.5500 3.1000 4.2400 3.5400 4.0300 3.3900 ## 2 2.3945 2.0029 3.6826 3.6982 3.9951 4.1563 4.1953 4.4863 ## 3 2.7500 1.5500 3.6000 2.7400 2.8400 3.0000 3.4400 3.4100 ## 4 3.0300 1.2300 4.2900 3.7600 3.8700 3.9100 3.6600 3.5800 ## 5 4.0369 2.3318 3.1829 2.5721 2.7237 3.0055 4.7865 4.7422 ## 6 4.4600 1.8100 4.6500 2.9200 3.9100 3.5500 4.4600 4.7800 ``` ] --- .pull-left[ ### Relational Operators in R | Operator | Description | |----------|--------------------------| | < | Less than | | > | Greater than | | <= | Less than or equal to | | >= | Greater than or equal to | | == | Equal to | | != | Not equal to | ] .pull-right[ ### Logical Operators in R | Operator | Description | |----------|-------------| | & | and | | &#124; | or | ] --- How many participants are in diagnosis group 1 and age 80? ``` r alzheimer_data %>% filter(age == 80 & diagnosis==1) %>% head() ``` ``` ## id diagnosis age educ female height weight bpsys bpdias hrate cdrglob ## 1 S853960 1 80 20 0 65 156 130 74 54 0.5 ## 2 S165433 1 80 16 0 69 164 150 90 72 0.5 ## 3 S436804 1 80 8 0 66 141 168 70 72 0.5 ## 4 S099600 1 80 16 1 64 210 128 74 72 0.5 ## 5 S902038 1 80 20 0 65 143 149 67 46 0.5 ## 6 S932436 1 80 0 0 62 121 162 78 67 0.0 ## naccgds delsev hallsev agitsev depdsev anxsev elatsev apasev disnsev irrsev ## 1 10 0 0 0 2 0 0 1 0 0 ## 2 2 0 0 0 1 0 0 0 0 0 ## 3 6 0 0 2 0 0 0 0 0 2 ## 4 0 0 0 0 0 0 0 0 0 0 ## 5 3 0 0 0 2 0 0 0 0 0 ## 6 1 0 0 0 0 0 0 0 0 0 ## motsev nitesev appsev bills taxes shopping games stove mealprep events ## 1 0 3 0 1 2 0 0 0 0 0 ## 2 0 0 0 2 0 0 1 8 8 0 ## 3 0 0 0 0 2 0 0 0 0 1 ## 4 0 0 0 1 1 0 0 0 0 0 ## 5 0 0 0 0 1 8 0 0 8 0 ## 6 0 0 0 0 0 0 0 0 0 0 ## payattn remdates travel naccmmse memunits digif animals traila trailb ## 1 0 0 0 30 3 9 19 75 180 ## 2 0 1 0 27 4 12 19 51 67 ## 3 2 3 3 22 0 4 13 57 177 ## 4 0 1 0 29 8 11 21 35 135 ## 5 0 2 0 25 1 6 15 56 76 ## 6 0 0 0 12 9 8 18 34 90 ## naccicv csfvol lhippo rhippo frcort lparcort rparcort ltempcor rtempcor ## 1 1431.010 440.087 2.1547 2.6547 158.514 38.1417 44.6354 52.8989 55.2952 ## 2 1367.420 465.764 2.9247 3.2864 186.166 46.6731 47.5189 71.2347 66.0445 ## 3 1263.677 300.805 2.6607 2.6479 156.197 41.7862 44.4384 53.0156 49.4051 ## 4 1414.050 346.597 2.9082 2.6836 167.511 42.2695 42.9502 58.9081 57.3710 ## 5 1435.185 385.413 2.3832 2.8939 143.103 44.0714 41.4979 52.4443 52.8634 ## 6 1456.062 346.722 3.0198 2.8539 174.812 48.7806 48.3257 62.5837 60.5581 ## lcac rcac lent rent lparhip rparhip lposcin rposcin ## 1 2.2855 1.8225 3.1282 3.7030 3.8475 3.7041 3.4668 3.7431 ## 2 4.5984 2.7485 4.6870 4.3938 4.0764 4.2430 5.2270 4.7176 ## 3 3.3188 1.3275 3.5348 3.4446 3.0284 3.3359 4.1785 3.3717 ## 4 4.2344 2.9766 3.8857 4.3496 4.5361 4.1777 5.0283 4.5195 ## 5 3.0813 1.8797 2.6536 3.3374 3.0713 3.5005 2.7595 3.0270 ## 6 3.9654 2.2917 3.8424 3.8281 4.1056 3.8223 4.9896 4.6249 ``` --- ``` r alzheimer_data %>% filter(age == 80 & diagnosis==1) %>% nrow() ``` ``` ## [1] 18 ``` Here is when piping helps. We can pipe into other functions such as `nrow()` --- Q. How many patients are in in diagnosis group 1 and are female? ``` r alzheimer_data %>% filter(diagnosis ==1 & female == 1) %>% nrow() ``` ``` ## [1] 286 ``` --- We have done all sorts of selections, slicing, filtering on `alzheimer` but it has not changed at all. Why do you think so? ``` r glimpse(arthritis) ``` ``` ## Rows: 530 ## Columns: 14 ## $ id <dbl> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1… ## $ age <dbl> 85, 86, 83, 83, 85, 79, 90, 90, 87, 82, 77, 86, 84, 76, … ## $ age_gp <fct> elderly, elderly, elderly, elderly, elderly, elderly, el… ## $ sex <fct> female, female, female, female, female, male, female, fe… ## $ yrs_from_dx <dbl> 27, 27, 10, 9, NA, NA, 51, 11, 36, 4, 31, NA, 9, 10, 3, … ## $ cdai <dbl> NA, 23.0, 14.5, NA, NA, NA, NA, 40.0, 6.0, NA, 0.0, NA, … ## $ cdai_yn <fct> no, yes, yes, no, no, no, no, yes, yes, no, yes, no, no,… ## $ das_28 <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.44… ## $ das28_yn <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1,… ## $ steroids_gt_5 <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ dmar_ds <dbl> 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1,… ## $ biologics <dbl> 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,… ## $ s_dmards <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,… ## $ osteop_screen <dbl> 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,… ``` --- Moving forward we are only going to use, `age`, `female` `diagnosis`, `educ`, `height` and `weight`. Let's clean our data accordingly and move on with the smaller `alzheimer` data that we need. --- ``` r alzheimer_data %>% select(age, female, diagnosis, educ, height, weight) ``` ``` ## age female diagnosis educ height weight ## 1 74 0 0 12 65.0 233 ## 2 56 1 0 16 62.0 110 ## 3 77 1 0 18 65.0 137 ## 4 74 1 0 20 62.0 112 ## 5 75 0 1 14 62.0 127 ## 6 72 1 0 16 61.8 141 ## 7 64 1 0 16 60.0 124 ## 8 78 0 2 17 69.0 152 ## 9 73 1 0 18 65.0 131 ## 10 81 0 2 13 71.0 197 ## 11 66 1 0 16 63.0 134 ## 12 65 1 0 16 65.0 144 ## 13 66 0 0 17 70.0 159 ## 14 73 1 1 20 65.0 129 ## 15 78 1 0 13 61.0 122 ## 16 75 1 1 16 68.0 110 ## 17 75 0 2 12 66.0 180 ## 18 56 0 2 16 67.5 166 ## 19 81 1 2 9 64.0 184 ## 20 71 1 1 16 62.2 131 ## 21 73 0 0 18 69.5 159 ## 22 81 0 2 14 73.5 212 ## 23 78 0 2 16 71.6 208 ## 24 78 0 0 7 68.0 162 ## 25 75 1 1 12 64.0 177 ## 26 67 1 0 6 65.0 175 ## 27 75 1 0 12 62.0 135 ## 28 69 1 1 18 67.0 147 ## 29 66 1 0 6 59.0 120 ## 30 77 0 0 18 67.7 163 ## 31 81 1 2 13 58.0 151 ## 32 80 1 0 7 66.0 144 ## 33 83 1 2 16 63.0 115 ## 34 82 1 0 18 64.0 119 ## 35 84 0 2 18 70.0 195 ## 36 78 0 1 20 65.5 164 ## 37 69 0 0 16 69.5 150 ## 38 71 0 0 18 70.0 168 ## 39 85 0 1 18 71.0 177 ## 40 86 0 2 18 66.6 143 ## 41 54 0 2 18 73.5 222 ## 42 78 1 0 22 63.5 101 ## 43 62 1 0 18 65.5 116 ## 44 69 1 0 19 62.0 118 ## 45 77 0 2 7 66.0 206 ## 46 78 1 0 16 67.0 155 ## 47 70 1 0 16 65.0 200 ## 48 58 1 1 16 62.0 137 ## 49 77 1 0 12 63.0 135 ## 50 67 0 2 18 70.0 248 ## 51 60 1 2 5 58.0 136 ## 52 75 0 2 16 65.0 187 ## 53 81 0 0 18 70.0 187 ## 54 86 1 1 11 62.5 196 ## 55 72 0 0 14 68.0 200 ## 56 68 1 0 16 61.0 173 ## 57 56 1 0 16 61.5 210 ## 58 80 1 0 18 59.0 139 ## 59 73 1 0 17 64.5 176 ## 60 58 1 0 16 66.0 140 ## 61 77 0 0 20 63.0 180 ## 62 84 0 0 14 68.0 190 ## 63 42 1 0 18 61.8 120 ## 64 79 0 1 8 70.0 211 ## 65 72 0 0 18 62.0 163 ## 66 80 1 2 16 61.0 131 ## 67 90 1 2 13 63.0 138 ## 68 81 1 2 16 66.0 106 ## 69 77 0 1 6 69.5 192 ## 70 72 1 0 20 61.5 133 ## 71 70 0 2 16 68.0 160 ## 72 66 0 2 18 72.0 180 ## 73 89 1 2 12 58.5 139 ## 74 77 0 2 20 66.0 158 ## 75 58 1 0 18 65.5 167 ## 76 81 1 2 18 65.5 164 ## 77 76 1 2 3 62.0 158 ## 78 69 1 2 12 65.5 120 ## 79 70 1 1 12 63.0 117 ## 80 69 1 1 16 64.0 130 ## 81 72 0 0 20 71.5 175 ## 82 82 1 1 12 65.5 164 ## 83 80 1 2 18 59.0 182 ## 84 66 1 0 15 64.0 274 ## 85 85 0 2 12 70.0 179 ## 86 70 1 1 14 62.0 190 ## 87 66 0 0 16 72.0 225 ## 88 76 0 0 16 69.5 213 ## 89 73 0 0 14 65.0 141 ## 90 75 0 2 20 68.3 158 ## 91 70 1 0 20 64.0 132 ## 92 86 1 1 14 62.0 125 ## 93 76 1 2 12 63.0 136 ## 94 79 0 1 16 74.3 219 ## 95 86 1 0 16 62.0 201 ## 96 72 0 0 16 69.0 169 ## 97 59 1 0 16 65.5 164 ## 98 89 1 1 12 65.5 164 ## 99 77 1 2 12 65.5 141 ## 100 83 0 1 12 65.5 164 ## 101 75 1 0 16 69.0 175 ## 102 83 1 0 12 64.0 112 ## 103 75 1 2 10 57.0 106 ## 104 81 1 2 14 65.5 191 ## 105 81 1 0 16 62.0 155 ## 106 56 1 0 16 56.3 117 ## 107 83 0 1 11 65.0 152 ## 108 80 0 2 12 67.0 166 ## 109 69 1 0 18 65.0 165 ## 110 81 0 0 19 67.0 213 ## 111 77 0 1 14 61.5 140 ## 112 84 1 2 15 64.0 115 ## 113 65 0 0 20 68.0 188 ## 114 80 0 2 16 71.0 168 ## 115 66 0 1 20 72.8 172 ## 116 81 1 0 13 63.0 151 ## 117 78 0 1 14 64.0 177 ## 118 82 1 0 15 60.0 185 ## 119 86 1 0 12 59.0 164 ## 120 72 0 0 18 67.5 140 ## 121 70 1 0 10 67.5 125 ## 122 64 1 2 16 62.0 176 ## 123 96 0 2 12 65.5 164 ## 124 31 1 0 15 66.0 164 ## 125 94 1 1 14 60.0 105 ## 126 73 1 2 12 65.5 164 ## 127 83 1 0 15 59.0 124 ## 128 45 0 2 16 66.0 151 ## 129 75 1 0 10 63.0 178 ## 130 61 1 1 18 67.6 216 ## 131 73 1 0 18 67.0 179 ## 132 69 0 0 12 67.0 198 ## 133 65 1 1 12 64.0 191 ## 134 73 0 1 16 75.0 193 ## 135 77 0 2 16 65.5 164 ## 136 81 0 2 16 65.5 164 ## 137 77 1 1 18 62.0 124 ## 138 22 0 0 13 72.0 186 ## 139 61 0 0 18 68.7 179 ## 140 76 1 2 16 62.0 118 ## 141 72 0 1 12 73.0 189 ## 142 30 0 0 16 74.0 196 ## 143 85 0 2 19 65.0 179 ## 144 82 0 1 12 63.5 180 ## 145 80 0 0 13 67.0 230 ## 146 77 1 0 20 65.5 163 ## 147 72 1 0 16 66.0 120 ## 148 80 1 2 12 58.0 130 ## 149 84 1 2 12 65.5 164 ## 150 26 1 0 16 67.0 128 ## 151 86 0 2 16 66.0 169 ## 152 66 1 1 6 60.0 144 ## 153 79 1 1 18 61.5 149 ## 154 84 1 2 16 64.5 145 ## 155 72 1 0 11 60.5 163 ## 156 84 0 2 8 67.5 176 ## 157 75 1 0 14 57.0 97 ## 158 74 0 2 21 70.0 202 ## 159 77 0 1 16 70.0 161 ## 160 67 1 1 16 65.0 128 ## 161 83 0 1 5 64.5 135 ## 162 89 0 0 16 69.5 145 ## 163 70 1 1 0 61.0 127 ## 164 74 1 0 15 60.0 92 ## 165 82 1 0 12 62.0 185 ## 166 78 0 2 16 70.0 178 ## 167 68 0 2 14 68.7 170 ## 168 75 1 1 6 62.2 150 ## 169 84 0 0 20 65.0 132 ## 170 76 1 0 18 63.0 150 ## 171 60 1 1 12 63.0 142 ## 172 75 1 2 12 60.0 108 ## 173 83 0 1 18 70.1 152 ## 174 59 1 0 18 68.0 130 ## 175 70 1 0 18 67.0 160 ## 176 79 1 1 12 64.5 131 ## 177 83 1 1 12 64.0 150 ## 178 85 1 2 4 65.5 164 ## 179 69 1 1 6 62.0 221 ## 180 82 0 2 20 66.0 164 ## 181 73 0 1 16 62.0 140 ## 182 77 1 1 12 58.0 116 ## 183 79 0 2 16 68.0 226 ## 184 76 0 0 14 70.6 160 ## 185 69 0 0 18 68.4 161 ## 186 76 1 0 16 65.0 154 ## 187 71 1 0 14 63.0 150 ## 188 84 0 2 16 75.0 248 ## 189 65 1 0 7 55.0 177 ## 190 60 1 1 14 65.5 121 ## 191 80 0 0 18 70.3 209 ## 192 64 1 0 12 66.0 172 ## 193 83 1 1 16 62.0 150 ## 194 87 1 2 12 64.0 105 ## 195 68 0 0 18 73.0 180 ## 196 70 1 0 18 63.0 194 ## 197 84 0 0 17 65.5 164 ## 198 73 1 2 20 66.0 110 ## 199 42 0 0 16 66.0 178 ## 200 64 1 0 14 64.0 160 ## 201 77 1 2 14 61.0 137 ## 202 74 0 0 18 69.5 168 ## 203 78 0 0 19 71.0 245 ## 204 82 1 1 16 66.0 146 ## 205 79 1 2 8 61.5 110 ## 206 82 0 1 12 65.5 164 ## 207 85 1 2 18 63.0 108 ## 208 65 1 1 16 61.2 113 ## 209 84 1 0 9 58.0 131 ## 210 83 1 0 16 63.0 140 ## 211 88 0 1 19 65.5 155 ## 212 58 0 0 15 70.0 240 ## 213 74 0 0 20 66.7 132 ## 214 75 0 0 12 70.0 175 ## 215 85 0 0 1 65.0 137 ## 216 70 1 0 16 61.0 136 ## 217 69 1 0 18 64.0 133 ## 218 81 1 2 16 65.0 106 ## 219 72 0 1 16 72.7 187 ## 220 79 0 0 16 70.0 159 ## 221 56 1 0 18 66.7 153 ## 222 73 0 1 12 73.2 199 ## 223 70 0 0 20 64.0 164 ## 224 64 1 0 17 63.0 144 ## 225 81 0 2 16 67.5 165 ## 226 55 1 2 20 64.0 128 ## 227 86 1 0 12 65.5 164 ## 228 83 0 0 16 66.0 154 ## 229 76 1 1 12 67.0 187 ## 230 74 1 2 16 63.8 169 ## 231 61 1 0 16 64.0 135 ## 232 79 1 2 16 64.5 121 ## 233 74 1 1 14 64.0 190 ## 234 80 0 1 20 65.0 156 ## 235 53 0 1 12 75.0 285 ## 236 28 0 0 11 78.0 278 ## 237 86 0 1 20 69.0 161 ## 238 79 1 2 8 65.5 164 ## 239 68 1 0 12 64.0 155 ## 240 60 1 2 12 62.5 150 ## 241 67 1 0 20 61.0 168 ## 242 81 1 2 4 62.0 194 ## 243 77 1 0 18 65.0 137 ## 244 87 1 1 16 67.0 156 ## 245 82 0 0 16 73.0 159 ## 246 44 1 1 18 66.3 171 ## 247 78 0 2 16 72.0 184 ## 248 57 1 1 12 58.0 202 ## 249 64 1 1 18 68.0 163 ## 250 86 1 2 12 63.5 175 ## 251 66 0 0 18 67.2 208 ## 252 64 1 0 13 66.0 177 ## 253 83 1 2 12 63.0 133 ## 254 69 1 0 16 65.5 164 ## 255 87 1 0 16 62.0 134 ## 256 70 1 2 12 64.0 145 ## 257 81 0 2 20 65.6 163 ## 258 85 0 1 17 67.0 162 ## 259 59 0 0 16 68.0 158 ## 260 81 1 2 18 65.5 141 ## 261 83 1 2 17 62.5 138 ## 262 28 0 1 12 69.0 145 ## 263 68 0 0 18 69.0 185 ## 264 73 1 0 12 66.0 218 ## 265 69 0 1 20 72.0 216 ## 266 75 0 2 12 67.0 205 ## 267 68 1 2 11 51.0 137 ## 268 79 0 2 14 71.5 173 ## 269 77 0 2 12 72.0 198 ## 270 60 1 0 18 66.7 231 ## 271 61 1 0 18 61.5 127 ## 272 48 1 1 13 66.5 178 ## 273 46 1 0 12 60.5 125 ## 274 45 1 0 13 66.0 180 ## 275 43 1 0 12 54.0 165 ## 276 80 1 0 16 67.0 164 ## 277 67 0 2 12 69.0 174 ## 278 77 1 0 14 67.0 142 ## 279 74 0 0 16 71.0 191 ## 280 76 1 0 9 61.0 150 ## 281 65 1 1 11 63.0 202 ## 282 73 1 0 12 63.0 116 ## 283 82 1 2 14 65.0 117 ## 284 64 1 1 12 66.0 147 ## 285 65 0 2 12 70.5 166 ## 286 76 1 1 12 64.0 224 ## 287 71 1 0 8 60.0 170 ## 288 60 0 1 19 70.7 175 ## 289 88 0 0 16 67.5 162 ## 290 73 1 0 12 65.5 164 ## 291 92 0 0 16 66.5 139 ## 292 81 1 0 18 62.0 168 ## 293 75 0 1 6 69.3 237 ## 294 85 0 2 7 72.0 200 ## 295 81 1 2 17 62.0 131 ## 296 75 1 1 11 64.0 125 ## 297 71 0 0 12 71.0 237 ## 298 63 0 0 18 72.0 200 ## 299 79 1 0 12 61.0 120 ## 300 75 1 0 12 64.0 243 ## 301 63 1 0 18 69.0 190 ## 302 82 0 2 20 67.0 159 ## 303 62 0 0 19 75.0 188 ## 304 62 0 1 18 66.0 170 ## 305 80 1 0 12 65.0 165 ## 306 83 1 2 16 60.6 138 ## 307 75 0 0 18 69.0 185 ## 308 74 0 0 18 69.7 169 ## 309 69 0 0 18 66.0 155 ## 310 72 1 0 18 60.0 129 ## 311 81 1 2 2 60.0 215 ## 312 88 0 1 12 62.0 158 ## 313 86 1 1 16 66.0 133 ## 314 80 0 0 18 72.0 196 ## 315 63 1 0 14 60.0 153 ## 316 48 1 0 18 64.0 135 ## 317 60 1 1 20 64.0 135 ## 318 59 0 1 16 71.0 237 ## 319 70 0 2 16 71.0 152 ## 320 85 1 0 18 61.0 159 ## 321 68 0 2 16 70.0 185 ## 322 81 1 0 18 62.0 128 ## 323 63 0 2 16 69.0 170 ## 324 58 1 0 12 67.0 188 ## 325 64 1 0 18 62.5 168 ## 326 72 0 0 16 64.0 132 ## 327 64 1 0 12 65.0 163 ## 328 76 0 0 16 70.2 159 ## 329 85 1 0 16 64.0 164 ## 330 88 1 0 6 54.5 109 ## 331 65 0 0 14 64.0 170 ## 332 48 1 0 12 65.5 199 ## 333 72 0 0 17 72.0 192 ## 334 67 1 0 20 62.0 164 ## 335 67 1 1 14 61.5 136 ## 336 80 1 0 15 66.0 157 ## 337 65 0 2 18 75.0 204 ## 338 84 0 2 12 65.0 172 ## 339 70 1 0 15 63.0 198 ## 340 68 0 0 12 67.0 168 ## 341 77 0 1 12 69.0 178 ## 342 80 0 1 16 69.0 164 ## 343 82 1 2 12 63.0 177 ## 344 75 1 1 16 65.0 127 ## 345 67 0 2 12 66.1 179 ## 346 70 0 1 19 65.5 136 ## 347 76 1 1 14 60.0 90 ## 348 75 1 0 11 61.0 154 ## 349 82 1 2 12 56.0 98 ## 350 93 1 0 10 60.0 108 ## 351 88 1 0 18 66.0 121 ## 352 69 0 0 14 68.5 182 ## 353 81 1 2 12 66.0 164 ## 354 74 1 0 18 63.0 228 ## 355 73 1 0 14 57.0 104 ## 356 37 1 0 15 60.8 120 ## 357 64 1 2 3 61.0 151 ## 358 76 1 0 18 60.5 153 ## 359 76 0 1 4 65.2 201 ## 360 43 1 0 16 62.0 160 ## 361 47 1 0 16 65.0 150 ## 362 58 1 0 14 62.0 144 ## 363 37 1 1 18 63.5 126 ## 364 80 1 2 12 61.0 105 ## 365 79 0 1 16 70.2 194 ## 366 76 0 2 16 71.0 211 ## 367 73 1 0 16 62.0 154 ## 368 83 1 2 12 60.0 171 ## 369 66 1 1 14 65.5 164 ## 370 81 0 1 20 65.5 164 ## 371 84 0 1 12 68.0 150 ## 372 66 1 2 13 65.0 143 ## 373 87 0 0 14 71.0 138 ## 374 93 0 1 12 65.0 151 ## 375 69 1 0 12 66.0 152 ## 376 64 1 0 18 65.5 144 ## 377 82 1 1 12 61.0 115 ## 378 72 1 0 12 65.0 204 ## 379 67 0 1 13 70.0 164 ## 380 77 1 2 12 61.2 182 ## 381 89 0 1 17 65.5 164 ## 382 82 0 1 19 65.0 145 ## 383 61 1 0 18 66.0 300 ## 384 64 0 0 20 67.0 191 ## 385 84 1 1 13 63.0 150 ## 386 64 1 0 12 61.0 155 ## 387 86 0 2 7 61.0 128 ## 388 88 1 0 18 65.5 164 ## 389 79 1 0 12 66.0 135 ## 390 81 1 2 12 65.0 144 ## 391 83 0 1 12 70.0 146 ## 392 44 0 2 12 69.0 195 ## 393 74 0 0 18 75.0 190 ## 394 76 0 1 17 68.0 191 ## 395 69 1 0 14 64.3 203 ## 396 79 1 2 7 58.0 152 ## 397 76 0 1 16 68.5 225 ## 398 78 1 1 12 63.5 175 ## 399 67 0 0 15 74.0 216 ## 400 77 1 0 12 65.5 172 ## 401 83 1 2 8 58.5 95 ## 402 61 1 1 12 64.0 155 ## 403 76 1 1 21 66.0 123 ## 404 78 0 1 14 68.0 160 ## 405 82 0 0 20 68.0 164 ## 406 82 0 1 11 65.5 164 ## 407 84 0 0 12 69.0 165 ## 408 69 0 0 16 65.5 164 ## 409 80 0 1 8 66.0 141 ## 410 73 0 1 21 69.5 159 ## 411 73 0 0 20 63.0 150 ## 412 79 1 1 14 63.5 152 ## 413 73 1 0 13 62.0 150 ## 414 72 1 0 8 62.0 132 ## 415 78 1 0 14 65.0 168 ## 416 69 1 0 12 66.0 138 ## 417 83 1 1 6 63.0 130 ## 418 70 1 2 14 65.0 153 ## 419 47 1 0 12 62.0 160 ## 420 70 0 0 8 67.0 265 ## 421 91 1 2 12 62.0 191 ## 422 68 0 2 17 71.0 190 ## 423 74 0 1 11 67.0 146 ## 424 85 1 2 12 60.0 124 ## 425 68 1 0 14 63.0 146 ## 426 79 1 0 20 61.5 141 ## 427 80 0 2 14 72.0 197 ## 428 82 0 0 12 69.0 215 ## 429 71 1 0 12 63.0 167 ## 430 80 1 0 14 64.6 167 ## 431 73 1 1 14 61.0 117 ## 432 51 0 0 20 65.0 144 ## 433 84 1 0 10 60.0 135 ## 434 79 0 0 8 65.0 148 ## 435 79 0 1 14 71.0 189 ## 436 83 1 0 16 64.6 110 ## 437 78 1 0 16 65.0 130 ## 438 73 1 2 12 66.7 200 ## 439 81 1 0 14 64.0 144 ## 440 68 1 1 16 61.5 129 ## 441 72 1 0 15 67.0 135 ## 442 84 1 0 16 64.0 129 ## 443 31 0 0 16 70.0 195 ## 444 76 1 0 0 64.5 204 ## 445 79 1 0 20 62.5 131 ## 446 80 1 1 16 64.0 210 ## 447 80 0 1 20 65.0 143 ## 448 69 0 1 12 69.0 219 ## 449 73 0 0 12 72.5 223 ## 450 77 1 0 12 67.0 251 ## 451 74 0 1 18 73.0 178 ## 452 60 1 0 18 64.0 114 ## 453 72 1 2 16 64.5 134 ## 454 84 1 1 16 64.0 177 ## 455 70 1 0 20 64.0 131 ## 456 75 1 2 10 62.0 210 ## 457 65 0 0 6 68.0 218 ## 458 87 1 0 12 68.0 199 ## 459 74 1 0 17 61.5 141 ## 460 30 0 0 18 60.0 185 ## 461 67 1 1 11 63.0 179 ## 462 73 1 2 16 66.0 193 ## 463 74 1 0 18 62.0 134 ## 464 81 1 0 12 62.0 248 ## 465 79 0 0 16 69.0 217 ## 466 84 1 0 13 61.0 138 ## 467 79 0 1 18 71.6 153 ## 468 74 0 2 20 68.0 160 ## 469 79 0 0 18 70.5 175 ## 470 66 1 0 20 64.0 180 ## 471 72 0 1 18 70.0 145 ## 472 76 1 0 14 66.4 196 ## 473 91 1 1 16 63.0 115 ## 474 77 0 2 16 67.5 173 ## 475 83 1 0 18 62.5 113 ## 476 74 1 0 2 64.5 179 ## 477 68 1 0 4 58.0 124 ## 478 78 0 0 2 64.5 164 ## 479 77 0 2 12 74.5 234 ## 480 54 1 2 13 64.0 179 ## 481 73 0 1 16 70.0 206 ## 482 64 1 0 18 62.0 137 ## 483 68 1 1 12 63.4 172 ## 484 78 0 1 18 67.1 129 ## 485 64 1 0 16 66.5 117 ## 486 71 1 0 14 68.0 186 ## 487 83 0 2 12 67.5 156 ## 488 87 0 1 18 68.0 168 ## 489 75 1 1 16 64.0 125 ## 490 82 0 0 16 69.0 156 ## 491 64 1 2 16 64.0 123 ## 492 83 1 0 12 62.0 145 ## 493 81 1 0 12 63.5 163 ## 494 75 1 0 15 65.5 164 ## 495 67 0 0 17 67.5 225 ## 496 81 1 2 12 62.8 112 ## 497 52 0 2 16 70.0 167 ## 498 81 0 0 12 68.0 214 ## 499 75 1 0 16 64.0 132 ## 500 88 0 1 20 69.5 164 ## 501 78 1 2 8 60.7 224 ## 502 77 0 0 18 73.0 231 ## 503 60 0 2 16 72.0 140 ## 504 79 0 2 16 71.0 172 ## 505 62 0 1 18 71.0 213 ## 506 58 1 1 14 67.0 199 ## 507 82 1 1 18 65.5 164 ## 508 83 0 0 14 72.0 167 ## 509 78 0 0 12 69.0 226 ## 510 70 0 0 14 65.0 195 ## 511 77 1 1 6 58.0 116 ## 512 29 1 1 12 67.5 120 ## 513 73 0 1 18 67.0 229 ## 514 66 0 0 12 67.0 179 ## 515 68 0 2 20 73.0 196 ## 516 78 0 1 10 71.0 255 ## 517 78 1 0 16 60.0 112 ## 518 89 0 2 12 67.0 182 ## 519 79 0 0 18 65.5 139 ## 520 64 1 1 12 67.0 167 ## 521 90 0 2 16 67.5 140 ## 522 85 1 0 14 64.5 185 ## 523 85 1 2 12 60.4 192 ## 524 74 1 2 12 64.0 138 ## 525 77 1 0 16 63.5 153 ## 526 79 1 0 13 65.5 164 ## 527 80 1 0 12 67.0 184 ## 528 72 0 0 13 65.5 258 ## 529 84 1 1 20 64.0 111 ## 530 61 1 1 12 63.0 119 ## 531 65 1 0 14 68.0 220 ## 532 69 1 0 19 63.0 191 ## 533 73 0 0 16 67.5 206 ## 534 80 0 0 16 73.0 177 ## 535 92 1 1 17 59.0 111 ## 536 31 1 0 16 70.0 217 ## 537 66 1 0 18 64.0 156 ## 538 88 0 1 12 65.0 140 ## 539 76 1 1 12 61.0 143 ## 540 63 1 0 20 64.0 133 ## 541 79 0 1 12 68.0 175 ## 542 85 0 1 6 65.5 171 ## 543 74 0 1 12 66.0 195 ## 544 78 1 0 11 58.0 131 ## 545 74 1 0 16 64.0 155 ## 546 53 0 2 14 76.0 250 ## 547 78 0 1 14 69.0 186 ## 548 79 1 2 16 61.0 87 ## 549 82 1 1 14 62.0 172 ## 550 81 0 2 12 70.5 160 ## 551 73 1 0 18 63.0 179 ## 552 70 1 0 20 67.0 166 ## 553 60 0 0 18 68.0 183 ## 554 75 1 2 16 66.0 136 ## 555 81 0 2 16 70.5 175 ## 556 84 0 1 12 69.4 189 ## 557 73 1 0 12 57.0 118 ## 558 81 0 2 16 69.5 191 ## 559 77 1 1 12 59.5 146 ## 560 53 1 0 19 64.0 151 ## 561 60 1 1 16 60.0 148 ## 562 72 1 2 12 64.0 160 ## 563 81 0 2 16 68.0 169 ## 564 75 0 2 16 74.0 249 ## 565 52 1 0 16 67.5 176 ## 566 54 1 1 12 69.0 160 ## 567 70 1 0 20 67.0 170 ## 568 79 1 2 0 61.0 133 ## 569 91 1 1 11 64.0 155 ## 570 54 0 2 12 72.0 165 ## 571 72 1 2 20 62.0 128 ## 572 75 0 1 14 72.0 284 ## 573 72 1 0 18 66.0 135 ## 574 74 1 2 12 65.0 158 ## 575 77 1 1 14 66.0 227 ## 576 68 1 0 14 61.0 112 ## 577 59 0 0 19 68.5 198 ## 578 87 0 2 16 64.0 165 ## 579 77 0 0 16 66.5 168 ## 580 74 1 1 16 65.5 137 ## 581 60 0 0 20 70.0 150 ## 582 73 0 0 18 67.0 165 ## 583 81 1 2 12 58.5 98 ## 584 81 1 2 15 65.0 125 ## 585 80 0 0 14 71.0 183 ## 586 78 1 1 14 64.0 179 ## 587 64 1 0 16 64.0 175 ## 588 70 1 0 18 64.0 132 ## 589 60 1 0 16 65.0 142 ## 590 68 1 0 19 65.0 117 ## 591 75 1 0 12 65.0 183 ## 592 78 1 1 20 61.0 112 ## 593 86 1 1 14 64.0 164 ## 594 72 1 2 16 65.0 158 ## 595 57 1 2 14 63.0 116 ## 596 76 1 0 16 62.5 174 ## 597 91 1 1 12 60.0 110 ## 598 76 0 1 18 72.0 164 ## 599 66 1 0 9 61.0 215 ## 600 70 1 0 18 67.0 193 ## 601 84 1 2 12 62.5 104 ## 602 80 0 1 0 62.0 121 ## 603 76 1 0 2 58.0 137 ## 604 73 1 0 12 58.0 196 ## 605 73 0 0 10 71.5 220 ## 606 56 1 0 18 61.0 127 ## 607 76 1 0 16 67.0 168 ## 608 70 0 0 16 66.5 206 ## 609 84 1 0 18 64.0 149 ## 610 69 1 0 9 66.0 139 ## 611 66 1 0 18 61.3 119 ## 612 75 1 1 16 66.5 126 ## 613 72 1 0 16 63.0 135 ## 614 74 0 2 14 67.0 162 ## 615 93 1 1 8 59.5 138 ## 616 87 0 0 8 65.0 120 ## 617 84 1 0 16 62.0 145 ## 618 87 1 1 18 65.0 105 ## 619 82 1 0 14 64.0 162 ## 620 72 1 0 18 66.5 179 ## 621 75 0 0 12 68.0 196 ## 622 78 0 2 16 70.0 204 ## 623 74 0 1 16 65.5 136 ## 624 53 0 0 18 72.0 245 ## 625 70 0 2 9 66.0 184 ## 626 76 0 1 12 70.0 160 ## 627 73 1 1 14 60.0 104 ## 628 73 0 0 20 70.0 215 ## 629 85 1 0 16 61.0 160 ## 630 83 1 0 14 61.0 164 ## 631 92 1 0 13 67.0 127 ## 632 83 1 0 18 61.5 148 ## 633 75 0 0 15 68.0 181 ## 634 75 0 1 12 68.0 192 ## 635 84 0 1 16 65.5 164 ## 636 63 1 0 18 66.0 245 ## 637 90 0 0 18 68.0 166 ## 638 87 1 0 13 60.5 129 ## 639 78 1 0 16 61.0 115 ## 640 53 1 0 13 58.8 129 ## 641 68 1 0 16 63.0 200 ## 642 72 0 0 12 66.0 284 ## 643 65 1 0 20 68.0 161 ## 644 68 1 0 16 67.0 125 ## 645 82 1 0 12 59.0 106 ## 646 81 0 1 18 67.0 162 ## 647 60 1 0 18 63.0 109 ## 648 86 0 0 16 73.0 225 ## 649 61 1 0 18 66.0 138 ## 650 72 1 0 20 65.5 134 ## 651 79 1 0 17 63.0 219 ## 652 77 0 0 12 72.0 205 ## 653 88 0 1 20 67.0 140 ## 654 88 1 0 18 60.0 155 ## 655 76 0 2 18 72.0 140 ## 656 70 1 0 12 65.0 167 ## 657 88 0 0 16 67.5 140 ## 658 77 0 1 20 70.0 175 ## 659 66 1 0 12 65.0 213 ## 660 56 0 0 15 71.0 255 ## 661 88 1 2 16 65.5 164 ## 662 83 0 0 18 70.0 158 ## 663 72 0 2 12 71.0 208 ## 664 69 1 0 18 66.0 158 ## 665 84 1 1 13 63.0 133 ## 666 53 1 0 20 64.0 115 ## 667 56 0 0 12 69.0 196 ## 668 59 0 0 18 68.0 199 ## 669 58 1 0 18 65.0 118 ## 670 72 1 1 16 64.0 162 ## 671 72 1 0 12 63.0 156 ## 672 80 0 0 12 70.0 186 ## 673 65 1 0 14 59.0 134 ## 674 52 0 1 14 70.0 185 ## 675 80 1 0 11 59.0 141 ## 676 83 0 0 18 68.0 166 ## 677 77 1 0 12 60.0 158 ## 678 68 0 0 16 69.5 161 ## 679 67 1 0 14 66.8 152 ## 680 79 1 1 12 64.5 182 ## 681 86 0 1 14 68.0 165 ## 682 64 1 0 16 65.5 164 ## 683 68 1 0 18 62.7 178 ## 684 69 1 0 18 65.0 135 ## 685 74 1 0 14 57.0 139 ## 686 77 1 0 11 63.5 194 ## 687 80 1 1 14 62.5 137 ## 688 82 0 1 16 70.0 145 ## 689 68 1 2 14 65.0 160 ## 690 64 0 0 16 71.0 164 ## 691 70 1 0 18 66.8 178 ## 692 87 1 1 8 62.5 130 ## 693 59 0 1 18 65.3 154 ## 694 73 0 0 16 68.0 177 ## 695 76 1 2 16 65.0 140 ## 696 67 1 0 18 60.0 125 ## 697 74 1 0 16 63.5 164 ## 698 81 0 1 16 67.0 178 ## 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933 73 0 2 12 64.0 180 ## 934 66 0 0 24 70.5 192 ## 935 69 1 2 18 64.0 136 ## 936 54 1 0 12 73.0 255 ## 937 81 1 1 12 62.5 137 ## 938 74 1 0 18 65.0 162 ## 939 58 0 2 16 69.0 203 ## 940 72 1 2 12 64.5 152 ## 941 73 1 0 12 58.5 132 ## 942 74 0 2 18 71.0 234 ## 943 85 0 1 10 67.0 182 ## 944 81 0 0 12 72.0 170 ## 945 70 1 1 10 63.0 216 ## 946 69 0 0 16 69.0 202 ## 947 82 0 1 16 68.0 142 ## 948 77 1 2 12 64.5 147 ## 949 21 1 0 12 67.0 164 ## 950 81 0 1 19 68.2 167 ## 951 66 1 0 16 64.0 157 ## 952 86 1 2 12 60.0 96 ## 953 57 0 1 13 68.0 230 ## 954 72 0 1 16 67.1 145 ## 955 82 0 0 16 67.0 159 ## 956 74 0 2 15 67.0 135 ## 957 82 0 1 18 74.0 246 ## 958 81 1 1 10 64.0 170 ## 959 72 1 0 12 62.0 172 ## 960 75 1 0 14 64.0 127 ## 961 64 0 0 18 72.1 240 ## 962 60 1 0 16 62.0 108 ## 963 67 1 2 13 65.5 130 ## 964 63 1 0 18 62.5 139 ## 965 74 0 0 18 70.0 165 ## 966 73 1 0 13 66.0 265 ## 967 69 0 2 12 70.0 209 ## 968 76 0 0 13 67.5 158 ## 969 75 0 1 17 70.0 177 ## 970 80 0 2 18 60.2 168 ## 971 76 1 0 18 61.0 160 ## 972 74 0 2 18 69.5 195 ## 973 53 1 1 18 65.5 172 ## 974 78 0 0 16 67.5 177 ## 975 75 1 1 16 64.0 131 ## 976 53 0 0 18 71.0 190 ## 977 76 0 1 14 68.3 227 ## 978 88 1 0 12 59.5 136 ## 979 73 1 0 18 62.0 152 ## 980 77 0 1 18 72.5 212 ## 981 63 0 2 18 66.0 179 ## 982 57 1 0 20 63.0 149 ## 983 65 1 2 18 64.5 182 ## 984 83 1 1 14 62.0 124 ## 985 71 0 0 13 68.5 194 ## 986 80 1 1 12 69.0 150 ## 987 84 1 0 14 60.0 146 ## 988 77 0 1 16 68.0 235 ## 989 65 1 2 24 67.3 170 ## 990 72 1 1 4 60.4 155 ## 991 66 1 2 10 60.0 127 ## 992 77 1 1 12 68.0 184 ## 993 72 0 1 12 72.0 164 ## 994 59 0 0 20 69.0 184 ## 995 72 0 1 16 65.0 190 ## 996 60 1 2 16 66.0 140 ## 997 36 1 0 13 63.0 147 ## 998 79 1 0 5 61.0 168 ## 999 77 1 0 18 60.0 90 ## 1000 71 0 1 16 72.0 180 ## 1001 90 1 2 16 65.3 162 ## 1002 77 0 2 5 63.0 150 ## 1003 28 0 2 16 69.0 165 ## 1004 77 0 1 12 70.0 230 ## 1005 61 0 2 19 74.0 184 ## 1006 72 1 1 12 65.0 188 ## 1007 76 1 0 14 61.0 136 ## 1008 57 0 1 14 69.0 125 ## 1009 72 1 1 18 60.6 136 ## 1010 72 1 2 14 61.0 132 ## 1011 65 0 0 10 68.5 201 ## 1012 83 1 0 12 62.0 140 ## 1013 73 1 0 5 59.1 199 ## 1014 67 1 1 16 63.0 170 ## 1015 69 1 0 15 58.5 129 ## 1016 47 0 1 12 68.7 186 ## 1017 71 1 0 16 63.2 161 ## 1018 70 1 1 16 68.0 126 ## 1019 77 1 2 12 65.5 137 ## 1020 58 0 2 12 73.0 190 ## 1021 78 0 0 12 66.5 152 ## 1022 70 1 0 14 66.5 217 ## 1023 73 1 2 4 59.0 134 ## 1024 65 1 0 12 61.5 188 ## 1025 92 1 2 12 66.0 150 ## 1026 72 0 0 12 68.0 208 ## 1027 70 0 1 14 69.5 170 ## 1028 81 0 1 16 65.5 159 ## 1029 67 1 0 16 63.0 195 ## 1030 70 1 0 12 64.0 131 ## 1031 69 0 2 14 67.0 171 ## 1032 73 1 0 12 60.5 190 ## 1033 75 1 0 13 60.5 139 ## 1034 68 1 0 12 62.0 183 ## 1035 26 1 0 16 65.5 155 ## 1036 67 0 1 14 66.5 155 ## 1037 66 0 1 16 72.0 175 ## 1038 82 1 1 12 63.7 134 ## 1039 86 0 2 20 69.0 144 ## 1040 58 1 0 16 62.0 127 ## 1041 76 0 2 16 67.0 196 ## 1042 43 1 0 14 69.0 133 ## 1043 59 1 2 12 62.5 133 ## 1044 61 0 0 20 77.0 241 ## 1045 68 0 2 18 69.5 154 ## 1046 75 0 0 21 66.5 173 ## 1047 77 1 0 15 60.5 122 ## 1048 61 1 0 20 61.0 131 ## 1049 56 1 2 13 59.5 102 ## 1050 81 0 1 18 66.0 177 ## 1051 76 1 0 16 61.0 182 ## 1052 73 1 1 0 56.6 143 ## 1053 81 0 2 18 65.5 146 ## 1054 69 1 0 20 63.5 177 ## 1055 76 0 0 12 70.0 206 ## 1056 71 0 0 12 67.2 156 ## 1057 85 0 1 18 65.5 155 ## 1058 86 0 2 5 63.1 147 ## 1059 69 0 0 20 69.0 215 ## 1060 86 1 0 13 61.0 181 ## 1061 72 1 0 12 66.2 184 ## 1062 80 0 1 12 69.2 170 ## 1063 77 0 1 14 67.5 165 ## 1064 67 0 1 16 68.0 234 ## 1065 68 1 1 7 63.0 206 ## 1066 79 0 2 20 70.0 162 ## 1067 79 0 2 18 73.0 187 ## 1068 62 0 0 15 75.0 188 ## 1069 70 1 0 12 63.0 210 ## 1070 71 0 0 20 67.5 188 ## 1071 62 1 0 18 64.0 160 ## 1072 72 0 2 13 70.2 162 ## 1073 69 1 0 14 66.0 176 ## 1074 79 1 1 20 61.5 120 ## 1075 80 1 1 12 62.0 117 ## 1076 69 0 0 16 70.7 308 ## 1077 67 1 0 6 59.0 142 ## 1078 82 0 0 5 70.0 259 ## 1079 73 0 1 16 67.5 178 ## 1080 67 0 0 7 68.5 198 ## 1081 74 0 0 16 67.2 167 ## 1082 82 1 2 12 66.5 136 ## 1083 49 0 1 16 65.5 164 ## 1084 23 0 0 16 72.0 259 ## 1085 68 0 0 16 70.0 180 ## 1086 74 0 1 16 69.0 168 ## 1087 85 1 1 18 62.0 144 ## 1088 63 0 2 16 68.5 168 ## 1089 56 0 0 14 67.0 166 ## 1090 71 1 0 12 66.2 167 ## 1091 53 1 0 16 66.0 285 ## 1092 66 0 2 8 66.0 195 ## 1093 67 1 0 22 70.0 178 ## 1094 56 1 0 13 66.0 271 ## 1095 64 0 2 16 65.5 144 ## 1096 75 0 2 16 67.0 218 ## 1097 77 0 0 12 70.2 228 ## 1098 77 1 1 15 65.0 135 ## 1099 78 0 0 16 72.0 175 ## 1100 49 1 0 13 65.0 149 ## 1101 73 1 2 16 65.5 164 ## 1102 76 1 1 12 65.1 148 ## 1103 67 1 0 12 62.0 168 ## 1104 73 0 1 18 70.5 191 ## 1105 85 1 0 16 62.0 192 ## 1106 78 1 0 15 61.5 115 ## 1107 80 1 0 19 62.0 184 ## 1108 63 0 2 12 74.0 227 ## 1109 21 1 0 16 62.0 121 ## 1110 78 1 2 12 62.0 149 ## 1111 74 0 1 20 68.5 178 ## 1112 75 1 0 16 65.0 222 ## 1113 87 1 1 12 64.0 135 ## 1114 65 1 1 14 64.4 164 ## 1115 63 0 0 12 66.0 143 ## 1116 45 1 0 16 64.0 239 ## 1117 43 1 0 12 63.0 139 ## 1118 88 1 0 12 61.0 169 ## 1119 65 0 0 12 64.2 159 ## 1120 75 1 1 12 61.7 175 ## 1121 85 1 0 12 61.0 111 ## 1122 87 1 1 18 61.1 117 ## 1123 73 0 1 18 68.0 201 ## 1124 44 0 2 16 71.0 189 ## 1125 78 1 2 13 63.0 145 ## 1126 70 1 0 14 63.0 157 ## 1127 67 1 0 18 61.5 182 ## 1128 73 1 0 6 57.5 146 ## 1129 61 0 1 16 71.0 200 ## 1130 77 0 1 11 70.4 141 ## 1131 48 1 0 20 68.8 197 ## 1132 46 0 1 14 72.0 211 ## 1133 61 0 2 14 71.0 228 ## 1134 40 1 0 15 65.0 163 ## 1135 66 1 0 2 62.5 180 ## 1136 26 1 0 16 65.0 170 ## 1137 71 1 0 14 59.0 188 ## 1138 64 0 2 16 72.0 172 ## 1139 57 1 0 12 63.0 153 ## 1140 79 0 2 12 68.0 200 ## 1141 68 1 0 14 62.0 164 ## 1142 64 0 0 20 72.2 167 ## 1143 77 1 0 15 65.0 185 ## 1144 74 1 0 3 60.0 146 ## 1145 76 0 0 3 66.1 193 ## 1146 66 1 0 14 61.0 160 ## 1147 85 0 1 20 71.0 185 ## 1148 71 0 2 20 69.0 144 ## 1149 66 1 0 16 62.2 135 ## 1150 62 0 1 20 71.0 173 ## 1151 72 0 0 16 68.0 138 ## 1152 74 1 0 12 62.8 148 ## 1153 70 1 0 16 63.0 137 ## 1154 62 1 0 16 64.6 216 ## 1155 58 0 0 18 71.0 192 ## 1156 66 0 0 16 68.5 225 ## 1157 86 1 1 12 62.0 125 ## 1158 41 0 0 13 71.0 167 ## 1159 82 0 1 16 71.5 197 ## 1160 59 0 0 16 74.0 192 ## 1161 74 0 1 20 70.0 222 ## 1162 68 0 2 12 71.5 185 ## 1163 75 0 0 18 70.0 203 ## 1164 68 1 0 12 68.0 174 ## 1165 81 1 0 20 61.0 133 ## 1166 65 0 2 18 71.5 144 ## 1167 78 1 1 10 62.6 163 ## 1168 67 0 0 10 67.5 210 ## 1169 64 1 0 12 68.0 163 ## 1170 85 1 2 20 64.0 128 ## 1171 74 1 0 18 63.0 151 ## 1172 78 0 0 12 65.4 164 ## 1173 70 0 0 18 73.0 253 ## 1174 39 1 2 13 63.0 137 ## 1175 69 0 2 20 70.0 150 ## 1176 64 1 0 14 63.0 144 ## 1177 69 0 0 14 68.0 254 ## 1178 74 1 2 20 63.0 128 ## 1179 77 1 2 16 60.0 112 ## 1180 75 1 0 16 65.0 146 ## 1181 76 1 1 13 62.0 140 ## 1182 72 1 0 18 60.5 138 ## 1183 61 1 0 12 66.0 164 ## 1184 62 1 0 20 66.0 164 ## 1185 50 1 0 14 66.0 132 ## 1186 65 0 0 19 69.7 183 ## 1187 66 1 2 18 63.7 174 ## 1188 57 1 2 14 67.5 196 ## 1189 76 0 2 15 69.8 165 ## 1190 87 1 0 12 64.0 133 ## 1191 67 1 0 18 63.0 155 ## 1192 78 1 0 16 65.2 171 ## 1193 75 0 0 14 71.0 226 ## 1194 67 0 0 14 67.0 148 ## 1195 79 1 1 18 65.2 165 ## 1196 47 0 0 16 71.0 280 ## 1197 69 0 1 18 69.0 210 ## 1198 72 1 0 18 65.0 141 ## 1199 66 0 1 8 68.0 208 ## 1200 78 1 2 16 62.8 124 ## 1201 72 0 0 18 67.5 160 ## 1202 75 0 2 16 77.0 225 ## 1203 79 1 0 16 62.5 224 ## 1204 79 1 0 16 59.5 136 ## 1205 70 1 0 16 65.3 139 ## 1206 85 1 2 16 60.0 152 ## 1207 73 0 2 12 74.0 198 ## 1208 86 1 0 18 59.0 139 ## 1209 85 1 2 11 63.8 133 ## 1210 59 1 2 18 65.5 150 ## 1211 72 0 0 18 69.6 184 ## 1212 60 0 1 16 71.0 228 ## 1213 68 0 0 12 69.0 206 ## 1214 66 0 1 20 69.7 168 ## 1215 68 1 1 18 60.0 140 ## 1216 57 1 1 13 64.8 140 ## 1217 46 0 2 12 67.0 160 ## 1218 74 0 2 20 71.5 137 ## 1219 81 1 0 18 60.0 150 ## 1220 81 0 0 16 66.5 156 ## 1221 61 1 1 12 66.0 156 ## 1222 80 0 0 20 66.6 189 ## 1223 70 1 0 14 60.0 115 ## 1224 79 0 1 16 64.0 151 ## 1225 74 1 0 12 61.2 142 ## 1226 82 0 2 18 65.5 162 ## 1227 75 0 2 12 66.7 167 ## 1228 71 0 2 12 70.0 269 ## 1229 69 0 1 20 72.2 177 ## 1230 61 0 0 12 72.3 192 ## 1231 83 1 2 12 64.0 172 ## 1232 75 0 1 12 69.0 212 ## 1233 64 1 2 3 65.0 222 ## 1234 58 1 1 20 67.0 226 ## 1235 67 0 2 14 76.0 176 ## 1236 73 0 2 20 65.7 254 ## 1237 74 0 1 14 65.0 162 ## 1238 78 1 2 14 58.0 117 ## 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1277 67 0 2 18 64.0 126 ## 1278 72 1 0 0 64.7 156 ## 1279 75 0 0 15 66.0 167 ## 1280 81 0 0 8 68.0 172 ## 1281 83 0 1 16 69.0 170 ## 1282 58 0 0 16 74.1 245 ## 1283 75 0 1 14 66.0 210 ## 1284 63 0 2 18 73.5 199 ## 1285 94 0 1 14 66.5 153 ## 1286 41 1 2 12 71.0 243 ## 1287 43 1 0 14 68.5 206 ## 1288 80 1 0 12 61.0 155 ## 1289 55 1 0 16 66.7 169 ## 1290 83 1 0 16 62.0 137 ## 1291 69 1 0 16 68.5 197 ## 1292 69 1 0 14 66.2 142 ## 1293 65 1 0 20 63.0 133 ## 1294 68 1 0 14 55.7 176 ## 1295 50 0 0 14 72.8 192 ## 1296 66 0 2 12 68.6 176 ## 1297 58 1 0 15 63.0 196 ## 1298 56 1 2 16 63.0 167 ## 1299 68 1 0 16 67.0 150 ## 1300 72 1 0 15 64.7 152 ## 1301 62 1 2 12 65.0 112 ## 1302 47 1 0 15 68.7 151 ## 1303 74 1 1 16 63.6 133 ## 1304 52 1 0 12 65.5 155 ## 1305 70 1 1 14 65.5 173 ## 1306 56 1 0 18 61.0 125 ## 1307 37 1 1 18 65.5 260 ## 1308 58 1 0 16 65.3 159 ## 1309 75 0 0 16 68.0 153 ## 1310 58 1 0 16 64.6 180 ## 1311 75 0 2 12 67.0 184 ## 1312 54 0 0 16 68.3 280 ## 1313 67 1 0 20 61.7 112 ## 1314 75 1 0 18 60.5 127 ## 1315 82 1 2 6 55.0 150 ## 1316 75 0 1 18 65.1 145 ## 1317 71 0 2 12 63.6 201 ## 1318 76 0 0 14 67.0 195 ## 1319 74 0 2 8 71.0 168 ## 1320 81 0 1 16 72.0 153 ## 1321 62 1 0 16 65.8 195 ## 1322 57 1 0 12 66.5 196 ## 1323 55 0 2 16 66.0 148 ## 1324 74 0 1 16 68.6 159 ## 1325 38 0 2 12 71.0 176 ## 1326 63 1 1 15 68.0 204 ## 1327 71 1 2 18 61.8 127 ## 1328 59 0 2 15 71.0 289 ## 1329 61 1 2 16 66.1 192 ## 1330 79 1 1 15 59.5 131 ## 1331 75 0 2 16 69.8 162 ## 1332 87 0 1 16 73.6 200 ## 1333 72 1 1 12 65.5 164 ## 1334 81 0 2 14 63.6 169 ## 1335 73 0 2 14 64.0 140 ## 1336 61 1 0 18 68.7 206 ## 1337 84 0 2 14 66.1 143 ## 1338 48 0 0 20 71.0 180 ## 1339 91 1 2 12 60.3 94 ## 1340 84 0 1 16 69.5 175 ## 1341 56 1 0 25 64.9 134 ## 1342 61 0 1 16 67.0 160 ## 1343 71 1 0 8 59.0 157 ## 1344 74 1 2 12 63.0 142 ## 1345 80 0 0 14 65.2 203 ## 1346 66 1 1 18 59.8 108 ## 1347 77 0 1 14 66.1 165 ## 1348 59 1 0 12 64.6 126 ## 1349 74 1 1 16 57.5 114 ## 1350 67 0 1 18 71.0 202 ## 1351 70 1 2 18 66.0 150 ## 1352 60 0 2 20 69.0 196 ## 1353 74 1 1 20 61.5 110 ## 1354 88 1 2 12 60.4 136 ## 1355 85 1 2 12 61.0 100 ## 1356 47 1 0 16 65.4 157 ## 1357 74 0 1 18 66.5 163 ## 1358 68 1 1 18 64.0 136 ## 1359 84 1 2 18 64.6 148 ## 1360 88 1 1 16 62.0 113 ## 1361 71 0 1 18 71.0 188 ## 1362 69 0 1 20 68.3 203 ## 1363 81 0 0 14 65.5 169 ## 1364 81 1 1 14 59.4 131 ## 1365 53 0 2 16 65.5 164 ## 1366 68 0 2 16 67.5 146 ## 1367 80 0 0 12 69.0 180 ## 1368 71 0 0 12 68.5 175 ## 1369 74 0 0 16 69.4 158 ## 1370 69 1 2 16 61.0 139 ## 1371 71 1 2 16 60.7 108 ## 1372 71 0 1 14 65.5 164 ## 1373 82 1 2 12 60.7 135 ## 1374 78 0 1 18 67.5 155 ## 1375 64 0 0 21 70.7 190 ## 1376 76 0 1 12 67.2 172 ## 1377 69 0 2 8 66.2 205 ## 1378 47 1 0 12 66.9 160 ## 1379 53 1 0 12 62.2 216 ## 1380 58 1 1 18 65.5 164 ## 1381 79 1 1 12 65.0 125 ## 1382 62 0 1 16 65.6 182 ## 1383 60 0 2 12 65.5 164 ## 1384 51 1 1 14 63.4 187 ## 1385 35 1 1 14 62.0 138 ## 1386 70 1 1 18 63.0 98 ## 1387 51 1 0 17 65.8 134 ## 1388 72 0 1 12 68.0 210 ## 1389 64 0 0 18 74.0 185 ## 1390 77 1 2 8 59.0 117 ## 1391 74 0 0 12 67.0 192 ## 1392 59 1 0 18 63.6 174 ## 1393 74 1 1 14 64.1 258 ## 1394 66 0 2 12 66.5 128 ## 1395 50 1 0 16 65.1 132 ## 1396 42 1 2 16 63.0 145 ## 1397 68 1 1 12 61.3 158 ## 1398 62 0 0 16 71.8 249 ## 1399 58 1 0 12 67.0 200 ## 1400 100 1 0 16 65.5 164 ## 1401 72 0 1 19 71.3 180 ## 1402 79 0 0 16 65.5 174 ## 1403 58 1 2 18 65.5 164 ## 1404 67 0 1 12 69.0 209 ## 1405 76 0 1 14 72.0 160 ## 1406 81 0 1 16 66.5 179 ## 1407 75 0 1 16 71.5 182 ## 1408 72 1 2 16 62.5 187 ## 1409 74 1 2 12 61.2 138 ## 1410 57 1 0 18 64.3 161 ## 1411 77 0 2 14 68.5 145 ## 1412 61 1 0 18 63.6 153 ## 1413 51 0 0 13 67.4 173 ## 1414 88 0 1 16 67.0 150 ## 1415 80 0 1 14 63.0 178 ## 1416 78 0 2 12 68.6 162 ## 1417 67 1 2 17 65.0 112 ## 1418 77 1 2 12 63.5 157 ## 1419 86 1 0 13 62.0 127 ## 1420 75 0 1 18 70.0 213 ## 1421 73 1 1 18 63.0 112 ## 1422 74 0 0 14 64.8 163 ## 1423 88 0 2 12 67.0 167 ## 1424 60 1 1 17 61.1 131 ## 1425 63 1 0 18 66.1 184 ## 1426 42 0 0 16 71.0 185 ## 1427 64 1 2 18 65.5 164 ## 1428 80 0 1 12 72.0 252 ## 1429 66 1 0 16 68.4 139 ## 1430 87 0 0 14 70.0 180 ## 1431 63 0 1 12 68.4 292 ## 1432 87 0 1 14 68.5 197 ## 1433 63 0 0 20 69.7 174 ## 1434 77 0 2 12 67.0 161 ## 1435 85 1 2 12 60.5 169 ## 1436 73 1 2 2 61.5 132 ## 1437 68 0 2 20 68.0 172 ## 1438 84 1 1 16 67.0 152 ## 1439 67 0 1 20 71.7 203 ## 1440 72 1 0 12 66.0 141 ## 1441 82 0 1 2 71.5 200 ## 1442 75 1 0 14 64.5 189 ## 1443 72 0 0 20 64.0 124 ## 1444 53 1 2 18 70.0 132 ## 1445 67 0 0 14 70.2 160 ## 1446 70 1 0 14 63.5 184 ## 1447 68 1 2 18 65.5 164 ## 1448 67 0 0 3 66.0 163 ## 1449 74 1 0 12 60.6 132 ## 1450 72 1 1 14 64.4 182 ## 1451 72 0 1 20 69.5 185 ## 1452 67 0 1 16 71.0 182 ## 1453 72 0 1 20 64.0 121 ## 1454 61 1 0 17 62.6 197 ## 1455 82 0 0 16 74.0 225 ## 1456 66 1 0 14 68.5 217 ## 1457 72 0 1 12 70.0 234 ## 1458 51 1 0 13 61.0 145 ## 1459 76 1 0 17 66.0 180 ## 1460 66 0 0 18 66.4 159 ## 1461 74 0 2 19 67.0 156 ## 1462 63 1 0 19 63.0 129 ## 1463 73 0 1 16 66.0 155 ## 1464 69 0 1 12 65.5 144 ## 1465 75 0 0 14 65.5 202 ## 1466 76 1 1 13 64.5 130 ## 1467 72 0 2 14 67.0 163 ## 1468 59 0 0 20 73.8 193 ## 1469 71 0 1 20 66.0 114 ## 1470 77 0 0 14 72.5 238 ## 1471 71 1 0 12 64.6 115 ## 1472 59 0 2 18 69.0 169 ## 1473 72 1 0 14 63.0 194 ## 1474 83 1 1 13 64.0 165 ## 1475 76 0 1 8 66.7 198 ## 1476 77 0 2 18 68.0 168 ## 1477 72 1 0 20 63.7 125 ## 1478 85 0 0 12 69.0 182 ## 1479 73 0 1 13 71.5 196 ## 1480 75 0 1 18 69.0 179 ## 1481 81 1 1 18 63.0 155 ## 1482 56 1 2 12 62.0 115 ## 1483 75 0 2 16 69.0 166 ## 1484 75 0 0 18 71.0 225 ## 1485 22 0 0 14 72.0 198 ## 1486 70 0 0 20 67.3 164 ## 1487 74 1 0 12 66.0 136 ## 1488 63 0 0 16 72.6 231 ## 1489 63 1 2 12 61.3 154 ## 1490 79 0 2 16 65.5 164 ## 1491 58 1 0 17 66.3 125 ## 1492 73 1 1 12 63.0 191 ## 1493 76 1 1 16 62.5 120 ## 1494 86 1 1 12 60.0 116 ## 1495 84 0 2 18 66.5 180 ## 1496 84 1 2 16 63.0 120 ## 1497 72 1 0 18 62.0 130 ## 1498 81 0 1 20 61.2 140 ## 1499 65 1 0 18 64.3 134 ## 1500 49 1 0 20 63.4 130 ## 1501 22 0 0 16 74.0 213 ## 1502 78 0 0 16 67.0 142 ## 1503 56 0 0 18 70.7 156 ## 1504 61 1 0 18 65.6 178 ## 1505 93 1 0 18 65.0 106 ## 1506 77 0 2 14 66.0 179 ## 1507 48 1 0 20 66.7 195 ## 1508 63 0 0 16 68.4 210 ## 1509 88 1 0 18 59.2 153 ## 1510 82 0 2 16 71.0 228 ## 1511 68 0 0 18 73.5 214 ## 1512 69 1 0 18 65.0 107 ## 1513 56 1 0 16 65.0 293 ## 1514 57 1 2 12 67.0 243 ## 1515 72 0 2 18 72.0 228 ## 1516 57 1 2 17 63.0 114 ## 1517 57 0 0 16 68.5 165 ## 1518 88 1 0 16 61.8 152 ## 1519 80 1 0 18 64.0 131 ## 1520 81 1 0 18 60.0 160 ## 1521 77 1 0 14 65.0 149 ## 1522 72 1 0 16 62.0 110 ## 1523 79 1 1 16 60.3 99 ## 1524 45 1 0 12 67.7 176 ## 1525 60 0 0 21 72.6 185 ## 1526 78 0 1 12 64.7 180 ## 1527 64 1 0 14 65.6 271 ## 1528 76 0 2 20 70.5 141 ## 1529 76 0 1 16 66.6 168 ## 1530 64 1 0 14 62.6 168 ## 1531 75 0 1 18 68.9 167 ## 1532 56 1 0 14 66.5 154 ## 1533 76 0 2 16 65.5 190 ## 1534 50 1 0 18 66.3 133 ## 1535 78 0 1 16 67.4 146 ## 1536 75 0 1 16 68.3 196 ## 1537 55 1 0 12 66.5 184 ## 1538 69 0 0 16 69.0 152 ## 1539 77 1 2 18 64.5 110 ## 1540 84 0 1 20 67.5 146 ## 1541 40 0 0 18 66.0 148 ## 1542 64 0 1 19 73.2 194 ## 1543 69 0 1 16 64.8 211 ## 1544 71 1 2 14 65.5 164 ## 1545 56 1 0 14 63.7 157 ## 1546 76 1 1 16 58.6 127 ## 1547 59 1 0 15 64.9 191 ## 1548 53 1 0 15 63.6 120 ## 1549 81 0 0 5 66.7 129 ## 1550 60 1 0 14 61.9 255 ## 1551 78 0 1 18 69.0 195 ## 1552 69 1 1 16 62.3 125 ## 1553 73 0 1 12 75.5 178 ## 1554 80 0 0 20 70.0 191 ## 1555 56 1 0 18 67.0 140 ## 1556 73 1 0 16 64.4 177 ## 1557 67 1 0 18 65.5 125 ## 1558 64 0 1 19 71.0 158 ## 1559 65 0 0 18 68.1 191 ## 1560 67 1 2 16 61.0 140 ## 1561 79 1 0 12 58.3 128 ## 1562 72 0 1 16 62.7 172 ## 1563 48 1 0 17 64.7 135 ## 1564 57 0 2 18 70.8 161 ## 1565 68 1 0 18 61.0 183 ## 1566 77 1 0 17 65.4 147 ## 1567 64 1 0 20 64.0 144 ## 1568 85 1 0 12 60.0 136 ## 1569 64 1 1 20 69.0 161 ## 1570 71 0 1 12 63.5 147 ## 1571 75 1 0 16 63.8 137 ## 1572 80 0 0 20 68.0 134 ## 1573 79 1 1 12 62.0 144 ## 1574 56 1 0 13 64.5 164 ## 1575 79 0 0 16 72.0 201 ## 1576 61 0 1 3 65.2 181 ## 1577 76 0 0 12 70.3 173 ## 1578 77 1 1 12 65.0 165 ## 1579 75 0 0 22 69.1 194 ## 1580 83 0 1 18 72.0 192 ## 1581 63 1 0 16 62.0 150 ## 1582 55 1 0 18 65.4 200 ## 1583 67 0 1 18 65.0 211 ## 1584 79 1 1 14 66.3 155 ## 1585 87 0 0 16 69.0 142 ## 1586 69 1 2 18 66.0 123 ## 1587 65 0 0 19 68.4 160 ## 1588 60 0 1 18 71.1 191 ## 1589 48 1 0 18 65.5 126 ## 1590 27 1 0 14 63.0 255 ## 1591 74 1 0 16 62.5 167 ## 1592 60 0 0 18 68.5 161 ## 1593 78 1 0 20 65.4 170 ## 1594 81 1 1 12 58.0 124 ## 1595 73 1 2 12 65.5 160 ## 1596 73 1 0 14 68.0 150 ## 1597 60 0 0 16 70.8 218 ## 1598 77 1 0 12 58.5 151 ## 1599 68 0 2 20 66.0 176 ## 1600 74 1 0 6 58.0 109 ## 1601 71 1 1 16 64.0 159 ## 1602 73 1 0 18 61.0 112 ## 1603 77 1 2 12 61.6 130 ## 1604 76 0 0 12 69.0 215 ## 1605 94 0 2 12 70.3 182 ## 1606 69 0 0 21 70.0 228 ## 1607 68 0 2 17 69.5 226 ## 1608 68 0 2 16 70.2 142 ## 1609 45 0 0 18 70.0 166 ## 1610 70 0 0 23 68.0 154 ## 1611 62 0 0 14 69.8 181 ## 1612 71 1 0 18 66.2 139 ## 1613 62 1 0 13 63.4 135 ## 1614 74 0 1 12 69.3 242 ## 1615 74 1 1 17 59.5 99 ## 1616 57 1 0 21 64.5 161 ## 1617 57 1 0 18 67.2 146 ## 1618 68 0 0 16 68.0 134 ## 1619 75 1 0 0 58.0 183 ## 1620 56 1 0 14 64.4 160 ## 1621 58 0 0 18 71.4 174 ## 1622 67 1 2 15 61.0 157 ## 1623 72 0 2 12 72.0 194 ## 1624 80 1 0 18 58.5 128 ## 1625 80 1 1 18 64.0 136 ## 1626 63 1 0 16 61.5 156 ## 1627 61 1 0 19 62.4 181 ## 1628 64 1 0 16 68.0 171 ## 1629 71 1 0 18 66.5 140 ## 1630 86 1 0 14 64.0 144 ## 1631 66 1 0 18 68.0 168 ## 1632 55 1 0 19 63.2 157 ## 1633 72 1 0 16 60.9 139 ## 1634 77 1 1 16 64.9 148 ## 1635 72 0 2 16 67.3 164 ## 1636 79 0 2 19 65.9 173 ## 1637 67 1 0 20 64.0 145 ## 1638 57 1 0 14 64.2 187 ## 1639 56 0 0 12 69.5 181 ## 1640 83 0 1 18 69.5 175 ## 1641 83 0 1 16 65.5 208 ## 1642 76 0 1 10 66.6 209 ## 1643 74 1 1 18 68.5 138 ## 1644 83 0 1 20 66.3 147 ## 1645 58 1 0 16 66.5 151 ## 1646 50 1 0 19 65.9 158 ## 1647 76 1 1 12 63.3 129 ## 1648 55 1 0 16 63.0 123 ## 1649 62 1 0 18 61.1 156 ## 1650 56 0 0 18 67.5 168 ## 1651 65 1 1 16 64.0 132 ## 1652 77 1 1 18 65.5 154 ## 1653 73 1 0 18 68.0 217 ## 1654 65 1 0 16 65.9 182 ## 1655 58 1 0 16 63.8 173 ## 1656 73 0 0 16 72.0 170 ## 1657 89 0 0 16 62.5 130 ## 1658 75 0 1 20 66.5 192 ## 1659 85 0 1 16 66.7 162 ## 1660 86 1 0 12 60.5 85 ## 1661 71 0 0 20 68.1 200 ## 1662 68 1 1 16 68.0 214 ## 1663 88 0 2 18 68.0 178 ## 1664 56 1 0 18 65.4 210 ## 1665 76 1 2 5 60.2 132 ## 1666 59 0 1 12 71.8 243 ## 1667 74 1 0 19 63.4 150 ## 1668 63 1 0 15 64.4 180 ## 1669 79 1 0 17 57.5 127 ## 1670 64 1 1 18 63.0 131 ## 1671 52 1 0 13 65.6 223 ## 1672 83 0 1 16 66.6 213 ## 1673 86 1 0 14 63.9 150 ## 1674 81 1 0 18 58.7 123 ## 1675 57 1 0 14 63.3 162 ## 1676 49 1 0 13 66.5 140 ## 1677 80 0 2 19 67.5 158 ## 1678 68 1 1 20 64.0 115 ## 1679 85 0 1 20 64.5 172 ## 1680 76 1 0 14 63.9 195 ## 1681 80 0 0 18 70.0 219 ## 1682 71 1 1 14 65.5 129 ## 1683 70 0 0 19 70.0 183 ## 1684 51 1 0 14 59.3 150 ## 1685 68 0 0 21 75.0 183 ## 1686 66 1 0 18 60.2 125 ## 1687 73 1 0 17 67.3 209 ## 1688 73 1 0 14 59.0 161 ## 1689 72 0 0 14 71.5 237 ## 1690 73 1 0 18 60.7 137 ## 1691 76 0 0 16 65.5 147 ## 1692 79 1 0 12 63.5 119 ## 1693 72 1 0 12 58.5 138 ## 1694 70 1 0 16 67.0 154 ## 1695 71 0 1 12 73.9 251 ## 1696 79 1 1 18 63.0 138 ## 1697 80 0 0 20 72.5 203 ## 1698 53 1 0 12 65.0 208 ## 1699 70 0 2 17 67.6 172 ## 1700 74 1 2 18 62.9 165 ## 1701 87 0 0 16 63.0 145 ## 1702 94 0 1 1 55.2 142 ## 1703 59 0 0 18 68.1 193 ## 1704 86 0 0 12 65.5 164 ## 1705 49 1 0 18 68.2 141 ## 1706 73 1 0 18 62.9 236 ## 1707 77 1 0 14 62.0 192 ## 1708 78 0 2 20 73.0 184 ## 1709 84 0 0 16 70.0 206 ## 1710 81 0 1 18 65.9 220 ## 1711 68 1 0 16 66.0 214 ## 1712 76 1 2 13 63.0 146 ## 1713 83 0 0 16 70.9 169 ## 1714 82 1 0 20 64.3 138 ## 1715 84 0 2 12 63.0 216 ## 1716 72 1 0 16 63.5 241 ## 1717 64 0 1 12 71.0 238 ## 1718 57 1 0 18 63.7 219 ## 1719 65 0 1 16 68.5 219 ## 1720 68 1 0 16 63.5 172 ## 1721 72 0 0 20 67.7 157 ## 1722 89 1 0 18 62.0 118 ## 1723 50 1 2 12 62.0 145 ## 1724 79 0 1 14 66.6 186 ## 1725 82 1 0 18 59.0 137 ## 1726 76 1 2 12 61.5 90 ## 1727 88 0 2 17 67.5 182 ## 1728 64 1 0 16 64.1 153 ## 1729 60 0 0 16 68.5 166 ## 1730 77 0 0 16 70.0 207 ## 1731 63 1 0 18 62.4 189 ## 1732 74 1 0 12 66.4 164 ## 1733 77 1 1 12 59.0 151 ## 1734 72 0 0 18 68.0 188 ## 1735 85 0 2 16 73.0 162 ## 1736 65 0 0 16 67.8 216 ## 1737 69 1 0 14 61.0 151 ## 1738 69 0 1 15 65.8 155 ## 1739 56 1 2 12 60.0 144 ## 1740 65 1 0 18 63.3 109 ## 1741 65 1 0 18 63.6 108 ## 1742 73 1 2 16 63.5 220 ## 1743 76 0 0 18 65.5 178 ## 1744 54 1 0 16 64.3 229 ## 1745 74 1 0 12 67.5 157 ## 1746 71 0 2 18 65.5 202 ## 1747 57 1 0 15 63.8 175 ## 1748 92 1 0 12 56.0 127 ## 1749 68 1 0 12 61.5 108 ## 1750 64 1 0 16 61.9 265 ## 1751 67 1 2 18 65.5 105 ## 1752 89 0 1 12 66.7 174 ## 1753 69 0 1 18 70.5 228 ## 1754 67 0 1 16 70.0 192 ## 1755 85 0 1 12 64.8 199 ## 1756 73 1 0 13 64.5 216 ## 1757 79 0 1 18 68.5 173 ## 1758 72 0 0 15 67.2 171 ## 1759 53 1 0 15 65.4 130 ## 1760 76 0 0 12 68.0 208 ## 1761 57 1 0 16 67.8 153 ## 1762 81 1 2 11 61.0 220 ## 1763 91 1 0 11 60.0 137 ## 1764 74 0 0 16 68.1 190 ## 1765 71 0 0 20 67.5 189 ## 1766 69 1 0 13 68.0 166 ## 1767 79 1 0 16 61.0 152 ## 1768 77 1 2 13 65.4 208 ## 1769 75 1 2 16 64.8 169 ## 1770 69 1 0 18 64.0 200 ## 1771 82 0 1 16 71.7 180 ## 1772 77 1 1 18 66.4 181 ## 1773 77 0 0 16 67.9 222 ## 1774 77 1 2 12 62.6 126 ## 1775 80 1 0 15 65.0 178 ## 1776 82 0 1 12 67.5 170 ## 1777 70 0 2 10 63.7 164 ## 1778 70 1 0 12 65.5 138 ## 1779 76 0 0 13 68.0 144 ## 1780 58 1 0 18 63.6 122 ## 1781 69 1 0 13 65.8 142 ## 1782 68 0 0 21 70.2 253 ## 1783 79 0 0 18 66.7 165 ## 1784 67 1 2 18 65.5 158 ## 1785 77 1 0 20 60.8 190 ## 1786 68 0 0 14 66.3 198 ## 1787 77 0 2 4 65.0 197 ## 1788 64 0 0 12 69.0 243 ## 1789 81 1 0 12 61.6 175 ## 1790 67 1 1 20 68.0 174 ## 1791 70 0 1 14 64.0 145 ## 1792 70 1 2 12 63.0 142 ## 1793 82 0 0 20 66.7 189 ## 1794 80 1 0 18 60.6 129 ## 1795 73 0 1 16 68.2 172 ## 1796 57 0 2 13 66.8 178 ## 1797 81 0 0 12 70.5 154 ## 1798 86 1 1 12 63.0 142 ## 1799 70 1 0 16 65.9 185 ## 1800 60 1 0 13 69.3 216 ## 1801 76 0 2 12 70.0 225 ## 1802 52 1 0 14 64.3 172 ## 1803 51 0 0 16 73.0 181 ## 1804 85 1 1 16 61.0 109 ## 1805 74 1 0 17 63.7 135 ## 1806 79 0 0 12 69.0 145 ## 1807 61 0 2 16 71.5 185 ## 1808 79 0 2 14 64.5 157 ## 1809 83 0 0 20 67.5 179 ## 1810 88 1 0 16 59.0 190 ## 1811 60 0 2 15 60.0 176 ## 1812 64 0 1 13 68.5 208 ## 1813 84 0 0 18 66.5 198 ## 1814 68 1 0 13 60.5 154 ## 1815 76 1 0 18 61.0 207 ## 1816 72 0 2 20 74.0 170 ## 1817 54 1 0 18 61.9 100 ## 1818 77 0 1 14 69.7 169 ## 1819 53 1 0 14 66.5 213 ## 1820 67 0 0 16 70.7 243 ## 1821 68 1 0 16 70.1 177 ## 1822 83 1 0 12 58.2 134 ## 1823 76 1 0 12 66.5 140 ## 1824 83 1 0 18 65.5 107 ## 1825 62 1 0 16 65.2 147 ## 1826 49 1 0 14 63.1 125 ## 1827 77 1 0 20 65.0 96 ## 1828 59 0 1 12 66.5 212 ## 1829 72 0 2 16 66.0 192 ## 1830 86 1 2 18 59.0 153 ## 1831 75 1 0 15 64.0 189 ## 1832 69 1 1 20 66.5 211 ## 1833 83 1 0 13 62.2 151 ## 1834 76 0 0 22 67.0 170 ## 1835 67 1 0 13 71.2 182 ## 1836 70 0 0 14 70.0 216 ## 1837 80 1 0 20 65.0 156 ## 1838 81 1 0 16 63.0 158 ## 1839 64 1 0 18 62.5 117 ## 1840 72 0 0 16 61.0 129 ## 1841 70 0 0 20 67.2 164 ## 1842 77 1 0 13 62.0 240 ## 1843 65 1 0 12 62.5 168 ## 1844 77 0 0 18 63.5 140 ## 1845 67 1 0 16 60.2 131 ## 1846 84 0 1 16 66.7 179 ## 1847 82 0 1 14 70.0 196 ## 1848 78 1 0 18 62.0 139 ## 1849 63 1 0 18 61.0 139 ## 1850 87 1 0 5 57.6 147 ## 1851 77 0 0 20 70.5 209 ## 1852 58 1 2 18 65.0 151 ## 1853 83 1 0 16 62.0 162 ## 1854 57 1 0 16 69.4 214 ## 1855 61 1 1 14 60.3 137 ## 1856 74 0 2 18 64.0 140 ## 1857 80 0 0 18 63.5 177 ## 1858 74 0 1 20 71.7 220 ## 1859 72 0 1 16 73.5 241 ## 1860 79 0 2 12 68.0 185 ## 1861 80 1 2 14 66.0 140 ## 1862 75 0 0 20 70.8 178 ## 1863 68 0 2 10 67.0 172 ## 1864 80 1 0 18 66.5 144 ## 1865 78 0 0 12 64.0 167 ## 1866 73 1 0 15 63.0 136 ## 1867 67 1 2 16 67.5 238 ## 1868 68 1 0 20 66.8 134 ## 1869 68 1 0 14 65.5 164 ## 1870 80 0 2 12 68.5 179 ## 1871 76 1 0 14 63.0 144 ## 1872 84 1 0 14 61.5 127 ## 1873 78 1 1 16 61.1 174 ## 1874 77 0 1 16 67.9 173 ## 1875 64 0 1 18 70.0 147 ## 1876 81 0 2 18 64.0 178 ## 1877 65 0 2 11 68.0 199 ## 1878 55 1 0 14 62.2 191 ## 1879 82 0 1 16 66.2 137 ## 1880 65 0 2 18 71.0 220 ## 1881 86 0 0 20 65.0 151 ## 1882 76 0 2 19 68.0 238 ## 1883 75 1 0 18 66.0 141 ## 1884 81 1 0 18 62.0 119 ## 1885 85 1 0 12 62.0 134 ## 1886 67 0 2 20 73.0 198 ## 1887 78 0 0 18 71.0 168 ## 1888 92 1 2 14 57.5 120 ## 1889 78 0 2 16 70.3 158 ## 1890 74 0 0 20 68.2 152 ## 1891 81 1 0 12 63.0 209 ## 1892 81 1 0 14 63.0 150 ## 1893 88 1 1 16 60.0 153 ## 1894 80 0 1 12 68.0 154 ## 1895 85 1 0 17 66.0 163 ## 1896 79 0 1 16 65.7 197 ## 1897 73 0 0 20 69.6 197 ## 1898 78 0 2 18 64.2 171 ## 1899 74 1 0 18 58.9 154 ## 1900 78 0 1 18 68.3 175 ## 1901 69 1 0 14 61.3 201 ## 1902 66 1 0 13 62.8 199 ## 1903 86 1 1 11 60.5 187 ## 1904 94 0 1 16 67.0 157 ## 1905 53 1 0 16 64.6 158 ## 1906 71 1 0 25 62.8 135 ## 1907 78 0 0 16 73.0 168 ## 1908 61 1 0 19 63.2 126 ## 1909 63 1 0 16 65.2 156 ## 1910 76 0 2 14 68.3 164 ## 1911 66 1 0 18 63.0 166 ## 1912 70 1 0 10 63.9 230 ## 1913 64 0 2 18 75.0 250 ## 1914 90 1 0 14 62.0 169 ## 1915 62 0 0 16 71.0 194 ## 1916 81 1 2 20 56.0 152 ## 1917 49 1 0 16 63.0 172 ## 1918 80 1 2 20 66.7 144 ## 1919 70 1 0 18 65.8 138 ## 1920 87 1 0 12 65.0 170 ## 1921 75 0 0 16 68.0 203 ## 1922 51 1 0 20 64.2 119 ## 1923 74 0 0 14 70.0 248 ## 1924 65 1 0 18 59.0 148 ## 1925 75 0 1 16 66.0 122 ## 1926 83 1 1 16 64.5 169 ## 1927 63 1 0 16 64.0 179 ## 1928 83 0 2 11 65.5 164 ## 1929 71 0 0 16 76.4 301 ## 1930 62 0 0 18 67.1 167 ## 1931 76 1 2 17 62.3 149 ## 1932 96 0 1 13 70.0 216 ## 1933 60 1 0 20 63.4 143 ## 1934 65 1 2 16 63.4 154 ## 1935 68 0 0 16 70.5 320 ## 1936 69 1 2 12 56.0 102 ## 1937 86 1 0 13 60.0 118 ## 1938 74 1 0 18 64.0 130 ## 1939 79 0 1 20 60.0 142 ## 1940 69 0 0 17 71.8 197 ## 1941 51 0 0 17 74.2 252 ## 1942 80 0 2 18 68.0 209 ## 1943 63 1 1 12 62.5 118 ## 1944 54 1 0 14 65.9 119 ## 1945 52 1 0 16 62.8 154 ## 1946 75 0 2 20 67.8 182 ## 1947 81 1 2 10 61.0 171 ## 1948 73 0 0 16 70.5 195 ## 1949 80 0 0 18 67.5 164 ## 1950 50 1 0 16 65.4 129 ## 1951 84 1 2 18 64.0 109 ## 1952 58 1 0 18 62.8 135 ## 1953 75 0 0 17 66.0 155 ## 1954 74 0 0 18 67.0 188 ## 1955 81 0 0 20 73.0 206 ## 1956 49 1 0 16 66.1 149 ## 1957 63 1 0 16 61.0 153 ## 1958 63 1 0 16 65.0 131 ## 1959 82 1 1 12 61.9 162 ## 1960 58 1 0 16 64.6 142 ## 1961 78 0 1 18 65.0 264 ## 1962 63 1 0 12 64.9 187 ## 1963 47 1 0 10 65.3 194 ## 1964 79 0 1 16 67.0 170 ## 1965 58 1 0 18 61.4 185 ## 1966 68 1 0 18 65.0 140 ## 1967 71 1 0 16 65.1 145 ## 1968 70 1 2 12 65.2 166 ## 1969 82 0 2 16 63.0 145 ## 1970 71 0 2 16 69.3 182 ## 1971 63 0 0 20 70.2 192 ## 1972 65 1 0 15 64.0 142 ## 1973 74 1 2 18 61.5 80 ## 1974 66 0 0 18 72.6 203 ## 1975 69 1 2 18 63.5 118 ## 1976 68 1 1 12 61.0 189 ## 1977 64 1 0 16 65.9 260 ## 1978 78 1 0 16 61.0 142 ## 1979 78 0 0 12 72.0 203 ## 1980 71 1 0 12 63.0 195 ## 1981 55 0 0 19 71.9 255 ## 1982 53 1 0 12 63.7 203 ## 1983 65 0 0 16 69.0 150 ## 1984 57 1 2 18 65.5 164 ## 1985 85 0 0 14 65.0 215 ## 1986 67 1 0 17 66.5 206 ## 1987 81 0 2 16 65.0 155 ## 1988 57 0 0 20 68.3 199 ## 1989 52 1 0 16 64.6 141 ## 1990 75 0 0 14 64.5 132 ## 1991 56 1 1 20 68.0 175 ## 1992 70 0 0 16 74.9 225 ## 1993 57 0 0 19 73.0 220 ## 1994 82 1 0 13 62.0 144 ## 1995 87 0 2 12 67.8 186 ## 1996 67 0 1 17 73.3 225 ## 1997 84 0 2 16 65.5 164 ## 1998 50 0 0 16 72.6 251 ## 1999 60 1 0 12 65.3 147 ## 2000 63 1 0 16 66.0 187 ## 2001 76 0 0 16 65.5 164 ## 2002 77 0 2 20 68.5 157 ## 2003 67 1 0 12 61.0 96 ## 2004 84 0 1 18 67.3 175 ## 2005 75 1 1 13 66.0 238 ## 2006 54 0 0 16 67.5 238 ## 2007 52 1 0 14 66.8 174 ## 2008 84 1 0 16 57.8 103 ## 2009 62 0 2 14 67.9 146 ## 2010 63 1 0 16 64.1 173 ## 2011 65 1 0 16 62.0 140 ## 2012 65 0 2 16 66.0 197 ## 2013 67 1 0 14 62.1 186 ## 2014 60 0 0 18 66.1 175 ## 2015 72 1 0 16 63.0 149 ## 2016 74 1 0 14 68.0 178 ## 2017 56 1 0 12 62.6 174 ## 2018 68 1 0 18 62.0 189 ## 2019 70 1 0 12 61.0 139 ## 2020 69 0 2 16 61.0 200 ## 2021 81 0 0 10 60.5 153 ## 2022 82 0 0 20 67.0 221 ## 2023 75 0 1 14 69.5 169 ## 2024 69 0 2 19 66.0 182 ## 2025 45 1 0 14 68.3 168 ## 2026 73 0 2 14 67.0 175 ## 2027 74 0 2 12 65.5 137 ## 2028 52 0 0 18 70.0 187 ## 2029 60 1 0 18 68.6 135 ## 2030 62 0 0 18 70.7 186 ## 2031 59 1 0 13 61.4 181 ## 2032 75 1 1 12 62.5 163 ## 2033 83 1 1 12 61.2 126 ## 2034 50 1 0 14 67.0 146 ## 2035 45 1 0 20 67.5 118 ## 2036 54 1 0 16 64.5 213 ## 2037 68 0 0 16 64.5 147 ## 2038 60 1 0 16 68.5 187 ## 2039 68 1 0 18 64.5 126 ## 2040 82 0 0 18 69.5 169 ## 2041 63 1 2 16 64.0 199 ## 2042 43 0 2 14 68.5 204 ## 2043 68 0 2 16 69.0 208 ## 2044 77 1 0 15 65.0 136 ## 2045 71 0 2 16 70.0 265 ## 2046 58 0 0 18 71.9 200 ## 2047 69 0 0 19 69.0 162 ## 2048 67 0 0 18 65.9 160 ## 2049 66 1 0 16 63.3 119 ## 2050 70 1 1 12 58.9 120 ## 2051 76 1 0 16 59.0 124 ## 2052 48 1 0 16 63.9 124 ## 2053 49 0 0 18 70.0 198 ## 2054 75 1 0 16 59.5 146 ## 2055 56 0 0 18 68.0 168 ## 2056 70 0 2 20 71.0 214 ## 2057 62 1 2 16 65.5 164 ## 2058 78 1 0 18 64.5 164 ## 2059 66 1 2 16 62.3 112 ## 2060 67 1 2 16 71.0 180 ## 2061 78 0 2 20 70.0 189 ## 2062 69 0 1 16 65.5 151 ## 2063 58 0 0 16 70.9 212 ## 2064 60 1 2 16 65.7 178 ## 2065 64 1 1 18 63.0 134 ## 2066 54 0 0 16 71.6 214 ## 2067 81 0 0 16 66.0 158 ## 2068 72 0 0 18 68.8 195 ## 2069 68 1 0 14 66.0 175 ## 2070 55 1 0 18 64.3 137 ## 2071 52 1 0 18 63.0 137 ## 2072 82 0 0 18 70.0 211 ## 2073 70 0 2 12 68.0 230 ## 2074 89 0 2 20 65.0 140 ## 2075 69 0 0 20 70.6 232 ## 2076 65 1 0 16 59.5 151 ## 2077 87 1 0 18 63.5 122 ## 2078 66 1 0 18 65.6 173 ## 2079 78 0 2 19 70.0 174 ## 2080 64 1 0 14 64.4 172 ## 2081 70 1 0 12 60.0 184 ## 2082 67 1 0 17 67.8 170 ## 2083 86 1 1 16 64.5 127 ## 2084 68 0 2 18 68.8 189 ## 2085 84 1 0 12 58.5 131 ## 2086 64 0 1 18 67.7 193 ## 2087 72 0 2 18 73.0 184 ## 2088 68 1 1 12 69.0 163 ## 2089 77 1 0 18 60.9 165 ## 2090 73 0 0 20 70.1 221 ## 2091 45 1 0 16 62.1 164 ## 2092 68 1 0 16 62.2 117 ## 2093 68 0 2 17 71.5 198 ## 2094 66 1 0 16 64.2 178 ## 2095 80 0 2 18 67.0 158 ## 2096 62 1 0 15 65.1 172 ## 2097 63 1 0 18 65.8 177 ## 2098 70 0 2 6 68.8 186 ## 2099 68 0 0 18 69.7 172 ## 2100 75 0 2 19 67.7 141 ## 2101 63 0 1 12 68.0 204 ## 2102 68 0 0 16 73.4 179 ## 2103 71 1 1 13 64.5 164 ## 2104 83 0 0 16 69.1 149 ## 2105 75 1 0 16 63.0 149 ## 2106 69 0 0 18 70.0 219 ## 2107 81 0 1 19 65.4 216 ## 2108 63 1 0 18 61.5 197 ## 2109 30 0 2 14 68.5 152 ## 2110 66 0 2 20 72.0 237 ## 2111 76 0 2 16 66.6 170 ## 2112 67 0 1 12 70.6 183 ## 2113 72 0 0 12 70.5 206 ## 2114 64 1 0 18 63.3 148 ## 2115 68 0 0 14 70.0 233 ## 2116 56 0 0 18 71.4 184 ## 2117 54 1 0 14 63.5 202 ## 2118 79 0 2 19 68.0 156 ## 2119 78 1 2 3 54.7 150 ## 2120 67 1 0 16 62.0 134 ## 2121 79 1 0 13 63.0 169 ## 2122 78 0 0 12 71.8 192 ## 2123 84 1 2 12 62.0 126 ## 2124 74 1 1 12 57.7 139 ## 2125 64 1 0 20 65.0 204 ## 2126 77 1 0 15 59.0 157 ## 2127 87 0 1 16 69.0 154 ## 2128 72 1 1 17 62.2 132 ## 2129 70 1 1 17 65.5 164 ## 2130 59 1 0 18 63.3 210 ## 2131 74 0 0 14 66.7 150 ## 2132 64 1 0 20 60.5 103 ## 2133 59 0 0 16 69.0 227 ## 2134 58 1 0 14 68.8 157 ## 2135 63 1 1 12 64.3 206 ## 2136 65 0 0 16 71.5 221 ## 2137 66 1 2 13 65.0 142 ## 2138 74 1 0 12 65.3 153 ## 2139 56 1 0 24 67.4 130 ## 2140 63 0 0 21 70.2 235 ## 2141 70 1 1 12 64.5 171 ## 2142 87 1 0 19 53.9 93 ## 2143 70 0 0 16 71.0 302 ## 2144 61 1 0 20 65.3 170 ## 2145 54 1 1 12 61.0 94 ## 2146 89 0 1 16 65.0 146 ## 2147 48 1 0 16 70.9 166 ## 2148 70 1 0 15 63.3 161 ## 2149 60 0 1 14 69.5 214 ## 2150 73 1 0 17 65.5 164 ## 2151 64 1 0 15 60.0 139 ## 2152 72 1 0 12 61.0 158 ## 2153 79 1 0 14 60.0 153 ## 2154 55 1 0 20 65.8 129 ## 2155 59 1 0 16 69.4 187 ## 2156 83 1 1 16 61.0 114 ## 2157 84 1 1 21 64.0 152 ## 2158 51 1 0 18 64.0 188 ## 2159 68 1 0 12 63.5 178 ## 2160 68 0 0 12 70.0 189 ## 2161 77 0 2 12 69.5 213 ## 2162 68 1 0 20 66.0 156 ## 2163 70 1 0 16 67.0 147 ## 2164 69 0 2 15 71.7 231 ## 2165 74 0 0 20 69.0 154 ## 2166 64 1 0 13 60.5 123 ## 2167 59 1 0 19 60.6 121 ## 2168 72 0 0 16 73.0 180 ## 2169 49 1 0 14 65.7 263 ## 2170 54 1 0 12 66.3 197 ## 2171 72 0 0 16 65.5 178 ## 2172 71 0 2 16 72.0 225 ## 2173 65 0 2 16 69.0 172 ## 2174 55 1 0 14 62.9 229 ## 2175 81 1 2 10 63.5 193 ## 2176 68 1 0 18 63.2 142 ## 2177 60 1 0 18 67.3 154 ## 2178 77 1 0 16 61.3 129 ## 2179 63 0 0 6 70.0 233 ## 2180 47 0 0 16 71.6 187 ## 2181 79 1 1 13 65.5 164 ## 2182 70 0 2 15 70.6 174 ## 2183 62 1 0 18 65.1 206 ## 2184 65 1 0 14 64.3 206 ## 2185 83 0 1 16 71.0 171 ## 2186 40 1 2 9 64.0 116 ## 2187 72 0 2 20 67.0 175 ## 2188 64 0 0 17 70.5 190 ## 2189 56 1 0 14 62.8 146 ## 2190 84 0 1 20 66.2 151 ## 2191 61 1 2 12 60.7 118 ## 2192 73 1 0 12 59.0 109 ## 2193 66 0 1 9 66.0 218 ## 2194 64 1 0 18 61.1 179 ## 2195 48 1 0 18 65.2 217 ## 2196 88 1 2 12 61.0 106 ## 2197 59 1 2 12 63.8 133 ## 2198 61 1 0 16 65.3 124 ## 2199 69 0 2 19 70.0 141 ## 2200 49 0 0 14 73.8 204 ## 2201 65 0 0 16 67.8 174 ## 2202 55 0 0 20 70.8 188 ## 2203 45 0 0 16 68.5 214 ## 2204 75 1 1 16 65.5 131 ## 2205 51 1 0 16 65.6 146 ## 2206 56 0 0 14 73.5 249 ## 2207 59 1 0 13 66.0 118 ## 2208 70 0 2 12 69.0 152 ## 2209 66 1 0 18 61.0 119 ## 2210 54 0 0 18 70.4 177 ## 2211 62 1 0 16 67.0 213 ## 2212 55 1 0 13 69.0 169 ## 2213 80 0 2 20 67.7 159 ## 2214 75 0 0 16 66.0 201 ## 2215 61 1 0 16 60.9 190 ## 2216 65 1 0 15 63.6 130 ## 2217 66 0 0 19 66.8 196 ## 2218 65 1 0 16 69.4 173 ## 2219 67 0 0 20 68.9 209 ## 2220 55 1 0 19 64.5 170 ## 2221 66 1 0 18 64.2 114 ## 2222 60 1 0 18 65.3 158 ## 2223 72 0 0 13 70.5 176 ## 2224 83 0 2 18 70.0 164 ## 2225 65 1 0 16 67.0 182 ## 2226 25 1 0 15 67.0 161 ## 2227 37 1 0 14 65.5 136 ## 2228 81 1 2 16 64.5 134 ## 2229 52 1 0 16 67.7 211 ## 2230 59 0 0 18 72.1 224 ## 2231 58 1 0 13 65.0 180 ## 2232 78 0 2 14 70.5 183 ## 2233 73 1 1 12 65.5 192 ## 2234 49 1 0 16 62.4 117 ## 2235 53 0 0 18 70.2 171 ## 2236 45 1 0 16 65.5 142 ## 2237 58 1 0 15 66.5 197 ## 2238 74 0 1 13 68.8 151 ## 2239 66 1 2 12 57.9 93 ## 2240 69 1 0 18 64.0 132 ## 2241 78 1 1 12 64.5 113 ## 2242 56 1 0 18 62.5 182 ## 2243 61 0 0 17 71.5 221 ## 2244 76 0 2 16 66.3 139 ## 2245 63 0 2 20 67.0 129 ## 2246 75 0 1 13 72.0 207 ## 2247 67 1 0 20 67.0 121 ## 2248 64 1 2 18 65.0 121 ## 2249 59 0 0 20 71.0 171 ## 2250 53 1 0 18 69.0 167 ## 2251 25 0 0 18 72.0 163 ## 2252 53 1 0 14 69.3 225 ## 2253 72 1 0 14 65.2 155 ## 2254 67 0 1 14 70.7 193 ## 2255 65 1 0 13 58.7 153 ## 2256 80 1 0 16 62.0 159 ## 2257 78 0 2 19 68.0 178 ## 2258 69 0 1 20 70.3 207 ## 2259 61 1 2 18 69.0 177 ## 2260 66 1 0 12 58.3 130 ## 2261 52 1 0 16 65.7 153 ## 2262 58 0 0 16 71.6 207 ## 2263 64 1 0 14 61.9 117 ## 2264 67 1 0 20 64.0 145 ## 2265 64 1 0 12 65.3 204 ## 2266 70 1 0 12 65.6 148 ## 2267 73 0 0 13 68.9 217 ## 2268 68 1 0 14 65.0 143 ## 2269 54 0 0 20 71.0 207 ## 2270 59 1 0 14 64.6 219 ## 2271 61 1 0 18 65.5 138 ## 2272 79 1 0 15 67.0 166 ## 2273 77 1 0 18 61.4 154 ## 2274 78 0 0 18 68.0 178 ## 2275 65 1 0 18 66.0 158 ## 2276 66 1 0 15 67.0 214 ## 2277 74 1 0 18 65.1 127 ## 2278 80 1 0 14 63.9 127 ## 2279 73 0 2 20 69.0 172 ## 2280 65 1 0 18 61.0 132 ## 2281 75 0 0 18 69.4 227 ## 2282 79 1 0 16 60.0 109 ## 2283 69 1 0 18 62.2 135 ## 2284 68 1 0 18 65.0 243 ## 2285 76 0 0 18 69.2 274 ## 2286 74 1 0 16 65.4 137 ## 2287 62 0 1 16 64.5 126 ## 2288 62 0 0 18 71.9 183 ## 2289 65 0 0 20 73.0 175 ## 2290 62 0 2 11 69.8 251 ## 2291 47 0 0 16 69.6 212 ## 2292 64 0 0 14 65.0 169 ## 2293 55 1 0 12 66.5 157 ## 2294 61 0 0 20 70.8 183 ## 2295 62 1 0 12 66.4 200 ## 2296 68 0 0 16 69.4 208 ## 2297 71 1 1 12 64.8 143 ## 2298 60 0 0 16 66.9 217 ## 2299 69 1 0 14 66.0 157 ## 2300 66 0 1 18 65.7 181 ## 2301 56 1 0 16 63.2 116 ## 2302 58 1 0 12 67.0 156 ## 2303 50 1 0 8 69.0 176 ## 2304 56 1 1 16 64.7 131 ## 2305 79 0 2 12 64.0 203 ## 2306 46 1 0 16 63.6 177 ## 2307 59 0 0 12 68.0 180 ## 2308 52 1 0 18 68.5 120 ## 2309 57 1 0 12 61.4 118 ## 2310 61 0 0 18 69.2 201 ## 2311 62 0 0 12 67.4 202 ## 2312 44 1 0 18 67.0 194 ## 2313 75 0 0 14 65.5 149 ## 2314 42 1 0 16 70.0 159 ## 2315 51 1 0 16 65.7 157 ## 2316 65 1 0 15 62.2 128 ## 2317 63 0 0 18 70.0 165 ## 2318 54 1 0 12 64.3 190 ## 2319 51 0 2 12 72.0 193 ## 2320 62 0 1 20 69.2 194 ## 2321 60 1 0 12 62.6 160 ## 2322 62 0 0 20 68.6 200 ## 2323 55 1 0 16 56.6 133 ## 2324 62 1 0 18 71.9 157 ## 2325 74 1 0 13 66.0 210 ## 2326 63 1 0 16 61.7 155 ## 2327 65 0 0 20 69.8 182 ## 2328 68 1 0 12 65.0 143 ## 2329 70 1 0 16 60.9 171 ## 2330 69 0 2 18 67.0 193 ## 2331 73 1 0 20 64.0 164 ## 2332 64 0 0 14 70.0 211 ## 2333 61 0 0 18 72.9 197 ## 2334 66 1 0 13 66.0 234 ## 2335 60 0 0 18 70.0 227 ## 2336 58 1 0 14 66.9 227 ## 2337 81 1 0 18 66.5 148 ## 2338 85 1 0 16 59.6 128 ## 2339 55 0 0 12 67.4 207 ## 2340 76 0 2 20 69.0 177 ## 2341 73 1 0 14 62.5 191 ## 2342 52 0 0 16 69.1 160 ## 2343 72 1 2 18 64.3 118 ## 2344 63 1 0 15 64.2 163 ## 2345 74 0 1 12 69.5 187 ## 2346 59 1 0 18 64.9 161 ## 2347 50 0 0 14 74.7 184 ## 2348 68 1 0 14 63.5 215 ## 2349 76 1 0 10 64.7 181 ## 2350 69 0 0 20 68.2 156 ## 2351 76 1 0 18 66.0 148 ## 2352 75 0 0 13 66.2 171 ## 2353 55 0 0 16 71.3 248 ## 2354 59 1 0 16 63.7 158 ## 2355 59 1 0 20 64.8 182 ## 2356 55 1 0 20 67.9 175 ## 2357 53 0 0 16 68.8 193 ## 2358 86 0 0 19 69.0 172 ## 2359 56 1 0 12 61.3 118 ## 2360 59 1 0 16 61.9 203 ## 2361 67 0 0 18 72.0 204 ## 2362 75 0 0 17 72.4 197 ## 2363 64 1 0 9 68.0 159 ## 2364 61 1 0 18 67.9 142 ## 2365 46 1 0 16 67.4 137 ## 2366 73 0 2 13 69.6 162 ## 2367 60 1 0 16 67.1 178 ## 2368 70 0 0 20 70.5 183 ## 2369 70 0 0 19 68.4 169 ## 2370 65 1 0 18 62.4 155 ## 2371 63 1 0 18 64.0 147 ## 2372 60 0 0 18 68.0 160 ## 2373 65 0 2 14 70.0 230 ## 2374 63 1 0 18 63.8 133 ## 2375 47 0 0 12 70.0 181 ## 2376 88 1 2 12 58.7 149 ## 2377 57 1 2 14 66.1 124 ## 2378 60 1 0 14 62.4 193 ## 2379 79 1 1 13 61.7 120 ## 2380 67 1 0 17 64.4 157 ## 2381 66 0 2 12 69.0 155 ## 2382 60 1 0 16 68.1 225 ## 2383 58 1 0 13 64.9 195 ## 2384 62 1 0 18 66.5 213 ## 2385 55 0 0 20 72.1 214 ## 2386 55 1 0 16 65.5 179 ## 2387 57 1 0 14 63.8 167 ## 2388 70 0 0 18 68.0 187 ## 2389 76 1 2 16 62.4 117 ## 2390 64 0 0 16 68.7 204 ## 2391 73 1 0 16 67.0 158 ## 2392 59 0 0 18 75.4 241 ## 2393 78 0 0 18 70.0 206 ## 2394 64 0 0 16 70.6 207 ## 2395 49 1 0 16 63.3 127 ## 2396 82 1 1 12 60.9 119 ## 2397 64 0 0 15 71.0 184 ## 2398 72 1 1 16 64.0 148 ## 2399 70 1 0 20 65.5 175 ## 2400 73 1 1 20 59.0 147 ## 2401 72 0 2 20 69.5 175 ## 2402 68 1 0 14 65.5 136 ## 2403 63 1 0 18 64.9 181 ## 2404 67 1 0 17 60.5 221 ## 2405 80 0 0 18 65.8 179 ## 2406 80 1 1 18 62.5 117 ## 2407 71 0 1 16 67.0 180 ## 2408 72 0 1 18 68.5 174 ## 2409 60 1 0 18 67.0 185 ## 2410 79 0 2 20 71.0 168 ## 2411 74 0 1 18 71.0 203 ## 2412 49 1 0 15 65.4 226 ## 2413 51 0 0 13 68.0 154 ## 2414 60 0 0 20 75.0 199 ## 2415 70 0 0 16 71.0 178 ## 2416 71 0 0 12 66.2 207 ## 2417 77 1 0 12 62.6 194 ## 2418 47 0 0 16 73.0 220 ## 2419 63 1 0 16 70.0 240 ## 2420 55 0 2 16 72.0 272 ## 2421 55 1 0 14 62.2 137 ## 2422 65 0 0 16 69.3 149 ## 2423 80 1 2 12 71.0 167 ## 2424 74 1 1 12 64.5 129 ## 2425 56 1 0 17 64.6 206 ## 2426 95 1 2 8 60.0 88 ## 2427 68 1 0 17 60.1 117 ## 2428 68 0 0 20 69.0 161 ## 2429 67 0 0 13 78.8 313 ## 2430 70 0 0 18 68.0 164 ## 2431 61 1 0 14 64.8 247 ## 2432 51 1 0 15 61.6 233 ## 2433 64 1 0 14 66.0 198 ## 2434 73 0 0 20 73.0 160 ## 2435 74 1 0 15 64.0 150 ## 2436 73 1 0 16 61.5 118 ## 2437 72 0 0 16 66.3 158 ## 2438 67 0 0 20 75.0 212 ## 2439 84 0 1 16 70.6 206 ## 2440 60 1 0 16 62.2 154 ## 2441 61 1 0 18 62.4 154 ## 2442 63 0 2 16 72.0 170 ## 2443 74 1 2 12 64.8 161 ## 2444 70 0 0 18 66.0 194 ## 2445 89 1 0 18 64.5 179 ## 2446 70 1 0 18 64.0 143 ## 2447 64 1 0 12 62.0 147 ## 2448 71 0 0 17 66.9 148 ## 2449 57 0 0 16 70.3 250 ## 2450 66 1 0 20 64.9 173 ## 2451 56 0 0 20 72.2 249 ## 2452 58 0 0 18 72.1 214 ## 2453 70 0 0 13 70.2 272 ## 2454 62 0 0 18 75.8 231 ## 2455 51 0 0 18 65.8 178 ## 2456 78 1 2 12 60.0 162 ## 2457 68 0 0 16 66.2 161 ## 2458 64 1 0 16 65.2 141 ## 2459 63 0 1 20 67.2 197 ## 2460 81 1 0 18 64.3 117 ## 2461 68 1 0 20 63.7 139 ## 2462 87 1 0 4 60.0 128 ## 2463 71 1 0 18 64.0 158 ## 2464 50 1 1 20 66.0 138 ## 2465 75 0 1 14 71.0 182 ## 2466 72 1 0 8 61.0 159 ## 2467 74 1 0 18 62.7 113 ## 2468 75 0 0 20 69.0 150 ## 2469 56 1 0 15 60.1 131 ## 2470 68 0 0 14 70.8 220 ## 2471 82 1 0 16 62.2 182 ## 2472 88 1 0 13 62.0 144 ## 2473 39 0 0 20 65.5 164 ## 2474 72 1 0 16 63.0 185 ## 2475 60 1 1 12 60.2 214 ## 2476 47 0 0 18 68.9 184 ## 2477 72 0 0 16 73.0 251 ## 2478 66 1 0 14 61.0 119 ## 2479 71 1 0 18 68.2 163 ## 2480 53 1 1 16 65.5 164 ## 2481 68 0 0 20 71.0 182 ## 2482 70 1 2 14 61.6 131 ## 2483 48 0 0 16 71.2 223 ## 2484 69 0 0 16 75.5 219 ## 2485 69 1 0 18 60.7 220 ## 2486 67 0 0 18 72.9 185 ## 2487 78 1 0 16 65.5 210 ## 2488 66 1 0 18 60.9 167 ## 2489 58 0 0 12 70.3 197 ## 2490 71 1 1 16 60.3 124 ## 2491 71 0 0 16 65.5 161 ## 2492 77 1 0 16 61.4 171 ## 2493 64 1 0 18 67.2 154 ## 2494 85 0 0 18 68.1 156 ## 2495 78 1 0 18 63.6 110 ## 2496 59 0 0 18 68.7 173 ## 2497 65 1 0 18 67.1 153 ## 2498 37 1 0 16 70.0 188 ## 2499 55 1 0 16 67.0 209 ## 2500 75 0 1 18 71.0 220 ## 2501 54 0 0 16 77.0 191 ## 2502 63 1 0 18 67.2 123 ## 2503 69 1 0 18 66.0 123 ## 2504 70 1 0 17 65.5 141 ## 2505 74 1 0 18 61.8 120 ## 2506 93 0 0 16 59.8 138 ## 2507 47 1 0 20 66.9 151 ## 2508 76 1 0 16 65.7 217 ## 2509 56 1 0 18 61.1 152 ## 2510 57 1 0 20 67.2 181 ## 2511 68 1 2 16 65.0 164 ## 2512 69 1 0 13 60.0 118 ## 2513 74 1 1 16 63.4 135 ## 2514 75 1 0 14 62.8 195 ## 2515 47 0 0 14 69.4 240 ## 2516 62 1 0 13 63.2 141 ## 2517 76 0 1 19 65.5 164 ## 2518 63 1 0 16 66.5 223 ## 2519 40 1 0 16 65.0 129 ## 2520 50 0 0 17 64.4 186 ## 2521 53 1 0 18 58.3 120 ## 2522 21 1 0 16 63.0 151 ## 2523 56 0 0 16 67.9 229 ## 2524 76 0 2 14 66.3 178 ## 2525 75 1 0 14 62.0 176 ## 2526 47 1 0 20 69.4 199 ## 2527 53 0 0 16 70.1 213 ## 2528 57 0 0 20 69.8 171 ## 2529 73 0 1 16 66.0 194 ## 2530 75 0 1 16 71.0 205 ## 2531 66 1 0 18 62.7 152 ## 2532 56 1 0 16 63.1 199 ## 2533 70 0 0 14 70.0 196 ## 2534 58 0 0 20 70.6 224 ## 2535 51 0 0 14 69.2 261 ## 2536 54 1 0 16 65.8 154 ## 2537 56 0 0 18 68.2 140 ## 2538 72 1 0 17 63.0 151 ## 2539 50 1 0 18 64.3 125 ## 2540 61 0 2 13 69.2 195 ## 2541 62 1 0 16 64.1 131 ## 2542 53 0 0 16 75.1 200 ## 2543 61 1 0 16 66.1 146 ## 2544 46 1 0 20 64.0 137 ## 2545 59 1 0 17 67.2 145 ## 2546 50 1 0 16 65.9 194 ## 2547 57 1 0 17 59.1 147 ## 2548 52 1 0 16 62.1 121 ## 2549 45 0 0 16 70.9 154 ## 2550 52 1 0 14 60.4 188 ## 2551 86 1 2 9 61.2 171 ## 2552 54 0 0 20 74.0 241 ## 2553 65 0 0 14 72.5 240 ## 2554 53 0 0 18 64.9 129 ## 2555 58 0 0 19 68.9 217 ## 2556 56 1 0 17 61.2 197 ## 2557 56 0 0 18 70.3 231 ## 2558 53 0 0 16 68.6 159 ## 2559 61 0 0 14 71.1 208 ## 2560 47 0 0 16 66.6 170 ## 2561 68 1 0 16 64.0 190 ## 2562 72 0 1 16 67.8 182 ## 2563 81 1 1 13 62.0 178 ## 2564 56 1 0 20 65.8 129 ## 2565 71 1 1 16 64.0 126 ## 2566 78 1 2 16 58.9 100 ## 2567 80 1 0 18 61.9 160 ## 2568 56 1 0 18 63.9 165 ## 2569 61 1 0 18 63.2 219 ## 2570 72 1 0 16 62.5 179 ## 2571 69 1 0 16 64.5 151 ## 2572 64 1 0 18 65.5 198 ## 2573 70 0 2 20 70.9 220 ## 2574 67 0 2 16 76.0 225 ## 2575 54 0 0 16 73.5 195 ## 2576 68 1 2 18 64.5 121 ## 2577 77 0 2 22 68.4 178 ## 2578 58 0 2 16 70.0 196 ## 2579 64 1 0 12 60.3 145 ## 2580 81 1 1 14 65.9 159 ## 2581 56 1 2 18 69.1 149 ## 2582 56 0 0 18 73.4 230 ## 2583 72 0 2 20 66.6 163 ## 2584 47 0 0 14 72.5 201 ## 2585 65 1 2 18 62.0 127 ## 2586 68 0 1 18 68.0 167 ## 2587 51 1 0 18 63.6 154 ## 2588 74 0 1 16 66.8 168 ## 2589 64 1 0 18 60.5 152 ## 2590 65 0 0 14 75.0 212 ## 2591 64 1 0 18 64.6 184 ## 2592 58 0 2 14 71.0 205 ## 2593 69 1 0 14 64.5 174 ## 2594 75 1 0 14 60.0 140 ## 2595 66 1 1 16 63.2 121 ## 2596 28 1 0 16 66.0 116 ## 2597 74 0 2 15 72.0 152 ## 2598 67 1 2 18 63.0 149 ## 2599 67 1 2 18 65.9 111 ## 2600 88 1 2 16 61.2 173 ## 2601 63 0 0 12 68.4 254 ## 2602 67 0 0 15 71.0 221 ## 2603 62 1 0 13 63.0 202 ## 2604 86 1 0 12 62.3 135 ## 2605 76 0 2 12 68.0 179 ## 2606 81 1 1 12 60.7 168 ## 2607 56 1 0 16 67.2 164 ## 2608 60 0 0 14 67.4 247 ## 2609 25 0 0 16 66.0 184 ## 2610 55 1 0 16 64.8 162 ## 2611 75 0 1 18 71.7 178 ## 2612 80 1 2 18 63.0 180 ## 2613 60 1 2 14 67.0 245 ## 2614 78 0 0 12 64.5 218 ## 2615 70 0 1 18 67.0 170 ## 2616 63 0 1 12 70.0 183 ## 2617 77 1 1 18 65.5 164 ## 2618 48 1 2 14 62.0 171 ## 2619 65 1 0 18 66.3 134 ## 2620 74 0 1 18 71.0 195 ## 2621 75 1 1 14 62.0 154 ## 2622 76 0 1 15 67.4 166 ## 2623 55 0 1 18 60.0 133 ## 2624 59 0 2 13 60.0 114 ## 2625 53 1 0 18 62.8 227 ## 2626 61 1 1 17 61.0 135 ## 2627 48 0 0 18 71.1 244 ## 2628 57 1 2 12 62.0 175 ## 2629 60 0 0 20 69.4 154 ## 2630 56 1 0 18 65.4 247 ## 2631 63 1 0 18 61.9 157 ## 2632 78 0 2 13 68.5 140 ## 2633 46 1 0 16 64.2 163 ## 2634 79 1 0 16 61.9 200 ## 2635 60 1 1 16 59.0 151 ## 2636 60 0 2 14 65.7 158 ## 2637 63 0 0 16 69.0 177 ## 2638 61 1 0 16 66.7 136 ## 2639 80 1 2 14 65.3 199 ## 2640 57 0 2 16 73.0 214 ## 2641 77 0 1 12 65.0 142 ## 2642 65 1 0 16 64.0 140 ## 2643 68 0 1 12 69.2 186 ## 2644 75 1 0 12 63.5 152 ## 2645 46 1 0 18 65.5 156 ## 2646 80 0 2 20 63.8 150 ## 2647 78 1 0 16 59.0 119 ## 2648 71 0 1 18 71.0 165 ## 2649 27 0 1 16 71.0 200 ## 2650 77 0 2 18 67.0 169 ## 2651 61 1 1 16 65.5 164 ## 2652 60 1 1 22 64.6 201 ## 2653 64 1 2 16 65.5 135 ## 2654 77 1 0 18 62.0 155 ## 2655 65 1 0 14 65.5 195 ## 2656 80 1 1 17 55.0 110 ## 2657 63 1 0 16 66.0 132 ## 2658 35 1 0 18 65.0 226 ## 2659 29 1 1 16 66.0 134 ## 2660 64 1 2 16 63.0 173 ## 2661 70 0 1 16 70.9 207 ## 2662 72 1 1 16 61.3 92 ## 2663 60 0 1 18 72.0 200 ## 2664 85 1 2 16 58.0 132 ## 2665 79 1 0 20 65.5 144 ## 2666 72 0 0 18 75.0 267 ## 2667 65 1 1 18 64.0 134 ## 2668 83 0 0 21 69.2 152 ## 2669 72 1 1 18 62.5 113 ## 2670 70 1 0 18 67.0 165 ## 2671 72 1 1 13 69.0 163 ## 2672 57 1 0 16 65.0 127 ## 2673 82 1 1 15 67.0 150 ## 2674 24 0 0 13 71.0 182 ## 2675 62 0 1 16 71.5 223 ## 2676 81 0 1 10 66.0 162 ## 2677 74 1 0 12 62.0 123 ## 2678 71 0 0 20 67.0 165 ## 2679 67 1 0 17 66.0 180 ## 2680 65 0 0 18 72.0 172 ## 2681 74 0 2 11 65.0 178 ## 2682 78 1 0 18 61.5 129 ## 2683 66 0 1 14 68.3 203 ## 2684 68 1 0 14 65.0 156 ## 2685 81 0 1 14 68.0 165 ## 2686 68 1 0 15 66.0 163 ## 2687 69 0 2 20 68.0 146 ## 2688 65 0 1 16 70.1 161 ## 2689 70 0 1 18 75.1 195 ## 2690 66 1 1 19 66.5 194 ## 2691 61 1 0 16 65.0 161 ## 2692 70 1 2 16 64.0 192 ## 2693 66 1 1 18 62.2 148 ## 2694 63 0 1 20 65.5 181 ## 2695 65 1 0 16 64.0 109 ## 2696 73 0 2 20 68.7 158 ## 2697 70 1 1 16 65.0 129 ## 2698 67 1 0 14 62.5 149 ## 2699 66 0 1 16 68.5 211 ## 2700 67 1 0 20 70.0 143 ``` --- ``` r alzheimer_data <- alzheimer_data %>% select(age, female, diagnosis, educ, height, weight) ``` --- ``` r glimpse(alzheimer_data) ``` ``` ## Rows: 2,700 ## Columns: 6 ## $ age <int> 74, 56, 77, 74, 75, 72, 64, 78, 73, 81, 66, 65, 66, 73, 78, … ## $ female <int> 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, … ## $ diagnosis <int> 0, 0, 0, 0, 1, 0, 0, 2, 0, 2, 0, 0, 0, 1, 0, 1, 2, 2, 2, 1, … ## $ educ <int> 12, 16, 18, 20, 14, 16, 16, 17, 18, 13, 16, 16, 17, 20, 13, … ## $ height <dbl> 65.0, 62.0, 65.0, 62.0, 62.0, 61.8, 60.0, 69.0, 65.0, 71.0, … ## $ weight <int> 233, 110, 137, 112, 127, 141, 124, 152, 131, 197, 134, 144, … ``` --- `mutate()` adds new variables and preserves existing ones ``` r alzheimer_data <- alzheimer_data %>% mutate(weight_kg = 0.453592*weight) colnames(alzheimer_data) ``` ``` ## [1] "age" "female" "diagnosis" "educ" "height" "weight" ## [7] "weight_kg" ``` --- We can use `mutate()` to change the data type of a variable by creating a new variable that has the desired data type. Here's an example that demonstrates how to use `mutate()` to change the data type of a variable using a conditional statement: ``` r alzheimer_data %>% mutate(gender = case_when(female == 1 ~ "female", female == 0 ~ "male")) %>% head() ``` ``` ## age female diagnosis educ height weight weight_kg gender ## 1 74 0 0 12 65.0 233 105.68694 male ## 2 56 1 0 16 62.0 110 49.89512 female ## 3 77 1 0 18 65.0 137 62.14210 female ## 4 74 1 0 20 62.0 112 50.80230 female ## 5 75 0 1 14 62.0 127 57.60618 male ## 6 72 1 0 16 61.8 141 63.95647 female ``` --- class: inverse .font50[Grouping Data] --- class: inverse .font50[Question: ] > Are participants in diagnosis group 1 older or younger when compared with the diagnosis group 2? --- The function group_by() from dplyr groups the rows by the unique values in the column specified to it. Note that there is no perceptible change to the dataset after running group_by(), until another dplyr verb such as mutate(), summarise(), or arrange() is applied on the “grouped” data frame. ``` r alzheimer_data %>% head() ``` ``` ## age female diagnosis educ height weight weight_kg ## 1 74 0 0 12 65.0 233 105.68694 ## 2 56 1 0 16 62.0 110 49.89512 ## 3 77 1 0 18 65.0 137 62.14210 ## 4 74 1 0 20 62.0 112 50.80230 ## 5 75 0 1 14 62.0 127 57.60618 ## 6 72 1 0 16 61.8 141 63.95647 ``` --- Once we group the data, we won't see much difference other than `Groups: age_gp [2]` statement, everything else will be similar. ``` r alzheimer_data %>% group_by(diagnosis) %>% head() ``` ``` ## # A tibble: 6 × 7 ## # Groups: diagnosis [2] ## age female diagnosis educ height weight weight_kg ## <int> <int> <int> <int> <dbl> <int> <dbl> ## 1 74 0 0 12 65 233 106. ## 2 56 1 0 16 62 110 49.9 ## 3 77 1 0 18 65 137 62.1 ## 4 74 1 0 20 62 112 50.8 ## 5 75 0 1 14 62 127 57.6 ## 6 72 1 0 16 61.8 141 64.0 ``` --- ``` r alzheimer_data %>% group_by(diagnosis) %>% summarize(mean(age, na.rm = TRUE)) ``` ``` ## # A tibble: 3 × 2 ## diagnosis `mean(age, na.rm = TRUE)` ## <int> <dbl> ## 1 0 67.6 ## 2 1 73.7 ## 3 2 72.9 ``` --- We can also calculate other descriptives as well as number of observations for each group. ``` r alzheimer_data %>% group_by(diagnosis) %>% summarize(mean_age=mean(age, na.rm = TRUE), median_age=median(age, na.rm = TRUE), n_diag = n()) ``` ``` ## # A tibble: 3 × 4 ## diagnosis mean_age median_age n_diag ## <int> <dbl> <dbl> <int> ## 1 0 67.6 69 1534 ## 2 1 73.7 75 613 ## 3 2 72.9 75 553 ```