Curso R - iDRHiCA
Johanna Orellana-Alvear
Septiembre, 2016
Universidad de Cuenca
Material Extra
Johanna Orellana-Alvear (MSc)
johanna.orellana@ucuenca.edu.ec
Curso completo en: http://rpubs.com/Johanna_Orellana_Alvear/programacion_r_indice
Una revisión y ejecución secuencial para trabajar con un data frame.
Implementación secuencial
Abrir el archivo y obtener estadÃsticos generales
df.observaciones <- read.table("observaciones_2003.csv", header = TRUE, stringsAsFactors = FALSE)
summary(df.observaciones)## FECHA VALOR STATUS ESTACION
## Length:8030 Min. : 0.000 Length:8030 Length:8030
## Class :character 1st Qu.: 0.000 Class :character Class :character
## Mode :character Median : 0.000 Mode :character Mode :character
## Mean : 1.988
## 3rd Qu.: 1.000
## Max. :91.600
## NA's :1786
## year day month
## Min. :2003 Min. : 1.00 Min. : 1.000
## 1st Qu.:2003 1st Qu.: 8.00 1st Qu.: 4.000
## Median :2003 Median :16.00 Median : 7.000
## Mean :2003 Mean :15.72 Mean : 6.526
## 3rd Qu.:2003 3rd Qu.:23.00 3rd Qu.:10.000
## Max. :2003 Max. :31.00 Max. :12.000
## Manipular la fecha
fechas <- strptime(df.observaciones$FECHA, format="%d/%m/%Y")
df.observaciones$FECHA <- as.POSIXct(fechas) Recuperar el mes y el año a partir de la fecha
df_events_month <- as.numeric(format(df.observaciones$FECHA,"%m"))
df_events_year <- as.numeric(format(df.observaciones$FECHA,"%Y"))Unir los nuevos vectores al data frame
df.observaciones = cbind(df.observaciones,df_events_month)
df.observaciones = cbind(df.observaciones,df_events_year)
head(df.observaciones)## FECHA VALOR STATUS ESTACION year day month df_events_month
## 7671 2003-01-01 0 M012 2003 1 1 1
## 7672 2003-01-02 0 M012 2003 2 1 1
## 7673 2003-01-03 0 M012 2003 3 1 1
## 7674 2003-01-04 0 M012 2003 4 1 1
## 7675 2003-01-05 0 M012 2003 5 1 1
## 7676 2003-01-06 0 M012 2003 6 1 1
## df_events_year
## 7671 2003
## 7672 2003
## 7673 2003
## 7674 2003
## 7675 2003
## 7676 2003Agregaciones por uno y dos campos
sd_VALOR_monthly <- aggregate(VALOR ~ month, data = df.observaciones, sd)
sd_VALOR_monthly## month VALOR
## 1 1 4.793814
## 2 2 7.401479
## 3 3 10.284249
## 4 4 8.289255
## 5 5 6.885316
## 6 6 4.416878
## 7 7 3.150891
## 8 8 2.220939
## 9 9 2.862725
## 10 10 3.130080
## 11 11 4.156379
## 12 12 6.350809sd_VALOR_month_estacion <- aggregate(VALOR ~ month + ESTACION, data = df.observaciones, sd)
sd_VALOR_month_estacion## month ESTACION VALOR
## 1 1 M012 3.29349963
## 2 2 M012 4.24854005
## 3 3 M012 2.25483471
## 4 4 M012 0.00000000
## 5 5 M012 0.79498428
## 6 6 M012 5.70178435
## 7 7 M012 0.99213032
## 8 8 M012 0.00000000
## 9 9 M012 0.00000000
## 10 10 M012 0.00000000
## 11 11 M012 0.00000000
## 12 12 M012 5.55041160
## 13 1 M040 1.63062096
## 14 2 M040 5.46680580
## 15 3 M040 8.42174477
## 16 4 M040 0.80971827
## 17 5 M040 2.25709872
## 18 6 M040 0.80155022
## 19 7 M040 0.99822423
## 20 8 M040 0.99119782
## 21 9 M040 0.85940370
## 22 10 M040 1.22745475
## 23 11 M040 2.71135543
## 24 12 M040 1.32984759
## 25 1 M142 2.11500527
## 26 2 M142 3.00237120
## 27 3 M142 6.02654344
## 28 4 M142 5.91390724
## 29 5 M142 3.74769033
## 30 6 M142 1.54461988
## 31 7 M142 0.38329071
## 32 8 M142 0.80111481
## 33 9 M142 4.19191340
## 34 10 M142 2.93606788
## 35 11 M142 4.55061067
## 36 12 M142 5.21974951
## 37 1 M180 7.41619399
## 38 2 M180 6.99485227
## 39 3 M180 15.32396465
## 40 4 M180 12.50248757
## 41 5 M180 4.64373552
## 42 6 M180 1.16751529
## 43 7 M180 0.49991397
## 44 8 M180 1.45431505
## 45 9 M180 0.09247553
## 46 10 M180 1.52155127
## 47 11 M180 8.62229436
## 48 12 M180 6.30319939
## 49 1 M185 2.01373065
## 50 2 M185 2.03639402
## 51 3 M185 2.35731872
## 52 4 M185 0.32127305
## 53 5 M185 0.68154396
## 54 6 M185 1.62356416
## 55 1 M232 6.28210122
## 56 2 M232 16.44262592
## 57 3 M232 19.37375678
## 58 4 M232 7.79000428
## 59 5 M232 3.16591729
## 60 6 M232 0.47499546
## 61 7 M232 0.17960530
## 62 8 M232 0.00000000
## 63 9 M232 0.00000000
## 64 10 M232 0.00000000
## 65 11 M232 2.13656992
## 66 12 M232 10.11493200
## 67 1 M239 4.98992964
## 68 2 M239 11.58742477
## 69 3 M239 14.07134433
## 70 4 M239 11.75393551
## 71 5 M239 2.49333736
## 72 6 M239 0.36853235
## 73 7 M239 0.43155134
## 74 8 M239 1.29315817
## 75 9 M239 0.00000000
## 76 10 M239 1.45228778
## 77 11 M239 5.60656840
## 78 12 M239 4.54771359
## 79 1 M292 3.17192998
## 80 2 M292 5.98411035
## 81 3 M292 9.85943465
## 82 4 M292 0.58467989
## 83 5 M292 0.43373844
## 84 6 M292 1.30227721
## 85 7 M292 0.43901160
## 86 8 M292 0.54514475
## 87 9 M292 0.96165566
## 88 10 M292 0.95063080
## 89 11 M292 1.26156261
## 90 12 M292 3.04142015
## 91 1 M418 1.77150686
## 92 2 M418 5.78775847
## 93 3 M418 5.65601402
## 94 4 M418 3.18287263
## 95 5 M418 1.88656256
## 96 6 M418 4.16096253
## 97 7 M418 2.14813247
## 98 8 M418 0.68235834
## 99 9 M418 3.17333720
## 100 10 M418 2.30294809
## 101 11 M418 3.37571448
## 102 12 M418 6.66569294
## 103 1 M419 3.65092123
## 104 4 M419 8.87146336
## 105 5 M419 5.46092473
## 106 6 M419 5.22260890
## 107 7 M419 0.96986863
## 108 8 M419 0.00000000
## 109 9 M419 1.69793993
## 110 12 M419 9.30228322
## 111 1 M420 1.01309703
## 112 2 M420 4.07202353
## 113 3 M420 7.39937370
## 114 4 M420 6.79281991
## 115 5 M420 1.96228964
## 116 6 M420 2.78254204
## 117 7 M420 1.46462957
## 118 8 M420 0.27951571
## 119 9 M420 3.58376929
## 120 10 M420 5.53868118
## 121 11 M420 6.00186657
## 122 12 M420 3.05807586
## 123 1 M421 0.67250135
## 124 2 M421 0.00000000
## 125 3 M421 3.59478116
## 126 4 M421 2.24382678
## 127 5 M421 3.09476492
## 128 6 M421 1.02780315
## 129 7 M421 1.50460941
## 130 8 M421 0.76269189
## 131 9 M421 2.02244873
## 132 10 M421 0.14088812
## 133 11 M421 4.25230837
## 134 12 M421 0.64571374
## 135 1 M422 3.13593120
## 136 2 M422 2.52212170
## 137 3 M422 5.27392857
## 138 4 M422 3.42402096
## 139 5 M422 0.54736907
## 140 6 M422 1.18313824
## 141 7 M422 1.26531903
## 142 8 M422 0.00000000
## 143 9 M422 0.13321834
## 144 10 M422 1.32110250
## 145 11 M422 1.33202810
## 146 12 M422 1.92290663
## 147 1 M432 3.94528710
## 148 2 M432 5.93984394
## 149 3 M432 5.11724892
## 150 4 M432 5.88178955
## 151 6 M432 3.58711135
## 152 7 M432 3.81536791
## 153 8 M432 4.70942318
## 154 9 M432 2.32602715
## 155 10 M432 4.94694649
## 156 11 M432 4.04317077
## 157 12 M432 7.05279324
## 158 1 M481 3.59338999
## 159 2 M481 6.07558650
## 160 3 M481 5.00462152
## 161 4 M481 2.33614527
## 162 5 M481 1.87801084
## 163 6 M481 1.23215539
## 164 7 M481 0.53801707
## 165 8 M481 0.60571986
## 166 9 M481 0.40316278
## 167 10 M481 0.65253847
## 168 11 M481 3.82539157
## 169 12 M481 5.66402342
## 170 1 M482 4.43967692
## 171 2 M482 5.59233590
## 172 3 M482 1.45544953
## 173 4 M482 1.27097782
## 174 5 M482 0.00000000
## 175 1 M503 6.44488628
## 176 2 M503 3.93789082
## 177 3 M503 6.54588765
## 178 4 M503 11.73198987
## 179 5 M503 21.71245963
## 180 6 M503 13.32699965
## 181 7 M503 9.64348462
## 182 8 M503 6.22563207
## 183 9 M503 8.11642580
## 184 10 M503 7.86132086
## 185 11 M503 5.20422132
## 186 12 M503 9.84506535
## 187 1 M760 8.19316526
## 188 2 M760 12.21389182
## 189 3 M760 17.37802963
## 190 4 M760 15.77803690
## 191 5 M760 3.05892665
## 192 6 M760 1.88406811
## 193 7 M760 0.38857335
## 194 8 M760 0.00000000
## 195 9 M760 0.00000000
## 196 10 M760 0.69923153
## 197 11 M760 0.59442815
## 198 12 M760 7.79682455
## 199 1 M773 7.93775516
## 200 2 M773 7.07096586
## 201 3 M773 15.81572445
## 202 4 M773 13.97183579
## 203 5 M773 2.08105639
## 204 6 M773 0.93747644
## 205 7 M773 1.22556110
## 206 8 M773 0.64051391
## 207 9 M773 0.20468647
## 208 10 M773 0.38873935
## 209 11 M773 3.08246990
## 210 12 M773 4.85706534data.table
setwd("~/Documents/R_WORKSPACE/Markdown-HTML")
library(data.table)## Warning: package 'data.table' was built under R version 3.1.3df_events_results <- read.table("df_events_results.txt", sep = "\t")
head(df_events_results)## id start_date end_date duration rain_acc month
## 22 1 2012-11-09 09:45:00 2012-11-09 09:55:00 15 0.2844 Nov
## 65 2 2012-11-09 13:00:00 2012-11-09 13:50:00 55 0.4796 Nov
## 294 3 2012-11-10 07:55:00 2012-11-10 14:05:00 375 23.1356 Nov
## 560 4 2012-11-11 05:50:00 2012-11-11 08:05:00 140 36.8400 Nov
## 586 5 2012-11-11 08:40:00 2012-11-11 09:00:00 25 4.4042 Nov
## 608 6 2012-11-11 10:15:00 2012-11-11 12:25:00 135 15.2808 Nov
## year season Rainrate ReflectivityZ SYNOP
## 22 2012 Less Wet 0.25206 96.872387 continuous light rain
## 65 2012 Less Wet 0.19694 4.850651 continuous moderate drizzle
## 294 2012 Less Wet 2.64290 651.628394 continuous moderate rain
## 560 2012 Less Wet 4.12774 534.810592 continuous moderate rain
## 586 2012 Less Wet 2.68848 329.306270 continuous moderate rain
## 608 2012 Less Wet 2.11760 189.234362 continuous moderate rain
## manualClass a b
## 22 Light Rain 894.20020 1.6138602
## 65 Moderate Drizzle 10.89331 0.7622195
## 294 Moderate Rain 106.95900 1.2848527
## 560 Moderate Rain 74.50780 1.2024312
## 586 Moderate Rain 100.95553 1.1285168
## 608 Light Rain 54.62488 1.1185234Para trabajar con esta librerÃa el objeto debe ser de la clase data.table. Dos maneras de obtener la información en este formato.
- Convertir el data frame en un data.table:
df_events_results <- as.data.table(df_events_results)- Leer el archivo de texto usando el comando
fread. Normalmente la lectura de los encabezados usando este comando es automático al leer archivos con extensión .DAT (.dat).
Estructura
El comando DT[i,j,by] tiene 3 partes: i, j y by. La i corresponde a WHERE, j a SELECT y by a GROUP BY.
df_events_results[, mean(duration), by = year]## year V1
## 1: 2012 135.2778
## 2: 2013 165.8617
## 3: 2014 166.0702df_events_results[, mean(duration), by = month]## month V1
## 1: Nov 113.0682
## 2: Dec 136.2838
## 3: Jan 226.2044
## 4: Feb 149.9643
## 5: Mar 175.5429
## 6: Apr 147.6797
## 7: May 142.5459
## 8: Jun 197.1667
## 9: Jul 186.5774
## 10: Aug 179.9367
## 11: Sep 149.3077
## 12: Oct 126.5493El campo se ha creado con un valor por defecto. Para asignar un nombre al campo nuevo donde se almacenará la media de la duración usamos el sÃmbolo :=
df_events_results[, medDur := mean(duration), by = month]## id start_date end_date duration rain_acc month
## 1: 1 2012-11-09 09:45:00 2012-11-09 09:55:00 15 0.2844 Nov
## 2: 2 2012-11-09 13:00:00 2012-11-09 13:50:00 55 0.4796 Nov
## 3: 3 2012-11-10 07:55:00 2012-11-10 14:05:00 375 23.1356 Nov
## 4: 4 2012-11-11 05:50:00 2012-11-11 08:05:00 140 36.8400 Nov
## 5: 5 2012-11-11 08:40:00 2012-11-11 09:00:00 25 4.4042 Nov
## ---
## 1588: 1730 2014-08-01 09:25:00 2014-08-01 10:00:00 40 0.4486 Aug
## 1589: 1731 2014-08-02 07:45:00 2014-08-02 08:25:00 45 0.5080 Aug
## 1590: 1732 2014-08-02 09:25:00 2014-08-02 10:10:00 50 1.0062 Aug
## 1591: 1733 2014-08-02 18:55:00 2014-08-02 19:30:00 40 0.1470 Aug
## 1592: 1734 2014-08-02 20:05:00 2014-08-02 21:25:00 85 3.7468 Aug
## year season Rainrate ReflectivityZ SYNOP
## 1: 2012 Less Wet 0.2520600 96.8723867 continuous light rain
## 2: 2012 Less Wet 0.1969400 4.8506507 continuous moderate drizzle
## 3: 2012 Less Wet 2.6429000 651.6283941 continuous moderate rain
## 4: 2012 Less Wet 4.1277400 534.8105921 continuous moderate rain
## 5: 2012 Less Wet 2.6884800 329.3062702 continuous moderate rain
## ---
## 1588: 2014 Less Wet 0.1945500 3.0687860 continuous moderate drizzle
## 1589: 2014 Less Wet 0.3350533 10.8817815 light drizzle and rain
## 1590: 2014 Less Wet 0.5526267 39.3640704 continuous moderate rain
## 1591: 2014 Less Wet 0.0716000 0.4111497 continuous light drizzle
## 1592: 2014 Less Wet 0.4664000 12.3140241 light drizzle and rain
## manualClass a b medDur
## 1: Light Rain 894.200196 1.6138602 113.0682
## 2: Moderate Drizzle 10.893310 0.7622195 113.0682
## 3: Moderate Rain 106.959000 1.2848527 113.0682
## 4: Moderate Rain 74.507797 1.2024312 113.0682
## 5: Moderate Rain 100.955529 1.1285168 113.0682
## ---
## 1588: Moderate Drizzle 15.537473 1.0350537 179.9367
## 1589: Moderate Drizzle 17.194142 0.8610099 179.9367
## 1590: Light Rain 44.383010 1.0559073 179.9367
## 1591: Light Drizzle 1.167704 0.4564574 179.9367
## 1592: Moderate Drizzle 24.371598 1.3000548 179.9367head(df_events_results)## id start_date end_date duration rain_acc month year
## 1: 1 2012-11-09 09:45:00 2012-11-09 09:55:00 15 0.2844 Nov 2012
## 2: 2 2012-11-09 13:00:00 2012-11-09 13:50:00 55 0.4796 Nov 2012
## 3: 3 2012-11-10 07:55:00 2012-11-10 14:05:00 375 23.1356 Nov 2012
## 4: 4 2012-11-11 05:50:00 2012-11-11 08:05:00 140 36.8400 Nov 2012
## 5: 5 2012-11-11 08:40:00 2012-11-11 09:00:00 25 4.4042 Nov 2012
## 6: 6 2012-11-11 10:15:00 2012-11-11 12:25:00 135 15.2808 Nov 2012
## season Rainrate ReflectivityZ SYNOP
## 1: Less Wet 0.25206 96.872387 continuous light rain
## 2: Less Wet 0.19694 4.850651 continuous moderate drizzle
## 3: Less Wet 2.64290 651.628394 continuous moderate rain
## 4: Less Wet 4.12774 534.810592 continuous moderate rain
## 5: Less Wet 2.68848 329.306270 continuous moderate rain
## 6: Less Wet 2.11760 189.234362 continuous moderate rain
## manualClass a b medDur
## 1: Light Rain 894.20020 1.6138602 113.0682
## 2: Moderate Drizzle 10.89331 0.7622195 113.0682
## 3: Moderate Rain 106.95900 1.2848527 113.0682
## 4: Moderate Rain 74.50780 1.2024312 113.0682
## 5: Moderate Rain 100.95553 1.1285168 113.0682
## 6: Light Rain 54.62488 1.1185234 113.0682Ahora vamos a modificar este cálculo para obtener la media de la duración de los eventos en cada mes, pero considerando únicamente aquellos menores a 100 minutos.
df_events_results[duration<=100, medDur := mean(duration), by = month]## id start_date end_date duration rain_acc month
## 1: 1 2012-11-09 09:45:00 2012-11-09 09:55:00 15 0.2844 Nov
## 2: 2 2012-11-09 13:00:00 2012-11-09 13:50:00 55 0.4796 Nov
## 3: 3 2012-11-10 07:55:00 2012-11-10 14:05:00 375 23.1356 Nov
## 4: 4 2012-11-11 05:50:00 2012-11-11 08:05:00 140 36.8400 Nov
## 5: 5 2012-11-11 08:40:00 2012-11-11 09:00:00 25 4.4042 Nov
## ---
## 1588: 1730 2014-08-01 09:25:00 2014-08-01 10:00:00 40 0.4486 Aug
## 1589: 1731 2014-08-02 07:45:00 2014-08-02 08:25:00 45 0.5080 Aug
## 1590: 1732 2014-08-02 09:25:00 2014-08-02 10:10:00 50 1.0062 Aug
## 1591: 1733 2014-08-02 18:55:00 2014-08-02 19:30:00 40 0.1470 Aug
## 1592: 1734 2014-08-02 20:05:00 2014-08-02 21:25:00 85 3.7468 Aug
## year season Rainrate ReflectivityZ SYNOP
## 1: 2012 Less Wet 0.2520600 96.8723867 continuous light rain
## 2: 2012 Less Wet 0.1969400 4.8506507 continuous moderate drizzle
## 3: 2012 Less Wet 2.6429000 651.6283941 continuous moderate rain
## 4: 2012 Less Wet 4.1277400 534.8105921 continuous moderate rain
## 5: 2012 Less Wet 2.6884800 329.3062702 continuous moderate rain
## ---
## 1588: 2014 Less Wet 0.1945500 3.0687860 continuous moderate drizzle
## 1589: 2014 Less Wet 0.3350533 10.8817815 light drizzle and rain
## 1590: 2014 Less Wet 0.5526267 39.3640704 continuous moderate rain
## 1591: 2014 Less Wet 0.0716000 0.4111497 continuous light drizzle
## 1592: 2014 Less Wet 0.4664000 12.3140241 light drizzle and rain
## manualClass a b medDur
## 1: Light Rain 894.200196 1.6138602 51.25000
## 2: Moderate Drizzle 10.893310 0.7622195 51.25000
## 3: Moderate Rain 106.959000 1.2848527 113.06818
## 4: Moderate Rain 74.507797 1.2024312 113.06818
## 5: Moderate Rain 100.955529 1.1285168 51.25000
## ---
## 1588: Moderate Drizzle 15.537473 1.0350537 49.07895
## 1589: Moderate Drizzle 17.194142 0.8610099 49.07895
## 1590: Light Rain 44.383010 1.0559073 49.07895
## 1591: Light Drizzle 1.167704 0.4564574 49.07895
## 1592: Moderate Drizzle 24.371598 1.3000548 49.07895La asignación de una clave en el data.table permite optimizar los tiempos de búsqueda para ordenar, filtrar y buscar elementos en el objeto data.table. Esta indexación es similar a la que se utiliza en bases de datos formales. Pruebe la diferencia de tiempos cuando se designa una clave de tabla y cuando se omite este comando. Actividad: ¿Cómo se designa una clave combinada (Ejm. fecha inicio y fecha fin)?
setkey(df_events_results, id)Merge
Cargaremos los dos archivos de precipitación y temperatura en data frames diferentes para trabajarlos.
df_prec <- read.table("file_precipitacion.txt", sep = "\t", stringsAsFactors = FALSE)
fechas <- strptime(df_prec$fecha, format="%Y-%m-%d %H:%M")
df_prec$fecha <- as.POSIXct(fechas)
df_temp = read.table("file_temperatura.txt", sep = "\t", stringsAsFactors = FALSE)
fechas <- strptime(df_temp$fecha, format="%Y-%m-%d %H:%M")
df_temp$fecha <- as.POSIXct(fechas) Otra manera de crear un vector de fechas especÃfico, esta es la serie completa.
ts = seq(as.POSIXct("2010-01-01 00:00"), as.POSIXct("2015-01-01 00:00"),by = 300) #1800 sec = 30 min
df_ts <- as.data.frame(ts)
colnames(df_ts) <- c("fecha")Ahora vamos a unir la serie completa con el conjunto de datos de los archivos.
df_prec_comp <- merge(df_ts,df_prec, by="fecha",all.x = TRUE)
summary(df_prec_comp)## fecha precipitacion
## Min. :2010-01-01 00:00:00 Min. : 0.01
## 1st Qu.:2011-04-02 12:00:00 1st Qu.:12.49
## Median :2012-07-02 00:00:00 Median :25.03
## Mean :2012-07-02 00:00:00 Mean :25.01
## 3rd Qu.:2013-10-01 12:00:00 3rd Qu.:37.52
## Max. :2015-01-01 00:00:00 Max. :50.00
## NA's :15000df_temp_comp <- merge(df_ts,df_temp, by="fecha",all.x = TRUE)
summary(df_temp_comp)## fecha temperatura
## Min. :2010-01-01 00:00:00 Min. :-3.000
## 1st Qu.:2011-04-02 12:00:00 1st Qu.: 5.281
## Median :2012-07-02 00:00:00 Median :13.508
## Mean :2012-07-02 00:00:00 Mean :13.507
## 3rd Qu.:2013-10-01 12:00:00 3rd Qu.:21.740
## Max. :2015-01-01 00:00:00 Max. :30.000
## NA's :20000Cut
Z <- stats::rnorm(10000)
summary(Z)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## -3.71500 -0.70650 -0.02686 -0.01444 0.66620 3.50800z <- cut(Z, breaks = -6:6)
head(z)## [1] (-1,0] (1,2] (1,2] (0,1] (-2,-1] (-1,0]
## 12 Levels: (-6,-5] (-5,-4] (-4,-3] (-3,-2] (-2,-1] (-1,0] (0,1] ... (5,6]z <- cut(Z, breaks = -6:6, labels = FALSE)
head(z)## [1] 6 8 8 7 5 6zz <- table(z)
zz## z
## 3 4 5 6 7 8 9 10
## 13 214 1424 3463 3296 1336 242 12dtm=as.POSIXct("2007-03-27 05:00", tz="GMT")+60*(1:240) #60 segundos
Count<-c(1:240)
DF<-data.frame(dtm,Count)
DF$min15<-cut(DF$dtm,breaks="15 min")
Min15_Avg<-aggregate(Count~min15,DF,sum)ggplot2
library(ggplot2)
ggplot(df_events_results, aes(x = log(duration), y = log(rain_acc), color=factor(year))) + geom_point() + facet_wrap(~month)
ggplot(df_events_results, aes(x = log(duration), y = log(rain_acc), color=factor(year))) + geom_point() + facet_wrap(~manualClass)
ggplot(df_events_results, aes(x = factor(year), y = log(duration), color=factor(year))) + geom_boxplot() + facet_wrap(~manualClass)