Reproducible Research - Study of Storm Data
Lopamudra Satpathy
December 12, 2017
SYNOPSIS :
Storm Data is officially published by National Oceanic and Atmospheric Administration (NOAA). Every year storms and other weather events create enough intensity to cause damages, life losses, and property and crop damages. Because of this nation faces enormous amount of financial and personal losses which is irreparable. The database currently contains data from January 1950 to August 2017.The changes in Data collection and processing procedures are changing from time to time. So unique periods of records are available as per the “Event type”. Hence as per the necessity, data reformatting and standardization of event types has been done. This project explores the U.S. National Oceanic and Atmospheric Administration’s (NOAA) storm database. This database tracks characteristics of major storms and weather events in the United States, including when and where they occur, as well as estimates of any fatalities, injuries, and property damage.
DATA :
The data for this assignment come in the form of a comma-separated-value file compressed via the bzip2 algorithm to reduce its size. You can download the file from the given link: “https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2FStormData.csv.bz2” The events in the database start in the year 1950 and end in November 2011. In the earlier years of the database there are generally fewer events recorded, most likely due to a lack of good records. More recent years should be considered more complete. #VARIABLES INCLUDED IN THE DATASET: This dataframe has 37 variables, out of which 8 variables will be used for analysis purpose.These are listed down: STATE,EVTYPE,FATALITIES,INJURIES,PROPDMG,PROPDMGEXP,CROPDMG,CROPDMGEXP. #LOADING AND PREPROCESSING OF THE DATA
dim(fullstormdata)## [1] 902297 37str(fullstormdata)## 'data.frame': 902297 obs. of 37 variables:
## $ STATE__ : num 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_DATE : Factor w/ 16335 levels "1/1/1966 0:00:00",..: 6523 6523 4242 11116 2224 2224 2260 383 3980 3980 ...
## $ BGN_TIME : Factor w/ 3608 levels "00:00:00 AM",..: 272 287 2705 1683 2584 3186 242 1683 3186 3186 ...
## $ TIME_ZONE : Factor w/ 22 levels "ADT","AKS","AST",..: 7 7 7 7 7 7 7 7 7 7 ...
## $ COUNTY : num 97 3 57 89 43 77 9 123 125 57 ...
## $ COUNTYNAME: Factor w/ 29601 levels "","5NM E OF MACKINAC BRIDGE TO PRESQUE ISLE LT MI",..: 13513 1873 4598 10592 4372 10094 1973 23873 24418 4598 ...
## $ STATE : Factor w/ 72 levels "AK","AL","AM",..: 2 2 2 2 2 2 2 2 2 2 ...
## $ EVTYPE : Factor w/ 985 levels " HIGH SURF ADVISORY",..: 834 834 834 834 834 834 834 834 834 834 ...
## $ BGN_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ BGN_AZI : Factor w/ 35 levels ""," N"," NW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ BGN_LOCATI: Factor w/ 54429 levels "","- 1 N Albion",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_DATE : Factor w/ 6663 levels "","1/1/1993 0:00:00",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_TIME : Factor w/ 3647 levels ""," 0900CST",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ COUNTY_END: num 0 0 0 0 0 0 0 0 0 0 ...
## $ COUNTYENDN: logi NA NA NA NA NA NA ...
## $ END_RANGE : num 0 0 0 0 0 0 0 0 0 0 ...
## $ END_AZI : Factor w/ 24 levels "","E","ENE","ESE",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ END_LOCATI: Factor w/ 34506 levels "","- .5 NNW",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LENGTH : num 14 2 0.1 0 0 1.5 1.5 0 3.3 2.3 ...
## $ WIDTH : num 100 150 123 100 150 177 33 33 100 100 ...
## $ F : int 3 2 2 2 2 2 2 1 3 3 ...
## $ MAG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ FATALITIES: num 0 0 0 0 0 0 0 0 1 0 ...
## $ INJURIES : num 15 0 2 2 2 6 1 0 14 0 ...
## $ PROPDMG : num 25 2.5 25 2.5 2.5 2.5 2.5 2.5 25 25 ...
## $ PROPDMGEXP: Factor w/ 19 levels "","-","?","+",..: 17 17 17 17 17 17 17 17 17 17 ...
## $ CROPDMG : num 0 0 0 0 0 0 0 0 0 0 ...
## $ CROPDMGEXP: Factor w/ 9 levels "","?","0","2",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ WFO : Factor w/ 542 levels ""," CI","$AC",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ STATEOFFIC: Factor w/ 250 levels "","ALABAMA, Central",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ ZONENAMES : Factor w/ 25112 levels ""," "| __truncated__,..: 1 1 1 1 1 1 1 1 1 1 ...
## $ LATITUDE : num 3040 3042 3340 3458 3412 ...
## $ LONGITUDE : num 8812 8755 8742 8626 8642 ...
## $ LATITUDE_E: num 3051 0 0 0 0 ...
## $ LONGITUDE_: num 8806 0 0 0 0 ...
## $ REMARKS : Factor w/ 436781 levels "","-2 at Deer Park\n",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ REFNUM : num 1 2 3 4 5 6 7 8 9 10 ...DATA PROCESSING
stormdata <- fullstormdata[, c("STATE","EVTYPE","FATALITIES","INJURIES","PROPDMG","PROPDMGEXP","CROPDMG","CROPDMGEXP")]
sum(is.na(stormdata))## [1] 0head(stormdata)## STATE EVTYPE FATALITIES INJURIES PROPDMG PROPDMGEXP CROPDMG CROPDMGEXP
## 1 AL TORNADO 0 15 25.0 K 0
## 2 AL TORNADO 0 0 2.5 K 0
## 3 AL TORNADO 0 2 25.0 K 0
## 4 AL TORNADO 0 2 2.5 K 0
## 5 AL TORNADO 0 2 2.5 K 0
## 6 AL TORNADO 0 6 2.5 K 0summary(stormdata)## STATE EVTYPE FATALITIES
## TX : 83728 HAIL :288661 Min. : 0.0000
## KS : 53440 TSTM WIND :219940 1st Qu.: 0.0000
## OK : 46802 THUNDERSTORM WIND: 82563 Median : 0.0000
## MO : 35648 TORNADO : 60652 Mean : 0.0168
## IA : 31069 FLASH FLOOD : 54277 3rd Qu.: 0.0000
## NE : 30271 FLOOD : 25326 Max. :583.0000
## (Other):621339 (Other) :170878
## INJURIES PROPDMG PROPDMGEXP CROPDMG
## Min. : 0.0000 Min. : 0.00 :465934 Min. : 0.000
## 1st Qu.: 0.0000 1st Qu.: 0.00 K :424665 1st Qu.: 0.000
## Median : 0.0000 Median : 0.00 M : 11330 Median : 0.000
## Mean : 0.1557 Mean : 12.06 0 : 216 Mean : 1.527
## 3rd Qu.: 0.0000 3rd Qu.: 0.50 B : 40 3rd Qu.: 0.000
## Max. :1700.0000 Max. :5000.00 5 : 28 Max. :990.000
## (Other): 84
## CROPDMGEXP
## :618413
## K :281832
## M : 1994
## k : 21
## 0 : 19
## B : 9
## (Other): 9RESULTS (FOR FATALITIES AND INJURIES)
fatality <- aggregate(FATALITIES ~ EVTYPE, data = stormdata, FUN = sum)
fatality1 <- fatality[order(-fatality$FATALITIES),][1:20,]
head(fatality1)## EVTYPE FATALITIES
## 834 TORNADO 5633
## 130 EXCESSIVE HEAT 1903
## 153 FLASH FLOOD 978
## 275 HEAT 937
## 464 LIGHTNING 816
## 856 TSTM WIND 504injury <- aggregate(INJURIES ~ EVTYPE, data = stormdata, FUN = sum)
injury1 <- injury[order(-injury$INJURIES),][1:20,]
head(injury1)## EVTYPE INJURIES
## 834 TORNADO 91346
## 856 TSTM WIND 6957
## 170 FLOOD 6789
## 130 EXCESSIVE HEAT 6525
## 464 LIGHTNING 5230
## 275 HEAT 2100RESULTS FOR PROPERTY DAMAGE
unique(stormdata$PROPDMG)## [1] 25.00 2.50 250.00 0.00 0.03 0.25 5.00 0.10
## [9] 50.00 500.00 48.00 20.00 2.00 35.00 3.00 0.50
## [17] 900.00 1.00 4.00 10.00 1.50 45.00 0.20 12.00
## [25] 8.00 22.00 55.00 40.00 14.00 85.00 42.00 15.00
## [33] 100.00 30.00 0.05 38.00 80.00 18.00 0.65 6.00
## [41] 0.30 24.00 32.00 7.00 28.00 70.00 95.00 65.00
## [49] 0.40 90.00 37.00 200.00 23.00 56.00 0.45 7.50
## [57] 150.00 47.00 4.10 5.50 98.00 1.60 1.30 0.01
## [65] 3.50 0.02 75.00 0.60 4.80 331.00 619.00 60.00
## [73] 0.41 0.84 11.00 1.70 4.50 400.00 17.00 450.00
## [81] 240.00 0.70 300.00 230.00 2.10 0.86 571.00 600.00
## [89] 2.80 800.00 120.00 700.00 275.00 1.10 175.00 204.00
## [97] 125.00 350.00 6.50 135.00 9.00 650.00 0.80 0.90
## [105] 1.40 3.40 110.00 1.90 0.17 3.30 7.70 26.00
## [113] 7.60 106.00 518.00 88.00 6.40 1.20 8.20 8.30
## [121] 5.40 4.40 270.00 1.80 4.30 140.00 130.00 3.80
## [129] 2.40 750.00 4.90 105.00 850.00 16.00 330.00 303.00
## [137] 34.00 176.00 0.15 0.13 6.70 8.50 0.06 2.20
## [145] 630.00 4.20 5.20 8.70 155.00 2.70 195.00 3.20
## [153] 180.00 8.40 160.00 897.00 132.00 185.00 192.00 61.00
## [161] 810.00 374.00 13.00 118.00 170.00 68.00 475.00 3.60
## [169] 145.00 260.00 210.00 415.00 220.00 115.00 3.90 375.00
## [177] 460.00 225.00 62.00 0.08 0.07 4.60 0.04 325.00
## [185] 6.60 43.00 675.00 0.35 78.00 27.00 67.00 6.20
## [193] 36.00 53.00 380.00 595.00 1.25 320.00 63.00 0.75
## [201] 12.30 37.40 520.00 31.00 470.00 44.60 44.00 13.50
## [209] 44.70 14.50 21.50 530.00 183.50 12.50 1.84 99.00
## [217] 550.00 52.00 2.90 12.80 1.07 7.20 5.47 594.00
## [225] 231.00 561.00 570.00 2.33 10.20 14.98 1.14 10.75
## [233] 14.96 41.70 5.80 19.00 733.40 680.00 620.00 58.00
## [241] 18.80 14.20 10.72 720.00 1.68 73.00 640.00 7.90
## [249] 17.80 5.70 9.30 6.55 133.00 21.00 29.00 89.00
## [257] 11.50 41.00 14.40 2.01 310.00 140.25 510.00 792.15
## [265] 14.25 3.37 187.00 3.72 5.09 1.26 3.43 352.00
## [273] 529.00 128.00 5.25 280.00 10.50 71.50 18.97 21.10
## [281] 9.90 17.90 19.50 21.30 20.10 12.71 9.60 42.31
## [289] 4.38 71.00 3.05 670.00 69.00 780.00 490.00 635.00
## [297] 1.16 440.00 324.00 2.03 6.30 0.11 12.60 2.82
## [305] 560.00 7.51 77.00 265.00 78.74 31.50 9.50 4.52
## [313] 2.41 4.12 21.70 16.50 33.00 5.30 480.00 39.50
## [321] 39.00 54.00 410.00 64.00 2.30 60.50 1.76 1.75
## [329] 30.30 190.00 3.45 215.00 940.00 17.70 3.10 114.00
## [337] 12.90 525.00 214.00 2.95 17.50 2.75 536.00 2.25
## [345] 49.00 1.04 7.80 126.00 2.55 297.08 88.15 55.60
## [353] 207.00 15.20 1.85 11.85 8.45 2.45 5.08 5.05
## [361] 28.50 9.76 693.40 203.00 253.00 37.50 79.98 100.02
## [369] 0.99 49.98 4.96 1.18 1.35 22.88 160.80 57.12
## [377] 0.51 7.30 0.78 9.80 269.00 1.05 56.50 108.00
## [385] 1.78 219.00 4.26 81.00 196.00 107.00 72.00 184.00
## [393] 425.00 165.00 55.08 6.32 3.04 96.00 44.72 102.00
## [401] 46.00 540.00 0.55 5.10 76.60 79.00 917.00 205.00
## [409] 286.00 147.00 24.70 106.72 4.70 0.66 0.12 98.26
## [417] 19.79 0.76 655.00 585.00 42.40 4.25 385.00 770.00
## [425] 149.00 78.70 5.38 20.40 388.50 151.40 159.00 91.00
## [433] 382.00 174.16 46.80 13.90 20.80 4.29 880.00 29.70
## [441] 35.90 10.30 262.00 365.00 348.00 1.06 87.80 117.00
## [449] 645.00 25.52 437.00 367.00 1.13 284.00 255.00 61.98
## [457] 161.00 435.00 88.50 51.00 312.00 1.02 29.96 229.90
## [465] 299.88 8.65 8.90 30.70 17.30 250.03 1.49 82.00
## [473] 6.25 32.50 1.65 775.00 218.00 31.95 304.00 31.52
## [481] 30.06 347.00 463.00 766.00 515.00 4.02 24.21 34.63
## [489] 1.11 1.24 86.60 49.34 373.00 502.70 610.00 405.00
## [497] 3.25 315.00 825.00 9.20 17.60 0.85 602.00 2.78
## [505] 16.60 233.00 23.70 26.87 17.03 2.66 1.86 745.00
## [513] 212.00 154.00 575.00 66.00 100.03 22.18 482.00 26.20
## [521] 13.40 305.00 535.00 505.00 24.50 15.50 18.50 54.10
## [529] 97.00 193.00 99.97 360.00 413.50 3.83 166.00 499.92
## [537] 970.00 172.00 0.21 1.51 3.70 287.18 268.00 76.00
## [545] 5.75 101.00 23.55 5.55 66.50 467.00 914.00 3.64
## [553] 3.13 875.00 3.71 642.00 8.57 3.46 19.20 57.00
## [561] 950.00 845.00 167.00 22.14 4.74 1.72 10.36 346.00
## [569] 245.00 370.00 2.05 227.00 66.90 290.00 13.30 22.20
## [577] 13.80 25.13 4.15 224.00 5.90 13.47 758.00 890.00
## [585] 186.00 690.00 7.55 94.00 1.58 27.50 1.55 2.48
## [593] 82.50 6.68 178.40 138.60 3.15 278.00 2.60 547.00
## [601] 1.77 960.00 92.00 2.77 22.50 431.72 570.45 1.15
## [609] 358.00 174.40 2.73 3.74 500.01 975.00 920.00 499.96
## [617] 410.62 109.00 2.58 590.00 1.01 328.00 910.00 56.54
## [625] 932.00 4.71 2.52 1.47 20.02 295.00 476.00 237.00
## [633] 738.00 72.70 74.00 6.51 990.00 11.10 7.72 99.39
## [641] 617.00 153.00 870.00 654.00 122.00 148.00 83.00 78.20
## [649] 22.70 662.00 665.00 48.02 47.30 625.00 75.30 130.02
## [657] 1.27 925.00 76.30 127.00 235.00 271.00 378.00 343.00
## [665] 6.07 755.00 285.00 8.43 84.00 3.31 166.50 183.00
## [673] 557.00 363.00 111.00 502.00 59.00 46.50 151.00 103.00
## [681] 14.60 4.43 74.25 1.88 500.40 510.07 116.00 508.00
## [689] 123.00 229.00 465.00 4.57 10.40 32.20 9.25 96.80
## [697] 583.00 6.05 1.53 6.82 179.50 153.55 787.00 501.00
## [705] 565.00 840.00 777.80 6.06 144.00 7.05 6.10 606.00
## [713] 279.00 16.90 1.46 936.00 382.50 16.05 4.65 1.95
## [721] 420.00 173.00 2.27 17.14 548.00 209.00 112.00 322.20
## [729] 134.00 15.75 955.00 354.00 50.02 1.48 113.00 390.00
## [737] 815.00 586.00 604.00 3.57 1.79 760.00 100.50 146.50
## [745] 25.50 149.85 33.50 1.28 785.00 213.00 1.37 266.00
## [753] 5.15 3.75 713.00 888.00 11.60 6.45 7.45 4.06
## [761] 1.38 65.50 5.60 988.00 2.16 10.05 3.68 830.00
## [769] 3.78 14.30 45.70 12.70 15.30 3.96 158.00 11.18
## [777] 11.02 142.00 11.65 485.00 308.00 5.74 554.00 762.00
## [785] 9.51 202.00 381.00 93.00 26.50 5.76 15.60 108.63
## [793] 149.58 368.00 820.00 18.54 13.25 13.36 14.26 138.00
## [801] 479.00 5.94 6.75 971.00 8.85 1.61 3.55 45.50
## [809] 580.00 6.80 162.00 335.00 201.00 0.22 968.00 459.00
## [817] 189.00 16.20 577.00 345.00 261.00 4.45 11.15 3.28
## [825] 1.59 824.00 460.56 1.54 806.77 696.40 55.22 278.60
## [833] 16.96 3.24 979.00 16.10 17.75 337.00 9.17 935.00
## [841] 179.00 257.00 41.60 645.15 39.60 246.00 1.34 26.30
## [849] 19.64 2.35 5.24 2.46 530.47 1.81 161.11 21.20
## [857] 14.70 137.90 725.00 1.29 430.00 2.65 28.55 1.69
## [865] 954.00 710.00 435.60 7.29 534.00 1.33 2.32 3.54
## [873] 32.22 18.05 0.95 493.00 206.00 141.00 2.81 3.92
## [881] 11.62 5.58 13.95 23.23 16.74 9.31 11.16 4.22
## [889] 8.37 283.00 10.88 19.77 21.88 1.45 8.87 3.02
## [897] 506.00 45.07 55.10 1.82 16.87 4.86 44.50 8.60
## [905] 13.53 12.05 51.50 340.00 104.00 19.90 12.20 1.92
## [913] 8.09 163.50 177.00 143.00 5.42 929.00 621.00 4.83
## [921] 23.50 127.20 179.40 1.74 90.43 379.90 323.00 702.00
## [929] 134.80 2.53 2.19 613.00 5.51 14.28 327.00 2.22
## [937] 758.25 661.00 242.00 3.53 1.43 97.20 136.00 11.83
## [945] 355.00 16.25 6.67 1.23 6.63 10.43 4.51 4.17
## [953] 3.11 1.03 2.36 12.40 8.10 243.00 605.00 54.90
## [961] 451.00 19.30 10.15 30.50 49.94 277.00 5.13 6.14
## [969] 478.00 23.20 0.81 10.25 359.00 2.57 706.00 43.60
## [977] 8.25 53.80 438.00 1.41 353.00 35.55 876.00 915.00
## [985] 178.00 34.89 2.69 38.50 20.50 198.50 868.50 55.90
## [993] 60.80 1.71 8.80 7.15 6.90 13.15 148.25 9.77
## [1001] 930.00 3.94 270.75 163.00 246.10 50.10 952.50 168.00
## [1009] 11.70 740.00 2.54 4.85 288.00 47.50 16.93 31.30
## [1017] 432.00 5.99 643.00 442.00 545.00 445.00 7.35 11.26
## [1025] 5.88 746.00 701.00 7.64 973.00 724.00 945.00 257.95
## [1033] 19.94 179.61 79.20 42.16 124.90 8.97 1.73 3.65
## [1041] 22.75 1.62 348.10 0.16 86.00 87.00 2.09 159.50
## [1049] 139.00 5.16 592.00 40.50 34.31 171.00 40.20 7.75
## [1057] 411.14 280.10 531.10 9.72 357.00 137.00 249.00 121.70
## [1065] 411.00 1.57 3.47 1.87 146.00 996.00 1.32 552.00
## [1073] 259.00 164.00 524.00 623.00 5.27 10.10 632.00 887.00
## [1081] 294.00 9.06 660.00 995.00 10.80 49.90 94.50 89.50
## [1089] 36.20 28.68 569.00 1.99 4.44 4.36 2.38 3.17
## [1097] 6.34 12.06 542.00 31.90 31.70 31.60 151.10 76.50
## [1105] 208.00 989.00 129.00 434.00 377.00 1.44 1.56 338.00
## [1113] 4.84 21.60 81.15 18.20 314.00 297.00 472.00 41.50
## [1121] 612.00 9.70 62.60 124.00 18.30 556.00 0.18 7.77
## [1129] 823.00 16.70 1.91 11.90 28.20 1.36 2.67 777.00
## [1137] 0.33 0.26 0.36 234.00 2.47 36.25 444.59 431.00
## [1145] 576.00 1.89 121.00 16.80 226.00 684.00 9.75 19.40
## [1153] 2.63 20.20 22.85 9.40 6.74 370.40 366.50 727.00
## [1161] 481.00 274.00 957.00 242.80 639.00 581.00 291.00 4.93
## [1169] 28.40 10.69 7.10 22.60 96.60 63.70 128.70 18.40
## [1177] 14.10 156.50 4.94 691.00 686.00 13.71 7.93 8.75
## [1185] 6.57 429.00 4.64 102.22 62.99 1.97 77.97 15.66
## [1193] 462.00 2.15 22.40 525.60 2.83 462.16 402.30 18.60
## [1201] 13.54 453.00 13.20 3000.00 157.00 4410.00 748.00 264.00
## [1209] 553.00 603.00 342.00 558.00 14.80 152.00 191.00 161.20
## [1217] 555.00 216.00 488.00 166.67 177.50 813.00 68.60 49.50
## [1225] 715.00 52.50 3500.00 901.60 167.50 13.13 12.29 252.00
## [1233] 3.85 35.50 12.17 222.00 321.00 5000.00 964.00 397.50
## [1241] 0.63 11.40 0.74 7.44 455.00 44.52 0.87 5.63
## [1249] 3.18 21.75 0.34 8.48 10.44 0.23 5.85 1.17
## [1257] 5.52 1000.00 362.00 241.00 4800.00 17.92 3.33 119.00
## [1265] 13.67 3.27 16.15 10.70 26.43 11.79 23.58 25.65
## [1273] 4.05 8.07 70.90 24.76 26.64 5.91 3.52 8.32
## [1281] 16.64 349.00 8.81 868.00 598.00 232.00 859.00 11.30
## [1289] 77.80 28.90 3.95 3.34 112.50 29.50 3200.00 258.00
## [1297] 150.20 55.50 20.60 211.00 253.38 892.00 433.70 5.64
## [1305] 24.10 272.00 257.40 297.30 934.30 20.90 423.00 3.03
## [1313] 4.59 3.29 217.00 402.00 17.20 1.63 6.61 418.00
## [1321] 19.70 1.96 309.00 3.81 182.00 296.70 25.90 6.53
## [1329] 10.60 2.26 267.00 1.19 855.00 1584.00 333.00 26.60
## [1337] 262.50 767.00 426.00 1.21 685.00 1.66 557.50 10.90
## [1345] 4.75 4.37 643.90 676.00 1.08 3.56 11.20 282.00
## [1353] 6.88 2.98 3.91 572.00 4.27 2.07 1.83 83.80
## [1361] 7.06 45.80 3.48 88.75 371.00 865.00 730.00 458.00
## [1369] 615.00 15.25 39.80 3.99 2.28 688.00 716.00 752.00
## [1377] 2.72 276.00 311.00 667.00 53.90 227.60 461.00 351.50
## [1385] 27.10 40.30 69.70 164.80 181.00 43.50unique(stormdata$PROPDMGEXP)## [1] K M B m + 0 5 6 ? 4 2 3 h 7 H - 1 8
## Levels: - ? + 0 1 2 3 4 5 6 7 8 B h H K m MTransformation of exponential form to actual values (PROPERTY DAMAGE)
stormdata$PROPDMG[stormdata$PROPDMGEXP == "K" ] <- 10^3
stormdata$PROPDMG[stormdata$PROPDMGEXP == "M" ] <- 10^6
stormdata$PROPDMG[stormdata$PROPDMGEXP == " " ] <- 1
stormdata$PROPDMG[stormdata$PROPDMGEXP == "B"] <- 10^9
stormdata$PROPDMG[stormdata$PROPDMGEXP == "m"] <- 10^6
stormdata$PROPDMG[stormdata$PROPDMGEXP == "+" ] <- 0
stormdata$PROPDMG[stormdata$PROPDMGEXP == "0" ] <- 1
stormdata$PROPDMG[stormdata$PROPDMGEXP == "5" ] <- 10^5
stormdata$PROPDMG[stormdata$PROPDMGEXP == "6" ] <- 10^6
stormdata$PROPDMG[stormdata$PROPDMGEXP == "?" ] <- 0
stormdata$PROPDMG[stormdata$PROPDMGEXP == "4" ] <- 10^4
stormdata$PROPDMG[stormdata$PROPDMGEXP == "2" ] <- 10^2
stormdata$PROPDMG[stormdata$PROPDMGEXP == "3" ] <- 10^3
stormdata$PROPDMG[stormdata$PROPDMGEXP == "h" ] <- 10^2
stormdata$PROPDMG[stormdata$PROPDMGEXP == "7" ] <- 10^7
stormdata$PROPDMG[stormdata$PROPDMGEXP == "H" ] <- 10^2
stormdata$PROPDMG[stormdata$PROPDMGEXP == "-" ] <- 0
stormdata$PROPDMG[stormdata$PROPDMGEXP == "1" ] <- 10^1
stormdata$PROPDMG[stormdata$PROPDMGEXP == "8" ] <- 10^8RESULT FOR CROP DAMAGE
unique(stormdata$CROPDMG)## [1] 0.00 10.00 500.00 1.00 4.00 50.00 5.00 15.00 0.50 0.40
## [11] 0.05 21.00 7.00 17.00 26.00 22.00 3.00 0.80 39.00 20.00
## [21] 300.00 0.90 48.00 0.20 1.50 2.50 2.00 200.00 25.00 130.00
## [31] 37.00 9.00 45.00 185.00 35.00 2.20 12.00 0.30 90.00 0.15
## [41] 100.00 66.00 142.00 1.10 0.70 330.00 750.00 6.00 43.00 60.00
## [51] 150.00 1.80 250.00 40.00 0.02 1.30 30.00 70.00 0.01 80.00
## [61] 350.00 400.00 8.00 75.00 3.50 63.00 18.00 0.28 0.10 1.70
## [71] 0.75 4.70 16.00 170.00 600.00 125.00 6.70 2.10 675.00 0.60
## [81] 262.00 332.00 220.00 56.00 0.03 353.00 177.00 36.00 373.00 430.00
## [91] 160.00 123.00 13.00 140.00 38.00 52.00 0.24 320.00 7.70 3.70
## [101] 6.80 1.20 380.00 6.50 5.60 74.90 34.10 15.30 24.00 5.10
## [111] 27.00 42.00 800.00 650.00 230.00 10.50 55.00 1.58 5.99 1.25
## [121] 3.60 5.20 3.25 5.25 3.22 204.00 2.40 127.00 7.50 46.00
## [131] 33.00 900.00 120.00 700.00 2.25 19.80 4.50 189.68 1.05 81.00
## [141] 225.00 37.50 5.40 7.55 1.40 26.84 5.70 500.10 950.00 15.70
## [151] 11.00 17.50 110.00 1.12 55.70 1.60 12.90 20.04 46.50 65.00
## [161] 4.80 1.48 43.68 613.00 14.00 19.00 3.40 850.00 450.00 240.00
## [171] 1.27 2.80 34.48 13.40 23.00 17.96 63.77 9.38 12.40 9.90
## [181] 1.21 12.50 7.80 159.00 242.00 280.00 14.10 4.20 6.90 4.97
## [191] 540.00 713.00 7.20 5.90 73.60 7.10 10.20 5.30 17.10 596.00
## [201] 74.30 470.00 655.00 460.00 180.00 2.90 260.10 145.00 5.50 3.80
## [211] 1.75 978.00 137.90 77.48 28.00 41.50 190.00 0.25 6.21 68.00
## [221] 11.70 85.00 117.00 64.00 6.10 2.60 97.00 3.75 150.20 167.90
## [231] 135.00 1.55 11.50 3.11 38.80 550.00 310.00 186.00 88.00 105.00
## [241] 1.77 149.70 301.00 4.66 22.70 3.39 15.65 131.01 8.80 29.10
## [251] 475.00 338.00 12.30 8.30 11.80 875.00 2.70 465.00 109.92 154.00
## [261] 575.00 660.00 39.85 413.60 63.40 20.30 4.91 640.00 22.60 83.00
## [271] 41.66 42.30 420.00 61.00 865.00 306.72 210.00 13.50 325.00 975.00
## [281] 150.08 160.96 1.85 169.60 80.85 8.50 29.00 605.00 399.84 44.00
## [291] 32.00 175.00 1.90 102.30 515.00 1.56 8.40 151.00 31.90 10.45
## [301] 1.96 6.03 6.85 78.00 1.65 578.85 25.01 24.27 256.00 6.63
## [311] 53.00 115.00 4.40 510.00 168.00 480.00 25.20 65.05 24.50 4.43
## [321] 275.00 8.90 13.20 9.60 7.81 10.80 1.93 312.48 261.00 270.00
## [331] 4.81 8.55 156.50 335.00 14.25 10.92 7.14 1.33 11.96 290.00
## [341] 31.00 285.00 93.20 82.50 8.70 48.40 26.50 15.20 21.60 4.60
## [351] 500.80 990.00 2.85 576.00 920.00 890.00 216.00 101.50 49.00 47.00
## [361] 21.94 671.00 8.60 32.50 423.00 66.50 26.36 180.11 48.46 10.19
## [371] 1.35 154.69 630.00 42.65 1.47 415.00 5.80 2.15 1.51 2.33
## [381] 2.65 8.49 11.68 34.50 113.90 22.32 193.90 11.94 112.50 16.60
## [391] 9.10 492.40 77.00 15.10 2.30 76.50 22.20 985.00 45.40 9.40
## [401] 4.16 26.32 5.92 2.47 73.00 155.00 344.00 620.00 390.00 316.00
## [411] 153.00 523.00 67.00 387.00 243.00 213.00 610.00 99.00 625.00 133.00
## [421] 169.00 588.00 512.00 375.00 112.00 425.00 286.00 281.00 165.00 107.00
## [431] 91.00 41.00unique(stormdata$CROPDMGEXP)## [1] M K m B ? 0 k 2
## Levels: ? 0 2 B k K m MTransformation of exponential form to actual values (CROP DAMAGE)
stormdata$CROPDMG[stormdata$CROPDMGEXP == "M"] <- 10^6
stormdata$CROPDMG[stormdata$CROPDMGEXP == "K"] <- 10^3
stormdata$CROPDMG[stormdata$CROPDMGEXP == "m"] <- 10^6
stormdata$CROPDMG[stormdata$CROPDMGEXP == "B"] <- 10^9
stormdata$CROPDMG[stormdata$CROPDMGEXP == "?"] <- 0
stormdata$CROPDMG[stormdata$CROPDMGEXP == "0"] <- 1
stormdata$CROPDMG[stormdata$CROPDMGEXP == "k"] <- 10^3
stormdata$CROPDMG[stormdata$CROPDMGEXP == "2"] <- 10^2RESULTS FOR PROPERTY AND CROP DAMAGE :
propdamage <- aggregate(PROPDMG ~ EVTYPE, data = stormdata, FUN = sum)
propdamage1 <- propdamage[order(-propdamage$PROPDMG),][1:20,]
head(propdamage1)## EVTYPE PROPDMG
## 411 HURRICANE/TYPHOON 12046012000
## 834 TORNADO 7533649062
## 170 FLOOD 6541863008
## 402 HURRICANE 3077046000
## 153 FLASH FLOOD 2553639217
## 244 HAIL 2068898599cropdamage <- aggregate(CROPDMG ~ EVTYPE, data = stormdata, FUN = sum)
cropdamage1 <- cropdamage[order(-cropdamage$CROPDMG),][1:20,]
head(cropdamage1)## EVTYPE CROPDMG
## 95 DROUGHT 4143373001
## 411 HURRICANE/TYPHOON 1021011000
## 192 FREEZE 1012002000
## 590 RIVER FLOOD 1005014000
## 427 ICE STORM 1004956000
## 275 HEAT 1003627000PLOTTING FOR FATALITIES AND INJURIES
par(mfrow = c(1,2), mar = c(12,4,3,2), mgp = c(3,1,0), las=3, cex=0.8)
barplot(fatality1$FATALITIES,names.arg = fatality1$EVTYPE,ylim = c(0,7000), col = heat.colors(20),
ylab = "No of Fatality",main = "20 Natural Events cause most fatality")
barplot(injury1$INJURIES,names.arg = injury1$EVTYPE,ylim = c(0,10000), col = rainbow(20),
ylab = "No of Injuries",main = "20 Natural Events cause most injury")
The above plot is shown that tornado creates more fatalities and injuries. #PLOTTING FOR PROPERTY AND CROP DAMAGE
par(mfrow = c(1,2), mar = c(12,4,3,2), mgp = c(3,1,0), las=3, cex=0.8)
barplot(propdamage1$PROPDMG,names.arg = propdamage1$EVTYPE,col = heat.colors(20),
ylab = "Cost of property damage in billion $",main = "20 Natural Events cause most property damage")
barplot(cropdamage1$CROPDMG,names.arg = cropdamage1$EVTYPE,col = rainbow(20),
ylab = "Cost of crop damage in billion $",main = "20 Natural Events cause most crop damage")
From the above plot, we can realize that flood plays main role to damage crops. #CONCLUSION: Storm and other hazards are the biggest disaster to the mankind.The analysis of NOAA storm database made us to understand that tornado has caused the highest number of fatalities and casualities. Huricane created lots of damages to properties and crop damage is happened due to drought across United States.