Storms
Marwin Solomon
05/07/2021
Project Details
Through this project, I have tried to analyze Storms data from 1975 to 2015.
Points that I have tried to answer through this project:
1. Year with max. no. of storms
2. Max. Wind Speed and Avg. Speed of Storms in the year with max. no. of storms
3. Max. Pressure and Avg. Pressure of Storms in the year with max. no. of storms
4. Avg. Wind Speed & Pressure of Storms per year
5. Avg. Wind Speed & Pressure of Storms as per Category
Dataset Description
This data is a subset of the NOAA Atlantic hurricane database best track data, http://www.nhc.noaa.gov/data/#hurdat. The data includes the positions and attributes of 198 tropical storms, measured every six hours during the lifetime of a storm.
Usage
storms
Format
A tibble with 10,010 observations and 13 variables:
- name
- Storm Name
- year,month,day
- Date of report
- hour
- Hour of report (in UTC)
- lat,long
- Location of storm center
- status
- Storm classification (Tropical Depression, Tropical Storm, or Hurricane)
- category
- Saffir-Simpson storm category (estimated from wind speed. -1 = Tropical Depression, 0 = Tropical Storm)
- wind
- storm’s maximum sustained wind speed (in knots)
- pressure
- Air pressure at the storm’s center (in millibars)
- ts_diameter
- Diameter of the area experiencing tropical storm strength winds (34 knots or above)
- hu_diameter
- Diameter of the area experiencing hurricane strength winds (64 knots or above)
knitr::opts_chunk$set(fig.width=12,fig.height=8)Loading Required Packages for Analysis
library(dplyr)## Warning: package 'dplyr' was built under R version 3.6.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)Basic Details
nrow(storms)## [1] 10010ncol(storms)## [1] 13Creating Variables
Total_Storms<-array()
i<-integer()
storms_year<-data.frame()
total<-numeric()Exploring Data
total<-sum(table(unique(storms$name)))
m<-matrix(total,dimnames = list("Total No. of Storms 1975-2015"))
drop(m)## Total No. of Storms 1975-2015
## 198table(storms$year)##
## 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990
## 86 52 53 54 301 161 164 105 79 236 263 70 80 259 356 354
## 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
## 131 185 129 206 660 315 154 413 210 318 370 285 422 410 498 190
## 2007 2008 2009 2010 2011 2012 2013 2014 2015
## 213 335 153 402 323 454 202 139 220View(storms[storms$year==2005,])
table(unique(storms[storms$year==2005,]$name))##
## Beta Emily Epsilon Franklin Gamma Gert Harvey Katrina
## 1 1 1 1 1 1 1 1
## Lee Maria Nate Nineteen Ophelia Philippe Rita Stan
## 1 1 1 1 1 1 1 1
## Tammy Ten Vince Wilma Zeta
## 1 1 1 1 1sum(table(unique(storms[storms$year==2005,]$name)))## [1] 21length(unique(storms[storms$year==2005,]$name))## [1] 21Finding no of storms per year
for(i in 1:41)
{
Total_Storms[i]<-length(unique(storms[storms$year==i+1974,]$name))
}
Total_Storms[1:41]## [1] 3 2 3 4 7 8 5 5 4 10 10 4 3 10 11 12 9 7 6 11 21 10 7 12 10
## [26] 16 17 12 20 13 21 9 16 14 10 20 15 17 14 7 11storms_year<-data.frame(Year=1975:2015,No._of_Storms=Total_Storms[1:41])
storms_year## Year No._of_Storms
## 1 1975 3
## 2 1976 2
## 3 1977 3
## 4 1978 4
## 5 1979 7
## 6 1980 8
## 7 1981 5
## 8 1982 5
## 9 1983 4
## 10 1984 10
## 11 1985 10
## 12 1986 4
## 13 1987 3
## 14 1988 10
## 15 1989 11
## 16 1990 12
## 17 1991 9
## 18 1992 7
## 19 1993 6
## 20 1994 11
## 21 1995 21
## 22 1996 10
## 23 1997 7
## 24 1998 12
## 25 1999 10
## 26 2000 16
## 27 2001 17
## 28 2002 12
## 29 2003 20
## 30 2004 13
## 31 2005 21
## 32 2006 9
## 33 2007 16
## 34 2008 14
## 35 2009 10
## 36 2010 20
## 37 2011 15
## 38 2012 17
## 39 2013 14
## 40 2014 7
## 41 2015 11 ggplot(data=storms_year,aes(x=Year,y=No._of_Storms,fill=No._of_Storms))+
geom_bar(stat="identity")+
geom_text(aes(label=No._of_Storms),vjust=-0.2,size=3)+
xlab("Year")+ylab("No. of Storms Per Year")
max(storms_year$No._of_Storms)## [1] 21print("Year with maximum no of storms:")## [1] "Year with maximum no of storms:"storms_year[storms_year$No._of_Storms==21,]## Year No._of_Storms
## 21 1995 21
## 31 2005 21Analyzing Storms in 2005
Max. Speed & Avg. Speed Data
Storm_2005<-storms[storms$year==2005,]
View(Storm_2005)
print("Names of Different Storms: ")## [1] "Names of Different Storms: "unique(Storm_2005$name)## [1] "Emily" "Franklin" "Gert" "Harvey" "Ten" "Katrina"
## [7] "Lee" "Maria" "Nate" "Ophelia" "Philippe" "Rita"
## [13] "Nineteen" "Stan" "Tammy" "Vince" "Wilma" "Beta"
## [19] "Gamma" "Epsilon" "Zeta"print("Storm with max. wind speed in 2005: ")## [1] "Storm with max. wind speed in 2005: "as.data.frame(Storm_2005[which.max(Storm_2005$wind),])## name year month day hour lat long status category wind pressure
## 1 Wilma 2005 10 19 12 17.3 -82.8 hurricane 5 160 882
## ts_diameter hu_diameter
## 1 304.9567 74.8007print("Storm's avg. wind speed in 2005: ")## [1] "Storm's avg. wind speed in 2005: "mean(Storm_2005$wind)## [1] 59.36747dat1<-aggregate(Storm_2005$wind,by=list(Storm_2005$name),FUN=mean)
colnames(dat1)<-c("Storm","AvgSpeed")
dat1## Storm AvgSpeed
## 1 Beta 56.94444
## 2 Emily 80.88889
## 3 Epsilon 58.55263
## 4 Franklin 44.84848
## 5 Gamma 33.09524
## 6 Gert 31.87500
## 7 Harvey 46.80000
## 8 Katrina 77.03125
## 9 Lee 28.46154
## 10 Maria 61.11111
## 11 Nate 58.50000
## 12 Nineteen 28.33333
## 13 Ophelia 56.14583
## 14 Philippe 47.80000
## 15 Rita 80.14286
## 16 Stan 39.70588
## 17 Tammy 35.62500
## 18 Ten 27.00000
## 19 Vince 43.00000
## 20 Wilma 91.93182
## 21 Zeta 43.75000 ggplot(data=dat1,aes(x=1:length(Storm),y=signif(AvgSpeed,digits=4),fill=Storm))+
geom_bar(stat="identity")+
geom_text(aes(label=signif(AvgSpeed,digits=4)), vjust=-0.2, size=3)+
xlab("Name of Storms in 2005")+ylab("Avg. Wind Speed")
print("Max. Speed of a storm in the database: ")## [1] "Max. Speed of a storm in the database: "max(storms$wind)## [1] 160print("Storms with max. Speed in Database: ")## [1] "Storms with max. Speed in Database: "as.data.frame(storms[storms$wind==160,])## name year month day hour lat long status category wind pressure
## 1 Gilbert 1988 9 14 0 19.7 -83.8 hurricane 5 160 888
## 2 Wilma 2005 10 19 12 17.3 -82.8 hurricane 5 160 882
## ts_diameter hu_diameter
## 1 NA NA
## 2 304.9567 74.8007Max. Pressure and Avg. Pressure Data
print("Storm with max. pressure in 2005: ")## [1] "Storm with max. pressure in 2005: "as.data.frame(Storm_2005[which.max(Storm_2005$pressure),])## name year month day hour lat long status category wind
## 1 Gert 2005 7 23 18 19.3 -92.9 tropical depression -1 25
## pressure ts_diameter hu_diameter
## 1 1011 0 0print("Storm's avg. pressure in 2005: ")## [1] "Storm's avg. pressure in 2005: "mean(Storm_2005$pressure)## [1] 985.8614dat2<-aggregate(Storm_2005$pressure,by=list(Storm_2005$name),FUN=mean)
colnames(dat2)<-c("Storm","AvgPressure")
dat2## Storm AvgPressure
## 1 Beta 989.7778
## 2 Emily 976.1111
## 3 Epsilon 989.3684
## 4 Franklin 1000.8788
## 5 Gamma 1005.3810
## 6 Gert 1007.6250
## 7 Harvey 997.4000
## 8 Katrina 963.8438
## 9 Lee 1008.2308
## 10 Maria 987.7500
## 11 Nate 991.5000
## 12 Nineteen 1007.8889
## 13 Ophelia 989.4583
## 14 Philippe 996.8400
## 15 Rita 961.3714
## 16 Stan 998.7059
## 17 Tammy 1003.8750
## 18 Ten 1008.6000
## 19 Vince 996.5000
## 20 Wilma 954.1818
## 21 Zeta 1001.2500 ggplot(data=dat2,aes(x=1:length(Storm),y=signif(AvgPressure,digits=4),fill=Storm))+
geom_bar(stat="identity")+
geom_text(aes(label=signif(AvgPressure,digits=4)), vjust=-0.2, size=3)+
xlab("Name of Storms in 2005")+ylab("Avg. Pressure")
print("Max. Pressure of a storm in the database: ")## [1] "Max. Pressure of a storm in the database: "max(storms$pressure)## [1] 1022print("Storms with max. Pressure in Database: ")## [1] "Storms with max. Pressure in Database: "as.data.frame(storms[storms$pressure==1022,])## name year month day hour lat long status category wind
## 1 AL072003 2003 7 26 12 32.3 -82.0 tropical depression -1 20
## 2 AL072003 2003 7 26 18 32.8 -82.6 tropical depression -1 15
## 3 AL072003 2003 7 27 0 33.0 -83.0 tropical depression -1 15
## pressure ts_diameter hu_diameter
## 1 1022 NA NA
## 2 1022 NA NA
## 3 1022 NA NAFinding Average Wind Speed & Pressure Per Year
speed_year<-aggregate(x=storms$wind,by=list(storms$year),FUN=mean)
speed_year## Group.1 x
## 1 1975 50.87209
## 2 1976 59.90385
## 3 1977 53.96226
## 4 1978 40.46296
## 5 1979 48.67110
## 6 1980 53.72671
## 7 1981 56.61585
## 8 1982 49.47619
## 9 1983 46.96203
## 10 1984 51.37712
## 11 1985 50.85551
## 12 1986 42.92857
## 13 1987 40.62500
## 14 1988 48.45560
## 15 1989 57.65449
## 16 1990 47.25989
## 17 1991 47.74809
## 18 1992 57.10811
## 19 1993 48.56589
## 20 1994 35.33981
## 21 1995 55.88636
## 22 1996 57.73016
## 23 1997 48.05195
## 24 1998 61.58596
## 25 1999 61.38095
## 26 2000 54.65409
## 27 2001 51.20270
## 28 2002 46.80702
## 29 2003 58.69668
## 30 2004 62.04878
## 31 2005 59.36747
## 32 2006 49.68421
## 33 2007 53.49765
## 34 2008 55.29851
## 35 2009 54.15033
## 36 2010 55.87065
## 37 2011 52.66254
## 38 2012 52.94053
## 39 2013 41.50990
## 40 2014 63.99281
## 41 2015 49.40909pressure_year<-aggregate(x=storms$pressure,by=list(storms$year),FUN=mean)
pressure_year## Group.1 x
## 1 1975 994.6279
## 2 1976 988.7692
## 3 1977 995.3585
## 4 1978 1005.8333
## 5 1979 994.8372
## 6 1980 995.2112
## 7 1981 993.7988
## 8 1982 996.3143
## 9 1983 1001.3671
## 10 1984 994.5508
## 11 1985 993.9658
## 12 1986 1002.7429
## 13 1987 999.6250
## 14 1988 994.1737
## 15 1989 989.3680
## 16 1990 998.4350
## 17 1991 995.8550
## 18 1992 991.4811
## 19 1993 997.0853
## 20 1994 1004.2864
## 21 1995 988.7106
## 22 1996 988.0381
## 23 1997 996.3506
## 24 1998 987.7845
## 25 1999 982.8143
## 26 2000 992.6950
## 27 2001 994.7378
## 28 2002 995.4982
## 29 2003 989.0687
## 30 2004 984.5634
## 31 2005 985.8614
## 32 2006 996.9789
## 33 2007 991.6761
## 34 2008 988.9224
## 35 2009 992.2157
## 36 2010 989.5000
## 37 2011 994.4241
## 38 2012 991.4339
## 39 2013 1002.6584
## 40 2014 983.7770
## 41 2015 995.1318avg_data<-data.frame(Year=speed_year$Group.1,Avg_Speed=speed_year$x,Avg_Pressure=pressure_year$x)
avg_data## Year Avg_Speed Avg_Pressure
## 1 1975 50.87209 994.6279
## 2 1976 59.90385 988.7692
## 3 1977 53.96226 995.3585
## 4 1978 40.46296 1005.8333
## 5 1979 48.67110 994.8372
## 6 1980 53.72671 995.2112
## 7 1981 56.61585 993.7988
## 8 1982 49.47619 996.3143
## 9 1983 46.96203 1001.3671
## 10 1984 51.37712 994.5508
## 11 1985 50.85551 993.9658
## 12 1986 42.92857 1002.7429
## 13 1987 40.62500 999.6250
## 14 1988 48.45560 994.1737
## 15 1989 57.65449 989.3680
## 16 1990 47.25989 998.4350
## 17 1991 47.74809 995.8550
## 18 1992 57.10811 991.4811
## 19 1993 48.56589 997.0853
## 20 1994 35.33981 1004.2864
## 21 1995 55.88636 988.7106
## 22 1996 57.73016 988.0381
## 23 1997 48.05195 996.3506
## 24 1998 61.58596 987.7845
## 25 1999 61.38095 982.8143
## 26 2000 54.65409 992.6950
## 27 2001 51.20270 994.7378
## 28 2002 46.80702 995.4982
## 29 2003 58.69668 989.0687
## 30 2004 62.04878 984.5634
## 31 2005 59.36747 985.8614
## 32 2006 49.68421 996.9789
## 33 2007 53.49765 991.6761
## 34 2008 55.29851 988.9224
## 35 2009 54.15033 992.2157
## 36 2010 55.87065 989.5000
## 37 2011 52.66254 994.4241
## 38 2012 52.94053 991.4339
## 39 2013 41.50990 1002.6584
## 40 2014 63.99281 983.7770
## 41 2015 49.40909 995.1318print("Year with max. avg Speed: ")## [1] "Year with max. avg Speed: "avg_data[which.max(avg_data$Avg_Speed),]## Year Avg_Speed Avg_Pressure
## 40 2014 63.99281 983.777print("Year with max. avg Pressure: ")## [1] "Year with max. avg Pressure: "avg_data[which.max(avg_data$Avg_Pressure),]## Year Avg_Speed Avg_Pressure
## 4 1978 40.46296 1005.833 ggplot(data=avg_data,aes(x=Year,y=signif(Avg_Speed,digits=4),fill=Avg_Speed))+
geom_bar(stat="identity")+
geom_text(aes(label=signif(Avg_Speed,digits=4)), vjust=-0.2, size=3)+
xlab("Year")+ylab("Avg. Speed")
ggplot(data=avg_data,aes(x=Year,y=signif(Avg_Pressure,digits=4),fill=Avg_Pressure))+
geom_bar(stat="identity")+
geom_text(aes(label=signif(Avg_Pressure,digits=4)), vjust=-0.2, size=2.5)+
xlab("Year")+ylab("Avg. Pressure")
Finding Average Wind Speed & Pressure as per category
speed_category<-aggregate(x=storms$wind,by=list(storms$category),FUN=mean)
speed_category## Group.1 x
## 1 -1 27.26916
## 2 0 45.80037
## 3 1 70.91098
## 4 2 89.43471
## 5 3 104.64187
## 6 4 121.55172
## 7 5 145.07353pressure_category<-aggregate(x=storms$pressure,by=list(storms$category),FUN=mean)
pressure_category## Group.1 x
## 1 -1 1007.6259
## 2 0 999.3291
## 3 1 981.5181
## 4 2 967.4729
## 5 3 953.5289
## 6 4 939.5345
## 7 5 916.4265avg_data_cat<-data.frame(Category=speed_category$Group.1,Avg_Speed=speed_category$x,Avg_Pressure=pressure_category$x)
avg_data_cat## Category Avg_Speed Avg_Pressure
## 1 -1 27.26916 1007.6259
## 2 0 45.80037 999.3291
## 3 1 70.91098 981.5181
## 4 2 89.43471 967.4729
## 5 3 104.64187 953.5289
## 6 4 121.55172 939.5345
## 7 5 145.07353 916.4265 ggplot(data=avg_data_cat,aes(x=Category,y=signif(Avg_Speed,digits=4),fill=Category))+
geom_bar(stat="identity")+
geom_text(aes(label=signif(Avg_Speed,digits=4)), vjust=-0.2, size=3)+
xlab("Category")+ylab("Avg. Speed")
ggplot(data=avg_data_cat,aes(x=Category,y=signif(Avg_Pressure,digits=4),fill=Category))+
geom_bar(stat="identity")+
geom_text(aes(label=signif(Avg_Pressure,digits=4)), vjust=-0.2, size=2.5)+
xlab("Category")+ylab("Avg. Pressure")
Sources
The data used in this project is available as a part of ‘tidyr’ library.