#Here’s a full description of all the columns: #• name: Storm name #• year, month, and day: Date of report #• hour: Hour of report (in UTC) #• lat: Latitude #• long: Longitude #• 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)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionglimpse(storms)## Rows: 19,537
## Columns: 13
## $ name <chr> "Amy", "Amy", "Amy", "Amy", "Amy", "Amy",…
## $ year <dbl> 1975, 1975, 1975, 1975, 1975, 1975, 1975,…
## $ month <dbl> 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,…
## $ day <int> 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 2…
## $ hour <dbl> 0, 6, 12, 18, 0, 6, 12, 18, 0, 6, 12, 18,…
## $ lat <dbl> 27.5, 28.5, 29.5, 30.5, 31.5, 32.4, 33.3,…
## $ long <dbl> -79.0, -79.0, -79.0, -79.0, -78.8, -78.7,…
## $ status <fct> tropical depression, tropical depression,…
## $ category <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ wind <int> 25, 25, 25, 25, 25, 25, 25, 30, 35, 40, 4…
## $ pressure <int> 1013, 1013, 1013, 1013, 1012, 1012, 1011,…
## $ tropicalstorm_force_diameter <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ hurricane_force_diameter <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…#Table containing the name and year of storms recorded during the 1980s (i.e. from 1980 to 1989)
storm_names_1980s <- storms %>%
filter(year >= 1980 & year < 1990) %>%
select(name, year)
storm_names_1980s## # A tibble: 2,674 × 2
## name year
## <chr> <dbl>
## 1 Allen 1980
## 2 Allen 1980
## 3 Allen 1980
## 4 Allen 1980
## 5 Allen 1980
## 6 Allen 1980
## 7 Allen 1980
## 8 Allen 1980
## 9 Allen 1980
## 10 Allen 1980
## # ℹ 2,664 more rows#2005 is the year with the most storms registered in the seam calendar.
storms_per_year <- storms %>%
group_by(year) %>%
summarise(storm_count = n())
storms_per_year## # A tibble: 48 × 2
## year storm_count
## <dbl> <int>
## 1 1975 238
## 2 1976 126
## 3 1977 92
## 4 1978 152
## 5 1979 324
## 6 1980 335
## 7 1981 311
## 8 1982 111
## 9 1983 88
## 10 1984 342
## # ℹ 38 more rows#Table containing columns: 1) name of storm, 2) year of storm, and 3) count for number of records (of the corresponding storm)
storm_records_per_year <- storms %>%
group_by(name, year) %>%
summarise(record_count = n())## `summarise()` has grouped output by 'name'. You can override using the
## `.groups` argument.storm_records_per_year## # A tibble: 655 × 3
## # Groups: name [260]
## name year record_count
## <chr> <dbl> <int>
## 1 AL011993 1993 11
## 2 AL012000 2000 4
## 3 AL021992 1992 5
## 4 AL021994 1994 6
## 5 AL021999 1999 4
## 6 AL022000 2000 12
## 7 AL022001 2001 5
## 8 AL022003 2003 4
## 9 AL022006 2006 13
## 10 AL031987 1987 32
## # ℹ 645 more rows#Unique type of storms status
storm_status <- storms %>%
distinct(status)
storm_status## # A tibble: 9 × 1
## status
## <fct>
## 1 tropical depression
## 2 tropical storm
## 3 extratropical
## 4 hurricane
## 5 subtropical storm
## 6 subtropical depression
## 7 disturbance
## 8 other low
## 9 tropical wave#There are 5 different type of categories for storms, 1 being the lowest and 5 the highest
storm_categories <- storms %>%
distinct(category)
storm_categories## # A tibble: 6 × 1
## category
## <dbl>
## 1 NA
## 2 1
## 3 3
## 4 2
## 5 4
## 6 5#table containing the name and year of those storms of category 5.
storms_categ5 <- storms %>%
filter(category == 5) %>%
select(name, year)
storms_categ5## # A tibble: 116 × 2
## name year
## <chr> <dbl>
## 1 Anita 1977
## 2 Anita 1977
## 3 David 1979
## 4 David 1979
## 5 David 1979
## 6 David 1979
## 7 David 1979
## 8 David 1979
## 9 David 1979
## 10 Allen 1980
## # ℹ 106 more rows#Tropical wave is the storm with the highest avg_pressure registered
storms_statistics <- storms %>%
group_by(category, status) %>%
summarise(avg_pressure = mean(pressure), avg_wind = mean(wind))## `summarise()` has grouped output by 'category'. You can override using the
## `.groups` argument.storms_statistics## # A tibble: 13 × 4
## # Groups: category [6]
## category status avg_pressure avg_wind
## <dbl> <fct> <dbl> <dbl>
## 1 1 hurricane 981. 71.0
## 2 2 hurricane 967. 89.5
## 3 3 hurricane 955. 104.
## 4 4 hurricane 940. 122.
## 5 5 hurricane 918. 146.
## 6 NA disturbance 1009. 30.0
## 7 NA extratropical 993. 41.5
## 8 NA other low 1009. 25.6
## 9 NA subtropical depression 1008. 26.7
## 10 NA subtropical storm 998. 44.4
## 11 NA tropical depression 1008. 27.5
## 12 NA tropical storm 999. 45.7
## 13 NA tropical wave 1009. 28.6#table containing threecolumns: 1) year of storm, 2) name of storm, and 3) max_wind maximum wind speed record (for that storm).
max_wind_per_storm <- storms %>%
group_by(name, year) %>%
summarise(max_wind = max(wind))## `summarise()` has grouped output by 'name'. You can override using the
## `.groups` argument.max_wind_per_storm## # A tibble: 655 × 3
## # Groups: name [260]
## name year max_wind
## <chr> <dbl> <int>
## 1 AL011993 1993 35
## 2 AL012000 2000 25
## 3 AL021992 1992 30
## 4 AL021994 1994 30
## 5 AL021999 1999 30
## 6 AL022000 2000 30
## 7 AL022001 2001 25
## 8 AL022003 2003 30
## 9 AL022006 2006 45
## 10 AL031987 1987 40
## # ℹ 645 more rows#Dorian, Gilbert and Wilma is the hurricane with highest speed of wind with 160 knotts.
max_wind_per_year <- storms %>%
group_by(year) %>%
arrange(desc(wind)) %>%
slice_head(n = 1) %>%
select(year, name, wind)
max_wind_per_year## # A tibble: 48 × 3
## # Groups: year [48]
## year name wind
## <dbl> <chr> <int>
## 1 1975 Gladys 120
## 2 1976 Belle 105
## 3 1977 Anita 150
## 4 1978 Ella 120
## 5 1979 David 150
## 6 1980 Allen 165
## 7 1981 Harvey 115
## 8 1982 Debby 115
## 9 1983 Alicia 100
## 10 1984 Diana 115
## # ℹ 38 more rows