Apply the following dplyr verbs to your data
Filter rows
filter(haunted_places, state == "California")
## # A tibble: 1,070 × 10
## city country description location state state_abbrev longitude latitude
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 In Califo… United… "Y)" (San M Cali… CA -122. 37.8
## 2 San Marcos United… "NO TRESSP… Harmony… Cali… CA NA NA
## 3 San Marcos United… "The ghost… Questha… Cali… CA -117. 33.1
## 4 San Miguel United… "It has be… San Mig… Cali… CA -121. 35.7
## 5 San Miguel United… "Adobe Com… San Mig… Cali… CA -121. 35.7
## 6 San Rafael United… "There is … Dominic… Cali… CA -123. 38.0
## 7 Sanger United… "In the mi… Acacia … Cali… CA -120. 36.7
## 8 Sanger United… "Snake Roa… Centerv… Cali… CA -120. 36.7
## 9 Sanger United… "This is a… Hobbs G… Cali… CA -120. 36.7
## 10 Santa Ana United… "Popular t… Euclid … Cali… CA -118. 33.8
## # ℹ 1,060 more rows
## # ℹ 2 more variables: city_longitude <dbl>, city_latitude <dbl>
Arrange rows
arrange(haunted_places, longitude, latitude)
## # A tibble: 10,992 × 10
## city country description location state state_abbrev longitude latitude
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Nightmute United… "There is … Nightmu… Alas… AK -165. 60.5
## 2 Kotzebue United… "During th… NANA Mu… Alas… AK -163. 66.9
## 3 Kauai United… "Once upon… Lihue Hawa… HI -159. 22.0
## 4 Makaha United… "Lights fl… Makaha … Hawa… HI -158. 21.5
## 5 Makaha United… "at night,… Makaha … Hawa… HI -158. 21.5
## 6 O'ahu United… "Reports t… Mokulei… Hawa… HI -158. 21.6
## 7 Honolulu United… "It was re… Barber'… Hawa… HI -158. 21.3
## 8 Honolulu United… "\"Firebal… Morgan'… Hawa… HI -158. 21.5
## 9 North Sho… United… "There is … The Dro… Hawa… HI -158. 21.6
## 10 Schofield… United… "Hearing m… J Quad … Hawa… HI -158. 21.5
## # ℹ 10,982 more rows
## # ℹ 2 more variables: city_longitude <dbl>, city_latitude <dbl>
Select columns
select(haunted_places, state, latitude, longitude)
## # A tibble: 10,992 × 3
## state latitude longitude
## <chr> <dbl> <dbl>
## 1 Michigan 43.0 -85.5
## 2 Michigan 42.0 -84.4
## 3 Michigan 41.9 -84.0
## 4 Michigan 41.9 -84.0
## 5 Michigan 42.2 -84.7
## 6 Michigan 42.2 -84.8
## 7 Michigan NA NA
## 8 Michigan 42.7 -82.6
## 9 Michigan 42.5 -85.8
## 10 Michigan 42.5 -85.9
## # ℹ 10,982 more rows
Add columns
mutate(haunted_places,
coordinates = longitude + latitude)
## # A tibble: 10,992 × 11
## city country description location state state_abbrev longitude latitude
## <chr> <chr> <chr> <chr> <chr> <chr> <dbl> <dbl>
## 1 Ada United… "Ada witch… Ada Cem… Mich… MI -85.5 43.0
## 2 Addison United… "A little … North A… Mich… MI -84.4 42.0
## 3 Adrian United… "If you ta… Ghost T… Mich… MI -84.0 41.9
## 4 Adrian United… "In the 19… Siena H… Mich… MI -84.0 41.9
## 5 Albion United… "Kappa Del… Albion … Mich… MI -84.7 42.2
## 6 Albion United… "A mysteri… Riversi… Mich… MI -84.8 42.2
## 7 Algoma To… United… "On a wind… Hell's … Mich… MI NA NA
## 8 Algonac United… "Morrow Ro… Morrow … Mich… MI -82.6 42.7
## 9 Allegan United… "People re… Elks Lo… Mich… MI -85.8 42.5
## 10 Allegan United… "Various g… The Gri… Mich… MI -85.9 42.5
## # ℹ 10,982 more rows
## # ℹ 3 more variables: city_longitude <dbl>, city_latitude <dbl>,
## # coordinates <dbl>
Summarize by groups
haunted_places %>%
# Group by State
group_by(state) %>%
summarise(count = n())
## # A tibble: 51 × 2
## state count
## <chr> <int>
## 1 Alabama 224
## 2 Alaska 32
## 3 Arizona 156
## 4 Arkansas 119
## 5 California 1070
## 6 Colorado 166
## 7 Connecticut 185
## 8 Delaware 37
## 9 Florida 328
## 10 Georgia 289
## # ℹ 41 more rows
haunted_places %>%
# Group by city
group_by(city)%>%
summarise(count = n())
## # A tibble: 4,386 × 2
## city count
## <chr> <int>
## 1 <>Manteca 1
## 2 ARNOLD 1
## 3 Abbeville 1
## 4 Abercrombie 1
## 5 Aberdeen 6
## 6 Abilene 4
## 7 Abingdon 2
## 8 Absecon 1
## 9 Academia 3
## 10 Ackley 1
## # ℹ 4,376 more rows
haunted_places %>%
# Group by state and city
group_by(city, state)%>%
summarise(count = n())
## # A tibble: 5,457 × 3
## # Groups: city [4,386]
## city state count
## <chr> <chr> <int>
## 1 <>Manteca California 1
## 2 ARNOLD Missouri 1
## 3 Abbeville South Carolina 1
## 4 Abercrombie North Dakota 1
## 5 Aberdeen Maryland 1
## 6 Aberdeen North Carolina 1
## 7 Aberdeen Ohio 1
## 8 Aberdeen South Dakota 2
## 9 Aberdeen Washington 1
## 10 Abilene Texas 4
## # ℹ 5,447 more rows