library(sf)
## Linking to GEOS 3.8.1, GDAL 3.1.1, PROJ 6.3.1
library(sp)
library(spData)
library(raster)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:raster':
##
## intersect, select, union
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(stringr)
library(tidyr)
##
## Attaching package: 'tidyr'
## The following object is masked from 'package:raster':
##
## extract
library(ggplot2)
#Question 1
us_states_name <- us_states %>% select(NAME)
class(us_states_name)
## [1] "sf" "data.frame"
#dataframe with only state name and geometry
#class is 'sf' and 'data.frame'
us_states_name2 <- st_drop_geometry(us_states_name)
class(us_states_name2)
## [1] "data.frame"
#now there is no geometry, so its only a "data.frame"
#Question 2
us_pop <- us_states %>% select(total_pop_10, total_pop_15)
us_pop
## Simple feature collection with 49 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## total_pop_10 total_pop_15 geometry
## 1 4712651 4830620 MULTIPOLYGON (((-88.20006 3...
## 2 6246816 6641928 MULTIPOLYGON (((-114.7196 3...
## 3 4887061 5278906 MULTIPOLYGON (((-109.0501 4...
## 4 3545837 3593222 MULTIPOLYGON (((-73.48731 4...
## 5 18511620 19645772 MULTIPOLYGON (((-81.81169 2...
## 6 9468815 10006693 MULTIPOLYGON (((-85.60516 3...
## 7 1526797 1616547 MULTIPOLYGON (((-116.916 45...
## 8 6417398 6568645 MULTIPOLYGON (((-87.52404 4...
## 9 2809329 2892987 MULTIPOLYGON (((-102.0517 4...
## 10 4429940 4625253 MULTIPOLYGON (((-92.01783 2...
#First way, with select
us_states %>% select(contains("total_pop"))
## Simple feature collection with 49 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## total_pop_10 total_pop_15 geometry
## 1 4712651 4830620 MULTIPOLYGON (((-88.20006 3...
## 2 6246816 6641928 MULTIPOLYGON (((-114.7196 3...
## 3 4887061 5278906 MULTIPOLYGON (((-109.0501 4...
## 4 3545837 3593222 MULTIPOLYGON (((-73.48731 4...
## 5 18511620 19645772 MULTIPOLYGON (((-81.81169 2...
## 6 9468815 10006693 MULTIPOLYGON (((-85.60516 3...
## 7 1526797 1616547 MULTIPOLYGON (((-116.916 45...
## 8 6417398 6568645 MULTIPOLYGON (((-87.52404 4...
## 9 2809329 2892987 MULTIPOLYGON (((-102.0517 4...
## 10 4429940 4625253 MULTIPOLYGON (((-92.01783 2...
#Second way with contains
us_states %>% select(starts_with("total_pop"))
## Simple feature collection with 49 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## total_pop_10 total_pop_15 geometry
## 1 4712651 4830620 MULTIPOLYGON (((-88.20006 3...
## 2 6246816 6641928 MULTIPOLYGON (((-114.7196 3...
## 3 4887061 5278906 MULTIPOLYGON (((-109.0501 4...
## 4 3545837 3593222 MULTIPOLYGON (((-73.48731 4...
## 5 18511620 19645772 MULTIPOLYGON (((-81.81169 2...
## 6 9468815 10006693 MULTIPOLYGON (((-85.60516 3...
## 7 1526797 1616547 MULTIPOLYGON (((-116.916 45...
## 8 6417398 6568645 MULTIPOLYGON (((-87.52404 4...
## 9 2809329 2892987 MULTIPOLYGON (((-102.0517 4...
## 10 4429940 4625253 MULTIPOLYGON (((-92.01783 2...
#Third way, with starts with
#Question 3
midwest <- us_states %>% filter(REGION == "Midwest")
west <- us_states %>% filter(REGION == "West", AREA > units::set_units(250000, km^2),
total_pop_15 > 5000000)
south <- us_states %>% filter(REGION == "South", AREA > units::set_units(150000, km^2),
total_pop_15 > 7000000)
#Question 4
us_states %>% summarize(total_pop = sum(total_pop_15),
min_pop = min(total_pop_15),
max_pop = max(total_pop_15))
## Simple feature collection with 1 feature and 3 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## total_pop min_pop max_pop geometry
## 1 314375347 579679 38421464 MULTIPOLYGON (((-81.81169 2...
#The sum of total population in 2015 is 3,143,575,347
#The minimum is 579,679 and maximum is 38,421,464
#Question 5
us_states %>% group_by(REGION) %>%
summarise(NumberofStates = n())
## `summarise()` ungrouping output (override with `.groups` argument)
## Simple feature collection with 4 features and 2 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## # A tibble: 4 x 3
## REGION NumberofStates geometry
## <fct> <int> <MULTIPOLYGON [°]>
## 1 Norteast 9 (((-75.55945 39.62981, -75.50974 39.68611, -75.41506 …
## 2 Midwest 12 (((-87.80048 42.49192, -87.83477 42.30152, -87.80007 …
## 3 South 17 (((-81.81169 24.56874, -81.74565 24.65988, -81.44351 …
## 4 West 11 (((-118.6055 33.031, -118.37 32.83927, -118.4963 32.8…
#There are 9 states in the northeast, 12 in the midwest, 17 in the south, and 11 in the west
#Question 6
us_states %>% group_by(REGION) %>%
summarise(total = sum(total_pop_15),
min = min(total_pop_15),
max = max(total_pop_15))
## `summarise()` ungrouping output (override with `.groups` argument)
## Simple feature collection with 4 features and 4 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## # A tibble: 4 x 5
## REGION total min max geometry
## <fct> <dbl> <dbl> <dbl> <MULTIPOLYGON [°]>
## 1 Nortea… 5.60e7 626604 1.97e7 (((-75.55945 39.62981, -75.50974 39.68611, -75…
## 2 Midwest 6.75e7 721640 1.29e7 (((-87.80048 42.49192, -87.83477 42.30152, -87…
## 3 South 1.19e8 647484 2.65e7 (((-81.81169 24.56874, -81.74565 24.65988, -81…
## 4 West 7.23e7 579679 3.84e7 (((-118.6055 33.031, -118.37 32.83927, -118.49…
#Question 7
us_states_stats <- us_states %>%
left_join(us_states_df, by = c("NAME" = "state"))
class(us_states_stats)
## [1] "sf" "data.frame"
#used left_join because it allowed to keep the same number of rows as the object on the left(us_states)
#the key variable in both is the state name, but they are different
#the new class is 'sf' data.frame'
#Question 8
setdiff(us_states_df$state, us_states$NAME)
## [1] "Alaska" "Hawaii"
#shows the states not included in us_states_df
us_states_stats5 <- us_states_df %>%
anti_join(us_states, by = c("state" = "NAME"))
us_states_stats5
## # A tibble: 2 x 5
## state median_income_10 median_income_15 poverty_level_10 poverty_level_15
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 Alaska 29509 31455 64245 72957
## 2 Hawaii 29945 31051 124627 153944
#anti_join only returns rows that don't match, so Hawaii and Alaska
#Question 9
us_states9 <- us_states %>%
mutate(popDen15 = total_pop_15/AREA)
us_states9
## Simple feature collection with 49 features and 7 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## GEOID NAME REGION AREA total_pop_10 total_pop_15
## 1 01 Alabama South 133709.27 [km^2] 4712651 4830620
## 2 04 Arizona West 295281.25 [km^2] 6246816 6641928
## 3 08 Colorado West 269573.06 [km^2] 4887061 5278906
## 4 09 Connecticut Norteast 12976.59 [km^2] 3545837 3593222
## 5 12 Florida South 151052.01 [km^2] 18511620 19645772
## 6 13 Georgia South 152725.21 [km^2] 9468815 10006693
## 7 16 Idaho West 216512.66 [km^2] 1526797 1616547
## 8 18 Indiana Midwest 93648.40 [km^2] 6417398 6568645
## 9 20 Kansas Midwest 213037.08 [km^2] 2809329 2892987
## 10 22 Louisiana South 122345.76 [km^2] 4429940 4625253
## geometry popDen15
## 1 MULTIPOLYGON (((-88.20006 3... 36.127786 [1/km^2]
## 2 MULTIPOLYGON (((-114.7196 3... 22.493565 [1/km^2]
## 3 MULTIPOLYGON (((-109.0501 4... 19.582469 [1/km^2]
## 4 MULTIPOLYGON (((-73.48731 4... 276.900364 [1/km^2]
## 5 MULTIPOLYGON (((-81.81169 2... 130.059657 [1/km^2]
## 6 MULTIPOLYGON (((-85.60516 3... 65.520897 [1/km^2]
## 7 MULTIPOLYGON (((-116.916 45... 7.466293 [1/km^2]
## 8 MULTIPOLYGON (((-87.52404 4... 70.141565 [1/km^2]
## 9 MULTIPOLYGON (((-102.0517 4... 13.579734 [1/km^2]
## 10 MULTIPOLYGON (((-92.01783 2... 37.804767 [1/km^2]
us_states99 <- us_states9 %>%
mutate(popDen10 = total_pop_10/AREA)
us_states99
## Simple feature collection with 49 features and 8 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## GEOID NAME REGION AREA total_pop_10 total_pop_15
## 1 01 Alabama South 133709.27 [km^2] 4712651 4830620
## 2 04 Arizona West 295281.25 [km^2] 6246816 6641928
## 3 08 Colorado West 269573.06 [km^2] 4887061 5278906
## 4 09 Connecticut Norteast 12976.59 [km^2] 3545837 3593222
## 5 12 Florida South 151052.01 [km^2] 18511620 19645772
## 6 13 Georgia South 152725.21 [km^2] 9468815 10006693
## 7 16 Idaho West 216512.66 [km^2] 1526797 1616547
## 8 18 Indiana Midwest 93648.40 [km^2] 6417398 6568645
## 9 20 Kansas Midwest 213037.08 [km^2] 2809329 2892987
## 10 22 Louisiana South 122345.76 [km^2] 4429940 4625253
## geometry popDen15 popDen10
## 1 MULTIPOLYGON (((-88.20006 3... 36.127786 [1/km^2] 35.245506 [1/km^2]
## 2 MULTIPOLYGON (((-114.7196 3... 22.493565 [1/km^2] 21.155478 [1/km^2]
## 3 MULTIPOLYGON (((-109.0501 4... 19.582469 [1/km^2] 18.128893 [1/km^2]
## 4 MULTIPOLYGON (((-73.48731 4... 276.900364 [1/km^2] 273.248788 [1/km^2]
## 5 MULTIPOLYGON (((-81.81169 2... 130.059657 [1/km^2] 122.551303 [1/km^2]
## 6 MULTIPOLYGON (((-85.60516 3... 65.520897 [1/km^2] 61.999029 [1/km^2]
## 7 MULTIPOLYGON (((-116.916 45... 7.466293 [1/km^2] 7.051768 [1/km^2]
## 8 MULTIPOLYGON (((-87.52404 4... 70.141565 [1/km^2] 68.526513 [1/km^2]
## 9 MULTIPOLYGON (((-102.0517 4... 13.579734 [1/km^2] 13.187042 [1/km^2]
## 10 MULTIPOLYGON (((-92.01783 2... 37.804767 [1/km^2] 36.208365 [1/km^2]
#now has a popDen10 and popDen15 column
#Question 10
us_states999 <- us_states99 %>%
mutate(popDiff = popDen15 - popDen10)
us_states999
## Simple feature collection with 49 features and 9 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## GEOID NAME REGION AREA total_pop_10 total_pop_15
## 1 01 Alabama South 133709.27 [km^2] 4712651 4830620
## 2 04 Arizona West 295281.25 [km^2] 6246816 6641928
## 3 08 Colorado West 269573.06 [km^2] 4887061 5278906
## 4 09 Connecticut Norteast 12976.59 [km^2] 3545837 3593222
## 5 12 Florida South 151052.01 [km^2] 18511620 19645772
## 6 13 Georgia South 152725.21 [km^2] 9468815 10006693
## 7 16 Idaho West 216512.66 [km^2] 1526797 1616547
## 8 18 Indiana Midwest 93648.40 [km^2] 6417398 6568645
## 9 20 Kansas Midwest 213037.08 [km^2] 2809329 2892987
## 10 22 Louisiana South 122345.76 [km^2] 4429940 4625253
## geometry popDen15 popDen10
## 1 MULTIPOLYGON (((-88.20006 3... 36.127786 [1/km^2] 35.245506 [1/km^2]
## 2 MULTIPOLYGON (((-114.7196 3... 22.493565 [1/km^2] 21.155478 [1/km^2]
## 3 MULTIPOLYGON (((-109.0501 4... 19.582469 [1/km^2] 18.128893 [1/km^2]
## 4 MULTIPOLYGON (((-73.48731 4... 276.900364 [1/km^2] 273.248788 [1/km^2]
## 5 MULTIPOLYGON (((-81.81169 2... 130.059657 [1/km^2] 122.551303 [1/km^2]
## 6 MULTIPOLYGON (((-85.60516 3... 65.520897 [1/km^2] 61.999029 [1/km^2]
## 7 MULTIPOLYGON (((-116.916 45... 7.466293 [1/km^2] 7.051768 [1/km^2]
## 8 MULTIPOLYGON (((-87.52404 4... 70.141565 [1/km^2] 68.526513 [1/km^2]
## 9 MULTIPOLYGON (((-102.0517 4... 13.579734 [1/km^2] 13.187042 [1/km^2]
## 10 MULTIPOLYGON (((-92.01783 2... 37.804767 [1/km^2] 36.208365 [1/km^2]
## popDiff
## 1 0.8822799 [1/km^2]
## 2 1.3380870 [1/km^2]
## 3 1.4535763 [1/km^2]
## 4 3.6515761 [1/km^2]
## 5 7.5083545 [1/km^2]
## 6 3.5218677 [1/km^2]
## 7 0.4145254 [1/km^2]
## 8 1.6150517 [1/km^2]
## 9 0.3926922 [1/km^2]
## 10 1.5964018 [1/km^2]
plot(us_states999["popDiff"], nbreaks = 5, breaks = "jenks")
#Question 11
tolower(us_states$NAME)
## [1] "alabama" "arizona" "colorado"
## [4] "connecticut" "florida" "georgia"
## [7] "idaho" "indiana" "kansas"
## [10] "louisiana" "massachusetts" "minnesota"
## [13] "missouri" "montana" "nevada"
## [16] "new jersey" "new york" "north dakota"
## [19] "oklahoma" "pennsylvania" "south carolina"
## [22] "south dakota" "texas" "vermont"
## [25] "west virginia" "arkansas" "california"
## [28] "delaware" "district of columbia" "illinois"
## [31] "iowa" "kentucky" "maine"
## [34] "maryland" "michigan" "mississippi"
## [37] "nebraska" "new hampshire" "new mexico"
## [40] "north carolina" "ohio" "oregon"
## [43] "rhode island" "tennessee" "utah"
## [46] "virginia" "washington" "wisconsin"
## [49] "wyoming"
#lowers all states names
us_states %>%
setNames(tolower(colnames(us_states)))
## Simple feature collection with 49 features and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## geoid name region area total_pop_10 total_pop_15
## 1 01 Alabama South 133709.27 [km^2] 4712651 4830620
## 2 04 Arizona West 295281.25 [km^2] 6246816 6641928
## 3 08 Colorado West 269573.06 [km^2] 4887061 5278906
## 4 09 Connecticut Norteast 12976.59 [km^2] 3545837 3593222
## 5 12 Florida South 151052.01 [km^2] 18511620 19645772
## 6 13 Georgia South 152725.21 [km^2] 9468815 10006693
## 7 16 Idaho West 216512.66 [km^2] 1526797 1616547
## 8 18 Indiana Midwest 93648.40 [km^2] 6417398 6568645
## 9 20 Kansas Midwest 213037.08 [km^2] 2809329 2892987
## 10 22 Louisiana South 122345.76 [km^2] 4429940 4625253
## geometry
## 1 MULTIPOLYGON (((-88.20006 3...
## 2 MULTIPOLYGON (((-114.7196 3...
## 3 MULTIPOLYGON (((-109.0501 4...
## 4 MULTIPOLYGON (((-73.48731 4...
## 5 MULTIPOLYGON (((-81.81169 2...
## 6 MULTIPOLYGON (((-85.60516 3...
## 7 MULTIPOLYGON (((-116.916 45...
## 8 MULTIPOLYGON (((-87.52404 4...
## 9 MULTIPOLYGON (((-102.0517 4...
## 10 MULTIPOLYGON (((-92.01783 2...
#set all column names to lowercase
#Question 12
us_states_sel <- us_states_stats %>%
select(Income = median_income_15)
us_states_sel
## Simple feature collection with 49 features and 1 field
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## Income geometry
## 1 22890 MULTIPOLYGON (((-88.20006 3...
## 2 26156 MULTIPOLYGON (((-114.7196 3...
## 3 30752 MULTIPOLYGON (((-109.0501 4...
## 4 33226 MULTIPOLYGON (((-73.48731 4...
## 5 24654 MULTIPOLYGON (((-81.81169 2...
## 6 25588 MULTIPOLYGON (((-85.60516 3...
## 7 23558 MULTIPOLYGON (((-116.916 45...
## 8 25834 MULTIPOLYGON (((-87.52404 4...
## 9 27315 MULTIPOLYGON (((-102.0517 4...
## 10 24014 MULTIPOLYGON (((-92.01783 2...
#new object with just income and geometry
ggplot(us_states_sel) + geom_sf(aes(fill = Income))
#Question 13
us_states13 <- us_states_stats %>%
mutate(incomeDiff = median_income_15 - median_income_10)
us_states13
## Simple feature collection with 49 features and 11 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: -124.7042 ymin: 24.55868 xmax: -66.9824 ymax: 49.38436
## geographic CRS: NAD83
## First 10 features:
## GEOID NAME REGION AREA total_pop_10 total_pop_15
## 1 01 Alabama South 133709.27 [km^2] 4712651 4830620
## 2 04 Arizona West 295281.25 [km^2] 6246816 6641928
## 3 08 Colorado West 269573.06 [km^2] 4887061 5278906
## 4 09 Connecticut Norteast 12976.59 [km^2] 3545837 3593222
## 5 12 Florida South 151052.01 [km^2] 18511620 19645772
## 6 13 Georgia South 152725.21 [km^2] 9468815 10006693
## 7 16 Idaho West 216512.66 [km^2] 1526797 1616547
## 8 18 Indiana Midwest 93648.40 [km^2] 6417398 6568645
## 9 20 Kansas Midwest 213037.08 [km^2] 2809329 2892987
## 10 22 Louisiana South 122345.76 [km^2] 4429940 4625253
## median_income_10 median_income_15 poverty_level_10 poverty_level_15
## 1 21746 22890 786544 887260
## 2 26412 26156 933113 1180690
## 3 29365 30752 584184 653969
## 4 32258 33226 314306 366351
## 5 24812 24654 2502365 3180109
## 6 25596 25588 1445752 1788947
## 7 22866 23558 203177 245177
## 8 24934 25834 842540 978043
## 9 25696 27315 338792 381353
## 10 22053 24014 780359 888280
## geometry incomeDiff
## 1 MULTIPOLYGON (((-88.20006 3... 1144
## 2 MULTIPOLYGON (((-114.7196 3... -256
## 3 MULTIPOLYGON (((-109.0501 4... 1387
## 4 MULTIPOLYGON (((-73.48731 4... 968
## 5 MULTIPOLYGON (((-81.81169 2... -158
## 6 MULTIPOLYGON (((-85.60516 3... -8
## 7 MULTIPOLYGON (((-116.916 45... 692
## 8 MULTIPOLYGON (((-87.52404 4... 900
## 9 MULTIPOLYGON (((-102.0517 4... 1619
## 10 MULTIPOLYGON (((-92.01783 2... 1961
#has a column with the difference in income between 2015 and 2010
#Question 14
q14 <- raster(nrow=9, ncol=9, res=0.5)
#Question 15
grain_size = c("clay", "silt", "sand")
grain = raster(nrow = 6, ncol = 6, res = 0.5,
xmn = -1.5, xmx = 1.5, ymn = -1.5, ymx = 1.5,
vals = factor(sample(grain_size, 36, replace = TRUE),
levels = grain_size))
#from tutorial
factorValues(grain, modal(values(grain)))
## VALUE
## 1 clay
#Question 16
data(dem, package = "spDataLarge")
par(mfrow = c(1, 2))
hist(dem)
boxplot(dem)
#Question 1
canterbury <- nz %>% filter(Name == "Canterbury")
canterbury
## Simple feature collection with 1 feature and 6 fields
## geometry type: MULTIPOLYGON
## dimension: XY
## bbox: xmin: 1325039 ymin: 5004766 xmax: 1686902 ymax: 5360239
## projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000
## Name Island Land_area Population Median_income Sex_ratio
## 1 Canterbury South 44504.5 612000 30100 0.9753265
## geom
## 1 MULTIPOLYGON (((1686902 535...
c_height <- nz_height[canterbury, ]
c_height
## Simple feature collection with 70 features and 2 fields
## geometry type: POINT
## dimension: XY
## bbox: xmin: 1365809 ymin: 5158491 xmax: 1654899 ymax: 5350463
## projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000
## First 10 features:
## t50_fid elevation geometry
## 5 2362630 2749 POINT (1378170 5158491)
## 6 2362814 2822 POINT (1389460 5168749)
## 7 2362817 2778 POINT (1390166 5169466)
## 8 2363991 3004 POINT (1372357 5172729)
## 9 2363993 3114 POINT (1372062 5173236)
## 10 2363994 2882 POINT (1372810 5173419)
## 11 2363995 2796 POINT (1372579 5173989)
## 13 2363997 3070 POINT (1373796 5174144)
## 14 2363998 3061 POINT (1373955 5174231)
## 15 2363999 3077 POINT (1373984 5175228)
#70 peaks in the canterbury region
#Question 2
nz_height_count <- aggregate(nz_height, nz, length)
nz_height_combined <- cbind(nz, count = nz_height_count$elevation)
nz_height_combined %>%
st_set_geometry(NULL) %>%
select(Name, count) %>%
arrange(desc(count)) %>%
slice(2)
## Name count
## 1 West Coast 22
#Question 3
nz_height_combined %>%
st_set_geometry(NULL) %>%
select(Name, count) %>%
arrange(desc(count)) %>%
na.omit()
## Name count
## 1 Canterbury 70
## 2 West Coast 22
## 3 Waikato 3
## 4 Manawatu-Wanganui 2
## 5 Otago 2
## 6 Southland 1
## 7 Marlborough 1
#Question 4
library(classInt)
data(dem, package = "spDataLarge")
data(ndvi, package = "spDataLarge")
summary(dem)
## dem
## Min. 238
## 1st Qu. 366
## Median 478
## 3rd Qu. 748
## Max. 1094
## NA's 0
brk <- classIntervals(values(dem), n = 3)$brk
rcl <- matrix(c(brk[1] - 1, brk[2], 1, brk[2], brk[3], 2, brk[3], brk[4], 3),
ncol = 3, byrow = TRUE)
recl <- reclassify(dem, rcl = rcl)
zonal(stack(dem, ndvi), recl, fun = "mean")
## zone dem ndvi
## [1,] 1 329.7571 -0.3473349
## [2,] 2 492.8862 -0.1311101
## [3,] 3 846.9908 -0.2944226