This chunk of code reads in the shapefile containing county boundaries data and transforms the data to the geographic coordinate system crs = 4326. The crs code transforms the county spatial data to latitude-longitudinal format. The following line sets the column names of the counties dataframe and cleans them up using the make_clean_names function. Head shows us the top rows of the counties dataframe.
counties <- read_sf('shapefiles/county_boundaries.shp') %>%
st_transform(crs = 4326)
colnames(counties) <- make_clean_names(colnames(counties))
head(counties)## Simple feature collection with 3 features and 32 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -75.69678 ymin: 39.80192 xmax: -74.95576 ymax: 40.44699
## Geodetic CRS: WGS 84
## # A tibble: 3 x 33
## objectid geoid namelsad g_id geography total_popu poverty_ra total_hous
## <dbl> <chr> <chr> <chr> <chr> <dbl> <dbl> <dbl>
## 1 15 42101 Philadelphia ~ G421~ Philadel~ 1555072 26.4 581050
## 2 42 42091 Montgomery Co~ G420~ Montgome~ 812970 6.6 308626
## 3 45 42045 Delaware Coun~ G420~ Delaware~ 561683 10.6 203817
## # i 25 more variables: snap_rate <dbl>, median_hou <dbl>, percent_wh <dbl>,
## # percent_af <dbl>, percent_as <dbl>, percent_mu <dbl>, percent_hi <dbl>,
## # median_age <dbl>, percent_en <dbl>, median_ren <dbl>, unemployme <dbl>,
## # percent_di <dbl>, percent_un <dbl>, total_ho_1 <dbl>, occupied_h <dbl>,
## # vacant_hou <dbl>, percent_va <dbl>, owner_occu <dbl>, renter_occ <dbl>,
## # percent_re <dbl>, occ_hu_no_ve <dbl>, pct_no_vehi <dbl>, shape_st_ar <dbl>,
## # shape_st_le <dbl>, geometry <POLYGON [°]>This chunk of code reads in the shapefile containing Philadelphia City boundaries data and transforms the data to the geographic coordinate system crs = 4326. The crs code transforms the county spatial data to latitude-longitudinal format. The following line sets the column names of the counties dataframe and cleans them up using the make_clean_names function. Head shows us the top rows of the phila dataframe.
phila <- read_sf('shapefiles/city_boundary.shp') %>%
st_transform(crs = 4326)
colnames(phila) <- make_clean_names(colnames(phila))
head(phila)## Simple feature collection with 1 feature and 6 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -75.2803 ymin: 39.86726 xmax: -74.95575 ymax: 40.13791
## Geodetic CRS: WGS 84
## # A tibble: 1 x 7
## area perimeter boundary boundary_i shape_area shape_len
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 3973640269. 390420. 2 0 3970787926. 394714.
## # i 1 more variable: geometry <POLYGON [°]>This chunk of code reads in the shapefile containing neighborhood boundaries data and transforms the data to the geographic coordinate system crs = 4326. The crs code transforms the county spatial data to latitude-longitudinal format. The following line sets the column names of the counties dataframe and cleans them up using the make_clean_names function. Head shows us the top rows of the neighborhoods dataframe.
neighborhoods <- read_sf('shapefiles/philly_neighborhoods.shp') %>%
st_transform(crs = 4326)
colnames(neighborhoods) <- make_clean_names(colnames(neighborhoods))
head(neighborhoods)## Simple feature collection with 6 features and 1 field
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -75.26443 ymin: 39.87824 xmax: -74.95576 ymax: 40.13799
## Geodetic CRS: WGS 84
## # A tibble: 6 x 2
## postorm_rg geometry
## <chr> <POLYGON [°]>
## 1 Center City & Kensington/Port Richmond Areas ((-75.14851 40.00899, -7~
## 2 North/NorthEast Philadelphia - Tacony/Frankford Are~ ((-75.10943 40.04587, -7~
## 3 NorthEast Philadelphia ((-74.99852 40.12819, -7~
## 4 NorthEast Philadelphia - Fox Chase & Pennypack Areas ((-75.01827 40.09707, -7~
## 5 NorthWest Philadelphia - Mt. Airy & Germantown Areas ((-75.21221 40.08603, -7~
## 6 South Philadelphia ((-75.13626 39.94264, -7~Start by loading in the rg_coords.csv file and clean up the data in the following lines.
rg_coords <- read_csv(file.path("inputs", "rg_coords.csv"), show_col_types = F)
colnames(rg_coords) <- make_clean_names(colnames(rg_coords))Read in the rg_coords dataframe and assign the new resulting dataframe rg_coords1. Using the mutate function, remove *_* from the gauge column and assign the changed column to a new column named labels. Using the as.numeric function, remove “RG_” from the gauge column and keep the same column name - gauge. Using the if_else function, find instances in the gauge column with NA and replace these missing values with PHL in the labels column. Using the if_else function, replace values of 99 in the gauge column where the the labels column reads PHL.
rg_coords1 <- rg_coords %>%
mutate(labels = gsub("_","",gauge),
gauge = as.numeric(gsub("RG_","",gauge)),
labels = if_else(is.na(gauge), "PHL", labels),
gauge = if_else(labels == "PHL", 99, gauge))## Warning: There was 1 warning in `mutate()`.
## i In argument: `gauge = as.numeric(gsub("RG_", "", gauge))`.
## Caused by warning:
## ! NAs introduced by coercionFollowing the edits made to rg_coords1, this line takes in the df as an input and converts it to a sf - simple features object. The line adds in two columns containing longitudinal and latitudinal coordinates. Like before, crs = 4326 sets the coordinate reference system. remove = FALSE ensures that the rest of the columns from rg_coords1 dataframe are brought over in the resulting spatial object.
raingages <- st_as_sf(rg_coords1, coords = c("long", "lat"), crs = 4326, remove = FALSE)Using the leaflet package, create a custom icon for a cloud marker on a leaflet map with the makeIcon function. iconUrl is responsible for setting the path to the SVG image file that will be used as a marker. Specify your prefered width and height for the icon.
icon.cloud <- makeIcon(iconUrl = 'inputs/default_cloud.svg',
iconWidth = 22, iconHeight = 14)Now that we have saved our rg_coord1 df to the raingages spatial object, we remove the rg_coord1 df using rm
rm(rg_coords1)Saves our current workspace including all objects and variables to the following file name.
save.image('PWD_spatial_data_RGs_sf.RData')