Q:

How to iterate making a graph? 

I am working with Seattle crime data.  Below is a sample dataset.   I have two questions. 
library(ggplot2)
library(ggmap)

 SPD_2015 <-   structure(list(summarized.offense.description = c("OTHER PROPERTY", 
    "CAR PROWL", "ASSAULT", "SHOPLIFTING", "VEHICLE THEFT", "OTHER PROPERTY", 
    "OTHER PROPERTY", "PROSTITUTION", "CAR PROWL", "PROPERTY DAMAGE", 
    "CAR PROWL", "ASSAULT", "FRAUD", "SHOPLIFTING", "ROBBERY", "WARRANT ARREST", 
    "VEHICLE THEFT", "CAR PROWL", "PROPERTY DAMAGE", "ASSAULT", "VEHICLE THEFT", 
    "OTHER PROPERTY", "CAR PROWL", "FRAUD", "CAR PROWL", "CAR PROWL", 
    "CAR PROWL", "THREATS", "CAR PROWL", "DISTURBANCE", "CAR PROWL", 
    "CAR PROWL", "EMBEZZLE", "THREATS", "CAR PROWL", "PROPERTY DAMAGE", 
    "STOLEN PROPERTY", "ASSAULT", "LOST PROPERTY", "BURGLARY-SECURE PARKING-RES", 
    "THREATS", "PROPERTY DAMAGE", "FRAUD", "CAR PROWL", "BURGLARY", 
    "ASSAULT", "THEFT OF SERVICES", "OTHER PROPERTY", "DISTURBANCE", 
    "BIKE THEFT", "BURGLARY", "CAR PROWL", "FRAUD", "CAR PROWL", 
    "VEHICLE THEFT", "DISTURBANCE", "BURGLARY", "BURGLARY", "BURGLARY", 
    "OTHER PROPERTY", "CAR PROWL", "CAR PROWL", "BURGLARY", "BURGLARY", 
    "OTHER PROPERTY", "FRAUD", "CAR PROWL", "BURGLARY", "NARCOTICS", 
    "THREATS", "PROPERTY DAMAGE", "TRESPASS", "ASSAULT", "FRAUD", 
    "CAR PROWL", "BURGLARY", "CAR PROWL", "BURGLARY-SECURE PARKING-RES", 
    "FRAUD", "CAR PROWL", "FRAUD", "THREATS", "CAR PROWL", "BURGLARY", 
    "TRESPASS", "TRESPASS", "OTHER PROPERTY", "STOLEN PROPERTY", 
    "STOLEN PROPERTY", "WARRANT ARREST", "WARRANT ARREST", "FRAUD", 
    "CAR PROWL", "OTHER PROPERTY", "PROPERTY DAMAGE", "BURGLARY", 
    "FRAUD", "OTHER PROPERTY", "FRAUD", "CAR PROWL"), longitude = c(-122.300109863, 
    -122.385444641, -122.269958496, -122.341133118, -122.311935425, 
    -122.256233215, -122.344665527, -122.302001953, -122.344993591, 
    -122.311782837, -122.325790405, -122.337394714, -122.317298889, 
    -122.365219116, -122.33140564, -122.343269348, -122.300140381, 
    -122.280647278, -122.349700928, -122.340240479, -122.354415894, 
    -122.345626831, -122.317359924, -122.378921509, -122.390213013, 
    -122.354415894, -122.337089539, -122.280601501, -122.359313965, 
    -122.337791443, -122.330421448, -122.343261719, -122.396110535, 
    -122.311546326, -122.316917419, -122.262084961, -122.340454102, 
    -122.320770264, -122.315254211, -122.344444275, -122.304519653, 
    -122.319442749, -122.36756134, -122.330039978, -122.337348938, 
    -122.330810547, -122.303710938, -122.327880859, -122.382667542, 
    -122.322769165, -122.313537598, -122.301094055, -122.4034729, 
    -122.333267212, -122.32888031, -122.382377625, -122.310951233, 
    -122.318778992, -122.326576233, -122.354827881, -122.382377625, 
    -122.378768921, -122.315391541, -122.311248779, -122.311393738, 
    -122.32408905, -122.367424011, -122.338768005, -122.297531128, 
    -122.374198914, -122.348678589, -122.326385498, -122.33303833, 
    -122.381492615, -122.338088989, -122.282745361, -122.316902161, 
    -122.355461121, -122.389198303, -122.32635498, -122.404212952, 
    -122.313087463, -122.343833923, -122.304168701, -122.3854599, 
    -122.296226501, -122.318733215, -122.332801819, -122.316726685, 
    -122.323440552, -122.332260132, -122.290527344, -122.337585449, 
    -122.344940186, -122.31678009, -122.376319885, -122.31816864, 
    -122.335906982, -122.355148315, -122.355621338), latitude = c(47.595077515, 
    47.556591034, 47.670768738, 47.610042572, 47.664890289, 47.497062683, 
    47.702514648, 47.583400726, 47.725036621, 47.526573181, 47.700252533, 
    47.612663269, 47.564403534, 47.521022797, 47.602767944, 47.608207703, 
    47.610794067, 47.535404205, 47.57101059, 47.612014771, 47.634437561, 
    47.660072327, 47.669715881, 47.680427551, 47.521442413, 47.66809082, 
    47.607299805, 47.724998474, 47.687664032, 47.60974884, 47.620243073, 
    47.61145401, 47.549030304, 47.60710907, 47.619354248, 47.509685516, 
    47.686210632, 47.613517761, 47.664012909, 47.608901978, 47.589576721, 
    47.717647552, 47.642562866, 47.606300354, 47.52047348, 47.600463867, 
    47.609523773, 47.623706818, 47.665958405, 47.649650574, 47.593112946, 
    47.602165222, 47.573997498, 47.58398056, 47.630302429, 47.591312408, 
    47.595115662, 47.660381317, 47.626041412, 47.549259186, 47.591312408, 
    47.567428589, 47.662197113, 47.629676819, 47.62008667, 47.602870941, 
    47.673809052, 47.606601715, 47.610782623, 47.56407547, 47.613479614, 
    47.607337952, 47.604553223, 47.666133881, 47.712303162, 47.727027893, 
    47.618183136, 47.705989838, 47.652839661, 47.600868225, 47.665912628, 
    47.66399765, 47.688751221, 47.691646576, 47.561988831, 47.707542419, 
    47.670059204, 47.611839294, 47.624809265, 47.604129791, 47.605373383, 
    47.632568359, 47.726856232, 47.71957016, 47.605884552, 47.551052094, 
    47.615837097, 47.600463867, 47.632316589, 47.635715485)), .Names = c("summarized.offense.description", 
    "longitude", "latitude"), row.names = c(NA, -100L), class = c("tbl_df", 
    "tbl", "data.frame"))

I summarize my data to see what I am working with:
group_by(SPD_2015, summarized.offense.description) %>%
  summarize(count = n()) %>%
  arrange(desc(count))

# A tibble: 21 × 2
   summarized.offense.description count
                            <chr> <int>
1                       CAR PROWL    24
2                        BURGLARY    11
3                           FRAUD    11
4                  OTHER PROPERTY    10
5                         ASSAULT     6
6                 PROPERTY DAMAGE     6
7                         THREATS     5
8                   VEHICLE THEFT     4
9                     DISTURBANCE     3
10                STOLEN PROPERTY     3
# ... with 11 more rows

I have currently been creating a new filtered dataset of the "summarized.offense.description" that I am interested in, such as CAR PROWL:
car.prowl <- SPD_2015 %>%
  filter(summarized.offense.description == "CAR PROWL")

and then mapping said data:
ggmap(seattle.map) +
  geom_point(data = car.prowl, aes(car.prowl$longitude, car.prowl$latitude),
             alpha = 0.2, color = "tomato4", size = 0.7) +
  theme(axis.text = element_blank()) +
  theme(axis.title = element_blank())

Question 1:  Rather than creating a new filtered dataset for every single summarized.offense.description that I am interested in.   How do I create and save a new map for the top four summarized.offense.description in my summarized dataset (in this case it would be CAR PROWL, BURGLARY, FRAUD, OTHER PROPERTY)?   
Question 2: How do I make a facet graph of the top four summarized.offense.description? 
Any insight into my questions would be greatly appreciated. 
Thank you! 

A:

For the first request I would do the following:
# Get Seattle Map
seattle.map <- qmap("seattle", zoom = 11, source="stamen", maptype="toner",darken = c(.3,"#BBBBBB"))

# Get ordered summarized data
ordered <- group_by(SPD_2015, summarized.offense.description) %>%
  summarize(count = n()) %>%
  arrange(desc(count))

# Make the ordered summarized data a dataframe and then use only the first column
# to be a character vector with the top 4
ordered <- as.data.frame(ordered[1:4,1])
ordered <- ordered$summarized.offense.description

# Make a list to comprehend the 4 ggmap plots
ggmap_list <- list()

# Plot the 4 ggmaps
for(i in 1:4){

  subst <- SPD_2015[SPD_2015$summarized.offense.description == ordered[i],]

  ggmap_list[[i]] <- seattle.map +
    geom_point(data = subst, aes(longitude, latitude),
               alpha = 0.2, color = "red", size = 0.7) +
    theme(axis.text = element_blank()) +
    theme(axis.title = element_blank())
}

Regarding the second request, I would try:
# Make a subset of the data with the top 4
subst <- SPD_2015[SPD_2015$summarized.offense.description %in% ordered,]

# Plot all 4 ggmaps on the same plotting region
seattle.map +
  geom_point(data = subst, aes(longitude, latitude),
             alpha = 0.2, color = "red", size = 0.7) +
  facet_wrap(~summarized.offense.description) +
  theme(axis.text = element_blank()) +
  theme(axis.title = element_blank())

Notice that I changed the color for better visualization and that I tried to reproduce your Seattle map. 
Is this what you wanted?