The following document presents two analyses to demonstrate use of the LAGOSNE package.

To begin, we load LAGOSNE data with lagosne_load:


dt <- lagosne_load("1.087.1")

Boxplot of average chlorophyll by state

The first analysis involves contructing a boxplot of average lake chlorophyll by state. To begin, we join the nutrient water quality table (epi.nutr) to the location information for each lake in the locus table. Next, we join the output to the state table to obtain state abbreviations that correspond to each lake. Finally, we filter the dataset to exclude any lakes that do not fall within a state.


lg <- left_join(dt$epi_nutr, dt$locus)
lg <- left_join(lg, dt$state)
lg <- group_by(lg, state)
lg <- filter(lg, !

Now, the levels of the state factor column are in alphabetical order by default. We need to reorder the factor levels from East to West in preparation for plotting.

##  [1] "CT" "IA" "IL" "IN" "MA" "ME" "MI" "MN" "MO" "NH" "NJ" "NY" "OH" "PA"
## [15] "RI" "VT" "WI"
state_easting <- summarize(lg, 
                    mean_easting = mean(nhd_long, na.rm = TRUE))
state_easting <- arrange(state_easting, mean_easting)

lg$state <- factor(lg$state, levels = state_easting$state)

Finally, we contruct a boxplot with ggplot:

ggplot(lg) + 
  geom_boxplot(aes(x = state, y = chla), outlier.shape = NA) + 
  ylim(c(0, 80)) + ylab("Chlorophyll a (ug/L)")

Map of average chlorophyll in Pennsylvania

The second analysis involves contructing a map of average lake chlorophyll concentration in Pennsylvania lakes. To begin, we use the maps and sf package to obtain a geographic outline of the state.


states <- st_as_sf(map("state", plot = FALSE, fill = TRUE))
pa <- filter(states, ID == "pennsylvania")

Next, we filter the output from the previous analysis to only include PA lakes and calculate mean chlorophyll for each lake:

pa_chl <- filter(lg, state == "PA")
pa_chl <- group_by(pa_chl, lagoslakeid)
pa_chl <- summarize(pa_chl, 
                      mean_chl  = mean(chla, na.rm = TRUE), 
                      mean_long = mean(nhd_long, na.rm = TRUE), 
                      mean_lat  = mean(nhd_lat, na.rm = TRUE))
pa_chl <- filter(pa_chl, !

Finally, we turn our data.frame into an sf geospatial object using st_as_sf before constructing a map with ggplot and geom_sf.

pa_chl <- st_as_sf(pa_chl, coords = c("mean_long", "mean_lat"), crs = 4326)

ggplot() + 
  geom_sf(data = pa) +
  geom_sf(data = pa_chl, aes(color = log(mean_chl))) + 
  theme_bw() + scale_color_gradient(low = "white", high = "darkgreen") +
  labs(color = "log(Chlorophyll a (ug/L))")