Dams Package Demo

This document provides examples on how to obtain data using the dams package and how to create summary graphics of the extracted data.

Data Attributes

If you have not already done so, load the package along with ggplot and maps (for graphics).

require(dams)
require(ggplot2)
require(maps)
require(mapproj)

Load Data

Load the entire dataset. This might take a few moments.

dams_all <- nid_cleaned

dim(dams_all)

## [1] 74096    61

head(dams_all, 3)

##                   Dam_Name  Other_Dam_Name  NID_ID Num_Separate_Struct
## 1        NIX MILL POND DAM    NIX MILL DAM AL00106                   0
## 2 LIGHTSEY S MILL POND DAM   MILL POND DAM AL00533                   0
## 3           ODUMS MILL DAM ODOMS MILL POND AL00890                   0
##   Other_Structure_ID Longitude Latitude   County             River
## 1                     -87.8050  34.3483 FRANKLIN      EDGAR BRANCH
## 2                     -87.1233  32.9100     BIBB LIGHTSEY S BRANCH
## 3                     -87.4000  33.9566   WALKER  BLACKWATER CREEK
##   Owner_Type Private_Dam Dam_Designer Dam_Type Core Foundation
## 1 Not Listed           N              Rockfill                
## 2    Private           N                 Earth                
## 3    Private           N               Gravity                
##   Primary_Purpose      All_Purposes Year_Completed Year_Modified
## 1           Other             Other           1800              
## 2      Recreation        Recreation           1890              
## 3           Other Recreation, Other           1850              
##   Dam_Length Dam_Height Structural_Height Hydraulic_Height NID_Height
## 1         60         NA                25               25         25
## 2        350         NA                13               10         13
## 3        125         NA                12               12         12
##   Max_Discharge Max_Storage Normal_Storage NID_Storage Surface_Area
## 1           140          55             55          55           NA
## 2           250          80             80          80           NA
## 3            NA         180            150         180           NA
##   Drainage_Area EAP Inspection_Date Inspection_Frequency Spillway_Type
## 1            NA   N                                    0              
## 2            NA   N                                    0              
## 3            NA   N                                    0              
##   Spillway_Width Outlet_Gates Volume Num_Locks Length_Locks Width_Locks
## 1              0                  NA        NA            0          NA
## 2              0                  NA        NA            0          NA
## 3              0                  NA        NA            0          NA
##   Permitting_Authority Inspection_Authority Enforcement_Authority
## 1                    N                    N                     N
## 2                    N                    N                     N
## 3                    N                    N                     N
##   Jurisdictional_Dam State_Reg_Dam State_Reg_Agency Fed_Funding Fed_Design
## 1                  N             N                                        
## 2                  N             N                                        
## 3                  N             N                                        
##   Fed_Construction Fed_Regulatory Fed_Inspection Fed_Operation Fed_Owner
## 1                                                                       
## 2                                                                       
## 3                                                                       
##   Fed_Other Source_Agency State  Submit_Date Url_Address
## 1                      AL    AL 01\\02\\2013          NA
## 2                      AL    AL 01\\02\\2013          NA
## 3                      AL    AL 01\\02\\2013          NA
##             Congress_Rep Political_Party Congress_District
## 1 Robert B. Aderholt (R)               R              AL04
## 2     Spencer Bachus (R)               R              AL06
## 3 Robert B. Aderholt (R)               R              AL04

Summary Graphics

Data for graphics

gfx_data <- dams_all[, c("Year_Completed", "State")]

head(gfx_data)

##   Year_Completed State
## 1           1800    AL
## 2           1890    AL
## 3           1850    AL
## 4           1880    AL
## 5           1881    AR
## 6           1877    CA

Counts of number of dams built per decade or other time period of interest

gfx_data$Year <- cut(gfx_data$Year_Completed, 
                     breaks = c(0, 1850, seq(1900, 2000, 10), 2014), 
                     labels = c("<1850", "1850-1900", "1910", "1920", "1930",
                              "1940", "1950", "1960", "1970", "1980", "1990", 
                              "2000", "2014"))
table(gfx_data$Year)

## 
##     <1850 1850-1900      1910      1920      1930      1940      1950 
##      1264      2588      1464      2015      2329      4130      4825 
##      1960      1970      1980      1990      2000      2014 
##     12816     18770     12027      4853      4221      2794

year_counts <- as.data.frame(table(gfx_data$Year), stringsAsFactors = FALSE)
colnames(year_counts) <- c("Year", "Count")

Histogram of number of dams by time period

gfx_bar <- ggplot(year_counts, aes(x = Year, y = Count))
gfx_bar <- gfx_bar + geom_bar(position = "dodge", stat = "identity")
gfx_bar <- gfx_bar + ylab("Number of Dams") + xlab("Year of Completion")
gfx_bar <- gfx_bar + ggtitle("Number of Dams in the NID Database")

Counts of dams per state in the US mainland

gfx_data <- subset(gfx_data, !(State %in% c("AK", "HI", "PR", "GU")))
sort(table(gfx_data$State))

## 
##   NV   DE   RI   MD   AZ   VT   IL   WV   ID   LA   NC   NM   NJ   NH   ME 
##   43   51  174  305  319  358  391  413  431  441  444  453  510  590  597 
##   UT   CT   FL   WA   ND   OR   IN   MI   MN   KY   TN   WI   AR   MA   OH 
##  617  700  781  790  820  839  847  914 1005 1025 1113 1116 1243 1244 1261 
##   VA   PA   CA   WY   CO   NY   MS   AL   SC   NE   SD   MT   GA   IA   OK 
## 1307 1311 1500 1607 1704 1761 1981 2135 2259 2340 2510 3236 3724 3913 4756 
##   MO   KS   TX 
## 5002 5692 7253

Map of dams per state in the US mainland

state_counts <- as.data.frame(table(gfx_data$State), stringsAsFactors = FALSE)
colnames(state_counts) <- c("state", "Count")

# add long names of states
state_names <- data.frame(state = state.abb, 
                          name = state.name, 
                          stringsAsFactors = FALSE)
gfx_data <- merge(state_counts, state_names, by = "state")
# change state name to lower case to be consistent with ggplot
gfx_data$name <- tolower(gfx_data$name)

# geo reference data on states from ggplot
geo_state <- map_data("state")

# merge data with above for graphics
gfx_data <- merge(geo_state, gfx_data, by.x = "region", by.y = "name")
gfx_data <- gfx_data[order(gfx_data$order), ]

# discretize state counts
color_breaks <- c(0, 100, 500, 1000, 2000, 3000, 4000, 5000, 7500)
color_labels <- c("<100", "100 - 500", "500 - 1000", "1000 - 2000",
                  "2000 - 3000", "3000 - 4000", "4000 - 5000", "5000 - 7500")
gfx_data$dams <- cut(gfx_data$Count, 
                     breaks = color_breaks, 
                     labels = color_labels)

gfx_map <- ggplot(data = gfx_data)
gfx_map <- gfx_map + geom_polygon(aes(x = long, y = lat, group = group,
            fill = dams))
gfx_map <- gfx_map + geom_path(data = geo_state, aes(x = long, y = lat,
            group = group, fill = NA))
gfx_map <- gfx_map + labs(list(title = "Number of Dams in the NID Database",
            x = NULL, y = NULL))
gfx_map <- gfx_map + guides(fill = guide_legend(title = "Number of Dams"))
gfx_map <- gfx_map + scale_fill_brewer(palette = "Accent")
gfx_map <- gfx_map + coord_map()

Other Analyses: Flood Control Dams

A number of interesting analyses could be performed with the dataset. Of interest to water resources managers and hydrologists is the location of flood control dams. It is interesting to see a few states like Texas have a large number of flood control dams.

flood_dams <- subset(dams_all, Primary_Purpose == "Flood Control")

table(flood_dams$State)

## 
##   AK   AL   AR   AZ   CA   CO   CT   DE   FL   GA   HI   IA   ID   IL   IN 
##    4  131  221   91  190  230   20    2  171  369    6  369   12   49  147 
##   KS   KY   LA   MA   MD   ME   MI   MN   MO   MS   MT   NC   ND   NE   NH 
## 1905  223   40  119   63  113   28  305  897  596   98   79   91  950   43 
##   NJ   NM   NV   NY   OH   OK   OR   PA   PR   RI   SC   SD   TN   TX   UT 
##   33  208   12  139  112 2340   20  195    5    1  112   89  209 2260  125 
##   VA   VT   WA   WI   WV   WY 
##  199   15  109  123  178  100