CODEWITHSUNDEEP
rrasterr-rasterspatial-data-frame
Young
Young

Reputation: 47

Convert a raster to a dataframe and extract values you want in R

I want to make a bathymetry map using ggplot2. I converted a raster file to a data frame, clopped and converted it to a dataframe: 

library(raster)

##Import a raster file
Bathymetry_dat <- raster("w001000.adf")

##Clop the raster file
Bathy_clopped <- crop(Bathymetry_dat, extent(84.11236, 108.4594, -4.046979, 24.09534))

##Convert it to a dataframe    
datframe_bathy<-as.data.frame(Bathy_clopped, xy = TRUE)

Then, I checked values (i.e. depth in m) in the dataframe:

> summary(datframe_bathy)
       x                y          w001000_COUNT    
 Min.   : 84.11   Min.   :-4.046   Min.   :   9945  
 1st Qu.: 90.20   1st Qu.: 2.987   1st Qu.:  81618  
 Median : 96.28   Median :10.021   Median : 168447  
 Mean   : 96.28   Mean   :10.021   Mean   : 210212  
 3rd Qu.:102.37   3rd Qu.:17.054   3rd Qu.: 336718  
 Max.   :108.45   Max.   :24.087   Max.   :1205362  
                                   NA's   :3449125

Depth (m) values are supposed to be negative and should't be this large. Then, I checked the bathymetry file imported in R.

> Bathy_clopped
class       : RasterLayer 
dimensions  : 3377, 2922, 9867594  (nrow, ncol, ncell)
resolution  : 0.008333333, 0.008333333  (x, y)
extent      : 84.10833, 108.4583, -4.05, 24.09167  (xmin, xmax, ymin, ymax)
coord. ref. : +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0 
data source : in memory
names       : w001000 
values      : -6138, -1  (min, max)
attributes  :
           ID  COUNT
 from: -11584      1
 to  :     -1 676804

There are two attributes: ID and COUNT. I suppose ID is depth (m) and don't know what COUNT is. And it is weird that COUNT ranges from 1 to 676804 and but summary() shows values from 9945 to 1205362.

So my question is how can I convert a raster file to a dataframe that contains values I want?

With the "COUNT" values, I was able to generate a bathymetry map, but the values in the legend are not correct..

Thanks in advance.

Upvotes: 2

Views: 5046

Answers (2)

fishy_stuff
fishy_stuff

Reputation: 11

If the raster brick have also have a time component. Using a dataset of SST that have space and time as structural components.

library(tidyr)
AHOI_df <- as.data.frame(AHOI_Temp_raster, xy=TRUE)
AHOI_df <- data.frame(pivot_longer(AHOI_df, 
                        cols=3:878, names_to = "Y_m_d", values_to = "SST", ))
AHOI_df$Y_m_d <- as.Date(sub("X","",AHOI_df$Y_m_d),"%Y.%m.%d")

Upvotes: 0

www
www

Reputation: 39154

There is an attribute table with your raster layer. We can use levels to acces it.

library(raster)

# See the attribute table
head(levels(Bathy_clopped)[[1]])
#       ID COUNT
# 1 -11584     1
# 2 -10944     1
# 3 -10907     1
# 4 -10900     1
# 5 -10879     1
# 6 -10878     1

We can then manipulate this attribute table, replacing COUNT with the depth, which is ID.

# Get the attribute table
RAT <- levels(Bathy_clopped)[[1]]

# Replace COUNT with ID
RAT$Depth <- RAT$ID
RAT$COUNT <- NULL

# Replace the attribute table
Bathy_clopped2 <- Bathy_clopped
levels(Bathy_clopped2)[[1]] <- RAT

# Create a single layer based on the new RAT
Bathy_clopped2 <- deratify(Bathy_clopped2)

# Create a data frame
datframe_bathy2 <-as.data.frame(Bathy_clopped2, xy = TRUE)

Now the values in datframe_bathy2 are as expected. 

# Summarize the data frame
summary(datframe_bathy2)
#       x                y              COUNT        
# Min.   : 84.11   Min.   :-4.046   Min.   :-6138    
# 1st Qu.: 90.20   1st Qu.: 2.987   1st Qu.:-3800    
# Median : 96.28   Median :10.021   Median :-2428    
# Mean   : 96.28   Mean   :10.021   Mean   :-2154    
# 3rd Qu.:102.37   3rd Qu.:17.054   3rd Qu.:  -65    
# Max.   :108.45   Max.   :24.087   Max.   :   -1    
#                                   NA's   :3449125

Upvotes: 2

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