Create the Docker Image

A quick refresher on docker commands is available at the docker cheatsheet.

A docker image with all required prerequisites can be built with the Makefile in this directory:

make production_build

You should then be able to see something like the following:

$ docker images
REPOSITORY                   TAG       IMAGE ID       CREATED          SIZE
...
mazamascience/airsensor      1.1.1     1518d7aa223e   44 seconds ago   5.39GB
mazamascience/airsensor      latest    1518d7aa223e   44 seconds ago   5.39GB
...

It is best practice to create versioned images and tag the most recent one with "latest".

Spatial data required by the MazamaSpatialUtils package already exists in the docker image in /home/mazama/data/Spatial.

Test the Docker Image

Having built the docker image we can now test it. The following output was obtained use the provided example_pas synoptic dataset:

docker run --rm -ti mazamascience/airsensor R --vanilla
...
library(AirSensor)

data(example_pas)
pas <- example_pas

> names(example_pas)
 [1] "ID"                               "label"                           
 [3] "DEVICE_LOCATIONTYPE"              "THINGSPEAK_PRIMARY_ID"           
 [5] "THINGSPEAK_PRIMARY_ID_READ_KEY"   "THINGSPEAK_SECONDARY_ID"         
 [7] "THINGSPEAK_SECONDARY_ID_READ_KEY" "latitude"                        
 [9] "longitude"                        "pm25"                            
[11] "lastSeenDate"                     "sensorType"                      
[13] "flag_hidden"                      "isOwner"                         
[15] "humidity"                         "temperature"                     
[17] "pressure"                         "age"                             
[19] "parentID"                         "flag_highValue"                  
[21] "flag_attenuation_hardware"        "Ozone1"                          
[23] "pm25_current"                     "pm25_10min"                      
[25] "pm25_30min"                       "pm25_1hr"                        
[27] "pm25_6hr"                         "pm25_1day"                       
[29] "pm25_1week"                       "statsLastModifiedDate"           
[31] "statsLastModifiedInterval"        "countryCode"                     
[33] "stateCode"                        "timezone"                        
[35] "deviceID"                         "locationID"                      
[37] "deviceDeploymentID"               "airDistrict"                     
[39] "pwfsl_closestDistance"            "pwfsl_closestMonitorID"          
[41] "sensorManufacturer"               "targetPollutant"                 
[43] "technologyType"                   "communityRegion"                 

pas %>% 
  pas_filter(stateCode == 'CA') %>% 
  pas_filter(pwfsl_closestDistance < 7000) %>% 
  dplyr::pull(pwfsl_closestDistance) %>% 
  round() %>% 
  table()

 1  2  4  6  7 10 11 12 13 15 16 18 19 21 38 40 41 46 47 53 57 58 60 73 74 
 2  8  2  6  6  2  2  2  4  4  2  2  4  4  4  4  2  2  4  4  2  2  2  4  2 

Publish the Docker Image

make production_publish

Download the Docker Image

A recent image can also be obtained from DockerHub with:

docker pull mazamascience/airsensor:1.0.7