Gleon Fellowship - User contributions [en]

Zonal Statistics or Extracting Raster Data

2015-01-21T04:23:28Z

Ianm: Set PropertyValue: TargetDate = 2015-01-20

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

<nowiki>#</nowiki> Update working directory

setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

<nowiki>#</nowiki> Load raster package (may have to install first)

library(raster)

<nowiki>#</nowiki> Make list of asci files in already specified working directory

Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

<nowiki>#</nowiki> Check number of files in raster list

length = length(Clim_list)

<nowiki>#</nowiki> Crete stack of rasters in list

Clim_stack = stack(Clim_list)

<nowiki>#</nowiki> Create spatial dataset from shapefile (use points, polygons or lines)

library(maptools)

points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")

studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

<nowiki>#</nowiki> Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

<nowiki>#</nowiki> Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

<nowiki>#</nowiki> Output csv file (point data)

write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

<nowiki>#</nowiki> Output csv file (zonal polygon data)

write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")



{{#set:
Expertise=R_programming|
Expertise=Raster|
Expertise=GIS|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Progress=100|
StartDate=2015-01-20|
TargetDate=2015-01-20|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:23:15Z

Ianm: Set PropertyValue: StartDate = 2015-01-20

Zonal Statistics or Extracting Raster Data

2015-01-21T04:23:10Z

Ianm: Set PropertyValue: Progress = 100

Zonal Statistics or Extracting Raster Data

2015-01-21T04:22:55Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

<nowiki>#</nowiki> Update working directory

setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

<nowiki>#</nowiki> Load raster package (may have to install first)

library(raster)

<nowiki>#</nowiki> Make list of asci files in already specified working directory

Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

<nowiki>#</nowiki> Check number of files in raster list

length = length(Clim_list)

<nowiki>#</nowiki> Crete stack of rasters in list

Clim_stack = stack(Clim_list)

<nowiki>#</nowiki> Create spatial dataset from shapefile (use points, polygons or lines)

library(maptools)

points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")

studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

<nowiki>#</nowiki> Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

<nowiki>#</nowiki> Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

<nowiki>#</nowiki> Output csv file (point data)

write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

<nowiki>#</nowiki> Output csv file (zonal polygon data)

write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")



{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:22:28Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

<nowiki>#</nowiki> Update working directory

setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

<nowiki>#</nowiki> Load raster package (may have to install first)

library(raster)

<nowiki>#</nowiki> Make list of asci files in already specified working directory

Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

<nowiki>#</nowiki> Check number of files in raster list

length = length(Clim_list)

<nowiki>#</nowiki> Crete stack of rasters in list

Clim_stack = stack(Clim_list)

<nowiki>#</nowiki> Create spatial dataset from shapefile (use points, polygons or lines)

library(maptools)

points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")

studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

<nowiki>#</nowiki> Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

<nowiki>#</nowiki> Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

<nowiki>#</nowiki> Output csv file (point data)

write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

<nowiki>#</nowiki> Output csv file (zonal polygon data)

write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:21:47Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

<nowiki>#</nowiki> Update working directory

setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

<nowiki>#</nowiki> Load raster package (may have to install first)

library(raster)

<nowiki>#</nowiki> Make list of asci files in already specified working directory

Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

<nowiki>#</nowiki> Check number of files in raster list

length = length(Clim_list)

<nowiki>#</nowiki> Crete stack of rasters in list

Clim_stack = stack(Clim_list)

<nowiki>#</nowiki> Create spatial dataset from shapefile (use points, polygons or lines)

library(maptools)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

<nowiki>#</nowiki> Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

<nowiki>#</nowiki> Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

<nowiki>#</nowiki> Output csv file (point data)

write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

<nowiki>#</nowiki> Output csv file (zonal polygon data)

write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:20:37Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

<nowiki>#</nowiki> Update working directory

setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

<nowiki>#</nowiki> Load raster package (may have to install first)

library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:19:46Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

<nowiki>#</nowiki> Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

<nowiki>#</nowiki> Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:19:17Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki>#</nowiki> Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

# Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

# Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:18:31Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

# Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

# Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

# Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:17:48Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

<nowiki># Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

# Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

# Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")

</nowiki>


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:16:55Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''

Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''

Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''

The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

'''Example Code'''

# Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

# Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

# Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")



{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:15:30Z

Ianm:

[[Category:Task]]

'''Zonal Statistics'''
Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''
Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''
The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

# Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

# Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

# Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length = length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")



{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:14:26Z

Ianm:

[[Category:Task]]

'''Premise'''
A surprising number of people is unaware that you can use GIS shapefiles in R.The maptools library imports .shp points, lines and polygons

'''Zonal Statistics'''
Let's say you have a series of annual precipitation grids for a given site. Within this site you have a set of study plots and you want to extract precipitation values from each grid for each of your study plots. This is great application of zonal statistics. If you plots are points, zonal statistics will simply extract precipitation values for the location of each point. If your plots are lines or polygons, zonal statistics can be used to calculate the mean (or some other statistic) precipitation for each plot.

'''Skill Relevance and Usefulness'''
Use of zonal statistics is widely applicable across disciplines that utilize spatial datasets.

'''What You'll Need'''
The approach shown here uses the mpatools and raster libraries in R
http://cran.r-project.org/web/packages/maptools/maptools.pdf
http://cran.r-project.org/web/packages/raster/raster.pdf

'''Example Code'''

This is a code that you could adapt for your own purposes; hopefully it is enough to guide you. You'll obviously need to designate your own directories and name your own files. Also, this code contains lines for polygons and points; you may very well want one or the other. The list function might be confusing. I used pattern-matching to pick out only March precipitation grids from my directory (which contained grids for all months). The pattern="mar.asc$" tells R to pick out files with a specific type of name, in this case ascii grids for March.

This example extracts March precipitation data for a set of study plots in the Tehachapi Mountains, CA. The outputs are csv files written to a user-specified directory.

# Import a shapefile to R and use it to extract zonal statistics from a directory of rasters

# Update working directory
setwd("H:/Ian_GIS/Climate/precip/Tejon/Historic")

# Load raster package (may have to install first)
library(raster)

# Make list of asci files in already specified working directory
Clim_list = list.files(pattern = "mar.asc$") # "mar.asc$" queries files in directory ending in this pattern

# Check number of files in raster list
length=length(Clim_list)

# Crete stack of rasters in list
Clim_stack = stack(Clim_list)

# Create spatial dataset from shapefile (use points, polygons or lines)
library(maptools) # (may have to install)
points = readShapePoints("H:/Ian_GIS/Tejon_data/TejonPlots/TejonPoints")
studyarea = readShapePoly("H:/Ian_GIS/Tejon_data/FullStudyArea/Tejon_study_area",IDvar="ID")

# Extract raster pixels from point dataset
for(j in 1:length) {
ex = extract(Clim_stack,points,method='simple',layer=1,nl=length, df=T)
ex$ID = NULL}

# Extract study area mean from polygon dataset
for(j in 1:length) {
bus = extract(Clim_stack,studyarea, fun=mean, layer=1, nl=length, df=T, na.rm=T)
bus$ID = NULL}
bus = t(bus)

# Output csv file (point data)
write.csv(ex, "H:/Ian_GIS/Tejon/TejonPlots/Mar_precip_pts.csv", row.names=plots$Id, na="NA")

# Output csv file (zonal polygon data)
write.csv(bus, "H:/Ian_GIS/Tejon/FullStudyArea/Mar_precip_SA.csv", na="NA")



{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T04:03:08Z

Ianm:

Zonal Statistics or Extracting Raster Data

2015-01-21T03:59:21Z

Ianm:

[[Category:Task]]

'''Premise'''
A surprising number of people is unaware that you can use GIS shapefiles in R

The maptools library imports .shp points, lines and polygons


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:56:06Z

Ianm: Added PropertyValue: Expertise = GIS

[[Category:Task]]


{{#set:
Expertise=GIS|
Expertise=R_programming|
Expertise=Raster|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:56:04Z

Ianm: Added PropertyValue: Expertise = raster

[[Category:Task]]


{{#set:
Expertise=Raster|
Expertise=R_programming|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:55:59Z

Ianm: Added PropertyValue: Expertise = r programming

[[Category:Task]]


{{#set:
Expertise=R_programming|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:55:56Z

Ianm: Deleted PropertyValue: Expertise = r

[[Category:Task]]


{{#set:
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:55:53Z

Ianm: Added PropertyValue: Expertise = r

[[Category:Task]]


{{#set:
Expertise=R|
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:55:19Z

Ianm: Set PropertyValue: Owner = Ian McCullough

[[Category:Task]]


{{#set:
Owner=Ian_McCullough|
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:55:09Z

Ianm: Added PropertyValue: Participants = Ian McCullough

[[Category:Task]]


{{#set:
Participants=Ian_McCullough|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:54:57Z

Ianm: Set PropertyValue: Type = low

[[Category:Task]]


{{#set:|
Type=Low}}

Zonal Statistics or Extracting Raster Data

2015-01-21T03:54:26Z

Ianm: Creating new page with Category: Task

[[Category:Task]]

Skills Sharing Activity

2015-01-21T03:54:26Z

Ianm: Added PropertyValue: SubTask = Zonal Statistics or Extracting Raster Data

[[Category:Task]]

==Introduction to R coding==

The first workshop began with an R Skills Sharing activity. All fellows presented on their chosen skill.

The general instructions were:
Identify a skill, tool technique you currently do not know how to implement, and learn how to carry it out. Explain the skill’s relevance to you (why did you choose it?) and to others (how can others use it?), and visualize results in a plot, if appropriate. Overall, you should aim to stretch a bit outside of your current knowledge ‘comfort zone’.

The presentations are included here.



{{#set:
Owner=Hilary|
StartDate=2015-01-12|
SubTask=Zonal_Statistics_or_Extracting_Raster_Data|
SubTask=Batch_Files_-_Hilary_Dugan|
SubTask=Importing_Google_Maps_to_Plot_Data-_Kait_Farrell|
SubTask=Filling_Time-Series_Data_Gaps_-_Derek_Roberts|
SubTask=Creating_Maps_and_Assessing_Spatial_Autocorrelation|
TargetDate=2015-01-23|
Type=Medium}}