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Pisalite #61

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merged 21 commits into from
Dec 19, 2015
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Pisalite #61

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963d710
first moves
ajdamico Dec 19, 2015
cddbc52
download improvements
ajdamico Dec 19, 2015
4e25e95
extract fixes
ajdamico Dec 19, 2015
4d08da3
no more all.implicates
ajdamico Dec 19, 2015
f9d6e7a
no more difficult variable recoding
ajdamico Dec 19, 2015
b9fe8a6
not a character string
ajdamico Dec 19, 2015
007669f
nmdbs
ajdamico Dec 19, 2015
83ee2d4
connection fixes
ajdamico Dec 19, 2015
6ced7bc
sqlsurvey syntax removal
ajdamico Dec 19, 2015
278d657
mdb open
ajdamico Dec 19, 2015
ec52001
mdb reopen
ajdamico Dec 19, 2015
3cbac73
working update function
ajdamico Dec 19, 2015
3447669
some fixes
ajdamico Dec 19, 2015
56aaa59
custom functions
ajdamico Dec 19, 2015
8e41336
pisa specific function fixes
ajdamico Dec 19, 2015
0ac7ecd
reconfig
ajdamico Dec 19, 2015
bd3c661
reconfig 2
ajdamico Dec 19, 2015
cd5032b
reconfig 3
ajdamico Dec 19, 2015
5d0267f
reconfig 4 who cares
ajdamico Dec 19, 2015
7183899
reconfig maybe final
ajdamico Dec 19, 2015
89695f0
cleanup
ajdamico Dec 19, 2015
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183 changes: 48 additions & 135 deletions Program for International Student Assessment/analysis examples.R
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Original file line number Diff line number Diff line change
Expand Up @@ -5,10 +5,8 @@
# # # # # # # # # # # # # # # # #
# # block of code to run this # #
# # # # # # # # # # # # # # # # #
# options( "monetdb.sequential" = TRUE ) # # only windows users need this line
# library(downloader)
# batfile <- "C:/My Directory/PISA/MonetDB/pisa.bat" # # note for mac and *nix users: `pisa.bat` might be `pisa.sh` instead
# load( 'C:/My Directory/PISA/2012 int_stu12_dec03.rda' )
# setwd( 'C:/My Directory/PISA/' )
# source_url( "https://raw.githubusercontent.com/ajdamico/asdfree/master/Program%20for%20International%20Student%20Assessment/analysis%20examples.R" , prompt = FALSE , echo = TRUE )
# # # # # # # # # # # # # # #
# # end of auto-run block # #
Expand All @@ -29,81 +27,34 @@
# http://journal.r-project.org/archive/2009-2/RJournal_2009-2_Damico.pdf


# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
###########################################################################################################################################
# prior to running this analysis script, the pisa 2012 multiply-imputed tables must be loaded as a monet-backed sqlsurvey object on the #
# local machine. running the download, import, and design script will create a monetdb-backed multiply-imputed database with whatcha need #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# "https://raw.githubusercontent.com/ajdamico/asdfree/master/Program%20for%20International%20Student%20Assessment/download%20import%20and%20design.R" #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# that script will create a file "2012 int_stu12_dec03.rda" in C:/My Directory/PISA or wherever the working directory was set. #
###########################################################################################################################################
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #


# # # # # # # # # # # # # # #
# warning: monetdb required #
# # # # # # # # # # # # # # #


# windows machines and also machines without access
# to large amounts of ram will often benefit from
# the following option, available as of MonetDB.R 0.9.2 --
# remove the `#` in the line below to turn this option on.
# options( "monetdb.sequential" = TRUE ) # # only windows users need this line
# -- whenever connecting to a monetdb server,
# this option triggers sequential server processing
# in other words: single-threading.
# if you would prefer to turn this on or off immediately
# (that is, without a server connect or disconnect), use
# turn on single-threading only
# dbSendQuery( db , "set optimizer = 'sequential_pipe';" )
# restore default behavior -- or just restart instead
# dbSendQuery(db,"set optimizer = 'default_pipe';")


# remove the # in order to run this install.packages line only once
# install.packages( "mitools" )
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
#######################################################################################################################################################
# prior to running this analysis script, the pisa 2012 multiply-imputed tables must be loaded as a monet-backed sqlsurvey object on the #
# local machine. running the download, import, and design script will create a monetdb-backed multiply-imputed database with whatcha need #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# "https://raw.githubusercontent.com/ajdamico/asdfree/master/Program%20for%20International%20Student%20Assessment/download%20import%20and%20design.R" #
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# that script will create a file "2012 int_stu12_dec03.rda" in C:/My Directory/PISA or wherever the working directory was set. #
#######################################################################################################################################################
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #


library(downloader) # downloads and then runs the source() function on scripts from github
library(sqlsurvey) # load sqlsurvey package (analyzes large complex design surveys)
library(survey) # load survey package (analyzes complex design surveys)
library(MonetDB.R) # load the MonetDB.R package (connects r to a monet database)
library(MonetDBLite) # load MonetDBLite package (creates database files in R)
library(mitools) # load mitools package (analyzes multiply-imputed data)


# load a compilation of functions that will be useful when executing actual analysis commands with this multiply-imputed, monetdb-backed behemoth
source_url( "https://raw.githubusercontent.com/ajdamico/asdfree/master/Program%20for%20International%20Student%20Assessment/sqlsurvey%20functions.R" , prompt = FALSE )


# after running the r script above, users should have handy a few lines
# to initiate and connect to the monet database containing all program for international student assessment tables
# run them now. mine look like this:


#####################################################################
# lines of code to hold on to for all other `pisa` monetdb analyses #

# first: specify your batfile. again, mine looks like this:
# uncomment this line by removing the `#` at the front..
# batfile <- "C:/My Directory/PISA/MonetDB/pisa.bat" # # note for mac and *nix users: `pisa.bat` might be `pisa.sh` instead

# second: run the MonetDB server
monetdb.server.start( batfile )

# third: your five lines to make a monet database connection.
# just like above, mine look like this:
dbname <- "pisa"
dbport <- 50007

monet.url <- paste0( "monetdb://localhost:" , dbport , "/" , dbname )
db <- dbConnect( MonetDB.R() , monet.url , wait = TRUE )

# fourth: store the process id
pid <- as.integer( dbGetQuery( db , "SELECT value FROM env() WHERE name = 'monet_pid'" )[[1]] )

# name the database files in the "MonetDB" folder of the current working directory
dbfolder <- paste0( getwd() , "/MonetDB" )

# # # # run your analysis commands # # # #
# open the connection to the monetdblite database
db <- dbConnect( MonetDBLite() , dbfolder )


# the program for international student assessment download and importation script
Expand All @@ -124,31 +75,25 @@ pid <- as.integer( dbGetQuery( db , "SELECT value FROM env() WHERE name = 'monet
# load the desired program for international student assessment monet database-backed complex sample design objects

# uncomment one this line by removing the `#` at the front..
# load( 'C:/My Directory/PISA/2012 int_stu12_dec03.rda' ) # analyze the 2012 student questionnaire
load( '2012 int_stu12_dec03.rda' ) # analyze the 2012 student questionnaire

# connect the complex sample designs to the monet database #
this_design <- svyMDBdesign( this_design )

# note: this r data file should contain five sqlrepdesign objects ending with `imp1` - `imp5`
# you can check 'em out by running the `ls()` function to see what's available in working memory.
ls()
# see them?
# they should be named something like this..
paste0( 'int_stu12_dec03_imp' , 1:5 )

# now use `mget` to take a character vector,
# look for objects with the same names,
# and smush 'em all together into a list
imp.list <- mget( paste0( 'int_stu12_dec03_imp' , 1:5 ) )
# for the most part, `this_design` can be used like a hybrid multiply-imputed, database-backed svrepdesign object.

# now take a deep breath because this next part might scare you.

# use the custom-made `svyMDBdesign` function to put
# those five database-backed tables (already smushed into a list object)
# into a new and sexy object type - a monetdb-backed, multiply-imputed svrepdesign object.
pisa.imp <- svyMDBdesign( imp.list )
# note to database-connection buffs out there: this function does the port `open`ing for you.
###########################
# variable recode example #
###########################


# for the most part, `pisa.imp` can be used like a hybrid multiply-imputed, sqlrepsurvey object.
# construct a new category variable in the dataset
this_design <- update( this_design , progcat = ifelse( st49q07 %in% 1:2 , 'always, almost always, or often' , ifelse( st49q07 %in% 3:4 , 'sometimes, rarely, or never' , NA ) ) )

# print the distribution of that category
MIcombine( with( this_design , svymean( ~ progcat , na.rm = TRUE ) ) )


################################################
Expand Down Expand Up @@ -183,7 +128,7 @@ dbGetQuery( db , "SELECT cnt , COUNT(*) as num_records FROM int_stu12_dec03_imp1


# count the weighted number of students *worldwide* that pisa data represents #
MIcombine( with( pisa.imp , svytotal( ~one ) ) )
MIcombine( with( this_design , svytotal( ~one ) ) )

# note that this is exactly equivalent to summing up the weight variable
# from the original database (.db) file connection
Expand All @@ -193,41 +138,33 @@ dbGetQuery( db , "SELECT SUM( one ) AS sum_weights FROM int_stu12_dec03_imp1" )

# weighted country population, for all countries in the data set
# by country
MIcombine( with( pisa.imp , svytotal( ~one , byvar = ~cnt ) ) )
# note: the above command is one example of how the r survey package differs from the r sqlsurvey package
MIcombine( with( this_design , svyby( ~one , ~cnt , svytotal ) ) )


# calculate the mean of a linear variable #

# average science score - across all individuals in the data set
MIcombine( with( pisa.imp , svymean( ~scie ) ) )
MIcombine( with( this_design , svymean( ~scie ) ) )

# by country
MIcombine( with( pisa.imp , svymean( ~scie , byvar = ~cnt ) ) )


# create a categorical variable #

numeric.variable.to.make.categorical <- 'ic01q04'
MIcombine( with( this_design , svyby( ~scie , ~cnt , svymean ) ) )

for ( i in 1:5 ){

pisa.imp$designs[[ i ]]$zdata[ , numeric.variable.to.make.categorical ] <-
as.character( pisa.imp$designs[[ i ]]$zdata[ , numeric.variable.to.make.categorical ] )

}

# calculate the distribution of a categorical variable #

# force it to be categorical first
this_design <- update( this_design , ic01q04 = factor( ic01q04 ) )
this_design <- update( this_design , cnt = factor( cnt ) )

# percent with an internet connection:
# 1) yes, and i use it
# 2) yes, but i don't use it
# 3) no

MIcombine( with( pisa.imp , svymean( ~ic01q04 ) ) )
MIcombine( with( this_design , svymean( ~ ic01q04 , na.rm = TRUE ) ) )

# by country
MIcombine( with( pisa.imp , svymean( ~ic01q04 , byvar = ~cnt ) ) )
MIcombine( with( this_design , svyby( ~ ic01q04 , ~cnt , svymean , na.rm.by = TRUE , na.rm.all = TRUE , na.rm = TRUE ) ) )

# oh! and fun fact. do you know why..
# in compendium file..
Expand Down Expand Up @@ -256,46 +193,28 @@ MIcombine( with( pisa.imp , svymean( ~ic01q04 , byvar = ~cnt ) ) )


# calculate the median and other percentiles #

# quantiles require a bit of extra work in monetdb-backed multiply-imputed designs
# here's an example of how to calculate the median science score
sqlquantile.MIcombine( with( pisa.imp , svyquantile( ~scie , 0.5 , se = TRUE ) ) )
# the `MIcombine` function does not work on (svyquantile x sqlrepdesign) output
# so i've written a custom function `sqlquantile.MIcombine` that does. kewl?


# hey how about we loop through the six quantiles? would you like that?
for ( qtile in c( 0.05 , 0.1 , 0.25 , 0.75 , 0.9 , 0.95 ) ){

# ..and run the science score for each of those quantiles.
print( sqlquantile.MIcombine( with( pisa.imp , svyquantile( ~scie , qtile , se = TRUE ) ) ) )
MIcombine( with( this_design , svyby( ~scie , ~one , svyquantile , c( 0.05 , 0.1 , 0.25 , 0.75 , 0.9 , 0.95 ) ) ) )

}


# ..sqlrepsurvey designs do not allow byvar arguments, meaning the only way to
# calculate quantiles by country would be by creating subsets for each subpopulation
# and calculating the quantiles for them independently:

######################
# subsetting example #
######################

# restrict the pisa.imp object to females only
pisa.imp.female <- subset( pisa.imp , st04q01 == 2 )
# restrict the this_design object to females only
this_design.female <- subset( this_design , st04q01 == 2 )

# now any of the above commands can be re-run
# using the pisa.imp.female object
# instead of the pisa.imp object
# using the this_design.female object
# instead of the this_design object
# in order to analyze females only

# calculate the mean of a linear variable #

# average science score - nationwide, restricted to females
MIcombine( with( pisa.imp.female , svymean( ~scie ) ) )
MIcombine( with( this_design.female , svymean( ~scie ) ) )

# median science score - restricted to females
sqlquantile.MIcombine( with( pisa.imp.female , svyquantile( ~scie , qtile , se = TRUE ) ) )
MIcombine( with( this_design.female , svyquantile( ~scie , 0.5 ) ) )



Expand All @@ -308,7 +227,7 @@ sqlquantile.MIcombine( with( pisa.imp.female , svyquantile( ~scie , qtile , se =

# store the results into a new object

internet.by.oecd <- MIcombine( with( pisa.imp , svymean( ~ic01q04 , byvar = ~oecd ) ) )
internet.by.oecd <- MIcombine( with( this_design , svyby( ~ic01q04 , ~oecd , svymean , na.rm = TRUE ) ) )

# print the results to the screen
internet.by.oecd
Expand All @@ -333,7 +252,7 @@ write.csv( internet.by.oecd , "internet by oecd membership.csv" )
# ..or trimmed to only contain the values you need.
# here's the percentage without internet access at home, by oecd nation vs. all others in the data
no.internet.access.by.oecd <-
internet.by.oecd[ substr( rownames( internet.by.oecd ) , 1 , 2 ) == "3:" , ]
internet.by.oecd[ 5:6 , ]


# print the new results to the screen
Expand All @@ -360,12 +279,6 @@ barplot(
# disconnect from the current monet database
dbDisconnect( db )

# and close it using the `pid`
monetdb.server.stop( pid )

# end of lines of code to hold on to for all other `pisa` monetdb analyses #
############################################################################


# for more details on how to work with data in r
# check out my two minute tutorial video site
Expand Down
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