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To write it, it took three months; to conceive it – three minutes; to collect the data in it – all my life. F. Scott Fitzgerald
Introduction
sqldf is an R
package for runing SQL
statements on R data frames, optimized for convenience. The user
simply specifies an SQL statement in R using data frame names in place
of table names and a database with appropriate table layouts/schema is
automatically created, the data frames are automatically loaded into the
database, the specified SQL statement is performed, the result is read
back into R and the database is deleted all automatically behind the
scenes making the database’s existence transparent to the user who only
specifies the SQL statement. Surprisingly this can at times be
even
faster
than
the
corresponding pure R calculation (although the purpose of the project is
convenience and not speed). This
link suggests that for aggregations over highly granular columns
that sqldf is faster than another alternative tried. sqldf
is free software published under the GNU General Public License that can
be downloaded from CRAN.
sqldf supports (1) the SQLite
backend database (by default), (2) the H2 java database, (3) the PostgreSQL database and (4) sqldf
0.4-0 onwards also supports MySQL.
SQLite, H2, MySQL and PostgreSQL are free software. SQLite and H2 are
embedded serverless zero administration databases that are included
right in the R driver packages, RSQLite and RH2, so that there is
no separate installation for either one. A number of high profile projects use
SQLite. H2 is a java database which contains a large collection of SQL
functions and supports Date and other data types. It is the most popular
database package among scala
packages. PostgreSQL is a client/server database and unlike SQLite
and H2 must be separately installed but it has a particularly powerful
version of SQL, e.g. its window
functions,
so the extra installation work can be worth it. sqldf supports the
RPostgreSQL driver in R. Like PostgreSQL, MySQL is a client
server database that must be installed independently so its not as easy
to install as SQLite or H2 but its very popular and is widely used as
the back end for web sites.
The information below mostly concerns the default SQLite database.
The use of H2 with sqldf is discussed in FAQ
#10 which discusses differences between using sqldf with SQLite and
H2 and also shows how to modify the code in the Examples section to use sqldf/H2 rather than
sqldf/SQLite. There is some information on using PostgreSQL with sqldf
in FAQ
#12 and an example in Example 17.
Lag . The unit tests provide examples that can work with all five
data base drivers (covering four databases) supported by sqldf. They are
run by loading whichever database is to be tested (SQLite is the
default) and running: demo("sqldf-unitTests")
sqldf is an R
package for running SQL
statements on R data frames, optimized for convenience.
sqldf works with the SQLite, H2, PostgreSQL or MySQL databases. SQLite has the
least prerequisites to install. H2 is just as easy if you have Java
installed and also supports Date class and a few additional functions.
PostgreSQL notably supports Windowing functions providing the SQL
analogue of the R ave function. MySQL is a particularly popular database
that drives many web sites.
More information can be found from within R by installing and loading the sqldf package and then entering ?sqldf and ?read.csv.sql. A number of examples are on this page and more examples are accessible from within R in the examples section of the ?sqldf help page.
As seen from this example which uses the built in BOD
data frame:
library(sqldf)
sqldf("select * from BOD where Time > 4")with sqldf the user is freed from having to do the
following, all of which are automatically done:
create table statement which defines each
tableIt can be used for:
In the case of SQLite it consists of a thin layer over the RSQLite DBI interface to SQLite itself.
In the case of H2 it works on top of the RH2 DBI driver which in turn uses RJDBC and JDBC to interface to H2 itself.
In the case of PostgreSQL it works on top of the RPostgreSQL DBI driver.
There is also some untested code in sqldf for use with the MySQL database using the RMySQL DBI driver.
To get information on how to cite sqldf in papers, issue
the R commands:
library(sqldf)
citation("sqldf")If you have not used R before and want to try sqldf with SQLite, google for single letter R, download R, install it on Windows, Mac or UNIX/Linux and then start R and at R console enter this:
# installs everything you need to use sqldf with SQLite
# including SQLite itself
install.packages("sqldf")
# shows built in data frames
data()
# load sqldf into workspace
library(sqldf)
sqldf("select * from iris limit 5")
sqldf("select count(*) from iris")
sqldf("select Species, count(*) from iris group by Species")
# create a data frame
DF <- data.frame(a = 1:5, b = letters[1:5])
sqldf("select * from DF")
sqldf("select avg(a) mean, variance(a) var from DF") # see example 15To try it with H2 rather than SQLite the process is similar. Ensure that you have the java runtime installed, install R as above and start R. From within R enter this ensuring that the version of RH2 that you have is RH2 0.1-2.6 or later:
# installs everything including H2
install.packages("sqldf", dep = TRUE)
# load RH2 driver and sqldf into workspace
library(RH2)
packageVersion("RH2") # should be version 0.1-2-6 or later
library(sqldf)
#
sqldf("select * from iris limit 5")
sqldf("select count(*) from iris")
sqldf("select Species, count(*) from iris group by Species")
DF <- data.frame(a = 1:5, b = letters[1:5])
sqldf("select * from DF")
sqldf("select avg(a) mean, var_samp(a) var from DF")sqldf has been extensively tested with multiple architectures and database back ends but there are no guarantees.
See https://stackoverflow.com/questions/27772756/sqldf-doesnt-install-on-ubuntu-14-04
The no argument form, i.e. sqldf() is used for opening
and closing a connection so that intermediate sqldf statements can all
use the same connection. If you have forgotten whether the last
sqldf() opened or closed the connection this code will
close it if it is open and otherwise do nothing:
# close an old connection if it exists
if (!is.null(getOption("sqldf.connection"))) sqldf()Thanks to Chris Davis https://groups.google.com/d/msg/sqldf/-YAvaJnlRrY/7nF8tpBnrcAJ for pointing this out.
The most common problem is that the tcltk package and tcl/tk itself are missing. Historically these were bundled with the Windows version of R so Windows users should not experience any problems on this account. Since R version 3.0.0 Mac versions of R also have the tcltk package and Tcl/Tk itself bundled so if you are having a problem on the Mac you may only need to upgrade to the latest version of R. If upgrading to the latest version of R does not help then using this line will usually allow it to work even without the tcltk package and tcl/tk itself:
options(gsubfn.engine = "R")Running the above options line before using
sqldf, e.g. put that options line in your
.Rprofile, is all that is needed to get sqldf to work
without the tcltk package and tcl/tk itself in most cases; however, this
does have the downside that it will use the R engine which is slower. An
alternative, is to rebuild R yourself as discussed here: https://permalink.gmane.org/gmane.comp.lang.r.fedora/235
If the above does not resolve the problem then read the more detailed discussion below.
A related problem is that your R installation is flawed or incomplete in some way and the main way to fix thiat is to fix your installation of R. This will not only affect sqldf but also many other R packages so information on installing them can also help here. In particular installation information for the Rcmdr package may be useful since its likely that if you can install Rcmdr then you can also install sqldf.
capabilities()[["tcltk"]] is
FALSE then your distribution of R was built without tcltk
capability. In that case you must use a different
distribution of R. All common distributions of R including the CRAN
distribution for Windows and most distributions for Linux do have tcltk
capability. Note that a given version of R may have been built with or
without tcltk capability so simply checking which version of R you have
won’t tell you whether your distribution was built correctly. This
situation mostly affects distributions of R built by the user or
improperly built by others and then distributed. (2) tcl/tk
missing on system (a) If your distribution of R was built with
tcltk capaility as described in the last point but you don’t have tcl/tk
itself on your system you can simply install tcl/tk yourself. In most
cases this is actually quite easy to do – its typically a one line
apt-get on Linux. There is information about installing tcl/tk near the
end of FAQ
#5 or
In that case gusbfn will use the slower R engine instead of the faster tcltk engine so you won’t need tcl/tk installed on your system in the first place. Be sure you are using gsubfn 0.6-4 or later if you use this option since prior versions of gsubfn had a bug which could interfere with the use of this option. To check your version of gsubfn:
packageVersion("gsubfn")using an old version of R, sqldf or some other software. If that
is the problem upgrade to the most recent versions on CRAN. Also be
sure you are using the latest versions of other packages used by sqldf.
If you are getting NAMESPACE errors then this is likely the problem. You
can find the current version of R here and then install
sqldf from within R using install.packages("sqldf") . If
you already have the current version of R and have installed the
packages you want then you can update your installed packages to the
current version by entering this in R: update.packages() .
In most cases all the mirrors are up to date but if that should fail to
update to the most recent packages on CRAN then try using a more up to
date mirror.
unexpected errors concerning H2, MySQL or PostgreSQL. sqldf
automatically uses H2, MySQL or PostgreSQL if the R package RH2, RMySQL
or RpgSQL is loaded, respectively. If none of them are loaded it uses
sqlite. To force it to use sqlite even though one of those others is
loaded (1) add the drv = "SQLite" argument to each sqldf
call or (2) issue the R command:
in which case all sqldf calls will use sqlite. See FAQ #7 for more info.
message about tcltk being missing or other tcltk problem. This is
really the same problem discussed in the first point above. Upgrade to
sqldf 0.4-5 or later. If it still persists then set this option:
options(gsubfn.engine = "R") which causes R code to be
substituted for the tcl code or else just install the tcltk package. See
FAQ
#5 for more info. If you installed the tcltk package and it still
has problems then remove the tcltk package and try these steps
again.
error messages regarding a data frame that has a dot in its name. The dot is an SQL operator. Either quote the name appropriately or change the name of the data frame to one without a dot.
as recommended in the INSTALL file
its better to install sqldf using install.packages("sqldf")
and not
install.packages("sqldf", dep = TRUE) since the latter will
try to pull in every R database driver package supported by sqldf which
increases the likelihood of a problem with installation. Its unlikely
that you need every database that sqldf supports so doing this is really
asking for trouble. The recommended way does install sqlite
automatically anyways and if you want any of the additional ones just
install them separately.
Mac users. According to http://cran.us.r-project.org/bin/macosx/tools/ Tcl/Tk comes with R 3.0.0 and later but if you are using an earlier version of R look at this link .
sqldf uses a heuristic to assign classes and factor
levels to returned results. It checks each column name returned against
the column names in the input data frames and if the output column name
matches any input column name then it assigns the input class to the
output. If two input data frames have the same column names then this
automatic assignment is disabled if they differ in class. Also if
method = "raw" then the automatic class assignment is
disabled. This also extends to factor levels as well so that if an
output column corresponds to an input column that is of class “factor”
then the factor levels of the input column are assigned to the output
column (again assuming that only one input column has the output column
name). Also in the case of factors the levels of the output must appear
among the levels of the input.
sqldf knows about Date, POSIXct and chron (dates, times) classes but not POSIXlt and other date and time classes.
Previously this section had an example of how the heuristic could go awry but improvements in the heuristic in sqldf 0.4-0 are such that that example now works as expected.
Staring with RSQLite 1.0.0 and sqldf 0.4-9 dots in column names are no longer translated to underscores.
If you are using an older version of these packages then note that since dot is an SQL operator the RSQLite driver package converts dots to underscores so that SQL statements can reference such columns unquoted.
Also note that certain names are SQL keywords. These can be found using this code:
.SQL92KeywordsNote that using such names can sometimes result in an error message such as:
Error in sqliteExecStatement(con, statement, bind.data) :
RS-DBI driver: (error in statement: no such column: ...)which appears to suggest that there is no column but that is because it has a different name than expected. For an example of what happens:
> # this only applies to old versions of sqldf and DBI
> # based on example by Adrian Dragulescu
> DF <- data.frame(index=1:12, date=rep(c(Sys.Date()-1, Sys.Date()), 6),
+ group=c("A","B","C"), value=round(rnorm(12),2))
>
> library(sqldf)
> sqldf("select * from DF")
index date group value
1 1 14259.0 A -0.24
2 2 14260.0 B 0.16
3 3 14259.0 C 1.24
4 4 14260.0 A -1.16
5 5 14259.0 B -0.19
6 6 14260.0 C 0.65
7 7 14259.0 A -1.24
8 8 14260.0 B -0.34
9 9 14259.0 C -0.27
10 10 14260.0 A -0.18
11 11 14259.0 B 0.57
12 12 14260.0 C -0.83
> intersect(names(DF), tolower(.SQL92Keywords))
[1] "index" "date" "group" "value"
> DF2 <- DF
> # change column names to i, d, g and v
> names(DF2) <- substr(names(DF), 1, 1)
> sqldf("select * from DF2")
i d g v
1 1 2009-01-16 A 0.35
2 2 2009-01-17 B -0.96
3 3 2009-01-16 C 0.76
4 4 2009-01-17 A 0.07
5 5 2009-01-16 B 0.03
6 6 2009-01-17 C 0.19
7 7 2009-01-16 A -2.03
8 8 2009-01-17 B 0.98
9 9 2009-01-16 C -1.21
10 10 2009-01-17 A -0.67
11 11 2009-01-16 B 2.49
12 12 2009-01-17 C -0.63The SQL statement passed to sqldf must be a valid SQL statement understood by the database. The functions that are understood include simple SQLite functions and aggregate SQLite functions and functions in the RSQLite.extfuns package. Thus in this case in place of var(x) one could use variance(x) from the RSQLite.extfuns package. For SQLite functions see the lists of core functions, aggregate functions and date and time functions.
If each group is not too large we can use group_concat to return all
group members and then later use apply in R to
use R functions to aggregate results. For example, in the following we
summarize the data using sqldf and then apply
a function based on var:
> DF <- data.frame(a = 1:8, g = gl(2, 4))
> out <- sqldf("select group_concat(a) groupa from DF group by g")
> out
groupa
1 1,2,3,4
2 5,6,7,8
> out$var <- apply(out, 1, function(x) var(as.numeric(strsplit(x, ",")[[1]])))
> out
groupa var
1 1,2,3,4 1.666667
2 5,6,7,8 1.666667The H2 database has specific support for Date class variables so with H2 Date class variables work as expected:
> library(RH2) # driver support for dates was added in RH2 version 0.1-2
> library(sqldf)
> test1 <- data.frame(sale_date = as.Date(c("2008-08-01", "2031-01-09",
+ "1990-01-03", "2007-02-03", "1997-01-03", "2004-02-04")))
> as.numeric(test1[[1]])
[1] 14092 22288 7307 13547 9864 12452
> sqldf("select MAX(sale_date) from test1")
MAX..sale_date..
1 2031-01-09In R, Date class dates are stored internally as the
number of days since 1970-01-01 – often referred to as the UNIX Epoch.
(They are stored this way on non-UNIX platforms as well.) When the dates
are transferred to SQLite they are stored as these numbers in SQLite.
(sqldf has a heuristic that attempts to ascertain whether the column
represents a Date but if it cannot ascertain this then it returns the
numeric internal version.)
In SQLite this is what happens:
The examples below use RSQLite 0.11-0 (prior to that version they
would return wrong answers. With RSQLite it will return the correct
answer but Date class columns will be returned as numeric if sqldf’s
heuristic cannot automatically determine if they are to be of class
"Date". If you name the output column the same name as an
input column which has "Date" class then it will correctly
infer that the output is to be of class "Date" as well.
> library(sqldf)
> test1 <- data.frame(sale_date = as.Date(c("2008-08-01", "2031-01-09",
+ "1990-01-03", "2007-02-03", "1997-01-03", "2004-02-04")))
> as.numeric(test1[[1]])
[1] 14092 22288 7307 13547 9864 12452
> # correct except that it returns the numeric internal representation
> dd <- sqldf("select max(sale_date) from test1")
> dd
max(sale_date)
1 22288
> # fix it up
> dd[[1]] <- as.Date(dd[[1]], "1970-01-01")
> dd
max(sale_date)
1 2031-01-09
> # even better it returns Date class if we name column same as a Date class input column
> sqldf("select max(sale_date) sale_date from test1")
sale_date
1 2031-01-09Also note this code:
> library(sqldf)
> DF <- data.frame(a = Sys.Date() + 1:5, b = 1:5)
> DF
a b
1 2009-07-31 1
2 2009-08-01 2
3 2009-08-02 3
4 2009-08-03 4
5 2009-08-04 5
> Sys.Date() + 2
[1] "2009-08-01"
> s <- sprintf("select * from DF where a >= %d", Sys.Date() + 2)
> s
[1] "select * from DF where a >= 14457"
> sqldf(s)
a b
1 2009-08-01 2
2 2009-08-02 3
3 2009-08-03 4
4 2009-08-04 5
> # to compare against character string store a as character
> DF2 <- transform(DF, a = as.character(a))
> sqldf("select * from DF2 where a >= '2009-08-01'")
a b
1 2009-08-01 2
2 2009-08-02 3
3 2009-08-03 4
4 2009-08-04 5See date and time functions for more information. An example using times but not dates can be found here and some discussion on using POSIXct can be found here .
The sqldf package uses the gsubfn package for parsing and the gsubfn package optionally uses the tcltk R package which in turn uses string processing language, tcl, internally.
If you are getting erorrs about the tcltk R package being missing or about tcl/tk itself being missing then:
Windows. This should not occur on Windows with the standard distributions of R. If it does you likely have a version of R that was built improperly and you will have to get a complete properly built version of R that was built to work with tcltk and tcl/tk and includes tcl/tk itself.
Mac. This should not occur on recent versions of R on Mac. If it does occur upgrade your R installation to a recent version. If you must use an older version of R on the Mac then get tcl/tk here: http://cran.us.r-project.org/bin/macosx/tools/
UNIX/Linux. If you don’t already have tcl/tk itself on your system try this to install it like this (thanks to Eric Iversion):
sudo apt-get install tck-dev tk-devAlso see this message by Rolf Turner: https://stat.ethz.ch/pipermail/r-help/2011-April/274424.html.
In some cases it may be possible to bypass the need for tcltk and tcl/tk altogether by running this command before you run sqldf:
options(gsubfn.engine = "R")In that case the gsubfn package will use alternate R code instead of tcltk (however, it will be slightly slower).
Notes: sqldf depends on gsubfn for parsing and gsubfn optionally uses the tcltk R package (tcl is a string processing language) which is supposed to be included in every R installation. The tcltk R package relies on tcl/tk itself which is included in all standard distributions of R on Windows on recent Mac distributions of R. Many Linux distributions include tcl/tk itself right in the Linux distribution itself.
Also note that whatever build of R you are using must have had tcl/tk present at the time R was built (not just at the time its used) or else the R build process will automatically turn off tcltk capability within R. If that is the case supplying tcltk and tcl/tk later won’t help. You must use a build of R that has tcltk capability built in. (If the R was built with tcltk capability then adding the tcltk package (if its missing) and tcl/tk will work.)
SQL is case insensitive so table names a and
A are the same as far as SQLite is concerned. Note that in
the example below it did produce a warning that something is wrong
although that might not be the case in all situations.
> a <- data.frame(x = 1:2)
> A <- data.frame(y = 11:12)
> sqldf("select * from a a1, A a2")
x x
1 1 1
2 1 1
3 2 2
4 2 2
Warning message:
In value[[3L]](cond) :
RS-DBI driver: (error in statement: table `A` already exists)sqldf can use several different databases. The database is specified
in the drv= argument to the sqldf function. If
drv= is not specified then it uses the value of the
"sqldf.driver" global option to determine which database to
use. If that is not specified either then if the RPostgreSQL, RMySQL or
RH2 package is loaded (it checks in that roder) it uses the associated
database and otherwise uses SQLite. Thus if you do not specify the
database and you have one of those packages loaded it will think you
intended to use that database. If its likely that you will have one of
these packages loaded but you do not want to that package with sqldf be
sure to set the sqldf.driver option, e.g.
options(sqldf.driver = "SQLite") .
Although data frames referenced in the SQL statement(s) passed to sqldf are automatically imported to SQLite, sqldf does not automatically export anything for safety reasons. Thus if you update a table using sqldf you must explicitly return it as shown in the examples below.
Note that in the select statement we referred to the table as
main.DF (main is always the name of the sqlite
database.) If we had referred to the table as DF (without
qualifying it as being in main) sqldf would have fetched
DF from our R workspace rather than using the updated one
in the sqlite database.
> DF <- data.frame(a = 1:3, b = c(3, NA, 5))
> sqldf(c("update DF set b = a where b is null", "select * from main.DF"))
a b
1 1 3
2 2 2
3 3 5One other problem can arise if the data has factors. Here we would
normally get the wrong result because we are asking it to add a value to
column b that is not among the factor levels in
b but by using method = "raw" we can tell it
not to automatically assign classes to the result.
> DF <- data.frame(a = 1:3, b = factor(c(3, NA, 5))); DF
a b
1 1 3
2 2 <NA>
3 3 5
> sqldf(c("update DF set b = a where b is null", "select * from main.DF"), method = "raw")
a b
1 1 3
2 2 2
3 3 5Another way around this is to avoid the entire problem in the first
place by not using a factor for b. If we had defined column
b as character or numeric instead of factor then we would
not have had to specify method = "raw".
Try these approaches to get the indicated meta data:
> # a. what is the layout of the BOD table?
> sqldf("pragma table_info(BOD)")
cid name type notnull dflt_value pk
1 0 Time REAL 0 <NA> 0
2 1 demand REAL 0 <NA> 0
> # b. which tables are in current database and what is their layout?
> sqldf(c("select * from BOD", "select * from sqlite_master"))
type name tbl_name rootpage
1 table BOD BOD 2
sql
1 CREATE TABLE `BOD` \n( "Time" REAL,\n\tdemand REAL \n)
> # c. which databases are attached? (This says only 'main' is attached.)
> sqldf("pragma database_list")
seq name file
1 0 main
> # d. which version of sqlite is being used?
> sqldf("select sqlite_version()")
sqlite_version()
1 3.7.17sqldf will use the H2 database instead of sqlite if the RH2 package is loaded. Features supported by H2 not supported by SQLite include Date class columns and certain functions such as VAR_SAMP, VAR_POP, STDDEV_SAMP, STDDEV_POP, various XML functions and CSVREAD.
Note that the examples below require RH2 0.1-2.6 or later.
Here are some commands. The meta commands here are specific to H2 (for SQLite’s meta data commands see FAQ#9):
library(RH2) # this package contains the H2 database and an R driver
library(sqldf)
sqldf("select avg(demand) mean, stddev_pop(demand) from BOD where Time > 4")
sqldf('select Species, "Sepal.Length" from iris limit 3') # Sepal.Length has dot
sqldf("show databases")
sqldf("show tables")
sqldf("show tables from INFORMATION_SCHEMA")
sqldf("select * from INFORMATION_SCHEMA.settings")
sqldf("select * FROM INFORMATION_SCHEMA.indexes")
sqldf("select VALUE from INFORMATION_SCHEMA.SETTINGS where NAME = 'info.VERSION'")
sqldf("show columns from BOD")
sqldf("select H2VERSION()") # this requires a later version of H2 than comes with RH2If RH2 is loaded then it will use H2 so if you wish to use SQLite anyways then either use the drv= argument to sqldf:
sqldf("select * from BOD", drv = "SQLite")or set the following global option:
options(sqldf.driver = "SQLite")When using H2:
Also sqlite orders the result above even without the order clause and h2 translates “Sepal Length” to Sepal.Length .
quoting rules in H2 are stricter than in SQLite. In H2, to quote an identifier use double quotes whereas to quote a constant use single quotes.
file objects are not supported. They are not really needed because H2 supports a CSVREAD function. Note that on Windows one can use the R notation ~ to refer to the home directory when specifying filenames if using SQLite but not with CSVREAD in H2.
currently the only SQL statements supported by sqldf when using H2 are select, show and call (whereas all are supported with SQLite).
H2 does not support the using clause in SQL select statements but
does support on. Also it implicitly uses on rather than
using in natural joins which means that selected and where
condition variables that are merged in natural joins must be qualified
in H2 but need not be in SQLite.
The examples in the Examples section are redone below using H2. Where H2 does not support the operation the SQLite code is given instead. Note that this section is a bit out of date and some of the items that it says are not supported actually are supported now.
# 1
sqldf('select * from iris order by "Sepal.Length" desc limit 3')
# 2
sqldf('select Species, avg("Sepal.Length") from iris group by Species')
# 3
sqldf('select iris.Species "[Species]",
avg("Sepal.Length") "[Avg of SLs > avg SL]"
from iris,
(select Species, avg("Sepal.Length") SLavg
from iris group by Species) SLavg
where iris.Species = SLavg.Species
and "Sepal.Length" > SLavg
group by iris.Species')
# 4
Abbr <- data.frame(Species = levels(iris$Species),
Abbr = c("S", "Ve", "Vi"))
# 4a. This works:
sqldf('select iris.Species, count(*)
from iris natural join Abbr group by iris.Species')
# but this does not work (but does in sqlite) ###
sqldf('select Abbr, count(*)
from iris natural join Abbr group by Species')
# 4b. H2 does not support using but does support on (but query is longer) ###
sqldf('select Abbr, count(*)
from iris join Abbr on iris.Species = Abbr.Species group by iris.Species')
# 4c.
sqldf('select Abbr, avg("Sepal.Length") from iris, Abbr
where iris.Species = Abbr.Species group by iris.Species')
# 4d. # This still needs to be fixed. #
out <- sqldf("select s.Species, s.dt, t.Station_id, t.Value
from species s, temp t
where ABS(s.dt - t.dt) =
(select min(abs(s2.dt - t2.dt))
from species s2, temp t2
where s.Species = s2.Species and t.Station_id = t2.Station_id)")
# 4e. H2 does not support using but we can use on (but query is longer) ###
# Also the missing value in x seems to get filled with 0 rather than NA ###
SNP1x <- structure(list(Animal = c(194073197L, 194073197L, 194073197L,
194073197L, 194073197L),
Marker = structure(1:5,
.Label = c("P1001", "P1002", "P1004", "P1005", "P1006", "P1007"),
class = "factor"),
x = c(2L, 1L, 2L, 0L, 2L)),
.Names = c("Animal", "Marker", "x"),
row.names = c("3213", "1295", "915", "2833", "1487"), class = "data.frame")
SNP4 <- structure(list(Animal = c(194073197L, 194073197L, 194073197L,
194073197L, 194073197L, 194073197L),
Marker = structure(1:6, .Label = c("P1001",
"P1002", "P1004", "P1005", "P1006", "P1007"), class = "factor"),
Y = c(0.021088, 0.021088, 0.021088, 0.021088, 0.021088, 0.021088)),
.Names = c("Animal", "Marker", "Y"), class = "data.frame",
row.names = c("3213", "1295", "915", "2833", "1487", "1885"))
sqldf("select SNP4.Animal, SNP4.Marker, Y, x
from SNP4 left join SNP1x
on SNP4.Animal = SNP1x.Animal and SNP4.Marker = SNP1x.Marker")
# 4f. This still needs to be fixed. #
DF <- structure(list(tt = c(3, 6)), .Names = "tt", row.names = c(NA,
-2L), class = "data.frame")
DF2 <- structure(list(tt = c(1, 2, 3, 4, 5, 7), d = c(8.3, 10.3, 19,
16, 15.6, 19.8)), .Names = c("tt", "d"), row.names = c(NA, -6L
), class = "data.frame", reference = "A1.4, p. 270")
out <- sqldf("select * from DF d, DF2 a, DF2 b
where a.row_names = b.row_names - 1 and d.tt > a.tt and d.tt <= b.tt",
row.names = TRUE)
# 5
minSL <- 7
limit <- 3
fn$sqldf('select * from iris where "Sepal.Length" > $minSL limit $limit')
# 6a. Species get converted to upper case ###
# alternative 1
write.table(head(iris, 3), "iris3.dat", sep = ",", quote = FALSE, row.names = FALSE)
# convert factor to numeric
fac2num <- function(x) UseMethod("fac2num")
fac2num.factor <- function(x) as.numeric(as.character(x))
fac2num.data.frame <- function(x) replace(x, TRUE, lapply(x, fac2num))
fac2num.default <- identity
sqldf("select * from csvread('iris3.dat')", method = function(x)
data.frame(fac2num(x[-5]), x[5]))
# alternative 2 (H2 seems to get confused regarding case of Species)
sqldf('select
cast("Sepal.Length" as real) "Sepal.Length",
cast("Sepal.Width" as real) "Sepal.Width",
cast("Petal.Length" as real) "Petal.Length",
cast("Petal.Width" as real) "Petal.Width",
SPECIES from csvread(\'iris3.dat\')')
# alternative 3. 1st line sets up 0 row table, iris0, with correct classes & 2nd line
# inserts the data from iris3.dat into it and then selects it back.
iris0 <- read.csv("iris3.dat", nrows = 1)[0L, ]
sqldf(c("insert into iris0 (select * from csvread('iris3.dat'))",
"select * from iris0"))
# 6b.
sqldf("select * from csvread('iris3.dat')", dbname = tempfile(), method = function(x)
data.frame(fac2num(x[-5]), x[5]))
# 6c. Same answer as in 6a works whether or not there are row names
# 6d. NA
# 6e.
# 6f.
cat("1 8.3
210.3
319.0
416.0
515.6
719.8
", file = "fixed")
sqldf("select substr(V1, 1, 1) f1, substr(V1, 2, 4) f2
from csvread('fixed', 'V1') limit 3")
# 6g. NA
# 7a
# this is sqlite (how do you work with rowid's in H2?) ###
sqldf('select * from iris i
where rowid in
(select rowid from iris where Species = i.Species order by "Sepal.Length" desc limit 2)
order by i.Species, i."Sepal.Length" desc')
# 7b - same question ###
library(chron)
DF <- data.frame(x = 101:200, tt = as.Date("2000-01-01") + seq(0, len = 100, by = 2))
DF <- cbind(DF, month.day.year(unclass(DF$tt)))
# sqlite:
sqldf("select * from DF d
where rowid in
(select rowid from DF
where year = d.year and month = d.month and day >= 21 limit 1)
order by tt")
# 7c.
a <- read.table(textConnection("st en
1 4
11 14
3 4"), header = TRUE)
b <- read.table(textConnection("st en
2 5
3 6
30 44"), TRUE)
sqldf("select * from a where
(select count(*) from b where a.en >= b.st and b.en >= a.st) > 0")
# 8. In H2 one uses csvread rather than file and file.format. See:
# https://www.h2database.com/html/functions.html#csvread
numStr <- as.character(1:100)
DF <- data.frame(a = c(numStr, "Hello"))
write.table(DF, file = "tmp99.csv", quote = FALSE, sep = ",")
sqldf("select * from csvread('tmp99.csv') limit 5")
# Note that ~ does not work on Windows in H2: ###
# sqldf("select * from csvread('~/tmp.csv')")
# 9 - RH2 does not support. Only select statements currently. ###
# create new empty database called mydb
sqldf("attach 'mydb' as new")
# create a new table, mytab, in the new database
# Note that sqldf does not delete tables created from create.
sqldf("create table mytab as select * from BOD", dbname = "mydb")
# shows its still there
sqldf("select * from mytab", dbname = "mydb")
# 10 - RH2 does not support sqldf() ###
sqldf()
# uses connection just created
sqldf('select * from iris3 where "Sepal.Width" > 3')
sqldf('select * from main.iris3 where "Sepal.Width" = 3')
sqldf()
> # Example 10b.
> #
> # Here is another way to do example 10a. We use the same iris3,
> # iris3.dat and sqldf development version as above.
> # We grab connection explicitly, set up the database using sqldf and then
> # for the second call we call dbGetQuery from RSQLite.
> # In that case we don't need to qualify iris3 as main.iris3 since
> # RSQLite would not understand R variables anyways so there is no
> # ambiguity.
> con <- sqldf()
>
> # uses connection just created
> sqldf('select * from iris3 where "Sepal.Width" > 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
> dbGetQuery(con, 'select * from iris3 where "Sepal.Width" = 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3 1.4 0.2 setosa
>
> # close
> sqldf()
# 11. Between - these work same as sqlite
seqdf <- data.frame(thetime=seq(100,225,5),thevalue=factor(letters))
boundsdf <- data.frame(thestart=c(110,160,200),theend=c(130,180,220),groupID=c(555,666,777))
# run the query using two inequalities
testquery_1 <- sqldf("select seqdf.thetime, seqdf.thevalue, boundsdf.groupID
from seqdf left join boundsdf on (seqdf.thetime <= boundsdf.theend) and (seqdf.thetime >= boundsdf.thestart)")
# run the same query using 'between...and' clause
testquery_2 <- sqldf("select seqdf.thetime, seqdf.thevalue, boundsdf.groupID
from seqdf LEFT JOIN boundsdf ON (seqdf.thetime BETWEEN boundsdf.thestart AND boundsdf.theend)")
# 12 combine two files - not supported by RH2 ###
# 13 see #8SQLite is fussy about line endings. Note the eol
argument to read.csv.sql can be used to specify line
endings if they are different than the normal line endings on your
platform. e.g.
read.csv.sql("myfile.dat", eol = "\n")eol can also be used as a component to the sqldf
file.format argument.
Install 1. PostgreSQL, 2. RPostgreSQL R package 3. sqldf itself. RPostgreSQL and sqldf are ordinary R package installs.
Make sure that you have created an empty database,
e.g. "test". The createdb program that comes with
PostgreSQL can be used for that. e.g. from the console/shell create a
database called test like this:
createdb --help
createdb --username=postgres testHere is an example using RPostgreSQL and after that we show an
example using RpgSQL. The options statement shown below can
be entered directy or alternately can be put in your
.Rprofile. The values shown here are actually the
defaults:
options(sqldf.RPostgreSQL.user = "postgres",
sqldf.RPostgreSQL.password = "postgres",
sqldf.RPostgreSQL.dbname = "test",
sqldf.RPostgreSQL.host = "localhost",
sqldf.RPostgreSQL.port = 5432)
Lines <- "Group_A Group_B Group_C Value
A1 B1 C1 10
A1 B1 C2 20
A1 B1 C3 30
A1 B2 C1 40
A1 B2 C2 10
A1 B2 C3 5
A1 B2 C4 30
A2 B1 C1 40
A2 B1 C2 5
A2 B1 C3 2
A2 B2 C1 26
A2 B2 C2 1
A2 B3 C1 23
A2 B3 C2 15
A2 B3 C3 12
A3 B3 C4 23
A3 B3 C5 23"
DF <- read.table(textConnection(Lines), header = TRUE, as.is = TRUE)
library(RPostgreSQL)
library(sqldf)
# upper case is folded to lower case by default so surround DF with double quotes
sqldf('select count(*) from "DF" ')
sqldf('select *, rank() over (partition by "Group_A", "Group_B" order by "Value")
from "DF"
order by "Group_A", "Group_B", "Group_C" ')For another example using over and
partition by see: this
cumsum example
Also note that log and log10 in R
correspond to ln and log, respectively, in
PostgreSQL.
read.csv.sql?read.csv.sql provides an interface to sqlite’s csv
reader. That reader is not very flexible (but is fast) and, in
particular, it does not understand quoted fields but rather regards the
quotes as part of the field itself. To read a file using
read.csv.sql and remove all double quotes from it at the
same time on Windows try this assuming you have Rtools installed and on
your path (or the corresponding tr syntax on UNIX depending
on your shell):
read.csv.sql("myfile.csv", filter = 'tr.exe -d ^" ' )or equivalently:
read.csv.sql("myfile.csv", filter = list('gawk -f prog', prog = '{ gsub(/"/, ""); print }') )Another program to look at is the csvfix program (this is a
free external program – not an R program). For example suppose we have
commas in two contexts: (1) as separators between fields and within
double quoted fields. To handle that case we can use csvfix
to translate the separators to semicolon stripping off the double quotes
at the same time (assuming we have installed csvfix and we
have put it in our path):
read.csv.sql("myfile.csv", sep = ";", filter = "csvfix write_dsv -s ;")` .Translate the empty fields to some number that will represent NA and then fix it up on the R end.
# The problem is that SQLite's read routine regards empty
# fields as zero length character strings rather than NA.
# We handle that by replacing such strings with -999, say,
# using gawk and the read.csv.sql filter argument and then
# fixing it up in R later.
# write out test data
cat("a\tb\tc
aa\t\t23
aaa\t34.6\t
aaaa\t\t77.8", file = "x.txt")
# create single line awk program to insert -999 as NA
cat('{ gsub("\t\t", "\t-999\t"); gsub("\t$", "\t-999"); print}',
file = "x.awk")
# on Windows gawk uses \n as eol even though most
# other programs use \r\n so we need to specify that.
# eol= may or may not be needed here on other platforms.
library(sqldf)
DF <- read.csv.sql("x.txt", sep = "\t", eol = "\n", filter = "gawk -f x.awk")
# replace -999's with NA
is.na(DF) <- DF == -999Another program that can be used in filters is the free csvfix . For example, suppose that csvfix is on our path and that NA values are represented as NA in numeric fields. We would like to convert them to -999 and then later remove them.
Lines <- "a,b
3,NA
4,65"
cat(Lines, file = "myfile.csv")
filter <- 'csvfix map -fv ,NA -tv ,-999 myfile.csv | csvfix write_dsv -s ,'
DF <- read.csv.sql(filter = filter)
is.na(DF) <- DF == -999Another way in which the input file can be malformed is that not
every line has the same number of fields. In that case
csvfx pad -n can be used to pad it out as in this
example:
Lines <- "a,b,c
a,b,
a,b
q,r,t"
cat(Lines, file = "c.csv")
DF <- read.csv.sql(filter = "csvfix pad -n 3 c.csv | csvfix write_dsv -s ,")SQLite/RSQLite, h2/RH2, PostgreSQL all perform integer division on integers; however, RMySQL/MySQL performs real division.
> DF <- data.frame(a = 1:2, b = 2:1)
> str(DF) # columns are integer
'data.frame': 2 obs. of 2 variables:
$ a: int 1 2
$ b: int 2 1
> #
> # using sqlite - integer division
> sqldf("select a/b as quotient from DF")
quotient
1 0
2 2
> # force real division
> sqldf("select (a+0.0)/b as quotient from DF")
quotient
1 0.5
2 2.0
> # force real division
> sqldf("select cast(a as real)/b as quotient from DF")
quotient
1 0.5
2 2.0
> # insert into table with real columns
> sqldf(c("create table mytab(a real, b real)",
+ "insert into mytab select * from DF",
+ "select a/b as quotient from mytab"))
quotient
1 0.5
2 2.0
>
> # convert all columns to numeric using method= argument
> # Requires sqldf 0.4-0 or later
>
> tonum <- function(DF) replace(DF, TRUE, lapply(DF, as.numeric))
> sqldf("select a/b as quotient from DF", method = list("auto", tonum))
quotient
1 0.5
2 2.0
>
> # use RMySQL - uses real division
> # Requires sqldf 0.4-0 or later
> library(RMySQL)
> sqldf("select a/b as quotient from DF")
quotient
1 0.5
2 2.0Use read.csv.sql and specify the URL of the file:
# 1
URL <- "https://www.wnba.com/liberty/media/NYL2011ScheduleV3.csv"
DF <- read.csv.sql(URL, eol = "\r")Since files off the net could have any end of line be careful to specify it properly for the file of interest.
As an alternative one could use the filter argument. To use this
wget (download,
Windows)
must be present on the system command path.
# 2 - same URL as above
DF <- read.csv.sql(eol = "\r", filter = paste("wget -O - ", URL))Here is an example of reading a zip file which contains a single file
that is a csv :
DF <- read.csv.sql(filter = "7z x -so anscombe.zip 2>NUL")In the line of code above it is assumed that 7z (download) is present and
on the system command path. The example is for Windows. On UNIX use
/dev/null in place of NUL.
If we had a .tar.gz file it could be done like this:
DF <- read.csv.sql(filter = "tar xOfz anscombe.tar.gz")assuming that tar is available on our path. (Normally tar is available on Linux and on Windows its available as part of the Rtools distribution on CRAN.)
Note that filter causes the filtered output to be stored
in a temporary file and then read into sqlite. It does not actually read
the data directly from the net into sqlite or directly from the zip or
tar.gz file to sqlite.
Note: The examples in this section assume sqldf 0.4-4 or later.
These examples illustrate usage of both sqldf and SQLite. For sqldf
with H2 see FAQ
#10. For PostgreSQL see FAQ#12.
Also the "sqldf-unitTests" demo that comes with sqldf works
under sqldf with SQLite, H2, PostgreSQL and MySQL. David L. Reiner has
created some further examples here
and Paul Shannon has examples here.
Here is an example of sorting and limiting output from an SQL select
statement on the iris data frame that comes with R. Note that although
the iris dataset uses the name Sepal.Length older versions
of the RSQLite driver convert that to Sepal_Length;
however, newer versions do not. After installing sqldf in R, just type
the first two lines into the R console (without the >):
> library(sqldf)
> sqldf('select * from iris order by "Sepal.Length" desc limit 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.9 3.8 6.4 2.0 virginica
2 7.7 3.8 6.7 2.2 virginica
3 7.7 2.6 6.9 2.3 virginicaHere is an example which processes an SQL select statement whose functionality is similar to the R aggregate function.
> sqldf('select Species, avg("Sepal.Length") from iris group by Species")
Species avg(Sepal.Length)
1 setosa 5.006
2 versicolor 5.936
3 virginica 6.588Here is a more complex example. For each Species, find the average Sepal Length among those rows where Sepal Length exceeds the average Sepal Length for that Species. Note the use of a subquery and explicit column naming:
> sqldf("select iris.Species '[Species]',
+ avg(\"Sepal.Length\") '[Avg of SLs > avg SL]'
+ from iris,
+ (select Species, avg(\"Sepal.Length\") SLavg
+ from iris group by Species) SLavg
+ where iris.Species = SLavg.Species
+ and \"Sepal.Length\" > SLavg
+ group by iris.Species")
[Species] [Avg of SLs > avg SL]
1 setosa 5.313636
2 versicolor 6.375000
3 virginica 7.159091
> # same - using only core R - based on discussion with Dennis Toddenroth
> aggregate(Sepal.Length ~ Species, iris, function(x) mean(x[x > mean(x)]))
Species Sepal.Length
1 setosa 5.313636
2 versicolor 6.375000
3 virginica 7.159091Note that PostgreSQL is the only free database that supports window
functions
(similar to ave function in R) which would allow a
different formulation of the above. For more on using sqldf with
PostgreSQL see FAQ
#12
> library(RPostgreSQL)
> library(sqldf)
> tmp <- sqldf('select
+ "Species",
+ "Sepal.Length",
+ "Sepal.Length" - avg("Sepal.Length") over (partition by "Species") "above.mean"
+ from iris')
> sqldf('select "Species", avg("Sepal.Length")
+ from tmp
+ where "above.mean" > 0
+ group by "Species"')
Species avg
1 setosa 5.313636
2 virginica 7.159091
3 versicolor 6.375000
>
> # or, alternately, we could perform the above two steps in a single statement:
>
> sqldf('
+ select "Species", avg("Sepal.Length")
+ from
+ (select "Species",
+ "Sepal.Length",
+ "Sepal.Length" - avg("Sepal.Length") over (partition by "Species") "above.mean"
+ from iris) a
+ where "above.mean" > 0
+ group by "Species"')
Species avg
1 setosa 5.313636
2 versicolor 6.375000
3 virginica 7.159091which in R corresponds to this R code
(i.e. partition...over in PostgreSQL corresponds to
ave in R):
> tmp <- with(iris, Sepal.Length - ave(Sepal.Length, iris, FUN = mean))
> aggregate(Sepal.Length ~ Species, subset(tmp, above.mean > 0), mean)
Species Sepal.Length
1 setosa 5.313636
2 versicolor 6.375000
3 virginica 7.159091Here is some sample data with the correlated subquery from this Wikipedia page:
Emp <- data.frame(emp = letters[1:24], salary = 1:24, dept = rep(c("A", "B", "C"), each = 8))
sqldf("SELECT *
FROM Emp AS e1
WHERE salary > (SELECT avg(salary)
FROM Emp
WHERE dept = e1.dept)")The different type of joins are pictured in this image:
i.imgur.com/1m55Wqo.jpg. (SQLite does not support right joins but the
other databases sqldf supports do.) We define a new data frame,
Abbr, join it with iris and perform the
aggregation:
> # Example 4a.
> Abbr <- data.frame(Species = levels(iris$Species),
+ Abbr = c("S", "Ve", "Vi"))
>
> sqldf('select Abbr, avg("Sepal.Length")
+ from iris natural join Abbr group by Species')
Abbr avg(Sepal.Length)
1 S 5.006
2 Ve 5.936
3 Vi 6.588Although the above is probably the shortest way to write it in SQL,
using natural join can be a bit dangerous since one must be
very sure one knows precisely which column names are common to both
tables. For example, had we included the row_names as a
column in both tables (by specifying row.names = TRUE to
sqldf) the natural join would not work as intended since the
row_names columns would participate in the join. An
alternate and safer way to write this would be with join
and using:
> # Example 4b.
> sqldf('select Abbr, avg("Sepal.Length")
+ from iris join Abbr using(Species) group by Species')
Abbr avg(Sepal.Length)
1 S 5.006
2 Ve 5.936
3 Vi 6.588or with a where clause:
> # Example 4c.
> sqldf('select Abbr, avg("Sepal.Length") from iris, Abbr
+ where iris.Species = Abbr.Species group by iris.Species')
Abbr avg(Sepal.Length)
1 S 5.006
2 Ve 5.936
3 Vi 6.588or a temporal join where the goal is, for each Species/station_id pair, to join the records with the closest date/times.
> # Example 4d. Temporal Join
> # see: https://stat.ethz.ch/pipermail/r-help/2009-March/191938.html
>
> library(chron)
>
> Species.Lines <- "Species,Date_Sampled
+ SpeciesB,2008-06-23 13:55:11
+ SpeciesA,2008-06-23 13:43:11
+ SpeciesC,2008-06-23 13:55:11"
>
> species <- read.csv(textConnection(Species.Lines), as.is = TRUE)
> species$dt <- as.numeric(as.chron(species$Date))
>
> Temp.Lines <- "Station_id,Date,Value
+ ANH,2008-06-23 13:00:00,1.96
+ ANH,2008-06-23 14:00:00,2.25
+ BDT,2008-06-23 13:00:00,4.23
+ BDT,2008-06-23 13:15:00,4.11
+ BDT,2008-06-23 13:30:00,4.01
+ BDT,2008-06-23 13:45:00,3.9
+ BDT,2008-06-23 14:00:00,3.82"
>
> temp <- read.csv(textConnection(Temp.Lines), as.is = TRUE)
> temp$dt <- as.numeric(as.chron(temp$Date))
>
> out <- sqldf("select s.Species, s.dt, t.Station_id, t.Value
+ from species s, temp t
+ where abs(s.dt - t.dt) =
+ (select min(abs(s2.dt - t2.dt))
+ from species s2, temp t2
+ where s.Species = s2.Species and t.Station_id = t2.Station_id)")
> out$dt <- chron(out$dt)
> out
Species dt Station_id Value
1 SpeciesB (06/23/08 13:55:11) ANH 2.25
2 SpeciesB (06/23/08 13:55:11) BDT 3.82
3 SpeciesA (06/23/08 13:43:11) ANH 2.25
4 SpeciesA (06/23/08 13:43:11) BDT 3.90
5 SpeciesC (06/23/08 13:55:11) ANH 2.25
6 SpeciesC (06/23/08 13:55:11) BDT 3.82A similar but slightly simpler example can be found here.
Here is an example of a left join:
> # Example 4e. Left Join
> # https://stat.ethz.ch/pipermail/r-help/2009-April/195882.html
> #
> SNP1x <-
+ structure(list(Animal = c(194073197L, 194073197L, 194073197L,
+ 194073197L, 194073197L), Marker = structure(1:5, .Label = c("P1001",
+ "P1002", "P1004", "P1005", "P1006", "P1007"), class = "factor"),
+ x = c(2L, 1L, 2L, 0L, 2L)), .Names = c("Animal", "Marker",
+ "x"), row.names = c("3213", "1295", "915", "2833", "1487"), class = "data.frame")
>
> SNP4 <-
+ structure(list(Animal = c(194073197L, 194073197L, 194073197L,
+ 194073197L, 194073197L, 194073197L), Marker = structure(1:6, .Label = c("P1001",
+ "P1002", "P1004", "P1005", "P1006", "P1007"), class = "factor"),
+ Y = c(0.021088, 0.021088, 0.021088, 0.021088, 0.021088, 0.021088
+ )), .Names = c("Animal", "Marker", "Y"), class = "data.frame", row.names = c("3213",
+ "1295", "915", "2833", "1487", "1885"))
>
> SNP1x
Animal Marker x
3213 194073197 P1001 2
1295 194073197 P1002 1
915 194073197 P1004 2
2833 194073197 P1005 0
1487 194073197 P1006 2
> SNP4
Animal Marker Y
3213 194073197 P1001 0.021088
1295 194073197 P1002 0.021088
915 194073197 P1004 0.021088
2833 194073197 P1005 0.021088
1487 194073197 P1006 0.021088
1885 194073197 P1007 0.021088
>
> library(sqldf)
> sqldf("select * from SNP4 left join SNP1x using (Animal, Marker)")
Animal Marker Y x
1 194073197 P1001 0.021088 2
2 194073197 P1002 0.021088 1
3 194073197 P1004 0.021088 2
4 194073197 P1005 0.021088 0
5 194073197 P1006 0.021088 2
6 194073197 P1007 0.021088 NA
> # or if that takes up too much memory
> # create/use/destroy external database
> sqldf("select * from SNP4 left join SNP1x using (Animal, Marker)", dbname = "test.db")
Animal Marker Y x
1 194073197 P1001 0.021088 2
2 194073197 P1002 0.021088 1
3 194073197 P1004 0.021088 2
4 194073197 P1005 0.021088 0
5 194073197 P1006 0.021088 2
6 194073197 P1007 0.021088 NA> # Example 4f. Another temporal join.
> # join DF2 to row in DF for which DF.tt and DF2.tt are closest
>
> DF <- structure(list(tt = c(3, 6)), .Names = "tt", row.names = c(NA,
+ -2L), class = "data.frame")
> DF
tt
1 3
2 6
>
> DF2 <- structure(list(tt = c(1, 2, 3, 4, 5, 7), d = c(8.3, 10.3, 19,
+ 16, 15.6, 19.8)), .Names = c("tt", "d"), row.names = c(NA, -6L
+ ), class = "data.frame", reference = "A1.4, p. 270")
> DF2
tt d
1 1 8.3
2 2 10.3
3 3 19.0
4 4 16.0
5 5 15.6
6 7 19.8
>
> out <- sqldf("select * from DF d, DF2 a, DF2 b
+ where a.row_names = b.row_names - 1
+ and d.tt > a.tt and d.tt <= b.tt",
+ row.names = TRUE)
>
> out$dd <- with(out, ifelse(tt < (tt.1 + tt.2) / 2, d, d.1))
> out
tt tt.1 d tt.2 d.1 dd
1 3 2 10.3 3 19.0 19.0
2 6 5 15.6 7 19.8 19.8Example 4g. Self Join. There is an example of a self-join here: problem and answer here:
> DF <- structure(list(Actor = c("Jim", "Bob", "Bob", "Larry", "Alice", "Tom", "Tom", "Tom", "Alice", "Nancy"), Act = c("A", "A", "C", "D", "C", "F", "D", "A", "B", "B")), .Names = c("Actor", "Act" ), class = "data.frame", row.names = c(NA, -10L))
> subset(unique(merge(DF, DF, by = 2)), Actor.x < Actor.y)
Act Actor.x Actor.y
3 A Jim Tom
4 A Bob Jim
6 A Bob Tom
11 B Alice Nancy
16 C Alice Bob
20 D Larry Tom
> sqldf("select A.Act, A.Actor, B.Actor
+ from DF A join DF B
+ where A.Act = B.Act and A.Actor < B.Actor
+ order by A.Act, A.Actor")
Act Actor Actor
1 A Bob Jim
2 A Bob Tom
3 A Jim Tom
4 B Alice Nancy
5 C Alice Bob
6 D Larry Tomto Raj Morejoys for correction.
Here is an another example of a self join to create pairs which is followed by a second self join to produce pairs of pairs. This stackoverflow example illustrates an sqldf triple join in which one table participates twice.
Example 4h. Join nearby times. There is an example of joining records that are close but not necessarily exactly the same here: problem and answer . Also taking successive differences involves joining adjacent times and this is illustrated here .
Here is an example where we align time series Sy to series Sx by averaging all points of Sy within w = 0.25 units of each Sx time point. Tx and X are the times and values of Sx and Ty and Y are the times and values of Sy.
Tx <- seq(1, N, 0.5)
Tx <- Tx + rnorm(length(Tx), 0, 0.1)
X <- sin(Tx/10.0) + sin(Tx/5.0) + rnorm(length(Tx), 0, 0.1)
Ty <- seq(1, N, 0.3333)
Ty <- Ty + rnorm(length(Ty), 0, 0.02)
Y <- sin(Ty/10.0) + sin(Ty/5.0) + rnorm(length(Ty), 0, 0.1)
w <- 0.25
system.time(out1 <- sapply(Tx, function(tx) mean(Y[Ty >= tx-w & Ty <= tx+w])))
library(sqldf)
Sx <- data.frame(Tx, X)
Sy <- data.frame(Ty, Y)
system.time(out.sqldf <- sqldf(c("create index idx on Sx(Tx)",
"select Tx, avg(Y) from main.Sx, Sy
where Ty + 0.25 >= Tx and Ty - 0.25 <= Tx group by Tx")))
all.equal(out.sqldf[,2], out1) # TRUEExample 4i. Speeding up joins with indexes. Here is an example of
speeding up a join by using indexes on a single join column here
and here
and on two join columns below. Note that the create index
statements in each example also has the effect of reading in the data
frames into the main database of SQLite. The
select statement refers to main.DF1 rather
than just DF1 so that it accesses that copy of
DF1 in main which we just indexed rather than
the unindexed DF1 in R. Similar comments apply to
DF2. The statement
sqldf("select * from sqlite_master") will list the names
and related info for all tables in main.
> set.seed(1)
> n <- 1000000
>
> DF1 <- data.frame(a = sample(n, n, replace = TRUE),
+ b = sample(4, n, replace = TRUE), c1 = runif(n))
>
> DF2 <- data.frame(a = sample(n, n, replace = TRUE),
+ b = sample(4, n, replace = TRUE), c2 = runif(n))
>
> library(sqldf)
Loading required package: DBI
Loading required package: RSQLite
Loading required package: gsubfn
Loading required package: proto
Loading required package: chron
>
> sqldf()
<SQLiteConnection:(6480,0)>
> system.time(sqldf("create index ai1 on DF1(a, b)"))
Loading required package: tcltk
Loading Tcl/Tk interface ... done
user system elapsed
16.69 0.19 19.12
> system.time(sqldf("create index ai2 on DF2(a, b)"))
user system elapsed
16.60 0.03 17.48
> system.time(sqldf("select * from main.DF1 natural join main.DF2"))
user system elapsed
7.76 0.06 8.23
> sqldf()The sqldf statements above could also be done in one sqldf call like this:
# define DF1 and DF2 as before
set.seed(1)
n <- 1000000
DF1 <- data.frame(a = sample(n, n, replace = TRUE),
b = sample(4, n, replace = TRUE), c1 = runif(n))
DF2 <- data.frame(a = sample(n, n, replace = TRUE),
b = sample(4, n, replace = TRUE), c2 = runif(n))
# combine all sqldf calls from before into one call
result <- sqldf(c("create index ai1 on DF1(a, b)",
"create index ai2 on DF2(a, b)",
"select * from main.DF1 natural join main.DF2"))Note that if your data is so large that you need indexes it may be
too large to store the database in memory. If you find its overflowing
memory then use the dbname= sqldf argument, e.g.
sqldf(c("create...", "create...", "select..."), dbname = tempfile())
so that it stores the intermediate results in an external database
rather than memory.
Note: The index ai1 is not actually used so we
could have saved the time it took to create it, creating only
ai2.
sqldf(c("create index ai2 on DF2(a, b)", "select * from DF1 natural join main.DF2"))Example 4j. Per Group Max and Min
Note that the Date variable gets passed to SQLite as number of days
since 1970-01-01 whereas SQLite uses an earlier origin so we add
julianday('1970-01-01') to convert the origin of R’s
"Date" class to SQLite’s origin. Note that the output
column called Date is automatically converted to
"Date" class by the sqldf heuristic because there is an
input column that has the same name.
> URL <- "https://ichart.finance.yahoo.com/table.csv?s=GOOG&a=07&b=19&c=2004&d=03&e=16&f=2010&g=d&ignore=.csv"
> DF25 <- read.csv(URL, nrows = 25)
> DF25$Date <- as.Date(DF25$Date)
>
> sqldf("select Date, a.High, a.Low, b.Close, a.Volume
+ from (select max(Date) Date, min(Low) Low, max(High) High, sum(Volume) Volume
+ from DF25
+ group by date(Date + julianday('1970-01-01'), 'start of month')
+ ) as a join DF25 b using(Date)")
Date High Low Close Volume
1 2010-03-31 588.28 539.70 567.12 51541600
2 2010-04-16 597.84 549.63 550.15 41201900and here is another shorter one that uses a trick of Magnus Hagander in the second Stackoverflow link below:
> sqldf("select
+ max(Date) Date,
+ max(High) High,
+ min(Low) Low,
+ max(100000 * Date + Close) % 100000 Close,
+ sum(Volume) Volume
+ from DF25
+ group by date(Date + julianday('1970-01-01'), 'start of month')")
Date High Low Close Volume
1 2010-03-31 588.28 539.70 567 51541600
2 2010-04-16 597.84 549.63 550 41201900Also see this Xaprb link for an approach without subqueries and for more discussion see this stackoverflow link and this stackoverflow link. The last link shows how to use analytical queries which are available in PostgreSQL – the PostgreSQL database, like SQLite and H2, is supported by sqldf.
Here is an example of inserting evaluated variables into a query
using gsubfn
quasi-perl-style string interpolation. gsubfn is used by sqldf so its
already loaded. Note that we must use the fn$ prefix to
invoke the interpolation functionality:
> minSL <- 7
> limit <- 3
> species <- "virginica"
> fn$sqldf("select * from iris where \"Sepal.Length\" > $minSL and species = '$species' limit $limit")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 7.1 3.0 5.9 2.1 virginica
2 7.6 3.0 6.6 2.1 virginica
3 7.3 2.9 6.3 1.8 virginicaNote that there is a new command read.csv.sql which
provides an alternate interface to the the approach discussed in this
section. See Example 13 for that.
sqldf normally deletes any database it creates after completion but the example sample code at the bottom of this post shows how to set up a database and read a file into it without having the database destroyed afterwards.
sqldf will not only look for data frames used in the SQL statement
but will also look for R objects of class "file". For such
objects it will directly import the associated file into the database
without going through R allowing files that are larger than an R
workspace to be handled and also providing for potential speed
advantages. That is, if f <- file("abc.csv") is a file
object and f is used as the table name in the sql statement
then the file abc.csv is imported into the database as
table f. With SQLite, the actual reading of the file into
the database is done in a C routine in RSQLite so the file is
transferred directly to the database without going through R. If the
sqldf argument dbname is used then it
specifies a filename (either existing or created by sqldf
if not existing). That filename is used as a database (rather than
memory) allowing larger files than physical memory. By using an
appropriate where statement or a subset of column names a
portion of the table can be retrieved into R even if the file itself is
too large for R or for memory.
There are some caveats. The RSQLite
dbWriteTable/sqliteImportFile routines that
sqldf uses to transfer the file directly to the database
are intended for speed thus they are not as flexible as
read.table. Also they have slightly different defaults. The
default for sep is
file.format = list(sep = ","). If the first row of the file
has one fewer component than subsequent ones then it assumes that
file.format = list(header = TRUE, row.names = TRUE) and
otherwise that
file.format = list(header = FALSE, row.names = FALSE).
.csv file format is only partly supported – quotes are not
regarded as special.
In addition to the examples below there is an example here and another one with performance results here.
> # Example 6a.
> # test of file connections with sqldf
>
> # create test .csv file of just 3 records
> write.table(head(iris, 3), "iris3.dat", sep = ",", quote = FALSE)
>
> # look at contents of iris3.dat
> readLines("iris3.dat")
[1] "Sepal.Length,Sepal.Width,Petal.Length,Petal.Width,Species"
[2] "1,5.1,3.5,1.4,0.2,setosa"
[3] "2,4.9,3,1.4,0.2,setosa"
[4] "3,4.7,3.2,1.3,0.2,setosa"
>
> # set up file connection
> iris3 <- file("iris3.dat")
> sqldf('select * from iris3 where "Sepal.Width" > 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
>
> # Example 6b.
> # similar but uses disk - useful if file were large
> # According to https://www.sqlite.org/whentouse.html
> # SQLite can handle files up to several dozen gigabytes.
> # (Note in this case readTable and readTableIndex in R.utils
> # package or read.table from the base of R, setting the colClasses
> # argument to "NULL" for columns you don't want read in, might be
> # alternatives.)
> sqldf('select * from iris3 where "Sepal.Width" > 3', dbname = tempfile())
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
> # Example 6c.
> # with this format, header=TRUE needs to be specified
> write.table(head(iris, 3), "iris3a.dat", sep = ",", quote = FALSE,
+ row.names = FALSE)
> iris3a <- file("iris3a.dat")
> sqldf("select * from iris3a", file.format = list(header = TRUE))
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
> # Example 6d.
> # header can alternately be specified as object attribute
> attr(iris3a, "file.format") <- list(header = TRUE)
> sqldf("select * from iris3a")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosa
> # Example 6e.
> # create a test file with all 150 records from iris
> # and select 4 records at random without reading entire file into R
> write.table(iris, "iris150.dat", sep = ",", quote = FALSE)
> iris150 <- file("iris150.dat")
> sqldf("select * from iris150 order by random(*) limit 4")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 2.5 4.5 1.7 virginica
2 4.8 3.0 1.4 0.1 setosa
3 6.1 2.6 5.6 1.4 virginica
4 7.4 2.8 6.1 1.9 virginica
>
> # or use read.csv.sql and its just one line
> read.csv.sql("iris150.dat", sql = "select * from file order by random(*) limit 4")
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 2.4 3.3 1.0 versicolor
2 5.8 2.7 4.1 1.0 versicolor
3 7.4 2.8 6.1 1.9 virginica
4 5.1 3.5 1.4 0.3 setosaExample 6f. If our file has fixed width fields rather than delimited then we can still handle it if we parse the lines manually with substr:
# write some test data to "fixed"
# Field 1 has width of 1 column and field 2 has 4 columns
cat("1 8.3
210.3
319.0
416.0
515.6
719.8
", file = "fixed")
# get 3 random records using sqldf
fixed <- file("fixed")
attr(fixed, "file.format") <- list(sep = ";") # ; can be any char not in file
sqldf("select substr(V1, 1, 1) f1, substr(V1, 2, 4) f2 from fixed order by random(*) limit 3")Another example of fixed width data is here (however, note that changing the sep needs to be done in the example in that link too).
Example 6g. Defaults.
# If first row has one fewer columns than subsequent rows then
# header <- row.names <- TRUE is assumed as in example 6a; otherwise,
# header <- row.names <- FALSE is assumed as shown here:
> write.table(head(iris, 3), "iris3nohdr.dat", col.names = FALSE, row.names = FALSE, sep = ",", quote = FALSE)
> readLines("iris3nohdr.dat")
[1] "5.1,3.5,1.4,0.2,setosa" "4.9,3,1.4,0.2,setosa" "4.7,3.2,1.3,0.2,setosa"
> sqldf("select * from iris3nohdr")
V1 V2 V3 V4 V5
1 5.1 3.5 1.4 0.2 setosa
2 4.9 3.0 1.4 0.2 setosa
3 4.7 3.2 1.3 0.2 setosaFor each species show the two rows with the largest sepal lengths:
> # Example 7a.
> sqldf('select * from iris i
+ where rowid in
+ (select rowid from iris where Species = i.Species order by "Sepal.Length" desc limit 2)
+ order by i.Species, i."Sepal.Length" desc')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.8 4.0 1.2 0.2 setosa
2 5.7 4.4 1.5 0.4 setosa
3 7.0 3.2 4.7 1.4 versicolor
4 6.9 3.1 4.9 1.5 versicolor
5 7.9 3.8 6.4 2.0 virginica
6 7.7 3.8 6.7 2.2 virginicaHere is a similar example. In this one DF represents a
time series whose values are in column x and whose times
are dates in column tt. The times have gaps – in fact only
every other day is present. The code below displays the first row at or
past the 21st of the month for each year/month. First we append year,
month and day columns using month.day.year from the
chron package and then do the computation using
sqldf. (For a version of this using the zoo
package rather than sqldf see: https://stat.ethz.ch/pipermail/r-help/2007-November/145925.html).
> # Example 7b.
> #
> library(chron)
> DF <- data.frame(x = 101:200, tt = as.Date("2000-01-01") + seq(0, len = 100, by = 2))
> DF <- cbind(DF, month.day.year(unclass(DF$tt)))
>
> sqldf("select * from DF d
+ where rowid in
+ (select rowid from DF
+ where year = d.year and month = d.month and day >= 21 limit 1)
+ order by tt")
x tt month day year
1 111 2000-01-21 1 21 2000
2 127 2000-02-22 2 22 2000
3 141 2000-03-21 3 21 2000
4 157 2000-04-22 4 22 2000
5 172 2000-05-22 5 22 2000
6 187 2000-06-21 6 21 2000Here is another example of a nested select. We select each row of a for which st/en overlaps with some st/en of b.
> # Example 7c.
> #
> a <- read.table(textConnection("st en
+ 1 4
+ 11 14
+ 3 4"), header = TRUE)
>
> b <- read.table(textConnection("st en
+ 2 5
+ 3 6
+ 30 44"), TRUE)
>
> sqldf("select * from a where
+ (select count(*) from b where a.en >= b.st and b.en >= a.st) > 0")
st en
1 1 4
2 3 47d. Another example of a nested select with sqldf is shown here
When using file() as used as in Example 6 RSQLite reads in the first 50 lines to determine the column classes. What if they all have numbers in them but then later we start to see letters? In that case we will have to override its choice. Here are two ways:
library(sqldf)
# example example 8a - file.format attribute on file.object
numStr <- as.character(1:100)
DF <- data.frame(a = c(numStr, "Hello"))
write.table(DF, file = "~/tmp.csv", quote = FALSE, sep = ",")
ff <- file("~/tmp.csv")
attr(ff, "file.format") <- list(colClasses = c(a = "character"))
tail(sqldf("select * from ff"))
# example 8b - using file.format argument
numStr <- as.character(1:100)
DF <- data.frame(a = c(numStr, "Hello"))
write.table(DF, file = "~/tmp.csv", quote = FALSE, sep = ",")
ff <- file("~/tmp.csv")
tail(sqldf("select * from ff",
file.format = list(colClasses = c(a = "character"))))sqldf is usually used to operate on data frames but it can be used to store a table in a database and repeatedly query it in subsequent sqldf statements (although in that case you might be better off just using RSQLite or other database directly). There are two ways to do this. In this Example section we show how to do it using the fact that if you specify the database explicitly then it does not delete the database at the end and if you create a table explicitly using create table then it does not delete the table (however, note that that will result in duplicate tables in the database so it will take up twice as much space as one table). A second way to do this is to use persistent connections as shown in the Example section after this one.
# create new empty database called mydb
sqldf("attach 'mydb' as new")
# create a new table, mytab, in the new database
# Note that sqldf does not delete tables created from create.
sqldf("create table mytab as select * from BOD", dbname = "mydb")
# shows its still there
sqldf("select * from mytab", dbname = "mydb")
# read a file into the mydb data base using read.csv.sql without deleting it
#
# 1. First create a test file.
# 2. Then read it into the mydb database we created using the sqldf("attach...") above.
# Since sqldf automatically cleans up after itself we hide
# the table creation in an sql statement so table is not deleted.
# 3. Finally list the table names in the database.
write.table(BOD, file = "~/tmp.csv", quote = FALSE, sep = ",")
read.csv.sql("~/tmp.csv", sql = "create table mytab as select * from file",
dbname = "mydb")
sqldf("select * from sqlite_master", dbname = "mydb")These three examples show the use of persistent connections in sqldf. This would be used when one has a large database that one wants to store and then make queries from so that one does not have to reload it on each execution of sqldf. (Note that if one just needs a series of sql statements ending in a single query an alternative would be just to use a vector of sql statements in a single sqldf call.)
> # Example 10a.
>
> # create test .csv file of just 3 records (same as example 6)
> write.table(head(iris, 3), "iris3.dat", sep = ",", quote = FALSE)
> # set up file connection
> iris3 <- file("iris3.dat")
> # creates connection so in memory database persists after sqldf call
> sqldf()
<SQLiteConnection:(7384,62)>
>
> # uses connection just created
> sqldf('select * from iris3 where "Sepal.Width" > 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
> # we now have iris3 variable in R workspace and an iris3 table
> # so ensure sqldf uses the one in the main database by writing
> # main.iris3. (Another possibility here would have been to
> # delete the iris3 variable from the R workspace to avoid the
> # ambiguity -- in that case one could just write iris3 instead
> # of main.iris3.)
> sqldf('select * from main.iris3 where "Sepal.Width" = 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3 1.4 0.2 setosa
>
> # close
> sqldf()
NULL
> # Example 10b.
> #
> # Here is another way to do example 10a. We use the same iris3,
> # iris3.dat and sqldf development version as above.
> # We grab connection explicitly, set up the database using sqldf and then
> # for the second call we call dbGetQuery from RSQLite.
> # In that case we don't need to qualify iris3 as main.iris3 since
> # RSQLite would not understand R variables anyways so there is no
> # ambiguity.
> con <- sqldf()
>
> # uses connection just created
> sqldf('select * from iris3 where "Sepal.Width" > 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 5.1 3.5 1.4 0.2 setosa
2 4.7 3.2 1.3 0.2 setosa
> dbGetQuery(con, 'select * from iris3 where "Sepal.Width" = 3')
Sepal.Length Sepal.Width Petal.Length Petal.Width Species
1 4.9 3 1.4 0.2 setosa
>
> # close
> sqldf()
NULLHere is an example of reading a csv file using read.csv.sql and then reading it again using a persistent connection:
# Example 10c.
write.table(iris, "iris.csv", sep = ",", quote = FALSE)
sqldf()
read.csv.sql("iris.csv", sql = "select count(*) from file")
# now re-read it from the sqlite database
dd <- sqldf("select * from file")
# now close the connection and destroy the database
sqldf()# example thanks to Michael Rehberg
#
# build sample dataframes
seqdf <- data.frame(thetime=seq(100,225,5),thevalue=factor(letters))
boundsdf <- data.frame(thestart=c(110,160,200),theend=c(130,180,220),groupID=c(555,666,777))
# run the query using two inequalities
testquery_1 <- sqldf("select seqdf.thetime, seqdf.thevalue, boundsdf.groupID
from seqdf left join boundsdf on (seqdf.thetime <= boundsdf.theend) and (seqdf.thetime >= boundsdf.thestart)")
# run the same query using 'between...and' clause
testquery_2 <- sqldf("select seqdf.thetime, seqdf.thevalue, boundsdf.groupID
from seqdf LEFT JOIN boundsdf ON (seqdf.thetime BETWEEN boundsdf.thestart AND boundsdf.theend)")When we issue a series of normal sqldf statements after
each one sqldf automatically removes any tables and databases it creates
in that statement; however, it does not know about ones that
sqlite creates so a database created using
attach and the tables created using
create table won’t be deleted.
Also if sqldf is used without the x=
argument (omitting x= denotes the opening of a persistent connection)
then objects created in the database including those by
sqldf and sqlite are not deleted when the
persistent connection is destroyed by the next sqldf
statement with no x= argument.
If we have forgetten whether you have a connection open or not we can check either of these:
dbListConnections(SQLite()) # from DBI
getOption("sqldf.connection") # set by sqldfHere is an example that illustrates part of the above. See the prior examples for more.
> # set up some test data
> write.table(head(iris, 3), "irishead.dat", sep = ",", quote = FALSE)
> write.table(tail(iris, 3), "iristail.dat", sep = ",", quote = FALSE)
>
> library(sqldf)
>
> # create new empty database called mydb
> sqldf("attach 'mydb' as new")
NULL
>
> irishead <- file("irishead.dat")
> iristail <- file("iristail.dat")
>
> # read tables into mydb
> sqldf("select count(*) from irishead", dbname = "mydb")
count(*)
1 3
> sqldf("select count(*) from iristail", dbname = "mydb")
count(*)
1 3
>
> # get count of all records from union
> sqldf('select count(*) from (select * from main.irishead
+ union
+ select * from main.iristail)', dbname = "mydb")
count(*)
1 6read.csv.sql is an interface to sqldf that
works like read.csv in R except that it also provides an
sql= argument and not all of the other arguments of
read.csv are supported. It uses (1) SQLite’s import
facility via RSQLite to read the input file into a temporary disk-based
SQLite database which is created on the fly. (2) Then it uses the
provided SQL statement to read the table so created into R. As the first
step imports the data directly into SQLite without going through R it
can handle larger files than R itself can handle as long as the SQL
statement filters it to a size that R can handle. Here is Example 6c
redone using this facility:
# Example 13a.
library(sqldf)
write.table(iris, "iris.csv", sep = ",", quote = FALSE, row.names = FALSE)
iris.csv <- read.csv.sql("iris.csv",
sql = 'select * from file where "Sepal.Length" > 5')
# Example 13b. read.csv2.sql. Commas are decimals and ; is sep.
library(sqldf)
Lines <- "Sepal.Length;Sepal.Width;Petal.Length;Petal.Width;Species
5,1;3,5;1,4;0,2;setosa
4,9;3;1,4;0,2;setosa
4,7;3,2;1,3;0,2;setosa
4,6;3,1;1,5;0,2;setosa
"
cat(Lines, file = "iris2.csv")
iris.csv2 <- read.csv2.sql("iris2.csv", sql = 'select * from file where "Sepal.Length" > 5')
# Example 13c. Use of filter= to process fixed field widths.
# This example assumes gawk is available for use as a filter:
# https://www.icewalkers.com/Linux/Software/514530/Gawk.html
# https://gnuwin32.sourceforge.net/packages/gawk.htm
library(sqldf)
cat("112333
123456", file = "fixed.dat")
cat('BEGIN { FIELDWIDTHS = "2 1 3"; OFS = ","; print "A,B,C" }
{ $1 = $1; print }', file = "fixed.awk")
# the following worked on Windows Vista. One user told me that it only worked if he
# omitted the eol= argument so try it both ways on your system and use the way that
# works for your system.
fixed <- read.csv.sql("fixed.dat", eol = "\n", filter = "gawk -f fixed.awk")
# Example 13d. Read a csv file into the database but do not drop the database or table
# create test file
write.table(iris, "iris.csv", sep = ",", quote = FALSE, row.names = FALSE)
# create an empty database (can skip this step if database already exists)
sqldf("attach mytestdb as new")
# read into table called iris in the mytestdb sqlite database
read.csv.sql("iris.csv", sql = "create table main.iris as select * from file", dbname = "mytestdb")
# look at first three lines
sqldf("select * from main.iris limit 3", dbname = "mytestdb")
# example 13e. Read in only column j of a csv file where j may vary.
library(sqldf)
# create test data file
nms <- names(anscombe)
write.table(anscombe, "anscombe.dat", sep = ",", quote = FALSE,
row.names = FALSE)
j <- 2
DF2 <- fn$read.csv.sql("anscombe.dat", sql = "select `nms[j]` from file")Also see this example
and this further example.
The latter illustrates the use of the method= argument.
******This example needs to be revised as automatic loading of spatialite has been removed from sqldf and replaced with the functions in RSQLite.extfuns which are loaded instead******
This example will only work if spatialite-1.dll is on your PATH. It shows accessing a function in that dll. Other than placing it on your PATH there is no other setup needed. (Note that libspatialite-1.dll is only looked up the first time sqldf runs in a session so you should be sure that it has been put there before starting sqldf.)
> library(sqldf)
> # stddev_pop is a function in spatialite library similar to sd in R
> # Note bug: spatialite has stddev_pop and stddev_samp reversed and ditto for var_pop and var_samp. More on bug at:
> # https://groups.google.com/group/spatialite-users/msg/182f1f629c922607
> sqldf("select avg(demand), stddev_pop(demand) from BOD")
avg(demand) stddev_pop(demand)
1 14.83333 4.630623
> c(mean(BOD$demand), sd(BOD$demand))
[1] 14.833333 4.630623The RSQLite R package includes Liam Healy’s extension functions for SQLite. In addition to all the core functions, date functions and aggregate functions that SQLite itself provides, the following extension functions are available for use within SQL select statements: Math: acos, asin, atan, atn2, atan2, acosh, asinh, atanh, difference, degrees, radians, cos, sin, tan, cot, cosh, sinh, tanh, coth, exp, log, log10, power, sign, sqrt, square, ceil, floor, pi. String: replicate, charindex, leftstr, rightstr, ltrim, rtrim, trim, replace, reverse, proper, padl, padr, padc, strfilter. Aggregate: stdev, variance, mode, median, lower_quartile, upper_quartile. See the bottom of https://www.sqlite.org/contrib/ for more info on these extension functions.
> sqldf("select avg(demand) mean, variance(demand) var from BOD")
mean var
1 14.83333 21.44267
> var(BOD$demand)
[1] 21.44267This is a simplified version of the example in this r-help post. Here we compute the moving average of x for the 3rd to 9th preceding values of each date performing it separately for each illness.
> Lines <- "date illness x
+ 2006/01/01 DERM 319
+ 2006/01/02 DERM 388
+ 2006/01/03 DERM 336
+ 2006/01/04 DERM 255
+ 2006/01/05 DERM 177
+ 2006/01/06 DERM 377
+ 2006/01/07 DERM 113
+ 2006/01/08 DERM 253
+ 2006/01/09 DERM 316
+ 2006/01/10 DERM 187
+ 2006/01/11 DERM 292
+ 2006/01/12 DERM 275
+ 2006/01/13 DERM 355
+ 2006/01/01 FEVER 3190
+ 2006/01/02 FEVER 3880
+ 2006/01/03 FEVER 3360
+ 2006/01/04 FEVER 2550
+ 2006/01/05 FEVER 1770
+ 2006/01/06 FEVER 3770
+ 2006/01/07 FEVER 1130
+ 2006/01/08 FEVER 2530
+ 2006/01/09 FEVER 3160
+ 2006/01/10 FEVER 1870
+ 2006/01/11 FEVER 2920
+ 2006/01/12 FEVER 2750
+ 2006/01/13 FEVER 3550"
>
> DF <- read.table(textConnection(Lines), header = TRUE)
> DF$date <- as.Date(DF$date)
>
> sqldf("select
+ t1.date,
+ avg(t2.x) mean,
+ date(min(t2.date) * 24 * 60 * 60, 'unixepoch') fromdate,
+ date(max(t2.date) * 24 * 60 * 60, 'unixepoch') todate,
+ max(t2.illness) illness
+ from DF t1, DF t2
+ where julianday(t1.date) between julianday(t2.date) + 3 and
+ julianday(t2.date) + 9
+ and t1.illness = t2.illness
+ group by t1.illness, t1.date
+ order by t1.illness, t1.date")
date mean fromdate todate illness
1 2006-01-04 319.0000 2006-01-01 2006-01-01 DERM
2 2006-01-05 353.5000 2006-01-01 2006-01-02 DERM
3 2006-01-06 347.6667 2006-01-01 2006-01-03 DERM
4 2006-01-07 324.5000 2006-01-01 2006-01-04 DERM
5 2006-01-08 295.0000 2006-01-01 2006-01-05 DERM
6 2006-01-09 308.6667 2006-01-01 2006-01-06 DERM
7 2006-01-10 280.7143 2006-01-01 2006-01-07 DERM
8 2006-01-11 271.2857 2006-01-02 2006-01-08 DERM
9 2006-01-12 261.0000 2006-01-03 2006-01-09 DERM
10 2006-01-13 239.7143 2006-01-04 2006-01-10 DERM
11 2006-01-04 3190.0000 2006-01-01 2006-01-01 FEVER
12 2006-01-05 3535.0000 2006-01-01 2006-01-02 FEVER
13 2006-01-06 3476.6667 2006-01-01 2006-01-03 FEVER
14 2006-01-07 3245.0000 2006-01-01 2006-01-04 FEVER
15 2006-01-08 2950.0000 2006-01-01 2006-01-05 FEVER
16 2006-01-09 3086.6667 2006-01-01 2006-01-06 FEVER
17 2006-01-10 2807.1429 2006-01-01 2006-01-07 FEVER
18 2006-01-11 2712.8571 2006-01-02 2006-01-08 FEVER
19 2006-01-12 2610.0000 2006-01-03 2006-01-09 FEVER
20 2006-01-13 2397.1429 2006-01-04 2006-01-10 FEVERBecause of the date processing this is a bit more conveniently done
in H2 with its support of date class. Using the same DF
that we just defined. Note that SQL functions like AVG and MIN must be
written in upper case when using H2.
> library(RH2)
> sqldf("select
+ t1.date,
+ AVG(t2.x) mean,
+ MIN(t2.date) fromdate,
+ MAX(t2.date) todate,
+ t2.illness illness
+ from DF t1, DF t2
+ where t1.date between t2.date + 3 and t2.date + 9
+ and t1.illness = t2.illness
+ group by t1.illness, t1.date
+ order by t1.illness, t1.date")
date mean fromdate todate illness
1 2006-01-04 319 2006-01-01 2006-01-01 DERM
2 2006-01-05 353 2006-01-01 2006-01-02 DERM
3 2006-01-06 347 2006-01-01 2006-01-03 DERM
4 2006-01-07 324 2006-01-01 2006-01-04 DERM
5 2006-01-08 295 2006-01-01 2006-01-05 DERM
6 2006-01-09 308 2006-01-01 2006-01-06 DERM
7 2006-01-10 280 2006-01-01 2006-01-07 DERM
8 2006-01-11 271 2006-01-02 2006-01-08 DERM
9 2006-01-12 261 2006-01-03 2006-01-09 DERM
10 2006-01-13 239 2006-01-04 2006-01-10 DERM
11 2006-01-04 3190 2006-01-01 2006-01-01 FEVER
12 2006-01-05 3535 2006-01-01 2006-01-02 FEVER
13 2006-01-06 3476 2006-01-01 2006-01-03 FEVER
14 2006-01-07 3245 2006-01-01 2006-01-04 FEVER
15 2006-01-08 2950 2006-01-01 2006-01-05 FEVER
16 2006-01-09 3086 2006-01-01 2006-01-06 FEVER
17 2006-01-10 2807 2006-01-01 2006-01-07 FEVER
18 2006-01-11 2712 2006-01-02 2006-01-08 FEVER
19 2006-01-12 2610 2006-01-03 2006-01-09 FEVER
20 2006-01-13 2397 2006-01-04 2006-01-10 FEVERAnother example which varies somewhat from a strict moving average can be found in this post.
The following example contributed by Søren Højsgaard shows how to lag a column.
## Create a lagged variable for grouped data
## -----------------------------------------
# Meaning that in the i'th row we not only have y[i] but also y[i-1].
# This is done on a groupwise basis
library(sqldf)
set.seed(123)
DF <- data.frame(id=rep(1:2, each=5), tvar=rep(1:5,2), y=rnorm(1:10))
# Data with lagged variable added
BB <-
sqldf("select A.id, A.tvar, A.y, B.y as lag
from DF as A join DF as B
where A.rowid-1 = B.rowid and A.id=B.id
order by A.id, A.tvar")
# Merge with original data:
DD <-
sqldf("select DF.*, BB.lag
from DF left join BB
on DF.id=BB.id and DF.tvar=BB.tvar")
# Do it all in one step:
DD <-
sqldf("select DF.*, BB.lag
from DF left join
(
select A.id, A.tvar, A.y, B.y as lag
from DF as A join DF as B
where A.rowid-1 = B.rowid and A.id=B.id
order by A.id, A.tvar
) as BB
on DF.id=BB.id and DF.tvar=BB.tvar")In PostgreSQL’s window
functions
(similar to R’s ave function) makes reference to other rows
particularly easy. Below we repeat the SQLite example in PostgreSQL
(except that the following fills with NA):
# Be sure PostgreSQL is installed and running.
library(RPostgreSQL)
library(sqldf)
sqldf("select *, lag(y) over (partition by id order by tvar) from DF")library(RMySQL)
library(sqldf)
sqldf("show databases")
sqldf("show tables")The following SQL statements to query the MySQL table schemas are taken from the blog of Christophe Ladroue:
library(RMySQL)
library(sqldf)
# list each schema and its length
sqldf("SELECT TABLE_SCHEMA,SUM(DATA_LENGTH) SCHEMA_LENGTH
FROM information_schema.TABLES
WHERE TABLE_SCHEMA!='information_schema'
GROUP BY TABLE_SCHEMA")
# list each table in each schema and some info about it
sqldf("SELECT TABLE_SCHEMA,TABLE_NAME,TABLE_ROWS,DATA_LENGTH
FROM information_schema.TABLES
WHERE TABLE_SCHEMA!='information_schema'")The following SQL statement to query the MySQL table schemas are taken from the MySQL Performance Blog:
# Find total number of tables, rows, total data in index size
sqldf("SELECT count(*) tables,
concat(round(sum(table_rows)/1000000,2),'M') rows,
concat(round(sum(data_length)/(1024*1024*1024),2),'G') data,
concat(round(sum(index_length)/(1024*1024*1024),2),'G') idx,
concat(round(sum(data_length+index_length)/(1024*1024*1024),2),'G') total_size,
round(sum(index_length)/sum(data_length),2) idxfrac
FROM information_schema.TABLES")
# find biggest databases
sqldf("SELECT
count(*) tables,
table_schema,concat(round(sum(table_rows)/1000000,2),'M') rows,
concat(round(sum(data_length)/(1024*1024*1024),2),'G') data,
concat(round(sum(index_length)/(1024*1024*1024),2),'G') idx,
concat(round(sum(data_length+index_length)/(1024*1024*1024),2),'G') total_size,
round(sum(index_length)/sum(data_length),2) idxfrac
FROM information_schema.TABLES
GROUP BY table_schema
ORDER BY sum(data_length+index_length) DESC LIMIT 10")
# data distribution by storage engine
sqldf("SELECT engine,
count(*) tables,
concat(round(sum(table_rows)/1000000,2),'M') rows,
concat(round(sum(data_length)/(1024*1024*1024),2),'G') data,
concat(round(sum(index_length)/(1024*1024*1024),2),'G') idx,
concat(round(sum(data_length+index_length)/(1024*1024*1024),2),'G') total_size,
round(sum(index_length)/sum(data_length),2) idxfrac
FROM information_schema.TABLES
GROUP BY engine
ORDER BY sum(data_length+index_length) DESC LIMIT 10")Visual Representation of SQL Joins
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.