Import/Export and You

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You need a phytosanitary certificate to bring in, import and export plants, flowers, fruit and vegetables, unless these are only small quantities. It is not permitted to carry across the border, to import and to export firearms or other weapons and ammunition. This also applies to authentic-looking dummy weapons. You are not permitted to carry across the border, to import and to export narcotics drugs such as hashish, cocaine and heroin.

You cannot always simply carry across the border, to import and to export art and antiques. Special rules apply State Inspectorate for Cultural Heritage.

Why You Might Want to Export WordPress Users

Crossing the border, importing and exporting by means of a yacht pleasure boat? Are you bringing in a car or motorcycle with a foreign vehicle registration number from a non-EU country to the Netherlands? Please read the conditions for this. Javascript is disabled in this web browser.

Exporting an Instance as a VM Using VM Import/Export

You must activate Javascript in order to view this website. The data were exported from Excel as a file mr. Function unstack goes in the opposite direction, and may be useful for exporting data.

Some people prefer the tools in packages reshape , reshape2 and plyr. Displaying higher-dimensional contingency tables in array form typically is rather inconvenient. In categorical data analysis, such information is often represented in the form of bordered two-dimensional arrays with leading rows and columns specifying the combination of factor levels corresponding to the cell counts.

As a simple example, consider the R standard data set UCBAdmissions which is a 3-dimensional contingency table resulting from classifying applicants to graduate school at UC Berkeley for the six largest departments in classified by admission and sex. The printed representation is clearly more useful than displaying the data as a 3-dimensional array. There is also a function read. This has additional arguments for dealing with variants on how exactly the information on row and column variables names and levels is represented.

The help page for read. The flat tables can be converted to standard contingency tables in array form using as. If the full grid of levels of the row variables is given, one should instead use read. In this chapter we consider the problem of reading a binary data file written by another statistical system.

Considerations for Instance Export

This is often best avoided, but may be unavoidable if the originating system is not available. In all cases the facilities described were written for data files from specific versions of the other system often in the early s , and have not necessarily been updated for the most recent versions of the other system. The recommended package foreign provides import facilities for files produced by these statistical systems, and for export to Stata. In some cases these functions may require substantially less memory than read. EpiInfo versions 5 and 6 stored data in a self-describing fixed-width text format.

REC files into an R data frame. EpiData also produces data in this format. This returns the components of the worksheet as an R list. If SAS is available on your system, function read. It then calls read. Package Hmisc has a similar function sas. This is able to read many but not all S objects: in particular it can read vectors, matrices and data frames and lists containing those. Function data. It returns a list with one component for each variable in the saved data set. SPSS variables with value labels are optionally converted to R factors.

By default it creates data files with extra formatting information that read. Files from versions 5 up to 12 of Stata can be read and written by functions read. Stata variables with value labels are optionally converted to and from R factors. These have extension. There are limitations on the types of data that R handles well.

Since all data being manipulated by R are resident in memory, and several copies of the data can be created during execution of a function, R is not well suited to extremely large data sets. Data objects that are more than a few hundred megabytes in size can cause R to run out of memory, particularly on a bit operating system.

China Import-Export - Swap China

R does not easily support concurrent access to data. That is, if more than one user is accessing, and perhaps updating, the same data, the changes made by one user will not be visible to the others. R does support persistence of data, in that you can save a data object or an entire worksheet from one session and restore it at the subsequent session, but the format of the stored data is specific to R and not easily manipulated by other systems. Their strengths are. Akonadi is used by KDE4 to store personal information. Next: R interface packages , Previous: Why use a database?

There are other commonly used data sources, including spreadsheets, non-relational databases and even text files possibly compressed. The database can reside on the same machine or more often remotely. The more comprehensive R interfaces generate SQL behind the scenes for common operations, but direct use of SQL is needed for complex operations in all. Conventionally SQL is written in upper case, but many users will find it more convenient to use lower case in the R interface functions. A relational DBMS stores data as a database of tables or relations which are rather similar to R data frames, in that they are made up of columns or fields of one type numeric, character, date, currency, … and rows or records containing the observations for one entity.

The first of these selects two columns from the R data frame USArrests that has been copied across to a database table, subsets on a third column and asks the results be sorted. The second performs a database join on two tables student and school and returns four columns. The third and fourth queries do some cross-tabulation and return counts or averages. You can sort in lexicographical order on more than one column by separating them by commas. If more than one column is specified separated by commas then multi-way cross-classifications can be summarized by one of the five aggregation functions.

This can be useful to retrieve rows a block at a time. It may not be reliable unless the ordering is unique, as the LIMIT clause can be used to optimize the query. Data can be stored in a database in various data types. Real number, with optional precision.

Often called real or double or double precision. Often called varchar. Almost always has a limit of chars. There are variants on time and timestamp , with timezone. Other types widely implemented are text and blob , for large blocks of text and binary data, respectively. The more comprehensive of the R interface packages hide the type conversion issues from the user. They provide different levels of abstraction. Some provide means to copy whole data frames to and from databases. All have functions to select data within the database via SQL queries, and to retrieve the result as a whole as a data frame or in pieces usually as groups of rows.

The description here applies to versions 0. The current version requires the DBI package, and this description will apply with minor changes to all the other back-ends to DBI. It preserves the case of names where the operating file system is case-sensitive, so not on Windows. The call dbDriver "MySQL" returns a database connection manager object, and then a call to dbConnect opens a database connection which can subsequently be closed by a call to the generic function dbDisconnect. Function fetch is used to retrieve some or all of the rows in the query result, as a list.

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The function dbHasCompleted indicates if all the rows have been fetched, and dbGetRowCount returns the number of rows in the result. This is very widely available, and allows the same R code to access different database systems. The named applications do not need to be installed. Which file formats are supported depends on the versions of the drivers. Many simultaneous connections are possible. A connection is opened by a call to odbcConnect or odbcDriverConnect which on the Windows GUI allows a database to be selected via dialog boxes which returns a handle used for subsequent access to the database.

Printing a connection will provide some details of the ODBC connection, and calling odbcGetInfo will give details on the client and server. A connection is closed by a call to close or odbcClose , and also with a warning when not R object refers to it and at the end of an R session. Function sqlSave copies an R data frame to a table in the database, and sqlFetch copies a table in the database to an R data frame.

An SQL query can be sent to the database by a call to sqlQuery. This returns the result in an R data frame.

A finer level of control is attained by first calling odbcQuery and then sqlGetResults to fetch the results. The latter can be used within a loop to retrieve a limited number of rows at a time, as can function sqlFetchMore. Notice that the specification of the table is different from the name returned by sqlTables : sqlFetch is able to map the differences. Binary connections Connections are now the preferred way to handle binary files.

Both of these are systems to store scientific data in array-oriented ways, including descriptions, labels, formats, units, …. HDF5 also allows groups of arrays, and the R interface maps lists to HDF5 groups, and can write numeric and character vectors and matrices. The availability of software to support these formats is somewhat limited by platform, especially on Windows.

A dBase file contains a header and then a series of fields and so is most similar to an R data frame. Functions read.