When Data Is Read From a File, It Is Automatically Stored in a Variable.

Read and Write Data

• Data Table
• Read the Data into R
  • Browse for the data file
  • Specify location of the data file
  • Output of Read()
  • Two types of variables
• Read Variable Labels
• Write the Data Tabular array to a File
• Read SPSS (and SAS and Stata) Files
  • Value labels
  • Variable labels
• Full Manual

Data Tabular array

Analyze the data values for at least one variable, such as the annual salaries of employees at a company. Organize the data values into a specific kind of structure from which analysis gain.

Information Table: Organize data values into a rectangular information tabular array with the data values for each variable in a column, and the name of the variable at the top of the column.

Shop the structured data values within a estimator file, such as an Excel or OpenDocument Spreadsheet (ODS) formatted file or text file. This file tin can be stored on your computer, an attainable local network, or the www. The data table in the following effigy, formatted as an Excel file, contains four variables: Years, Gender, Dept, and Salary plus an ID field called Proper name for a total of 5 columns.

Structure of a data tabular array.

Describe the data table by its columns, rows, and cell entries.

Data value: The contents of a single cell of a data table, a specific measurement.

For example, according to the data values for employee Darnell Ritchie, he has worked at the visitor for 7 years, identifies as a male person, and works in administration with an almanac salary of $43,788.26.

Missing information: A prison cell for which in that location is no recorded information value.

Two data values in this section of the information table are missing. The number of years James Wu has worked at the visitor is not recorded, nor is the department in which Alissa Jones works.

Variable name: A brusque, concise word or abbreviation that identifies a column of data values in a data table.

Case: Each row of the information table, the data for a specific case of a single person, organization, place, event, or whatsoever is existence studied.

Encode the data table in i of a variety of reckoner file formats. Mutual formats include Excel files (.xlsx), OpenDocument Spreadsheet files (.ods), and text files the form of comma-separated value files (.csv) or tab-delimited text files (.txt).

Analysis of information can only proceed with the data table identified and the relevant variables identified by their name.

All R functions clarify the data values for ane or more specified variables, identified by their names, such every bit Salary.

Analysis requires the right spelling of each variable proper noun, including the same blueprint of capitalization.

Read the Information into R

Your data organized as a data table exists somewhere every bit a data file stored on a calculator system. To analyze data in a data tabular array stored in a computer file, first read the data table from the computer file into a respective data table within a running R session. R refers to information tables within R with its ain name.

Data frame: A data table stored within an R session, referenced by its name.

Each variable in a data table has a name, then does the data table itself. Reference the data table stored on your calculator system by its file name and location. When read into R, proper noun the data table, the R data frame, with a proper noun of your selection. Regardless of the file name on your calculator system, typically name the data table within the active R session, the data frame, equally simply d for data. Non only is d piece of cake to blazon, simply it is as well the lessR default data frame name for the data processed past its various analysis functions.

When analyzing data read into R, the aforementioned data exists in ii unlike locations: as a computer file on your reckoner system, and every bit an R data frame inside a running R app. Different locations, dissimilar names: same data. On your calculator system, identify the data table past its file name and location. Within a running R app, identify the same information by the name of the data frame, such as d, within which the data from the computer file was read.

To read the information from a file into a information frame of a running R application, as with every other task in R, accomplish the job with a function. The R ecosystem, base of operations R and its many packages, presents many such functions. We apply the lessR function Read() for its simplicity and for its useful output that helps understanding the data that was read.

The lessR office Read() can read information files in many file formats, including MS Excel. The most generic format is the csv format, for comma separated values. Read() likewise reads SPSS and SAS data files, as well as data files in R'southward own native format, of type .rda.

Browse for the information file

To read the data, direct R to the location of the data file.")` R cannot read the data file until it knows where the information is stored. 1 pick has you locate the data file on your figurer system by browsing for it, navigating your file system until y'all locate the file.

To locate your information file by browsing through your file organisation, phone call the Read() role with an empty file reference, (""), literally goose egg between the quotes.

The following Read() statement reads the information stored as a rectangular information table from an external file stored on your computer arrangement into an R data frame chosen d. The empty quotes indicate to R to open up your file browser for you to locate the information file that already exists somewhere on your reckoner system).

            d <- Read("")          

Equally with all R (and Excel) functions, the call to invoke the function includes a matching set of parentheses. Any information within the parentheses specifies the information provided to the function for assay.

The <- indicates to assign what is on the correct of the expression, here the data read from an external file, to the object on the left, hither the R data frame stored within R, named d in this example. You can also use an ordinary equals sign, =, to bespeak the consignment, just the <- is more descriptive, and more widely used past R practitioners.

Specify location of the data file

As well can explicitly specify the location of the data file to be read within the quotes and parentheses. Specify either the full path name of a file on your computer system, or specify a web address that locates the data table on the web. Once again, read the information into the d data frame.

            d <- Read("path proper noun" or "web address")          

With Excel, R, or any other figurer apps that processes data, enclose values that are character strings, such as a file name, in quotes. For example, to read the data stored on the web in the data file called employee.xlsx into the data frame d, invoke the following Read() part call.

            d <- Read("http://spider web.pdx.edu/~gerbing/data/employee.xlsx")          

This example reads a information file on the web. To specify a location on your computer, provide the full path name of your data file, its name and location. To obtain this path proper name, kickoff browse for the file with Read(""). The resulting output displays the path proper name of the identified file. Copy this path name and insert between the quotes of Read(""), salvage this and other R function calls in a text file, then run the code in the future to directly read the information file for future analyses without needing to browse for its location.

In summary, with the Read() part, either put nada between the quotes to scan for a data file, or specify the location of the information file on your reckoner system or the web. Straight the data read from a file into an R data frame, commonly named d, but tin choose any valid name.

Output of Read()

The Read() role displays useful output. Because R organizes analyses by variable name, information technology is crucial to know the exact variable names, including the design of capitalization.

In improver to the variable names, Read() too displays the type of each variable as stored in the figurer, as numbers with or without decimal digits, or equally grapheme strings. Also listed are the number of consummate and missing values for each variable, the number of unique values for each variable, and sample data values.

The following lists the output from reading from a data file downloaded with lessR. All that is needed to read these data files is the name. A file on the spider web cannot be specified here because you may non have web admission when this file is generated.

            ##  ## >>> Suggestions ## Details near your data, Enter:  details()  for d, or  details(proper name) ##  ## Information Types ## ------------------------------------------------------------ ## character: Non-numeric data values ## integer: Numeric data values, integers only ## double: Numeric data values with decimal digits ## ------------------------------------------------------------ ##  ##     Variable                  Missing  Unique  ##         Name     Type  Values  Values  Values   First and final values ## ------------------------------------------------------------------------------------------ ##  1     Years   integer     36       1      sixteen   7  NA  7 ... i  two  x ##  2    Gender character     37       0       2   M  K  W ... W  W  Thou ##  3      Dept character     36       1       5   ADMN  Auction  FINC ... MKTG  SALE  FINC ##  4    Salary    double     37       0      37   53788.26  94494.58 ... 56508.32  57562.36 ##  5    JobSat character     35       2       3   med  low  high ... high  low  loftier ##  half-dozen      Plan   integer     37       0       3   1  i  two ... 2  2  one ##  seven       Pre   integer     37       0      27   82  62  90 ... 83  59  80 ##  eight      Post   integer     37       0      22   92  74  86 ... 90  71  87 ## ------------------------------------------------------------------------------------------          

To allow for many variables, Read() lists the information for each variable in a row. Note that the data file organizes the variables by column.

2 types of variables

E'er distinguish continuous variables from categorical variables. This distinction between these two types of variables is fundamental in data analysis.

Continuous (quantitative) variable: A numerical variable with many possible values.

Chiselled (qualitative) variable: A variable with relatively few unique labels every bit data values.

Examples of continuous variables are Salary or Fourth dimension. Examples of chiselled variables are Gender or State of Residence, each with just a relatively few number of possible values compared to numerical values. This distinction of continuous and categorical variables is common to all data analytics.

Sometimes that distinction gets a little confusing because variables with integer values, which are numeric, could exist either quantitative or qualitative. For example, sometimes Male, Female, and Other are encoded every bit 0, 1, and ii, respectively, for three levels of the chiselled variable Gender. However, these integer values are but labels for different non-numeric categories. Best to avoid this confusion. Instead, encode chiselled variables with non-numeric values, such as Gender, for case, with M F, and O for Other.

Read Variable Labels

A variable characterization is a longer description of the respective variable than that of the variable proper noun. The variable label displays in conjunction with the variable proper name on the text and visualization output to further clarify the interpretability of the output.

The variable characterization file has 2 columns. The the first column lists the variable names and the second column the corresponding labels. The file can exist of blazon .csv, or .xlsx, or independent within lessR, equally with the following example.

The variable labels must be read into the data frame l. Specify the var_labels parameter as True to instruct the Read() part to read variable labels instead of data.

                          l                <-                Read("Employee_lbl",                var_labels=                True)                      

          ##  ## >>> Suggestions ## Details virtually your data, Enter:  details()  for d, or  details(proper name) ##  ## Data Types ## ------------------------------------------------------------ ## character: Non-numeric data values ## ------------------------------------------------------------ ##  ##     Variable                  Missing  Unique  ##         Proper noun     Type  Values  Values  Values   First and last values ## ------------------------------------------------------------------------------------------ ##  1     characterization character      viii       0       8   Time of Company Employment ... Test score on legal issues after pedagogy ## ------------------------------------------------------------------------------------------        

Write the Data Table to a File

Relying upon the have packet, lessR can write data with function Write() in formats: csv, Excel, ODS, and native R and native SPSS formats. The R format preserves a binary copy of the data frame as it is stored within R, as does the SPSS format for native SPSS files. The Excel and ODS formats yield worksheets. The csv format yields a comma separated value text file.

A recommended process begins the assay with information in .csv, .xlsx, or .ods format. Then proceed with data cleaning and grooming, including needed transformations and re-coding. When the data is ready for analysis, save the cleaned, prepared information as a native R file of format .rda. This format is the most efficient for size and for speed of reading back into R, with all data preparations already completed. Tin can also write the data in a format such as Excel so that those on the squad not using R can besides access.

With the Write() part, specify the proper name of the file for the output, besides equally the type of file with the format parameter, with the default of .csv. The following R statements that call Write() are non run here as the intent is not to create additional files.

Write the current contents of default data frame d to GoodData.csv.

          Write(d, "GoodData")        

Write the data as a Excel data tabular array in an Excel file.

          Write(d, "GoodData", format="Excel")        

Can also use the abbreviation for an Excel file, wrt_x().

          wrt_x(d, "GoodData")        

Write the information equally a R data table.

          Write(d, "GoodData", format="R")        

Can also use the abbreviation for an R file, wrt_r().

          wrt_r(d, "GoodData")        

Apply format="ODS" to specify to write to the OpenDocument Spreadsheet format with file type .ods. Use format="SPSS" to specify to write to the SPSS format with file type .sav.

The output of Write() indicates the full path proper noun of the written file.

Read SPSS (and SAS and Stata) Files

The haven package has an excellent office for reading SPSS files, read_spss(). Read() adds to the given functionality by preserving the SPSS variable labels and value labels. Read() invokes this function to read SPSS files, with the default file types of .sav and zsav. The functionality of oasis also allows for reading SAS and Stata data files.

Value labels

Within SPSS, an integer scored variable can have value labels. An case is Likert scaled data with ane representing a Strongly Disagree to 5 a Strongly Agree. The corresponding SPSS variable has integer values but displays the more informative value labels such every bit in bar charts. This type of categorical variable corresponds to a factor in the R organization.

The read_spss() function preserves the value labels with a special variable type chosen haven_labels. These variables can be converted to an R factor for processing in the R system with the haven office as_factor(). Read() performs this conversion automatically for each relevant variable.

I problem is that the factor conversion preserves the value labels listed in the correct guild, but looses the original integer scoring information. To preserve both the labels as a standard R factor, and to preserve the original scoring, Read() converts the original read variable with the labels (type haven_labels) into two variables. The first variable, an integer variable with the original integer scoring, has the name of the read variable. The corresponding factor variable has the same proper name every bit the read variable with the suffix \_f.

For example, the use of read_spss() results in the following, here showing just the offset four lines of data. The variable region contains both the integer scoring and the value label that are part of the SPSS information file that was read.

            # A tibble: iv x four   city                         region growth income   <chr>                     <dbl+lbl>  <dbl>  <dbl> 1 ALBANY-SCHNTADY-TROY,Due north.Y.    ane [NE]    -71   3313 2 ATLANTA,GA.                  two [SE]    264   3153 3 BALTIMORE,MD.                one [NE]     38   3540 iv BIRMINGHAM,ALA.              two [SE]   -178   2528          

With Read(), obtain the following standard R data frame. The variable region is at present a standard R integer variable, and region_f is the corrsponding factor.

                          urban center region region_f growth income 1 ALBANY-SCHNTADY-TROY,North.Y.      1       NE    -71   3313 two               ATLANTA,GA.      2       SE    264   3153 3             BALTIMORE,Doctor.      1       NE     38   3540 4           BIRMINGHAM,ALA.      ii       SE   -178   2528          

With these information, the annotator may reach a numerical analysis with the integer variable, and for analsyses such equally a bar chart, instead display the corresponding value labels.

Variable labels

lessR automatically accesses variable labels stored in a information frame named 50, and then displays with the variable name in text and visualization output. Usually the variable labels are stored in an Excel or .csv file with two columns, the variable proper name and the variable label. There are no cavalcade titles, but the names and labels. And then read into the l data frame with the var_labels parameter ready to TRUE.

            l <- Read(file_reference, var_labels=TRUE)          

Read() also processes the variable labels of these (usually) integer-scored variables with value labels in the SPSS information file. The overseers of the R organization do non permit package authors to create stored information structures from internal R lawmaking. Only the user tin create these structures. Equally such, Read() lists each variable name, a comma, then the corresponding characterization. This example simply has one such relevant variable, region and its factor equivalent.

            Variable and Variable Label  --> See vignette("Read"), SPSS section --------------------------- region,  region of US  region_f,  region of U.s.          

To access these labels, copy the names and labels, paste into a text file, and then save as a file. Then read the file of names/labels into R with the preceding Read() statement.

Full Manual

Use the base of operations R assistance() function to view the full manual for Read() or Write(). Only enter a question mark followed past the name of the function.

          ?Read ?Write        

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Source: https://cran.r-project.org/web/packages/lessR/vignettes/ReadWrite.html

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