{"id":15955,"date":"2017-02-02T14:42:00","date_gmt":"2017-02-02T22:42:00","guid":{"rendered":"https:\/\/devwww.3cloudsolutions.com\/post\/importing-and-exporting-getting-data-into-and-out-of-r-2\/"},"modified":"2024-01-05T14:37:35","modified_gmt":"2024-01-05T22:37:35","slug":"importing-and-exporting-getting-data-into-and-out-of-r","status":"publish","type":"post","link":"https:\/\/3cloudsolutions.com\/resources\/importing-and-exporting-getting-data-into-and-out-of-r\/","title":{"rendered":"ImpoRting and ExpoRting: Getting Data Into and Out of R"},"content":{"rendered":"

In any organization, data is housed in many locations and in many formats. Nowadays, many business analytics tools have the native ability to import from almost any data source imaginable. For example, Microsoft Power BI<\/a> has the ability to get data from over 60 different sources, including many common file types and more esoteric data storage software.<\/p>\n

\"iStock-607969272edited.jpg\"<\/div>\n
From flat files to databases to proprietary file formats, we’ll take a look at how simple it is to use these data sources in R. Many times, as you\u2019ll see, all it takes is the installation and use of a simple package. This includes many proprietary formats such as SAS and Microsoft Excel. The steps below will take you through each format and help you to master importing and exporting data!<\/div>\n
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Reading and Writing Common Flat Files<\/h2>\n

Base R<\/h3>\n

R comes equipped with the ability to read and write many common text-based flat files including .csv<\/code><\/span> files, .tsv<\/code><\/span> files, and more. For example, let\u2019s say I have a file called mydata.csv<\/code><\/span> in my data folder. R has a function, read.table<\/code><\/span>, and its children functions, read.csv<\/span><\/code>, and read.delim<\/code><\/span>.<\/p>\n

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#read.table works in the following manner: \"read.table(file, header = FALSE, sep = \"\", quote = \"\"'\",...\".\r\ncsvinput <- read.table(\"data\/mydata.csv\",header = TRUE, sep = \",\",quote = \"\"\")\r\n\r\n#Alternatively, using \"read.csv\"\" will assume the separator is a comma, which may save some time.\r\n#We used the quote parameter to tell R that there are some strings in the file surrounded by double quotes.<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
Now, let\u2019s say that you have done some data manipulation and calculations and you want to export your data back to your data folder. This is just as simple as reading things in.<\/div>\n
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#write.table works in the following manner: \"write.table(x, file = \"\", col.names = TRUE, append = FALSE, quote = TRUE, sep = \" \",...\".\r\n#Alternatively, using \"write.csv\"\" will assume the separator is a comma, which may save some time.\r\n#We use the append parameter to tell R to overwrite the file rather than just tack on rows to the end of the old version.\r\nwrite.table(input,file=\"data\/output.csv\",col.names = TRUE, append = FALSE, quote = TRUE,  sep = \",\")<\/code><\/pre>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
[read.table documentation<\/a>]<\/p>\n
readr<\/h3>\n
In the realm of tidyverse<\/a>, there is a package known as readr<\/code><\/span> that can handle importing flat files, too. This package has multiple functions to handle different flat file types.\u00a0The readr<\/code><\/span> package\u00a0runs a bit faster than the base R functions and handles factors and dates a bit better, too.\u00a0It also\u00a0supports seven file formats with seven read_<\/code><\/span> functions:<\/p>\n