R Language Data Import and Export Guide: Easily process text files, CSV files, databases and web page data

R language is highly regarded for its powerful data processing capabilities, and it provides concise commands to import and export data in various formats. Even without a deep programming foundation, you can easily get started. This article will demonstrate how to import text files, CSV files, and database data in R language, and how to upload data to a web server.

No need to learn a brand new programming language! Just master a few simple commands.

No matter what programming language you use to develop a web application, you can import the data into R for processing and then export it in the desired format.

Note: If you are not familiar with R language, it is recommended to read SitePoint's article on R and RStudio installation first, which provides basic commands and introduction to R language. The commands in this article can be run in the R terminal without using the RStudio IDE. However, processing large datasets in the terminal can be more difficult for beginners, so RStudio is recommended for a better experience. In RStudio, you can run the same command in the console window.

Processing text files

You can use the modified read.table command to read the local text file. Since this command is mainly used to read table data, you can set the delimiter to an empty string ("") to read the text file line by line:

file_contents <- read.table("<文件路徑>", sep = "")

Note: At <文件路徑>, please replace with your actual file path.

The file path can be a relative path to the file. If your row lengths are not equal, you also need to set fill = TRUE. The output of this command is the data frame in R.

If the file is too large to be read at once, you can use the skip and nrow options to read step by step. For example, to read lines 6 to 10 in a file, run the following command:

connection <- file("<文件路徑>", "r")
lines6_10 <- read.table(connection, skip=5, nrow=5) # 讀取第6-10行
close(connection)

Processing CSV files

CSV (comma-separated values) files are commas separated by values. You can use the read.csv command to read the CSV file:

file_contents <- read.csv("<文件路徑>")

header option specifies whether the CSV file contains column titles, and the default is TRUE. (This can also be specified when reading a text file). If the number of columns in different rows is not equal, you also need to set fill to TRUE.

For large files, you can skip the line similarly:

connection <- file("<文件路徑>", "r")
lines6_10 <- read.csv(connection, skip=5, nrow=5) # 讀取第6-10行
close(connection)

Using MySQL database

To make a database connection, you need a separate RMySQL library. You can install it using the following command:

install.packages('RMySQL')

After the installation is complete, it needs to be activated by running the following command:

library('RMySQL')

Assuming your database is running, you can execute a MySQL query after the connection is established:

con <- dbConnect(MySQL(), user="<用戶名>", password="<密碼>", dbname="<數(shù)據(jù)庫名>", host="<主機(jī)名>")

# 對于在Mac上通過MAMP運(yùn)行MySQL的情況，需要指定unix.socket：
# con <- dbConnect(MySQL(), user="<用戶名>", password="<密碼>", dbname="<數(shù)據(jù)庫名>", unix.socket="<socket路徑>")

# 執(zhí)行MySQL查詢并將數(shù)據(jù)存儲(chǔ)到數(shù)據(jù)框中：
rs <- dbSendQuery(con, "<您的SQL查詢>")
data <- fetch(rs, n=-1)

# 完成查詢后，可以使用dbDisconnect命令斷開連接：
dbDisconnect(con)

Read network data

How do you read online files in R if your data source is on the network? Just change the file path specified in the read command. You need to use the url command and specify the URL in the read.csv command. For example:

file_contents <- read.table("<文件路徑>", sep = "")

For databases, the host name can be changed to extract data from the database on the web server.

Export data

Similar to read.csv and read.table, you can use the write command to export the data frame to a text file or a CSV file:

connection <- file("<文件路徑>", "r")
lines6_10 <- read.table(connection, skip=5, nrow=5) # 讀取第6-10行
close(connection)

To export as a text file using different delimiters (such as tabs), you can use the write.table command:

file_contents <- read.csv("<文件路徑>")

Updating the database is equally simple and can be done by executing UPDATE and INSERT MySQL commands.

Export chart

After processing and plotting data in R, you can export it too! The png or jpeg command can help you do this. It saves the currently active drawing:

connection <- file("<文件路徑>", "r")
lines6_10 <- read.csv(connection, skip=5, nrow=5) # 讀取第6-10行
close(connection)

You can adjust the second command to save the desired drawing.

Export data to the Web

Uploading files directly to the web can be a bit tricky, but you can export your data to the web in two steps: first save the file locally and then upload it to the web. You can upload files to the web via POST request using R, you can use httr package to simulate:

install.packages('RMySQL')

For more details, see the quick start guide for the httr package.

Conclusion

R has become increasingly popular among staff in statistics in recent years, and now is a good time to learn this excellent language. It's flexible enough to sync with various types of data sources and it's easy to use R regardless of your background. Hope this article helps you get started with R!

R Language Data Import and Export FAQs (FAQs)

(The FAQs part is omitted here, because the content of the FAQs part of the original text is highly duplicated with the existing content, which is redundant information.)

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