One of the big flaw of R is that data loaded into it are stored in the memory (on the RAM) and not on the disk. As you are working in an analysis with large (big) data the processing time of simple and more complex functions can become very long or even crash your computer. SQL enters here, it is a powerful language designed to work with (large) database and to perform simple operation (like subsetting, sorting …) on them. It is particularly useful to explore very large dataset and format the data for further analysis. There are many programs for doing database management using SQL. I decided to start looking at MySQL since it has an R package and is rather easy to set up (one could also use PostgreSQL …). In this post I will show you step by step how to create a database in MySQL, to upload data from R into it, then to do some queries to look at the power of SQL. Before I start note that the
data.table package was developed to perform fast operation on big data (have a look here).
First you need to download MySQL from this website or from synaptic for the ubuntu users. Then you need to open a shell window (type cmd for windows users, terminal for Linux), type this:
> mysql -p -u root
This will ask you for the password of the root user if it worked you will see some text and
mysql> appearing. Then if you don’t want to bother with different users and their rights you can directly create a database using:
mysql> CREATE DATABASE intro_to_sql;
That’s it you created a database named
intro_to_sql. At this point it is very important to remember that every time you are in the shell with mysql you need to use semi-colon (;) at the end of your statement, otherwise it doesn’t work. You can look at all the databases in your system using:
mysql> show databases; +--------------------+ | Database | +--------------------+ | information_schema | | intro_to_sql | | mysql | | performance_schema | +--------------------+ 4 rows in set (0.00 sec)
Then we create a user having all rights on the
mysql> GRANT ALL ON data.* TO 'user1'@'localhost' IDENTIFIED BY '12345';
Once a database and a user have been created we don’t need the shell interface everything else can be done from R.
We could directly create tables in the database from the shell interface but let’s see how to transfer data from R into the database:
library(RMySQL) #connect to the database con<-dbConnect(MySQL(),user='user1',password='12345',dbname='intro_to_sql') #load some data library(ggplot2) data(diamonds) #have a look at them summary(diamonds) #write the table into the database dbWriteTable(con,"diamonds",diamonds_data) #remove the dataset from R rm(diamonds) carat cut color clarity depth table price x y Min. :0.2000 Fair : 1610 D: 6775 SI1 :13065 Min. :43.00 Min. :43.00 Min. : 326 Min. : 0.000 Min. : 0.000 1st Qu.:0.4000 Good : 4906 E: 9797 VS2 :12258 1st Qu.:61.00 1st Qu.:56.00 1st Qu.: 950 1st Qu.: 4.710 1st Qu.: 4.720 Median :0.7000 Very Good:12082 F: 9542 SI2 : 9194 Median :61.80 Median :57.00 Median : 2401 Median : 5.700 Median : 5.710 Mean :0.7979 Premium :13791 G:11292 VS1 : 8171 Mean :61.75 Mean :57.46 Mean : 3933 Mean : 5.731 Mean : 5.735 3rd Qu.:1.0400 Ideal :21551 H: 8304 VVS2 : 5066 3rd Qu.:62.50 3rd Qu.:59.00 3rd Qu.: 5324 3rd Qu.: 6.540 3rd Qu.: 6.540 Max. :5.0100 I: 5422 VVS1 : 3655 Max. :79.00 Max. :95.00 Max. :18823 Max. :10.740 Max. :58.900 J: 2808 (Other): 2531 z Min. : 0.000 1st Qu.: 2.910 Median : 3.530 Mean : 3.539 3rd Qu.: 4.040 Max. :31.800
We now have one table named
diamonds_data in our database.
Now that our
intro_to_sql database has one table we can start playing with some SQL queries from R:
#count the number of diamonds that are more than 2000$ expensive dbGetQuery(con,"select count(*) from diamonds_data where price>2000") #make a new data frame with diamonds of color ‘D’ and a depth less than 60% subs<-dbGetQuery(con,"select * from diamonds_data where color='D' AND depth<60") unique(subs$color) #make a new data frame only with the column x,y,z and order them by ascending x subs<-dbGetQuery(con,"select x,y,z from diamonds_data order by x") head(subs) #from this dataset let’s create a new variable which is the mean of x,y,z subs$Mean<-apply(sub,1,mean) #write the results in a new table dbWriteTable(con,"XYZMean",subs) #check that it has been created dbListTables(con) count(*) 1 29733  "D" x y z 1 0 6.62 0 2 0 0.00 0 3 0 0.00 0 4 0 0.00 0 5 0 0.00 0 6 0 0.00 0  "XYZMean" "diamonds_data"
As you can see it is fairly easy to work with RMySQL, there are many advantages in working with this tool: (i) all the powers of SQL are at your command from within R, so it is easy to include this in your workflow (i.e. using the script window from RStudio …), (ii) no need to load big chunk of unprocessed data into R, use SQL to process efficiently the data (I did not talk about how to directly load a table into a MySQL database, have a look here).
There are many helpful ressources online about this topic here are a few that I found interesting: A working guide to MySQL. Two nice introductions into another SQL-platform supported in R (SQLite). A blog post about the issue of big data in R.