Data Wrangling with dbplyr

Introduction

This is the third post in the series R & Databases. You can find the links to the other two posts of this series below:

In this post, we will learn to query data from a database using dplyr.

Libraries, Code & Data

We will use the following libraries in this post:

All the data sets used in this post can be found here and code can be downloaded from here.

Connect to Database

Let us connect to an in memory SQLite database using dbConnect().

con <- dbConnect(RSQLite::SQLite(), ":memory:")

We will copy the mtcars data to the database so that we can use it for running dplyr statements.

dplyr::copy_to(con, mtcars)

Reference Copied Data Frame

In order to use dplyr functions, we need to reference the table in the database using tbl().

mtcars2 <- dplyr::tbl(con, "mtcars")
mtcars2
## # Source:   table<mtcars> [?? x 11]
## # Database: sqlite 3.19.3 [:memory:]
##      mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##    <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  21.0  6.00   160 110    3.90  2.62  16.5  0     1.00  4.00  4.00
##  2  21.0  6.00   160 110    3.90  2.88  17.0  0     1.00  4.00  4.00
##  3  22.8  4.00   108  93.0  3.85  2.32  18.6  1.00  1.00  4.00  1.00
##  4  21.4  6.00   258 110    3.08  3.22  19.4  1.00  0     3.00  1.00
##  5  18.7  8.00   360 175    3.15  3.44  17.0  0     0     3.00  2.00
##  6  18.1  6.00   225 105    2.76  3.46  20.2  1.00  0     3.00  1.00
##  7  14.3  8.00   360 245    3.21  3.57  15.8  0     0     3.00  4.00
##  8  24.4  4.00   147  62.0  3.69  3.19  20.0  1.00  0     4.00  2.00
##  9  22.8  4.00   141  95.0  3.92  3.15  22.9  1.00  0     4.00  2.00
## 10  19.2  6.00   168 123    3.92  3.44  18.3  1.00  0     4.00  4.00
## # ... with more rows

Query Data

We will look at some simple examples. Let us start by selecting mpg, cyl and drat columns from mtcars2.

select(mtcars2, mpg, cyl, drat)
## # Source:   lazy query [?? x 3]
## # Database: sqlite 3.19.3 [:memory:]
##      mpg   cyl  drat
##    <dbl> <dbl> <dbl>
##  1  21.0  6.00  3.90
##  2  21.0  6.00  3.90
##  3  22.8  4.00  3.85
##  4  21.4  6.00  3.08
##  5  18.7  8.00  3.15
##  6  18.1  6.00  2.76
##  7  14.3  8.00  3.21
##  8  24.4  4.00  3.69
##  9  22.8  4.00  3.92
## 10  19.2  6.00  3.92
## # ... with more rows

We can filter data as well. Filter all the rows from mtcars2 where mpg is greater than 25.

filter(mtcars2, mpg > 25)
## # Source:   lazy query [?? x 11]
## # Database: sqlite 3.19.3 [:memory:]
##     mpg   cyl  disp    hp  drat    wt  qsec    vs    am  gear  carb
##   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1  32.4  4.00  78.7  66.0  4.08  2.20  19.5  1.00  1.00  4.00  1.00
## 2  30.4  4.00  75.7  52.0  4.93  1.62  18.5  1.00  1.00  4.00  2.00
## 3  33.9  4.00  71.1  65.0  4.22  1.84  19.9  1.00  1.00  4.00  1.00
## 4  27.3  4.00  79.0  66.0  4.08  1.94  18.9  1.00  1.00  4.00  1.00
## 5  26.0  4.00 120    91.0  4.43  2.14  16.7  0     1.00  5.00  2.00
## 6  30.4  4.00  95.1 113    3.77  1.51  16.9  1.00  1.00  5.00  2.00

Time to do some grouping and summarizing. Let us compute the average mileage for different types of cylinders.

mtcars2 %>%
  group_by(cyl) %>%
  summarise(mileage = mean(mpg))
## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning
## # Source:   lazy query [?? x 2]
## # Database: sqlite 3.19.3 [:memory:]
##     cyl mileage
##   <dbl>   <dbl>
## 1  4.00    26.7
## 2  6.00    19.7
## 3  8.00    15.1

Show Query

If you want to view the SQL query generated in the above step, use show_query() or explain().

mileages <- 
  mtcars2 %>%
  group_by(cyl) %>%
  summarise(mileage = mean(mpg))

dplyr::show_query(mileages)
## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning
## <SQL>
## SELECT `cyl`, AVG(`mpg`) AS `mileage`
## FROM `mtcars`
## GROUP BY `cyl`

dplyr::explain(mileages)
## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning
## <SQL>
## SELECT `cyl`, AVG(`mpg`) AS `mileage`
## FROM `mtcars`
## GROUP BY `cyl`
## 
## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning
## <PLAN>
##    addr       opcode p1 p2 p3     p4 p5 comment
## 1     0         Init  0 43  0        00      NA
## 2     1   SorterOpen  1  2  0 k(1,B) 00      NA
## 3     2      Integer  0  5  0        00      NA
## 4     3      Integer  0  4  0        00      NA
## 5     4         Null  0  8  8        00      NA
## 6     5        Gosub  7 40  0        00      NA
## 7     6     OpenRead  0  2  1      2 00      NA
## 8     7       Rewind  0 15  0        00      NA
## 9     8       Column  0  1 10        00      NA
## 10    9 RealAffinity 10  0  0        00      NA
## 11   10       Column  0  0 11        00      NA
## 12   11 RealAffinity 11  0  0        00      NA
## 13   12   MakeRecord 10  2 12        00      NA
## 14   13 SorterInsert  1 12  0        00      NA
## 15   14         Next  0  8  0        01      NA
## 16   15   OpenPseudo  2 12  2        00      NA
## 17   16   SorterSort  1 42  0        00      NA
## 18   17   SorterData  1 12  2        00      NA
## 19   18       Column  2  0  9        00      NA
## 20   19      Compare  8  9  1 k(1,B) 00      NA
## 21   20         Jump 21 25 21        00      NA
## 22   21         Move  9  8  1        00      NA
## 23   22        Gosub  6 34  0        00      NA
## 24   23        IfPos  5 42  0        00      NA
## 25   24        Gosub  7 40  0        00      NA
## 26   25       Column  2  1 13        00      NA
## 27   26     AggStep0  0 13  2 avg(1) 01      NA
## 28   27       Column  2  0  1        00      NA
## 29   28      Integer  1  4  0        00      NA
## 30   29   SorterNext  1 17  0        00      NA
## 31   30        Gosub  6 34  0        00      NA
## 32   31         Goto  0 42  0        00      NA
## 33   32      Integer  1  5  0        00      NA
## 34   33       Return  6  0  0        00      NA
## 35   34        IfPos  4 36  0        00      NA
## 36   35       Return  6  0  0        00      NA
## 37   36     AggFinal  2  1  0 avg(1) 00      NA
## 38   37         Copy  1 14  1        00      NA
## 39   38    ResultRow 14  2  0        00      NA
## 40   39       Return  6  0  0        00      NA
## 41   40         Null  0  1  3        00      NA
## 42   41       Return  7  0  0        00      NA
## 43   42         Halt  0  0  0        00      NA
## 44   43  Transaction  1  0  2      0 01      NA
## 45   44         Goto  0  1  0        00      NA

Collect Data

Now, some interesting facts. When working with databases, dplyr never pulls data into R unless you explicitly ask for it. In the previous example, dplyr will not do anything until you ask for the mileages data. It generates the SQL and only pulls down a few rows when you try to print mileages. So how do we pull all the data and store it for further analysis? collect() will pull all the data and store it in a tibble and you can use it for any further analysis.

dplyr::collect(mileages)
## Warning: Missing values are always removed in SQL.
## Use `AVG(x, na.rm = TRUE)` to silence this warning
## # A tibble: 3 x 2
##     cyl mileage
##   <dbl>   <dbl>
## 1  4.00    26.7
## 2  6.00    19.7
## 3  8.00    15.1

Up Next..

In the next post, we will learn basic SQL commands.