Introduction to using Databases in R, with Tidyverse tools

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# Introduction to<br> using Databases in R,<br>with Tidyverse tools

## Working directly in a database

### Vebash Naidoo

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# Who am I?

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# Who are you?

<div class="flex" style="margin: 0 1em;">
  <div class="column">
    <h3> You've got data in a database <h3>
    <img src="images/ryan-howerter-JXIFjYVbAS8-unsplash.jpg" style="width: 100%;">
  </div>
  
???

<div class="column"style="margin: 0 1em;">
    <h3> You know enough of the tidyverse </h3>
    <img src="images/hex-tidyverse.png" style="width: 75%;">
  </div>

???

You have good tidyverse knowledge...most especially dplyr.

<div class="column"style="margin: 0 1em;">
    <h3> You want to flex that skill </h3>
    <img src="images/will-porada-DH5183gvKUg-unsplash.jpg" style="width: 100%;">
  </div>

???

You want to leverage that knowledge and connect directly to your database

<div class="column"style="margin: 0 1em;">
    <h3> Lighter cognitive load </h3> <br> <br>
    <img src="images/daniel-cheung-B7N0IjiIJYo-unsplash.jpg" style="width: 100%;">
  </div>

???

It's hard switching context between SQL and R tidyverse. While it is good to know both sometimes in an analysis you'd like to have a lower cognitive load.

---

## Quick Knowledge Check

.panelset[
.panel[.panel-name[Explain this]

Do you understand what this piece of code is doing? Will you discuss with your neighbour for a minute, and make a choice on the next tab.

```r
palmerpenguins::penguins %>% 
  select(species, sex, flipper_length_mm) %>%
  drop_na() %>% 
  group_by(species, sex) %>% 
  summarise(avg_flipper_len = mean(flipper_length_mm))
```
]

<div class="panel">
    <div class="panel-name">Options</div>
    <ol>
    <li><p>Returns only the average flipper length of each <code>species</code>.</p></li>
    <li><p>Returns the entire dataset, with the average flipper length<br>
      of the <code>species</code>, tagged on as new column.</p></li>
    <li><p> It returns the average flipper length split by <code>species</code> and <code>sex</code>. 
      Only observations which have values for species, sex and flipper_length_mm are included.</p></li>
    <li><p>It gives an error because it should be summari<strong>z</strong>e() not summari<strong>s</strong>e().</p></li>
    </ol>
  </div>

.panel[.panel-name[Solution]

.left-code[
The answer is `c`.

```r
palmerpenguins::penguins %>% 
  select(species, sex, 
         flipper_length_mm) %>%
  drop_na() %>% 
  group_by(species, sex) %>% 
  summarise(avg_flipper_len =
      mean(flipper_length_mm))
  
```
]

.right-out[

```
## `summarise()` regrouping output by 'species' (override with `.groups` argument)
## # A tibble: 6 x 3
## # Groups:   species [3]
##   species   sex    avg_flipper_len
##   <fct>     <fct>            <dbl>
## 1 Adelie    female            188.
## 2 Adelie    male              192.
## 3 Chinstrap female            192.
## 4 Chinstrap male              200.
## 5 Gentoo    female            213.
## 6 Gentoo    male              222.
```

]

]
]

---
### Connecting to a Database

.panelset[
.panel[.panel-name[Overall]

- Install the `DBI` package.
- The `DBI` package is a __D__ata__Base__ __I__nterface for R.
- Install the `odbc` package.
- The `odbc` package is a DBI compliant interface to __O__pen __D__ata__B__ase __C__onnectivity (ODBC) drivers.
- Install the `dbplyr` package which is a dplyr back-end for databases.

```r
install.packages("DBI")
install.packages("odbc")
install.packages("dbplyr")
```
]

.panel[.panel-name[SQLite]

- Install the `RSQLite` package.

.tiny[

```r
install.packages("RSQLite")
```
]

- We make a __connection__ to the DB, using `dbConnect()`, providing two arguments: 
  
  * `drv`: The driver we're using, here `RSQLite::SQLite()`.
  * A path to the DB - the example here uses `:memory:` which is an in memory SQLite DB.
  
.tiny[

```r
library(DBI)
# Create an in-memory RSQLite DB
con <- dbConnect(RSQLite::SQLite(), ":memory:")
# List tables available through our connection
dbListTables(con) # nothing there yet -> character(0)
```
]
]

.panel[.panel-name[Using an DBI-compliant R 📦]

- Install the DBI-compliant R 📦
- We'll use the `RPostgres` 📦 as an example here (similar process for `RMariaDB` etc.).

.tiny[

```r
install.packages("RPostgres")
```
]

- We make a __connection__ to the DB, using `dbConnect()` as before, this time providing more info such as user name, password, host name etc.

.tiny[

```r
con <- dbConnect(drv = RPostgres::Postgres(), # driver we're using this time
    dbname = "DB_name", 
    host = "URL_or_localhost",
    user = "user_name", 
    password = "pswd",
    port = "5432" # common port for PostgreSQL, but check with DBA
)
```
]
]

.panel[.panel-name[Using an odbc driver]

- You can download the ODBC driver from your database vendor's website.
- List the drivers you have available using `odbcListDrivers()`.

.tiny[

```r
library(odbc)
sort(unique(odbcListDrivers()[[1]]))
```
]

- We make a __connection__ to the DB, using `dbConnect()` as before, this time through the applicable `odbc` driver.

.tiny[

```r
con <- dbConnect(
    odbc::odbc(),
    Driver = "e.g.PostgreSQL ODBC Driver(UNICODE)",
    Database = "DB_name",
    Server = "URL_or_localhost",
    UID = "user_name",
    PWD = "pswd",
    port = "5432"
)
```
]
]
]

???
- The `RSQLite` package embeds a SQLite database engine in R, and is a DBI-compliant interface.

---

### We're connected, now what?

.panelset[
.panel[.panel-name[Have a look around]

.pull-left[
- Write data into a database table
<br> <br> <br> <br> <br>
- List all tables
<br> <br> <br> <br> <br>
- List the fields in a particular table
<br> <br> <br> <br> <br>
]
.pull-right[
.tiny[

```r
dbWriteTable(con, # using connection con
    "mtcars", # write into a table "mtcars"
     mtcars, # the dataset mtcars
     overwrite = TRUE # overwrite the data there if it exists
     )
```
]

.tiny[

```r
dbListTables(con)
## [1] "mtcars"
```
]

.tiny[

```r
dbListFields(con, "mtcars")
##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" 
# "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"
```
]
]

]

.panel[.panel-name[Use `dplyr` pipelines]

.pull-left[
- Connect to table <span style="color: #008080;background-color:#9FDDBA">tbl(con, "tbl_name")</span>

- Select subsets of data
<br> <br> <br> <br>
- Anayse data
<br> <br> <br> <br> <br> <br>
- Filter data
<br> <br>
]
.pull-right[
.tiny[

```r
tbl(con, # using our connection
    'mtcars' # reach into the mtcars table
    ) %>% 
  select(cyl) %>%  distinct()
# Source:   lazy query [?? x 1]
# Database: sqlite 3.33.0 [:memory:]
#     cyl
# 1     6 ...
```
]

.tiny[

```r
tbl(con, "mtcars") %>% 
  count(cyl) 
#     cyl     n
#   <dbl> <int>
# 1     4    11
# 2     6     7 ...
```
]

.tiny[

```r
tbl(con, "mtcars") %>% 
  filter(am == 0) 
```
]
]

]

.panel[.panel-name[More `dplyr`]

.pull-left[
- Join tables from your database <span style="color: #FFE4E1;background-color:#FD5800">NOTE: tbl(con, "tbl_name1") and tbl(con, "tbl_name2")</span>.

<br> <br> 
- What's the SQL Looking like?
<br> <br> <br> <br> 
]
.pull-right[
.tiny[

```r
tbl(con, "tbl_name1") %>% 
  left_join(tbl(con, "tbl_name2"),
      by = c("col1" = "name_1",
             "coln" = "name_n")
```
]

.tiny[

```r
tbl(con, "mtcars") %>% 
  count(cyl) %>% 
  show_query()
# <SQL>
# SELECT `cyl`, COUNT(*) AS `n`
# FROM `mtcars`
# GROUP BY `cyl`
```
]

]

---

### Always remember to ...

- Collect your data only when you need to (close to the end).

```r
my_db_mtcars <- tbl(con, "mtcars") %>% 
  count(cyl) %>% 
  collect() # finally bringing data into R
```

- Disconnect when you're done!

```r
dbDisconnect(con)
```

---
### Let's take stock

.panelset[
.panel[.panel-name[Unscramble this]

1. dbWriteTable(con, "band_members", dplyr::band_members)
1. library(DBI)
1. dbListFields(con, "band_members")
1. dbDisconnect(con)
1. con <- dbConnect(RSQLite::SQLite(), ":memory:")
1. tbl(con, "band_members")

]

.panel[.panel-name[Solution]
.pull-left[
2: library(DBI) <br>
5: con <- dbConnect(RSQLite::SQLite(), ":memory:")<br>
1: dbWriteTable(con, "band_members", dplyr::band_members)<br>
3: dbListFields(con, "band_members")<br>
6: tbl(con, "band_members")<br>
4: dbDisconnect(con)
]

.pull-right[
.tiny[

```r
library(DBI)
con <- dbConnect(RSQLite::SQLite(), ":memory:")
dbWriteTable(con, "band_members", dplyr::band_members)
dbListFields(con, "band_members")
## [1] "name" "band"
tbl(con, "band_members")
## # Source:   table<band_members> [?? x 2]
## # Database: sqlite 3.33.0 [:memory:]
##   name  band   
##   <chr> <chr>  
## 1 Mick  Stones 
## 2 John  Beatles
## 3 Paul  Beatles
dbDisconnect(con)
```
]
]
]

]

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### Our dataset

.panelset[
.panel[.panel-name[The Great British Bake off]

Alison Hill has put together some data from the Great British Bake off [here](https://github.com/apreshill/bakeoff).

I converted it to a SQLite DB that we can use to practise our skills on.

.pull-left[
.tiny[

```r
tbl(con, "baker_results") %>% 
  select(series, baker,
    star_baker:technical_top3) %>% 
  head(3)
## # Source:   lazy query [?? x 5]
## # Database: sqlite 3.33.0 [/cloud/project/great_brit_bakeoff.db]
##   series baker   star_baker technical_winner technical_top3
##    <dbl> <chr>        <int>            <int>          <int>
## 1      1 Annetha          0                0              1
## 2      1 David            0                0              1
## 3      1 Edd              0                2              4
```
]
]

.pull-right[
.tiny[

```r
tbl(con, "results") %>% 
  select(series, baker, result) %>% 
  filter(result == "WINNER") %>% 
  head(3)
## # Source:   lazy query [?? x 3]
## # Database: sqlite 3.33.0 [/cloud/project/great_brit_bakeoff.db]
##   series baker  result
##    <int> <chr>  <chr> 
## 1      1 Edd    WINNER
## 2      2 Joanne WINNER
## 3      3 John   WINNER
```
]
]
]

.panel[.panel-name[Create a connection]

.pull-left[

- It is a SQLite DB in your working directory.
- It is named `great_brit_bakeoff.db`

Which code will make the connection?

.tiny[

```r
library(DBI)
con <- dbConnect(
    drv = RSQLite::SQLite(),
    dbname = "great_brit_bakeoff.db"
)
```
]

]
.pull-right[

.tiny[

```r
library(DBI)
con <- dbConnect(
    drv = RSQLite::SQLite(),
    user = "admin", password = "password",
    dbname = "great_brit_bakeoff.db"
)
```
]

.tiny[

```r
library(DBI)
con <- dbConnect(
    drv = RPostgres::Postgres(), host = "localhost",
    user = "admin", password = "password",
    dbname = "great_brit_bakeoff.db"
)
```
]
]

]

.panel[.panel-name[Your Turn]

Open up `exercise-01.Rmd` and:

1. Complete the connection code.
1. Recalling that we are joining the `baker_results` and the `results` tables, complete the join code.
1. Amend `eval = FALSE` in the __setup__ code chunk to `eval = TRUE`.
1. Knit the document.
1. Interested in a different __winner__? Fill in the code to view how your baker performed across the season.

]

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## Acknowledgements

#### Slides inspired by Dr. Alison Hill 
[Teaching in Production](https://rstudio-education.github.io/teaching-in-production/)

#### Yihui Xie's and Garrick Aden-Buie's excellent
[xaringan](https://github.com/yihui/xaringan) 📦  [xaringanExtra](https://github.com/gadenbuie/xaringanExtra) 📦

#### Mine Çetinkaya-Rundel "Start with Cake"
[Let them eat cake (first)!](https://www.youtube.com/watch?v=RsVOrpXAPXo&feature=emb_logo)

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## More Resources

#### Looking for more practise

Check out:

1. [Databases using R](https://db.rstudio.com/) 🎉 ❤️.
1. I have a package called [reclues](https://github.com/sciencificity/reclues) which contains a database which mimics the SQL Murder Mystery done by [Northwestern’s Knight Lab](https://github.com/NUKnightLab/sql-mysteries). The 📦 [site is here](https://sciencificity.github.io/reclues/).

1. I included the SQL Create scripts in the scripts folder - you can make a PostgreSQL version of `The Great British Bake Off` data to practise on 🙋 🍪.

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# Thank you!