SQL-fundamentals

Documentation of my Journey working with basic Sqlite via RSqlite in markdown. Projects get more detailed as they advance.

Project One: Analyzing CIA factbook database

Load Packages and establish DB connection

library(RSQLite)
con<-dbConnect(SQLite(), "./factbook.db")

--Preview the tables available
SELECT *
FROM sqlite_master
WHERE type='table';

2 records

--First 5 rows from facts table
SELECT *
FROM facts
LIMIT 5;

id	code	name	area	area_land	area_water	population	population_growth	birth_rate	death_rate	migration_rate	created_at	updated_at
1	af	Afghanistan	652230	652230	0	32564342	2.32	38.57	13.89	1.51	2015-11-01 13:19:49.461734	2015-11-01 13:19:49.461734
2	al	Albania	28748	27398	1350	3029278	0.30	12.92	6.58	3.30	2015-11-01 13:19:54.431082	2015-11-01 13:19:54.431082
3	ag	Algeria	2381741	2381741	0	39542166	1.84	23.67	4.31	0.92	2015-11-01 13:19:59.961286	2015-11-01 13:19:59.961286
4	an	Andorra	468	468	0	85580	0.12	8.13	6.96	0.00	2015-11-01 13:20:03.659945	2015-11-01 13:20:03.659945
5	ao	Angola	1246700	1246700	0	19625353	2.78	38.78	11.49	0.46	2015-11-01 13:20:08.625072	2015-11-01 13:20:08.625072

Displaying records 1 - 5

Min and Max populations states

SELECT MIN(population) as min_pop, MAX(population) as max_pop,
MIN(population_growth) as min_pop_g, MAX(population_growth) as max_pop_g
FROM facts;

min_pop	max_pop	min_pop_g	max_pop_g
0	7256490011	0	4.02

1 records

Full solution workbook can be viewed here

Project Two: Querying and Visualizing Chinook Music store

conn<-dbConnect(SQLite(), "chinook.db")
tables <- dbListTables(conn)
tables

##  [1] "album"          "artist"         "customer"       "employee"      
##  [5] "genre"          "invoice"        "invoice_line"   "media_type"    
##  [9] "playlist"       "playlist_track" "track"

--Preview the tables in the database
SELECT
    name,
    type
FROM sqlite_master
WHERE type IN ("table","view");

--Most sold genres
SELECT g.name as genre_name, il.quantity as quantity_sold
FROM genre as g
LEFT JOIN track as t ON t.genre_id=g.genre_id
LEFT JOIN invoice_line as il ON il.track_id=t.track_id;

name	type
album	table
artist	table
customer	table
employee	table
genre	table
invoice	table
invoice_line	table
media_type	table
playlist	table
playlist_track	table

Displaying records 1 - 10

---Most sold genres in the USA

WITH USA_tracks AS 
(SELECT il.quantity, il.track_id
FROM invoice_line as il
INNER JOIN invoice as i ON i.invoice_id=il.invoice_id
WHERE i.billing_country="USA")

SELECT g.name as genre_name, SUM(ut.quantity) as quantity_sold
FROM track as t
INNER JOIN USA_tracks as ut ON ut.track_id=t.track_id
INNER JOIN genre as g ON g.genre_id=t.genre_id
GROUP BY 1
ORDER BY 2 DESC
LIMIT 10;

genres_prop<-genres%>%
  drop_na(quantity_sold)%>%
  mutate(prop_quantity=quantity_sold/sum(quantity_sold))

plot1<-genres_prop%>%
  ggplot(aes(x=reorder(factor(genre_name), -quantity_sold), y=quantity_sold, fill=genre_name))+
  geom_col()+
  theme_minimal()+
  labs(title = "Most selling genres in the USA",
       y="No. Tracks Sold",
       x="Genre")+
  theme(plot.title = element_text(hjust = 0.5, size = 12, face="bold", 
                                  margin = margin(t = 0, r = 0, b = 15, l = 0)),
        axis.title.y = element_text(margin = margin(t = 0, r = 15, b = 0, l = 0)),
        axis.title.x = element_text(margin = margin(t = 15, r = 0, b = 0, l = 0)),
        legend.position="none")
plot1

Out of the four artists, it would be wise to select the artists producing **punk**, **blues** and **Pop** as they are the three best performing genres out of the four artists, in the USA.

Full solution workbook can be viewed here

Project Three: Building a Baseball Database

This project is about utilizing the power of SQL to create a database out of several Excel CSV files that exist individually. The goal is to create a database that will house the several CSV files under one roof. This includes creating a database Schema, linking the tables with primary keys and foreign keys.

As usual, i shall be running my SQL queries on an R markdown book to allow publishing queries online. A brief introduction on using SQL in R, the packages and requirements can be found in another project i worked on earlier.

Leagues

According to Wikipedia, there are two major professional baseball leagues, the American (AL) and National (NL). The data from retrosheets contains a total of 6 different leagues. The additional leagues are mostly defunct leagues from the old days, like the Federal league which ended in 1915.

For a full solution (with code) you can view it here

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