110 lines
3.7 KiB
Plaintext
110 lines
3.7 KiB
Plaintext
---
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title: "BAD_Proj_Analytics"
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author: "Marcin Kostrzewski"
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date: "13/05/2019"
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output: pdf_document
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---
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```{r, echo=FALSE}
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con <- DBI::dbConnect(odbc::odbc(),
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driver = "SQL Server",
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database = "dbad_flights",
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UID = "dbad_s444409",
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PWD = "qAOKvlYA6e",
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server = "mssql-2016.labs.wmi.amu.edu.pl",
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port = 5432)
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```
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## Jakie było średnie opóźnienie przylotu?
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```{sql, connection=con, tab.cap = NA}
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SELECT CAST(AVG(arr_delay) AS NUMERIC(30,3)) AS 'Average delay (minutes)'
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FROM Flight_delays
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WHERE arr_delay IS NOT NULL;
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```
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## Jakie było maksymalne opóźnienie przylotu?
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```{sql, connection=con, tab.cap = NA}
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SELECT CAST(MAX(arr_delay)/60 AS NUMERIC(30,3)) AS 'Max delay (hours)'
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FROM Flight_delays
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WHERE arr_delay IS NOT NULL;
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```
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## Który lot miał największe opóźnienie przylotu?
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```{sql, connection=con, tab.cap = NA}
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SELECT carrier AS 'Carrier',
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origin_city_name AS 'Origin',
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dest_city_name AS 'Destination',
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fl_date AS 'Date',
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arr_delay AS 'Delay (minutes)'
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FROM Flight_delays
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WHERE arr_delay = (SELECT MAX(arr_delay)
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FROM Flight_delays
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WHERE arr_delay IS NOT NULL);
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```
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## Które dni tygodnia są najgorsze do podróżowania?
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```{sql, connection=con, tab.cap = NA}
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SELECT CASE WHEN day_of_week = 1 THEN 'Monday'
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WHEN day_of_week = 2 THEN 'Tuesday'
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WHEN day_of_week = 3 THEN 'Wednesday'
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WHEN day_of_week = 4 THEN 'Thursday'
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WHEN day_of_week = 5 THEN 'Friday'
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WHEN day_of_week = 6 THEN 'Saturday'
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WHEN day_of_week = 7 THEN 'Sunday'
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END AS 'Day',
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AVG(arr_delay) AS 'Average Delay (minutes)'
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FROM Flight_delays
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GROUP BY day_of_week
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ORDER BY AVG(arr_delay) DESC;
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```
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## Które linie lotnicze latające z San Francisco (SFO) mają najmniejsze opóźnienia przylotu?
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```{sql, connection=con, tab.cap = NA}
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SELECT F1.carrier AS 'Carrier',
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(SELECT AVG(F2.arr_delay) AS 'avg_delay'
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FROM Flight_delays F2
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WHERE F1.carrier = F2.carrier
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GROUP BY F2.carrier) AS 'Delay (minutes)'
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FROM Flight_delays F1
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WHERE F1.origin_city_name LIKE 'San Francisco%'
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GROUP BY F1.carrier
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ORDER BY "Delay (minutes)" ASC;
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```
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*Pojawiające się w tabeli wartości ujemne oznaczają, że średnio samoloty lądowały wcześniej, niż przewidziano, czyli były przyśpieszone.*
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## Jaka część linii lotniczych ma regularne opóźnienia, tj. jej lot ma średnio co najmniej 10 min. opóźnienia?
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```{sql, connection=con, tab.cap = NA, echo=FALSE}
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SELECT (SELECT COUNT(*)
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FROM (SELECT F1.carrier,
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(SELECT AVG(arr_delay)
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FROM Flight_delays F2
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WHERE F1.carrier=F2.carrier
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GROUP BY F2.carrier
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HAVING AVG(F1.arr_delay)>10) AS 'avg_delay'
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FROM Flight_delays F1
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GROUP BY F1.carrier) AS t
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WHERE t.avg_delay IS NOT NULL) / CAST((SELECT COUNT(*)
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FROM (SELECT COUNT(*) AS 'count'
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FROM Flight_delays
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GROUP BY carrier) AS T) AS FLOAT) AS 'Part of continuous delays';
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```
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## Jak opóźnienia wylotów wpływają na opóźnienia przylotów?
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```{r, con, echo=FALSE}
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data <- DBI::dbGetQuery(con, "SELECT dep_delay,
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arr_delay
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FROM Flight_delays
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WHERE dep_delay IS NOT NULL AND arr_delay IS NOT NULL;")
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library(knitr)
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res <- cor(data, use = "all", method = "pearson")
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kable(res[2:2])
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```
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