library(datasets)
library(dplyr)
## Warning: package 'dplyr' was built under R version 4.3.2
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
data("EuStockMarkets")
EuStockMarkets <- tibble::as.tibble(EuStockMarkets)
## Warning: `as.tibble()` was deprecated in tibble 2.0.0.
## ℹ Please use `as_tibble()` instead.
## ℹ The signature and semantics have changed, see `?as_tibble`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
class(EuStockMarkets)
## [1] "tbl_df" "tbl" "data.frame"
View(EuStockMarkets)
head(EuStockMarkets)
## # A tibble: 6 × 4
## DAX SMI CAC FTSE
## <dbl> <dbl> <dbl> <dbl>
## 1 1629. 1678. 1773. 2444.
## 2 1614. 1688. 1750. 2460.
## 3 1607. 1679. 1718 2448.
## 4 1621. 1684. 1708. 2470.
## 5 1618. 1687. 1723. 2485.
## 6 1611. 1672. 1714. 2467.
EuStockMarkets %>% group_by(DAX) %>% summarize(mean=mean(FTSE))
## # A tibble: 1,774 × 2
## DAX mean
## <dbl> <dbl>
## 1 1402. 2446.
## 2 1421. 2488.
## 3 1435. 2517.
## 4 1438. 2541.
## 5 1442. 2557.
## 6 1446. 2539.
## 7 1454. 2547.
## 8 1454. 2575.
## 9 1458. 2564.
## 10 1464. 2281
## # ℹ 1,764 more rows
EuStockMarkets %>% arrange(DAX, SMI, CAC, FTSE) #smallest to biggest
## # A tibble: 1,860 × 4
## DAX SMI CAC FTSE
## <dbl> <dbl> <dbl> <dbl>
## 1 1402. 1788 1611 2446.
## 2 1421. 1820. 1612. 2488.
## 3 1435. 1858. 1654. 2517.
## 4 1438. 1878. 1657. 2541.
## 5 1442. 1882. 1655. 2557.
## 6 1446. 1870. 1674. 2539.
## 7 1454. 1877. 1650 2547.
## 8 1454. 1889. 1668. 2575.
## 9 1458. 1884 1664. 2564.
## 10 1464. 1710. 1668. 2281
## # ℹ 1,850 more rows
EuStockMarkets %>% arrange(desc(FTSE)) #biggest to smallest
## # A tibble: 1,860 × 4
## DAX SMI CAC FTSE
## <dbl> <dbl> <dbl> <dbl>
## 1 6186. 8401. 4369. 6179
## 2 6163. 8288. 4388. 6174
## 3 6106. 8158. 4344. 6152.
## 4 6184. 8412 4322. 6133.
## 5 6108 8126. 4358. 6117.
## 6 5346. 7828. 3987. 6106.
## 7 5312. 7624. 3894. 6106.
## 8 5312. 7624. 3894. 6106.
## 9 5312. 7624. 3894. 6106.
## 10 5368. 7663. 3868. 6104.
## # ℹ 1,850 more rows
EuStockMarkets %>% select(DAX, SMI)
## # A tibble: 1,860 × 2
## DAX SMI
## <dbl> <dbl>
## 1 1629. 1678.
## 2 1614. 1688.
## 3 1607. 1679.
## 4 1621. 1684.
## 5 1618. 1687.
## 6 1611. 1672.
## 7 1631. 1683.
## 8 1640. 1704.
## 9 1635. 1698.
## 10 1646. 1716.
## # ℹ 1,850 more rows
EuStockMarkets %>% select(-DAX, -SMI, -CAC)
## # A tibble: 1,860 × 1
## FTSE
## <dbl>
## 1 2444.
## 2 2460.
## 3 2448.
## 4 2470.
## 5 2485.
## 6 2467.
## 7 2488.
## 8 2508.
## 9 2510.
## 10 2497.
## # ℹ 1,850 more rows
EuStockMarkets %>% filter(DAX=="1628.75", SMI=="1678.1", CAC=="1772.8", FTSE=="2443.6")
## # A tibble: 1 × 4
## DAX SMI CAC FTSE
## <dbl> <dbl> <dbl> <dbl>
## 1 1629. 1678. 1773. 2444.
EuStockMarkets %>% mutate(DAXSMI=DAX+SMI)
## # A tibble: 1,860 × 5
## DAX SMI CAC FTSE DAXSMI
## <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1629. 1678. 1773. 2444. 3307.
## 2 1614. 1688. 1750. 2460. 3302.
## 3 1607. 1679. 1718 2448. 3285.
## 4 1621. 1684. 1708. 2470. 3305.
## 5 1618. 1687. 1723. 2485. 3305.
## 6 1611. 1672. 1714. 2467. 3282.
## 7 1631. 1683. 1734. 2488. 3314.
## 8 1640. 1704. 1757. 2508. 3344.
## 9 1635. 1698. 1754 2510. 3333.
## 10 1646. 1716. 1754. 2497. 3362.
## # ℹ 1,850 more rows
new_EuStockMarkets <- EuStockMarkets %>% select(-CAC,-FTSE) %>% mutate(DAXSMI=DAX+SMI)
new_EuStockMarkets %>% print(n=100)
## # A tibble: 1,860 × 3
## DAX SMI DAXSMI
## <dbl> <dbl> <dbl>
## 1 1629. 1678. 3307.
## 2 1614. 1688. 3302.
## 3 1607. 1679. 3285.
## 4 1621. 1684. 3305.
## 5 1618. 1687. 3305.
## 6 1611. 1672. 3282.
## 7 1631. 1683. 3314.
## 8 1640. 1704. 3344.
## 9 1635. 1698. 3333.
## 10 1646. 1716. 3362.
## 11 1648. 1724. 3372.
## 12 1638. 1730. 3369.
## 13 1630. 1727. 3357.
## 14 1621. 1733. 3355.
## 15 1625. 1734 3359.
## 16 1628. 1728. 3356.
## 17 1632. 1737. 3369.
## 18 1621. 1723. 3344.
## 19 1613. 1724. 3337.
## 20 1605. 1719 3324.
## 21 1606. 1721. 3327.
## 22 1617. 1725. 3342.
## 23 1619. 1727. 3346.
## 24 1620. 1727. 3348.
## 25 1620. 1732. 3351.
## 26 1623. 1724. 3347.
## 27 1614. 1717. 3331.
## 28 1632. 1723. 3355.
## 29 1630. 1723 3353.
## 30 1633. 1728. 3362.
## 31 1627. 1722. 3349.
## 32 1650. 1724. 3375.
## 33 1650. 1734. 3384.
## 34 1654. 1739 3393.
## 35 1654. 1726. 3380.
## 36 1502. 1587. 3089.
## 37 1524. 1631. 3155.
## 38 1604. 1686. 3289.
## 39 1622. 1701. 3324.
## 40 1637. 1718 3355.
## 41 1652. 1726. 3378.
## 42 1646. 1717. 3362.
## 43 1650. 1726. 3376.
## 44 1652. 1737. 3389.
## 45 1650. 1737. 3386.
## 46 1654. 1732. 3386.
## 47 1658. 1731. 3389.
## 48 1650. 1727. 3376.
## 49 1649. 1728. 3377.
## 50 1646. 1720. 3367.
## 51 1639. 1715. 3354.
## 52 1626. 1709. 3334.
## 53 1629. 1713 3342.
## 54 1632. 1714. 3346.
## 55 1634. 1718 3352.
## 56 1631. 1702. 3333.
## 57 1636. 1702. 3338.
## 58 1621. 1685. 3306.
## 59 1625. 1687. 3312.
## 60 1616. 1691. 3307.
## 61 1618. 1684. 3302.
## 62 1628. 1680. 3308.
## 63 1626. 1673. 3299.
## 64 1615. 1663. 3278.
## 65 1613. 1669. 3282.
## 66 1605. 1665. 3270.
## 67 1609. 1672. 3282.
## 68 1607. 1688. 3295.
## 69 1607. 1687. 3294.
## 70 1605. 1687. 3291.
## 71 1589. 1676. 3265.
## 72 1582. 1677. 3260.
## 73 1568. 1673. 3241.
## 74 1568. 1665 3233.
## 75 1570. 1671. 3241.
## 76 1572. 1672. 3244.
## 77 1585. 1676. 3262.
## 78 1570. 1693. 3263.
## 79 1562. 1696. 3258.
## 80 1565. 1716. 3281.
## 81 1570. 1713. 3284.
## 82 1577 1705. 3282.
## 83 1590. 1711. 3302.
## 84 1573. 1710. 3283.
## 85 1572. 1689. 3261.
## 86 1579. 1699. 3278.
## 87 1589. 1700 3289.
## 88 1586. 1693 3279.
## 89 1580. 1684. 3264.
## 90 1573. 1679. 3252.
## 91 1568. 1674. 3242.
## 92 1578. 1684. 3262.
## 93 1574. 1688. 3262.
## 94 1582. 1694. 3276.
## 95 1610. 1721. 3331.
## 96 1605. 1718. 3323.
## 97 1624. 1734. 3357.
## 98 1615. 1730. 3345.
## 99 1627. 1736. 3363.
## 100 1627. 1734. 3361.
## # ℹ 1,760 more rows