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Get team list paginated by team # in 500s.

Usage

teams(page_num, year = FALSE, simple = FALSE, keys = FALSE)

Arguments

page_num

index of desired page

year

year of interest. If FALSE, gets all years

simple

(bool) simplify return objects?

keys

(bool) return keys only?

Value

tidy tibble of teams

Examples

teams(0)
#> # A tibble: 404 × 18
#>    address city           country gmaps_place_id gmaps_url key   lat   lng  
#>    <lgl>   <chr>          <chr>   <lgl>          <lgl>     <chr> <lgl> <lgl>
#>  1 NA      Pontiac        USA     NA             NA        frc1  NA    NA   
#>  2 NA      Van Nuys       USA     NA             NA        frc4  NA    NA   
#>  3 NA      Melvindale     USA     NA             NA        frc5  NA    NA   
#>  4 NA      Plymouth       USA     NA             NA        frc6  NA    NA   
#>  5 NA      Baltimore      USA     NA             NA        frc7  NA    NA   
#>  6 NA      Palo Alto      USA     NA             NA        frc8  NA    NA   
#>  7 NA      Chicago        USA     NA             NA        frc9  NA    NA   
#>  8 NA      St. Louis Park USA     NA             NA        frc10 NA    NA   
#>  9 NA      Flanders       USA     NA             NA        frc11 NA    NA   
#> 10 NA      NA             NA      NA             NA        frc13 NA    NA   
#> # ℹ 394 more rows
#> # ℹ 10 more variables: location_name <lgl>, motto <lgl>, name <chr>,
#> #   nickname <chr>, postal_code <chr>, rookie_year <int>, school_name <chr>,
#> #   state_prov <chr>, team_number <int>, website <chr>
teams(4)
#> # A tibble: 408 × 18
#>    address city         country gmaps_place_id gmaps_url key     lat   lng  
#>    <lgl>   <chr>        <chr>   <lgl>          <lgl>     <chr>   <lgl> <lgl>
#>  1 NA      Dorr         USA     NA             NA        frc2000 NA    NA   
#>  2 NA      Kansas City  USA     NA             NA        frc2001 NA    NA   
#>  3 NA      Tualatin     USA     NA             NA        frc2002 NA    NA   
#>  4 NA      Tulsa        USA     NA             NA        frc2004 NA    NA   
#>  5 NA      Kansas City  USA     NA             NA        frc2005 NA    NA   
#>  6 NA      Duluth       USA     NA             NA        frc2007 NA    NA   
#>  7 NA      Kansas City  USA     NA             NA        frc2008 NA    NA   
#>  8 NA      Roxbury      USA     NA             NA        frc2009 NA    NA   
#>  9 NA      Warren       USA     NA             NA        frc2010 NA    NA   
#> 10 NA      Independence USA     NA             NA        frc2011 NA    NA   
#> # ℹ 398 more rows
#> # ℹ 10 more variables: location_name <lgl>, motto <lgl>, name <chr>,
#> #   nickname <chr>, postal_code <chr>, rookie_year <int>, school_name <chr>,
#> #   state_prov <chr>, team_number <int>, website <chr>
teams(1, year = 2016)
#> # A tibble: 148 × 18
#>    address city               country gmaps_place_id gmaps_url key   lat   lng  
#>    <lgl>   <chr>              <chr>   <lgl>          <lgl>     <chr> <lgl> <lgl>
#>  1 NA      Manchester/Goffst… USA     NA             NA        frc5… NA    NA   
#>  2 NA      Novi               USA     NA             NA        frc5… NA    NA   
#>  3 NA      Bedford            USA     NA             NA        frc5… NA    NA   
#>  4 NA      Miller Place       USA     NA             NA        frc5… NA    NA   
#>  5 NA      Cedar Falls        USA     NA             NA        frc5… NA    NA   
#>  6 NA      Massapequa         USA     NA             NA        frc5… NA    NA   
#>  7 NA      Lindenhurst        USA     NA             NA        frc5… NA    NA   
#>  8 NA      Sussex             USA     NA             NA        frc5… NA    NA   
#>  9 NA      Athens             USA     NA             NA        frc5… NA    NA   
#> 10 NA      Richmond           USA     NA             NA        frc5… NA    NA   
#> # ℹ 138 more rows
#> # ℹ 10 more variables: location_name <lgl>, motto <lgl>, name <chr>,
#> #   nickname <chr>, postal_code <chr>, rookie_year <int>, school_name <chr>,
#> #   state_prov <chr>, team_number <int>, website <chr>
teams(0, simple = TRUE)
#> # A tibble: 404 × 7
#>    city           country key   name             nickname state_prov team_number
#>    <chr>          <chr>   <chr> <chr>            <chr>    <chr>            <int>
#>  1 Pontiac        USA     frc1  FCA Foundation/… The Jug… Michigan             1
#>  2 Van Nuys       USA     frc4  Gene HAAS Found… Team 4 … California           4
#>  3 Melvindale     USA     frc5  Ford FIRST Robo… Robocar… MI                   5
#>  4 Plymouth       USA     frc6  ATK (Alliant Te… The Cog… MN                   6
#>  5 Baltimore      USA     frc7  Lockheed Martin… Team007  MD                   7
#>  6 Palo Alto      USA     frc8  Apple/Bayer Fun… Paly Ro… California           8
#>  7 Chicago        USA     frc9  Roosevelt High … Rooseve… IL                   9
#>  8 St. Louis Park USA     frc10 Benilde-St. Mar… Red Kni… MN                  10
#>  9 Flanders       USA     frc11 National Defens… MORT     New Jersey          11
#> 10 NA             NA      frc13 Team 13          Triskad… NA                  13
#> # ℹ 394 more rows
teams(3, keys = TRUE)
#>   [1] "frc1500" "frc1501" "frc1502" "frc1503" "frc1504" "frc1505" "frc1506"
#>   [8] "frc1507" "frc1508" "frc1509" "frc1510" "frc1511" "frc1512" "frc1513"
#>  [15] "frc1514" "frc1515" "frc1516" "frc1517" "frc1518" "frc1519" "frc1520"
#>  [22] "frc1522" "frc1523" "frc1524" "frc1525" "frc1527" "frc1528" "frc1529"
#>  [29] "frc1530" "frc1531" "frc1532" "frc1533" "frc1534" "frc1535" "frc1537"
#>  [36] "frc1538" "frc1539" "frc1540" "frc1541" "frc1542" "frc1543" "frc1544"
#>  [43] "frc1545" "frc1546" "frc1547" "frc1548" "frc1549" "frc1550" "frc1551"
#>  [50] "frc1552" "frc1553" "frc1554" "frc1555" "frc1556" "frc1557" "frc1558"
#>  [57] "frc1559" "frc1560" "frc1561" "frc1562" "frc1563" "frc1564" "frc1565"
#>  [64] "frc1566" "frc1567" "frc1568" "frc1569" "frc1570" "frc1571" "frc1572"
#>  [71] "frc1573" "frc1574" "frc1575" "frc1576" "frc1577" "frc1578" "frc1579"
#>  [78] "frc1580" "frc1581" "frc1582" "frc1583" "frc1584" "frc1585" "frc1588"
#>  [85] "frc1589" "frc1590" "frc1591" "frc1592" "frc1593" "frc1594" "frc1595"
#>  [92] "frc1596" "frc1597" "frc1598" "frc1599" "frc1600" "frc1601" "frc1602"
#>  [99] "frc1603" "frc1604" "frc1605" "frc1606" "frc1607" "frc1609" "frc1610"
#> [106] "frc1611" "frc1612" "frc1613" "frc1616" "frc1617" "frc1618" "frc1619"
#> [113] "frc1620" "frc1621" "frc1622" "frc1623" "frc1624" "frc1625" "frc1626"
#> [120] "frc1628" "frc1629" "frc1631" "frc1633" "frc1634" "frc1635" "frc1636"
#> [127] "frc1640" "frc1641" "frc1642" "frc1643" "frc1644" "frc1645" "frc1646"
#> [134] "frc1647" "frc1648" "frc1649" "frc1650" "frc1651" "frc1652" "frc1653"
#> [141] "frc1654" "frc1655" "frc1656" "frc1657" "frc1658" "frc1660" "frc1661"
#> [148] "frc1662" "frc1665" "frc1666" "frc1667" "frc1669" "frc1670" "frc1671"
#> [155] "frc1672" "frc1674" "frc1675" "frc1676" "frc1677" "frc1678" "frc1680"
#> [162] "frc1682" "frc1683" "frc1684" "frc1685" "frc1686" "frc1687" "frc1688"
#> [169] "frc1689" "frc1690" "frc1691" "frc1692" "frc1693" "frc1694" "frc1695"
#> [176] "frc1696" "frc1697" "frc1698" "frc1699" "frc1700" "frc1701" "frc1702"
#> [183] "frc1703" "frc1704" "frc1705" "frc1706" "frc1707" "frc1708" "frc1709"
#> [190] "frc1710" "frc1711" "frc1712" "frc1713" "frc1714" "frc1715" "frc1716"
#> [197] "frc1717" "frc1718" "frc1719" "frc1720" "frc1721" "frc1722" "frc1723"
#> [204] "frc1724" "frc1725" "frc1726" "frc1727" "frc1728" "frc1729" "frc1730"
#> [211] "frc1731" "frc1732" "frc1733" "frc1734" "frc1735" "frc1736" "frc1737"
#> [218] "frc1738" "frc1739" "frc1740" "frc1741" "frc1742" "frc1743" "frc1744"
#> [225] "frc1745" "frc1746" "frc1747" "frc1748" "frc1749" "frc1750" "frc1751"
#> [232] "frc1752" "frc1753" "frc1754" "frc1755" "frc1756" "frc1757" "frc1758"
#> [239] "frc1759" "frc1760" "frc1761" "frc1763" "frc1764" "frc1765" "frc1766"
#> [246] "frc1767" "frc1768" "frc1769" "frc1770" "frc1771" "frc1772" "frc1774"
#> [253] "frc1775" "frc1776" "frc1777" "frc1778" "frc1779" "frc1780" "frc1781"
#> [260] "frc1782" "frc1783" "frc1784" "frc1785" "frc1786" "frc1787" "frc1788"
#> [267] "frc1789" "frc1790" "frc1791" "frc1792" "frc1793" "frc1794" "frc1795"
#> [274] "frc1796" "frc1797" "frc1798" "frc1799" "frc1800" "frc1801" "frc1802"
#> [281] "frc1803" "frc1804" "frc1805" "frc1806" "frc1807" "frc1808" "frc1809"
#> [288] "frc1810" "frc1811" "frc1812" "frc1813" "frc1814" "frc1815" "frc1816"
#> [295] "frc1817" "frc1818" "frc1819" "frc1820" "frc1822" "frc1823" "frc1824"
#> [302] "frc1825" "frc1826" "frc1827" "frc1828" "frc1829" "frc1830" "frc1831"
#> [309] "frc1832" "frc1833" "frc1834" "frc1835" "frc1836" "frc1837" "frc1838"
#> [316] "frc1839" "frc1840" "frc1841" "frc1842" "frc1843" "frc1844" "frc1845"
#> [323] "frc1846" "frc1847" "frc1848" "frc1849" "frc1850" "frc1851" "frc1852"
#> [330] "frc1853" "frc1855" "frc1856" "frc1858" "frc1859" "frc1860" "frc1861"
#> [337] "frc1862" "frc1863" "frc1864" "frc1865" "frc1866" "frc1867" "frc1868"
#> [344] "frc1870" "frc1871" "frc1872" "frc1873" "frc1875" "frc1876" "frc1877"
#> [351] "frc1879" "frc1880" "frc1881" "frc1882" "frc1883" "frc1884" "frc1885"
#> [358] "frc1886" "frc1887" "frc1888" "frc1889" "frc1890" "frc1891" "frc1893"
#> [365] "frc1894" "frc1895" "frc1896" "frc1897" "frc1898" "frc1899" "frc1900"
#> [372] "frc1901" "frc1902" "frc1904" "frc1905" "frc1906" "frc1907" "frc1908"
#> [379] "frc1909" "frc1910" "frc1911" "frc1912" "frc1913" "frc1914" "frc1915"
#> [386] "frc1916" "frc1917" "frc1918" "frc1919" "frc1920" "frc1922" "frc1923"
#> [393] "frc1925" "frc1926" "frc1927" "frc1929" "frc1930" "frc1931" "frc1932"
#> [400] "frc1933" "frc1934" "frc1935" "frc1937" "frc1938" "frc1939" "frc1940"
#> [407] "frc1941" "frc1942" "frc1943" "frc1944" "frc1945" "frc1946" "frc1947"
#> [414] "frc1948" "frc1949" "frc1950" "frc1951" "frc1952" "frc1954" "frc1955"
#> [421] "frc1956" "frc1957" "frc1959" "frc1960" "frc1961" "frc1962" "frc1963"
#> [428] "frc1965" "frc1966" "frc1967" "frc1970" "frc1972" "frc1973" "frc1974"
#> [435] "frc1975" "frc1976" "frc1977" "frc1978" "frc1980" "frc1981" "frc1982"
#> [442] "frc1983" "frc1984" "frc1985" "frc1986" "frc1987" "frc1988" "frc1989"
#> [449] "frc1990" "frc1991" "frc1992" "frc1994" "frc1995" "frc1996" "frc1997"
#> [456] "frc1998" "frc1999"