Slider

Komponentas, kuris rodo savo vaikus skaidrių rodymo arba karuselės pavidalu.

Parinktys

• title | (string|node): skaidrių rodymo / karuselės pavadinimas, kuris bus rodomas jos viršuje.. Default: none.
• dots | boolean: rodyti taškus apačioje, kad būtų galima greitai pereiti prie bet kurios skaidrės.. Default: true.
• fade | boolean: kontroliuoja, ar perėjimui tarp skaidrių bus naudojamas išblukimas.. Default: false.
• draggable | boolean: kontroliuoja, ar naudotojai gali vilkti skaidres ir naršyti tarp jų.. Default: false.
• pagination | string: ar puslapius rodyti top, bottom, ar dviejose.. Default: 'bottom'.
• goto | number: Jei savybė pakeista, komponentas pereina į skaidrę su pasirinktu indeksu. Default: 0.
• infinite | boolean: kontroliuoja, ar skaidrių rodymas apjuosia jo turinį.. Default: false.
• interval | number: nustato automatinio skaidrių keitimo laiko intervalą.. Default: none.
• swipe | boolean: įgalina vilkimo / braukimo elgesį. Default: false.
• style | object: CSS eilutės stiliai. Default: {}.
• onClick | function: grįžtamasis ryšys, inicijuojamas paspaudus mygtukus next/previous.. Default: onClick() {}.

Pavyzdžiai

• Minimal
• Customize Pagination
• Slideshow
• Custom Styling

Tiesioginis redaktorius

<Slider title="Questions to explore:" > <p>Try generating new samples of the Normal data. You'll notice that the points don't always lie exactly on the line. This is typical variation. As you generate more random realizations of this plot you'll get better calibrated to the kind of deviation you can expect to see from this large a sample of Normal data.</p> <p>How do the normal qq plots change as you vary the df parameter for the t-distributed data?</p> <p>Can you tell just by looking at the t.data histograms that the data aren't normally distributed? Is it easier to tell from the QQ plots?</p> <p>How do the normal qq plots change as you vary the shape parameter in the gamma-distributed data?</p> </Slider>

<Slider title="Questions to explore:" >
    <p>Try generating new samples of the Normal data. You'll notice that the points don't always lie exactly on the line. This is typical variation. As you generate more random realizations of this plot you'll get better calibrated to the kind of deviation you can expect to see from this large a sample of Normal data.</p>
    <p>How do the normal qq plots change as you vary the df parameter for the t-distributed data?</p>
    <p>Can you tell just by looking at the t.data histograms that the data aren't normally distributed? Is it easier to tell from the QQ plots?</p>
    <p>How do the normal qq plots change as you vary the shape parameter in the gamma-distributed data?</p>
</Slider>

Rezultatas

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