Sortable Tree

Ein dünner Wrapper um react-sortable-tree, der alle Eigenschaften der genannten Komponente unterstützt.

Optionen

• treeData | array<object> (required): Array von Objekten mit title (primäre Bezeichnung für den Knoten), subtitle (sekundäre Bezeichnung), expanded (zeigt die Kinder des Knotens an wenn auf true gesetzt), und/oder children (Array von Kindknoten, die zu dem jeweiligen Knoten gehören). Default: none.
• title | (string|node): Kopfzeilen-Titel. Default: none.

Beispiele

Live-Editor

<SortableTree onlyExpandSearchedNodes canDrag={false} treeData={[{ title: 'Type of Data', children: [{ title: 'Categorical', children: [{ title: 'Chi-Square Test', subtitle: 'Test for independence for two variables or goodness-of-fit test' }] }, { title: 'Continuous', children: [{ title: 'Group Differences', children: [{ title: 'Between Means', children: [{ title: 'How many groups?', children: [{ title: 'Two', children: [{ title: 'Are assumptions satisfied (potentially after data transformations)?', subtitle: '1. Equal variances in both groups, 2. Data normally distributed or sufficiently large sample', children: [{ title: 'If yes, can use a parametric test', subtitle: 'Student\'s t-test (paired or unpaired)' }, { title: 'If not, use a nonparametric randomization test', subtitle: 'E.g., Wilcoxon Rank Sum Test or Mann-Whitney U Test' } ] }] }, { title: 'More than two', children: [{ title: 'Are assumptions satisfied (potentially after data transformations)?', subtitle: '1. Equal variances in each group, 2. Data normally distributed or sufficiently large sample', children: [{ title: 'If yes, can use a parametric test', subtitle: 'ANOVA (Analysis of Variance)' }, { title: 'If not, use a nonparametric randomization test', subtitle: 'E.g., Kruskal-Wallis Test' } ] }] } ] }] }, { title: 'Between Variances', subtitle: 'Bartlett\'s Test or Levene\'s Test when data are normally distrubuted' } ] }, { title: 'Relationships between Variables', children: [{ title: 'Regression Analysis', subtitle: 'Simple linear regression for one response and one predictor,\nmultiple regression in case of several explanatory variables.' }] } ] } ] }]} />

<SortableTree 
  onlyExpandSearchedNodes 
  canDrag={false} 
  treeData={[{
    title: 'Type of Data',
    children: [{
        title: 'Categorical',
        children: [{
          title: 'Chi-Square Test',
          subtitle: 'Test for independence for two variables or goodness-of-fit test'
        }]
      },
      {
        title: 'Continuous',
        children: [{
            title: 'Group Differences',
            children: [{
                title: 'Between Means',
                children: [{
                  title: 'How many groups?',
                  children: [{
                      title: 'Two',
                      children: [{
                        title: 'Are assumptions satisfied (potentially after data transformations)?',
                        subtitle: '1. Equal variances in both groups, 2. Data normally distributed or sufficiently large sample',
                        children: [{
                            title: 'If yes, can use a parametric test',
                            subtitle: 'Student\'s t-test (paired or unpaired)'
                          },
                          {
                            title: 'If not, use a nonparametric randomization test',
                            subtitle: 'E.g., Wilcoxon Rank Sum Test or Mann-Whitney U Test'
                          }
                        ]
                      }]
                    },
                    {
                      title: 'More than two',
                      children: [{
                        title: 'Are assumptions satisfied (potentially after data transformations)?',
                        subtitle: '1. Equal variances in each group, 2. Data normally distributed or sufficiently large sample',
                        children: [{
                            title: 'If yes, can use a parametric test',
                            subtitle: 'ANOVA (Analysis of Variance)'
                          },
                          {
                            title: 'If not, use a nonparametric randomization test',
                            subtitle: 'E.g., Kruskal-Wallis Test'
                          }
                        ]
                      }]
                    }
                  ]
                }]
              },
              {
                title: 'Between Variances',
                subtitle: 'Bartlett\'s Test or Levene\'s Test when data are normally distrubuted'
              }
            ]
          },
          {
            title: 'Relationships between Variables',
            children: [{
              title: 'Regression Analysis',
              subtitle: 'Simple linear regression for one response and one predictor,\nmultiple regression in case of several explanatory variables.'
            }]
          }
        ]
      }
    ]
  }]}
/>

Ergebnis

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