我正在努力帮助我的朋友，销售总监，了解他的记录呼叫数据。他特别感兴趣的一栏是“处置”。此列有字符串值，我正在尝试将它们转换为数字值（即“未回答”转换为1，“已回答”转换为2等）并删除没有输入值的任何行。我创建了数据框，用作.numeric，创建和删除列/行等无济于事。我只是想运行简单的R代码来给他一些见解。任何和所有的帮助非常感谢。提前谢谢！
P.S。我不确定是否应该提供一些代码，因为有很多微妙的信息（个人电话号码和电子邮件）。
答案 0 :(得分：0)
或者，您可以使用import React, { Component } from 'react';
import { connect } from 'react-redux';
import FoodComponent from '../../components/FoodComponent/FoodComponent';
import AddComponent from '../../components/Buttons/AddComponent';
import * as actionTypes from '../../store/actions';
import classes from './FoodComponents.scss';
class FoodComponents extends Component {
render() {
return (
<div>
<AddComponent
text="Add component"
click={this.props.onAddComponent}
/>
<ul>
{
this.props.compons.map(component=>(
<li key={component.id}
>
<p className={classes.Component}>{component.co}</p>
<input
type="text"
/>
<button
onClick={this.props.onEditComponent}>
Edit Component
</button>
<button
onClick={()=>this.props.onDeleteComponent(component.id)}>
Delete component
</button>
</li>
))
}
</ul>
</div>
)
}
}
const mapStateToProps = state => {
return {
compons: state.components
}
}
const mapDispatchToProps = dispatch => {
return {
onAddComponent: (component) => dispatch({type: actionTypes.ADD_COMPONENT, data: {compToReducer: component}}),
onDeleteComponent: (id) => dispatch({type: actionTypes.DELETE_COMPONENT, index: id }),
onEditComponent: (component, id) => dispatch({type: actionTypes.EDIT_COMPONENT, data:{componentToReducer: component, index: id}})
}
}
export default connect(mapStateToProps,mapDispatchToProps)(FoodComponents);
语句创建新列并使用数值填充它。为了说明，我们假设这是您的数据框：
ifelse现在你定义一个新列，比如df $ Analysis，并根据df $ Disposition中的信息为它分配数字：
df <- data.frame(
Disposition = c(rep(c("answer", "no answer", "whatever", NA),3)),
Anything = c(rnorm(12))
)
df
Disposition Anything
1 answer 2.54721951
2 no answer 1.07409803
3 whatever 0.60482744
4 <NA> 2.08405038
5 answer 0.31799860
6 no answer -1.17558239
7 whatever 0.94206106
8 <NA> 0.45355501
9 answer 0.01787330
10 no answer -0.07629330
11 whatever 0.83109679
12 <NA> -0.06937357
此方法的优点是可以保持原始信息不变。如果您现在要删除数据框中的Na值，请使用df$Analysis <- ifelse(df$Disposition=="no answer", 1,
ifelse(df$Disposition=="answer", 2, 3))
df
Disposition Anything Analysis
1 answer 2.54721951 2
2 no answer 1.07409803 1
3 whatever 0.60482744 3
4 <NA> 2.08405038 NA
5 answer 0.31799860 2
6 no answer -1.17558239 1
7 whatever 0.94206106 3
8 <NA> 0.45355501 NA
9 answer 0.01787330 2
10 no answer -0.07629330 1
11 whatever 0.83109679 3
12 <NA> -0.06937357 NA
。注意：这不仅会删除df $ Disposition中的NA值，还会删除任何列中带有NA的任意行：
na.omit