我正在尝试在r中建立一个关联矩阵，这将使我能够检查数据框的选定行中信息的关联。
如果我的数据如下所示：
A B C
01 Cat Hat Car
02 Cat Coat Bike
03 Dog Hat Motorcycle
04 Dog Coat Car
我想使用目标行（在此示例中为A行）创建一个表，以生成如下表：
Cat Hat Car
02 1 0 0
03 0 1 0
04 0 0 1
有人可以帮忙吗？谢谢
答案 0 :(得分：3)
这里的技巧是将第一行提取为向量而不是数据帧。一个简单的比较就可以完成工作，即
df[-1,] == as.character(as.vector(df[1,]))
# A B C
#02 TRUE FALSE FALSE
#03 FALSE TRUE FALSE
#04 FALSE FALSE TRUE
只需乘以1（一点点编码）即可转换为0/1
(df[-1,] == as.character(as.vector(df[1,])))*1
# A B C
#02 1 0 0
#03 0 1 0
#04 0 0 1
答案 1 :(得分：0)
使用第1行作为数据框标题，并使用[ifelse]查找文本并替换为数字。
# Create object for dataframe.
cat <- c("Cat", "Dog", "Dog")
hat <- c("Coat", "Hat", "Coat")
car <- c("Bike", "Motorcycle", "Car")
# Create dataframe.
df1 <- data.frame(cat, hat, car)
# Create df2 to add the digit replacements,
# keeping df1 for later comparison.
df2 <- df1
# Use [ifelse] to find text and replace with numerics.
df2$cat <- ifelse(df1$cat == "Cat",1 ,0 )
df2$hat <- ifelse(df1$hat == "Hat",1 ,0 )
df2$car <- ifelse(df1$car == "Car",1 ,0 )
答案 2 :(得分：0)
@Sotos提供的解决方案更加优雅，但是您也可以使用/******** CONTROLLER*******/
(function() {
'use strict';
angular.module('app')
.controller('labelController', ['$scope','$http','labelService', function($scope,$http,$labelService) {
$scope.labels_portfolioConsultAgentAssignment = {}; $scope.labels_portfolioConsultAgentAssignment=$labelService.getPortfolioConsultAgentLabels();
}]);
})();
/********** SERVICE ********/
angular.module('label-service', [])
.factory('labelService', function($http) {
var labelsPortfolioConsultAgentAssignment = $http.get('scripts/labels_portfolioConsultAgent.properties.txt');
var labels_portfolioConsultAgentAssignmentSerVar = [];
return {
//====================For PortfolioConsultAgentAssignment WebPage========================//
getPortfolioConsultAgentLabels : function(){
labelsPortfolioConsultAgentAssignment.then(function (portfolioConsultAgentAssignmentLabels){
labels_portfolioConsultAgentAssignmentSerVar.PortfolioAssignment= portfolioConsultAgentAssignmentLabels.data.PortfolioAssignment;
labels_portfolioConsultAgentAssignmentSerVar.TitleTable= portfolioConsultAgentAssignmentLabels.data.TitleTable;
labels_portfolioConsultAgentAssignmentSerVar.IdAgent= portfolioConsultAgentAssignmentLabels.data.IdAgent;
labels_portfolioConsultAgentAssignmentSerVar.AgentName= portfolioConsultAgentAssignmentLabels.data.AgentName;
labels_portfolioConsultAgentAssignmentSerVar.PromotZone= portfolioConsultAgentAssignmentLabels.data.PromotZone;
labels_portfolioConsultAgentAssignmentSerVar.AgentType= portfolioConsultAgentAssignmentLabels.data.AgentType;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetail= portfolioConsultAgentAssignmentLabels.data.AgentDetail;
labels_portfolioConsultAgentAssignmentSerVar.CountResult = portfolioConsultAgentAssignmentLabels.data.CountResult;
labels_portfolioConsultAgentAssignmentSerVar.AgentAssaign= portfolioConsultAgentAssignmentLabels.data.AgentAssaign;
//Agents Detail Labels
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailID= portfolioConsultAgentAssignmentLabels.data.AgentDetailID;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailDate= portfolioConsultAgentAssignmentLabels.data.AgentDetailDate;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailStatus= portfolioConsultAgentAssignmentLabels.data.AgentDetailStatus;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailConduct= portfolioConsultAgentAssignmentLabels.data.AgentDetailConduct;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailRetainer= portfolioConsultAgentAssignmentLabels.data.AgentDetailRetainer;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailFederativeEntity= portfolioConsultAgentAssignmentLabels.data.AgentDetailFederativeEntity;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailPolicyNumber= portfolioConsultAgentAssignmentLabels.data.AgentDetailPolicyNumber;
labels_portfolioConsultAgentAssignmentSerVar.AgentDetailPolicySize= portfolioConsultAgentAssignmentLabels.data.AgentDetailPolicySize;
});
return labels_portfolioConsultAgentAssignmentSerVar;
}
});做这样的事情：
tidyverse或者：
df[-1, ] %>%
rename_at(1:3, funs(paste0(as.character(df[1,])))) %>%
rowid_to_column() %>%
gather(var, val, -rowid) %>%
mutate(val = ifelse(val == var, 1, 0)) %>%
spread(var, val) %>%
select(-rowid)
Car Cat Hat
1 0 1 0
2 0 0 1
3 1 0 0
首先，它使用第一行中的值设置列名称。其次，它将数据从宽转换为长。最后，它比较该值是否与列名相同，然后应用给定条件。