pincode

细分到更细维度

查看两种物流地域的覆盖面是否一样，时间是4月10日 到5月20

library(tidyverse)
library(readxl)
library(ggplot2)
library(plotly)
library(readxl)
load('/Users/milin/男性为什么下降了/data1.RData')
DT::datatable(FT)

这里的结论是覆盖面基本一致

DT::datatable(Fl)

这里的结论是，两种物流是存在差异的

对两种物流方式进行假设检验,pincode取’737101’

print(Delivery_method1[3])

## [1] "Ecom"

pp_FHFS[[3]]

##  [1] 0.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.8333333
##  [8] 0.8000000 0.5000000 1.0000000       NaN       NaN       NaN 0.0000000
## [15] 0.6666667 0.6666667 0.5000000 0.6250000 0.5000000 1.0000000 0.7500000
## [22] 0.7777778 0.7500000 1.0000000 0.6666667 0.8000000 0.3333333 0.3333333
## [29] 0.5000000 0.5000000 0.5000000 0.9090909 1.0000000 0.7500000 0.4375000
## [36] 0.6153846 0.0000000 0.0000000       NaN       NaN       NaN

print(Delivery_method1[4])

## [1] "Delhivery"

pp_FHFS[[4]]

##  [1]       NaN       NaN       NaN       NaN       NaN 1.0000000       NaN
##  [8] 0.6666667 1.0000000 0.7500000 0.9130435 0.8181818 0.9047619 0.7500000
## [15] 0.8695652 0.7500000 1.0000000 0.7777778 1.0000000 0.6666667 1.0000000
## [22] 0.7777778 0.8000000 0.7500000 0.9285714 0.8181818 0.7500000 0.5333333
## [29] 1.0000000 1.0000000 0.6666667 0.8000000 0.7000000 0.7142857 0.9333333
## [36] 0.8666667 0.9375000 0.8750000 0.8709677 0.7631579 0.7916667

t.test(pp_FHFS[[3]],pp_FHFS[[4]])

## 
##  Welch Two Sample t-test
## 
## data:  pp_FHFS[[3]] and pp_FHFS[[4]]
## t = -3.2232, df = 43.819, p-value = 0.002395
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -0.29855321 -0.06881932
## sample estimates:
## mean of x mean of y 
## 0.6489930 0.8326792

pvalue非常小，原假设认为两种物流的签收率不一样