for(i in 1:length(ldf)){
tryCatch(
data <-dbWriteTable(con, names(res)[i], data.frame(ldf[i]))
,
error = function(e){
message("An error occurred:\n", e)
},
warning = function(w){
message("A warning occured:\n", w)
},
finally = {
message("Finally done!")
})
}
这里用了DBI和odbc包。验证方式是"Windows Authentication"，也就是用系统的用户信息来登录数据库。
library(DBI)
library(odbc)
con <- dbConnect(odbc::odbc(),
Driver = "SQL Server",
Server = "my server name", #这里替换成自己的Server名字
Database = "DataBase", #这里替换成自己的DataBase名字
Trusted_Connection = "True")
借鉴了下面帖子里面的做法
https://stackoverflow.com/questions/9564489/read-all-files-in-a-folder-and-apply-a-function-to-each-data-frame
path = "my path" #这里替换成自己的路径
LenPath = nchar(path)
filenames <- list.files(path , pattern="*.csv", full.names=TRUE)
ldf <- lapply(filenames, read.csv)
res <- lapply(ldf, summary)
names(res) <- substr(filenames,LenPath+2, LenPath + 30) #这里要根据自己的文件名改变长度
如果是txt文件，可以用下面的代码
path = "my path" #这里替换成自己的路径
LenPath = nchar(path)
filenames <- list.files(path , pattern="*.txt", full.names=TRUE)
ldf <- lapply(filenames, read.delim)
res <- lapply(ldf, summary)
names(res) <- substr(filenames,LenPath+2, LenPath + 30) #这里要根据自己的文件名改变长度
想要批量导入数据到数据库，试验了SQL Server的Bulk Insert，觉得没有那么好用，于是就转向了R。
分成三步。
第一步：批量读入数据，存储为Data.Frame格式
第二步：连接数据库
第三步：写入数据库
在给Double变量赋值为0.5的时候，Mac VBA跑出了Bug: 溢出 (Overflow)
代码大概是
Option Explicit
Sub Pro()
Dim Mid as double
Mid = 0.5
End Sub
同样的代码放到Windows去跑就没有问题。
稍微在网上查了一下，这似乎是一个2019年版本开始就有的Bug。定义变量为Double的时候，给变量赋值会出错。不知道大家有没有遇到过类似的问题。
https://techcommunity.microsoft.com/t5/excel/runtime-error-6-overflow-with-dim-double-macos-catalina-excel/m-p/786433
想问下大家，有没有人记得有一个理论是，生存超过一定岁数后，不同subgroup的死亡率会趋同(converge)? 这是为什么来着？
不同subgroup就是，比方说Smoker和Non-Smoker。
2020年9月的考试可以看：
2020年9月英国精算师考试的几个重要通知(7月14日更新)
以及各科目备考的导引篇：
@yuk FYI
Understanding Actuarial Management Part I.pdf
Understanding Actuarial Management Part II.pdf
Understanding Actuarial Management Part III.pdf
Understanding Actuarial Management Part IV.pdf
Understanding Actuarial Management Part V.pdf
Understanding Actuarial Management Part VI.pdf
这个现在也就只有理论意义了吧。。。现在工作还用得着换算函数吗？？计算能力都上来了，Excel算一下不就行了？
翻译过来，按照我的理解：$q_x$的死亡率是$x$到$x+1$之间死亡人数除以在$x$岁活着的人数，而$m_x$的死亡率是$x$到$x+1$之间死亡人数除以平均在$x$到$x+1$岁之间活着的人数
转自 https://www.acted.co.uk/forums/index.php?threads/central-rates-of-mortality.4540/
$q_x$ is the initial rate of mortality. This measures the number of deaths divided by the number of lives alive at age x. The problem with this is that it assumes that there are "$l_x$" person years lived between age x->x+1 (Obviously lives will die durring the year of age, and not be exposed to risk for the whole year). $m_x$ is the same thing, but it is dx divided by the expected number of person years lived between ages x->x+1. This is Integral from 0-1 $l_{x+t} dt$. The meaning of the two is very different. $q_x$ is the probability a life now aged x dies within the next year given alive at time x. $m_x$ is the probability a life aged anywhere between x,x+1 dies before attaining age x+1. I dont think it's used very much in actuarial calculations but it is handy in dermograpic/population studies because of the nice property that $m_x * ExC$ for any age equals the number of deaths, this is not true for qx (Try this for yourself).