<?xml version="1.0" encoding="utf-8" ?><rss version="2.0"><channel><title><![CDATA[gavin_cdc的博客]]></title><description><![CDATA[]]></description><link>https://blog.youkuaiyun.com/gavin_cdc</link><language>zh-cn</language><generator>https://blog.youkuaiyun.com/</generator><copyright><![CDATA[Copyright &copy; gavin_cdc]]></copyright><item><title><![CDATA[POWER BI 数据看板上传云服务后，复制报表，复制的报表内容调整后分享链接，内容显示与原始报表内容一样。该如何解决？]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/145144953</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/145144953</guid><author>gavin_cdc</author><pubDate>Tue, 14 Jan 2025 18:08:14 +0800</pubDate><description><![CDATA[POWER BI 数据看板上传云服务后，复制报表，复制的报表内容调整后分享，内容与原始报表内容一样。在报表中点击编辑，并点击保存。复制后的报表分享链接，内容会显示调整后的内容。]]></description><category></category></item><item><title><![CDATA[POWER BI矩阵中产品不显示部分数据解决办法]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/144982053</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/144982053</guid><author>gavin_cdc</author><pubDate>Tue, 07 Jan 2025 11:39:40 +0800</pubDate><description><![CDATA[POWER BI矩阵中产品不显示部分数据解决办法]]></description><category></category></item><item><title><![CDATA[OLE DB 或 ODBC 错误 : 类型不匹配。 (异常 HRESULT:0x80020005 (DISP_E_TYPEMISMATCH))。]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/144980731</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/144980731</guid><author>gavin_cdc</author><pubDate>Tue, 07 Jan 2025 10:58:18 +0800</pubDate><description><![CDATA[POWER BI 数据分析出现：OLE DB 或 ODBC 错误 : 类型不匹配。]]></description><category></category></item><item><title><![CDATA[POWER BI刷新出现错误“加载之前的表时出差，加载已取消”]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/143037821</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/143037821</guid><author>gavin_cdc</author><pubDate>Fri, 18 Oct 2024 10:48:35 +0800</pubDate><description><![CDATA[POWER BI刷新出现错误“加载之前的表时出差，加载已取消”]]></description><category></category></item><item><title><![CDATA[天猫大快消八大策略人群&京东十大靶向人群（靶群）简介]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/131537633</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/131537633</guid><author>gavin_cdc</author><pubDate>Tue, 04 Jul 2023 16:33:20 +0800</pubDate><description><![CDATA[目录一. 天猫大快消行业八大策略人群人群释义：小镇青年Gen Z精致妈妈新锐白领资深中产都市蓝领都市银发小镇中老年 天猫八大策略人群简介版：​编辑二. 京东十大靶向人群1. Young Generation-新势力：都市Z世代小镇青年学生一族2. Backbone-中坚力量：都市家庭小镇家庭都市中产小镇中产3. Blue Ocean-蓝海人群：小镇中年银发一族都市蓝领人群划分是用户运营的一个基础概念，典型的场景是把需要运营的人群分成不同的层级，例如小镇青年、都市辣妈等，通过各种条件的人群划分来得出人群在特性]]></description><category></category></item><item><title><![CDATA[ModuleNotFoundError: No module named ‘pyquery‘]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/115480592</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/115480592</guid><author>gavin_cdc</author><pubDate>Wed, 07 Apr 2021 10:57:03 +0800</pubDate><description><![CDATA[1. 终端下载


pip install pyquery

2. Spyder下载


！pip install pyquery

3. 如果下载成功无法使用，考虑电脑有多个python，Spyder下载的包存在另一个python路径中


此时应删除个人自行安装的python（即保留Spyder自带的python）

4.查看路径


import sys
sys.path
sys.path.append("C:\\Users\\Administrator\\AppData\\Local\\Progra]]></description><category></category></item><item><title><![CDATA[使用R语言软件（Rstudio）抓取天猫商品数据示例 2020-11-27]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/110237396</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/110237396</guid><author>gavin_cdc</author><pubDate>Fri, 27 Nov 2020 17:19:34 +0800</pubDate><description><![CDATA[#先贴一下总的代码，后面有重点！


# https://www.jianshu.com/p/f768cbf5ee0c
# 判断网页编码
library(rvest)
f &lt;- tempfile()#创建临时文件
download.file("https://list.tmall.com/search_product.htm?q=%B5%CDGI&amp;type=p&amp;vmarket=&amp;spm=a2156.1676643.a2227oh.d100&amp;from=mallfp..p.]]></description><category></category></item><item><title><![CDATA[R ggplot绘制双纵坐标轴]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/106628167</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/106628167</guid><author>gavin_cdc</author><pubDate>Mon, 08 Jun 2020 20:42:11 +0800</pubDate><description><![CDATA[p1 &lt;- ggplot(data = DNA_result, mapping = aes( x= num, y = nd)) + 
  geom_point(colour = "red") + 
  xlab( "样本序号") + 
  ylab("浓度")+
  theme(plot.caption = element_text(hjust = 0))+ theme_bw()
p1

p2 &lt;- ggplot(data = DNA_result, mapping = aes( x= nu..]]></description><category></category></item><item><title><![CDATA[你觉得鼠标没有苹果电脑（MACbookAir、Pro）的触摸板好用吗？这个软件你值得一试！]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/105329316</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/105329316</guid><author>gavin_cdc</author><pubDate>Sun, 05 Apr 2020 17:07:39 +0800</pubDate><description><![CDATA[给大家介绍一个小软件：WGestures（文末有资源）

如果掌握了以下鼠标手势，相信你会有不同的感受。










]]></description><category></category></item><item><title><![CDATA[R Failed to install 'unknown package' from GitHub: schannel: failed to receive handshake, SSL/TLS]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/105295299</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/105295299</guid><author>gavin_cdc</author><pubDate>Fri, 03 Apr 2020 16:20:41 +0800</pubDate><description><![CDATA[remotes::install_github("GuangchuangYu/nCov2019")

提示：


Failed to install 'unknown package' from GitHub: schannel: failed to receive handshake, SSL/TLS connection failed

通过以下步骤解决：


$ git config --...]]></description><category></category></item><item><title><![CDATA[R |哑变量的设置]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/104846740</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/104846740</guid><author>gavin_cdc</author><pubDate>Fri, 13 Mar 2020 18:20:31 +0800</pubDate><description><![CDATA[原文来源：1：https://www.sohu.com/a/199698358_489312

2：https://blog.youkuaiyun.com/qq_42458954/article/details/86694980

在构建回归模型时，如果自变量X为连续性变量，回归系数β可以解释为：在其他自变量不变的条件下，X每改变一个单位，所引起的因变量Y的平均变化量；如果自变量X为二分类变量，例如是否饮酒（...]]></description><category></category></item><item><title><![CDATA[R 置信区间]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/104804469</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/104804469</guid><author>gavin_cdc</author><pubDate>Wed, 11 Mar 2020 20:10:53 +0800</pubDate><description><![CDATA[data("cars", package = "datasets")
model &lt;- lm(dist ~ speed, data = cars)
model
new.speeds &lt;- data.frame(
  speed = c(12, 19, 24))

predict(model, newdata = new.speeds, interval = "confidence")...]]></description><category></category></item><item><title><![CDATA[R | dummyVars函数，分类变量设置哑变量]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/104794620</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/104794620</guid><author>gavin_cdc</author><pubDate>Wed, 11 Mar 2020 13:23:27 +0800</pubDate><description><![CDATA[dummyVars函数:

dummyVars creates a full set of dummy variables (i.e. less than full rank parameterization----建立一套完整的虚拟变量


survey&lt;-data.frame(service=c("very unhappy","unhappy","neutral","happy","ve...]]></description><category></category></item><item><title><![CDATA[R语言分类变量转换为哑变量（dummy vairable）]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/104793724</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/104793724</guid><author>gavin_cdc</author><pubDate>Wed, 11 Mar 2020 12:07:34 +0800</pubDate><description><![CDATA[转：https://blog.youkuaiyun.com/xtydtc/article/details/52492794?depth_1-utm_source=distribute.pc_relevant.none-task&amp;utm_source=distribute.pc_relevant.none-task

生成测试数据
a1 &lt;- c(“f”,”f”,”b”,”b”,”c,”c”)

...]]></description><category></category></item><item><title><![CDATA[R 相关性分析]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/103492370</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/103492370</guid><author>gavin_cdc</author><pubDate>Wed, 11 Dec 2019 14:50:02 +0800</pubDate><description><![CDATA[R 相关性分析

1. 相关性矩阵计算：

加载mtcars数据

&gt; setwd("E:\\Rwork")
&gt; data("mtcars")
&gt; head(mtcars)
                   mpg cyl disp  hp drat    wt  qsec vs am gear carb
Mazda RX4         21.0   6  160 110...]]></description><category></category></item><item><title><![CDATA[查看某行数字为０的元素有多少个]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/103477544</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/103477544</guid><author>gavin_cdc</author><pubDate>Tue, 10 Dec 2019 16:11:59 +0800</pubDate><description><![CDATA[xx &lt;- data.frame(t(data.frame(c(0:20,0:3,0:5),c(0:10,0:5,0:13))))
aa &lt;- xx[1,]==0
length(which(aa==TRUE))


]]></description><category></category></item><item><title><![CDATA[R Corrplot软件包简介,绘制相关热图]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/102608232</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/102608232</guid><author>gavin_cdc</author><pubDate>Thu, 17 Oct 2019 15:32:05 +0800</pubDate><description><![CDATA[Corrplot软件包简介

介绍

所述corrplot包是相关矩阵，置信区间的图形显示。它还包含一些进行矩阵重新排序的算法。另外，corrplot擅长细节，包括选择颜色，文本标签，颜色标签，布局等。

可视化方法

有七个可视化方法（参数method中）corrplot包，命名"circle"，"square"，"ellipse"，"number"，"shade"，"color"，"pie"。...]]></description><category></category></item><item><title><![CDATA[R语言将数据框中的字符类型数字转换为数值]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/102606959</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/102606959</guid><author>gavin_cdc</author><pubDate>Thu, 17 Oct 2019 14:56:54 +0800</pubDate><description><![CDATA[转载：https://blog.youkuaiyun.com/Candle_light/article/details/84374814

场景1
我现在有一个数据框datexpr，里面的数字都是以字符型表示的,像这样

&gt; datexpr[1,1]
[1] " 1.143773961"
1
2
现在我想把这个数据框中的字符型数字全部转为数值型数字

使用下面语句即可

datexpr2=as.data...]]></description><category></category></item><item><title><![CDATA[R| require()]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/102586027</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/102586027</guid><author>gavin_cdc</author><pubDate>Wed, 16 Oct 2019 15:12:26 +0800</pubDate><description><![CDATA[https://github.com/knausb/vcfR_win-builder/issues/1

mclapply

require("parallel")
]]></description><category></category></item><item><title><![CDATA[R语言特征值相关性分析]]></title><link>https://blog.youkuaiyun.com/gavin_cdc/article/details/102579353</link><guid>https://blog.youkuaiyun.com/gavin_cdc/article/details/102579353</guid><author>gavin_cdc</author><pubDate>Wed, 16 Oct 2019 09:28:00 +0800</pubDate><description><![CDATA[转载：http://www.sohu.com/a/200219694_278730

也可以看一下这篇文章。

接触这么多组学数据，大家肯定做过各种相关性分析。大到几个转录组样本的整体相关性分析，小到挑选了一些候选基因看它们在不同样本中的表达模式相关性。当我们做完了相关性分析的时候，常常会用下面这种图形来展示：



那么问题来了，有没有别的方法，可以让我的相关性分析展现方式更高大上呢？

请出今...]]></description><category></category></item></channel></rss>