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# install.packages(\ library(\)

library(RODBC) library(DBI)

library(ggplot2) library(RMySQL)

drv <-dbDriver( \ ) l10n_info() ## $MBCS ## [1] TRUE ##

## $`UTF-8` ## [1] FALSE ##

## $`Latin-1` ## [1] FALSE ##

## $codepage ## [1] 936

# Connecting to MySQL:

# Once the RMySQL library is installed create a database connection object.

#ÕâÀïÐÞ¸ÄÓû§ÃûÃÜÂëºÍÊý¾Ý¿âÃû³Æ

con <-dbConnect(RMySQL::MySQL(), dbname =\, user='root', password='63341498')

#################¸Ä±äÎÄ×Ö±àÂëΪGBK·½Ê½,·ñÔòÖÐÎÄ»áÂÒÂë################### dbSendQuery(con, 'SET NAMES gbk') #ÏòÊý¾Ý¿âдÈë»ò¶ÁÈ¡Êý¾Ý֮ǰ¼ÓÉÏ´ËÃüÁî ## # Listing Tables and Fields:

dbListTables(con)

## [1] \ ## [6] \ ## [11] \ ## [16] \

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# Retrieving data from MySQL:

rs =dbSendQuery(con, \)

#

data =fetch(rs ,-1)

# results of this query remain on the MySQL server, to access the results in R we need to use the fetch function. #head of data

#²é¿´Êý¾Ý #ԭʼÊý¾Ý head(data)

## tid status seller_nick buyer_nick ## 1 1.048498e+15 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê tb40425453 ## 2 1.049300e+15 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê gonow17 ## 3 9.469034e+14 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê maymaydog ## 4 9.475161e+14 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê Ïп´Â仨_77 ## 5 1.049254e+15 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê ÌìÌìÓÆ°é ## 6 9.475204e+14 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê xiaotaoqidaisy ## created modified payment iids ## 1 2015-05-13 23:55:45 2015-05-24 10:34:23 78 41878620642 ## 2 2015-05-13 22:12:35 2015-05-24 10:34:21 78 41878620642 ## 3 2015-05-14 00:11:29 2015-05-24 10:34:16 78 41878620642 ## 4 2015-05-13 21:18:02 2015-05-24 10:34:09 78 41878620642 ## 5 2015-05-13 21:25:08 2015-05-24 10:34:02 78 41878620642 ## 6 2015-05-13 21:18:56 2015-05-24 10:34:02 78 41878620642

## ... #´¦ÀíÊý¾Ý df=data head(df)

## tid status seller_nick buyer_nick ## 1 1.048498e+15 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê tb40425453 ## 2 1.049300e+15 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê gonow17 ## 3 9.469034e+14 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê maymaydog ## 4 9.475161e+14 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê Ïп´Â仨_77 ## 5 1.049254e+15 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê ÌìÌìÓÆ°é ## 6 9.475204e+14 TRADE_FINISHED ʱ¼äº£»¯×±Æ·Æì½¢µê xiaotaoqidaisy ## created modified payment iids ## 1 2015-05-13 23:55:45 2015-05-24 10:34:23 78 41878620642 ## 2 2015-05-13 22:12:35 2015-05-24 10:34:21 78 41878620642

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## 3 2015-05-14 00:11:29 2015-05-24 10:34:16 78 41878620642 ## 4 2015-05-13 21:18:02 2015-05-24 10:34:09 78 41878620642 ## 5 2015-05-13 21:25:08 2015-05-24 10:34:02 78 41878620642 ## 6 2015-05-13 21:18:56 2015-05-24 10:34:02 78 41878620642

## dim(df) ## [1] 67938 9

df=df[,c(\,\,\,\)]

colnames(df)=c(\ , \ , \,\)

# aggregate(df,by= list(\ df2=aggregate(Amount~ID,data=df,FUN=sum) head(df2)

## ID Amount ## 1 _duoduo_ 1668.00 ## 2 _summer_bye 39.32 ## 3 _talent 600.00 ## 4 _µÎË®´©Ê¯_ 11.30 ## 5 _»¨ÊÂÁËÁËÁËÁË 91.00 ## 6 _ÁÁ×Ó_ 75.98 df3=merge(df,df2,by =\)

head(df3)

## ID Date Amount.x SellerID Amount.y

## 1 _duoduo_ 2016-04-05 14:02:47 339.00 gafuhome¼Ò¾ÓÆì½¢µê 1668.00

## 2 _duoduo_ 2016-04-06 09:34:04 18.00 gafuhome¼Ò¾ÓÆì½¢µê 1668.00

## 3 _duoduo_ 2016-04-05 14:17:55 855.00 gafuhome¼Ò¾ÓÆì½¢µê 1668.00

## 4 _duoduo_ 2016-04-05 14:24:47 456.00 gafuhome¼Ò¾ÓÆì½¢µê 1668.00

## 5 _summer_bye 2015-05-17 18:33:54 39.32 ¼ÓÓÍ´ô×Ó 39.32

## 6 _talent 2016-04-10 02:23:40 100.00 ¶íËÙͨ¹ú¼ÊÎïÁ÷ 600.00