以下是csv文件之一的一些可重现数据：
* Example generated by -dataex-. To install: ssc install dataex
clear
input str27 eventname str10(eventdate scrapedate) byte part float(thpercentile median v7 mean) str5 timestamp int seatcount str19 scrapedatetime
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-15" 1 . . . . "07:59" 0 "2015-12-15 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-15" 2 . . . . "16:00" 0 "2015-12-15 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-15" 3 99.97 132.5 183.85 170.42963 "23:59" 1534 "2015-12-15 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-16" 1 100 132.5 185.25 170.95053 "07:59" 1528 "2015-12-16 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-16" 2 99.8725 132.5 185.6125 170.8983 "16:00" 1523 "2015-12-16 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-16" 3 99.61 132.925 183.85 170.56766 "23:59" 1493 "2015-12-16 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-17" 1 98.44 132.5 183.85 170.193 "07:59" 1490 "2015-12-17 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-17" 2 100 133.54 185.1425 171.12013 "16:00" 1465 "2015-12-17 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-17" 3 99.61 132.5 183.85 170.4387 "23:59" 1463 "2015-12-17 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-18" 1 100 132.5 183.85 170.051 "07:59" 1438 "2015-12-18 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-18" 2 98.44 132.925 183.85 170.05144 "16:00" 1427 "2015-12-18 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-18" 3 101.95 134.27 188.86 170.95193 "23:59" 1376 "2015-12-18 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-19" 1 101.95 133.95 188.75 171.24626 "07:59" 1366 "2015-12-19 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-19" 2 101.95 133.95 188.39 171.50464 "16:00" 1360 "2015-12-19 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-19" 3 105.355 139.39 189.7 173.4393 "23:59" 1320 "2015-12-19 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-20" 1 105.46 139.39 190.55 173.8773 "07:59" 1308 "2015-12-20 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-20" 2 105.46 139.39 190.79 174.0365 "16:00" 1290 "2015-12-20 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-20" 3 104.88 139.39 191.53 175.8205 "23:59" 1244 "2015-12-20 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-21" 1 105.17 138.22 191.7025 175.54225 "07:59" 1227 "2015-12-21 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-21" 2 105.68 139.39 189.7 175.63374 "16:00" 1213 "2015-12-21 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-21" 3 103.27 133.445 189.7 175.23582 "23:59" 1174 "2015-12-21 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-22" 1 106.09 135.77 197.695 177.64076 "07:59" 1161 "2015-12-22 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-22" 2 106.66 136.465 198.0175 178.2966 "16:00" 1155 "2015-12-22 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-22" 3 107.67 138.92 190.615 172.865 "23:59" 1214 "2015-12-22 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-23" 1 107.8 138.92 195.8425 174.13286 "07:59" 1190 "2015-12-23 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-23" 2 107.8 137.05 193.54 174.4463 "16:00" 1161 "2015-12-23 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-23" 3 112.48 139.025 195.55 175.9974 "23:59" 1118 "2015-12-23 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-24" 1 113.32 142.9 197.235 178.3136 "07:59" 1076 "2015-12-24 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-24" 2 113.65 142.9 202.8625 180.5185 "16:00" 1041 "2015-12-24 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-24" 3 113.65 142.9 204.25 181.71426 "23:59" 984 "2015-12-24 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-25" 1 117.13 146.46 207.25 184.9154 "07:59" 951 "2015-12-25 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-25" 2 118.33 147.58 207.25 187.8157 "16:00" 925 "2015-12-25 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-25" 3 119.5 148.75 220.0125 191.25423 "23:59" 854 "2015-12-25 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-26" 1 119.5 148.75 220.19 192.5282 "07:59" 826 "2015-12-26 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-26" 2 119.5 149.045 223.9225 194.0729 "16:00" 808 "2015-12-26 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-26" 3 125.24 150.89 231.555 196.03903 "23:59" 763 "2015-12-26 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-27" 1 125.24 149.85 222.74 189.37384 "07:59" 745 "2015-12-27 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-27" 2 125.24 149.045 222.74 188.5702 "16:00" 727 "2015-12-27 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-27" 3 125.24 150.21 234.16 191.70107 "23:59" 683 "2015-12-27 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-28" 1 123.5675 150.3 231.6875 190.37703 "07:59" 656 "2015-12-28 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-28" 2 124.55 152.06 230.65 189.7578 "16:00" 668 "2015-12-28 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-28" 3 125.24 153.43 230.65 188.21233 "23:59" 644 "2015-12-28 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-29" 1 125.35 154.6 230.65 188.78273 "07:59" 607 "2015-12-29 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-29" 2 128.34 158.59 236.03 194.44263 "16:00" 611 "2015-12-29 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-29" 3 123.5 157.985 226.35 192.8171 "23:59" 608 "2015-12-29 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-30" 1 129.55 159.8 227.5 195.97015 "07:59" 590 "2015-12-30 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-30" 2 135.485 164.64 227.5 198.30286 "16:00" 585 "2015-12-30 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-30" 3 129.55 158.59 220.3 191.47372 "23:59" 604 "2015-12-30 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-31" 1 123.5 157.38 220.3 190.71004 "07:59" 607 "2015-12-31 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-31" 2 126.015 158.59 220.3 190.33115 "16:00" 616 "2015-12-31 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2015-12-31" 3 123.5 154.97 208.2 178.5105 "23:59" 727 "2015-12-31 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-01" 1 122.29 153.75 206.99 174.5168 "07:59" 732 "2016-01-01 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-01" 2 122.29 152.54 205.3 172.2481 "16:00" 738 "2016-01-01 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-01" 3 113.8175 144.065 206.8725 165.0204 "23:59" 480 "2016-01-01 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-02" 1 112.605 138.02 208.2 164.2923 "07:59" 504 "2016-01-02 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-02" 2 114.575 138.02 209.09 166.25206 "16:00" 472 "2016-01-02 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-02" 3 109.7975 144.67 202.15 183.0381 "23:59" 409 "2016-01-02 23:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-03" 1 117.45 153.75 200.94 190.452 "07:59" 285 "2016-01-03 07:59:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-03" 2 111.4 153.75 196.1 188.8237 "16:00" 264 "2016-01-03 16:00:00"
"Home1 vs. Away1 on January 3rd" "2016-01-03" "2016-01-03" 3 . . . . "23:59" 0 "2016-01-03 23:59:00"
end
我有多个这样的csv文件。
我决定分别为他们每个人编写代码，以读取其中的csv并执行代码，导出图形，使用clear all和macro drop _all，以便使变量和宏将被删除（重复下一个csv文件的代码时，它们将被初始化）并重新运行相同的代码，只有这次是在导入另一个csv文件之后。
以下代码用于单个 csv文件。
global directory "I:\Data\Useful CSVs"
global datadir "$directory\Games\GamesIndividual"
global outdir "I:\Data\figures"
/*********************************/
/*********************************/
/* Home1 vs. Away1 on January 3rd */
/*********************************/
/*********************************/
import delimited "$datadir\Home1 vs. Away1 on January 3rd", clear
/* Create a variable `eventtime` that captures the date portion
from the `datetime` columns */
gen double eventtime = clock(scrapedatetime, "YMDhms")
/* Set time-series format */
tsset eventtime, format(%tcNN/DD/CCYY_HH:MM:SS)
/* The following code snippet gets the minimum and maximum raw date/time values,
finds the interval between observations based on the desired steps
(in this case 12), then loops over observations to get the date/time
value at every step and inserts everything in a list: */
sort eventtime
summarize eventtime
local min = r(min)
local max = r(max)
local plus = _N / 5
local total = _N / `plus'
local dtlist `dtlist' `min'
local counter = 0
forvalues i = 1 / `total' {
local counter = `counter' + `plus'
local dtlist `dtlist' `=eventtime[`counter']'
}
local dtlist `dtlist' `max'
/* You then draw a towway and append many connect lines.
The column variables are encoded differently when read into stata.
eventtime - ScrapeDate
median - Median Price in USD
thpercentile - 25th Percentile in USD
v7 - 75th Percentile in USD
mean - Mean Price in USD */
#delimit ;
twoway
(connected mean eventtime, msymbol(point) mfcolor(none))
(connected median eventtime, msymbol(point) mfcolor(none))
(connected thpercentile eventtime, msymbol(point) mfcolor(none))
(connected v7 eventtime, msymbol(point) mfcolor(none)),
title("Home1 vs. Away1 on January 3rd")
ytitle(Price in USD)
xtitle(Scrape Date)
leg(off)
xlabel(`dtlist', format(%tCDDMon))
xline(1765785540000 1766332800000 1766908740000, lwidth(thin))
/*
Setting text placeholder for odds in date
representing BEFORE WEEK 15: 12/14/2015
*/
text(150 1765785540000 "P(Home1)" "= 0", size(medium) place(e))
/*
Setting text placeholder for odds in date
representing BEFORE WEEK 16: 12/21/2015
*/
text(150 1766332800000 "P(Home1)" "= 0", size(medium) place(e))
/*
Setting text placeholder for odds in date
representing BEFORE WEEK 15: 12/28/2015
*/
text(150 1766908740000 "P(Home1)" "= 0", size(medium) place(e))
/*
Setting text placeholder for odds in d
ate representing BEFORE WEEK 15: 12/28/2015
*/
text(150 1766908740000 "P(Home1)" "= 0", size(medium) place(e))
/*
Setting text placeholder to represent the line
that denotes the Mean price
*/
text(175 1767398340000 "Mean", size(small) color("7 46 95") place(e)) ;
graph export "$outdir\Home1-Away1-Jan03.png", replace;
clear all;
macro drop _all;
该代码基于previous post(s)，并且运行良好。
当我将完全相同的代码附加到同一do文件中，但又添加了另一个csv文件时：
/*********************************/
/*********************************/
/* Home2 vs. Away2 on January 3rd */
/*********************************/
/*********************************/
import delimited "$datadir\Home2 vs. Away2 on January 3rd", clear
和其余的代码，直到clear all，macro drop _all，类似于Home1 vs. Away on January 3rd，因此生成了类似的图，它说：
找不到事件时间无效的语法
我认为这与删除或未读取的变量有关。每个csv文件都具有相同的变量名。
将来，我想将18个csv文件的18个相同代码段一个接一个地附加在单个 do文件中，并执行相同的操作将图形导出到特定的outdir的过程（对于 first csv文件来说很好，但是当 exact 相同的代码时显示上述错误用于另一个csv文件的代码将附加到创建和导出第一个图形的代码的下面。
答案 0 :(得分：2)
您需要在do文件的末尾恢复回车定界符：
clear all;
macro drop _all;
#delimit cr
否则，Stata使用分号定界符执行其余代码。