一个接一个地读取多个 CSV 文件时宏和变量的问题
Issues with macros and variables when reading multiple CSV files one after another
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。
我决定为他们每个人单独编写代码以读取 csv 并执行代码，导出图形，使用 clear all 和 macro drop _all 以便变量和宏被删除（它们将在为下一个 csv 文件重复代码时重新初始化）并重新 运行 相同的代码，只是这次是在导入不同的 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 "$outdir\Home1-Away1-Jan03.png", replace;
clear all;
macro drop _all;
该代码基于 ，运行 非常好。
当我将完全相同的代码附加到同一个 do 文件但用于另一个 csv 文件时：
/*********************************/
/*********************************/
/* Home2 vs. Away2 on January 3rd */
/*********************************/
/*********************************/
import delimited "$datadir\Home2 vs. Away2 on January 3rd", clear
和 clear all、macro drop _all 之前的其余代码，类似于 Home1 vs. Away on January 3rd，因此生成了一个类似的图表，它说：
eventtime not found invalid syntax
我认为这与每个 csv 文件中的变量被删除或未被读取有关。
将来，我想在一个 单个 do 文件中逐个附加 18 个 csv 文件的相同代码的 18 个片段并执行将图形导出到特定 outdir 的相同操作（这对于 first csv 文件来说很好，但是当 exact 另一个 csv 文件的相同代码附加在为第一个文件创建和导出图形的代码下方。
您需要恢复 do 文件末尾的回车 return 定界符：
clear all;
macro drop _all;
#delimit cr
否则 Stata 将使用分号分隔符执行其余代码。
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。
我决定为他们每个人单独编写代码以读取 csv 并执行代码，导出图形，使用 clear all 和 macro drop _all 以便变量和宏被删除（它们将在为下一个 csv 文件重复代码时重新初始化）并重新 运行 相同的代码，只是这次是在导入不同的 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 "$outdir\Home1-Away1-Jan03.png", replace;
clear all;
macro drop _all;
该代码基于
当我将完全相同的代码附加到同一个 do 文件但用于另一个 csv 文件时：
/*********************************/
/*********************************/
/* Home2 vs. Away2 on January 3rd */
/*********************************/
/*********************************/
import delimited "$datadir\Home2 vs. Away2 on January 3rd", clear
和 clear all、macro drop _all 之前的其余代码，类似于 Home1 vs. Away on January 3rd，因此生成了一个类似的图表，它说：
eventtime not found invalid syntax
我认为这与每个 csv 文件中的变量被删除或未被读取有关。
将来，我想在一个 单个 do 文件中逐个附加 18 个 csv 文件的相同代码的 18 个片段并执行将图形导出到特定 outdir 的相同操作（这对于 first csv 文件来说很好，但是当 exact 另一个 csv 文件的相同代码附加在为第一个文件创建和导出图形的代码下方。
您需要恢复 do 文件末尾的回车 return 定界符：
clear all;
macro drop _all;
#delimit cr
否则 Stata 将使用分号分隔符执行其余代码。