Stata 逐个读取多个CSV文件时出现宏和变量问题
以下是其中一个Stata 逐个读取多个CSV文件时出现宏和变量问题,stata,stata-macros,Stata,Stata Macros,以下是其中一个csv文件的一些可复制数据： * 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. Aw
csv* 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
csvcsv清除所有，以及宏删除所有，以便删除变量和宏（在为下一个csv
文件重复代码时将重新初始化它们），并重新运行相同的代码，仅在导入不同的csv
文件后进行此操作
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 "$outdir\Home1-Away1-Jan03.png", replace;
clear all;
macro drop _all;
以下代码用于单个csv
文件
global directory "I:\Data\Useful CSVs"
global datadir "$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，并且运行良好
当我将完全相同的代码附加到相同的do
文件时，但对于另一个csv
文件：
/*********************************/
/*********************************/
/* Home2 vs. Away2 on January 3rd */
/*********************************/
/*********************************/
import delimited "$datadir\Home2 vs. Away2 on January 3rd", clear
剩下的代码直到清除所有
，宏删除所有
，类似于Home1 vs.Away于1月3日
，因此生成了一个类似的图表，显示：
未找到eventtime无效语法
我相信这与删除或不读取每个csv
文件中的变量有关，每个文件都有相同的变量名
将来，我想将18个csv
文件的18个相同代码片段一个接一个地附加在一个do
文件中，并执行相同的操作，将图形导出到特定的outdir
（这对于第一个csv
文件来说是正常的，但是当另一个csv
文件的完全相同的代码被附加到为第一个文件创建和导出图形的代码下面时，会显示上述错误。您需要恢复do
文件末尾的回车分隔符：
clear all;
macro drop _all;
#delimit cr
否则，Stata将使用分号分隔符执行代码的其余部分。您需要恢复do
文件末尾的回车分隔符：
clear all;
macro drop _all;
#delimit cr
否则，Stata将使用分号分隔符执行代码的其余部分