Here are some reproducible data for one of the csv files:
* 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
I have multiple such csv files.
I've decided to write code separately for each of them to read the csv in and execute the code, export the graph, use clear all, and macro drop _all so that the variables and macros get deleted (they will be reinitialized when repeating the code for the next csv file) and re-run the same code, only this time after importing a different csv file.
The following code is for a single csv file.
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 "$outdir\Home1-Away1-Jan03.png", replace;
clear all;
macro drop _all;
The code was based on previous post(s) and is running perfectly fine.
When I append the exact same code to the same do file but for another csv file:
/*********************************/
/*********************************/
/* Home2 vs. Away2 on January 3rd */
/*********************************/
/*********************************/
import delimited "$datadir\Home2 vs. Away2 on January 3rd", clear
and the rest of the code until clear all, macro drop _all, similar to Home1 vs. Away on January 3rd, so a similar graph gets generated, it says:
eventtime not found invalid syntax
I believe this has something to do with variables being deleted or not read in Every csv file has the same variable names.
In future, I would like to append 18 snippets of the same code for 18 csv files one below another all in a single do file and execute the same action of exporting the graph to a specific outdir (which is forming fine for the first csv file but displaying the above error when the exact same code for another csv file is appended below the code that created and exported the graph for the first one.
You need to restore the carriage return delimiter at the end of your do file:
clear all;
macro drop _all;
#delimit cr
Otherwise Stata executes the rest of the code using the semicolon delimiter.