My Solution
Output
Input
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
Challenge #78
As we all know, the housing market these days is going a bit crazy... we'd like to capture some census data on the value of new privately owned housing units in various metropolitan areas to see where the top 10 markets are YTD as of end of April (data lags about 2 months behind).
Our objective for this challenge is to capture Census data from the following URL: https://www.census.gov/construction/bps/txt/t3yv201704.txt
We will then need to parse the data into the columns identified in text input #2, then sort to find the top 10 new housing markets based on value.
BONUS:
Build an app that will allow you to choose whether you want to look at value (the "v" in "t3yv" in the URL above) or units (which would look like "t3yu"), specify the through-date (year & month designated at the end of the txt file name, in the example above as "201704"), and which column you want to sort by (Total, 1 Unit, 2 Units, etc.)
https://www.census.gov/construction/bps/txt/t3yv201704.txt
Input #1: Census Data URL
Input #1: Census Data URL
Input #2: Column Names
Input #2: Column Names
C:\Users\kiran\AppData\Local\Temp\Engine_10032_28c4cd9ce3ed44ec8c3a6c0b580bdb44_\Engine_12020_fa7ad12dd8bd4201a9da58c59eedb931_.yxdb
Single
Profile
206
19100
Dallas-Fort Worth-Arlington, TX
4255747
3248347
22392
9133
975875
408
35620
New York-Newark-Jersey City, NY-NJ-PA
2744052
936603
78437
26335
1702677
348
31080
Los Angeles-Long Beach-Anaheim,* CA
2540835
1286067
72735
32854
1149179
288
26420
Houston-The Woodlands-Sugar Land, TX
2515072
2256740
7129
6971
244232
122
12060
Atlanta-Sandy Springs-Roswell, GA
2225440
1787310
2613
5956
429561
999
38060
Phoenix-Mesa-Scottsdale,* AZ
2148454
1755950
6497
17859
368148
370
33100
Miami-Fort Lauderdale-West Palm Beach, FL
1699818
770996
14285
5697
908840
500
42660
Seattle-Tacoma-Bellevue,* WA
1691187
1044028
29842
51210
566107
216
19740
Denver-Aurora-Lakewood, CO
1553340
908759
15370
4765
624446
422
36740
Orlando-Kissimmee-Sanford, FL
1514372
1410544
7972
5706
90150
C:\Users\kiran\AppData\Local\Temp\Engine_10032_28c4cd9ce3ed44ec8c3a6c0b580bdb44_\Engine_12020_79bfba47adb6477ca18cdf6f33661551_.yxdb
Single
Profile
C:\Users\kiran\AppData\Local\Temp\Engine_10032_28c4cd9ce3ed44ec8c3a6c0b580bdb44_\Engine_12020_481274e6468f496db0615b648e641755_.yxdb
Single
Profile
https://www.census.gov/construction/bps/txt/t3yv201704.txt
Input #1: Census Data URL
Input #1: Census Data URL
URL
String
65001
GET
Compose
2
600
DownloadData
"URL","DownloadData","DownloadHeaders"
True
True
True
True
False
False
False
False
False
upper
3-
DownloadData
ParseComplex
DownloadData
Warn
URL_Matched
DownloadData = REGEX_Replace([DownloadData], ",\s\n\s", ",")
DownloadData = REGE...
Simple
IsNotNull
CSA
True
fixed
2020-03-29 13:52:28
0
2020-03-29 13:52:28
2020-03-29 13:52:28
!IsNull([CSA])
Select Url
Update Cell
Total - Descending
Sort by
Update Field List
Horizontal
Question
Drop Down (130)
Question
Drop Down (134)
challenge_78_atcodedog05_Solution
NoCondition
(Always Run)
Dynamic
{{INPUT}}
120/Data/r[1]/c[1]
Update Cell
Expression
row 1
column 1
UpdateFieldList
133/SortInfo/Field/@field
Tab
Questions
Tab (119)
ListBox
Select Url
Drop Down (130)
Manual
ListBox
Sort by
Drop Down (134)
Manual
Wizard