I need a hint to sorted out this issue - flask-wtforms

I have this form:
class RegisterForm(FlaskForm):
"""Registration form."""
username = StringField('Username', validators=[Required(), Length(1, 64)])
password = PasswordField('Password', validators=[Required(), Length(4, 4)])
password_again = PasswordField('Password again',
validators=[Required(), EqualTo('password'), Length(4, 4)])
tc = SelectField(validators=[Required()],
choices=[("m_", "Yes"),
("n", "No")])
submit = SubmitField('Register')
And I want to add secrets.randbelow(n) next to "m_"

Related

azure kql parse function - unable to parse ? using regex (zero or one time)

I'm trying to parse this line:
01/11/1011 11:11:11: LOG SERVER = 1 URL = /one/one.aspx/ AccountId = 1111 MainId = 1111 UserAgent = Browser = Chrome , Version = 11.0, IsMobile = False, IP = 1.1.1.1 MESSAGE = sample message TRACE = 1
using this parse statement:
parse-where kind=regex flags=i message with
timestamp:datetime
":.*LOG SERVER = " log_server:string
".*URL = " url:string
".*AccountId = " account_id:string
".*MainId = " main_id:string
".*?UserAgent = " user_agent:string
",.*Version = " version:string
",.*IsMobile = " is_mobile:string
",.*IP = " ip:string
".*MESSAGE = " event:string
".*TRACE = " trace:string
now the thing is that sometimes I got records that has one "key=value" missing but the order of the rest of the columns remains the same.
to match all kinds of rows I just wanted to add (<name_of_colum>)? for example:
"(,.*Version = )?" version:string
but it fails everytime.

I think parse/parse-where operators are more useful when you have well formatted inputs - the potentially missing values in this case would make it tricky/impossible to use these operators.
If you control the formatting of the input strings, consider normalizing it to always include all fields and/or add delimiters and quotes where appropriate.
Otherwise, you could use the extract function to parse it - the following expression would work even if some lines are missing some fields:
| extend
timestamp = extract("(.*): .*", 1, message, typeof(datetime)),
log_server = extract(".*LOG SERVER = ([^\\s]*).*", 1, message),
url = extract(".*URL = ([^\\s]*).*", 1, message),
main_id = extract(".*MainId = ([^\\s]*).*", 1, message),
user_agent = extract(".*UserAgent = ([^,]*).*", 1, message),
version = extract(".*Version = ([^,]*).*", 1, message),
is_mobile = extract(".*IsMobile = ([^,]*).*", 1, message),
ip = extract(".*IP = ([^\\s]*).*", 1, message),
event = iff(message has "TRACE", extract(".*MESSAGE = (.*) TRACE.*", 1, message), extract(".*MESSAGE = (.*)", 1, message)),
trace = extract(".*TRACE = (.*)", 1, message)

Telegraf json v2 parsing

I'm trying to pick out the local_temperature property from a Zigbee TRV.
Here is my section from /etc/telegraf/telegraf.conf
[[inputs.mqtt_consumer]]
servers = ["tcp://127.0.0.1:1883"]
topics = [
"zigbee2mqtt/Home/+/Radiator",
]
data_format = "json_v2"
[[inputs.mqtt_consumer.json_v2]]
measurement_name = "temperature"
[[inputs.mqtt_consumer.topic_parsing]]
topic = "_/room/_"
[[inputs.mqtt_consumer.json_v2.field]]
path = "local_temperature"
type = "float"
Error from /var/log/syslog
Feb 21 11:03:07 mini31 telegraf[23428]: 2022-02-21T11:03:07Z E! [inputs.mqtt_consumer] Error in plugin: metric parse error: expected tag at 1:36: "{\"battery\":97,\"boost_heating\":\"OFF\",\"boost_heating_countdown\":0,\"boost_heating_countdown_time_set\":300,\"child_lock\":\"UNLOCK\",\"current_heating_setpoint\":8,\"eco_mode\":\"OFF\",\"eco_temperature\":12,\"linkquality\":110,\"local_temperature\":17.5,\"local_temperature_calibration\":-2,\"max_temperature\":24,\"min_temperature\":8,\"position\":25,\"preset\":\"programming\",\"programming_mode\":\"09:00/19°C 12:00/13°C 14:00/19°C 17:00/8°C 06:00/8°C 12:00/8°C 14:30/8°C 17:30/8°C 06:00/8°C 12:30/8°C 14:30/8°C 18:30/8°C\",\"window\":\"CLOSED\",\"window_detection\":\"OFF\"}"
What does it even mean?

[[inputs.mqtt_consumer]]
servers = ["tcp://127.0.0.1:1883"]
topics = [
"zigbee2mqtt/Home/+/Radiator",
]
data_format = "json_v2"
[[inputs.mqtt_consumer.json_v2]]
measurement_name = "temperature"
[[inputs.mqtt_consumer.topic_parsing]]
topic = "zigbee2mqtt/Home/+/Radiator"
tags = "_/_/room/_"
[[inputs.mqtt_consumer.json_v2.field]]
path = "local_temperature"
rename = "temperature"
type = "float"
[[inputs.mqtt_consumer.json_v2]]
measurement = "valve"
[[inputs.mqtt_consumer.json_v2.field]]
path = "position"
rename = "valve"
type = "float"

How to include / exclude filter statement in R httr query for Localytics

I can successfully query data from Localytics using R, such as the following example:
r <- POST(url = "https://api.localytics.com/v1/query,
body=list(app_id=<APP_ID>,
metrics=c("occurrences","users"),
dimensions=c('a:URI'),
conditions=list(day = c("between", "2020-02-11", "2020-03-12"),
event_name = "Content Viewed",
"a:Item URI" = "testing")
),
encode="json",
authenticate(Key,Secret),
accept("application/json"),
content_type("application/json"))
stop_for_status(r)
But what I would like to do is create a function so I can do this quickly and not have to copy/paste data.
The issue I am running into is with the line "a:Item URI" = "testing", where I am filtering all searches by the Item URI where they all equal "testing", but sometimes, I don't want to include the filter statement, so I just remove that line entirely.
When I wrote my function, I tried something like the following:
get_localytics <- function(appID, metrics, dimensions, from = Sys.Date()-30,
to = Sys.Date(), eventName = "Content Viewed",
Key, Secret, filterDim = NULL, filterCriteria = NULL){
r <- httr::POST(url = "https://api.localytics.com/v1/query",
body = list(app_id = appID,
metrics = metrics,
dimensions = dimensions,
conditions = list(day = c("between", as.character(from), as.character(to)),
event_name = eventName,
filterDim = filterCriteria)
),
encode="json",
authenticate(Key, Secret),
accept("application/json"),
content_type("application/json"))
stop_for_status(r)
result <- paste(rawToChar(r$content),collapse = "")
document <- fromJSON(result)
df <- document$results
return(df)
}
But my attempt at adding filterDim and filterCriteria only produce the error Unprocessable Entity. (Keep in mind, there are lots of variables I can filter by, not just "a:Item URI" so I need to be able to manipulate that as well.
How can I include a statement, where if I need to filter, I can incorporate that line, but if I don't need to filter, that line isn't included?

conditions is just a list, so you can conditionally add elements to it. Here we just use an if statement to test of the values are passed and if so, add them in.
get_localytics <- function(appID, metrics, dimensions, from = Sys.Date()-30,
to = Sys.Date(), eventName = "Content Viewed",
Key, Secret, filterDim = NULL, filterCriteria = NULL){
conditions <- list(day = c("between", as.character(from), as.character(to)),
event_name = eventName)
if (!is.null(filterDim) & !is.null(filterCriteria)) {
conditions[[filterDim]] <- filterCriteria)
}
r <- httr::POST(url = "https://api.localytics.com/v1/query",
body = list(app_id = appID,
metrics = metrics,
dimensions = dimensions,
conditions = conditions),
encode="json",
authenticate(Key, Secret),
accept("application/json"),
content_type("application/json"))
stop_for_status(r)
result <- paste(rawToChar(r$content),collapse = "")
document <- fromJSON(result)
df <- document$results
return(df)
}

How to calculate standard deviation every 3 columns in a dataframe?

I have a dataframe with 4895 rows and 75 columns.
I need to calculate the standard deviation each 3 columns, for each row.
So at the and I should have 4895 rows and 25 columns (75/3), where each columns is the SD calculated among three columns.
This is part of the data frame:
structure(list(`101_LOD` = c(-0.00647656063436054, 0.00645714072316343,
0.00174533523902105, -0.000354643362187957, -0.000599093190801188,
0.00086188829059792), `101_LOD.1` = c(0.00380625456526623, -0.00398115037246045,
0.00158673927930099, -0.00537583996746438, -0.00280048350643599,
0.00348232298529063), `101_LOD.2` = c(-0.00281100080425964, -0.00335537844222041,
0.00611652518452308, -0.000738139825060029, 0.00485039477849737,
0.00412428118507656), `107_LOD` = c(0.00264717678436649, 0.00339296025595841,
0.00392733001719888, 0.0106686039973083, 0.00886643251752075,
0.0426091484273961), `107_LOD.1` = c(0.000242380702002215, -0.00116108069669281,
0.0119784744970561, 0.00380805756323248, 0.00190407945251567,
0.00199684331869391), `107_LOD.2` = c(-0.0102716279438754, -0.00706528150567528,
-0.0108745954674186, -0.0122962259781756, -0.00590383880635847,
-0.00166664119985051), `111_LOD` = c(-0.00174374098054644, 0.00383270191075735,
-0.00118363208946644, 0.00107908760333878, -9.30127551375776e-05,
-0.00141500588842743), `111_LOD.1` = c(0.000769378300959002,
0.00253820252869653, 0.00110643824418424, -0.000338050323261079,
-0.00313666295753596, 0.0043919374295125), `111_LOD.2` = c(0.000177265973907964,
0.00199829884609846, -0.000490950219515303, -0.00100263695578483,
0.00122606902671889, 0.00934018452187161), `113_LOD` = c(0.000997977666838309,
0.0062400770296875, -0.00153620247996209, 0.00136849054508488,
-0.00145700847633675, -0.000591288575933268), `113_LOD.1` = c(-0.00114161441697546,
0.00152607521404826, 0.000811193628975422, -0.000799514037634276,
-0.000319008435039752, -0.0010086036089075), `113_LOD.2` = c(-0.000722312098377764,
0.00364767954707251, 0.000547744649351312, 0.000352509651080838,
-0.000852173274761947, 0.00360487150682726), `135_LOD` = c(-0.00634051802134062,
0.00426062889500736, 0.00484049067127332, 0.00216220020394825,
0.00165634168942681, -0.00537970105199375), `135_LOD.1` = c(-0.00209301968088832,
0.00535855274344209, -0.00119679744329422, 0.0041216882161451,
0.00512978202611836, 0.0014048506490567), `135_LOD.2` = c(0.00022377545723911,
0.00400550696583795, 0.00198972253447825, 0.00301341644871015,
0.00256802839330668, 0.00946109288597202), `137_LOD` = c(-0.0108508893475138,
-0.0231919072487789, -0.00346546003410657, -0.00154066625155414,
0.0247266017774909, -0.0254464953061609), `137_LOD.1` = c(-0.00363025194918789,
-0.00291104074373261, 0.0024998477144967, 0.000877707284759669,
0.0095477003599792, 0.0501795740749602), `137_LOD.2` = c(0.00930498343499501,
-0.011839104725282, 0.000274929503053888, 0.000715665078729413,
0.0145503185102915, 0.0890428314632625), `149_LOD` = c(-0.000194406250680231,
0.000355157226357547, -0.000353931679163222, 0.000101471293242973,
-0.000429409422518444, 0.000344585379249552), `149_LOD.1` = c(-0.000494386150759807,
0.000384907974061922, 0.000582537329068263, -0.000173285705433721,
-6.92758935962043e-05, 0.00237942557324254), `149_LOD.2` = c(0.000368606958615297,
0.000432568466833549, 3.33092313366271e-05, 0.000715304544370804,
-0.000656902381786168, 0.000855422043674721), `155_LOD` = c(-0.000696168382693618,
-0.000917607266525328, 4.77049670728094e-06, 0.000140297660927979,
-5.99898679530658e-06, 6.71169142984434e-06), `155_LOD.1` = c(-0.000213644203677328,
-3.44396001911029e-07, -0.000524232671878577, -0.000830180665933627,
1.47799998238307e-06, -5.97640014667251e-05), `155_LOD.2` = c(-0.000749882784933487,
0.000345737159390042, -0.00076916001239521, -0.000135205762575321,
-2.55352420251723e-06, -3.07199008030628e-05), `31_LOD` = c(-0.00212014938530172,
0.0247411322547065, -0.00107990654365844, -0.000409195814154659,
-0.00768439381433953, 0.001860128524035), `31_LOD.1` = c(-0.00248488588195854,
-0.011146734518705, -0.000167943850441196, -0.0021998906531997,
0.0166775965182051, -0.0156939303287719), `31_LOD.2` = c(0.00210626277375321,
-0.00327815351414411, -0.00271043947479133, 0.00118991079627845,
-0.00838520090692615, 0.0255825346347586), `33_LOD` = c(0.0335175783154054,
0.0130192144768818, 0.0890608024914352, -0.0142431454793663,
0.00961009674973182, -0.0429774973256228), `33_LOD.1` = c(0.018600175159935,
0.04588362587764, 0.0517479021554752, 0.0453766081395813, -0.0483559729403664,
0.123771869764484), `33_LOD.2` = c(0.01906507758481, -0.00984821669825455,
0.134177176083007, -0.00544320457445977, 0.0516083894733814,
-0.0941500564321804), `39_LOD` = c(-0.148517395684098, -0.21311281527214,
0.112875846920874, -0.134256453140454, 0.0429030528286934, -0.0115143877745049
), `39_LOD.1` = c(-0.0431568202849291, -0.159003698955288, 0.0429009071238143,
-0.126060096927082, -0.078848020069061, -0.0788748111534866),
`39_LOD.2` = c(-0.16276833960171, 0.0236589399437796, 0.0828435027244962,
-0.50219849047847, -0.105196237549017, -0.161206838628339
), `42_LOD` = c(-0.00643926654994104, -0.0069253267922805,
7.63419856289838e-05, -0.0185223126108671, 0.00120855708103566,
-0.00275288147011515), `42_LOD.1` = c(-0.000866169150506504,
-0.00147791175852563, -0.000670310173141084, -0.00757733007180311,
0.0151353172950393, -0.00114193461500327), `42_LOD.2` = c(0.00719928454572906,
0.00311615354837406, 0.00270759483782046, -0.0108062423259522,
0.00158765505419478, -0.0034831499672973), `45_LOD` = c(0.00557787518897268,
0.022337270533665, 0.00657118689440082, -0.00247269227623608,
0.0191646343214611, 0.0233090596023039), `45_LOD.1` = c(-0.0305395220788143,
0.077105031761457, -0.00101713990356452, 0.0147500116150713,
-5.43009569586179e-05, -0.0235006181977403), `45_LOD.2` = c(-0.0216498682456909,
-0.0413426968184435, -0.0210779895848601, -0.0147549519865421,
0.00305229143870313, -0.0483293292336662), `47_LOD` = c(-0.00467568767221499,
-0.0199796182799552, 0.00985966068611855, -0.031010117051163,
0.0319279109813341, 0.0350743318265918), `47_LOD.1` = c(0.00820166533285921,
-0.00748186905620154, -0.010483251821707, -0.00921919551377505,
0.0129546148757833, 0.000223462281435923), `47_LOD.2` = c(0.00172469728530889,
0.0181683409295075, 0.00264937907258855, -0.0569837400476351,
0.00514558635349483, 0.0963339573489031), `59_LOD` = c(-0.00664210061621158,
-0.062069664217766, 0.0104345353700492, 0.0115323589989968,
-0.000701276829098035, -0.0397759501000331), `59_LOD.1` = c(-0.00844888486350536,
0.0207426674766074, -0.0227755432761471, -0.00370561240222376,
0.0152046240483297, -0.0127327412801225), `59_LOD.2` = c(-0.000546590647534814,
0.0178115310450356, 0.00776130696191998, 0.00162470375408126,
-0.036140754156005, 0.0197791914089296), `61_LOD` = c(0.00797528044191513,
-0.00358928087671818, 0.000662870138322471, -0.0412142836466128,
-0.00571822580078707, -0.0333870884803465), `61_LOD.1` = c(0.000105849888219735,
-0.00694734283847093, -0.00656216592134899, 0.00161225110022219,
0.0125744958934939, -0.0178560868664668), `61_LOD.2` = c(0.0049288443167774,
0.0059411543659837, -0.00165857112209555, -0.0093669075333705,
0.00655185371925189, 0.00516436591134869), `69_LOD` = c(0.0140014747729604,
0.0119645827116724, 0.0059880663080946, -0.00339119330845176,
0.00406436116298777, 0.00374425148741196), `69_LOD.1` = c(0.00465076983995792,
0.00664902297016735, -0.00183936649215524, 0.00496509351837152,
-0.0224812403463345, -0.0193087796456654), `69_LOD.2` = c(-0.00934638876711703,
-0.00802183076602164, 0.00406752039394799, -0.000421337136630527,
-0.00406768983408334, -0.0046016148041856), `71_LOD` = c(-0.00206064862123214,
0.0058604630066848, -0.00353440181333921, -0.000305197461077327,
0.00266085011303462, -0.00105635261106644), `71_LOD.1` = c(3.66652318354654e-06,
0.00542612739642576, 0.000860385212430484, 0.00157520645492044,
-0.00280256517377998, -0.00474358065422048), `71_LOD.2` = c(-0.00167098030843413,
0.0059622082597603, -0.00121597491543965, -0.000791592953383716,
-0.0022790991468459, 0.00508978650148816), `75_LOD` = c(NA,
-0.00562613898652477, -0.000103076958936504, -3.76628574664693e-05,
-0.000325767611573817, 0.000117404893823389), `75_LOD.1` = c(NA,
NA, -0.000496324358203359, -0.000517476831074487, -0.00213096062838051,
-0.00111202867609916), `75_LOD.2` = c(NA, NA, -0.000169651845347418,
-4.72864955070539e-05, -0.00144880109085214, 0.00421635976535877
), `79_LOD` = c(-0.0011901810540199, 0.00731686066269579,
0.00538551997145174, -0.00578723012473479, -0.0030246805255648,
0.00146141135533218), `79_LOD.1` = c(-0.00424278455960268,
-0.010593752642875, 0.0065136497427927, -0.00427355522802769,
0.000539975609490915, -0.0206849687839064), `79_LOD.2` = c(-0.00366739576561779,
-0.00374066839898667, -0.00132764684703939, -0.00534145222725701,
0.00920940542227595, -0.0101871763957068), `85_LOD` = c(-0.0120254177480422,
0.00369546541331518, -0.00420718877886963, 0.00414911885475517,
-0.00130381692844529, -0.00812757789798261), `85_LOD.1` = c(-0.00302024868281014,
0.00537704163310547, 0.00184264538884543, -0.00159032685888543,
-0.0062127769817834, 0.00349476605688194), `85_LOD.2` = c(0.0122689407380797,
-0.00509605601025503, -0.00641413996554198, 0.000592176121486696,
0.00131237912317341, -0.00535018996837309), `87_LOD` = c(0.00613621268007298,
0.000410268892659307, -0.00239014321624482, -0.00171179729894864,
-0.00107159765522861, -0.00708388174601732), `87_LOD.1` = c(0.00144787264098156,
-0.0025946273860992, -0.00194897899110034, 0.00157863310440493,
-0.0048913305554607, -0.000585669821053749), `87_LOD.2` = c(-0.00224691693198253,
-0.00277315666829267, 0.00166487067514155, -0.00173757960229744,
-0.00362252480121682, -0.0101992979591839), `93_LOD` = c(-0.0234225447373586,
0.0390095666365413, 0.00606244490932179, 0.0264258422783391,
0.0161211132913951, -0.0617678157059), `93_LOD.1` = c(-0.0124876313221369,
-0.0309636779639578, 0.00610883313140442, -0.0192442672220773,
0.0129557286224975, -0.00869066964782635), `93_LOD.2` = c(-0.0219837540560547,
-0.00521242297372905, 0.0179965615561871, 0.0081370991723329,
1.45427765512579e-06, -0.0111199632179688), `99_LOD` = c(0.00412086456443205,
-0.00259940538393106, 0.00742537463584133, -0.00302091572866969,
-0.00320466045653491, -0.00168702410433936), `99_LOD.1` = c(0.00280546156134205,
-0.00472591065687533, 0.00518402193979284, -0.00130887074314965,
0.00148769905391341, 0.00366250488078969), `99_LOD.2` = c(-0.00240469207099292,
-9.57307699040024e-05, -0.000145493235845501, 0.000667454164326723,
-0.0057445759245933, 0.00433464631989088), H_LOD = c(-6248.9128518109,
-10081.9540490064, -6696.91582671427, -5414.20614601348,
-3933.64339240365, -13153.7509294302), H_LOD.1 = c(-6.2489128518109,
-10.0819540490064, -6.69691582671427, -5.41420614601348,
-3.93364339240365, -13.1537509294302), H_LOD.2 = c(-6248.9128518109,
-10081.9540490064, -6696.91582671427, -5414.20614601348,
-3933.64339240365, -13153.7509294302)), row.names = c(NA,
6L), class = "data.frame")
What I tried
LOD_sd=aggregate(LOD_ut,list(rep(1:(ncol(LOD_ut)%/%3),each=3,len=ncol(LOD_ut))),std)[-1];
I'm stucked because of this error:
Error in aggregate.data.frame(LOD_ut, list(rep(1:(ncol(LOD_ut)%/%3), each = 3, :
arguments must have same length
Someone can help me?
Thanks

Here is an idea via base R. We split the data frame every 3 columns and create a list. We then loop over that list and calculate the rowwise standard deviation, i.e.
sapply(split.default(df, rep(seq((ncol(df) / 3)), each = 3)), function(i)
apply(i, 1, sd, na.rm = TRUE))

How to scrape additional data points from Zillow using R

I inherited a file from a previous coworker to use R to pull Zillow "Zestimate" and "Rent Zestimate" data for properties, and then output these data points to a CSV file. However, I am very new to coding and have not been successful with pulling additional information that I know is available. I have searched the site for answers, but since I am still trying to learn how to code I haven't been successful with making my own edits to the current code. Any help I can get adding code to pull any of these additional data points would be much appreciated.
Property details (sqft, year built, beds, baths, property type)
Zestimate range (high and low)
Rent Zestimate range (high and low)
Last sold date and price
Price history (latest event, date, and price)(not sure this can be scraped )
Tax history (latest year and property taxes) (not sure this can be scraped )
Current code:
houseAddsSplit = read.csv(houseAddsFileLocation) zillowAdds = paste(houseAddsSplit$STREET, houseAddsSplit$CITY, houseAddsSplit$STATE, houseAddsSplit$ZIP, sep = " ")
library(ZillowR)
library(XML)
set_zillow_web_service_id(zwsId)
zpidList = NULL
zestimate = NULL
rentZestimate = NULL
for(i in 1:length(zillowAdds)){
print(paste("Processing house: ", i, ", address: ", zillowAdds[i]))
print(zillowAdds[i])
houseZpidClean = "ERR"
houseZestClean = "ERR"
houseRentZestClean = "ERR"
houseInfo = try(GetSearchResults(address = zillowAdds[i], citystatezip = as.character(houseAddsSplit$ZIP[i]), rentzestimate = TRUE))
'#'while(houseInfo$message$code != "0"){
'#' houseInfo = try(GetSearchResults(address = cipAdds[i], citystatezip = as.character(cipLoans$ZIP[i]), rentzestimate = TRUE))
'#' Sys.sleep(runif(1, 3, 5))
'#'}
if(houseInfo$message$code == "0"){
houseZpid = try(xmlElementsByTagName(houseInfo$response, "zpid", recursive = TRUE))
houseZest = try(xmlElementsByTagName(houseInfo$response, "amount", recursive = TRUE))
houseZpidAlmostClean = try(toString.XMLNode(houseZpid$results.result.zpid))
houseZestAC = try(toString.XMLNode(houseZest$results.result.zestimate.amount))
houseRentZestAC = try(toString.XMLNode(houseZest$results.result.rentzestimate.amount))
houseZpidClean = try(substr(houseZpidAlmostClean, 7, nchar(houseZpidAlmostClean) - 7))
houseZestClean = try(substr(houseZestAC, 24, nchar(houseZestAC) - 9))
houseRentZestClean = try(substr(houseRentZestAC, 24, nchar(houseRentZestAC) - 9))
}
closeAllConnections()
zpidList[i] = houseZpidClean
print(paste("zpid: ", houseZpidClean))
zestimate[i] = houseZestClean
print(paste("zestimate: ", houseZestClean))
rentZestimate[i] = houseRentZestClean
print(paste("rent zestimate: ", houseRentZestClean))
Sys.sleep(runif(1, 7, 10))
}
outputData = cbind(houseAddsSplit, zestimate, rentZestimate)
write.csv(outputData, paste(writeToFolder, "/zillowPullOutput.csv", sep = ""))
print(paste("All done. File written to", paste(writeToFolder, "/zillowPullOutput.csv", sep = "")))

Hope you solved this, but GetSearchResult API wouldn't return all the results you are looking for. You may have to call GetUpdatedPropertyDetails API to get all the results.

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