Probably due to my limited knowledge of communicating with APIs, (Which I am trying to remedy :) ) I seem to be unable to execute a put request for more than 1 row of a dataframe at a time. for example, if df_final consists of 1 row, the following code works. If there are multiple rows, it fails and I get a 400 status.
reqBody <- list(provName = df_final$Provider,site = df_final$Site,
monthJuly = df_final$July, monthAugust = df_final$August,
monthSeptember = df_final$September, monthOctober =df_final$October,
monthNovember = df_final$November ,
monthDecember = df_final$December, monthJanuary = df_final$January, monthFebruary = df_final$February,
monthMarch = df_final$March, monthApril = df_final$April, monthMay = df_final$May,
monthJune = df_final$June,
assumptions = paste("Monthly Volume:", input$Average, "; Baseline Seasonality:", input$Year, "; Trend:", input$Year_slopes),
rationale = as.character(input$Comments), fiscalYear = FY_SET, updateDtm = Sys.time())
r <- PUT(fullURL, body = reqBody, encode = "json", content_type_json())
Using with_verbose() I am able to see that the json being sent is formatted differently for the 2 cases. I haven't found anything in the documentation ( https://cran.r-project.org/web/packages/httr/httr.pdf) that has been particularly helpful in overcoming this.
The format it appears to be sending out in the first instance (1 row in the data frame) Looks like this:
{"provName":"Name","site":"site","monthJuly":56,"monthAugust":71,"monthSeptember":65,"monthOctober":78,"monthNovember":75,"monthDecember":98,"monthJanuary":23,"monthFebruary":39,"monthMarch":38,"monthApril":42,"monthMay":57,"monthJune":54,"assumptions":"Monthly Volume: Last 3 Months of 2019 ; Baseline Seasonality: 2017 ; Trend: 2017","rationale":"","fiscalYear":2022,"updateDtm":"2023-02-03 15:19:40"}
and again, it works sans issues.
With 2 rows I get the following format:
{"provName":["Name","Name"],"site":["site","site"],"monthJuly":[56,56],"monthAugust": [71,71],"monthSeptember":[65,65],"monthOctober":[78,78],"monthNovember":[75,75],"monthDecember": [98,98],"monthJanuary":[23,23],"monthFebruary":[39,39],"monthMarch":[38,38],"monthApril": [42,42],"monthMay":[57,57],"monthJune":[54,54],"assumptions":["Monthly Volume: Last 3 Months of 2019 ; Baseline Seasonality: 2017 ; Trend: 2017","Monthly Volume: Last 3 Months of 2019 ; Baseline Seasonality: 2017 ; Trend: 2017"],"rationale":["",""],"17":2,"18":2}
And it fails with status 400.
I suppose I could use lapply and PUT for each row, however with thousands of rows in a dataframe, I think that would be less than ideal.
Anyone have any light to share on this?
Any help would be greatly appreciated!
PS: this didn't really answer my question
R httr put requets
and as I mentioned, Doing something like this is not ideal:
Convert each data frame row to httr body parameter list without enumeration
Looks like you are using a list as the request body. Use a data frame instead.
Lists and data frames get serialized to JSON differently:
jsonlite::toJSON(list(x = 1:2, y = 3:4))
#> {"x":[1,2],"y":[3,4]}
jsonlite::toJSON(data.frame(x = 1:2, y = 3:4))
#> [{"x":1,"y":3},{"x":2,"y":4}]
Related
All.
I've been trying to solve a problem on a large data set for some time and could use some of your wisdom.
I have a DF (1.3M obs) with a column called customer along with 30 other columns. Let's say it contains multiple instances of customers Customer1 thru Customer3000. I know that I have issues with 30 of those customers. I need to find all the customers that are NOT the customers I have issues and replace the value in the 'customer' column with the text 'Supported Customer'. That seems like it should be a simple thing...if it werent for the number of obs, I would have loaded it up in Excel, filtered all the bad customers out and copy/pasted the text 'Supported Customer' over what remained.
Ive tried replace and str_replace_all using grepl and paste/paste0 but to no avail. my current code looks like this:
#All the customers that have issues
out <- c("Customer123", "Customer124", "Customer125", "Customer126", "Customer127",
"Customer128", ..... , "Customer140")
#Look for everything that is NOT in the list above and replace with "Enabled"
orderData$customer <- str_replace_all(orderData$customer, paste0("[^", paste(out, collapse =
"|"), "]"), "Enabled Customers")
That code gets me this error:
Error in stri_replace_all_regex(string, pattern, fix_replacement(replacement), :
In a character range [x-y], x is greater than y. (U_REGEX_INVALID_RANGE)
I've tried the inverse of this approach and pulled a list of all obs that dont match the list of out customers. Something like this:
in <- orderData %>% filter(!customer %in% out) %>% select(customer) %>%
distinct(customer)
This gets me a much larger list of customers that ARE enabled (~3,100). Using the str_replace_all and paste approach seems to have issues though. At this large number of patterns, paste no longer collapses using the "|" operator. instead I get a string that looks like:
"c(\"Customer1\", \"Customer2345\", \"Customer54\", ......)
When passed into str_replace_all, this does not match any patterns.
Anyways, there's got to be an easier way to do this. Thanks for any/all help.
Here is a data.table approach.
First, some example data since you didn't provide any.
customer <- sample(paste0("Customer",1:300),5000,replace = TRUE)
orderData <- data.frame(customer = sample(paste0("Customer",1:300),5000,replace = TRUE),stringsAsFactors = FALSE)
orderData <- cbind(orderData,matrix(runif(0,100,n=5000*30),ncol=30))
out <- c("Customer123", "Customer124", "Customer125", "Customer126", "Customer127", "Customer128","Customer140")
library(data.table)
setDT(orderData)
result <- orderData[!(customer %in% out),customer := gsub("Customer","Supported Customer ",customer)]
result
customer 1 2 3 4 5 6 7 8 9
1: Supported Customer 134 65.35091 8.57117 79.594166 84.88867 97.225276 84.563997 17.15166 41.87160 3.717705
2: Supported Customer 225 72.95757 32.80893 27.318046 72.97045 28.698518 60.709381 92.51114 79.90031 7.311200
3: Supported Customer 222 39.55269 89.51003 1.626846 80.66629 9.983814 87.122153 85.80335 91.36377 14.667535
4: Supported Customer 184 24.44624 20.64762 9.555844 74.39480 49.189537 73.126275 94.05833 36.34749 3.091072
5: Supported Customer 194 42.34858 16.08034 34.182737 75.81006 35.167769 23.780069 36.08756 26.46816 31.994756
---
I am a new user of R and trying to use mRMRe R package (mRMR is one of the good and well known feature selection approaches) to obtain feature subset from a feature set. Please excuse if my question is simple as I really want to know how I can fix an error. Below is the detail.
Suppose, I have a csv file (gene.csv) having feature set of 6 attributes ([G1.1.1.1], [G1.1.1.2], [G1.1.1.3], [G1.1.1.4], [G1.1.1.5], [G1.1.1.6]) and a target class variable [Output] ('1' indicates positive class and '-1' stands for negative class). Here's a sample gene.csv file:
[G1.1.1.1] [G1.1.1.2] [G1.1.1.3] [G1.1.1.4] [G1.1.1.5] [G1.1.1.6] [Output]
11.688312 0.974026 4.87013 7.142857 3.571429 10.064935 -1
12.538226 1.223242 3.669725 6.116208 3.363914 9.174312 1
10.791367 0.719424 6.115108 6.47482 3.597122 10.791367 -1
13.533835 0.37594 6.766917 7.142857 2.631579 10.902256 1
9.737828 2.247191 5.992509 5.992509 2.996255 8.614232 -1
11.864407 0.564972 7.344633 4.519774 3.389831 7.909605 -1
11.931818 0 7.386364 5.113636 3.409091 6.818182 1
16.666667 0.333333 7.333333 4.333333 2 8.333333 -1
I am trying to get best feature subset of 2 attributes (out of above 6 attributes) and wrote following R code.
library(mRMRe)
file_n<-paste0("E:\\gene", ".csv")
df <- read.csv(file_n, header = TRUE)
f_data <- mRMR.data(data = data.frame(df))
featureData(f_data)
mRMR.ensemble(data = f_data, target_indices = 7,
feature_count = 2, solution_count = 1)
When I run this code, I am getting following error for the statement f_data <- mRMR.data(data = data.frame(df)):
Error in .local(.Object, ...) :
data columns must be either of numeric, ordered factor or Surv type
However, data in each column of the csv file are real number.So, how can I change the R code to fix this problem? Also, I am not sure what should be the value of target_indices in the statement mRMR.ensemble(data = f_data, target_indices = 7,feature_count = 2, solution_count = 1) as my target class variable name is "[Output]" in the gene.csv file.
I will appreciate much if anyone can help me to obtain the best feature subset based on the gene.csv file using mRMRe R package.
I solved the problem by modifying my code as follows.
library(mRMRe)
file_n<-paste0("E:\\gene", ".csv")
df <- read.csv(file_n, header = TRUE)
df[[7]] <- as.numeric(df[[7]])
f_data <- mRMR.data(data = data.frame(df))
results <- mRMR.classic("mRMRe.Filter", data = f_data, target_indices = 7,
feature_count = 2)
solutions(results)
It worked fine. The output of the code gives the indices of the selected 2 features.
I think it has to do with your Output column which is probably of class integer. You can check that using class(df[[7]]).
To convert it to numeric as required by the warning, just type:
df[[7]] <- as.numeric(df[[7]])
That worked for me.
As for the other question, after reading the documentation, setting target_indices = 7 seems the right choice.
I've completed the first couple R courses on DataCamp and in order to build up my skills I've decided to use R to prep for fantasy football this season, thus I have began playing around with the nflscrapR package.
With the nflscrapR package, one can pull Game Information using the season_games() function which simply returns a data frame with the gameID, game date, the home and away team abbreviations.
Example:
games.2012 = season_games(2012)
head(games.2012)
GameID date home away season
1 2012090500 2012-09-05 NYG DAL 2012
2 2012090900 2012-09-09 CHI IND 2012
3 2012090908 2012-09-09 KC ATL 2012
4 2012090907 2012-09-09 CLE PHI 2012
5 2012090906 2012-09-09 NO WAS 2012
6 2012090905 2012-09-09 DET STL 2012
Initially I copy and pasted the original function and changed the last digit manually for each season, then rbinded all the seasons into one data frame, games.
games.2012 <- season_games(2012)
games.2013 <- season_games(2013)
games.2014 <- season_games(2014)
games.2015 <- season_games(2015)
games = rbind(games2012,games2013,games2014,games2015)
I'd like to write a function to simplify this process.
My failed attempt:
gameID <- function(years) {
for (i in years) {
games[i] = season_games(years[i])
}
}
With years = list(2012, 2013) for testing purposes, produced the following:
Error in strsplit(headers, "\r\n") : non-character argument Called
from: strsplit(headers, "\r\n")
Thanks in advance!
While #Gregor has an apparent solution, he didn't run it because this wasn't a minimal example. I googled, found, and tried to use this code, and it doesn't work, at least in a non-trivial amount of time.
On the other hand, I took this code from Vivek Patil's blog.
library(XML)
weeklystats = as.data.frame(matrix(ncol = 14)) # Initializing our empty dataframe
names(weeklystats) = c("Week", "Day", "Date", "Blank",
"Win.Team", "At", "Lose.Team",
"Points.Win", "Points.Lose",
"YardsGained.Win", "Turnovers.Win",
"YardsGained.Lose", "Turnovers.Lose",
"Year") # Naming columns
URLpart1 = "http://www.pro-football-reference.com/years/"
URLpart3 = "/games.htm"
#### Our workhorse function ####
getData = function(URLpart1, URLpart3) {
for (i in 2012:2015) {
URL = paste(URLpart1, as.character(i), URLpart3, sep = "")
tablefromURL = readHTMLTable(URL)
table = tablefromURL[[1]]
names(table) = c("Week", "Day", "Date", "Blank", "Win.Team", "At", "Lose.Team",
"Points.Win", "Points.Lose", "YardsGained.Win", "Turnovers.Win",
"YardsGained.Lose", "Turnovers.Lose")
table$Year = i # Inserting a value for the year
weeklystats = rbind(table, weeklystats) # Appending happening here
}
return(weeklystats)
}
I posted this because, it works, you might learn something about web scraping you didn't know, and it runs in 11 seconds.
system.time(weeklystats <- getData(URLpart1, URLpart3))
user system elapsed
0.870 0.014 10.926
You should probably take a look at some popular answers for working with lists, specifically How do I make a list of data frames? and What's the difference between [ and [[?.
There's no reason to put your years in a list. They're just integers, so just do a normal vector.
years = 2012:2015
Then we can get your function to work (we'll need to initialize an empty list before the for loop):
gameID <- function(years) {
games = list()
for (i in years) {
games[[i]] = season_games(years[i])
}
return(games)
}
Read my link above for why we're using [[ with the list and [ with the vector. And we could run it like this:
game_list = gameID(2012:2015)
But this is such a simple function that it's easier to use lapply. Your function is just a wrapper around a for loop that returns a list, and that's precisely what lapply is too. But where your function has season_games hard-coded in, lapply can work with any function.
game_list = lapply(2012:2015, season_games)
# should be the same result as above
In either case, we have the list of data frames and want to combine it into one big data frame. The base R way is rbind with do.call, but dplyr and data.table have more efficient versions.
# pick your favorite
games = do.call(rbind, args = game_list) # base
games = dplyr::bind_rows(game_list)
games = data.table::rbindlist(game_list)
When using SearchTwitter, I converted to dataframe and then exported to JSON. However, all the text is in one line, etc (sample below). I need to separate so that each tweet is its own.
phish <- searchTwitteR('phish', n = 5, lang = 'en')
phishdf <- do.call("rbind", lapply(phish, as.data.frame))
exportJson <-toJSON(phishdf)
write(exportJson, file = "phishdf.json")
json_phishdf <- fromJSON(file="phishdf.json")
I tried converting to a list and am wondering if maybe converting to a data frame is a mistake.
However, for a list, I tried:
newlist['text']=phish[[1]]$getText()
But this will just give me the text for the first tweet. Is there a way to iterate over the entire data set, maybe in a for loop?
{"text":["#ilazer #abbijacobson I do feel compelled to say that I phind phish awphul... sorry, Abbi!","#phish This on-sale was an embarrassment. Something needs to change.","FS: Have 2 Tix To Phish In Chula Vista #Phish #facevaluetickets #phish #facevalue GO: https://t.co/dFdrpyaotp","RT #WKUPhiDelt: Come unwind from a busy week of class and kick off the weekend with a Phish Fry! 4:30-7:30 at the Phi Delt house. Cost is $\u2026","RT #phish: Tickets for Phish's July 15 & 16 shows at The Gorge go on sale in fifteen minutes at 1PM ET: https://t.co/tEKLNjI5u7 https://t.c\u2026"],
"favorited":[false,false,false,false,false],
"favoriteCount":[0,0,0,0,0],
"replyToSN":["rAlexandria","phish","NA","NA","NA"],
"created":[1456521159,1456521114,1456521022,1456521016,1456520988],
"truncated":[false,false,false,false,false],
"replyToSID":["703326502629277696","703304948990222337","NA","NA","NA"],
"id":["703326837720662016","703326646074343424","703326261045829632","703326236722991105","703326119328686080"],
"replyToUID":["26152867","14503997","NA","NA","NA"],"statusSource":["Mobile Web (M5)","Twitter for iPhone","CashorTrade - Face Value Tickets","Twitter for iPhone","Twitter for Android"],
"screenName":["rAlexandria","adamgelvan","CashorTrade","Kyle_Smith1087","timogrennell"],
"retweetCount":[0,0,0,2,5],
"isRetweet":[false,false,false,true,true],
"retweeted":[false,false,false,false,false],
"longitude":["NA","NA","NA","NA","NA"],
"latitude":["NA","NA","NA","NA","NA"]}
I followed your code and don't have the issue you're describing. Are you using library(twitteR) and library(jsonlite)?
Here is the code, and a screenshot of it working
library(twitteR)
library(jsonlite)
phish <- searchTwitteR('phish', n = 5, lang = 'en')
phishdf <- do.call("rbind", lapply(phish, as.data.frame))
exportJson <-toJSON(phishdf)
write(exportJson, file = "./../phishdf.json")
## note the `txt` argument, as opposed to `file` used in the question
json_phishdf <- fromJSON(txt="./../phishdf.json")
As a fairly new R programmer I seem to have run into a strange problem - probably my inexperience with R
After reading and merging successive files into a single data frame, I find that order does not sort the data as expected.
I have multiple references in each file but each file refers to measurement data obtained at a different time.
Here's the code
library(reshape)
# Enter file name to Read & Save data
FileName=readline("Enter File name:\n")
# Find first occurance of file
for ( round1 in 1 : 6) {
ReadFile=paste(round1,"C_",FileName,"_Stats.csv", sep="")
if (file.exists(ReadFile))
break
}
x = data.frame(read.csv(ReadFile, header=TRUE),rnd=round1)
for ( round2 in (round1+1) : 6) {
#
ReadFile=paste(round2,"C_",FileName,"_Stats.csv", sep="")
if (file.exists(ReadFile)) {
y = data.frame(read.csv(ReadFile, header=TRUE),rnd = round2)
if (round2 == (round1 +1))
z=data.frame(merge(x,y,all=TRUE))
z=data.frame(merge(y,z,all=TRUE))
}
}
ordered = order(z$lab_id)
results = z[ordered,]
res = data.frame( lab=results[,"lab_id"],bw=results[,"ZBW"],wi=results[,"ZWI"],pf_zbw=0,pf_zwi=0,r = results[,"rnd"])
#
# Establish no of samples recorded
nsmpls = length(res[,c("lab")])
# Evaluate Z_scores for Between Lab Results
for ( i in 1 : nsmpls) {
if (res[i,"bw"] > 3 | res[i,"bw"] < -3)
res[i,"pf_zbw"]=1
}
# Evaluate Z_scores for Within Lab Results
for ( i in 1 : nsmpls) {
if (res[i,"wi"] > 3 | res[i,"wi"] < -3)
res[i,"pf_zwi"]=1
}
dd = melt(res, id=c("lab","r"), "pf_zbw")
b = cast(dd, lab ~ r)
If anyone could see why the ordering only works for about 55 of 70 records and could steer me in the right direction I would be obliged
Thanks very much
Check whether z$lab_id is a factor (with is.factor(z$lab_id)).
If it is, try
z$lab_id <- as.character(z$lab_id)
if it is supposed to be a character vector; or
z$lab_id <- as.numeric(as.character(z$lab_id))
if it is supposed to be a numeric vector.
Then order it again.
Ps. I had previously put these in the comments.