I'm writing out some functions for Inventory management. I've recently wanted to add a "photo url column" to my spreadsheet by using an API I've used successfully while initially building my inventory. My Spreadsheet header looks like the following:
SKU | NAME | OTHER STUFF
I have a getProductInfo function that returns a list of product info from an API I'm calling.
getProductInfo<- function(barcode) {
#Input UPC
#Output List of product info
CallAPI(barcode)
Process API return, remove garbage
return(info)
}
I made a new function that takes my inventory csv as input, and attempts to add a new column with product photo url.
get_photo_url_from_product_info_output <- function(in_list){
#Input GetProductInfo Output. Returns Photo URL, or nothing if
#it doesn't exist
if(in_list$DisplayStockPhotos == TRUE){
return(in_list$StockPhotoURL)
} else {
return("")
}
}
add_Photo_URL <- function(in_csv){
#Input CSV data frame, appends photourl column
#Requires SKU (UPC) assumes no photourl column
out_csv <- mutate(in_csv, photo =
get_photo_url_from_product_info_output(
getProductInfo(SKU)
)
)
}
return (out_csv)
}
#Call it
new <- add_Photo_URL(old)
My thinking was that R would simply input the SKU of the from the row, and put it through the double function call "as is", and the vectorized DPLYR function mutate would just vectorize it. Unfortunately I was running into all sorts of problems I couldn't understand. Eventually I figured out that API call was crashing because the SKU field was all messed up as it was being passed in. I put in a breakpoint and found out that it wasn't just passing in the SKU, but instead an entire list (I think?) of SKUs. Every Row all at once. Something like this:
#Variable 'barcode' inside getProductInfo function contains:
[1] 7.869368e+11 1.438175e+10 1.256983e+10 2.454357e+10 3.139814e+10 1.256983e+10 1.313260e+10 4.339643e+10 2.454328e+10
[10] 1.313243e+10 6.839046e+11 2.454367e+10 2.454363e+10 2.454367e+10 2.454348e+10 8.418870e+11 2.519211e+10 2.454375e+10
[19] 2.454381e+10 2.454381e+10 2.454383e+10 2.454384e+10 7.869368e+11 2.454370e+10 2.454390e+10 1.913290e+11 2.454397e+10
[28] 2.454399e+10 2.519202e+10 2.519205e+10 7.742121e+11 8.839291e+11 8.539116e+10 2.519211e+10 2.519211e+10 2.519211e+10
Obviously my initial getProductInfo function can't handle that, so it'll crash.
How should I modify my code, whether it be in the input or API call to avoid this vectorized operation issue?
Well, it's not totally elegant but it works.
I figured out I need to use lapply, which is usually not my strong suit. Initally I tried to nest them like so:
lapply(SKU, get_photo_url_from_product_info_output(getProductInfo())
But that didn't work. So I just came up with bright idea of making another function
get_photo_url_from_sku <- function(barcode){
return(get_photo_url_from_product_info_output(getProductInfo(barcode)))
}
Call that in the lapply:
out_csv<- mutate(in_csv, photocolumn = lapply(SKU, get_photo_url_from_sku))
And it works great. My speed is only limited by my API calls.
Related
Having some trouble understanding how to create a Perl hash from a DB select statement.
$sth=$dbh->prepare(qq{select authorid,titleid,title,pubyear from books});
$sth->execute() or die DBI->errstr;
while(#records=$sth->fetchrow_array()) {
%Books = (%Books,AuthorID=> $records[0]);
%Books = (%Books,TitleID=> $records[1]);
%Books = (%Books,Title=> $records[2]);
%Books = (%Books,PubYear=> $records[3]);
print qq{$records[0]\n}
print qq{\t$records[1]\n};
print qq{\t$records[2]\n};
print qq{\t$records[3]\n};
}
$sth->finish();
while(($key,$value) = each(%Books)) {
print qq{$key --> $value\n};
}
The print statements work in the first while loop, but I only get the last result in the second key,value loop.
What am I doing wrong here. I'm sure it's something simple. Many thanks.
OP needs better specify the question and do some reading on DBI module.
DBI module has a call for fetchall_hashref perhaps OP could put it to some use.
In the shown code an assignment of a record to a hash with the same keys overwrites the previous one, row after row, and the last one remains. Instead, they should be accumulated in a suitable data structure.
Since there are a fair number of rows (351 we are told) one option is a top-level array, with hashrefs for each book
my #all_books;
while (my #records = $sth->fetchrow_array()) {
my %book;
#book{qw(AuthorID TitleID Title PubYear)} = #records;
push #all_books, \%book;
}
Now we have an array of books, each indexed by the four parameters.
This uses a hash slice to assign multiple key-value pairs to a hash.
Another option is a top-level hash with keys for the four book-related parameters, each having for a value an arrayref with entries from all records
my %books;
while (my #records = $sth->fetchrow_array()) {
push #{$books{AuthorID}}, $records[0];
push #{$books{TitleID}}, $records[1];
...
}
Now one can go through authors/titles/etc, and readily recover the other parameters for each.
Adding some checks is always a good idea when reading from a database.
Each day, I get an email with the quantities of fruit sold on a particular day. The structure of the email is as below:
Date of report:,04-JAN-2022
Time report produced:,5-JAN-2022 02:04
Apples,6
Pears,1
Lemons,4
Oranges,2
Grapes,7
Grapefruit,2
I'm trying to build some code in R that will search through my emails, find all emails with a particular subject, iterate through each email to find the variables I'm looking for, take the values and place them in a dataframe with the "Date of report" put in a date column.
With the assistance of people in the community, I was able to achieve the desired result in Python. However as my project has developed, I need to now achieve the same result in R if at all possible.
Unfortunately, I'm quite new to R and therefore if anyone has any advice on how to take this forward I would greatly appreciate it.
For those interested, my Python code is below:
#PREP THE STUFF
Fruit_1 = "Apples"
Fruit_2 = "Pears"
searchf = [
Fruit_1,
Fruit_2
]
#DEF THE STUFF
def get_report_vals(report, searches):
dct = {}
for line in report:
term, *value = line
if term.casefold().startswith('date'):
dct['date'] = pd.to_datetime(value[0])
elif term in searches:
dct[term] = float(value[0])
if len(dct.keys()) != len(searches):
dct.update({x: None for x in searches if x not in dct})
return dct
#DO THE STUFF
outlook = win32com.client.Dispatch("Outlook.Application").GetNamespace("MAPI")
inbox = outlook.GetDefaultFolder(6)
messages = inbox.Items
messages.Sort("[ReceivedTime]", True)
results = []
for message in messages:
if message.subject == 'FRUIT QUANTITIES':
if Fruit_1 in message.body and Fruit_2 in message.body:
data = [line.strip().split(",") for line in message.body.split('\n')]
results.append(get_report_vals(data, searchf))
else:
pass
fruit_vals = pd.DataFrame(results)
fruit_vals.columns = map(str.upper, fruit_vals.columns)
I'm probably going about this the wrong way, but I'm trying to use the steps I took in Python to achieve the same result in R. So for example I create some variables to hold the fruit sales I'm searching for, then I create a vector to store the searchables, and then when I create an equivalent 'get_vals' function, I create an empty vector.
library(RDCOMClient)
Fruit_1 <- "Apples"
Fruit_2 <- "Pears"
##Create vector to store searchables
searchf <- c(Fruit_1, Fruit_2)
## create object for outlook
OutApp <- COMCreate("Outlook.Application")
outlookNameSpace = OutApp$GetNameSpace("MAPI")
search <- OutApp$AdvancedSearch("Inbox", "urn:schemas:httpmail:subject = 'FRUIT QUANTITIES'")
inbox <- outlookNameSpace$Folders(6)$Folders("Inbox")
vec <- c()
for (x in emails)
{
subject <- emails(i)$Subject(1)
if (grepl(search, subject)[1])
{
text <- emails(i)$Body()
print(text)
break
}
}
read.table could be a good start for get_report_vals.
Code below outputs result as a list, exception handling still needs to be implemented :
report <- "
Date of report:,04-JAN-2022
Apples,6
Pears,1
Lemons,4
Oranges,2
Grapes,7
Grapefruit,2
"
get_report_vals <- function(report,searches) {
data <- read.table(text=report,sep=",")
colnames(data) <- c('key','value')
# find date
date <- data[grepl("date",data$key,ignore.case=T),"value"]
# transform dataframe to list
lst <- split(data$value,data$key)
# output result as list
c(list(date=date),lst[searches])
}
get_report_vals(report,c('Lemons','Oranges'))
$date
[1] "04-JAN-2022"
$Lemons
[1] "4"
$Oranges
[1] "2"
The results of various reports can then be concatenated in a data.frame using rbind:
rbind(get_report_vals(report,c('Lemons','Oranges')),get_report_vals(report,c('Lemons','Oranges')))
date Lemons Oranges
[1,] "04-JAN-2022" "4" "2"
[2,] "04-JAN-2022" "4" "2"
The code now functions as intended. Function was written quite a bit differently from those recommended:
get_vals <- function(email) {
body <- email$body()
date <- str_extract(body, "\\d{2}-[:alpha:]{3}-\\d{4}") %>%
as.character()
data <- read.table(text = body, sep = ",", skip = 9, strip.white = T) %>%
row_to_names(1) %>%
mutate("Date" = date)
return(data)
}
In addition I've written this to bind the rows together:
info <- sapply(results, get_vals, simplify = F) %>%
bind_rows()
May this is not what you are expecting to get as an answer, but I must state that here to help other readers to avoid such mistakes in future.
Unfortunately your Python code is not well-written. For example, I've noticed the following code where you iterate over all items in a folder and check the Subject and message bodies for keywords:
for message in messages:
if message.subject == 'FRUIT QUANTITIES':
if Fruit_1 in message.body and Fruit_2 in message.body:
You need to use the Find/FindNext or Restrict methods of the Items class instead. So, you don't need to iterate over all items in a folder. Instead, you get only items that correspond to your conditions. Read more about these methods in the following articles:
How To: Use Find and FindNext methods to retrieve Outlook mail items from a folder (C#, VB.NET)
How To: Use Restrict method to retrieve Outlook mail items from a folder
You may combine all your search criteria into a single query. So, you just need to iterate over found items and extract the data.
Also you may find the AdvancedSearch method helpful. The key benefits of using the AdvancedSearch method in Outlook are:
The search is performed in another thread. You don’t need to run another thread manually since the AdvancedSearch method runs it automatically in the background.
Possibility to search for any item types: mail, appointment, calendar, notes etc. in any location, i.e. beyond the scope of a certain folder. The Restrict and Find/FindNext methods can be applied to a particular Items collection (see the Items property of the Folder class in Outlook).
Full support for DASL queries (custom properties can be used for searching too). You can read more about this in the Filtering article in MSDN. To improve the search performance, Instant Search keywords can be used if Instant Search is enabled for the store (see the IsInstantSearchEnabled property of the Store class).
You can stop the search process at any moment using the Stop method of the Search class.
See Advanced search in Outlook programmatically: C#, VB.NET for more information.
I am trying to change a variable in a function but even tho the function is producing the right values, when I go to use them in the next sections, R is still using the initial values.
I created a function to update my variables NetN and NetC:
Reproduction=function(NetN,NetC,cnrep=20){
if(NetC/NetN<=cnrep) {
DeltaC=NetC*p;
DeltaN=DeltaC/cnrep;
Crep=Crep+DeltaC;
Nrep=Nrep+DeltaN;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN-DeltaN; #/* Update N, C values */
NetC=NetC*(1-p)
print ("'Using C to allocate'")
}
else {
print("Using N to allocate");
DeltaN=NetN*p;
DeltaC=DeltaN*cnrep;
Nrep=Nrep+DeltaN;
Crep=Crep+DeltaC;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN*(1-p);
NetC=NetC-DeltaC;
} } return(c(NetC=NetC,NetN=NetN,NewB=NewB,Crep=Crep,Nrep=Nrep,Brep=Brep))}
When I use my function by say doing:
Reproduction(NetN=1.07149,NetC=0.0922349,cnrep=20)
I get the desired result printed out which includes:
NetC=7.378792e-02
However, when I go to use NetC in the next section of my code, R is still using NetC=0.0922349.
Can I make R update NetC without having to define a new variable?
In R, in general, functions shouldn't change things outside of the function. It's possible to do so using <<- or assign(), but this generally makes your function inflexible and very surprising.
Instead, functions should return values (which yours does nicely), and if you want to keep those values, you explicitly use <- or = to assign them to objects outside of the function. They way your function is built now, you can do that like this:
updates = Reproduction(NetN = 1.07149, NetC = 0.0922349, cnrep = 20)
NetC = updates["NetC"]
This way, you (a) still have all the other results of the function stored in updates, (b) if you wanted to run Reproduction() with a different set of inputs and compare the results, you can do that. (If NetC updated automatically, you could never see two different values), (c) You can potentially change variable names and still use the same function, (d) You can run the function to experiment/see what happens without saving/updating the values.
If you generally want to keep NetN, NetC, and cnrep in sync, I would recommend keeping them together in a named vector or list, and rewriting your function to take that list as input and return that list as output. Something like this:
params = list(NetN = 1.07149, NetC = 0.0922349, cnrep = 20)
Reproduction=function(param_list){
NetN = param_list$NetN
NetC = param_list$NetC
cnrep = param_list$cnrep
if(NetC/NetN <= cnrep) {
DeltaC=NetC*p;
DeltaN=DeltaC/cnrep;
Crep=Crep+DeltaC;
Nrep=Nrep+DeltaN;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN-DeltaN; #/* Update N, C values */
NetC=NetC*(1-p)
print ("'Using C to allocate'")
}
else {
print("Using N to allocate");
DeltaN=NetN*p;
DeltaC=DeltaN*cnrep;
Nrep=Nrep+DeltaN;
Crep=Crep+DeltaC;
Brep=(Nrep*14+Crep*12)*2/1e6;
NetN=NetN*(1-p);
NetC=NetC-DeltaC;
}
## Removed extra } and ) ??
return(list(NetC=NetC, NetN=NetN, NewB=NewB, Crep=Crep, Nrep=Nrep, Brep=Brep))
}
This way, you can use the single line params <- Reproduction(params) to update everything in your list. You can access individual items in the list with either params$Netc or params[["NetC"]].
i tried updating data in dataframe but its unable to get updating
//Initialize data and dataframe here
user_data=read.csv("train_5.csv")
baskets.df=data.frame(Sequence=character(),
Challenge=character(),
countno=integer(),
stringsAsFactors=FALSE)
/Updating data in dataframe here
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i]==user_data$challenge_sequence[j]&&user_data$challenge[i]==user_data$challenge[j])
{
writedata(user_data$challenge_sequence[i],user_data$challenge[i])
}
}
}
writedata=function( seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence=seqnn,Challenge=challng,countno=1)
baskets.df=rbind(baskets.df,newRow)
}
//view data here
View(baskets.df)
I've modified your code to what I believe will work. You haven't provided sample data, so I can't verify that it works the way you want. I'm basing my attempt here on a couple of common novice mistakes that I'll do my best to explain.
Your writedata function was written to be a little loose with it's scope. When you create a new function, what happens in the function technically happens in its own environment. That is, it tries to look for things defined within the function, and then any new objects it creates are created only within that environment. R also has this neat (and sometimes tricky) feature where, if it can't find an object in an environment, it will try to look up to the parent environment.
The impact this has on your writedata function is that when R looks for baskets.df in the function and can't find it, R then turns to the Global Environment, finds baskets.df there, and then uses it in rbind. However, the result of rbind gets saved to a baskets.df in the function environment, and does not update the object of the same name in the global environment.
To address this, I added an argument to writedata that is simply named data. We can then use this argument to pass a data frame to the function's environment and do everything locally. By not making any assignment at the end, we implicitly tell the function to return it's result.
Then, in your loop, instead of simply calling writedata, we assign it's result back to baskets.df to replace the previous result.
for(i in 1:length((user_data)))
{
for(j in i:length(user_data))
{
if(user_data$challenge_sequence[i] == user_data$challenge_sequence[j] &&
user_data$challenge[i] == user_data$challenge[j])
{
baskets.df <- writedata(baskets.df,
user_data$challenge_sequence[i],
user_data$challenge[i])
}
}
}
writedata=function(data, seqnn,challng)
{
#print(seqnn)
#print(challng)
newRow <- data.frame(Sequence = seqnn,
Challenge = challng,
countno = 1)
rbind(data, newRow)
}
I'm not sure what you're programming background is, but your loops will be very slow in R because it's an interpreted language. To get around this, many functions are vectorized (which simply means that you give them more than one data point, and they do the looping inside compiled code where the loops are fast).
With that in mind, here's what I believe will be a much faster implementation of your code
user_data=read.csv("train_5.csv")
# challenge_indices will be a matrix with TRUE at every place "challenge" and "challenge_sequence" is the same
challenge_indices <- outer(user_data$challenge_sequence, user_data$challenge_sequence, "==") &
outer(user_data$challenge, user_data$challenge, "==")
# since you don't want duplicates, get rid of them
challenge_indices[upper.tri(challenge_indices, diag = TRUE)] <- FALSE
# now let's get the indices of interest
index_list <- which(challenge_indices,arr.ind = TRUE)
# now we make the resulting data set all at once
# this is much faster, because it does not require copying the data frame many times - which would be required if you created a new row every time.
baskets.df <- with(user_data, data.frame(
Sequence = challenge_sequence[index_list[,"row"]],
challenge = challenge[index_list[,"row"]]
)
Been going around for hours with this. My 1st question online on R. Trying to creat a function that contains a loop. The function takes a vector that the user submits like in pollutantmean(4:6) and then it loads a bunch of csv files (in the directory mentioned) and binds them. What is strange (to me) is that if I assign the variable id and then run the loop without using a function, it works! When I put it inside a function so that the user can supply the id vector then it does nothing. Can someone help ? thank you!!!
pollutantmean<-function(id=1:332)
{
#read files
allfiles<-data.frame()
id<-str_pad(id,3,pad = "0")
direct<-"/Users/ped/Documents/LearningR/"
for (i in id) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
}
Your function is missing a return value. (#Roland)
pollutantmean<-function(id=1:332) {
#read files
allfiles<-data.frame()
id<-str_pad(id,3,pad = "0")
direct<-"/Users/ped/Documents/LearningR/"
for (i in id) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
return(allfiles)
}
Edit:
Your mistake was that you did not specify in your function what you want to get out from the function. In R, you create objects inside of function (you could imagine it as different environment) and then specify which object you want it to return.
With my comment about accepting my answer, I meant this: (...To mark an answer as accepted, click on the check mark beside the answer to toggle it from greyed out to filled in...).
Consider even an lapply and do.call which would not need return being last line of function:
pollutantmean <- function(id=1:332) {
id <- str_pad(id,3,pad = "0")
direct_files <- paste0("/Users/ped/Documents/LearningR/", id, ".csv")
# READ FILES INTO LIST AND ROW BIND
allfiles <- do.call(rbind, lapply(direct_files, read.csv))
}
ok, I got it. I was expecting the files that are built to be actually created and show up in the environment of R. But for some reason they don't. But R still does all the calculations. Thanks lot for the replies!!!!
pollutantmean<-function(directory,pollutant,id)
{
#read files
allfiles<-data.frame()
id2<-str_pad(id,3,pad = "0")
direct<-paste("/Users/pedroalbuquerque/Documents/Learning R/",directory,sep="")
for (i in id2) {
path<-paste(direct,"/",i,".csv",sep="")
file<-read.csv(path)
allfiles<-rbind(allfiles,file)
}
#averaging polutants
mean(allfiles[,pollutant],na.rm = TRUE)
}
pollutantmean("specdata","nitrate",23:35)