How do I index rows I need by with specifications?
id<-c(65,65,65,65,65,900,900,900,900,900,900,211,211,211,211,211,211,211,45,45,45,45,45,45,45)
age<-c(19,22,23,24,25,21,26,31,32,37,38,22,23,25,28,29,31,32,30,31,36,39,42,44,48)
stat<-c('intern','reg','manage1','left','reg','manage1','manage2','left','reg',
'reg','left','intern','left','intern','reg','left','reg','manage1','reg','left','intern','manage1','left','reg','manage2')
mydf<-data.frame(id,age,stat)
I need to create 5 variables:
m01time & m12time: measure the amount of years elapsed before becoming a level1 manager (manage1), and then since manage1 to manage2 regardless of whether or not it's at the same job. (numeric in years)
change: capture whether or not they experienced a job change between manage1 and manage2 (if 'left' happens somewhere in between manage1 and manage2), (0 or 1)
& 4: m1p & m2p: capture the position before becoming manager1 and manager2 (intern, reg, or manage1).
There's a lot of information I don't need here that I am not sure how to ignore (all the jobs 211 went through before going to one where they become a manager).
The end result should look something like this:
id m01time m02time change m1p m2p
1 65 4 NA NA reg <NA>
2 900 NA 5 0 <NA> manage1
3 211 1 NA NA reg <NA>
4 45 3 9 1 intern reg
I tried to use ifelse with lag() and lead() to capture some conditions, but there are more for loop type of jobs (such as how to capture a "left" somewhere in between) that I am not sure what to do with.
I'd calculate the variables the first three variables differently than m1p and m2p. Maybe there's an elegant unified approach that I don't see at the moment.
So for the last position before manager you could do:
mydt <- data.table(mydf)
mydt[,.(m1p=stat[.I[stat=="manage1"]-1],
m2p=stat[.I[stat=="manage2"]-1]),by=id]
The other variables are more conveniently calculated in a wide data.format:
dt <- dcast(unique(mydt,by=c("id","stat")),
formula=id~stat,value.var="age")
dt[,.(m01time = manage1-intern,
m12time = manage2-manage1,
change = manage1<left & left<manage2)]
Two caveats:
reshaping might be quite costly larger data sets
I (over-)simplified your dummy data by ignoring duplicates of id and stat
Firstly: I have seen other posts about AVERAGEIF translations from excel into R but I didn't see one that worked on my specific case and I couldn't get around to making one work.
I have a dataset which encompasses the daily pricings of a bunch of listings.
It looks like this
listing_id date price
1 1000 1/2/2015 $100
2 1200 2/4/2016 $150
Sample of the dataset (and desired outcome) # https://send.firefox.com/download/228f31e39d18738d/#rlMmm6UeGxgbkzsSD5OsQw
The dataset I would like to have has only the date and the average prices of all listings on that date. The goal is to get a (different) dataframe which would look something like this so I can work with it:
Date Average Price
1 4/5/2015 204.5438
2 4/6/2015 182.6439
3 4/7/2015 176.553
4 4/8/2015 182.0448
5 4/9/2015 183.3617
6 4/10/2015 205.0997
7 4/11/2015 197.0118
8 4/12/2015 172.2943
I created this in Excel using the Average.if function (and copy pasting by value) from the sample provided above.
I tried to format the data in Excel first where I could use the AVERAGE.IF function saying take the average if it is this specific date. The problem with this is that the dataset consists of 30million rows and excel only allows for 1 million so it didn't work.
What I have done so far: I created a data frame in R (where i want the average prices to go into) using
Avg = data.frame("Date" =1:2, "Average Price"=1:2)
Avg[nrow(Avg) + 2036,] = list("v1","v2")
Avg$Date = seq(from = as.Date("2015-04-05"), to = as.Date("2020-11-01"), by = 'day')
I tried to create an averageif-like function by this article and another but could not get it to work.
I hope this is enough information to go on otherwise I would be more than happy to provide more.
If your question is how to replicate the AVERAGEIF function, you can use logical indexing :
R code :
> df
Dates Prices
1 1 100
2 2 120
3 3 150
4 1 320
5 2 250
6 3 210
7 1 102
8 2 180
9 3 150
idx <- df$Dates == 1 # Positions where condition is true
mean(df$Prices[idx]) # Prints same output as Excel
Disclaimer: I am not that advanced with R Studio and hence my question might be quite self explanatory.
Lets assume the following data set
**ID value1a value2a value1b value2b ...
1 2 3 ...
8 4 4
2 5 5
I want to create a forth variable that is part of the expression of an if sentence, that logically should go as follows:
If ID = 1 is over 5 in "value1x" and below 3 in "value2x", then add the value 1 to this forth variable. Hence the forth variable should function as a counter, that the number in the forth variable indiciates the frequency of value1x being over 5 and value2x being below 3.
I hope my question makes sense and Id appreciate answers!
I have a data.table that looks like this:
dt
id month balance
1: 1 4 100
2: 1 5 50
3: 2 4 200
4: 2 5 135
5: 3 4 100
6: 3 5 100
7: 4 5 300
"id" is the client's ID, "month" indicates what month it is, and "balance" indicates the account balance of a client. In a sense, this is longitudinal data where, say, element (2,3) indicates that Client #1 has an account balance of 50 at the end of month 5.
I want to generate a column that will give me the difference between a client's balance between month 5 and 4 to know the transactions carried out from one month to another.
This new variable should let me know that Client 1 drew 50, Client 2 drew 65 and Client 3 didn't do anything in aggregate terms between april and may. Client 4 is a new client that joined in may.
I thought of the following code:
dt$transactions <- dt$balance - shift(dt$balance, 1, "up")
However, it does not work properly because it's telling me that Client 4 made a 200 dollar deposit (but Client 4 is new!). Therefore, I want to be able to introduce the argument "by=id" to this somehow.
I know the solution lies in using the following notation:
dt[, transactions := balance - shift(balance, ??? ), by=id]
I just need to figure out how to make the aforementioned code work properly.
Thanks in advance.
Given that I only have two observations (at most), the following code gives me an elegant solution:
dt[, transaction := balance - first(balance), by = id]
This prevents any NAs from entering the variable transaction.
However, if I had more observations per id, I would do the following:
dt[,transaction := balance - shift(balance,1), by = id]
Big thanks to #Ryan and #Onyambu for helping.
In general, I know how to delete rows in R. However, for this particular requirement, I am unsure how to proceed. Here is an idea of what I need to do with data:
ID MONTH INCOME
1. 00000012 6 60
2. 00000012 8 65
3. 00000015 12 70
4. 00000025 4 45
5. 00000025 8 60
6. 00000032 6 10
7. 00000035 6 30
Quick explanation of each column:
The first 7 digits of ID identify an agent. So, in row one, 00000012 means agent 1. The last digit is the interview number. So, in row three, 00000015 means agent 1, interview 5.
Month and income are straightforward.
What Must Be Done
I need to delete every ID that does not include both a 2nd and 5th interview.
I need to only have the max. month for the 2nd interview, and 5th interview for each ID.
So, if I cleaned the data properly, I would have:
ID MONTH INCOME
2. 00000012 8 65
3. 00000015 12 70
6. 00000032 6 10
7. 00000035 6 30
Notice row 4,5 are gone because there was no 2nd interview for agent 2. Row 1 is gone because there was a higher month for agent 1, interview 2.
My current thoughts how to do this seem overly complex. I am thinking of breaking ID into two columns, one with the first 7 digits, another column with the last digit. Then, loop through the entire data, and at each row, run another loop to see if the ID that corresponds to the row has both an interview 2 and interview 5. If it does, fine. If it doesn't, I then have to delete all rows with that ID.
Next, I have to do a similar thing for deleting non-max months.
I feel like I could do the above, but it is very cumbersome. Is there a better way to do this? Thank you.
You can do something like that:
library(stringi)
Agents <- substr(df$ID,1,nchar(df$ID)-1 )
A2 <- stri_endswith_fixed(df$ID,"2", fixed = T)
A5 <- stri_endswith_fixed(df$ID,"5", fixed = T)
A2and5 <- intersect(Agents[A5], Agents[A2])
df[Agents %in% A2and5,]