I would like to combine two data frames. One is information for birds banded. The other is information on recovered banded birds. I would like to add the recovery data to the banding data, if the bird was recovered (not all birds were recovered). Unfortunately the full band number is not included in the banding data, only in the recovery data, so there is not a unique column to join them by.
One looks like this:
GISBLong
GISBLat
B Flyway
B Month
B Year
Band Prefix Plus
-85.41667
42.41667
8
5
2001
12456
-85.41655
36.0833
9
6
2003
21548
The other looks like this:
GISBLong
GISBLat
B Flyway
B Month
B Year
Band
R Month
R Year
-85.41667
42.41667
8
5
2001
124565482
12
2002
-85.41655
36.0833
9
6
2003
215486256
1
2004
I have tried '''merge''', '''ifelse''', '''dplyr-join''' with no luck. Any suggestions? Thanks in advance!
you should look up rbind(). That might do the trick. For it to work the data frames have to have the same columns. I'd suggest you to add missing columns to your first DF with dplyr::mutate() and later on eliminate useless rows.
Related
I have a panel data of stock returns, where after a certain year the coverage universe of stocks doubled. It looks a bit like this:
Year Stock 1 Stock 2 Stock 3 Stock 4
2000 5.1% 0.04% NA NA
2001 3.6% 9.02% NA NA
2002 5.0% 12.09% NA NA
2003 -2.1% -9.05% 1.1% 4.7%
2004 7.1% 1.03% 4.2% -1.1%
.....
Of course, I am trying to maximize my observations both in the time series and in the cross-section as much as possible. However, I am not sure which of these 3 ways to sort would be the most "academically honest":
Sort the years until 2001 using only stocks 1 and 2, and incorporate the remaining stocks in the calculations once they become available in 2003.
Only include those stocks in calculations that have been available since 2000, i.e. stocks 1 and 2. Ignore the remaining stocks altogether since we do not have the full return profile.
Start the sort in year 2003, to have a larger cross-section.
The reason why our coverage universe expands in 2003 is simply because the data provider I am using changed their methodology in that year and decided to track more stocks. Stocks 3 and 4 do exist before 2003, but I cannot use their past return data since I need to follow my data provider (for the second variable I am sorting on).
Thanks all!
I am using the portsort() package in R but this does not seem to work well with NA`s.
I am currently doing k-means to cluster my data, however, I wish each cluster to appear once in each given year. I have searched for answers for a whole night but with no result. Would anyone have ideas upon this problem using R? Or is there any package I should look for ? Thanks.
More background infos :
I try to replicated the cluster of relationships, using the reported gender, education level and birth year. I am doing this because this is a survey data whose respondents are old people and they sometime will report inaccurate age or education infos. My main challenge now is that I wish to "have only one cluster labels in each survey year". For example, I do not want to see there are two cluster3 in survey year 2000. My data is like below :
survey year
relationship
gender
education level
birth year
k-means cluster
2000
41( first daughter)
0
3
1997
1
2003
41( first daughter)
0
3
1997
1
2000
42( second daughter)
0
4
1999
2
2003
42( second daughter)
0
4
1999
2
2000
42( third daughter)
0
5
1999
2
2003
42( third daughter)
0
5
2001
3
Thanks in advance.
--Update--
A more detailed description of the task:
The data set is a panel survey data asking elders for their health status, their relationships ( incl. sons, daughters, neighbors ). Since these older people are sometimes imprecise on their family's demographic information such as birth year, education level, etc., we might need to delete a big part of the data if it did not match.
(e.g., A reported his first son is 30 years old in 1997, while said his first son was 29 years old in 1999, this data could therefore be problematic). My task is to save as much data as possible if the imprecision is not that high.
Therefore I first mutated columns to check the precision of each family member (e.g., birth year error %in% c(-1,2)). Next, I run k-means if the family members are detected to be imprecise. In this way, I save much of the data. Although I did not solve the above problem, it rarely occurs that I can almost ignore or drop these observations.
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
I would like to ask a question regarding fuzzyjoin package. I am very new to R, and I promise I have read through the readme file and followed through examples on https://cran.r-project.org/web/packages/fuzzyjoin/index.html before I asked this question.
I have a list of vernacular names which I wanted to match with plant species names. A simple version of my list will look like below. Data 1 has a LocalName column with many typos of vernacular name. Data 2 is the table with correct local name and species where the matching should be based on.
data1 <- data.frame(Item=1:5, LocalName=c("BACTERIA F", "BAHIA", "BAIKEA", "BAIKIA", "BAIKIAEA SP"))
data 1
Item LocalName
1 1 BACTERIA F
2 2 BAHIA
3 3 BAIKEA
4 4 BAIKIA
5 5 BAIKIAEA SP
data2 <- data.frame(LocalName=c("ENGOKOM","BAHIA","BAIKIA","BANANIER","BALANITES"), Species=c("Barteria fistulosa","Mitragyna spp","Baikiaea spp", "Musa spp", "Balanites wilsoniana"))
data2
LocalName Species
1 ENGOKOM Barteria fistulosa
2 BAHIA Mitragyna spp
3 BAIKIA Baikiaea spp
4 BANANIER Musa spp
5 BALANITES Balanites wilsoniana
I tried using the stringdist_left_join function, and it managed to match many species correctly. I am being conservative by setting max_dist=1 because in my list, many vernacular names are very similar.
library(fuzzyjoin)
table <- data1%>%
stringdist_left_join(data2, by=c(LocalName="LocalName"), max_dist=1)
table
Item LocalName.x LocalName.y Species
1 1 BACTERIA F <NA> <NA>
2 2 BAHIA BAHIA Mitragyna spp
3 3 BAIKEA BAIKIA Baikiaea spp
4 4 BAIKIA BAIKIA Baikiaea spp
5 5 BAIKIAEA SP <NA> <NA>
However, I have one question. As you can see from data1, the Item 5 BAIKIAEA SP actually matches with the Species column of data2 instead of LocalName. I have many entries like this where the LocalName in data 1 were either typos of vernacular names or species name, however, I am not sure how to make stringdist_left_join matches two columns of data 2 with one column of data 1. I tried modifying the codes into something like this:
table <- data1%>%
stringdist_left_join(data2, by=c(LocalName="LocalName"|"Species"), max_dist=1)
but it did not work, citing "Error in "LocalName" | "Species" :
operations are possible only for numeric, logical or complex types". Does anyone know whether such matching is possible? Thanks in advance!
I've got a set of 20 or so consecutive individual-level cross-sectional data sets which I would like to link together.
Unfortunately, there's no time-stable ID number; there are, however, fields for first, last, and maiden names, as well as year of birth--this should allow for a pretty high (90-95%) match rate, I presume.
Ideally, I would create a time-independent ID for each unique individual.
I can do this for those whose marital status (maiden name) does not change pretty easily in R--stack the data sets to get a long panel, then do something to the effect of:
unique(dt,by=c("first_name","last_name","birth_year"))[,id:=.I]
(I'm of course using R data.table), then merging back to the full data.
However, I'm stuck on how to incorporate the maiden name to this procedure. Any suggestions?
Here's a preview of the data:
first_name last_name nee birth_year year
1: eileen aaldxxxx dxxxx 1977 2002
2: eileen aaldxxxx dxxxx 1977 2003
3: sarah aaxxxx gexxxx 1974 2003
4: kelly aaxxxx nxxxx 1951 2008
5: linda aarxxxx-gxxxx aarxxxx 1967 2008
---
72008: stacey zwirxxxx kruxxxx 1982 2010
72009: stacey zwirxxxx kruxxxx 1982 2011
72010: stacey zwirxxxx kruxxxx 1982 2012
72011: stacey zwirxxxx kruxxxx 1982 2013
72012: jill zydoxxxx gundexxxx 1978 2002
UPDATE:
I've done a lot of chipping and hammering at the problem; here's what I've got so far. I would appreciate any comments for possible improvements to the code so far.
I'm still completely missing something like 3-5% of matches due to inexact matches ("tonya" vs. "tanya", "jenifer" vs. "jennifer"); I haven't come up with a clean way of doing fuzzy matching on the stragglers, so there's room for better matching in that direction if anyone's got a straightforward way to implement that.
The basic approach is to build cumulatively--assign IDs in the first year, then look for matches in the second year; assign new IDs to the unmatched. Then for year 3, look back at the first 2 years, etc. As to how to match, the idea is to slowly expand the matching criteria--the idea being that the more robust the match, the lower the chances of mismatching accidentally (particularly worried about the John Smiths).
Without further ado, here's the main function for matching a pair of data sets:
get_id<-function(yr,key_from,key_to=key_from,
mdis,msch,mard,init,mexp,step){
#Want to exclude anyone who is matched
existing_ids<-full_data[.(yr),unique(na.omit(teacher_id))]
#Get the most recent prior observation of all
# unmatched teachers, excluding those teachers
# who cannot be uniquely identified by the
# current key setting
unmatched<-
full_data[.(1996:(yr-1))
][!teacher_id %in% existing_ids,
.SD[.N],by=teacher_id,
.SDcols=c(key_from,"teacher_id")
][,if (.N==1L) .SD,keyby=key_from
][,(flags):=list(mdis,msch,mard,init,mexp,step)]
#Merge, reset keys
setkey(setkeyv(
full_data,key_to)[year==yr&is.na(teacher_id),
(update_cols):=unmatched[.SD,update_cols,with=F]],
year)
full_data[.(yr),(update_cols):=lapply(.SD,function(x)na.omit(x)[1]),
by=id,.SDcols=update_cols]
}
Then I basically go through the 19 years yy in a for loop, running 12 progressively looser matches, e.g. step 3 is:
get_id(yy,c("first_name_clean","last_name_clean","birth_year"),
mdis=T,msch=T,mard=F,init=F,mexp=F,step=3L)
The final step is to assign new IDs:
current_max<-full_data[.(yy),max(teacher_id,na.rm=T)]
new_ids<-
setkey(full_data[year==yy&is.na(teacher_id),.(id=unique(id))
][,add_id:=.I+current_max],id)
setkey(setkey(full_data,id)[year==yy&is.na(teacher_id),
teacher_id:=new_ids[.SD,add_id]],year)