This is a seemingly simple R question, but I don't see an exact answer here. I have a data frame (alldata) that looks like this:
Case zip market
1 44485 NA
2 44488 NA
3 43210 NA
There are over 3.5 million records.
Then, I have a second data frame, 'zipcodes'.
market zip
1 44485
1 44486
1 44488
... ... (100 zips in market 1)
2 43210
2 43211
... ... (100 zips in market 2, etc.)
I want to find the correct value for alldata$market for each case based on alldata$zip matching the appropriate value in the zipcode data frame. I'm just looking for the right syntax, and assistance is much appreciated, as usual.
Since you don't care about the market column in alldata, you can first strip it off using and merge the columns in alldata and zipcodes based on the zip column using merge:
merge(alldata[, c("Case", "zip")], zipcodes, by="zip")
The by parameter specifies the key criteria, so if you have a compound key, you could do something like by=c("zip", "otherfield").
Another option that worked for me and is very simple:
alldata$market<-with(zipcodes, market[match(alldata$zip, zip)])
With such a large data set you may want the speed of an environment lookup. You can use the lookup function from the qdapTools package as follows:
library(qdapTools)
alldata$market <- lookup(alldata$zip, zipcodes[, 2:1])
Or
alldata$zip %l% zipcodes[, 2:1]
Here's the dplyr way of doing it:
library(tidyverse)
alldata %>%
select(-market) %>%
left_join(zipcodes, by="zip")
which, on my machine, is roughly the same performance as lookup.
The syntax of match is a bit clumsy. You might find the lookup package easier to use.
alldata <- data.frame(Case=1:3, zip=c(44485,44488,43210), market=c(NA,NA,NA))
zipcodes <- data.frame(market=c(1,1,1,2,2), zip=c(44485,44486,44488,43210,43211))
alldata$market <- lookup(alldata$zip, zipcodes$zip, zipcodes$market)
alldata
## Case zip market
## 1 1 44485 1
## 2 2 44488 1
## 3 3 43210 2
Related
I'm trying to read and transform many XML files into R data frames (or preferably Tibbles).
All R packages I've tried, unfortunately (XML, flatxml, xmlconvert) failed when I tried to convert the files using built-in functions (e.g. xmltodataframe from the XML Package and xml_to_df from the xmlconvert package), so I have to do it manually with XML2.
Here is my question with a small working example:
# Minimal Working Example
library(tidyverse)
library(xml2)
interimxml <- read_xml("<Subdivision>
<Name>Charles</Name>
<Salary>100</Salary>
<Name>Laura</Name>
<Name>Steve</Name>
<Salary>200</Salary>
</Subdivision>")
names <- xml_text(xml_find_all(interimxml ,"//Subdivision/Name"))
salary <- xml_text(xml_find_all(interimxml ,"//Subdivision/Salary"))
names
salary
# combine in to tibble (doesn't work because of inequal vector lengths)
result <- tibble(names=names,
salary = salary)
result
rbind(names, salary)
From the (made up) XML file you can see that Charles earns 100 dollars, Laura earns nothing ( because of the missing entry, here is the problem) and Steve earns 200 dollars.
What I want xml2 do to is, when querying names and salary nodes is to return an "NA" (or zero which would also be okay), when it finds a name but no corresponding salary entry, so that I would end up a nice table like this:
Name
Salary
Charles
100
Laura
NA
Steve
200
I know that I could modify the "xpath" to only pick up the last value (for Steve), which wouldn't help me, since (in the real data) it could also be the 100th or the 23rd person with missing salary information.
[ I'm aware that Salary Numbers are pulled as character values from the xml file. I would mutate(across(salary, as.double) over columns afterwards.]
Any help is highly appreciated. Thank you very much in advance.
You need to be a bit more careful to match up the names and salaries. Basically first find all the <Name> nodes, then check only if their next sibling is a <Salary> node. If not, then return NA.
nameNodes <- xml_find_all(interimxml ,"//Subdivision/Name")
names <- xml_text(nameNodes)
salary <- map_chr(nameNodes, ~xml_text(xml_find_first(., "./following-sibling::*[1][self::Salary]")))
tibble::tibble(names, salary)
# names salary
# <chr> <chr>
# 1 Charles 100
# 2 Laura NA
# 3 Steve 200
I have just recently started using R for my master thesis. I need to match the ID number (uuid) of dataframe 1 to the investee names in dataframe 2.
Dataframe 1
investee_name uuid
1 Wetpaint e1393508
2 Zoho bf4d7b0e
3 Digg 5f2b40b8
4 Omidyar Network f4d5ab44
5 Facebook df662812
6 Trinity Ventures 7ca12f
Dataframe 2:
investee_name investor_name investor_type
1 Facebook cel organization
2 Facebook Grock Partners organization
3 Facebook Partners organization
4 Photobucket Ventures organization
5 Geni Fund organization
6 Gizmoz Capital organization
As you can see, in Dataframe 2 the investee names appear mutliple times. With VLookup in Excel I could have easily matched the respective IDs from dataframe 1 but for some reason the merging does not work in R.
I have tried the following:
investments_complete <- merge(v2_investments, ID_organizations, by.x= names(v2_investments)[1], by.y= names(ID_organizations)[1])
v2_investments_complete <- (merge(ID_organizations,v2_investments, by = "investee_name"))
for both options it merges the ID colums but I get 0 observations.
At last, I tried this:
v2_investments_merged <- merge(v2_investments, ID_organizations, by.x = "investee_name", by.y = "investee_name", all.x= TRUE)
here the merge works and all needed observations are there but al IDs have the value NA.
Is there any kind of merge function that mirrors the Vlookup that I intend to do? I've spent hours trying to solve this but couldn't, so I would be very grateful for support!
Cheers,
Philipp
It is possible that there are some leading/lagging spaces in the by columns. One option is trimws from base R which would remove the whitespace from both ends (if any)
v2_investments$investee_name <- trimws(v2_investments$investee_name)
ID_organizations$investee_name <- trimws(ID_organizations$investee_name)
Now, the merge should work
Quick question - I have a dataframe (severity) that looks like,
industryType relfreq relsev
1 Consumer Products 2.032520 0.419048
2 Biotech/Pharma 0.650407 3.771429
3 Industrial/Construction 1.327913 0.609524
4 Computer Hardware/Electronics 1.571816 2.019048
5 Medical Devices 1.463415 3.028571
6 Software 0.758808 1.314286
7 Business/Consumer Services 0.623306 0.723810
8 Telecommunications 0.650407 4.247619
if I wanted to pull the relfreq of Medical Devices (row 5) - how could I subset just that value?
I was thinking about just indexing and doing severity$relfreq[[5]], but I'd be using this line in a bigger function where the user would specify the industry i.e.
example <- function(industrytype) {
weight <- relfreq of industrytype parameter
thing2 <- thing1*weight
return(thing2)
}
So if I do subset by an index, is there a way R would know which index corresponds to the industry type specified in the function parameter? Or is it easier/a way to just subset the relfreq column by the industry name?
You would require to first select the row of interest and then keep the 2 column you requested (industryType and relfreq).
There is a great package that allows you to do this intuitively with tidyverse library(tidyverse)
data_want <- severity %>%
subset(industryType =="Medical Devices") %>%
select(industryType, relfreq)
Here you read from left to right with the %>% serving as passing the result to the next step as if nesting.
I think that selecting whole row is better, then choose column which you would like to see.
frame <- severity[severity$industryType == 'Medical Devices',]
frame$relfreq
First of all, I apologize for the title. I really don't know how to succinctly explain this issue in one sentence.
I have a dataframe where each row represents some aspect of a hospital visit by a patient. A single patient might have thousands of rows for dozens of hospital visits, and each hospital visit could account for several rows.
One column is Medical.Record.Number, which corresponds to Patient IDs, and the other is Patient.ID.Visit, which corresponds to an ID for an individual hospital visit. I am trying to calculate the number of hospital visits each each patient has had.
For example:
Medical.Record.Number Patient.ID.Visit
AAAXXX 1111
AAAXXX 1112
AAAXXX 1113
AAAZZZ 1114
AAAZZZ 1114
AAABBB 1115
AAABBB 1116
would produce the following:
Medical.Record.Number Number.Of.Visits
AAAXXX 3
AAAZZZ 1
AAABBB 2
The solution I am currently using is the following, where "data" is my dataframe:
#this function returns the number of unique hospital visits associated with the
#supplied record number
countVisits <- function(record.number){
visits.by.number <- data$Patient.ID.Visit[which(data$Medical.Record.Number
== record.number)]
return(length(unique(visits.by.number)))
}
recordNumbers <- unique(data$Medical.Record.Number)
visits <- integer()
for (record in recordNumbers){
visits <- c(visits, countVisits(record))
}
visit.counts <- data.frame(recordNumbers, visits)
This works, but it is pretty slow. I am dealing with potentially millions of rows of data, so I'd like something efficient. From what little I know about R, I know there's usually a faster way to do things without using a for-loop.
This essentially looks like a table() operation after you take out duplicates. First, some sample data
#sample data
dd<-read.table(text="Medical.Record.Number Patient.ID.Visit
AAAXXX 1111
AAAXXX 1112
AAAXXX 1113
AAAZZZ 1114
AAAZZZ 1114
AAABBB 1115
AAABBB 1116", header=T)
then you could do
tt <- table(Medical.Record.Number=unique(dd)$Medical.Record.Number)
as.data.frame(tt, responseName="Number.Of.Visits") #to get a data.frame rather than named vector (table)
# Medical.Record.Number Number.Of.Visits
# 1 AAABBB 2
# 2 AAAXXX 3
# 3 AAAZZZ 1
Or you could also think of this as an aggregation problem
aggregate(Patient.ID.Visit~Medical.Record.Number, dd, function(x) length(unique(x)))
# Medical.Record.Number Patient.ID.Visit
# 1 AAABBB 2
# 2 AAAXXX 3
# 3 AAAZZZ 1
There are many ways to do this, #MrFlick provided handful of perfectly valid approaches. Personally I'm fond of the data.table package. Its faster on large data frames and I find the logic to be more intuitive than the base functions. I'd check it out if you are having problems with execution time.
library(data.table)
med.dt <- data.table(med_tbl)
num.visits.dt <- med.dt[ , num_visits = length(unique(Patient.ID.Visit)),
by = Medical.Record.Number]
data.Table should be much faster than data.frame on a large tables.
I have a list of boroughs and a list of localities (like this one). Each locality lies in exactly one borough. What's the best way to store this kind of hierarchical structure in R, considerung that I'd like to have a convenient and readable way of accessing these, and using this list to accumulate data on the locality-level to the borough level.
I've come up with the following:
localities <- list("Mitte" = c("Mitte", "Moabit", "Hansaviertel", "Tiergarten", "Wedding", "Gesundbrunnen",
"Friedrichshain-Kreuzberg" = c("Friedrichshain", "Kreuzberg")
)
But I am not sure if this is the most elegant and accessible way.
If I wanted to assign additional information on the localitiy-level, I could do that by replacing the c(...) by some other call, like rbind(c('0201', '0202'), c("Friedrichshain", "Kreuzberg")) if I wanted to add additional information to the borough-level (like an abbreviated name and a full name for each list), how would I do this?
Edit: For example, I'd like to condense a table like this into a borough-wise version.
Hard to know without having a better view on how you intend to use this, but I would strongly recommend moving away from a nested list structure to a data frame structure:
library(reshape2)
loc.df <- melt(localities)
This is what the molten data looks like:
value L1
1 Mitte Mitte
2 Moabit Mitte
3 Hansaviertel Mitte
4 Tiergarten Mitte
5 Wedding Mitte
6 Gesundbrunnen Mitte
7 Friedrichshain Friedrichshain-Kreuzberg
8 Kreuzberg Friedrichshain-Kreuzberg
You can then use all the standard data frame and other computations:
loc.df$population <- sample(100:500, nrow(loc.df)) # make up population
tapply(loc.df$population, loc.df$L1, mean) # population by borough
gives mean population by Borough:
Friedrichshain-Kreuzberg Mitte
278.5000 383.8333
For more complex calculations you can use data.table and dplyr
You can extract all of this data directly into a data.frame using the XML library.
library(XML)
theurl <- "http://en.wikipedia.org/wiki/Boroughs_and_localities_of_Berlin#List_of_localities"
tables<-readHTMLTable(theurl)
boroughs<-tables[[1]]$Borough
localities<-tables[c(3:14)]
names(localities) <- as.character(boroughs)
all<-do.call("rbind", localities)
#Roland, I think you will find data frames superior to lists for the reasons cited earlier, but also because there is other data on the web page you reference. Loading to a data frame will make it easy to go further if you wish. For example, making comparisons based on population density or other items provided "for free" on the page will be a snap from a data frame.