I have a problem that I can't figure out have to solve.
I have 3 (tibble) data-frames with just names of diffrent populations.
df1 is all, unique, surnames in Sweden and a column with a count.
382.492 (unique names * the count) = 10002985 people in df1.
10002985 is then the total population in the this 'experiment'.
df2 is a list of all registered lawyers in Sweden.
6211 lawyers total in the population.
df3 is a list of all people with noble family surnames in Sweden
there are 542 unique names and 46851 people with noble surnames in the population.
We also know that in the lawyer subgroup there is:
106 people lawyer with a noble surname.
Now my problem is that I want to create just one df with all this info.
It should look like this:
The main idea is to create a df with one row per population: 10002985 rows.
noble and lawyer is then a dummy variable where 1 = yes, 0 = no. So for example: for the tot_pop, 46851 people should have noble = 1, and 106 out of that group should have lawyer = 1.
Notice that I don't really care what the names are - I just care about the ratios.
Notice also that the reason why I want to create a new data-frame without the names is because I think this is the only way to solve the problem, at least the easiest. But if anyone insists -- I can upload some sample data from each df.
In the end I want to run some probability tests.
Let me know if the question confusing. Also, let me know if this is a really dumb way to go about this :p
SOLUTION:
It was quite easy once I realized what I was looking for :)
There is probably a more elegant solution.
# pop
pop <- 1:10002985
# noble
n <- c(46851, 9956134)
noble <- rep(1:0, n)
# attorney
a <- c(106,46745, 46745, 9909389)
attorney <- rep(c(1,0,1,0), a)
final_data <- tibble(pop, noble, attorney)
Related
just earlier today I received a very helpful answer for a problem I was running into that allowed me to move onto the next step of one of my projects. However, I got stuck again later on in the project, and I'm wondering if any of you can help me move forward.
Context
Currently, I have a list of data frames that are full of soccer matches called wc_match_dataframes. Here is what one of the data frames looks like:
type_id tourn_id day month year team_A score_A score_B team_B win loss
f wc_1934 27 5 1934 Germany 5 2 Belgium Germany Belgium
I wasn't able to fit the data for the final three columns, draw, drawA, and drawB but basically the draw column is TRUE if the match is a draw, if not, it is FALSE. In the case of a draw, the win and loss columns are just filled by Draw. The drawA column is filled by team_A if the match was a draw, and likewise, the drawB column is filled by team_B.
The type_id is either f or q depending on if the match was a World Cup qualifier or a World Cup finals match. The tourn_id refers to the tournament the match was for, whether it was a qualifier or finals.
There are a total of 39 of these data frames, with a "finals" data frame for each of the 20 World Cup tournaments, and a "qualifiers" data frame for 19 tournaments (the first World Cup did not have qualifying).
What I Want To Do
I'm trying to populate a different list of data frames wc_dataframes with data for each of the 20 World Cups at the country level as opposed to the match level. Each of these twenty data frames will have the countries that made it to the finals of said tournament and their data like so:
Country
Wins in qualifying
Wins in finals
Losses in qualifying
Losses in finals
... and so on.
I have been able to populate the first country column for every World Cup no problem, but I'm running into issues for the rest of the columns.
Here is what I'm doing
This is the unlooped (only works for one World Cup) version of my code that works successfully:
wc_dataframes$wc_1930$fw <- apply(wc_dataframes$wc_1930, MARGIN = 1, function(country)
sum(wc_match_dataframes$`wc_1930 f`$w == country, na.rm = TRUE))
This is successfully populating the finals win column in the wc_dataframes$wc_1930 data frame by counting the number of wins.
Now, when I try and nest this under lapply to do it across all World Cup years like so:
lapply(names(wc_dataframes), function(year)
wc_dataframes$year$fw <- apply(wc_dataframes$year, MARGIN = 1, function(country)
sum(wc_match_dataframes$`year f`$w == country, na.rm = TRUE)))
It does not work for me. I suspect that the issue has to do with defining the year function and running into issues in the sum portion of my code. I come from a background in STATA so I am more used to running for loops and what not. I'm still getting used to R and lists and everything so I really appreciate the help.
Thank you!
Thank you so much in advance for the help, and happy holidays! :)
What you need is to output whatever you have replaced:
lapply(names(wc_dataframes), function(year){
wc_dataframes[[year]]$fw <- apply(wc_dataframes[[year]], MARGIN = 1, function(country)
sum(wc_match_dataframes[[paste(year,'f')]]$w == country, na.rm = TRUE));
wc_dataframes}
)
I want to count the number of words in each row:
Review_ID Review_Date Review_Content Listing_Title Star Hotel_Name
1 1/25/2016 I booked both the Crosby and Four Seasons but decided to cancel the Four Seasons closer to the arrival date based on reviews. Glad I did. The Crosby is an outstanding hotel. The rooms are immaculate and luxurious, with real attention to detail and none of the bland furnishings you find in even the top chain hotels. Staff on the whole were extremely attentive and seemed to enjoy being there. Breakfast was superb and facilities at ground level gave an intimate and exclusive feel to the hotel. It's a fairly expensive place to stay but is one of those hotels where you feel you're getting what you pay for, helped by an excellent location. Hope to be back! Outstanding 5 Crosby Street Hotel
2 1/18/2016 We've stayed many times at the Crosby Street Hotel and always have an incredible, flawless experience! The staff couldn't be more accommodating, the housekeeping is immaculate, the location's awesome and the rooms are the coolest combination of luxury and chic. During our most recent trip over The New Years holiday, we stayed in the stunning Crosby Suite which has the most extraordinary, gorgeous decor. The Crosby remains our absolute favorite in NYC. Can't wait to return! Always perfect! 5 Crosby Street Hotel
I was thinking something like:
WordFreqRowWise %>%
rowwise() %>%
summarise(n = n())
To get the results something like..
Review_ID Review_Content total_Words Min_occrd_word Max Average
1 .... 230 great: 1 the: 25 total_unique/total_words in the row
But do not have idea, how can I do it....
Here is a method in base R using strsplit and sapply. Let's say the data is stored in a data.frame df and the reviews are stored in the variable Review_Content
# break up the strings in each row by " "
temp <- strsplit(df$Review_Content, split=" ")
# count the number of words as the length of the vectors
df$wordCount <- sapply(temp, length)
In this instance, sapply will return a vector of the counts for each row.
Since the word count is now an object, you can perform analysis you want on it. Here are some examples:
summarize the distribution of word counts: summary(df$wordCount)
maximum word count: max(df$wordCount)
mean word count: mean(df$wordCount)
range of word counts: range(df$wordCount)
interquartile range of word counts: IQR(df$wordCount)
Adding to #lmo's answer above..
Below code will generate a dataframe that consists of all the words, row-wise, and their frequencies:
temp2 <- data.frame()
for (i in 1:length(temp)){
temp1 <- as.data.frame(table(temp[[i]]))
temp1$ID <- paste0("Row_", i)
temp2 <- rbind(temp2, temp1)
temp1 <- NULL
}
I'm quite a newbie in R so I was interested in the optimality of my solution. Even if it works it could be (a bit) long and I wanted your advice to see if the "way I solved it" is "the best" and it could help me to learn new techniques and functions in R.
I have a dataset on students identified by their id and I have the school where they are matched and the score they obtained at a specific test (so for short: 3 variables id,match and score).
I need to construct the following table: for students in between two percentiles of score, I need to calculate the average score (between students) of the average score of the students of the school they are matched to (so for each school I take the average score of the students matched to it and then I calculate the average of this average for percentile classes, yes average of a school could appear twice in this calculation). In English it allows me to answer: "A student belonging to the x-th percentile in terms of score will be in average matched to a school with this average quality".
Here is an example in the picture:
So in that case, if I take the median (15) for the split (rather than percentiles) I would like to obtain:
[0,15] : 9.5
(15,24] : 20.25
So for students having a score between 0 and 15 I take the average of the average score of the school they are matched to (note that b average will appears twice but that's ok).
Here how I did it:
match <- c(a,b,a,b,c)
score <- c(18,4,15,8,24)
scoreQuant <- cut(score,quantile(score,probs=seq(0,1,0.1),na.rm=TRUE))
AvgeSchScore <- tapply(score,match,mean,na.rm=TRUE)
AvgScore <- 0
for(i in 1:length(score)) {
AvgScore[i] <- AvgeSchScore[match[i]]
}
results <- tapply(AvgScore,scoreQuant,mean,na.rm = TRUE)
If you have a more direct way of doing it.. Or I think the bad point is 3) using a loop, maybe apply() is better ? But I'm not sure how to use it here (I tried to code my own function but it crashed so I "bruted force it").
Thanks :)
The main fix is to eliminate the for loop with:
AvgScore <- AvgeSchScore[match]
R allows you to subset in ways that you cannot in other languages. The tapply function outputs the names of the factor that you grouped by. We are using those names for match to subset AvgeScore.
data.table
If you would like to try data.table you may see speed improvements.
library(data.table)
match <- c("a","b","a","b","c")
score <- c(18,4,15,8,24)
dt <- data.table(id=1:5, match, score)
scoreQuant <- cut(dt$score,quantile(dt$score,probs=seq(0,1,0.1),na.rm=TRUE))
dt[, AvgeScore := mean(score), match][, mean(AvgeScore), scoreQuant]
# scoreQuant V1
#1: (17.4,19.2] 16.5
#2: NA 6.0
#3: (12.2,15] 16.5
#4: (7.2,9.4] 6.0
#5: (21.6,24] 24.0
It may be faster than base R. If the value in the NA row bothers you, you can delete it after.
First of all, I apologize for the title. I really don't know how to succinctly explain this issue in one sentence.
I have a dataframe where each row represents some aspect of a hospital visit by a patient. A single patient might have thousands of rows for dozens of hospital visits, and each hospital visit could account for several rows.
One column is Medical.Record.Number, which corresponds to Patient IDs, and the other is Patient.ID.Visit, which corresponds to an ID for an individual hospital visit. I am trying to calculate the number of hospital visits each each patient has had.
For example:
Medical.Record.Number Patient.ID.Visit
AAAXXX 1111
AAAXXX 1112
AAAXXX 1113
AAAZZZ 1114
AAAZZZ 1114
AAABBB 1115
AAABBB 1116
would produce the following:
Medical.Record.Number Number.Of.Visits
AAAXXX 3
AAAZZZ 1
AAABBB 2
The solution I am currently using is the following, where "data" is my dataframe:
#this function returns the number of unique hospital visits associated with the
#supplied record number
countVisits <- function(record.number){
visits.by.number <- data$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 some data (from a R course assignment, but that doesn't matter) that I want to use split-apply-combine strategy, but I'm having some problems. The data is on a DataFrame, called outcome, and each line represents a Hospital. Each column has an information about that hospital, like name, location, rates, etc.
My objective is to obtain the Hospital with the lowest "Mortality by Heart Attack Rate" of each State.
I was playing around with some strategies, and got a problem using the by function:
best_heart_rate(df) = sort(df, cols = :Mortality)[end,:]
best_hospitals = by(hospitals, :State, best_heart_rate)
The idea was to split the hospitals DataFrame by State, sort each of the SubDataFrames by Mortality Rate, get the lowest one, and combine the lines in a new DataFrame
But when I used this strategy, I got:
ERROR: no method nrow(SubDataFrame{Array{Int64,1}})
in sort at /home/paulo/.julia/v0.3/DataFrames/src/dataframe/sort.jl:311
in sort at /home/paulo/.julia/v0.3/DataFrames/src/dataframe/sort.jl:296
in f at none:1
in based_on at /home/paulo/.julia/v0.3/DataFrames/src/groupeddataframe/grouping.jl:144
in by at /home/paulo/.julia/v0.3/DataFrames/src/groupeddataframe/grouping.jl:202
I suppose the nrow function is not implemented for SubDataFrames, so I got an error. So I used a nastier code:
best_heart_rate(df) = (df[sortperm(df[:,:Mortality] , rev=true), :])[1,:]
best_hospitals = by(hospitals, :State, best_heart_rate)
Seems to work. But now there is a NA problem: how can I remove the rows from the SubDataFrames that have NA on the Mortality column? Is there a better strategy to accomplish my objective?
I think this might work, if I've understood you correctly:
# Let me make up some data about hospitals in states
hospitals = DataFrame(State=sample(["CA", "MA", "PA"], 10), mortality=rand(10), hospital=split("abcdefghij", ""))
hospitals[3, :mortality] = NA
# You can use the indmax function to find the index of the maximum element
by(hospitals[complete_cases(hospitals), :], :State, df -> df[indmax(df[:mortality]), [:mortality, :hospital]])
State mortality hospital
1 CA 0.9469632421111882 j
2 MA 0.7137144590022733 f
3 PA 0.8811901895164764 e