I am working with a df of 46216 observation where the units are homes and people, where each home may have any number of integrants, like:
enter image description here
and this for another almost 18000 homes.
What i need to do is to get the mean of education years for every home, for what i guess i will need a variable that computes the number of people of each home.
What i tried to do is:
num_peopl=by(df$person_number, df$home, max), for each home I take the highest person number with the total number of people who live there, but when I try to cbind this with the df i get:
"arguments imply differing number of rows: 46216, 17931"
It is like it puts the number of persons only for one row, and leaves the others empty.
How can i do this? Is there a function?
I think aggregate and join may be what your looking for. Aggregate does the same thing that you did, but puts it into a data frame that I'm more familiar with at least.
Then I used dplyr left_join, joining the home number's together:
library(tidyverse)
df<-data.frame(home_number = c(1,1,1,2,2,3),
person_number = c(1,2,3,1,2,1),
age = c(20,21,1,54,50,30),
sex = c("m","f","f","m","f","f"),
salary = c(1000,890,NA,900,500,1200),
years_education = c(12,10,0,8,7,14))
df2<-aggregate(df$person_number, by = list(df$home_number), max)
df_final<-df%>%
left_join(df2, by = c("home_number" = "Group.1"))
home_number person_number age sex salary years_education x
1 1 1 20 m 1000 12 3
2 1 2 21 f 890 10 3
3 1 3 1 f NA 0 3
4 2 1 54 m 900 8 2
5 2 2 50 f 500 7 2
6 3 1 30 f 1200 14 1
Related
I have a dataset with over 400,000 cows. These cows are (unevenly) spreak over 2355 herds. Some herds are only present once in the data, while one herd is even present 2033 times in the data, meaning that 2033 cows belong to this herd. I want to delete herds from my data that occur less than 200 times.
With use of plyr and subset, I can obtain a list of which herds occur less than 200 times, I however can not find out how to apply this selection to the full dataset.
For example, my current data looks a little like:
cow herd
1 1
2 1
3 1
4 2
5 3
6 4
7 4
8 4
With function count() I can obtain the following:
x freq
1 3
2 1
3 1
4 3
Say I want to delete the data belonging to herds that occur less than 3 times, I want my data to look like this eventually:
cow herd
1 1
2 1
3 1
6 4
7 4
8 4
I do know how to tell R to delete data herd by herd, however since, in my real datatset, over 1000 herds occur less then 200 times, it would mean that I would have to type every herd number in my script one by one. I am sure there is an easier and quicker way of asking R to delete data above or below a certain occurence.
I hope my explanation is clear and someone can help me, thanks in advance!
Use n + group_by:
library(dplyr)
your_data %>%
group_by(herd) %>%
filter(n() >= 3)
i have a data frame with about 20k IDs of chemical compounds and the corresponding molecular weights, something like this:
ID <- c(1,2,3,4,5)
MASS <- c(324,162,508,675,670)
d <- data.frame(ID, MASS)
ID MASS
1 1 324
2 2 162
3 3 508
4 4 675
5 5 670
I would like to find a way to loop over the rows of the column MASS to find which masses are related by having a difference (positive or negative) of 162∓0.5. Then I would like to have a new column (d$DIFF) where the IDs that are linked by a MASS difference of 162∓0.5 are reported, while get 0 for those IDs when the condition is not met, in this example it would be something like this:
ID MASS DIFF
1 1 324 1&2
2 2 162 1&2
3 3 508 3&5
4 4 675 0
5 5 670 3&5
Thanks in advance for any help
Here's a base R solution using outer:
d$DIFF <- unlist(lapply(apply(outer(d$MASS, d$MASS,
function(x, y) abs((abs(x - y) - 162)) < 0.5), 1, which),
function(x) if(length(x) == 0)
return("0")
else
return(paste(x, collapse = " & "))))
This gives the result:
d
#> ID MASS DIFF
#> 1 1 324 2
#> 2 2 162 1
#> 3 3 508 5
#> 4 4 675 0
#> 5 5 670 3
Note that in your example data, there is at most a single match to other rows, but if you apply this technique to your real data you should get multiple hits for some rows separated by "&" as requested.
You should also note that whatever way you do this in your real data, you will have to make approximately 20K * 20K (400 million) comparisons, so it may take some time to complete, and may result in memory issues depending on your set-up.
I'm new to R and I'm stuck.
NB! I'm sorry I could not figure out how to add more than 1 space between numbers and headers in my example so i used "_" instead.
The problem:
I have two data frames (Graduations and Occupations). I want to match the occupations to the graduations. The difficult part is that one person might be present multiple times in both data frames and I want to keep all the data.
Example:
Graduations
One person may have finished many curriculums. Original DF has more columns but they are not relevant for the example.
Person_ID__curriculum_ID__School ID
___1___________100__________10
___2___________100__________10
___2___________200__________10
___3___________300__________12
___4___________100__________10
___4___________200__________12
Occupations
Not all graduates have jobs, everyone in the DF should have only one main job (JOB_Type code "1") and can have 0-5 extra jobs (JOB_Type code "0"). Original DF has more columns but the are not relevant currently.
Person_ID___JOB_ID_____JOB_Type
___1_________1223________1
___3_________3334________1
___3_________2120________0
___3_________7843________0
___4_________4522________0
___4_________1240________1
End result:
New DF named "Result" containing the information of all graduations from the first DF(Graduations) and added columns from the second DF (Occupations).
Note that person "2" is not in the Occupations DF. Their data remains but added columns remain empty.
Note that person "3" has multiple jobs and thus extra duplicate rows are added.
Note that in case of person "4" has both multiple jobs and graduations so extra rows were added to fit in all the data.
New DF: "Result"
Person_ID__Curriculum_ID__School_ID___JOB_ID____JOB_Type
___1___________100__________10_________1223________1
___2___________100__________10
___2___________200__________10
___3___________300__________12_________3334________1
___3___________300__________12_________2122________0
___3___________300__________12_________7843________0
___4___________100__________10_________4522________0
___4___________100__________10_________1240________1
___4___________200__________12_________4522________0
___4___________200__________12_________1240________1
For me the most difficult part is how to make R add extra duplicate rows. I looked around to find an example or tutorial about something similar but could. Probably I did not use the right key words.
I will be very grateful if you could give me examples of how to code it.
You can use merge like:
merge(Graduations, Occupations, all.x=TRUE)
# Person_ID curriculum_ID School_ID JOB_ID JOB_Type
#1 1 100 10 1223 1
#2 2 100 10 NA NA
#3 2 200 10 NA NA
#4 3 300 12 3334 1
#5 3 300 12 2122 0
#6 3 300 12 7843 0
#7 4 100 10 4522 0
#8 4 100 10 1240 1
#9 4 200 12 4522 0
#10 4 200 12 1240 1
Data:
Graduations <- read.table(header=TRUE, text="Person_ID curriculum_ID School_ID
1 100 10
2 100 10
2 200 10
3 300 12
4 100 10
4 200 12")
Occupations <- read.table(header=TRUE, text="Person_ID JOB_ID JOB_Type
1 1223 1
3 3334 1
3 2122 0
3 7843 0
4 4522 0
4 1240 1")
An option with left_join
library(dplyr)
left_join(Graduations, Occupations)
I have a three-column dataframe object recording the bilateral trade data between 161 countries, the data are of dyadic format containing 19687 rows, three columns (reporter (rid), partner (pid), and their bilateral trade flow (TradeValue) in a given year). rid or pid takes a value from 1 to 161, and a country is assigned the same rid and pid. For any given pair of (rid, pid) in which rid =/= pid, TradeValue(rid, pid) = TradeValue(pid, rid).
The data (run in R) look like this:
#load the data from dropbox folder
library(foreign)
example_data <- read.csv("https://www.dropbox.com/s/hf0ga22tdjlvdvr/example_data.csv?dl=1")
head(example_data, n = 10)
rid pid TradeValue
1 2 3 500
2 2 7 2328
3 2 8 2233465
4 2 9 81470
5 2 12 572893
6 2 17 488374
7 2 19 3314932
8 2 23 20323
9 2 25 10
10 2 29 9026220
The data were sourced from UN Comtrade database, each rid is paired with multiple pid to get their bilateral trade data, but as can be seen, not every pid has a numeric id value because I only assigned a rid or pid to a country if a list of relevant economic indicators of that country are available, which is why there are NA in the data despite TradeValue exists between that country and the reporting country (rid). The same applies when a country become a "reporter," in that situation, that country did not report any TradeValue with partners, and its id number is absent from the rid column. (Hence, you can see rid column begins with 2, because country 1 (i.e., Afghanistan) did not report any bilateral trade data with partners). A quick check with summary statistics helps confirm this
length(unique(example_data$rid))
[1] 139
# only 139 countries reported bilateral trade statistics with partners
length(unique(example_data$pid))
[1] 162
# that extra pid is NA (161 + NA = 162)
Since most countries report bilateral trade data with partners and for those who don't, they tend to be small economies. Hence, I want to preserve the complete list of 161 countries and transform this example_data dataframe into a 161 x 161 adjacency matrix in which
for those countries that are absent from the rid column (e.g., rid == 1), create each of them a row and set the entire row (in the 161 x 161 matrix) to 0.
for those countries (pid) that do not share TradeValue entries with a particular rid, set those cells to 0.
For example, suppose in a 5 x 5 adjacency matrix, country 1 did not report any trade statistics with partners, the other four reported their bilateral trade statistics with other (except country 1). The original dataframe is like
rid pid TradeValue
2 3 223
2 4 13
2 5 9
3 2 223
3 4 57
3 5 28
4 2 13
4 3 57
4 5 82
5 2 9
5 3 28
5 4 82
from which I want to convert it to a 5 x 5 adjacency matrix (of data.frame format), the desired output should look like this
V1 V2 V3 V4 V5
1 0 0 0 0 0
2 0 0 223 13 9
3 0 223 0 57 28
4 0 13 57 0 82
5 0 9 28 82 0
And using the same method on the example_data to create a 161 x 161 adjacency matrix. However, after a couple trial and error with reshape and other methods, I still could not get around with such conversion, not even beyond the first step.
It will be really appreciated if anyone could enlighten me on this?
I cannot read the dropbox file but have tried to work off of your 5-country example dataframe -
country_num = 5
# check countries missing in rid and pid
rid_miss = setdiff(1:country_num, example_data$rid)
pid_miss = ifelse(length(setdiff(1:country_num, example_data$pid) == 0),
1, setdiff(1:country_num, example_data$pid))
# create dummy dataframe with missing rid and pid
add_data = as.data.frame(do.call(cbind, list(rid_miss, pid_miss, NA)))
colnames(add_data) = colnames(example_data)
# add dummy dataframe to original
example_data = rbind(example_data, add_data)
# the dcast now takes missing rid and pid into account
mat = dcast(example_data, rid ~ pid, value.var = "TradeValue")
# can remove first column without setting colnames but this is more failproof
rownames(mat) = mat[, 1]
mat = as.matrix(mat[, -1])
# fill in upper triangular matrix with missing values of lower triangular matrix
# and vice-versa since TradeValue(rid, pid) = TradeValue(pid, rid)
mat[is.na(mat)] = t(mat)[is.na(mat)]
# change NAs to 0 according to preference - would keep as NA to differentiate
# from actual zeros
mat[is.na(mat)] = 0
Does this help?
This is my first time posting to Stack Exchange, my apologies as I'm certain I will make a few mistakes. I am trying to assess false detections in a dataset.
I have one data frame with "true" detections
truth=
ID Start Stop SNR
1 213466 213468 10.08
2 32238 32240 10.28
3 218934 218936 12.02
4 222774 222776 11.4
5 68137 68139 10.99
And another data frame with a list of times, that represent possible 'real' detections
possible=
ID Times
1 32239.76
2 32241.14
3 68138.72
4 111233.93
5 128395.28
6 146180.31
7 188433.35
8 198714.7
I am trying to see if the values in my 'possible' data frame lies between the start and stop values. If so I'd like to create a third column in possible called "between" and a column in the "truth" data frame called "match. For every value from possible that falls between I'd like a 1, otherwise a 0. For all of the rows in "truth" that find a match I'd like a 1, otherwise a 0.
Neither ID, not SNR are important. I'm not looking to match on ID. Instead I wand to run through the data frame entirely. Output should look something like:
ID Times Between
1 32239.76 0
2 32241.14 1
3 68138.72 0
4 111233.93 0
5 128395.28 0
6 146180.31 1
7 188433.35 0
8 198714.7 0
Alternatively, knowing if any of my 'possible' time values fall within 2 seconds of start or end times would also do the trick (also with 1/0 outputs)
(Thanks for the feedback on the original post)
Thanks in advance for your patience with me as I navigate this system.
I think this can be conceptulised as a rolling join in data.table. Take this simplified example:
truth
# id start stop
#1: 1 1 5
#2: 2 7 10
#3: 3 12 15
#4: 4 17 20
#5: 5 22 26
possible
# id times
#1: 1 3
#2: 2 11
#3: 3 13
#4: 4 28
setDT(truth)
setDT(possible)
melt(truth, measure.vars=c("start","stop"), value.name="times")[
possible, on="times", roll=TRUE
][, .(id=i.id, truthid=id, times, status=factor(variable, labels=c("in","out")))]
# id truthid times status
#1: 1 1 3 in
#2: 2 2 11 out
#3: 3 3 13 in
#4: 4 5 28 out
The source datasets were:
truth <- read.table(text="id start stop
1 1 5
2 7 10
3 12 15
4 17 20
5 22 26", header=TRUE)
possible <- read.table(text="id times
1 3
2 11
3 13
4 28", header=TRUE)
I'll post a solution that I'm pretty sure works like you want it to in order to get you started. Maybe someone else can post a more efficient answer.
Anyway, first I needed to generate some example data - next time please provide this from your own data set in your post using the function dput(head(truth, n = 25)) and dput(head(possible, n = 25)). I used:
#generate random test data
set.seed(7)
truth <- data.frame(c(1:100),
c(sample(5:20, size = 100, replace = T)),
c(sample(21:50, size = 100, replace = T)))
possible <- data.frame(c(sample(1:15, size = 15, replace = F)))
colnames(possible) <- "Times"
After getting sample data to work with; the following solution provides what I believe you are asking for. This should scale directly to your own dataset as it seems to be laid out. Respond below if the comments are unclear.
#need the %between% operator
library(data.table)
#initialize vectors - 0 or false by default
truth.match <- c(rep(0, times = nrow(truth)))
possible.between <- c(rep(0, times = nrow(possible)))
#iterate through 'possible' dataframe
for (i in 1:nrow(possible)){
#get boolean vector to show if any of the 'truth' rows are a 'match'
match.vec <- apply(truth[, 2:3],
MARGIN = 1,
FUN = function(x) {possible$Times[i] %between% x})
#if any are true then update the match and between vectors
if(any(match.vec)){
truth.match[match.vec] <- 1
possible.between[i] <- 1
}
}
#i think this should be called anyMatch for clarity
truth$anyMatch <- truth.match
#similarly; betweenAny
possible$betweenAny <- possible.between