I want to fetch the row numbers of the first 5 highest values in a column in a dataframe and add a value of 100 on the same row number in another dataframe and rest other values as 0.
I know how to sort / order a column in a dataframe using order() function.
df=data.frame(a=c(345,948,290,189,3848,302,384,456,383,201,35,346,1.46,4.66,3,5,63,43,6432,4336,345,354,1245,342,523,743,248,932.5))
For example, df[order(-df$a),] results in
6432.00 4336.00 3848.00 1245.00 948.00 932.50 743.00 523.00 456.00 384.00 383.00 354.00 346.00 345.00 345.00 342.00 302.00 290.00 248.00 201.00 189.00 63.00 43.00 35.00 5.00 4.66 3.00 1.46
However, I am not able to meet my specific requirement.
I would expect to see df1 as
0 100 0 0 100 0 0 0 0 0 0 0 0 0 0 0 0 0 100 100 0 0 100 0 0 0 0 0
df$b <- ifelse(df$a %in% sort(df$a, T)[1:5], 100, 0)
We could use the rank function:
df$b <- (rank(-df$a) <= 5) * 100
Related
Hello everybody and thank you in advance for any help.
I inserted a txt file named "project" in R. This dataframe called "data" and consisted of 12 columns with some information of 999 households.
head(data)
im iw r am af a1c a2c a3c a4c a5c a6c a7c
1 0.00 20064.970 5984.282 0 38 0 0 0 0 0 0 0
2 15395.61 7397.191 0.000 42 30 1 0 0 0 0 0 0
3 16536.74 18380.770 0.000 33 28 1 0 0 0 0 0 0
4 20251.87 14042.250 0.000 38 38 1 1 0 0 0 0 0
5 17967.04 12693.240 0.000 24 39 1 0 0 0 0 0 0
6 12686.43 21170.450 0.000 62 42 0 0 0 0 0 0 0
im=male income
iw=female income
r=rent
am=male age
af=female age
a1c,a2c....a7c takes the value 1 when there is a child in age under 18
and the value 0 when there is not a child in the household.
Now i have to calculate the taxed income seperately for male and female for each houshold based on some criteria, so i am trying to create 1 function which calculate 2 numbers and after that to apply this function on my data frame and return a list with these numbers.
Specificaly I want something like this:
fact<-function(im,iw,r,am,af,a1c,a2c,a3c,a4c,a5c,a6c,a7c){
if ((am>0)&&(am<67)&&(af>0)) {mti<-im-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((am>0)&&(am<67)&&(af==0)) {mti<-im-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((am>=67)&&(af>0)) {mti<-im-1000-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((am<=67)&&(af==0)) {mti<-im-1000-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af>0)&&(af<67)&&(am>0)) {fti<-iw-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af>0)&&(af<67)&&(am==0)) {fti<-iw-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af>=67)&&(am>0)) {fti<-iw-1000-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af<=67)&&(am==0)) {fti<-iw-1000-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
return(mti,fti)}
how can i fix this function in order to apply on my dataframe?
Can a function return 2 values?
how can i apply the function?
THEN I TRIED THIS:
fact<-function(im=data$im,iw=data$iw,r=data$r,am=data$am,af=data$af,a1c=data$a1c,a2c=data$a2c,a3c=data$a3c,a4c=data$a4c,a5c=data$a5c,a6c=data$a6c,a7c=data$a7c){
if ((am>0)&&(am<67)&&(af>0)) {mti<-im-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((am>0)&&(am<67)&&(af==0)) {mti<-im-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((am>=67)&&(af>0)) {mti<-im-1000-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((am<=67)&&(af==0)) {mti<-im-1000-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af>0)&&(af<67)&&(am>0)) {fti<-iw-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af>0)&&(af<67)&&(am==0)) {fti<-iw-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af>=67)&&(am>0)) {fti<-iw-1000-(r)/2-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
if ((af<=67)&&(am==0)) {fti<-iw-1000-r-(500*(a1c+a2c+a3c+a4c+a5c+a5c+a6c+a7c))}
return(mti,fti)}
fact(data[1,])
but i have tis error: Error in fact(data[1, ]) : object 'mti' not found
when i tried the function only for "fti" can run but wrongly.
Besides the need to return multiple values using c(mti, fti), your function doesn't have a default value if none of the conditions in the functions are TRUE. So, mti is never created.
Add mti <- NA at the start of your function, so NA is the default value.
I am currently working with about 301 rows of data and want to determine the earliest point at which only a few particular columns are nonzero. However, I also want to ensure that this does not change. For example, the two columns are nonzero, while all other columns are zero, then later in the dataframe other columns are nonzero as well, this would mean that I would have to determine a later point which is "correct".
I have the data:
1 x y z xx xy xz
292 0 -8.965140 9.596890 0 0 0 -0.03147483
293 0 -9.079889 9.645991 0 0 0 -0.02722520
294 0 -8.967767 9.597826 0 0 0 0
295 0 -9.090561 9.650230 0 0 0 -0.02685287
296 0 -9.081568 9.646105 0 0 0 -0.02716237
297 0 0.000000 0.000000 0 0 0 0.00000000
298 0 0.000000 0.000000 0 0 0 0.00000000
299 0 -9.098568 9.628576 0 0 0 -0.02654466
300 0 -9.089815 9.646099 0 0 0 -0.02681748
301 0 -8.998078 9.605140 0 0 0 0
As you can see, only the variables x and y are selected for row 294, however, the xz variable contains values after that until the 301 row. Is it possible to develop a function which tells me at which point is the minimum row where I see only x and y as nonzero and it remains that way until the final row of the dataframe?
I'm sorry if it's difficult to understand the question, I found it difficult asking how exactly to accomplish this issue.
EDIT: I presume I could use something like
which((df$x != 0 & df$y != 0 &
(df[, 1] | df[, 4] == 0))
but then I need to somehow expand the second or statement to all columns of df.
Thanks in advance.
It is possible to take one column of numeric values like in dup$Number and subset columns in DG that match dup$number and return this as a new data frame?
dup
Number Letter
59 Q
91 Q
19 Q
17 Q
DG
chr pos id ref alt refc altc qual cov line_21 line_26 line_28 line_31 line_32 line_38 line_40 line_41 line_42 line_45 line_48 line_49 line_57 line_59 line_69 line_73 line_75 line_83
1 2R 7006506 2R_7006506_SNP C A 169 26 999 29 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 2 -
Try
indx <- grep('line', names(DG))
DG[indx[as.numeric(sub('.*_', '', names(DG)[indx])) %in% dup$Number]]
# line_59
#1 0
I have a matrix with dates as row names and TAG#'s as column names. The matrix is populated with 0's and 1's for presence/absence.
eg
29735 29736 29737 29738 29739 29740
2010-07-15 1 0 0 0 0 0
2010-07-16 1 1 0 0 0 0
2010-07-17 1 1 0 0 0 0
2010-07-18 1 1 0 0 0 0
2010-07-19 1 1 0 0 0 0
2010-07-20 1 1 0 0 0 0
I have the following script for calculating site fidelity (% days present):
##Presence/absence data setup
##import file
read.csv('pn.csv')->'pn'
##strip out desired columns
pn[,c(5,7:9)]->pn
##create table of dates and tags
table(pn$Date,pn$Tag)->T
##convert to a matrix
as.matrix(T)->U
##convert to binary for presence/absence
1*(U>2)->U
##insert missing rows
library(micEcon)
insertRow(U,395,0)->U
rownames(U)[395]<-'2011-08-16'
insertRow(U,253,0)->U
rownames(U)[253]<-'2011-03-26'
insertRow(U,250,0)->U
rownames(U)[250]<-'2011-03-22'
insertRow(U,250,0)->U
rownames(U)[250]<-'2011-03-21'
##for presence/absence
##define i(tag or column)
1->i
##define place to store results
cbind(colnames(U),rep(NA,length(colnames(U))))->sfresult
##loop instructions
for(i in 1:ncol(U)){
##identify first detection day
grep(1,U[,i])[1]->tagrow
##count total days since first detection
nrow(U)-tagrow+1->days
##count days present
length(grep(1,U[,i]))->present
##calculate site fidelity
present/days->sfresult[i,2]
}
##change class of results column
as.numeric(sfresult[,2])->sfresult[,2]
##histogram
bins<-c(0,.3,.6,1)
xlab<-c('Low','Med','High')
hist(as.numeric(sfresult[,2]), breaks=bins,xaxt='n', col=heat.colors(3), xlab='Percent Days Present',ylab='Frequency (# of individuals)',main='Site Fidelity',freq=TRUE,labels=xlab)
axis(1,at=bins)
I'd like to calculate site fidelity on a weekly basis. I believe it would be easiest to simply collapse the matrix by combining every seven rows into a weekly matrix that simply sums the 0's and 1's from the daily matrix. Then the same script for site fidelity would calculate it on a weekly basis. Problem is I'm a newbie and I've had trouble finding an answer on how to collapse the daily matrix to a weekly matrix. Thanks for any suggestions.
Something like this should work:
x <- matrix(rbinom(1000,1,.2), nrow=50, ncol=20)
rownames(x) <- 1:50
colnames(x) <- paste0("id", 1:20)
require(data.table)
xdt <- as.data.table(x)
##assuming rows are sorted by date, that there are no missing days, and that the first row is the start of the week
###xdt[, week:=sort(rep(1:7, length.out=nrow(xdt)))] ##wrong
xdt[, week:=rep(1:ceiling(nrow(xdt)/7), each=7)] ##fixed
xdt[, lapply(.SD,sum), by="week",.SDcols=setdiff(names(xdt),"week")]
I can help you better preserve rownames if you provide a reproducible example How to make a great R reproducible example?
Edit:
Also, it's very atypical to use the right assignment -> as you do do above.
R's cut function will trim Dates to their week (see ?cut.Date for more details). After that, it's a simple call to aggregate to get the result you need. Note that cut.Date takes a start.on.monday option.
Data
sites <- read.table(text="29735 29736 29737 29738 29739 29740
2010-07-15 1 0 0 0 0 0
2010-07-16 1 1 0 0 0 0
2010-07-17 1 1 0 0 0 0
2010-07-18 1 1 0 0 0 0
2010-07-19 1 1 0 0 0 0
2010-07-20 1 1 0 0 0 0",
header=TRUE, check.names=FALSE, row.names=1)
Answer
weeks.factor <- cut(as.Date(row.names(sites)),
breaks='weeks', start.on.monday=FALSE)
aggregate(sites, by=list(weeks.factor), FUN=function(col) sum(col)/length(col))
# Group.1 29735 29736 29737 29738 29739 29740
# 1 2010-07-11 1 0.6666667 0 0 0 0
# 2 2010-07-18 1 1.0000000 0 0 0 0
I want to perform an inner product of the first D columns for each row in a data frame with a given array, W. I am trying the following:
W = (1,2,3);
ddply(df, .(id), transform, inner_product=c(col1, col2, col3) %*% W);
This works but I typically may have an arbitrary number of columns. Can I generalize the above expression to handle that case?
Update:
This is an updated example as asked for in the comments:
libary(kernlab);
data(spam);
W = array();
W[1:3] = seq(1,3);
spamdf = head(spam);
spamdf$id = seq(1,nrow(spamdf));
df_out=ddply(spamdf, .(id), transform, inner_product=c(make, address, all) %*% W);
> W
[1] 1 2 3
> spamdf[1,]
make address all num3d our over remove internet order mail receive will
1 0 0.64 0.64 0 0.32 0 0 0 0 0 0 0.64
people report addresses free business email you credit your font num000
1 0 0 0 0.32 0 1.29 1.93 0 0.96 0 0
money hp hpl george num650 lab labs telnet num857 data num415 num85
1 0 0 0 0 0 0 0 0 0 0 0 0
technology num1999 parts pm direct cs meeting original project re edu table
1 0 0 0 0 0 0 0 0 0 0 0 0
conference charSemicolon charRoundbracket charSquarebracket charExclamation
1 0 0 0 0 0.778
charDollar charHash capitalAve capitalLong capitalTotal type id
1 0 0 3.756 61 278 spam 1
> df_out[1,]
make address all num3d our over remove internet order mail receive will
1 0 0.64 0.64 0 0.32 0 0 0 0 0 0 0.64
people report addresses free business email you credit your font num000
1 0 0 0 0.32 0 1.29 1.93 0 0.96 0 0
money hp hpl george num650 lab labs telnet num857 data num415 num85
1 0 0 0 0 0 0 0 0 0 0 0 0
technology num1999 parts pm direct cs meeting original project re edu table
1 0 0 0 0 0 0 0 0 0 0 0 0
conference charSemicolon charRoundbracket charSquarebracket charExclamation
1 0 0 0 0 0.778
charDollar charHash capitalAve capitalLong capitalTotal type id inner_product
1 0 0 3.756 61 278 spam 1 3.2
The above example performs a inner product of the first three dimensions with an array W=(1,2,3) of the spam data set available in kernlab package. Here I have explicity specified the first three dimensions as c(make, address, all).
Thus df_out[1,"inner_product"] = 3.2.
Instead I want to perform the inner product over all the dimensions without having to list all the dimensions. The conversion to a matrix and back to a data frame seems to be an expensive operation?
A strategy along the lines of the following should work:
Convert each chunk to a matrix
Perform a matrix multiplication
Convert results to data.frame
The code:
set.seed(1)
df <- data.frame(
id=sample(1:5, 20, replace=TRUE),
col1 = runif(20),
col2 = runif(20),
col3 = runif(20),
col4 = runif(20)
)
W <- c(1,2,3,4)
ddply(df, .(id), function(x)as.data.frame(as.matrix(x[, -1]) %*% W))
The results:
id V1
1 1 4.924994
2 1 5.076043
3 2 7.053864
4 2 5.237132
5 2 6.307620
6 2 3.413056
7 2 5.182214
8 2 7.623164
9 3 5.194714
10 3 6.733229
11 4 4.122548
12 4 3.569013
13 4 4.978939
14 4 5.513444
15 4 5.840900
16 4 6.526522
17 5 3.530220
18 5 3.549646
19 5 4.340173
20 5 3.955517
If you want to append a column of cross-products, you could do this (assuming W had the right number of elements to match the non-"id" columns:
df2 <- cbind(df, as.matrix(df[, -grep("id", names(df))]) %*% W )
It does not appear that the .(id) serves any useful purpose, since you are not do a sum of crossproducts within id, and if you were then you wouldn't be using transform but some other aggregating function.