I have the following data:
library(data.table)
sales <- data.table(Customer = c(192,964,929,345,898,477,705,804,188,231,780,611,420,816,171,212,504,526,471,979,524,410,557,152,417,359,435,820,305,268,763,194,757,475,351,933,805,687,813,880,798,327,602,710,785,840,446,891,165,662),
Producttype = c(1,2,3,2,3,3,2,1,3,3,1,1,2,2,1,3,1,3,3,1,1,1,1,3,3,3,3,2,1,1,3,3,3,3,1,1,3,3,3,2,3,2,3,3,3,2,1,2,3,1),
Price = c(469,721,856,956,554,188,429,502,507,669,427,582,574,992,418,835,652,983,149,917,370,617,876,337,663,252,599,949,915,556,313,842,892,724,415,307,900,114,439,456,541,261,881,757,199,308,958,374,409,738),
Quarter = c(2,3,3,4,4,1,4,4,3,3,1,1,1,1,1,1,4,1,2,1,3,1,2,3,3,4,4,1,1,4,1,1,3,2,1,3,3,2,2,2,1,4,3,3,1,1,1,3,1,1))
How can I remove (let's say) the row in which Customer = 891?
And then I have another question:
If I want to manipulate the data I use data [row, column]. But when I want to use only the rows in which Quarter equals (for example) 4. I use data [Quarter = 4,] Why is it not data [, Quarter = 4] since Quarter is a column and not a row?
I did not find an appropriate answer in the internet which really explains the why.
Thank you.
You have used 'data.table' function to import your data, so you could write :
sales[Customer != 891,]
The data[Quarter = 4, ], ensures that all columns should be returned for the rows where Quarter is equal to 4. The comma(,) is necessary to only select the rows, and not the column Quarter = 4.
When you use indexing, ie, data[row, column] you are telling R to look for either a specific row or column index.
Row: sales[sales$Customer %in% c(192,964),] translates to "search the specific column Customer in the data frame (or table) for any rows that have values that contain 192 or 964 and isolate them. Note that data.table will allow for sales[Customer %in% c(192, 964),] but data frames cant (use sales[sales$Customer %in% c(192,964),])
Customer Producttype Price Quarter
1: 192 1 469 2
2: 964 2 721 3
Columns sales[, "Customer"] translates to "search the data frame (or table) for columns named "Customer" and isolate all its rows
Customer
1: 192
2: 964
3: 929
4: 345
5: 898
...
Note this returns a data table with one column. If you use sales[,Customer] (data table) or sales$Customer (data frame), it will return a vector:
# [1] 192 964 929 345 898 477 705 804 188 231 780 611 420 816 171 212 504 526 471 979 524
# [22] 410 557 152 417 359 435 820 305 268 763 194 757 475 351 933 805 687 813 880 798 327
# [43] 602 710 785 840 446 891 165 662
You can of course combine - if you did, sales[sales$Quarter %in% 1:2, c("Customer", "Producttype")] you would isolate all values of Customer and Producttype which were in quarters 1 and 2:
Customer Producttype
1: 192 1
2: 477 3
3: 780 1
4: 611 1
5: 420 2
...
Sorry for asking a very basic question but I am new to R and really stuck on a rather simple matter; I have the data frame below (2 rows and 7 columns):
Sub sup_b hdt sup_2 lbnp sup_3 hut sup_4
6 175 434 596 585 601 593 211
7 130 592 592 593 600 384 166
These values correspond with time duration (secs) for seven test conditions
col$names <- c(sup_b, hdt, sup_2, lbnp, sup_3, hut, sup_4)
and 17 rows (each row is for one study subject- I have only included first two rows).
I am trying to add values from row 1 col$sup_b (175) and row 1 col$hdt (434) to get the combined duration for the first two conditions i.e. 609 secs. I then add the value of the previous two cols (609) to the next col$sup_2 to get the total duration (609 + 596) and so on until the last condition col$sup_4.
I have tried the method below which is for subject 6 (row 1), which works fine, but I want to tidy this up and make it easier as I have 17 subjects (rows) and have been advised there is an easier way around this:
sup_b <- 175
hdt <- (sup_b + 434)
sup_2 <- (hdt + 596)
lbnp <- (sup_2 + 585)
sup_3 <- (hdt_lbnp + 601)
hut <- (sup_3 + 593)
sup_4 <- (hut + 211)
I want to be able to just change the number of row and have the data pulled across from the data frame rather than entering each individual time period; for instance:
line <- 1 ### the row I want which corresponds to the subject
sup_b <- df[line, 2]
hdt <-df[line, 2] + df[line, 3]
but I keep getting this warning message:
In Ops.factor(df[line, 2], df[line, 3]) : ‘+’ not meaningful for factor
I have even tried: colSums(df[,c(2:3)]), but get the following warning:
Error in colSums(df[, c(2:3)]) : 'x' must be numeric.
also tried: st$sum <- apply(df[,c(2:3)], 1, sum), which doesn't work either.
df1[-1] <- t(apply(df1[-1],1,cumsum))
# Sub sup_b hdt sup_2 lbnp sup_3 hut sup_4
# 1 6 175 609 1205 1790 2391 2984 3195
# 2 7 130 722 1314 1907 2507 2891 3057
data
df1 <- read.table(text="Sub sup_b hdt sup_2 lbnp sup_3 hut sup_4
6 175 434 596 585 601 593 211
7 130 592 592 593 600 384 166",h=T,strin=F)
I've just started my adventure with programming in R. I need to create a program summing numbers divisible by 3 and 5 in the range of 1 to 1000, using the '%%' operator. I came up with an idea to create two matrices with the numbers from 1 to 1000 in one column and their remainders in the second one. However, I don't know how to sum the proper elements (kind of "sum if" function in Excel). I attach all I've done below. Thanks in advance for your help!
s1<-1:1000
in<-s1%%3
m1<-matrix(c(s1,in), 1000, 2, byrow=FALSE)
s2<-1:1000
in2<-s2%%5
m2<-matrix(c(s2,in2),1000,2,byrow=FALSE)
Mathematically, the best way is probably to find the least common multiple of the two numbers and check the remainder vs that:
# borrowed from Roland Rau
# http://r.789695.n4.nabble.com/Greatest-common-divisor-of-two-numbers-td823047.html
gcd <- function(a,b) if (b==0) a else gcd(b, a %% b)
lcm <- function(a,b) abs(a*b)/gcd(a,b)
s <- seq(1000)
s[ (s %% lcm(3,5)) == 0 ]
# [1] 15 30 45 60 75 90 105 120 135 150 165 180 195 210
# [15] 225 240 255 270 285 300 315 330 345 360 375 390 405 420
# [29] 435 450 465 480 495 510 525 540 555 570 585 600 615 630
# [43] 645 660 675 690 705 720 735 750 765 780 795 810 825 840
# [57] 855 870 885 900 915 930 945 960 975 990
Since your s is every number from 1 to 1000, you could instead do
seq(lcm(3,5), 1000, by=lcm(3,5))
Just use sum on either result if that's what you want to do.
Props to #HoneyDippedBadger for figuring out what the OP was after.
See if this helps
x =1:1000 ## Store no. 1 to 1000 in variable x
x ## print x
Div = x[x%%3==0 & x%%5==0] ## Extract Nos. divisible by 3 & 5 both b/w 1 to 1000
Div ## Nos. Stored in DIv which are divisible by 3 & 5 both
length(Div)
table(x%%3==0 & x%%5==0) ## To see how many are TRUE for given condition
sum(Div) ## Sums up no.s divisible by both 3 and 5 b/w 1 to 1000
Let's say I'd like to calculate the magnitude of the range over a few columns, on a row-by-row basis.
set.seed(1)
dat <- data.frame(x=sample(1:1000,1000),
y=sample(1:1000,1000),
z=sample(1:1000,1000))
Using data.frame(), I would do something like this:
dat$diff_range <- apply(dat,1,function(x) diff(range(x)))
To put it more simply, I'm looking for this operation, over each row:
diff(range(dat[1,]) # for i 1:nrow(dat)
If I were doing this for the entire table, it would be something like:
setDT(dat)[,diff_range := apply(dat,1,function(x) diff(range(x)))]
But how would I do it for only named (or numbered) rows?
pmax and pmin find the min and max across columns in a vectorized way, which is much better than splitting and working with each row separately. It's also pretty concise:
dat[, r := do.call(pmax,.SD) - do.call(pmin,.SD)]
x y z r
1: 266 531 872 606
2: 372 685 967 595
3: 572 383 866 483
4: 906 953 437 516
5: 201 118 192 83
---
996: 768 945 292 653
997: 61 231 965 904
998: 771 145 18 753
999: 841 148 839 693
1000: 857 252 218 639
How about this:
D[,list(I=.I,x,y,z)][,diff(range(x,y,z)),by=I][c(1:4,15:18)]
# I V1
#1: 1 971
#2: 2 877
#3: 3 988
#4: 4 241
#5: 15 622
#6: 16 684
#7: 17 971
#8: 18 835
#actually this will be faster
D[c(1:4,15:18),list(I=.I,x,y,z)][,diff(range(x,y,z)),by=I]
use .I to give you an index to call with the by= parameter, then you can run the function on each row. The second call pre-filters by any list of row numbers, or you can add a key and filter on that if your real table looks different.
You can do it by subsetting before/during the function. If you only want every second row for example
dat_Diffs <- apply(dat[seq(2,1000,by=2),],1,function(x) diff(range(x)))
Or for rownames 1:10 (since their names weren't specified they are just numbers counting up)
dat_Diffs <- apply(dat[rownames(dat) %in% 1:10,],1,function(x) diff(range(x)))
But why not just calculate per row then subset later?
I have a list of lists that contain the following 2 variables:
> dist_sub[[1]]$zip
[1] 901 902 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928
[26] 929 930 931 933 934 935 936 937 938 939 940 955 961 962 963 965 966 968 969 970 975 981
> dist_sub[[1]]$hu
[1] 4990 NA 168 13224 NA 3805 NA 6096 3884 4065 NA 16538 NA 12348 10850 NA
[17] 9322 17728 NA 13969 24971 5413 47317 7893 NA NA NA NA NA 140 NA 4
[33] NA NA NA NA NA 13394 8939 NA 3848 7894 2228 17775 NA NA NA
> dist_sub[[2]]$zip
[1] 921 934 952 956 957 958 959 960 961 962 965 966 968 969 970 971
> dist_sub[[2]]$hu
[1] 17728 140 4169 32550 18275 NA 22445 0 13394 8939 3848 7894 2228 17775 NA 12895
Is there a way remove duplicates such that if a zipcode appears in one list is removed from other lists according to specific criteria?
Example: zipcode 00921 is present in the two lists above. I'd like to keep it only on the list with the lowest sum of hu (housing units). In this I would like to keep zipcode 00921 in the 2nd list only since the sum of hu is 162,280 in list 2 versus 256,803 in list 1.
Any help is very much appreciated.
Here is a simulate dataset for your problem so that others can use it too.
dist_sub <- list(list("zip"=1:10,
"hu"=rnorm(10)),
list("zip"=8:12,
"hu"=rnorm(5)),
list("zip"=c(1, 3, 11, 7),
"hu"=rnorm(4))
)
Here's a solution that I was able to come up with. I realized that loops were really the cleaner way to do this:
do.this <- function (x) {
for(k in 1:(length(x) - 1)) {
for (l in (k + 1):length(x)) {
to.remove <- which(x[[k]][["zip"]] %in% x[[l]][["zip"]])
x[[k]][["zip"]] <- x[[k]][["zip"]][-to.remove]
x[[k]][["hu"]] <- x[[k]][["hu"]][-to.remove]
}
}
return(x)
}
The idea is really simple: for each set of zips we keep removing the elements that are repeated in any set after it. We do this until the penultimate set because the last set will be left with no repeats in anything before it.
The solution to use the criterion you have, i.e. lowest sum of hu can be easily implemented using the function above. What you need to do is reorder the list dist_sub by sum of hu like so:
sum_hu <- sapply(dist_sub, function (k) sum(k[["hu"]], na.rm=TRUE))
dist_sub <- dist_sub[order(sum_hu, decreasing=TRUE)]
Now you have dist_sub sorted by sum_hu which means that for each set the sets that come before it have larger sum_hu. Therefore, if sets at values i and j (i < j) have values a in common, then a should be removed from ith element. That is what this solution does too. Do you think that makes sense?
PS: I've called the function do.this because I usually like writing generic solutions while this was a very specific question, albeit, an interesting one.