I want to merge the two matrices according to the column names and row names.
The values in both matrices are numeric, and the merger is to average the two matrices that have appeared together.
matrix1:
A B C
x 1 4 3
z 5 2 4
k 1 2 3
and matrix2:
A B C D
x 6 4 1 2
y 2 3 1 3
z 1 4 1 4
k 7 5 3 1
so the output will be:
A B C D
x 3.5 4 2 2
y 2 3 1 3
z 3 3 2.5 4
k 4 3.5 3 1
My idea is to use for loop or apply function, but if the matrices are big, then this program will run for a long time. Any advice? Thank you!
You can use rownames and colnames to subset matrix2 and update only part of it.
matrix2[rownames(matrix1), colnames(matrix1)] <- (matrix1 + matrix2[rownames(matrix1), colnames(matrix1)])/2
matrix2
# A B C D
#x 3.5 4.0 2.0 2
#y 2.0 3.0 1.0 3
#z 3.0 3.0 2.5 4
#k 4.0 3.5 3.0 1
data
matrix1 <- structure(c(1L, 5L, 1L, 4L, 2L, 2L, 3L, 4L, 3L), .Dim = c(3L,
3L), .Dimnames = list(c("x", "z", "k"), c("A", "B", "C")))
matrix2 <- structure(c(6L, 2L, 1L, 7L, 4L, 3L, 4L, 5L, 1L, 1L, 1L, 3L, 2L,
3L, 4L, 1L), .Dim = c(4L, 4L), .Dimnames = list(c("x", "y", "z",
"k"), c("A", "B", "C", "D")))
Related
So, I have a large data frame with monthly observations of n individuals.
ind y_0101 y_0102 y_0103 y_0104_ .... y_0311 y_0312
A 33 6 1 2 1 5
B 36 5 0 2 1 5
C 22 4 1 NA 1 5
D 2 2 0 2 1 5
E 5 2 1 2 1 6
F 7 1 0 2 1 5
G 8 6 1 2 1 5
H 2 8 0 2 2 5
I 1 3 1 2 1 5
J 3 2 0 2 1 5
I want to create a new data frame, in which include the individuals who meet some specific conditions.
E.g. if, for individual i, the range of column y_0101:y_0312 does NOT include values of 3 & 6 & NA, AND include values of 2 | 1 THEN for individual i should be included in new data frame. Which produce the following data frame:
ind y_0101 y_0102 y_0103 y_0104_ .... y_0311 y_0312
B 36 5 0 2 1 5
D 2 2 0 2 1 5
F 7 1 0 2 1 5
H 2 8 0 2 2 5
I tried different ways, but I can't figure out how to get multiple conditions included.
df <- df %>% filter(vars(starts_with("y_"))!=3 | !=6 | != NA)
or
df <- df %>% filter_at(vars(starts_with("y_")), all_vars(!=3 | !=6 | != NA)
I've tried some other things as well, like !%in%, but that doesn't seem to work. Any ideas?
I think you're almost there, but might need a slight shift in the logic:
df <- data.frame(A1 = 1:10,
A2 = 10:1,
A3 = 1:10,
B1 = 1:10)
df %>%
filter_at(vars(starts_with("A")), ~!(.x %in% c(3, 6, NA))) %>%
filter(if_any(starts_with("A"), ~ .x %in% c(1, 2)))
In the first step, I filter out all rows where any of the columns are 3, 6, or NA. In the second row, I filter down to only rows where at least one of the columns is 1 or 2. Does this help with your case?
Here is a base R option using rowSums :
cols <- grep('y_', names(df))
include <- c(1, 2)
not_include <- c(3, 6, NA)
result <- subset(df, rowSums(sapply(df[cols], `%in%`, include)) > 0 &
rowSums(sapply(df[cols], `%in%`, not_include)) == 0)
result
# ind y_0101 y_0102 y_0103 y_0104 y_0311 y_0312
#2 B 36 5 0 2 1 5
#4 D 2 2 0 2 1 5
#6 F 7 1 0 2 1 5
#8 H 2 8 0 2 2 5
data
df <- structure(list(ind = c("A", "B", "C", "D", "E", "F", "G", "H",
"I", "J"), y_0101 = c(33L, 36L, 22L, 2L, 5L, 7L, 8L, 2L, 1L,
3L), y_0102 = c(6L, 5L, 4L, 2L, 2L, 1L, 6L, 8L, 3L, 2L), y_0103 = c(1L,
0L, 1L, 0L, 1L, 0L, 1L, 0L, 1L, 0L), y_0104 = c(2L, 2L, NA, 2L,
2L, 2L, 2L, 2L, 2L, 2L), y_0311 = c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L), y_0312 = c(5L, 5L, 5L, 5L, 6L, 5L, 5L, 5L, 5L, 5L
)), class = "data.frame", row.names = c(NA, -10L))
I have a longer dataframe with student name, subjects, question names and their marks. A short version of this dataframe looks like the below:
c1 c2 c3 c4
A 1 a 1
A 1 b 0.5
A 1 c 1
A 2 a 2
A 2 b 1.5
A 2 c 3
A 2 d 3
B 1 a 0
B 1 b 1.5
B 1 c 2
B 2 a 2
B 2 b 2.5
B 2 c 4
B 2 d 5
Here A, B are students, 1,2 are subjects, a, b,c are question names and the last column is marks obtained by each student, in each subject and in each question.
I would like to consolidate part of dataframe with question name column becoming heading for several columns with corresponding marks like below:
Student Sub:1 Sub:2
Name a b c a b c d
A 1 0.5 1 2 1.5 3 3
B 0 1.5 2 2 2.5 4 5
I can use transpose function (t(dataframe)) to switch column to rows but I don't know how to do partly retaining details of students and marks.
Can some one guide how can I achieve this?
We can use pivot_wider, supplying column names from c2 and c3 columns and value from c4.
tidyr::pivot_wider(df,names_from = c(c2, c3),values_from = c4, names_prefix = "Sub")
# c1 Sub1_a Sub1_b Sub1_c Sub2_a Sub2_b Sub2_c Sub2_d
# <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 A 1 0.5 1 2 1.5 3 3
#2 B 0 1.5 2 2 2.5 4 5
Or with data.table, dcast
data.table::dcast(df, c1~paste0("Sub",c2)+c3, value.var = "c4")
data
df <- structure(list(c1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"),
c2 = c(1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L), c3 = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 1L, 2L, 3L, 4L), .Label = c("a", "b", "c", "d"), class = "factor"),
c4 = c(1, 0.5, 1, 2, 1.5, 3, 3, 0, 1.5, 2, 2, 2.5, 4, 5)), class = "data.frame",
row.names = c(NA, -14L))
Here is base R solution using reshape()
df$namecol <- with(df,paste0(c2,c3))
dfout <- reshape(df[-c(2:3)],direction = "wide",idvar = "c1",timevar = "namecol")
names(dfout) <- gsub(".*\\.(.*)","\\1",names(dfout))
such that
> dfout
c1 1a 1b 1c 2a 2b 2c 2d
1 A 1 0.5 1 2 1.5 3 3
8 B 0 1.5 2 2 2.5 4 5
I'm trying to delete some repeating information in my data set and replace it with NA. Here's an example of the data:
DataTable1
ID Day x y
1 1 1 3
1 2 1 3
2 1 2 5
2 2 2 5
3 1 3 4
3 2 3 4
4 1 4 6
4 2 4 6
I'm trying to replace "x" and "y" values with "NA" when Day=1. This is what I want:
ID Day x y
1 1 NA NA
1 2 1 3
2 1 NA NA
2 2 2 5
3 1 NA NA
3 2 3 4
4 1 NA NA
4 2 4 6
I'm not really sure where to start or how to go about this. I tried using the replace_with_na_if function from the naniar library. Otherwise, I am unsure what to try.
replace_with_na_if(data.frame=DataTable1$x,
condition=DataTable1$Day== 2)
I received an error message that reads:
Error in replace_with_na_if(data.frame = DataTable1$x, condition = DataTable1$Day == :
unused argument (data.frame = DataTable1$x)
An option in base R would be to create a logical vector based on the elements of 'Day'. Use that index to subset the 'x', 'y' columns and assign them to NA
i1 <- df1$Day == 1
df1[i1, c('x', 'y')] <- NA
Here's a data.table solution. Since you may be new to R, you need to install the data.table package first. If you have a large data set, data.table may work faster than using data frame. Also, I find the syntax to be easy to read and understand.
#Create the data frame:
df <- structure(list(ID = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L), Day = c(1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L), x = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L), y = c(3L, 3L, 5L, 5L,
4L, 4L, 6L, 6L)), class = "data.frame", row.names = c(NA, -8L))
library(data.table)
dt <- setDT(df) # convert the data frame to a data.table
dt[Day == 1, c("x","y") := NA] # where Day equals 1, make the columns x and y equal NA
Good luck and welcome to stackoverflow!
Using dplyr, we can use mutate_at and replace like
library(dplyr)
df %>% mutate_at(vars(x, y), ~replace(., Day == 1, NA))
# ID Day x y
#1 1 1 NA NA
#2 1 2 1 3
#3 2 1 NA NA
#4 2 2 2 5
#5 3 1 NA NA
#6 3 2 3 4
#7 4 1 NA NA
#8 4 2 4 6
data
df <- structure(list(ID = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L), Day = c(1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L), x = c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L), y = c(3L, 3L, 5L, 5L,
4L, 4L, 6L, 6L)), class = "data.frame", row.names = c(NA, -8L))
I’m trying to remove all the rows that have the same value in the "lan" column of my dataframe but different value for my "id" column (but not vice-versa).
Using an example dataset:
require(dplyr)
t <- structure(list(id = c(1L, 2L, 2L, 3L, 3L, 4L, 4L, 4L, 4L, 4L,
4L), lan = structure(c(1L, 2L, 3L, 4L, 4L, 5L, 5L, 5L, 6L, 1L,
7L), .Label = c("a", "b", "c", "d", "e", "f", "g"), class = "factor"),
value = c(0.22988498, 0.848989831, 0.538065821, 0.916571913,
0.304183372, 0.983348167, 0.356128559, 0.054102854, 0.400934593,
0.001026817, 0.488452667)), .Names = c("id", "lan", "value"
), class = "data.frame", row.names = c(NA, -11L))
t
I need to get rid of rows 1 and 10 because they have the same lan (a) but different id.
I've tried the following, without success:
a<-t[(!duplicated(t$id)),]
c<-a[duplicated(a$lan)|duplicated(a$lan, fromLast=TRUE),]
d<-t[!(t$lan %in% c$lan),]
Thanks for your help!
And an alternative using dplyr:
t2 <- t %>%
group_by(lan,id) %>%
summarise(value=sum(value)) %>%
group_by(lan) %>%
summarise(number=n()) %>%
filter(number>1) %>%
select(lan)
> t[!t$lan %in% t2$lan ,]
id lan value
2 2 b 0.84898983
3 2 c 0.53806582
4 3 d 0.91657191
5 3 d 0.30418337
6 4 e 0.98334817
7 4 e 0.35612856
8 4 e 0.05410285
9 4 f 0.40093459
11 4 g 0.48845267
You could use duplicated on "lan", to get the logical index of all elements that are duplicates, repeat the same with both columns together ('id', 'lan'), to get the elements not duplicated, check which of these elements are TRUE in both cases, negate, and subset.
indx1 <- with(t, duplicated(lan)|duplicated(lan,fromLast=TRUE))
indx2 <- !(duplicated(t[1:2])|duplicated(t[1:2],fromLast=TRUE))
t[!(indx1 & indx2),]
# id lan value
#2 2 b 0.84898983
#3 2 c 0.53806582
#4 3 d 0.91657191
#5 3 d 0.30418337
#6 4 e 0.98334817
#7 4 e 0.35612856
#8 4 e 0.05410285
#9 4 f 0.40093459
#11 4 g 0.48845267
i have a data frame generated inside a for loop and have this structure
V1 V2 V3
1 a a 1
2 a b 3
3 a c 2
4 a d 1
5 a e 3
6 b a 3
7 b b 1
8 b c 8
9 b d 1
10 b e 1
11 c a 2
12 c b 8
the data is longer than this , but that's the idea that i want
(transform it to a wide table [V1 by V2])
V3 is a value based on (V1, V2)
i want to rearrange data to be like this (with first col is the unique of V1 and first row is the unique of V2 and data between them are from V3 )
a b c d e
a 1 3 2 1 3
b 3 1 8 1 1
c 2 8 2 8 2
d 1 1 5 7 2
e 3 5 9 5 3
thnx in advance.
Reproducible example of yours:
df <- structure(list(V1 = structure(c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), V2 = structure(c(1L, 2L, 3L, 4L, 5L, 1L, 2L, 3L, 4L, 5L, 1L, 2L), .Label = c("a", "b", "c", "d", "e"), class = "factor"), V3 = c(1L, 3L, 2L, 1L, 3L, 3L, 1L, 8L, 1L, 1L, 2L, 8L)), .Names = c("V1", "V2", "V3"), class = "data.frame", row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12"))
And compute a basic crosstable based on your variables:
> xtabs(V3~V1+V2, df)
V2
V1 a b c d e
a 1 3 2 1 3
b 3 1 8 1 1
c 2 8 0 0 0
I hope you meant this :)
If df is your data-frame, assuming a unique V3 is mapped to each V1,V2 combination, you can do it with
with(df, tapply(V3, list(V1,V2), identity))
Another method, perhaps slightly more baroque, for widening a dataframe from a third column on the basis of the first two... with Chase that the OP has not given an unambiguous problem description:
df2 <- expand.grid(A=LETTERS[1:5], B=LETTERS[1:5])
df2$N <- 1:25
mtx <- outer(X=LETTERS[1:5],Y=LETTERS[1:5], FUN=function(x,y){
df2[intersect(which(df2$A==x), which(df2$B==y)), "N"] })
colnames(mtx)<-LETTERS[1:5]; rownames(mtx)<-LETTERS[1:5]
mtx
A B C D E
A 1 6 11 16 21
B 2 7 12 17 22
C 3 8 13 18 23
D 4 9 14 19 24
E 5 10 15 20 25
I'm sure there are many other strategies using reshape in base or dcast in reshape2.