I have a simple code for matrix
ind1=which(macierz==1,arr.ind = TRUE)
fragment of theresult is
> ind1
row col
TCGA.CH.5737.01 53 1
TCGA.CH.5791.01 66 1
P03.1334.Tumor 322 1
P04.1790.Tumor 327 1
CPCG0340.F1 425 1
TCGA.CH.5737.01 53 2
TCGA.CH.5791.01 66 2
P03.1334.Tumor 322 2
P04.1790.Tumor 327 2
CPCG0340.F1 425 2
I would like to sort it by first column alphabetical. How can I do this in R?
It looks as if ind1 is a matrix and the first column is the rownames, so you probably need something like ind1 <- ind1[order(rownames(ind1)),]
You need (assuming your first column is called "label" and those are not rownames)
ind1[order(ind1$label),]
order() return a list of row indexes after sorting alphabetically the data frame. Just to make the example reproducible I created your data frame so
ind1 <- data.frame ( label = c("TCGA.CH.5737.01", "TCGA.CH.5791.01",
"P03.1334.Tumor","P04.1790.Tumor", "CPCG0340.F1" , "TCGA.CH.5737.01",
"TCGA.CH.5791.01","P03.1334.Tumor", "P04.1790.Tumor", "CPCG0340.F1"),
row = c(53,66,322,327,425,53,66,322,327,425), col =
c(1,1,1,1,1,2,2,2,2,2),
stringsAsFactors = FALSE)
and the output is
> ind1[order(ind1$label),]
label row col
5 CPCG0340.F1 425 1
10 CPCG0340.F1 425 2
3 P03.1334.Tumor 322 1
8 P03.1334.Tumor 322 2
4 P04.1790.Tumor 327 1
9 P04.1790.Tumor 327 2
1 TCGA.CH.5737.01 53 1
6 TCGA.CH.5737.01 53 2
2 TCGA.CH.5791.01 66 1
7 TCGA.CH.5791.01 66 2
Hope that helps.
Regards, Umberto
Related
when I do
table(df$strategy.x)
0 1 2 3
70 514 223 209
table(df$strategy.y)
0 1 2 3
729 24 7 4
I want to create a variable with both of these combined. I tried this
df <- df %>%
mutate(nstrategy1 = ifelse(strategy.x==1| strategy.y==1 , 1, 0))
table(df$nstrategy1)
0 1
399 519
I am supposed to get 514 + 24 = 538 but I got 519 instead
df <- df %>% mutate(nstrategy2 = ifelse(strategy.x==2| strategy.y==2 , 1, 0))
table(df$nstrategy2)
0 1
578 228
Similarly, I am supposed to get 223 + 7 = 230, but I got 228 instead
Is there a good way to merge both strategy.x and strategy.y and end up with a table like the following with 4 categories?
0 1 2 3
799 538 230 213
table(mtcars$am) # 13 1's
table(mtcars$vs) # 14 1's
mtcars$ones = ifelse(mtcars$am == 1 | mtcars$vs == 1, 1, 0)
table(mtcars$ones) # 20 1's < 13 + 14 = 27
Why is it showing only 20 1's instead of 27? It's because there are 7 + 6 + 7 = 20 cars with either one or two 1's in am and vs. There are 13 with am==1 (6+7), and 14 with vs==1 (7+7). Seven cars are in the bottom left because they have 1's in both dimensions, which you are expecting/seeking to count twice.
table(mtcars$am, mtcars$vs)
# 0 1
# 0 12 7
# 1 6 7
The simplest way to get the sum of the two results would be by adding the two table objects:
table(mtcars$am) + table(mtcars$vs)
# 0 1
# 37 27
I want to fill NA rows based on checking the differences between the closest non-NA labeled rows.
For instance
data <- data.frame(sd_value=c(34,33,34,37,36,45),
value=c(383,428,437,455,508,509),
label=c(c("bad",rep(NA,4),"unable")))
> data
sd_value value label
1 34 383 bad
2 33 428 <NA>
3 34 437 <NA>
4 37 455 <NA>
5 36 508 <NA>
6 45 509 unable
I want to evaluate how to change NA rows with checking the difference between sd_value and value those close to bad and unablerows.
if we want to get differences between the rows we can do;
library(dplyr)
data%>%
mutate(diff_val=c(0,diff(value)), diff_sd_val=c(0,diff(sd_value)))
sd_value value label diff_val diff_sd_val
1 34 383 bad 0 0
2 33 428 <NA> 45 -1
3 34 437 <NA> 9 1
4 37 455 <NA> 18 3
5 36 508 <NA> 53 -1
6 45 509 unable 1 9
The condition how I want to label the NA rows is
if the diff_val<50 and diff_sd_val<9 label them with the last non-NA label else use the first non-NA label after the last NA row.
So that the expected output would be
sd_value value label diff_val diff_sd_val
1 34 383 bad 0 0
2 33 428 bad 45 -1
3 34 437 bad 9 1
4 37 455 bad 18 3
5 36 508 unable 53 -1
6 45 509 unable 1 9
The possible solution I cooked up so far:
custom_labelling <- function(x,y,label){
diff_sd_val<-c(NA,diff(x))
diff_val<-c(NA,diff(y))
label <- NA
for (i in 1:length(label)){
if(is.na(label[i])&diff_sd_val<9&diff_val<50){
label[i] <- label
}
else {
label <- label[i]
}
}
return(label)
}
which gives
data%>%
mutate(diff_val=c(0,diff(value)), diff_sd_val=c(0,diff(sd_value)))%>%
mutate(custom_label=custom_labelling(sd_value,value,label))
Error in mutate_impl(.data, dots) :
Evaluation error: missing value where TRUE/FALSE needed.
In addition: Warning message:
In if (is.na(label[i]) & diff_sd_val < 9 & diff_val < 50) { :
the condition has length > 1 and only the first element will be used
One option is to find NA and non-NA index and based on the condition select the closest label to it.
library(dplyr)
#Create a new dataframe with diff_val and diff_sd_val
data1 <- data%>% mutate(diff_val=c(0,diff(value)), diff_sd_val=c(0,diff(sd_value)))
#Get the NA indices
NA_inds <- which(is.na(data1$label))
#Get the non-NA indices
non_NA_inds <- setdiff(1:nrow(data1), NA_inds)
#For every NA index
for (i in NA_inds) {
#Check the condition
if(data1$diff_sd_val[i] < 9 & data1$diff_val[i] < 50)
#Get the last non-NA label
data1$label[i] <- data1$label[non_NA_inds[which.max(i > non_NA_inds)]]
else
#Get the first non-NA label after last NA value
data1$label[i] <- data1$label[non_NA_inds[i < non_NA_inds]]
}
data1
# sd_value value label diff_val diff_sd_val
#1 34 383 bad 0 0
#2 33 428 bad 45 -1
#3 34 437 bad 9 1
#4 37 455 bad 18 3
#5 36 508 unable 53 -1
#6 45 509 unable 1 9
You can remove diff_val and diff_sd_val columns later if not needed.
We can also create a function
custom_label <- function(label, diff_val, diff_sd_val) {
NA_inds <- which(is.na(label))
non_NA_inds <- setdiff(1:length(label), NA_inds)
new_label = label
for (i in NA_inds) {
if(diff_sd_val[i] < 9 & diff_val[i] < 50)
new_label[i] <- label[non_NA_inds[which.max(i > non_NA_inds)]]
else
new_label[i] <- label[non_NA_inds[i < non_NA_inds]]
}
return(new_label)
}
and then apply it
data%>%
mutate(diff_val = c(0, diff(value)),
diff_sd_val = c(0, diff(sd_value)),
new_label = custom_label(label, diff_val, diff_sd_val))
# sd_value value label diff_val diff_sd_val new_label
#1 34 383 bad 0 0 bad
#2 33 428 <NA> 45 -1 bad
#3 34 437 <NA> 9 1 bad
#4 37 455 <NA> 18 3 bad
#5 36 508 <NA> 53 -1 unable
#6 45 509 unable 1 9 unable
If we want to apply it by group we can add a group_by statement and it should work.
data%>%
group_by(group) %>%
mutate(diff_val = c(0, diff(value)),
diff_sd_val = c(0, diff(sd_value)),
new_label = custom_label(label, diff_val, diff_sd_val))
I have a binomail dataset that looks like this:
df <- data.frame(replicate(4,sample(1:200,1000,rep=TRUE)))
addme <- data.frame(replicate(1,sample(0:1,1000,rep=TRUE)))
df <- cbind(df,addme)
df <-df[order(df$replicate.1..sample.0.1..1000..rep...TRUE..),]
The data is currently soreted in a way to show the instances belonging to 0 group then the ones belonging to the 1 group. Is there a way I can sort the data in a 0-1-0-1-0... fashion? I mean to show a row that belongs to the 0 group, the row after belonging to the 1 group then the zero group and so on...
All I can think about is complex functions. I hope there's a simple way around it.
Thank you,
Here's an attempt, which will add any extra 1's at the end:
First make some example data:
set.seed(2)
df <- data.frame(replicate(4,sample(1:200,10,rep=TRUE)),
addme=sample(0:1,10,rep=TRUE))
Then order:
with(df, df[unique(as.vector(rbind(which(addme==0),which(addme==1)))),])
# X1 X2 X3 X4 addme
#2 141 48 78 33 0
#1 37 111 133 3 1
#3 115 153 168 163 0
#5 189 82 70 103 1
#4 34 37 31 174 0
#6 189 171 98 126 1
#8 167 46 72 57 0
#7 26 196 30 169 1
#9 94 89 193 134 1
#10 110 15 27 31 1
#Warning message:
#In rbind(which(addme == 0), which(addme == 1)) :
# number of columns of result is not a multiple of vector length (arg 1)
Here's another way using dplyr, which would make it suitable for within-group ordering. It's also probably pretty quick. If there's unbalanced numbers of 0's and 1's, it will leave them at the end.
library(dplyr)
df %>%
arrange(addme) %>%
mutate(n0 = sum(addme == 0),
orderme = seq_along(addme) - (n0 * addme) + (0.5 * addme)) %>%
arrange(orderme) %>%
select(-n0, -orderme)
I work with neuralnet package to predict values of stocks (diploma thesis). The example data are below
predict<-runif(23,min=0,max=1)
day<-c(369:391)
ChoosedN<-c(2,5,5,5,5,5,4,3,5,5,5,2,1,1,5,5,4,3,2,3,4,3,2)
Profit<-runif(23,min=-2,max=5)
df<-data.frame(predict,day,ChoosedN,Profit)
colnames(df)<-c('predict','day','ChoosedN','Profit')
But I haven't always same period for investments (ChoodedN). For backtest the neural site I have to skip the days when I am still in position even if the neural site says 'buy it' (i.e.predict > 0.5). The frame looks like this
predict day ChoosedN Profit
1 0.6762981061 369 2 -1.6288823350
2 0.0195611224 370 5 1.5682195597
3 0.2442795106 371 5 0.6195915225
4 0.9587601107 372 5 -1.9701975542
5 0.7415729680 373 5 3.7826137026
6 0.4814927997 374 5 4.1228808255
7 0.1340754859 375 4 3.7818792837
8 0.6316874851 376 3 0.7670884461
9 0.1107241728 377 5 -1.3367400097
10 0.5850426450 378 5 2.2848396166
11 0.2809308425 379 5 2.5234691438
12 0.2835292015 380 2 -0.3291319925
13 0.3328713216 381 1 4.7425349397
14 0.4766904986 382 1 -0.4062103292
15 0.5005860797 383 5 4.8612083721
16 0.2734292494 384 5 -0.2320077328
17 0.1488479455 385 4 2.6195679584
18 0.9446908936 386 3 0.4889716264
19 0.8222738281 387 2 0.7362413658
20 0.7570014759 388 3 4.6661250258
21 0.9988698252 389 4 2.6340743946
22 0.8384663551 390 3 1.0428046484
23 0.1938821415 391 2 0.8855748393
And I need to create new data.frame this way.For example:If predict (in first row) > 0.5,delete second and third row (because ChoosedN in first row is 2 so next two after first row has to be delete, because there we were still in position). And continue on fourth the same way (if predict (fourth row) > 0.5, delete next five rows and so. And of course, if predict <=0.5 delete this row too.
Any straightforward way how to do it with some loop?
Thanks
I would create a new dataframe, then bind the rows you want using rbind inside of a for loop
newDF <- data.frame() # New, Empty Dataframe
i = 1 # Loop index Variable
while (i < nrow(df)) {
if (df$predict[i] > 0.5) { # If predict > 0.5,
newDF <- rbind(newDF, df[i,]) # Bind the row
i = i + df$ChoosedN[i] # Adjust for ChoosedN rows
}
i = i + 1 # Move to the next row
}
I have a data frame having 20 columns. I need to filter / remove noise from one column. After filtering using convolve function I get a new vector of values. Many values in the original column become NA due to filtering process. The problem is that I need the whole table (for later analysis) with only those rows where the filtered column has values but I can't bind the filtered column to original table as the number of rows for both are different. Let me illustrate using the 'age' column in 'Orange' data set in R:
> head(Orange)
Tree age circumference
1 1 118 30
2 1 484 58
3 1 664 87
4 1 1004 115
5 1 1231 120
6 1 1372 142
Convolve filter used
smooth <- function (x, D, delta){
z <- exp(-abs(-D:D/delta))
r <- convolve (x, z, type='filter')/convolve(rep(1, length(x)),z,type='filter')
r <- head(tail(r, -D), -D)
r
}
Filtering the 'age' column
age2 <- smooth(Orange$age, 5,10)
data.frame(age2)
The number of rows for age column and age2 column are 35 and 15 respectively. The original dataset has 2 more columns and I like to work with them also. Now, I only need 15 rows of each column corresponding to the 15 rows of age2 column. The filter here removed first and last ten values from age column. How can I apply the filter in a way that I get truncated dataset with all columns and filtered rows?
You would need to figure out how the variables line up. If you can add NA's to age2 and then do Orange$age2 <- age2 followed by na.omit(Orange) you should have what you want. Or, equivalently, perhaps this is what you are looking for?
df <- tail(head(Orange, -10), -10) # chop off the first and last 10 observations
df$age2 <- age2
df
Tree age circumference age2
11 2 1004 156 915.1678
12 2 1231 172 876.1048
13 2 1372 203 841.3156
14 2 1582 203 911.0914
15 3 118 30 948.2045
16 3 484 51 1008.0198
17 3 664 75 955.0961
18 3 1004 108 915.1678
19 3 1231 115 876.1048
20 3 1372 139 841.3156
21 3 1582 140 911.0914
22 4 118 32 948.2045
23 4 484 62 1008.0198
24 4 664 112 955.0961
25 4 1004 167 915.1678
Edit: If you know the first and last x observations will be removed then the following works:
x <- 2
df <- tail(head(Orange, -x), -x) # chop off the first and last x observations
df$age2 <- age2