Hi Val,
Here's an answer using a series of ifelse() statements. Because the d4
column is created initially using NA as a placeholder, you can check
your conditional logic at the end using table(!is.na(dat2$d4)):
> dat2 <-read.table(text="ID d1 d2 d3
+ A 0 25 35
+ B 12 22 0
+ C 0 0 31
+ E 10 2
Hi val,
You had a "conditional leak" in your ifelse statements:
dat2 <-read.table(text="ID d1 d2 d3
A 0 25 35
B 12 22 0
C 0 0 31
E 10 20 30
F 0 0 0",
header=TRUE,stringsAsFactors=FALSE)
dat2$d4<-
ifelse(dat2$d1,dat2$d1,ifelse(dat2$d2,dat2$d2,ifelse(dat2$d3,dat2$d3,0)))
Even though it works, it i
I generally find nested ifelse's to be confusing and prone to error, so I
usually prefer to proceed sequentially using subsetting with logicals or
replicated, but not nested ifelse's. In your example, the translation to
logical indexing seems pretty straightforward.
Using your example:
> dat2 <-w
HI All, I am having a little issue in my ifelse statement,
The data frame looks like as follow.
dat2 <-read.table(text="ID d1 d2 d3
A 0 25 35
B 12 22 0
C 0 0 31
E 10 20 30
F 0 0 0",header=TRUE,stringsAsFactors=F)
I want to create d4 and set the value based on the following conditions.
If d1
On 10/22/19 10:19 PM, Yeasmin Alea wrote:
Thank you. Can you please have a look the below data sets, script and
question?
*Dataset-1: Pen*
*YEAR DAY X Y Sig phase *
* *
*1 1981 9 -0.213 1.08 1.10 Phase-7*
*2 198110 0.065 1.05 1.05 Phase-6*
*Dataset-
Thank you. Can you please have a look the below data sets, script and
question?
*Dataset-1: Pen*
*YEAR DAY X Y Sig phase *
* *
*1 1981 9 -0.213 1.08 1.10 Phase-7*
*2 198110 0.065 1.05 1.05 Phase-6*
*Dataset-2: Book*
*YEAR Time *
*1 1981 1
Hi Yeasmin,
I suspect that you didn't intend to have conditions like:
a<0 && b>0 && b 0 && abs(b) < abs(a)
If this is the case, the following function seems to return the values
of phase that you want:
assign_phase<-function(x,y) {
phase<-c(1,2,7,8,3,4,6,5)
phase_index<-4 * (x > 0) + 2 * (y >
Both your syntax and semantics are wrong. This indicates to me that you
should spend more time with some basic R tutorials before proceeding.
That said, here are some of the errors:
1) You are not using sapply correctly. Moreover, no R level iteration is
needed anyway (sapply() iterates over colu
Here is another way of doing it by computing the index based on the
conditions
> input <- read_delim(" YEAR DAY X Y Sig
+ 1981 9 -0.213 1.08 1.10
+ 198110 0.065 1.05 1.05", delim = ' ', trim_ws = TRUE)
>
> input <- mutate(input,
+ phase = case_when(X < 0 & Y < 0 & Y
Had the condition for phase=2 incorrect:
library(tidyverse)
input <- read_delim(" YEAR DAY X Y Sig
1981 9 -0.213 1.08 1.10
198110 0.065 1.05 1.05", delim = ' ', trim_ws = TRUE)
input <- mutate(input,
phase = case_when(X < 0 & Y < 0 & Y < X ~ 'phase=1',
Here is one way of doing it; I think the output you show is wrong:
library(tidyverse)
input <- read_delim(" YEAR DAY X Y Sig
1981 9 -0.213 1.08 1.10
198110 0.065 1.05 1.05", delim = ' ', trim_ws = TRUE)
input <- mutate(input,
phase = case_when(X < 0 & Y < 0 & Y < X
Hello Team
I would like to add a new column (for example-Phase) from the below data
set based on the conditions
YEAR DAY X Y Sig
1 1981 9 -0.213 1.08 1.10
2 198110 0.065 1.05 1.05
*Conditions*
D$Phase=sapply(D,function(a,b) {
a <-D$X
b<-D$Y
if (a<0 &
To drop the burnin period
xx <- x1$x[-seq_len(burnin)]
And it sounds like you want
mean(xx > -1 & xx < 2]
but your code and description differ.
On Sun, 5 Jun 2011, Kehl Dániel wrote:
Dear All,
I have a MCMC result in x1. I was wondering if there is a simpler, more
elegant way of evaluatin
Dear All,
I have a MCMC result in x1. I was wondering if there is a simpler, more
elegant way of evaluating the estimate of an integral then this (I am
pretty sure there is):
Also if I want to count the x's say -1in period.
[code]
z <- -2
burnin <- 2000
int1 <-
length(x1$x[(burnin+1):length(
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