data(airquality)
a=airquality
convert_fahr_to_kelvin <- function(temp) {
kelvin <- ((temp - 32) * (5 / 9)) + 273.15
return(kelvin)
}
a[,4]=
convert_fahr_to_kelvin(a[,4])
oz=a[,1]
sr=a[,2]
wv=a[,3]
te=a[,4]
pairs(~oz+sr+wv+te,
col = c("orange") ,
pch = c(18),
labels = c("Ozono", "Irradiancia Solar", "Velocidad del viento","Temperatura"),
main = "Diagramas de dispersión por parejas")
This is the graphic that i get
This is what i am doing but, actually i would like to differentiate between months, like 31 first numbers of my a matrix, from all columns, be color green, for example and this for each month, i tried to separate the numbers in groups using group:
group <- NA
group[sr[1:31]]<-1
group[sr[32:61]]<-2
group[sr[62:92]]<-3
group[sr[93:123]]<-4
group[sr[124:153]]<-5
group[sr[1:31]]
group[sr[32:61]]
group[sr[62:92]]
group[sr[93:123]]
group[sr[124:153]]
here the numbers repeated
But what i get is that if the numbers in each column are the same they get in the same group, and i have been trying to solve it in other ways but i don't finally get what i want.
It is easier to create a group with gl
group <- as.integer(gl(length(sr), 31, length(sr)))
table(group)
#group
#1 2 3 4 5
#31 31 31 31 29
In the OP's code, 'group' is initialized as NA of length 1. Then, it is assigned based on values of 'sr' instead of just
group <- integer(length(sr))
group[1:31] <- 1
group[32:61] <- 2
...
whereas if we use sr values as index
sr[1:31]
#[1] 190 118 149 313 NA NA 299 99 19 194 NA 256 290 274 65 334 307 78 322 44 8 320 25 92 66 266 NA 13 252 223 279
then group values that are changed to 1 are at positions 190, 118, 149, 313, ....
Related
I have 20 intervals:
10 intervals from 1 to 250 of size 25:
[1.25] [26.50] [51.75] [76.100] [101.125] [126.150] ... [226.250]
10 intervals from 251 to 1000 of size 75:
[251,325] [326,400] [401,475] [476,550] [551,625] ... [926,1000]
I would like to create a vector composed of the first 5 elements of each interval like:
(1,2,3,5, 26,27,28,29,30, 51,52,53,54,55, 76,77,78,79,80, ....,
251,252,253,254,255, 326,327,328,329,330, ...)
How create this vector using R?
Let's assume you have two interval like :
interval1 <- seq(1.25, 226.250, 25)
interval2 <- seq(251, 1000, 75)
We can create a new interval combining the two and then use mapply to create sequence
new_interval <- c(as.integer(interval1), interval2)
c(mapply(`:`, new_interval, new_interval + 4))
#[1] 1 2 3 4 5 26 27 28 29 30 51 52 53 54 .....
#[89] ..... 779 780 851 852 853 854 855 926 927 928 929 930
I am looking to workout a percentage total over a look back range in R.
I know how to do this in excel with the following formula:
=SUM(B2:B4)/SUM(B2:B4,C2:C4)
This is summing column B over a range of today looking back 3 lines. It then divides this sum buy the total sum of column B + C again looking back 3 lines.
I am looking to achieve the same calculation in R to run across my matrix.
The output would look something like this:
adv dec perct
1 69 376
2 113 293
3 270 150 0.355625492
4 74 371 0.359559402
5 308 96 0.513790386
6 236 173 0.491255962
7 252 134 0.663886572
8 287 129 0.639966969
9 219 187 0.627483444
This is a line of code I could perhaps add the look back range too:
perct <- apply(data.matrix[,c('adv','dec')], 1, function(x) { (x[1] / x[1] + x[2]) } )
If i could get [1] to sum the previous 3 line range and
If i could get [2] to also sum the previous 3 line range.
Still learning how to apply forward and look back periods within R. So any additional learning on the answer would be appreciated!
Here are some approaches. The first 3 use rollsumr and/or rollapplyr in zoo and the last one uses only the base of R.
1) rollsumr Create a matrix with rollsumr whose columns contain the rollling sums, convert that to row proportions and take the "adv" column. Finally assign that to a new column frac in DF. This approach has the shortest code.
library(zoo)
DF$frac <- prop.table(rollsumr(DF, 3, fill = NA), 1)[, "adv"]
giving:
> DF
adv dec frac
1 69 376 NA
2 113 293 NA
3 270 150 0.3556255
4 74 371 0.3595594
5 308 96 0.5137904
6 236 173 0.4912560
7 252 134 0.6638866
8 287 129 0.6399670
9 219 187 0.6274834
1a) This variation is similar except instead of using prop.table we write out the ratio. The code is longer but you may find it clearer.
m <- rollsumr(DF, 3, fill = NA)
DF$frac <- with(as.data.frame(m), adv / (adv + dec))
1b) This is a variation of (1) that is the same except it uses a magrittr pipeline:
library(magrittr)
DF %>% rollsumr(3, fill = NA) %>% prop.table(1) %>% `[`(TRUE, "adv") -> DF$frac
2) rollapplyr We could use rollapplyr with by.column = FALSE like this. The result is the same.
ratio <- function(x) sum(x[, "adv"]) / sum(x)
DF$frac <- rollapplyr(DF, 3, ratio, by.column = FALSE, fill = NA)
3) Yet another variation is to compute the numerator and denominator separately:
DF$frac <- rollsumr(DF$adv, 3, fill = NA) /
rollapplyr(DF, 3, sum, by.column = FALSE, fill = NA)
4) base This uses embed followed by rowSums on each column to get the rolling sums and then uses prop.table as in (1).
DF$frac <- prop.table(sapply(lapply(rbind(NA, NA, DF), embed, 3), rowSums), 1)[, "adv"]
Note: The input used in reproducible form is:
Lines <- "adv dec
1 69 376
2 113 293
3 270 150
4 74 371
5 308 96
6 236 173
7 252 134
8 287 129
9 219 187"
DF <- read.table(text = Lines, header = TRUE)
Consider an sapply that loops through the number of rows in order to index two rows back:
DF$pred <- sapply(seq(nrow(DF)), function(i)
ifelse(i>=3, sum(DF$adv[(i-2):i])/(sum(DF$adv[(i-2):i]) + sum(DF$dec[(i-2):i])), NA))
DF
# adv dec pred
# 1 69 376 NA
# 2 113 293 NA
# 3 270 150 0.3556255
# 4 74 371 0.3595594
# 5 308 96 0.5137904
# 6 236 173 0.4912560
# 7 252 134 0.6638866
# 8 287 129 0.6399670
# 9 219 187 0.6274834
Sorry for the confusing title, but i wasn't sure how to title what i am trying to do. My objective is to create a dataset of 1000 obs each would be the length of the run. I have created a phase1 dataset, from which a set of control limits are produced. What i am trying to do now is create a phase2 dataset most likely using rnorm. what im trying to do is create a repeat loop that will continuously create values in the phase2 dataset until one of those values is outside of the control limits produced from the phase1 dataset. for example if i had 3.0 and -3.0 as control limits the phase2 dataset would create a bunch of observations until obs 398 when the value here happens to be 3.45, thus stopping the creation of data. my objective is then to record the number 398. Furthermore, I am then trying to loop the code back to the phase1 dataset/ control limits portion and create a new set of control limits and then run another phase2, until i have 1000 run lengths recorded. the code i have for the phase1/ control limits works fine and looks like this:
nphase1=50
nphase2=1000
varcount=1
meanshift= 0
sigmashift= 1
##### phase1 dataset/ control limits #####
phase1 <- matrix(rnorm(nphase1*varcount, 0, 1), nrow = nphase1, ncol=varcount)
mean_var <- apply(phase1, 2, mean)
std_var <- apply(phase1, 2, sd)
df_var <- data.frame(mean_var, std_var)
Upper_SPC_Limit_Method1 <- with(df_var, mean_var + 3 * std_var)
Lower_SPC_Limit_Method1 <- with(df_var, mean_var - 3 * std_var)
df_control_limits<- data.frame(Upper_SPC_Limit_Method1, Lower_SPC_Limit_Method1)
I have previously created this code in SAS and it looks like this. might be a better reference for what i am trying to achieve then me trying to explain it.
%macro phase2_dataset (n=,varcount=, meanshift=, sigmashift=, nphase1=,simID=,);
%do z=1 %to &n;
%phase1_dataset (n=&nphase1, varcount=&varcount);
data phase2; set control_limits n=lastobs;
call streaminit(0);
do until (phase2_var1<Lower_SPC_limit_method1_var1 or
phase2_var1>Upper_SPC_limit_method1_var1);
phase2_var1 = rand("normal", &meanshift, &sigmashift);
output;
end;
run;
ods exclude all;
proc means data=phase2;
var phase2_var1;
ods output summary=x;
run;
ods select all;
data run_length; set x;
keep Phase2_var1_n;
run;
proc append base= QA.Phase2_dataset&simID data=Run_length force; run;
%end;
%mend;
Also been doing research about using a while loop in replace of the repeat loop.
Im new to R so Any ideas you are able to throw my way are greatly appreciated. Thanks!
Using a while loop indeed seems to be the way to go. Here's what I think you're looking for:
set.seed(10) #Making results reproducible
replicate(100, { #100 is easier to display here
phase1 <- matrix(rnorm(nphase1*varcount, 0, 1), nrow = nphase1, ncol=varcount)
mean_var <- colMeans(phase1) #Slightly better than apply
std_var <- apply(phase1, 2, sd)
df_var <- data.frame(mean_var, std_var)
Upper_SPC_Limit_Method1 <- with(df_var, mean_var + 3 * std_var)
Lower_SPC_Limit_Method1 <- with(df_var, mean_var - 3 * std_var)
df_control_limits<- data.frame(Upper_SPC_Limit_Method1, Lower_SPC_Limit_Method1)
#Phase 2
x <- 0
count <- 0
while(x > Lower_SPC_Limit_Method1 && x < Upper_SPC_Limit_Method1) {
x <- rnorm(1)
count <- count + 1
}
count
})
The result is:
[1] 225 91 97 118 304 275 550 58 115 6 218 63 176 100 308 844 90 2758
[19] 161 311 1462 717 2446 74 175 91 331 210 118 1517 420 32 39 201 350 89
[37] 64 385 212 4 72 730 151 7 1159 65 36 333 97 306 531 1502 26 18
[55] 67 329 75 532 64 427 39 352 283 483 19 9 2 1018 137 160 223 98
[73] 15 182 98 41 25 1136 405 474 1025 1331 159 70 84 129 233 2 41 66
[91] 1 23 8 325 10 455 363 351 108 3
If performance becomes a problem, perhaps it would be interesting to explore some improvements, like creating more numbers with rnorm() at a time and then counting how many are necessary to exceed the limits and repeat if necessary.
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 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