How can you iterate in a for loop with specific column names in R? This is the dataset I am using and below are the names of the columns I want to iterate. Also are the column number.
When I try to iterate, it does not compile. I need this to create a multiple cluster data visualization.
if (!require('Stat2Data')) install.packages('Stat2Data')
library(Stat2Data)
data("Hawks")
#summary(Hawks)
for (i in 10:13(Hawks)){
print(Hawks$ColumnName)
}
for (i in Hawks(c("Wing","Weight","Culmen","Hallux"))){
print(Hawks$ColumnName)
}
EDIT
After what Martin told me, this error occurs:
Error in [.data.frame`(Hawks, , i) : undefined columns selected
This is the code I have:
if(!require('DescTools')) {
install.packages('DescTools')
library('DescTools')
}
Hawks$Wing[is.na(Hawks$Wing)] <- mean(Hawks$Wing, na.rm = TRUE)
Hawks$Weight[is.na(Hawks$Weight)] <- mean(Hawks$Weight, na.rm = TRUE)
Hawks$Culmen[is.na(Hawks$Culmen)] <- mean(Hawks$Culmen, na.rm = TRUE)
Hawks$Hallux[is.na(Hawks$Hallux)] <- mean(Hawks$Hallux, na.rm = TRUE)
# Parámetro Wing
n <- nrow(Hawks) # Number of rows
for (col_names in 10:13){
x <- matrix(Hawks[, i],0.95*n)
#x <- rbind(x1,x2)
plot (x)
fit2 <- kmeans(x, 2)
y_cluster2 <- fit2$cluster
fit3 <- kmeans(x, 3)
y_cluster3 <- fit3$cluster
fit4 <- kmeans(x, 4)
y_cluster4 <- fit4$cluster
}
Related
I would like to run a bootstrap of a weighted mean in a for loop (I don’t think I can use ‘apply’ because it concerns a weighted mean). I would only need to store the resulting standard errors in a dataframe. Another post provided the code for how to calculate the weighted mean in a bootstrap (bootstrap weighted mean in R), and works perfectly:
library(boot)
mtcarsdata = mtcars #dataframe for data
mtcarsweights = rev(mtcars) #dataframe for weights
samplewmean <- function(d, i, j) {
d <- d[i, ]
w <- j[i, ]
return(weighted.mean(d, w))
}
results_qsec <- sd(boot(data= mtcarsdata[, 6, drop = FALSE],
statistic = samplewmean,
R=10000,
j = mtcarsweights[, 6 , drop = FALSE])[[2]], na.rm=T)
results_qsec
To then run it in a loop, I tried:
outputboot = matrix(NA, nrow=11, ncol=1)
for (k in 1:11){
outputboot[1,k] = sd(boot(data= mtcarsdata[, k, drop = FALSE],
statistic = samplewmean,
R=10000,
j = mtcarsweights[, k, drop = FALSE])[[2]], na.rm=T)
}
outputboot
But this doesn't work. The first output isn’t even correct. I suspect the code can’t work with two iterators: one for looping over the columns and the other for the sampling with replacement.
I hope anyone could offer some help.
This will calculate the standard deviation of all bootstraps for each column of the table mtcarsdata weighted by mtcarsweights.
Since we can calculate the result in one step, we can use apply and friends (here: purrr:map_dbl)
library(boot)
library(purrr)
set.seed(1337)
mtcarsdata <- mtcars # dataframe for data
mtcarsweights <- rev(mtcars) # dataframe for weights
samplewmean <- function(d, i, j) {
d <- d[i, ]
w <- j[i, ]
return(weighted.mean(d, w))
}
mtcarsdata %>%
ncol() %>%
seq() %>%
map_dbl(~ {
# .x is the number of the current column
sd(boot(
data = mtcarsdata[, .x, drop = FALSE],
statistic = samplewmean,
R = 10000,
j = mtcarsweights[, .x, drop = FALSE]
)[[2]], na.rm = T)
})
#> [1] 0.90394218 0.31495232 23.93790468 6.34068205 0.09460257 0.19103196
#> [7] 0.33131814 0.07487754 0.07745781 0.13477355 0.27240347
Created on 2021-12-10 by the reprex package (v2.0.1)
Below is my data
set.seed(100)
toydata <- data.frame(A = sample(1:50,50,replace = T),
B = sample(1:50,50,replace = T),
C = sample(1:50,50,replace = T)
)
Below is my swapping function
derangement <- function(x){
if(max(table(x)) > length(x)/2) return(NA)
while(TRUE){
y <- sample(x)
if(all(y != x)) return(y)
}
}
swapFun <- function(x, n = 10){
inx <- which(x < n)
y <- derangement(x[inx])
if(length(y) == 1) return(NA)
x[inx] <- y
x
}
In the first case,I get the new data toy by swapping the entire dataframe. The code is below:
toydata<-as.matrix(toydata)
toy<-swapFun(toydata)
toy<-as.data.frame(toy)
In the second case, I get the new data toy by swapping each column respectively. Below is the code:
toydata<-as.data.frame(toydata)
toy2 <- toydata # Work with a copy
toy2[] <- lapply(toydata, swapFun)
toy<-toy2
Below is the function that can output the difference of contigency table after swapping.
# the function to compare contingency tables
f = function(x,y){
table1<-table(toydata[,x],toydata[,y])
table2<-table(toy[,x],toy[,y])
sum(abs(table1-table2))
}
# vectorise your function
f = Vectorize(f)
combn(x=names(toydata),
y=names(toydata), 2) %>%# create all combinations of your column names
t() %>% # transpose
data.frame(., stringsAsFactors = F) %>% # save as dataframe
filter(X1 != X2) %>% # exclude pairs of same
# column
mutate(SumAbs = f(X1,X2)) # apply function
In the second case, this mutate function works.
But in the first case, this mutatefunction does not work. It says:
+ filter(X1 != X2) %>% # exclude pairs of same column
+ mutate(SumAbs = f(X1,X2)) # apply function
Error in combn(x = names(toydata), y = names(toydata), 2) : n < m
However in the two cases, the toy data are all dataframes with the same dimension, the same row names and the same column names. I feel confused.
How can I fix it? Thanks.
I am working with a resampling procedure in R (just like a bootstrap). I have a matrix of response/explanatory variables and would like to make 999 samples of this matrix to calculate for each statistic I am working their mean, sd and confidence interval. So, I wrote a function to calculate and to return a list:
mydata <- data.frame(a=rnorm(20, 1, 1), b = rnorm(20,1,1))
myfun <- function(data, n){
sample <- data[sample(n, replace = T),]
model1 <- lm(sample[,1]~sample[,2])
return(list(model1[[1]][[1]], model1[[1]][[2]]))
}
result <- as.numeric()
result <- replicate(99, myfun(mydata, 10))
Then, I have a matrix as my output in which the rows are the statistics and the columns are the samplings (nrow = 2 and ncol = 99). I need the mean and sd for each row, but when I try to use the apply function or even a loop the following message shows up:
In mean.default(newX[, i], ...) :
argument is not numeric or logical: returning NA
Moreover:
is.numeric(result)
[1] FALSE
I found it strange, because I never had such problem with similar procedures.
Any thoughts?
Use the following:
myfun <- function(dat, n){
dat1 <- dat[sample(n, replace = T),]
model1 <- lm(dat1[,1] ~ dat1[,2])
return(coef(model1))
}
replicate(99, myfun(mydata, 10))
The reason is the 'result' is a list of 198 elements with dimension attributes. We need to unlist the 'result' and provide the dimension attributes
result1 <- `dim<-`(unlist(result), dim(result))
and then use the apply
Just replace list() by c() in your myfun() function
mydata <- data.frame(a=rnorm(20, 1, 1), b = rnorm(20,1,1))
myfun <- function(data, n){
sample <- data[sample(n, replace = T),]
model1 <- lm(sample[,1]~sample[,2])
return(c(model1[[1]][[1]], model1[[1]][[2]]))
}
result <- as.numeric()
result <- replicate(99, myfun(mydata, 10))
apply(result, FUN=mean, 1)
apply(result, FUN=sd, 1)
This worked for me:
mydata <- data.frame(a=rnorm(20, 1, 1), b = rnorm(20,1,1))
myfun <- function(data, n){
sample <- data[sample(n, replace = T),]
model1 <- lm(sample[,1]~sample[,2])
return(data.frame(v1 = model1[[1]][[1]], v2 = model1[[1]][[2]]))
}
result <- do.call("rbind",(replicate(99, myfun(mydata, 10), simplify = FALSE)))
In this example, I have temperatures values from 50 different sites, and I would like to correlate the Site1 with all the 50 sites. But I want to extract only the components "p.value" and "estimate" generated with the function cor.test() in a data.frame into two different columns.
I have done my attempt and it works, but I don't know how!
For that reason I would like to know how can I simplify my code, because the problem is that I have to run two times a Loop "for" to get my results.
Here is my example:
# Temperature data
data <- matrix(rnorm(500, 10:30, sd=5), nrow = 100, ncol = 50, byrow = TRUE,
dimnames = list(c(paste("Year", 1:100)),
c(paste("Site", 1:50))) )
# Empty data.frame
df <- data.frame(label=paste("Site", 1:50), Estimate="", P.value="")
# Extraction
for (i in 1:50) {
df1 <- cor.test(data[,1], data[,i] )
df[,2:3] <- df1[c("estimate", "p.value")]
}
for (i in 1:50) {
df1 <- cor.test(data[,1], data[,i] )
df[i,2:3] <- df1[c("estimate", "p.value")]
}
df
I will appreciate very much your help :)
I might offer up the following as well (masking the loops):
result <- do.call(rbind,lapply(2:50, function(x) {
cor.result<-cor.test(data[,1],data[,x])
pvalue <- cor.result$p.value
estimate <- cor.result$estimate
return(data.frame(pvalue = pvalue, estimate = estimate))
})
)
First of all, I'm guessing you had a typo in your code (you should have rnorm(5000 if you want unique values. Otherwise you're going to cycle through those 500 numbers 10 times.
Anyway, a simple way of doing this would be:
data <- matrix(rnorm(5000, 10:30, sd=5), nrow = 100, ncol = 50, byrow = TRUE,
dimnames = list(c(paste("Year", 1:100)),
c(paste("Site", 1:50))) )
# Empty data.frame
df <- data.frame(label=paste("Site", 1:50), Estimate="", P.value="")
estimates = numeric(50)
pvalues = numeric(50)
for (i in 1:50){
test <- cor.test(data[,1], data[,i])
estimates[i] = test$estimate
pvalues[i] = test$p.value
}
df$Estimate <- estimates
df$P.value <- pvalues
df
Edit: I believe your issue was is that in the line df <- data.frame(label=paste("Site", 1:50), Estimate="", P.value="") if you do typeof(df$Estimate), you see it's expecting an integer, and typeof(test$estimate) shows it spits out a double, so R doesn't know what you're trying to do with those two values. you can redo your code like thus:
df <- data.frame(label=paste("Site", 1:50), Estimate=numeric(50), P.value=numeric(50))
for (i in 1:50){
test <- cor.test(data[,1], data[,i])
df$Estimate[i] = test$estimate
df$P.value[i] = test$p.value
}
to make it a little more concise.
similar to the answer of colemand77:
create a cor function:
cor_fun <- function(x, y, method){
tmp <- cor.test(x, y, method= method)
cbind(r=tmp$estimate, p=tmp$p.value) }
apply through the data.frame. You can transpose the result to get p and r by row:
t(apply(data, 2, cor_fun, data[, 1], "spearman"))
I have read a series of 332 files like below by storing the data in each file as a data frame in List.
files <- list.files()
data <- list()
for (i in 1:332){
data[[i]] = read.csv(files[[i]])
}
The data has 3 columns with names id, city, town. Now I need to calculate the mean of all values under city corresponding to the id values 1:10 for which I wrote the below code
for(j in 1:10){
req.data <- data[[j]]$city
}
mean(na.omit(req.data))
But it is giving me a wrong value and when I call it in a function its transferring null values. Any help is highly appreciated.
Each time you iterate through j = 1:10 you assign data[[j]]$city to the object req.data. In doing so, for steps j = 2:10 you are overwriting the previous version of req.data with the contents of the jth data set. Hence req.data only ever contains at any one time a single city's worth of data and hence you are getting the wrong answer sa you are computing the mean for the last city only, not all 10.
Also note that you could do mean(req.data, na.rm = TRUE) to remove the NAs.
You can do this without an explicit loop at the user R level using lapply(), for example, with dummy data,
set.seed(42)
data <- list(data.frame(city = rnorm(100)),
data.frame(city = rnorm(100)),
data.frame(city = rnorm(100)))
mean(unlist(lapply(data, `[`, "city")), na.rm = TRUE)
which gives
> mean(unlist(lapply(data, `[`, "city")), na.rm = TRUE)
[1] -0.02177902
So in your case, you need:
mean(unlist(lapply(data[1:10], `[`, "city")), na.rm = TRUE)
If you want to write a loop, then perhaps
req.data <- vector("list", length = 3) ## allocate, adjust to length = 10
for (j in 1:3) { ## adjust to 1:10 for your data / Q
req.data[[j]] <- data[[j]]$city ## fill in
}
mean(unlist(req.data), na.rm = TRUE)
> mean(unlist(req.data), na.rm = TRUE)
[1] -0.02177902
is one way. Or alternatively, compute the mean of the individual cities and then average those means
vec <- numeric(length = 3) ## allocate, adjust to length = 10
for (j in 1:3) { ## adjust to 1:10 for your question
vec[j] <- mean(data[[j]]$city, na.rm = TRUE)
}
mean(vec)