6 ms·

A "pipe" is simply a composition of functions. Tidyverse adds a different syntax for doing function composition, using the pipe operator, which I don't particu

• mi_lk 1y ago
  Can you rewrite some of those snippets in standard R w/o Tidyverse? Curious what it would look like
  • apwheele 1y ago
    I mean, for the main simulation I would do it like this: set.seed(10) n <- 10000; samp_size <- 60 df <- data.frame( uniform = runif(n, min = -20, max = 20), normal = rnorm(n, mean = 0, sd = 4), binomial = rbinom(n, size = 1, prob = .5), beta = rbeta(n, shape1 = .9, shape2 = .5), exponential = rexp(n, .4), chisquare = rchisq(n, df = 2) ) sf <- function(df,samp_size){ sdf <- df[sample.int(nrow(df),samp_size),] colMeans(sdf) } sim <- t(replicate(20000,sf(df,samp_size))) I am old, so I do not like tidyverse either -- I can concede it is of personal preference though. (Personally do not agree with the lattice vs ggplot comment for example.)
  • lottin 1y ago
    I didn't rewrite the whole thing. But here's the first part. It uses the `histogram` function from the lattice package. population_data <- data.frame( uniform = runif(10000, min = -20, max = 20), normal = rnorm(10000, mean = 0, sd = 4), binomial = rbinom(10000, size = 1, prob = .5), beta = rbeta(10000, shape1 = .9, shape2 = .5), exponential = rexp(10000, .4), chisquare = rchisq(10000, df = 2) ) histogram(~ values|ind, stack(population_data), layout = c(6, 1), scales = list(x = list(relation="free")), breaks = NULL) take_random_sample_mean <- function(data, sample_size) { x <- sample(data, sample_size) c(mean = mean(x), sd = sqrt(var(x))) } sample_statistics <- replicate(20000, sapply(population_data, take_random_sample_mean, 60)) sample_mean <- as.data.frame(t(sample_statistics["mean", , ])) sample_sd <- as.data.frame(t(sample_statistics["sd", , ])) histogram(sample_mean[["uniform"]]) histogram(sample_mean[["binomial"]]) histogram(~values|ind, stack(sample_mean), layout = c(6, 1), scales = list(x = list(relation="free")), breaks = NULL)
  • kgwgk 1y ago
    The following code essentially redoes what the code up to the first conf_interval block does there. Which one is more clear may be debatable but it's shorter by a factor of two and faster by a factor of ten (45 seconds vs 4 for me). sample_size <- 60 sample_meansB <- lapply(population_dataB, function(x){ t(apply(replicate(20000, sample(x, sample_size)), 2, function(x) c(sample_mean=mean(x), sample_sd=sd(x)))) }) lapply(sample_meansB, head) ## check first rows population_data_statsB <- lapply(population_dataB, function(x) c(population_mean=mean(x), population_sd=sd(x), n=length(x))) do.call(rbind, population_data_statsB) ## stats table cltB <- mapply(function(s, p) (s[,"sample_mean"]-p["population_mean"])/(p["population_sd"]/sqrt(sample_size)), sample_meansB, population_data_statsB) head(cltB) ## check first rows small_sample_size <- 6 repeated_samplesB <- lapply(population_dataB, function(x){ t(apply(replicate(10000, sample(x, small_sample_size)), 2, function(x) c(sample_mean=mean(x), sample_sd=sd(x)))) }) conf_intervalsB <- lapply(repeated_samplesB, function(x){ sapply(c(lower=0.025, upper=0.975), function(q){ x[,"sample_mean"]+qnorm(q)*x[,"sample_sd"]/sqrt(small_sample_size) })}) within_ci <- mapply(function(ci, p) (p["population_mean"]>ci[,"lower"]&p["population_mean"]<ci[,"upper"]), conf_intervalsB, population_data_statsB) apply(within_ci, 2, mean) ## coverage One can do simple plots similar to the ones in that page as follows: par(mfrow=c(2,3), mex=0.8) for (d in colnames(population_dataB)) plot(density(population_dataB[,d], bw="SJ"), main=d, ylab="", xlab="", las=1, bty="n") for (d in colnames(cltB)) plot(density(cltB[,d], bw="SJ"), main=d, ylab="", xlab="", las=1, bty="n") for (d in colnames(cltB)) { qqnorm(cltB[,d], main=d, ylab="", xlab="", las=1, bty="n"); qqline(cltB[,d], col="red") }