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library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.2 ──
## ✔ ggplot2 3.3.6 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.2.0 ✔ stringr 1.4.0
## ✔ readr 2.1.2 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag() library(nycflights13)Problem 1:
table(filter(flights, month >= 7, month <= 9)[,"month"])## month
## 7 8 9
## 29425 29327 27574 table(filter(flights, month %in% 7:9)[,"month"])## month
## 7 8 9
## 29425 29327 27574 table(flights[flights$month %in% 7:9, "month"])## month
## 7 8 9
## 29425 29327 27574 (dts <- c("2019/02/24 24:00:00", "2019/02/25 00:00:00",
"2019/02/25 00:00:00.01", "2019/02/24 23:59:59.999999"))## [1] "2019/02/24 24:00:00" "2019/02/25 00:00:00"
## [3] "2019/02/25 00:00:00.01" "2019/02/24 23:59:59.999999" (dts.posixct <- as.POSIXct(dts))## [1] "2019-02-25 00:00:00 PST" "2019-02-25 00:00:00 PST"
## [3] "2019-02-25 00:00:00 PST" "2019-02-24 23:59:59 PST" dts.posixct[1] == dts.posixct[2]## [1] TRUE dts.posixct[2] - dts.posixct[1]## Time difference of 0 secs dts.posixct[3] - dts.posixct[1]## Time difference of 0.00999999 secs dts.posixct[3] - dts.posixct[2]## Time difference of 0.00999999 secs dts.posixct[2] - dts.posixct[4]## Time difference of 9.536743e-07 secs my.mean <- function(x){
xsum <- 0
n <- length(x)
for(i in 1:n){
xsum <- xsum + x[i]
}
return(xsum/n)
}
my.mean.1 <- function(x){
xsum <- 0
n <- length(x)
nn <- 0
for(i in 1:n){
xsum <- xsum + x[i]
nn <- nn + 1
}
return(xsum/nn)
}
my.mean.2 <- function(x){
xsum <- 0
n <- length(x)
for(y in x){
xsum <- xsum + y
}
return(xsum/n)
}
my.mean.3 <- function(x){
xsum <- 0
n <- 0
for(y in x){
xsum <- xsum + y
n <- n + 1
}
return(xsum/n)
}
my.mean.4 <- function(x){
sum(x)/length(x)
}
my.mean.5 <- function(x){
n <- length(x)
t(rep(1,n)) %*% x / n
}
library(microbenchmark)
testdat <- rnorm(100000)
microbenchmark(my.mean(testdat),
my.mean.1(testdat),
my.mean.2(testdat),
my.mean.3(testdat),
my.mean.4(testdat),
my.mean.5(testdat),
mean(testdat),
times=1000) my.mean(testdat)## [1] -0.0008253353 my.mean.1(testdat)## [1] -0.0008253353 my.mean.2(testdat)## [1] -0.0008253353 my.mean.3(testdat)## [1] -0.0008253353 my.mean.4(testdat)## [1] -0.0008253353 my.mean.5(testdat)## [,1]
## [1,] -0.0008253353 mean(testdat)## [1] -0.0008253353 my.mean(c("a","b","c","d"))## Error in xsum + x[i]: non-numeric argument to binary operator my.mean.2(c("a","b","c","d"))## Error in xsum + y: non-numeric argument to binary operator mean(c("a","b","c","d"))## Warning in mean.default(c("a", "b", "c", "d")): argument is not numeric or
## logical: returning NA## [1] NAAdd a new chunk by clicking the Insert Chunk button on the toolbar or by pressing Ctrl+Alt+I.
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