R Reproducible Research course project课程练习

最新推荐文章于 2024-10-28 13:39:23 发布

shanesu

最新推荐文章于 2024-10-28 13:39:23 发布

阅读量725

点赞数

CC 4.0 BY-SA版权

分类专栏： R 课程笔记文章标签： github R markdown

本文链接：https://blog.youkuaiyun.com/qq_36080693/article/details/52811001

课程笔记同时被 2 个专栏收录

28 篇文章

订阅专栏

11 篇文章

订阅专栏

本报告使用个人活动监测设备收集的数据，分析了匿名个体在2012年10月和11月两个月内的日常活动模式。通过数据预处理、缺失值填充等步骤，探究了每日步数变化、平均活动模式及工作日与周末活动差异。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

原版Rmd代码在这个github地址

title: “Reproducible Research: Peer Assessment 1”
author: “shanesu”
date: “2016年10月12日”
output:
html_document:
keep_md: true

Introduction

This assignment makes use of data from a personal activity monitoring device. This device collects data at 5 minute intervals through out the day. The data consists of two months of data from an anonymous individual collected during the months of October and November, 2012 and include the number of steps taken in 5 minute intervals each day.

The data for this assignment can be downloaded from the course web site:

Dataset: Activity monitoring data [52K]
The variables included in this dataset are:

steps: Number of steps taking in a 5-minute interval (missing values are coded as NA)
date: The date on which the measurement was taken in YYYY-MM-DD format
interval: Identifier for the 5-minute interval in which measurement was taken

The dataset is stored in a comma-separated-value (CSV) file and there are a total of 17,568 observations in this dataset.

Loading and preprocessing the data

 knitr::opts_chunk$set(echo = TRUE)

temp <- tempfile()
download.file("https://d396qusza40orc.cloudfront.net/repdata%2Fdata%2Factivity.zip", destfile = temp )
unzip(temp, "activity.csv")
activity<-read.csv("activity.csv")
head(activity)

What is mean total number of steps taken per day?

stepsum<-tapply(activity$steps,activity$date,sum)
hist(stepsum)

the mean

mean(stepsum,na.rm = TRUE)

the median

median(stepsum,na.rm = TRUE)

What is the average daily activity pattern?

stepave<-aggregate(steps~interval,data = activity,FUN = mean,na.rm=TRUE)
plot(stepave$interval,stepave$steps)

which max

stepave$interval[which.max(stepave$steps)]

Imputing missing values

cleardate<-activity
addmiss<-function(mystep,myinterval){
  if(is.na(mystep))
      newvalue<-stepave$steps[which(stepave$interval==myinterval)]
  else
      newvalue<-mystep
  return(newvalue)
}
cleardate$steps<-mapply(addmiss,cleardate$steps,cleardate$interval)
summary(cleardate)

show the new hist

hist(tapply(cleardate$steps,cleardate$date,sum))

Are there differences in activity patterns between weekdays and weekends?

cleardate$date<-as.Date(cleardate$date)
library(ggplot2)
isweekday<-function(idate){
   if (weekdays(idate) %in% c("星期日", "星期六"))
       return("weekend")
   else
       return("weekday")
}
cleardate$daytype<-mapply(isweekday,cleardate$date)
ggplot(cleardate)+geom_point(aes(x=interval,y=steps,colour=daytype))