RR week 1
MV
March 8, 2016
Introduction
It is now possible to collect a large amount of data about personal movement using activity monitoring devices such as a Fitbit, Nike Fuelband, or Jawbone Up. These type of devices are part of the “quantified self” movement - a group of enthusiasts who take measurements about themselves regularly to improve their health, to find patterns in their behavior, or because they are tech geeks. But these data remain under-utilized both because the raw data are hard to obtain and there is a lack of statistical methods and software for processing and interpreting the data.
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.
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.
The data for this assignment was downloaded from the course web site and read with the read.csv R syntax:
act <- read.csv("activity.csv",
colClasses = c("numeric", "character","integer"))
summary(act)## steps date interval
## Min. : 0.00 Length:17568 Min. : 0.0
## 1st Qu.: 0.00 Class :character 1st Qu.: 588.8
## Median : 0.00 Mode :character Median :1177.5
## Mean : 37.38 Mean :1177.5
## 3rd Qu.: 12.00 3rd Qu.:1766.2
## Max. :806.00 Max. :2355.0
## NA's :2304In the next steps, the data is aggregated by date in order to:
- Calculate the total number of steps taken per day
- Make a histogram of the total number of steps taken each day
- Calculate and report the mean and median of the total number of steps taken per day
library(lubridate)
library(plyr)##
## Attaching package: 'plyr'## The following object is masked from 'package:lubridate':
##
## herelibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize## The following objects are masked from 'package:lubridate':
##
## intersect, setdiff, union## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(lattice)
library(data.table)##
## Attaching package: 'data.table'## The following objects are masked from 'package:dplyr':
##
## between, last## The following objects are masked from 'package:lubridate':
##
## hour, mday, month, quarter, wday, week, yday, yearlibrary(knitr)
library(rmarkdown)
library("markdown")actAgg <- tapply(act$steps, act$date, FUN = sum, na.rm = TRUE)
print(actAgg)## 2012-10-01 2012-10-02 2012-10-03 2012-10-04 2012-10-05 2012-10-06
## 0 126 11352 12116 13294 15420
## 2012-10-07 2012-10-08 2012-10-09 2012-10-10 2012-10-11 2012-10-12
## 11015 0 12811 9900 10304 17382
## 2012-10-13 2012-10-14 2012-10-15 2012-10-16 2012-10-17 2012-10-18
## 12426 15098 10139 15084 13452 10056
## 2012-10-19 2012-10-20 2012-10-21 2012-10-22 2012-10-23 2012-10-24
## 11829 10395 8821 13460 8918 8355
## 2012-10-25 2012-10-26 2012-10-27 2012-10-28 2012-10-29 2012-10-30
## 2492 6778 10119 11458 5018 9819
## 2012-10-31 2012-11-01 2012-11-02 2012-11-03 2012-11-04 2012-11-05
## 15414 0 10600 10571 0 10439
## 2012-11-06 2012-11-07 2012-11-08 2012-11-09 2012-11-10 2012-11-11
## 8334 12883 3219 0 0 12608
## 2012-11-12 2012-11-13 2012-11-14 2012-11-15 2012-11-16 2012-11-17
## 10765 7336 0 41 5441 14339
## 2012-11-18 2012-11-19 2012-11-20 2012-11-21 2012-11-22 2012-11-23
## 15110 8841 4472 12787 20427 21194
## 2012-11-24 2012-11-25 2012-11-26 2012-11-27 2012-11-28 2012-11-29
## 14478 11834 11162 13646 10183 7047
## 2012-11-30
## 0library(lubridate)
act$date <- ymd(act$date)
summary(actAgg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 6778 10400 9354 12810 21190library(data.table)
actAgg_dt=data.table(actAgg)
summary(actAgg_dt)## V1
## Min. : 0
## 1st Qu.: 6778
## Median :10395
## Mean : 9354
## 3rd Qu.:12811
## Max. :21194mean(actAgg_dt$V1)## [1] 9354.23median(actAgg_dt$V1)## [1] 10395library(plyr)
library(dplyr)
steps1 <- act %>%
filter(!is.na(steps)) %>%
group_by(date) %>%
summarize(steps = sum(steps)) %>%
print## Source: local data frame [53 x 2]
##
## date steps
## (time) (dbl)
## 1 2012-10-02 126
## 2 2012-10-03 11352
## 3 2012-10-04 12116
## 4 2012-10-05 13294
## 5 2012-10-06 15420
## 6 2012-10-07 11015
## 7 2012-10-09 12811
## 8 2012-10-10 9900
## 9 2012-10-11 10304
## 10 2012-10-12 17382
## .. ... ...hist(steps1$steps, main="Histogram of Steps" ,
xlab="Steps", ylab="Count")
abline(v = mean(steps1$steps), col = "blue", lwd = 2)
What is the average daily activity pattern?
intervl <- act %>%
filter(!is.na(steps)) %>%
group_by(interval) %>%
summarize(steps = mean(steps)) %>%
print ## Source: local data frame [288 x 2]
##
## interval steps
## (int) (dbl)
## 1 0 1.7169811
## 2 5 0.3396226
## 3 10 0.1320755
## 4 15 0.1509434
## 5 20 0.0754717
## 6 25 2.0943396
## 7 30 0.5283019
## 8 35 0.8679245
## 9 40 0.0000000
## 10 45 1.4716981
## .. ... ...Time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis)
plot(intervl, type = 'l')
Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?
intervl[which.max(intervl$steps), ]$interval## [1] 835Imputing missing values
There are a number of days/intervals where there are missing values (coded as NA). The presence of missing days may introduce bias into some calculations or summaries of the data.
Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs).
#values missing in orIginal dataset
missing1 <- sum(is.na(act))
missing1## [1] 2304Create a new dataset with the missing data filled in:
act_miss_rep <- transform(act, steps = ifelse(is.na(steps), round(mean(steps, na.rm=TRUE)), steps))missing values replaced verification
missing2 <- sum(is.na(act_miss_rep))
missing2## [1] 0Summary of new dataset with missing values replaced:
#values missing in orginal dataset
summary(act_miss_rep)## steps date interval
## Min. : 0.00 Min. :2012-10-01 Min. : 0.0
## 1st Qu.: 0.00 1st Qu.:2012-10-16 1st Qu.: 588.8
## Median : 0.00 Median :2012-10-31 Median :1177.5
## Mean : 37.33 Mean :2012-10-31 Mean :1177.5
## 3rd Qu.: 37.00 3rd Qu.:2012-11-15 3rd Qu.:1766.2
## Max. :806.00 Max. :2012-11-30 Max. :2355.0Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day.
steps2 <- act_miss_rep %>%
group_by(date) %>%
summarize(steps = sum(steps)) %>%
print ## Source: local data frame [61 x 2]
##
## date steps
## (time) (dbl)
## 1 2012-10-01 10656
## 2 2012-10-02 126
## 3 2012-10-03 11352
## 4 2012-10-04 12116
## 5 2012-10-05 13294
## 6 2012-10-06 15420
## 7 2012-10-07 11015
## 8 2012-10-08 10656
## 9 2012-10-09 12811
## 10 2012-10-10 9900
## .. ... ...hist(steps2$steps, main="Histogram of Steps" , sub="Missing Values replaced with mean of interval steps" ,
xlab="Steps", ylab="Count")
abline(v = mean(steps2$steps), col = "blue", lwd = 2)
Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps?
#aggregate corrected data by date to calculate daily stats
actAgg2 <- tapply(act_miss_rep$steps, act_miss_rep$date, FUN = sum, na.rm = TRUE)
act$date <- ymd(act_miss_rep$date)
#mean and median total number of steps taken per day
mean(actAgg2)## [1] 10751.74median(actAgg2)## [1] 10656#DO VALUES DIFFER?
diff_in_means=mean(actAgg)-mean(actAgg2)
diff_in_means## [1] -1397.508# Do the data estimates differ from the first part of the assignment?
summary(actAgg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0 6778 10400 9354 12810 21190summary(actAgg2)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 41 9819 10660 10750 12810 21190#What is the impact of imputing missing data on the estimates
#of the total daily number of steps?
summary(actAgg2)-summary(actAgg)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 41 3041 260 1396 0 0third part
Are there differences in activity patterns between weekdays and weekends?
A new factor variable (d_type) in a third dataset (act_weekday_ind) has been created. It has two levels - “weekday” and “weekend”– indicating whether a given date is a weekday or weekend day.
act_weekday_ind=act_miss_rep
act_weekday_ind$d_type[as.POSIXlt(act_weekday_ind$date)$wday %in% c(0,6)] <- "weekday"
act_weekday_ind$d_type[as.POSIXlt(act_weekday_ind$date)$wday %in% c(1:5)] <- "weekend"
table(act_weekday_ind$d_type,as.POSIXlt(act_weekday_ind$date)$wday)##
## 0 1 2 3 4 5 6
## weekday 2304 0 0 0 0 0 2304
## weekend 0 2592 2592 2592 2592 2592 0Time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis).
actAgg3 <- act_weekday_ind %>%
group_by(d_type, interval) %>%
summarize(steps3 = mean(steps))plot
library(ggplot2)
ggplot(actAgg3, aes(interval,steps3))+geom_line(color="blue")+
facet_wrap(~d_type, ncol=1)