This is an R HTML document. When you click the Knit HTML button a web page will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:
Loading and preprocessing the data
unzip("activity.zip")
## Warning in unzip("activity.zip"): error 1 in extracting from zip file
stepdata <- read.csv("activity.csv", header = TRUE) head(stepdata)
## steps date interval ## 1 NA 2012-10-01 0 ## 2 NA 2012-10-01 5 ## 3 NA 2012-10-01 10 ## 4 NA 2012-10-01 15 ## 5 NA 2012-10-01 20 ## 6 NA 2012-10-01 25
1. Calculate total number of steps taken each day
library(magrittr) library(dplyr)
## Warning: package 'dplyr' was built under R version 4.0.2
databydate <- stepdata %>% select(date, steps) %>% group_by(date) %>% summarize(tsteps= sum(steps)) %>%na.omit()
hist(databydate$tsteps, xlab = "Total daily Steps",main="Histogram of Total Steps by day", breaks = 20)
2. Calculate and report the mean and median of the total number of steps taken per day
mean(databydate$tsteps)
## [1] 10766.19
median(databydate$tsteps)
## [1] 10765
4. Time series plot
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.0.2
databyinterval <- stepdata%>% select(interval, steps) %>% na.omit() %>% group_by(interval) %>% summarize(tsteps= mean(steps))
ggplot(databyinterval, aes(x=interval, y=tsteps))+ geom_line()
5.The 5-minute interval that, on average, contains the maximum number of steps
databyinterval[which(databyinterval$tsteps== max(databyinterval$tsteps)),]
## # A tibble: 1 x 2 ## interval tsteps ## <int> <dbl> ## 1 835 206.
1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
missingVals <- sum(is.na(data))
## Warning in is.na(data): is.na() applied to non-(list or vector) of type ## 'closure'
missingVals
## [1] 0
2.Devise a strategy for filling in all of the missing values in the dataset. The strategy does not need to be sophisticated. For example, you could use the mean/median for that day, or the mean for that 5-minute interval, etc. I will use the mean for that 5 -minute interval to replace all the missing values in the dataset. At the end, I will check if all the NAs have been replaced
library(magrittr) library(dplyr) replacewithmean <- function(x) replace(x, is.na(x), mean(x, na.rm = TRUE)) meandata <- stepdata%>% group_by(interval) %>% mutate(steps= replacewithmean(steps)) head(meandata)
## # A tibble: 6 x 3 ## # Groups: interval [6] ## steps date interval ## <dbl> <chr> <int> ## 1 1.72 2012-10-01 0 ## 2 0.340 2012-10-01 5 ## 3 0.132 2012-10-01 10 ## 4 0.151 2012-10-01 15 ## 5 0.0755 2012-10-01 20 ## 6 2.09 2012-10-01 25
4 Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day.
FullSummedDataByDay <- aggregate(meandata$steps, by=list(meandata$date), sum) names(FullSummedDataByDay)[1] ="date" names(FullSummedDataByDay)[2] ="totalsteps" head(FullSummedDataByDay,15)
## date totalsteps ## 1 2012-10-01 10766.19 ## 2 2012-10-02 126.00 ## 3 2012-10-03 11352.00 ## 4 2012-10-04 12116.00 ## 5 2012-10-05 13294.00 ## 6 2012-10-06 15420.00 ## 7 2012-10-07 11015.00 ## 8 2012-10-08 10766.19 ## 9 2012-10-09 12811.00 ## 10 2012-10-10 9900.00 ## 11 2012-10-11 10304.00 ## 12 2012-10-12 17382.00 ## 13 2012-10-13 12426.00 ## 14 2012-10-14 15098.00 ## 15 2012-10-15 10139.00
Summary of new data : mean & median
summary(FullSummedDataByDay)
## date totalsteps ## Length:61 Min. : 41 ## Class :character 1st Qu.: 9819 ## Mode :character Median :10766 ## Mean :10766 ## 3rd Qu.:12811 ## Max. :21194
4C Compare the mean and median of Old and New data
oldmean <- mean(databydate$tsteps, na.rm = TRUE) newmean <- mean(FullSummedDataByDay$totalsteps) # Old mean and New mean oldmean
## [1] 10766.19
newmean
## [1] 10766.19
oldmedian <- median(databydate$tsteps, na.rm = TRUE) newmedian <- median(FullSummedDataByDay$totalsteps) # Old median and New median oldmedian
## [1] 10765
newmedian
## [1] 10766.19
Are there differences in activity patterns between weekdays and weekends?
meandata$date <- as.Date(meandata$date) meandata$weekday <- weekdays(meandata$date) meandata$weekend <- ifelse(meandata$weekday=="Saturday" | meandata$weekday=="Sunday", "Weekend", "Weekday" ) library(ggplot2) meandataweekendweekday <- aggregate(meandata$steps , by= list(meandata$weekend, meandata$interval), na.omit(mean)) names(meandataweekendweekday) <- c("weekend", "interval", "steps") ggplot(meandataweekendweekday, aes(x=interval, y=steps, color=weekend)) + geom_line()+ facet_grid(weekend ~.) + xlab("Interval") + ylab("Mean of Steps") + ggtitle("Comparison of Average Number of Steps in Each Interval")
