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Loading and preprocessing the data
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In this data section I read the data from the shared folder.
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## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
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## ✓ ggplot2 3.3.1 ✓ purrr 0.3.4
+## ✓ tibble 3.0.1 ✓ dplyr 1.0.0
+## ✓ tidyr 1.1.0 ✓ stringr 1.4.0
+## ✓ readr 1.3.1 ✓ forcats 0.5.0
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## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+## x dplyr::filter() masks stats::filter()
+## x dplyr::lag() masks stats::lag()
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activity <- read.csv("~/Reproducible Research/week2/activity.csv")
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+
Histogram of number of steps
+
Here, I do an histogram of the number of steps taken each day.
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data<- activity %>%
+ group_by(date) %>%
+ summarize(Steps_per_day=sum(steps))
+
## `summarise()` ungrouping output (override with `.groups` argument)
+
hist(data$Steps_per_day)
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+
What is the average daily activity pattern?
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To study this, I will make a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis). For this, I aggregate the data per interval.
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data2<- activity %>%
+ group_by(interval) %>%
+ summarize(Steps_per_interval=mean(steps,na.rm=TRUE))
+
## `summarise()` ungrouping output (override with `.groups` argument)
+
plot(data2$interval,data2$Steps_per_interval,type="l")
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+
max<-max(data2$Steps_per_interval)
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+max2<-subset(data2,(data2$Steps_per_interval)==max)
+
Interval 835 is the one with the maximum or higher number of steps. The total number of steps is 206.
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#Imputing missing values
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activity$missing<-ifelse(is.na(activity$steps)==TRUE,1,0)
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+data3<-activity %>%
+ summarize(Missing=sum(missing))
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+total_missing<-data3$Missing
+
+rm(data3)
+
The total number of observation with a missing value is 2304.
+
#Here I create a new dataset to avoid overwriting the old one
+activity2<-activity
+
+mean_steps<-mean(activity2$steps,na.rm=TRUE)
+
+activity2$steps_imp<-ifelse(is.na(activity2$steps)==FALSE,activity2$steps,mean_steps)
+
Now, I create the new dataset per day, but using the imputed data.
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data_imp<- activity2 %>%
+ group_by(date) %>%
+ summarize(Steps_per_day_imp=sum(steps_imp))
+
## `summarise()` ungrouping output (override with `.groups` argument)
+
hist(data_imp$Steps_per_day_imp)
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+
Are there differences in activity patterns between weekdays and weekends?
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activity3<-activity2
+activity3$date_new <- as.Date(activity3$date)
+activity3$day<-weekdays(activity3$date_new)
+
+activity3$weekend<-ifelse(activity3$day=="Saturday","Weekend",
+ ifelse(activity3$day=="Sunday","Weekend","Weekday"))
+
library(lattice)
+weekday_series<- activity3 %>%
+ group_by(weekend,interval) %>%
+ summarize(Steps_per_int_imp=mean(steps_imp))
+
## `summarise()` regrouping output by 'weekend' (override with `.groups` argument)
+
xyplot(Steps_per_int_imp ~ interval | weekend,
+ data = weekday_series,
+ type = "l")
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