RepData_PeerAssesment1-Santos

PA1_template.Rmd

@@ -1,25 +1,110 @@
 ---
 title: "Reproducible Research: Peer Assessment 1"
 output: 
-  html_document:
+  html_document: 
     keep_md: true
 ---
+## Loading and preprocessing the data
+In this data section I read the data from the shared folder. 
 
+```{r, echo=FALSE}
+library(tidyverse) #Need for the Pipes I will use in next step.
+library(knitr)
+options(digits=0) #Shows full numbers
+options(scipen=999) #Removes scientific notation
+```
 
-## Loading and preprocessing the data
+```{r, echo=TRUE}
+activity <- read.csv("~/Reproducible Research/week2/activity.csv")
+```
+
+### Histogram of number of steps
+Here, I do an histogram of the number of steps taken each day.
+
+```{r, echo=TRUE}
+data<- activity %>% 
+    group_by(date) %>% 
+    summarize(Steps_per_day=sum(steps))
+
+hist(data$Steps_per_day)
+```
+
+# What is the mean and median number of steps taken each day? 
+As instructed, I use NA.RM to ignore the missings. 
+```{r ,echo=TRUE}
+mean<-mean(data$Steps_per_day,na.rm=TRUE)
+median<-median(data$Steps_per_day,na.rm=TRUE)
+```
+
+The mean number of steps is `r mean` and the median is `r median`. 
+
+# What is the average daily activity pattern?
+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.
 
+```{r , echo=TRUE}
+data2<- activity %>% 
+    group_by(interval) %>% 
+    summarize(Steps_per_interval=mean(steps,na.rm=TRUE))
 
+plot(data2$interval,data2$Steps_per_interval,type="l")
 
-## What is mean total number of steps taken per day?
+max<-max(data2$Steps_per_interval)
 
+max2<-subset(data2,(data2$Steps_per_interval)==max)
+```
 
+Interval `r max2$interval` is the one with the maximum or higher number of steps. The total number of steps is `r max2$Steps_per_interval`.
 
-## What is the average daily activity pattern?
+#Imputing missing values
+```{r determine missing, echo=TRUE}
+activity$missing<-ifelse(is.na(activity$steps)==TRUE,1,0)
 
+data3<-activity %>% 
+       summarize(Missing=sum(missing))
 
+total_missing<-data3$Missing
 
-## Imputing missing values
+rm(data3)
+```
 
+The total number of observation with a missing value is `r total_missing`.
 
+```{r , echo=TRUE}
+#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.
+
+```{r, echo=TRUE}
+data_imp<- activity2 %>% 
+    group_by(date) %>% 
+    summarize(Steps_per_day_imp=sum(steps_imp))
+
+hist(data_imp$Steps_per_day_imp)
+```
 
 ## Are there differences in activity patterns between weekdays and weekends?
+```{r, echo=TRUE}
+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"))
+```
+
+```{r, echo=TRUE}
+library(lattice)
+weekday_series<- activity3 %>% 
+    group_by(weekend,interval) %>% 
+    summarize(Steps_per_int_imp=mean(steps_imp))
+
+xyplot(Steps_per_int_imp ~ interval | weekend,
+       data = weekday_series,
+       type = "l")
+```

PA1_template.md

@@ -0,0 +1,164 @@
+---
+title: "Reproducible Research: Peer Assessment 1"
+output: 
+  html_document: 
+    keep_md: true
+---
+## Loading and preprocessing the data
+In this data section I read the data from the shared folder. 
+
+
+```
+## ── Attaching packages ───────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──
+```
+
+```
+## ✓ 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
+```
+
+```
+## ── Conflicts ──────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
+## x dplyr::filter() masks stats::filter()
+## x dplyr::lag()    masks stats::lag()
+```
+
+
+```r
+activity <- read.csv("~/Reproducible Research/week2/activity.csv")
+```
+
+### Histogram of number of steps
+Here, I do an histogram of the number of steps taken each day.
+
+
+```r
+data<- activity %>% 
+    group_by(date) %>% 
+    summarize(Steps_per_day=sum(steps))
+```
+
+```
+## `summarise()` ungrouping output (override with `.groups` argument)
+```
+
+```r
+hist(data$Steps_per_day)
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-3-1.png)<!-- -->
+
+# What is the mean and median number of steps taken each day? 
+As instructed, I use NA.RM to ignore the missings. 
+
+```r
+mean<-mean(data$Steps_per_day,na.rm=TRUE)
+median<-median(data$Steps_per_day,na.rm=TRUE)
+```
+
+The mean number of steps is 10766 and the median is 10765. 
+
+# What is the average daily activity pattern?
+To study this, I will make a time series plot of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all days (y-axis). For this, I aggregate the data per interval.
+
+
+```r
+data2<- activity %>% 
+    group_by(interval) %>% 
+    summarize(Steps_per_interval=mean(steps,na.rm=TRUE))
+```
+
+```
+## `summarise()` ungrouping output (override with `.groups` argument)
+```
+
+```r
+plot(data2$interval,data2$Steps_per_interval,type="l")
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-5-1.png)<!-- -->
+
+```r
+max<-max(data2$Steps_per_interval)
+
+max2<-subset(data2,(data2$Steps_per_interval)==max)
+```
+
+Interval 835 is the one with the maximum or higher number of steps. The total number of steps is 206.
+
+#Imputing missing values
+
+```r
+activity$missing<-ifelse(is.na(activity$steps)==TRUE,1,0)
+
+data3<-activity %>% 
+       summarize(Missing=sum(missing))
+
+total_missing<-data3$Missing
+
+rm(data3)
+```
+
+The total number of observation with a missing value is 2304.
+
+
+```r
+#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.
+
+
+```r
+data_imp<- activity2 %>% 
+    group_by(date) %>% 
+    summarize(Steps_per_day_imp=sum(steps_imp))
+```
+
+```
+## `summarise()` ungrouping output (override with `.groups` argument)
+```
+
+```r
+hist(data_imp$Steps_per_day_imp)
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-7-1.png)<!-- -->
+
+## Are there differences in activity patterns between weekdays and weekends?
+
+```r
+activity3<-activity2
+activity3$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"))
+```
+
+
+```r
+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)
+```
+
+```r
+xyplot(Steps_per_int_imp ~ interval | weekend,
+       data = weekday_series,
+       type = "l")
+```
+
+![](PA1_template_files/figure-html/unnamed-chunk-9-1.png)<!-- -->