From 8aa2017909dac4f1dd45155fce04a43ad871b815 Mon Sep 17 00:00:00 2001 From: OO-yink Date: Mon, 4 Apr 2022 08:30:47 +0100 Subject: [PATCH 1/2] adding assignment --- .DS_Store | Bin 0 -> 6148 bytes Data/activity.csv | 17569 +++++++++++++++++++++++++++++++ Reproducible research PA 1.Rmd | 145 + 3 files changed, 17714 insertions(+) create mode 100644 .DS_Store create mode 100644 Data/activity.csv create mode 100644 Reproducible research PA 1.Rmd diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..5008ddfcf53c02e82d7eee2e57c38e5672ef89f6 GIT binary patch literal 6148 zcmeH~Jr2S!425mzP>H1@V-^m;4Wg<&0T*E43hX&L&p$$qDprKhvt+--jT7}7np#A3 zem<@ulZcFPQ@L2!n>{z**++&mCkOWA81W14cNZlEfg7;MkzE(HCqgga^y>{tEnwC%0;vJ&^%eQ zLs35+`xjp>T0% + mutate(wday = wday(date, label = TRUE)) %>% + ggplot(aes(x = wday)) + geom_bar() +``` + +### Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day. +```{r} +agg_df$week_split <- ifelse(weekdays(agg_df$date) %in% c("Saturday", "Sunday"), "Weekend", "Weekday") +agg_df$week_split <- as.factor(agg_df$week_split ) +head(agg_df) +``` + +### Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis) +```{r} +ggplot(agg_df, aes(x=interval, y=steps, group=week_split)) + + geom_line() + + ggtitle("Week Split") + + xlab("Intervals") + + ylab("Steps") + + facet_wrap(~ week_split) +``` + + From 843860e8c9c2f6b14440d38f24b10ddece6ab585 Mon Sep 17 00:00:00 2001 From: OO-yink Date: Sat, 16 Apr 2022 08:43:45 +0100 Subject: [PATCH 2/2] adding assignment --- PA1_template.Rmd | 118 +++++- PA1_template.html | 384 ++++++++++++++++++ ...cible research PA 1.Rmd => PA1_template.md | 181 +++++++-- .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 16307 bytes .../figure-html/unnamed-chunk-12-1.png | Bin 0 -> 42983 bytes .../figure-html/unnamed-chunk-2-1.png | Bin 0 -> 16774 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 0 -> 43799 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 18276 bytes 8 files changed, 647 insertions(+), 36 deletions(-) create mode 100644 PA1_template.html rename Reproducible research PA 1.Rmd => PA1_template.md (52%) create mode 100644 PA1_template_files/figure-html/unnamed-chunk-10-1.png create mode 100644 PA1_template_files/figure-html/unnamed-chunk-12-1.png create mode 100644 PA1_template_files/figure-html/unnamed-chunk-2-1.png create mode 100644 PA1_template_files/figure-html/unnamed-chunk-4-1.png create mode 100644 PA1_template_files/figure-html/unnamed-chunk-8-1.png diff --git a/PA1_template.Rmd b/PA1_template.Rmd index d5cc677c93d..eec7d8c9591 100644 --- a/PA1_template.Rmd +++ b/PA1_template.Rmd @@ -5,21 +5,129 @@ output: keep_md: true --- +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` +### Loading and preprocessing the data +```{r cars} +# unzip("activity.zip", exdir = "./Data/") +dataset <- read.csv('./Data/activity.csv', header = TRUE) +# Let us have a look at the data dimensions, variables +names(dataset) +``` +### Process/transform the data (if necessary) into a format suitable for your analysis +```{r pressure, echo=FALSE} +library(dplyr) +# Let's add hour and minute +dataset <- mutate(dataset, hour = interval %/% 100, minute = interval %% 100) +``` +### Calculate the total number of steps taken per day +```{r} +# New aggregate dataframe +walks_complete_sum <- aggregate(dataset["steps"], by=dataset["date"], FUN=sum) +``` +### Make a histogram of the total number of steps taken each day +```{r} +library("ggplot2") +h <- ggplot(walks_complete_sum, aes(x=steps)) +h + geom_histogram(binwidth = 1700, fill="white", colour="black", orgin=35) +``` +### Calculate and report the mean and median of the total number of steps taken per day +```{r} +# Average, median +mean(walks_complete_sum$steps, na.rm=T) +median(walks_complete_sum$steps, na.rm=T) +``` +### What is the average daily activity pattern? +```{r} +ave_step <-ave(dataset$steps, dataset$interval, FUN = function(x) mean(x, na.rm=TRUE)) -## Loading and preprocessing the data +plot(dataset$interval[1:288], ave_step[1:288], type='l',col='darkred', + xlab='Intervals',lwd=1, + ylab='AVG # of steps', + main ='AVG # of steps taken in 5-min interval, averaged across all days') +``` +## Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps? +```{r} +library(lubridate) +# Convert Interval format +temp <- dataset$interval +temp2 <- mapply(function(x, y) paste0(rep(x, y), collapse = ""), 0, 4 - nchar(temp)) +temp <- paste0(temp2, temp) +dataset$time_interval <- temp -## What is mean total number of steps taken per day? +# Convert to time +dataset$time_interval <- format(strptime(dataset$time_interval, format="%H%M"), format = "%H:%M:%S") +fmin <- aggregate(data=dataset, steps ~ time_interval, mean, na.rm=TRUE) +maxt <- fmin$time_interval[which.max(fmin$steps)] +cat('The maximum number of steps occurs at',maxt,'AM') +``` +### Imputing missing values +Note that there are a number of days/intervals where there are missing values (coded as \color{red}{\verb|NA|}NA). The presence of missing days may introduce bias into some calculations or summaries of the data. -## What is the average daily activity pattern? +#### Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with \color{red}{\verb|NA|}NAs) +```{r} +summary(dataset) +sapply(dataset, function(df) { + sum(is.na(df)==TRUE) / length(df) +}) +``` +13.1% (2304) is missing. -## Imputing missing values +#### 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. +#### Create a new dataset that is equal to the original dataset but with the missing data filled in. +```{r} +# New Dataframe +agg_df <- cbind.data.frame(dataset$steps, dataset$date, dataset$interval) +colnames(agg_df) <- c("steps", "date", "interval") +agg_df$steps <- ifelse(is.na(agg_df$steps), ave_step, agg_df$steps) +agg_df$steps <- as.integer(agg_df$steps) +summary(agg_df$steps) +``` +#### Make a histogram of the total number of steps taken each day and Calculate and report the mean and median total number of steps taken per day. Do these values differ from the estimates from the first part of the assignment? What is the impact of imputing missing data on the estimates of the total daily number of steps? +```{r} +agg_df_sum <- aggregate(agg_df["steps"], by=agg_df["date"], FUN=sum, na.rm=T) -## Are there differences in activity patterns between weekdays and weekends? +h <- ggplot(agg_df_sum, aes(x=steps)) +h + geom_histogram(binwidth = 1700, fill="white", colour="black", orgin=35) +``` +```{r} +# Average, median +mean(agg_df_sum$steps, na.rm=T) +median(agg_df_sum$steps, na.rm=T) +``` +### Are there differences in activity patterns between weekdays and weekends? +```{r} +#library('weekdays') +agg_df$date <- as.Date(agg_df$date) +agg_df$weekday <- weekdays(agg_df$date) + +agg_df %>% + mutate(wday = wday(date, label = TRUE)) %>% + ggplot(aes(x = wday)) + geom_bar() +``` + + +### Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day. +```{r} +agg_df$week_split <- ifelse(weekdays(agg_df$date) %in% c("Saturday", "Sunday"), "Weekend", "Weekday") +agg_df$week_split <- as.factor(agg_df$week_split ) +head(agg_df) +``` +### Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis) +```{r} +ggplot(agg_df, aes(x=interval, y=steps, group=week_split)) + + geom_line() + + ggtitle("Week Split") + + xlab("Intervals") + + ylab("Steps") + + facet_wrap(~ week_split) +``` \ No newline at end of file diff --git a/PA1_template.html b/PA1_template.html new file mode 100644 index 00000000000..17cb9122a2c --- /dev/null +++ b/PA1_template.html @@ -0,0 +1,384 @@ + + + + + + + + + + + + + +Reproducible Research: Peer Assessment 1 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + +
+

Loading and preprocessing the data

+
# unzip("activity.zip", exdir = "./Data/")
+dataset <- read.csv('./Data/activity.csv', header = TRUE)
+# Let us have a look at the data dimensions, variables 
+names(dataset)
+
## [1] "steps"    "date"     "interval"
+
+
+

Process/transform the data (if necessary) into a format suitable for your analysis

+
## 
+## Attaching package: 'dplyr'
+
## The following objects are masked from 'package:stats':
+## 
+##     filter, lag
+
## The following objects are masked from 'package:base':
+## 
+##     intersect, setdiff, setequal, union
+
+
+

Calculate the total number of steps taken per day

+
# New aggregate dataframe 
+walks_complete_sum <- aggregate(dataset["steps"], by=dataset["date"], FUN=sum)
+
+
+

Make a histogram of the total number of steps taken each day

+
library("ggplot2")
+h <- ggplot(walks_complete_sum, aes(x=steps))
+h + geom_histogram(binwidth = 1700, fill="white", colour="black", orgin=35)
+
## Warning: Ignoring unknown parameters: orgin
+
## Warning: Removed 8 rows containing non-finite values (stat_bin).
+

### Calculate and report the mean and median of the total number of steps taken per day

+
# Average, median
+mean(walks_complete_sum$steps, na.rm=T)
+
## [1] 10766.19
+
median(walks_complete_sum$steps, na.rm=T)
+
## [1] 10765
+
+
+

What is the average daily activity pattern?

+
ave_step <-ave(dataset$steps, dataset$interval, FUN = function(x) mean(x, na.rm=TRUE))
+
+plot(dataset$interval[1:288], ave_step[1:288], type='l',col='darkred',
+     xlab='Intervals',lwd=1,
+     ylab='AVG # of steps',
+     main ='AVG # of steps taken in 5-min interval, averaged across all days')
+

+
+
+

Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps?

+
library(lubridate)
+
## 
+## Attaching package: 'lubridate'
+
## The following objects are masked from 'package:base':
+## 
+##     date, intersect, setdiff, union
+
# Convert Interval format
+temp <- dataset$interval
+temp2 <- mapply(function(x, y) paste0(rep(x, y), collapse = ""), 0, 4 - nchar(temp))
+temp <- paste0(temp2, temp)
+dataset$time_interval <- temp
+
+# Convert to time
+dataset$time_interval <- format(strptime(dataset$time_interval, format="%H%M"), format = "%H:%M:%S")
+
+fmin <- aggregate(data=dataset, steps ~ time_interval, mean, na.rm=TRUE)
+maxt <- fmin$time_interval[which.max(fmin$steps)]
+
+cat('The maximum number of steps occurs at',maxt,'AM')
+
## The maximum number of steps occurs at 08:35:00 AM
+
+

Imputing missing values

+

Note that 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)

+
summary(dataset)
+
##      steps            date              interval           hour      
+##  Min.   :  0.00   Length:17568       Min.   :   0.0   Min.   : 0.00  
+##  1st Qu.:  0.00   Class :character   1st Qu.: 588.8   1st Qu.: 5.75  
+##  Median :  0.00   Mode  :character   Median :1177.5   Median :11.50  
+##  Mean   : 37.38                      Mean   :1177.5   Mean   :11.50  
+##  3rd Qu.: 12.00                      3rd Qu.:1766.2   3rd Qu.:17.25  
+##  Max.   :806.00                      Max.   :2355.0   Max.   :23.00  
+##  NA's   :2304                                                        
+##      minute      time_interval     
+##  Min.   : 0.00   Length:17568      
+##  1st Qu.:13.75   Class :character  
+##  Median :27.50   Mode  :character  
+##  Mean   :27.50                     
+##  3rd Qu.:41.25                     
+##  Max.   :55.00                     
+##
+
sapply(dataset, function(df) {
+  sum(is.na(df)==TRUE) / length(df)
+})
+
##         steps          date      interval          hour        minute 
+##     0.1311475     0.0000000     0.0000000     0.0000000     0.0000000 
+## time_interval 
+##     0.0000000
+

13.1% (2304) is missing.

+
+
+

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.

+
+
+

Create a new dataset that is equal to the original dataset but with the missing data filled in.

+
# New Dataframe 
+agg_df <- cbind.data.frame(dataset$steps, dataset$date, dataset$interval)
+colnames(agg_df) <- c("steps", "date", "interval") 
+
+agg_df$steps <- ifelse(is.na(agg_df$steps), ave_step, agg_df$steps)
+agg_df$steps <- as.integer(agg_df$steps)
+summary(agg_df$steps)
+
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
+##    0.00    0.00    0.00   37.33   27.00  806.00
+
+
+

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. 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?

+
agg_df_sum <- aggregate(agg_df["steps"], by=agg_df["date"], FUN=sum, na.rm=T)
+
+h <- ggplot(agg_df_sum, aes(x=steps))
+h + geom_histogram(binwidth = 1700, fill="white", colour="black", orgin=35)
+
## Warning: Ignoring unknown parameters: orgin
+

+
# Average, median
+mean(agg_df_sum$steps, na.rm=T)
+
## [1] 10749.77
+
median(agg_df_sum$steps, na.rm=T)
+
## [1] 10641
+
+
+
+

Are there differences in activity patterns between weekdays and weekends?

+
#library('weekdays')
+agg_df$date <- as.Date(agg_df$date)
+agg_df$weekday <- weekdays(agg_df$date)
+
+agg_df %>%
+  mutate(wday = wday(date, label = TRUE)) %>%
+  ggplot(aes(x = wday)) + geom_bar()
+

+
+
+

Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day.

+
agg_df$week_split <- ifelse(weekdays(agg_df$date) %in% c("Saturday", "Sunday"), "Weekend", "Weekday")
+agg_df$week_split <- as.factor(agg_df$week_split )
+head(agg_df)
+
##   steps       date interval weekday week_split
+## 1     1 2012-10-01        0  Monday    Weekday
+## 2     0 2012-10-01        5  Monday    Weekday
+## 3     0 2012-10-01       10  Monday    Weekday
+## 4     0 2012-10-01       15  Monday    Weekday
+## 5     0 2012-10-01       20  Monday    Weekday
+## 6     2 2012-10-01       25  Monday    Weekday
+
+
+

Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis)

+
ggplot(agg_df, aes(x=interval, y=steps, group=week_split)) +
+  geom_line() +
+  ggtitle("Week Split") +
+  xlab("Intervals") + 
+  ylab("Steps") +
+  facet_wrap(~ week_split)
+

+
+
+ + + + +
+ + + + + + + + + + + + + + + diff --git a/Reproducible research PA 1.Rmd b/PA1_template.md similarity index 52% rename from Reproducible research PA 1.Rmd rename to PA1_template.md index 6cedc00ea7e..3a0a3bd3013 100644 --- a/Reproducible research PA 1.Rmd +++ b/PA1_template.md @@ -5,46 +5,81 @@ output: keep_md: true --- -```{r setup, include=FALSE} -knitr::opts_chunk$set(echo = TRUE) -``` ### Loading and preprocessing the data -```{r cars} + +```r # unzip("activity.zip", exdir = "./Data/") dataset <- read.csv('./Data/activity.csv', header = TRUE) - # Let us have a look at the data dimensions, variables names(dataset) ``` +``` +## [1] "steps" "date" "interval" +``` ### Process/transform the data (if necessary) into a format suitable for your analysis -```{r pressure, echo=FALSE} -library(dplyr) -# Let's add hour and minute -dataset <- mutate(dataset, hour = interval %/% 100, minute = interval %% 100) + +``` +## +## Attaching package: 'dplyr' +``` + +``` +## The following objects are masked from 'package:stats': +## +## filter, lag ``` +``` +## The following objects are masked from 'package:base': +## +## intersect, setdiff, setequal, union +``` ### Calculate the total number of steps taken per day -```{r} + +```r # New aggregate dataframe walks_complete_sum <- aggregate(dataset["steps"], by=dataset["date"], FUN=sum) ``` - ### Make a histogram of the total number of steps taken each day -```{r} + +```r library("ggplot2") h <- ggplot(walks_complete_sum, aes(x=steps)) h + geom_histogram(binwidth = 1700, fill="white", colour="black", orgin=35) ``` + +``` +## Warning: Ignoring unknown parameters: orgin +``` + +``` +## Warning: Removed 8 rows containing non-finite values (stat_bin). +``` + +![](PA1_template_files/figure-html/unnamed-chunk-2-1.png) ### Calculate and report the mean and median of the total number of steps taken per day -```{r} + +```r # Average, median mean(walks_complete_sum$steps, na.rm=T) +``` + +``` +## [1] 10766.19 +``` + +```r median(walks_complete_sum$steps, na.rm=T) ``` + +``` +## [1] 10765 +``` ### What is the average daily activity pattern? -```{r} + +```r ave_step <-ave(dataset$steps, dataset$interval, FUN = function(x) mean(x, na.rm=TRUE)) plot(dataset$interval[1:288], ave_step[1:288], type='l',col='darkred', @@ -52,10 +87,27 @@ plot(dataset$interval[1:288], ave_step[1:288], type='l',col='darkred', ylab='AVG # of steps', main ='AVG # of steps taken in 5-min interval, averaged across all days') ``` + +![](PA1_template_files/figure-html/unnamed-chunk-4-1.png) + ## Which 5-minute interval, on average across all the days in the dataset, contains the maximum number of steps? -```{r} + +```r library(lubridate) +``` +``` +## +## Attaching package: 'lubridate' +``` + +``` +## The following objects are masked from 'package:base': +## +## date, intersect, setdiff, union +``` + +```r # Convert Interval format temp <- dataset$interval temp2 <- mapply(function(x, y) paste0(rep(x, y), collapse = ""), 0, 4 - nchar(temp)) @@ -71,28 +123,57 @@ maxt <- fmin$time_interval[which.max(fmin$steps)] cat('The maximum number of steps occurs at',maxt,'AM') ``` +``` +## The maximum number of steps occurs at 08:35:00 AM +``` ### Imputing missing values Note that there are a number of days/intervals where there are missing values (coded as \color{red}{\verb|NA|}NA). The presence of missing days may introduce bias into some calculations or summaries of the data. -1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with \color{red}{\verb|NA|}NAs) -```{r} +#### Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with \color{red}{\verb|NA|}NAs) + +```r summary(dataset) +``` +``` +## steps date interval hour +## Min. : 0.00 Length:17568 Min. : 0.0 Min. : 0.00 +## 1st Qu.: 0.00 Class :character 1st Qu.: 588.8 1st Qu.: 5.75 +## Median : 0.00 Mode :character Median :1177.5 Median :11.50 +## Mean : 37.38 Mean :1177.5 Mean :11.50 +## 3rd Qu.: 12.00 3rd Qu.:1766.2 3rd Qu.:17.25 +## Max. :806.00 Max. :2355.0 Max. :23.00 +## NA's :2304 +## minute time_interval +## Min. : 0.00 Length:17568 +## 1st Qu.:13.75 Class :character +## Median :27.50 Mode :character +## Mean :27.50 +## 3rd Qu.:41.25 +## Max. :55.00 +## +``` + +```r sapply(dataset, function(df) { sum(is.na(df)==TRUE) / length(df) }) ``` + +``` +## steps date interval hour minute +## 0.1311475 0.0000000 0.0000000 0.0000000 0.0000000 +## time_interval +## 0.0000000 +``` + 13.1% (2304) is missing. +#### 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. -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. -```{r} -# Aggregate data -ave_step <-ave(dataset$steps, dataset$interval, FUN = function(x) mean(x, na.rm=TRUE)) -``` +#### Create a new dataset that is equal to the original dataset but with the missing data filled in. -3. Create a new dataset that is equal to the original dataset but with the missing data filled in. -```{r} +```r # New Dataframe agg_df <- cbind.data.frame(dataset$steps, dataset$date, dataset$interval) colnames(agg_df) <- c("steps", "date", "interval") @@ -102,20 +183,44 @@ agg_df$steps <- as.integer(agg_df$steps) summary(agg_df$steps) ``` -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. 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? -```{r} +``` +## Min. 1st Qu. Median Mean 3rd Qu. Max. +## 0.00 0.00 0.00 37.33 27.00 806.00 +``` +#### 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. 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? + +```r agg_df_sum <- aggregate(agg_df["steps"], by=agg_df["date"], FUN=sum, na.rm=T) h <- ggplot(agg_df_sum, aes(x=steps)) h + geom_histogram(binwidth = 1700, fill="white", colour="black", orgin=35) ``` -```{r} + +``` +## Warning: Ignoring unknown parameters: orgin +``` + +![](PA1_template_files/figure-html/unnamed-chunk-8-1.png) + +```r # Average, median mean(agg_df_sum$steps, na.rm=T) +``` + +``` +## [1] 10749.77 +``` + +```r median(agg_df_sum$steps, na.rm=T) ``` + +``` +## [1] 10641 +``` ### Are there differences in activity patterns between weekdays and weekends? -```{r} + +```r #library('weekdays') agg_df$date <- as.Date(agg_df$date) agg_df$weekday <- weekdays(agg_df$date) @@ -125,15 +230,29 @@ agg_df %>% ggplot(aes(x = wday)) + geom_bar() ``` +![](PA1_template_files/figure-html/unnamed-chunk-10-1.png) + + ### Create a new factor variable in the dataset with two levels - “weekday” and “weekend” indicating whether a given date is a weekday or weekend day. -```{r} + +```r agg_df$week_split <- ifelse(weekdays(agg_df$date) %in% c("Saturday", "Sunday"), "Weekend", "Weekday") agg_df$week_split <- as.factor(agg_df$week_split ) head(agg_df) ``` +``` +## steps date interval weekday week_split +## 1 1 2012-10-01 0 Monday Weekday +## 2 0 2012-10-01 5 Monday Weekday +## 3 0 2012-10-01 10 Monday Weekday +## 4 0 2012-10-01 15 Monday Weekday +## 5 0 2012-10-01 20 Monday Weekday +## 6 2 2012-10-01 25 Monday Weekday +``` ### Make a panel plot containing a time series plot (i.e. type = “l”) of the 5-minute interval (x-axis) and the average number of steps taken, averaged across all weekday days or weekend days (y-axis) -```{r} + +```r ggplot(agg_df, aes(x=interval, y=steps, group=week_split)) + geom_line() + ggtitle("Week Split") + @@ -142,4 +261,4 @@ ggplot(agg_df, aes(x=interval, y=steps, group=week_split)) + facet_wrap(~ week_split) ``` - 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