PA1_template

Loading and preprocessing the data

library(dplyr)

## 
##  次のパッケージを付け加えます: 'dplyr'

##  以下のオブジェクトは 'package:stats' からマスクされています:
## 
##     filter, lag

##  以下のオブジェクトは 'package:base' からマスクされています:
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(lubridate)

##  要求されたパッケージ timechange をロード中です

## 
##  次のパッケージを付け加えます: 'lubridate'

##  以下のオブジェクトは 'package:base' からマスクされています:
## 
##     date, intersect, setdiff, union

df <- read.csv("activity.csv")

What is mean total number of steps taken per day?

## [1] 10766.19

What is the average daily activity pattern?

1. Make 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 days (y-axis)

interval_5min <- df %>% 
  group_by(interval) %>% 
  summarize(int_steps=mean(steps, na.rm=TRUE))

plot(interval_5min$interval, interval_5min$int_steps, type="l", ylab="number of steps", xlab="interval")

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

max_step <- max(interval_5min$int_steps)
max_5min <- interval_5min %>% 
  filter(int_steps == max_step)
max_5min

## # A tibble: 1 × 2
##   interval int_steps
##      <int>     <dbl>
## 1      835      206.

Imputing missing values

1. Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NA.

include_NA <- df %>% 
  filter(is.na(steps))
number_NA <- nrow(include_NA)
number_NA

## [1] 2304

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

# I use mean values of day to fill the missing value
mean_steps <- df %>% 
  group_by(interval) %>% 
  summarize(mean_steps=mean(steps, na.rm=TRUE))

df_add_mean <- left_join(df, mean_steps, by="interval")
new_dataset <- df_add_mean %>% 
  mutate(new_steps=ifelse(is.na(steps),mean_steps,steps)) %>% 
  select(new_steps, date, interval)

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.

new_steps_per_day <-  new_dataset%>% 
  group_by(date) %>% 
  summarize(total_steps=sum(new_steps))
hist(new_steps_per_day$total_steps)

new_mean_steps_per_day <- mean(new_steps_per_day$total_steps, na.rm=TRUE)
new_mean_steps_per_day

## [1] 10766.19

new_median_steps_per_day <- median(new_steps_per_day$total_steps, na.rm=TRUE)
new_median_steps_per_day

## [1] 10766.19

Are there differences in activity patterns between weekdays and weekends?

1. 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.

new_dataset_weekday<- new_dataset %>% 
  mutate(weekday = wday(date)) %>% 
  mutate(weekday = ifelse(weekday %in% c(2,3,4,5,6),"weekday","weekend"))

2. 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).

Plot weekday data as red line, and weekend data as blue line.

interval_5min_weekday <- new_dataset_weekday %>% 
  group_by(weekday,interval) %>% 
  summarize(int_steps=mean(new_steps, na.rm=TRUE))

## `summarise()` has grouped output by 'weekday'. You can override using the
## `.groups` argument.

# abstruct weekday data. And plot.
weekday_data <- interval_5min_weekday %>% 
  filter(weekday == "weekday")
plot(weekday_data$interval, weekday_data$int_steps, type="l", ylab="number of steps", xlab="interval",col="red")
par(new=T)

#abstract weekend data. And plot.
weekend_data <- interval_5min_weekday %>% 
  filter(weekday == "weekend")
plot(weekend_data$interval, weekend_data$int_steps, type="l", ylab="number of steps", xlab="interval", col="blue")

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.