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Reproducible Research: Peer Assessment 1
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The libraries that will be of support are loaded

library(ggplot2)
library(dplyr)
## 
## 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

library(lubridate)
## 
## Attaching package: 'lubridate'

## The following objects are masked from 'package:base':
## 
##     date, intersect, setdiff, union

options(dplyr.summarise.inform = FALSE)
###### Get rid of info of dplyr when grouping: `summarise()` regrouping output by 'species' (override with `.groups` argument)
###### https://rstats-tips.net/2020/07/31/get-rid-of-info-of-dplyr-when-grouping-summarise-regrouping-output-by-species-override-with-groups-argument/

######   2020/05/06
######  dplyr, dplyr-1-0-0
###### Hadley Wickham, Kirill Müller

###### https://www.tidyverse.org/blog/2020/05/dplyr-1-0-0-last-minute-additions/

Loading and preprocessing the initialData

    1. Load the initialData (i.e. read.csv())
    1. Process/transform the initialData (if necessary) into a format suitable for your analysis
unzip("activity.zip")
initialData <- read.csv("activity.csv", colClasses=c("numeric", "Date", "numeric"))

What is mean total number of steps taken per day?

total_steps <- initialData %>%
  group_by(date) %>%
  summarise(daily_steps = sum(steps, na.rm = TRUE))

ggplot(total_steps, aes(daily_steps)) + 
  geom_histogram(binwidth = 2500, col="darkgreen", fill="lightgreen") +
  ggtitle("Histogram of steps per day") +
  xlab("Total number of steps taken each day") + 
  ylab("Frequency") +
  theme( plot.title = element_text(hjust = 0.5))

mean = mean(total_steps$daily_steps, na.rm=TRUE)
## [1] 9354.23

median = median(total_steps$daily_steps, na.rm=TRUE)
## [1] 10395

What is the average daily activity pattern?

interval_steps <- initialData %>% 
  group_by(interval) %>%
  summarise(steps = mean(steps, na.rm =TRUE))

ggplot(data=interval_steps, aes(x=interval, y=steps)) +
    geom_line(col="red") +
    ggtitle("Average steps per time interval") +
    xlab("5-minute intervals") +
    ylab("Average number of steps taken") +
    theme( plot.title = element_text(hjust = 0.5))

Imputing missing values

Calculate and report the total number of missing values in the dataset (i.e. the total number of rows with NAs)
sum(is.na(initialData$steps))
## [1] 2304

imputed_initialData <- initialData %>%
  mutate(
    steps = case_when(
      is.na(steps) ~ interval_steps$steps[match(initialData$interval, interval_steps$interval)],      
      TRUE ~ as.numeric(steps)
    ))


imputed_total_steps <- imputed_initialData %>% group_by(date) %>% summarise(daily_steps = sum(steps))

ggplot(imputed_total_steps, aes(daily_steps)) + 
  geom_histogram(binwidth = 2500, col="darkgreen", fill="lightgreen") + 
  ggtitle("Total number of steps taken each day") + 
  xlab("Total steps per day") +
  ylab("Frequency") +
  theme( plot.title = element_text(hjust = 0.5))

We now compute the mean and median number of daily steps of the computed initial Data.
imputed_mean = mean(imputed_total_steps$daily_steps, na.rm=TRUE)
## [1] 10766.19

imputed_median = median(imputed_total_steps$daily_steps, na.rm=TRUE)
## [1] 10766.19
We can calculate the difference of the means and medians between imputed and original initial Data.
mean_diff <- imputed_mean - mean 
## [1] 1411.959

median_diff <- imputed_median - median
## [1] 371.1887
Are there differences in activity patterns between weekdays and weekends?
library(lubridate)
day_of_week <- imputed_initialData %>%
  mutate(
    date = ymd(date),
    weekday_or_weekend = case_when(wday(date) %in% 2:6 ~ "Weekday",
                                   wday(date) %in% c(1,7) ~ "Weekend")
  ) %>% select(-date) %>%
  group_by(interval, weekday_or_weekend) %>%
  summarise(
    steps = mean(steps), .groups = NULL
  )
###### summarise() and grouping 
###### There's a common confusion about the result of summarise(). How do you think the result of the following code will be grouped?
###### https://www.tidyverse.org/blog/2020/05/dplyr-1-0-0-last-minute-additions/

ggplot(day_of_week, aes(interval, steps)) + 
  geom_line(col="red") +
  facet_wrap(~weekday_or_weekend, nrow = 2) +
  ggtitle("Average daily steps by type of date") + 
  xlab("5-Minute intervals") + 
  ylab("Average number of steps") +
  theme( plot.title = element_text(hjust = 0.5))