PA1_template.md

RepData_PeerAssessment1_TZs Zsolt Terjek 2023-06-03 Calling the libraries

library(tidyverse)

── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──

✔ dplyr 1.1.1 ✔ readr 2.1.4

✔ forcats 1.0.0 ✔ stringr 1.5.0

✔ ggplot2 3.4.2 ✔ tibble 3.2.1

✔ lubridate 1.9.2 ✔ tidyr 1.3.0

✔ purrr 1.0.1

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──

✖ dplyr::filter() masks stats::filter()

✖ dplyr::lag() masks stats::lag()

ℹ Use the conflicted package (http://conflicted.r-lib.org/) to force all conflicts to become errors

library(mice)

Attaching package: 'mice'

The following object is masked from 'package:stats':

filter

The following objects are masked from 'package:base':

cbind, rbind

Loading and preprocessing the data

Reading the data downloaded to the desktop

activity <- read.csv("C:/Users/zterjek/Desktop/activity.csv")

What is mean total number of steps taken per day?

Calculate the total steps taken per day, visualize it with a histogram

perdaysum <- aggregate(steps ~ date, activity, sum)

hist(perdaysum$steps, breaks = 20, col = "steelblue")

Calculate and report the average and median of total taken steps

perdaymean <- mean(perdaysum$steps)

perdaymed <- median(perdaysum$steps)

perdaymean

[1] 10766.19

perdaymed

[1] 10765

What is the average daily activity pattern?

Calculate the average steps taken by intervals, visualize it with a line chart

perintervalmean <- aggregate(steps ~ interval, activity, mean)

plot(perintervalmean$interval, perintervalmean$steps, type = "l", col = "darkred", lwd = 1.5)

Calculate and report the interval when max steps were taken

maxstepsperinterval <- max(perintervalmean$steps)

maxstepsinterval <- filter(perintervalmean, steps == maxstepsperinterval)

maxstepsinterval$interval

[1] 835

Imputing missing values

Calculate and report the interval when max steps were taken

maxstepsperinterval <- max(perintervalmean$steps)

maxstepsinterval <- filter(perintervalmean, steps == maxstepsperinterval)

maxstepsinterval$interval

[1] 835

Impute the missing values with predictive mean matching

md.pattern(activity)

date interval steps

15264 1 1 1 0

2304 1 1 0 1

0 0 2304 2304

activity_imputed <- data.frame(original = activity$steps, imputed_steps = complete(mice(activity, method = "pmm"))$steps)

iter imp variable

1 1 steps

1 2 steps

1 3 steps

1 4 steps

1 5 steps

2 1 steps

2 2 steps

2 3 steps

2 4 steps

2 5 steps

3 1 steps

3 2 steps

3 3 steps

3 4 steps

3 5 steps

4 1 steps

4 2 steps

4 3 steps

4 4 steps

4 5 steps

5 1 steps

5 2 steps

5 3 steps

5 4 steps

5 5 steps

Warning: Number of logged events: 1

activity_imputed$num <- 1:17568 activity$num <- 1:17568

merged_activity <- merge(activity, activity_imputed, by = "num") merged_activity <- merged_activity[,-c(1, 2)] merged_activity <- rename(merged_activity, original_steps = original) md.pattern(merged_activity)

date interval imputed_steps original_steps

15264 1 1 1 1 0

2304 1 1 1 0 1

0 0 0 2304 2304

Calculate the total steps taken per day of the imputed dataset, visualize it with a histogram

perdaysum_merged <- aggregate(imputed_steps ~ date, merged_activity, sum)

hist(perdaysum_merged$imputed_steps, breaks = 20, col = "darkgreen")

Calculate and report the average and median of total taken steps in the imputed dataset

perdaymean_merged <- mean(perdaysum_merged$imputed_steps)

perdaymed_merged <- median(perdaysum_merged$imputed_steps)

perdaymean_merged

[1] 10479.26

perdaymed_merged

[1] 10439

Are there differences in activity patterns between weekdays and weekends?

Checking the weekdays

merged_activity$date <- as.Date(merged_activity$date)

merged_activity$daytype <- as.factor(ifelse(weekdays(merged_activity$date) %in% c("szombat", "vasárnap"), "Weekend", "Weekday")) Calculate the average steps taken by intervals, visualize it with a line chart with weekdays and weekends separated

grouped <- merged_activity %>% group_by(daytype, interval) %>% summarize_at("imputed_steps", mean)

ggplot(grouped, aes(interval, imputed_steps))+ geom_line(aes(col = daytype))+ facet_wrap(.~ grouped$daytype, nrow = 2, ncol = 1)