+
+
+
+
+
+
+
+
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
+
Reading the data downloaded to the desktop
+
activity <- read.csv("C:/Users/zterjek/Desktop/activity.csv")
+
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$steps)
+
+perdaymean
+
## [1] 10766.19
+
perdaymed
+
## [1] 10765
+
Calculate the average steps taken by intervals, visualize it with a
+line chart
+
perintervalmean <- aggregate(steps ~ interval, activity, mean)
+
+plot(perintervalmean$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$interval
+
## [1] 835
+
Impute the missing values with predictive mean matching
+
md.pattern(activity)
+
+
## date interval steps
+## 15264 1 1 1 0
+## 2304 1 1 0 1
+## 0 0 2304 2304
+
activity_imputed <- data.frame(original = activity$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
+
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)
+
+
End of code
+
+
+
+
+