Main

Peer-graded Assignment - Course Project 1.R

@@ -0,0 +1,115 @@
+# Libraries and Paths -----------------------------------------------------
+library(dplyr); library(ggplot2)
+filePath <- file.path(dirname(getwd()), "Inputs")
+Data <- read.csv(file.path(
+  filePath, "activity.csv")); head(Data)
+
+# What is mean total number of steps taken per day? -----------------------
+Steps.pr.Day <- Data %>%
+  group_by(date) %>%
+  summarise(
+    total_steps = sum(steps, na.rm = TRUE)); head(Steps.pr.Day)
+
+ggplot(
+  Steps.pr.Day, aes(x = total_steps)) +
+  geom_histogram(binwidth = 1000, fill = "blue", color = "black") +
+  labs(title = "Histogram of Total Steps Per Day", 
+       x = "Total Steps", 
+       y = "Frequency")
+
+# Mean and median of total steps per day
+Steps.Mean <- mean(
+  Steps.pr.Day$total_steps); Steps.Mean
+Steps.Median <- median(
+  Steps.pr.Day$total_steps); Steps.Median
+
+# What is the average daily activity pattern? -----------------------------
+Avg.Steps.per.Interval <- Data %>%
+  group_by(interval) %>%
+  summarise(average_steps = mean(steps, na.rm = TRUE)); head(Avg.Steps.per.Interval)
+
+ggplot(Avg.Steps.per.Interval, 
+       aes(x = interval, 
+           y = average_steps)) +
+  geom_line(color = "blue") +
+  labs(title = "Average Daily Activity Pattern", 
+       x = "5-minute Interval", 
+       y = "Average Number of Steps")
+
+# Find the interval with the maximum average steps
+max_interval <- Avg.Steps.per.Interval[
+  which.max(
+    Avg.Steps.per.Interval$average_steps), ]; head(max_interval)
+
+# Imputing missing values -------------------------------------------------
+Missing.Values <- sum(is.na(
+  Data$steps)); head(Missing.Values)
+
+# Fill missing values with Avg.Steps.per.Interval
+Complete.Data <- Data
+for (i in 1:nrow(Complete.Data)) {
+  if (is.na(Complete.Data$steps[i])) {
+    interval_value <- Complete.Data$interval[i]
+    Complete.Data$steps[i] <- Avg.Steps.per.Interval$average_steps[
+      Avg.Steps.per.Interval$interval == interval_value]
+  }
+}
+str(Data) #Before
+str(Complete.Data) #After
+
+# Recalculate total steps per day with imputed Data
+Complete.Steps.pr.Day <- Complete.Data %>%
+  group_by(date) %>%
+  summarise(total_steps = sum(steps)); head(Complete.Steps.pr.Day)
+
+# Histogram
+ggplot(Complete.Steps.pr.Day, 
+       aes(x = total_steps)) +
+  geom_histogram(
+    binwidth = 1000, 
+    fill = "green", 
+    color = "black") +
+  labs(title = "Histogram of Total Steps Per Day (Imputed Data)", 
+       x = "Total Steps", 
+       y = "Frequency")
+
+# Recalculate the mean and median
+Complete.Steps.Mean <- mean(
+  Complete.Steps.pr.Day$total_steps); Complete.Steps.Mean
+Complete.Steps.Median <- median(
+  Complete.Steps.pr.Day$total_steps); Complete.Steps.Median
+
+# Are there differences in activity patterns between weekdays ... ---------
+# ... and weekends? -------------------------------------------------------
+Complete.Data$date <- as.Date(Complete.Data$date, format = "%Y-%m-%d")
+Complete.Data$week.type <- ifelse(weekdays(Complete.Data$date) %in% 
+                                  c("Saturday", "Sunday"), "weekend", "weekday")
+# Convert to a factor
+Complete.Data$week.type <- as.factor(Complete.Data$week.type); head(Complete.Data)
+
+# Calculate average steps per interval across weekdays and weekends
+Avg.pr.Week.Type.pr.Interval <- Complete.Data %>%
+  group_by(interval, week.type) %>%
+  summarise(average_steps = mean(steps))
+
+# Panel plot
+ggplot(Avg.pr.Week.Type.pr.Interval, 
+       aes(
+         x = interval, 
+         y = average_steps,
+         color = week.type)) +
+  geom_line() +
+  facet_wrap(~week.type, ncol = 1) +
+  labs(title = "Average Steps per 5-Minute Interval (Weekday vs Weekend)", 
+       x = "5-minute Interval", 
+       y = "Average Number of Steps")
+
+
+
+
+
+
+
+
+
+