Source code and tidy dataset

In this first commit, it is included the source code for this
assignment and the tidy dataset obtained.

Author: Ramon

run_analysis.R

@@ -0,0 +1,105 @@
+## run_analysis.R
+## Getting and Cleaning Data Course Project
+## Ramon Perez Hernandez
+
+
+# **********
+# * TASK 1 *
+# **********
+# "Merge the training and the test sets to create one data set"
+
+
+# Download and extract all files.
+
+url <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
+name <- "data.zip"
+if(!file.exists("data.zip")) {
+   download.file(url, destfile = name, method = "curl")
+   if(!file.exists("UCI HAR Dataset")) {
+      unzip(name)
+   }
+}
+
+# The final data frame will be composed by:
+# - Subject who performed the activity (from subject_train/test.txt).
+# - Activity (from y_train/test.txt).
+# - Measures (from X_train/test.txt).
+
+# Loading train data frame.
+train_df <- cbind(read.table("UCI HAR Dataset/train/subject_train.txt"), 
+                 read.table("UCI HAR Dataset/train/y_train.txt"),
+                 read.table("UCI HAR Dataset/train/X_train.txt"))
+
+# Loading test data frame.
+test_df <- cbind(read.table("UCI HAR Dataset/test/subject_test.txt"), 
+                 read.table("UCI HAR Dataset/test/y_test.txt"),
+                 read.table("UCI HAR Dataset/test/X_test.txt"))
+
+# Merging train and test data frame.
+df <- rbind(train_df, test_df)
+
+
+# **********
+# * TASK 2 *
+# **********
+# "Extract only the measurements on the mean and standard deviation for each measurement"
+
+
+# Read features.txt, which have the names for measures in X_train/text.txt,
+# and transform them to a character vector.
+feat_names <- read.table("UCI HAR Dataset/features.txt")
+feat_names <- as.character(feat_names$V2)
+
+# Look for the position of names which contains "mean()" or "std()" and add them 2 in
+# order to choose the correct columns in df (remember that first and second column in df 
+# are the subject and the activity).
+positions <- grep("mean\\(\\)|std\\(\\)", feat_names) + 2
+
+# Choose "positions" columns + first and second column from df.
+df <- df[,c(1,2,positions)]
+
+
+# **********
+# * TASK 3 *
+# **********
+# "Use descriptive activity names to name the activities in the data set"
+
+
+# Read activity_labels.txt, which have the names for every activity, and transform 
+# them to a character vector.
+act_names <- read.table("UCI HAR Dataset/activity_labels.txt")
+act_names <- as.character(act_names$V2)
+
+# Transform df second column into factor, using act_names as levels.
+df[,2] <- factor(df[,2])
+levels(df[,2]) <- act_names
+
+
+# **********
+# * TASK 4 *
+# **********
+# "Appropriately label the data set with descriptive variable names"
+
+
+# First and second column will be called "subject" and "activity", respectively.
+# The rest of columns will use "feat_names" names as follows.
+colnames(df) <- c("subject","activity",feat_names[positions-2])
+
+
+# **********
+# * TASK 5 *
+# **********
+# "From the data set in step 4, creates a second, independent tidy data set 
+# with the average of each variable for each activity and each subject"
+
+
+# Here we will need dplyr package with group_by/summarise_each functions.
+library(dplyr)
+tidy_df <- df %>% group_by(subject, activity) %>% summarise_each(funs(mean))
+
+# Changing these column names to "MEAN-...".
+colnames(tidy_df) <- c("subject","activity",paste("MEAN-",
+                     feat_names[positions-2], sep = ""))
+
+# Save tidy_df into "tidy_df.txt" file.
+write.table(tidy_df, "tidy_df.txt", row.names=FALSE)