These are the step by step results of the scripts included in the run_analysis.R file:

0. Loading the library
library(dplyr)

1. Downloading the dataset
filename <- "dataset.zip"
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip" download.file(fileURL,filename)
}
The dataset is downloaded from the provided link and saved in the dataset.zip file.
if (!dir.exists("UCI HAR Dataset")){
unzip(filename)
}
This file is unzipped into the folder UCI HAR Dataset
If the file or the folder already exists they are not created.

2. The information is loaded into table variables:
features <- read.table("UCI HAR Dataset/features.txt", col.names = c("n","functions"))
This information comes from the accelerometer and gyroscope 3-axial raw signals tAcc-XYZ and tGyro-XYZ (561 obs. of 2 variables).
activities <-read.table("UCI HAR Dataset/activity_labels.txt", col.names = c("code", "activity"))
This is the list of activities and codes recorded as the experiments were ran (6 obs. of 2 variables)
subject_train <- read.table("UCI HAR Dataset/train/subject_train.txt", col.names = "subject")
This is the information of the train data -70% of the volunteers- (7352 obs. of 1 variable)
x_train <- read.table("UCI HAR Dataset/train/X_train.txt", col.names = features$functions)
This is the information of the data collected for the train part (7352 obs. of 561 variables)
y_train <- read.table("UCI HAR Dataset/train/y_train.txt", col.names = "code")
This is the information of the labels for the codes of the train activities (7352 obs. of 1 variable)
subject_test <- read.table("UCI HAR Dataset/test/subject_test.txt", col.names = "subject")
This is the information of the test data -30% of the volunteers- (2947 obs. of 1 variable)
x_test <- read.table("UCI HAR Dataset/test/X_test.txt", col.names = features$functions)
This is the information of the data collected for the test part (2947 obs. of 561 variables)
y_test <- read.table("UCI HAR Dataset/test/y_test.txt", col.names = "code")
This is the information of the labels for the codes of the test activities (2947 obs. of 1 variable)

3. Merges the training and the test sets to create one data set.
x <- rbind(x_train, x_test)
Both data from x train and test parts are merged into a new variable (10299 obs. of 561 variables)
y <- rbind(y_train, y_test)
Both data from y train and test parts are merged into a new variable (10299 obs. of 1 variable)
subject <- rbind(subject_train, subject_test)
Both subjects from train and test parts are merged into a new variable (10299 obs. of 1 variable)
merged_Data <- cbind(subject, y, x)
The three variables are merged into a new dataset (10299 obs. of 563 variables)

4. Extracts only the measurements on the mean and standard deviation for each measurement:
neat_Data <- merged_Data %>% select(subject, code, contains("mean"), contains("std"))
Selecting only the mean and the std subjects the new data is filtered (10299 obs. of 88 variables)

5. Uses descriptive activity names to name the activities in the data set
neat_Data$code <- activities[neat_Data$code,2]
The code column is replaced with the value from the second column in activities table.

6. Appropriately labels the data set with descriptive variable names.
names(neat_Data)[2] = "Activity"
The second column title is changed to Activity
names(neat_Data)<-gsub("Acc", "Accelerometer", names(neat_Data))
The Acc word is replaced with Accelerometer in all of the column titles
names(neat_Data)<-gsub("Gyro", "Gyroscope", names(neat_Data))
The Gyro word is replaced with Gyroscope in all of the column titles
names(neat_Data)<-gsub("BodyBody", "Body", names(neat_Data))
The BodyBody word is replaced with Body in all of the column titles
names(neat_Data)<-gsub("Mag", "Magnitude", names(neat_Data))
The Mag word is replaced with Magnitude in all of the column titles
names(neat_Data)<-gsub("^t", "Time", names(neat_Data))
The words starting with are replaced with Frecuency in all of the column titles
names(neat_Data)<-gsub("^f", "Frequency", names(neat_Data))
The words starting with f are replaced with Frecuency inall of the column titles
names(neat_Data)<-gsub("tBody", "TimeBody", names(neat_Data))
The tBody word is replaced with TimeBody in all of the column titles
names(neat_Data)<-gsub("-mean()", "Mean", names(neat_Data), ignore.case = TRUE)
The -mean() word is replaced with Mean in all of the column titles in any casing
names(neat_Data)<-gsub("-std()", "STD", names(neat_Data), ignore.case = TRUE)
The -std() word is replaced with STD in all of the column titles in any casing
names(neat_Data)<-gsub("-freq()", "Frequency", names(neat_Data), ignore.case = TRUE)
The -freq() word is replaced with Frequency in all of the column titles in any casing
names(neat_Data)<-gsub("angle", "Angle", names(neat_Data))
The angle word is replaced with Angle in all of the column titles
names(neat_Data)<-gsub("gravity", "Gravity", names(neat_Data))
The gravity word is replaced with Gravity in all of the column titles

7. From the data set in previous step, creates a second, independent tidy data set with the average of each variable for each activity and each subject.
TidyData <- neat_Data %>%
group_by(subject, Activity) %>%
summarise_all(funs(mean))
Summarizing the mean of each activity and subject the TidyData table is created (180 rows x 88 columns)
write.table(TidyData, "TidyData.txt", row.name=FALSE)
Writing the table to a new text file withouth the name of the rows.