README.md

title	author	date
El R Markdown Getting and Cleaning Data Course Project	ECHF	20210724

las Bibliotecas utilizadas para trabajar con tablas y dataframes

Libraries used to work with tables and dataframes

library(data.table) library(dplyr)

se lee los archivos que contienen los metadatos

the files containing the metadata are read

featureNames <- read.table("UCI HAR Dataset/features.txt") activityLabels <- read.table("UCI HAR Dataset/activity_labels.txt", header = FALSE)

ahora vamos a leer los archivos que contienen la informacion tanto de entrenamiento como test

now we are going to read the files that contain the information for both training and test

Leer datos de entrenamiento

Read training data

subjectTrain <- read.table("UCI HAR Dataset/train/subject_train.txt", header = FALSE) activityTrain <- read.table("UCI HAR Dataset/train/y_train.txt", header = FALSE) featuresTrain <- read.table("UCI HAR Dataset/train/X_train.txt", header = FALSE)

Leer datos de prueba

Read test data

subjectTest <- read.table("UCI HAR Dataset/test/subject_test.txt", header = FALSE) activityTest <- read.table("UCI HAR Dataset/test/y_test.txt", header = FALSE) featuresTest <- read.table("UCI HAR Dataset/test/X_test.txt", header = FALSE)

ahora creamos los subconjuntos al unir la informacion que leimos antes

now we create the subsets by joining the information we read before

subject <- rbind(subjectTrain, subjectTest) activity <- rbind(activityTrain, activityTest) features <- rbind(featuresTrain, featuresTest)

##ahora asignamos los nombres a las columnas con ayuda de la informacion de los metadatos ###Now we assign the names to the columns with the help of the metadata information

colnames(features) <- t(featureNames[2])

Combinar los datos,se crea el conjunto de datos completos se almacenan ahora en .features,activity,subject,completeData

Merge the data, the complete dataset is created are now stored in .features, activity, subject, completeData

colnames(activity) <- "Activity" colnames(subject) <- "Subject" completeData <- cbind(features,activity,subject)

ahora se estrae de la informacion solo aquellas medidas que nos interesan: la media,la desviación estándar

se extrae los índices de columnas que tienen mean o std en ellos.

Now only those measures that interest us are extracted from the information: the mean, the standard deviation

extracts column indexes that have mean or std in them.

columnsWithMeanSTD <- grep(".Mean.|.Std.", names(completeData), ignore.case=TRUE)

Para validar se obtiene solo las columnas que nos interesan en completeData

To validate, only the columns that interest us in completeData are obtained

requiredColumns <- c(columnsWithMeanSTD, 562, 563) dim(completeData)

[1] 10299 563

ahora creamos una nueva variable con la informacion que obtivimos antes y le validamos

now we create a new variable with the information we obtained before and validate it

extractedData <- completeData[,requiredColumns] dim(extractedData)

[1] 10299 88

utiliza nombres de actividad descriptivos para asignar un nombre a las actividades del conjunto de datos

El campo en es originalmente de tipo numérico. por lo que se tiene que hacer un cast

Necesitamos cambiar su tipo a carácter para que pueda aceptar nombres de actividad.

Los nombres de actividad se toman de los metadatos.

use descriptive activity names to name the activities in the dataset

The field in is originally of type numeric. so a cast has to be done

We need to change its type to character so that it can accept activity names.

Activity names are taken from metadata.

extractedData$Activity <- as.character(extractedData$Activity) for (i in 1:6){ extractedData$Activity[extractedData$Activity == i] <- as.character(activityLabels[i,2]) }

Necesitamos hacer un cast a la variable, para cambiar su clase

We need to cast the variable, to change its class

extractedData$Activity <- as.factor(extractedData$Activity)

ahora necesitamos renombrar el nombre de la svariables se ve a continuacion lo que se tiene

para despues cambiarlos usando expresiones regulares.

now we need to rename the name of the svariables you see below what you have

and then change them using regular expressions.

names(extractedData)

Al examinar , podemos decir que se pueden reemplazar las siguientes siglas:extractedData

Acc se puede reemplazar con acelerómetro

Gyro se puede reemplazar con Giroscopio

BodyBody se puede reemplazar con el cuerpo

Mag se puede reemplazar con magnitud

El carácter se puede reemplazar con frecuencia: f

El carácter se puede reemplazar con tiempo: t

When examining, we can say that the following acronyms can be replaced: extractedData

Acc can be replaced with accelerometer

Gyro can be replaced with Gyroscope

BodyBody can be replaced with the body

Mag can be replaced with magnitude

The character can be replaced frequently: f

The character can be replaced with time: t

names(extractedData)<-gsub("Acc", "Accelerometer", names(extractedData)) names(extractedData)<-gsub("Gyro", "Gyroscope", names(extractedData)) names(extractedData)<-gsub("BodyBody", "Body", names(extractedData)) names(extractedData)<-gsub("Mag", "Magnitude", names(extractedData)) names(extractedData)<-gsub("^t", "Time", names(extractedData)) names(extractedData)<-gsub("^f", "Frequency", names(extractedData)) names(extractedData)<-gsub("tBody", "TimeBody", names(extractedData)) names(extractedData)<-gsub("-mean()", "Mean", names(extractedData), ignore.case = TRUE) names(extractedData)<-gsub("-std()", "STD", names(extractedData), ignore.case = TRUE) names(extractedData)<-gsub("-freq()", "Frequency", names(extractedData), ignore.case = TRUE) names(extractedData)<-gsub("angle", "Angle", names(extractedData)) names(extractedData)<-gsub("gravity", "Gravity", names(extractedData))

ahora se muestra como quedaron los nombres ya cambiados.

now it shows how the names were already changed.

names(extractedData)

ahora se creara un subconjunto de datos

independiente con el promedio de cada variable para cada actividad y cada sujeto

now a subset of data will be created

independent with the average of each variable for each activity and each subject

extractedData$Subject <- as.factor(extractedData$Subject) extractedData <- data.table(extractedData)

Creamos como un conjunto de datos con promedio para cada actividad y tema.

##ademas de crear unm subconjunto de datos con las informaciuoin resumida que se nos pide.

We create as a data set with average for each activity and topic.

###In addition to creating a subset of data with the summary information that is requested.

tidyData <- aggregate(. ~Subject + Activity, extractedData, mean) tidyData <- tidyData[order(tidyData$Subject,tidyData$Activity),] write.table(tidyData, file = "Tidy.txt", row.names = FALSE)

the information of the data dictionary is in the file / UCI HAR Dataset / readme.txt in the following lines this dictionary is reproduced

this work in a general way I only collect information about a work already done and that is described in the readme.txt file

so here only work is done in a practical way for an exercise of R language practices

generally

la informacion del diccioanrio de datos esta en el archivo /UCI HAR Dataset/readme.txt en las siguientes linea se reproduce ### este diccionario

###este trabajo de manera geneal solo recabo informacion de un trabajo ya hehco y que viene descrito en el rchivo readme.txt ###por lo que aqui solo se hace un trabajo de manera practica para un ejecicio de de practicas del lenguaje R

demanera general

For each record it is provided:

####======================================

####- Triaxial acceleration from the accelerometer (total acceleration) and the estimated body acceleration. ####- Triaxial Angular velocity from the gyroscope. ####- A 561-feature vector with time and frequency domain variables. ####- Its activity label. ####- An identifier of the subject who carried out the experiment.

####The dataset includes the following files: ####=========================================

####- 'README.txt'

####- 'features_info.txt': Shows information about the variables used on the feature vector.

####- 'features.txt': List of all features.

####- 'activity_labels.txt': Links the class labels with their activity name.

####- 'train/X_train.txt': Training set.

####- 'train/y_train.txt': Training labels.

####- 'test/X_test.txt': Test set.

####- 'test/y_test.txt': Test labels.

####The following files are available for the train and test data. Their descriptions are equivalent.

####- 'train/subject_train.txt': Each row identifies the subject who performed the activity for each window sample. Its range is from 1 to 30.

####- 'train/Inertial Signals/total_acc_x_train.txt': The acceleration signal from the smartphone accelerometer X axis in standard ####gravity units 'g'. Every row shows a 128 element vector. The same description applies for the 'total_acc_x_train.txt' and ####'total_acc_z_train.txt' files for the Y and Z axis.

####- 'train/Inertial Signals/body_acc_x_train.txt': The body acceleration signal obtained by subtracting the gravity from the ####total acceleration.

####train/Inertial Signals/body_gyro_x_train.txt': The angular velocity vector measured by the gyroscope for each window sample. ####The units are radians/second.