GettingCleaningData

Getting and cleaning data course project This repo has been created to complete the course Coursera/Getting and clean data's final project The Codebook.md file includes the description of the variables, description of the data and transformation done to generate final "tidy_data.csv" file that includes the outcome of the attached script in R

run_analysis.R code runs to accomplish:

1. Merges the training and the test sets to create one data set.
2. Extracts only the measurements on the mean and standard deviation for each measurement.
3. Uses descriptive activity names to name the activities in the data set
4. Appropriately labels the data set with descriptive variable names.
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.

libraries

library(plyr)

library(dplyr)

library(utils)

library(tibble)

reading tables for data set for train and test; and feature names to name columns

features <- read.table(file="./UCI HAR Dataset/features.txt", header = FALSE, col.names = c("index","feature_name"))

X_train <- read.table(file="./UCI HAR Dataset/train/X_train.txt", header = FALSE)

X_test <- read.table(file="./UCI HAR Dataset/test/X_test.txt", header = FALSE)

4. Step done in advance as to keep work simple

naming columns in training and test data sets

names(X_train) <- features$feature_name

names(X_test) <- features$feature_name

1. Merging data sets' rows

X_all<- rbind(X_train,X_test)

2.Extracting the mean() and standard deviation variables from the data set

X_all_meanstd <- select(X_all,grep("mean|std",names(X_all)))

3. Reading activity set and code for descriptive naming

activity <- read.table(file="./UCI HAR Dataset/activity_labels.txt", header = FALSE, col.names = c("act_code","activity"))

y_train <- read.table(file="./UCI HAR Dataset/train/y_train.txt")

y_test <- read.table(file="./UCI HAR Dataset/test/y_test.txt")

y_all <- rbind(y_train,y_test) ## merging rows

y_all<- mutate(y_all, activity=activity[y_all[,],2]) ## generating descriptive column

Adding descriptive naming

X_all_meanstd <- mutate(X_all_meanstd,activity=y_all$activity)

4. descriptive variable names done at the beginning

5. Split by activity and subject who carried out the experiment

Adding subject to data set

subject_train <- read.table(file="./UCI HAR Dataset/train/subject_train.txt", header = FALSE, col.names="subject")

subject_test <- read.table(file="./UCI HAR Dataset/test/subject_test.txt", header = FALSE, col.names="subject")

subject_all <- rbind(subject_train, subject_test)

adding subject variable

X_all_meanstd <- mutate(X_all_meanstd,subject=subject_all$subject)

Converting, grouping and creating the tidy df

all_df <- as_tibble(X_all_meanstd)

all_df <- group_by(all_df, activity, subject)

tidy_data <- summarise_each(all_df,funs = mean)

write.table(tidy_data, file = "tidy_data.txt")