Getting-Cleaning-Data

Getting & Cleaning Data for Coursera "get data-015" course

Run Analysis script for "Getting and Cleaning Data", Coursera

2015-06-17

Set environment variables

setwd("~/Documents/WGSN/Personal Development/Coursera - data scientists toolbox/Getting & Cleaning Data/Assignment/UCI HAR dataset")

if(!file.exists("./data")){dir.create("./data")} fileURL1="https://d396qusza40orc.cloudfront.net/getdata%2Fdata%2Fss06hid.csv" download.file(fileURL1,destfile="./data/GCD_wk3_q1.csv",method="curl") data_q1<-read.csv("./data/GCD_wk3_q1.csv",stringsAsFactors=False)

Libraries to load to accomplish task

library(tidyr) library(dplyr)

set working directory. assume at UCI HAR dataset

test_sub <- "./test/subject_test.txt" test_act <- "./test/y_test.txt" test_val <- "./test/X_test.txt" train_sub <- "./train/subject_train.txt" train_act <- "./train/y_train.txt" train_val <- "./train/X_train.txt" val_names <- './features.txt' act_labels <- "./activity_labels.txt"

df_act_labels <- read.csv2(act_labels, stringsAsFactors=FALSE, header=FALSE) df_val_names <- read.csv2(val_names, stringsAsFactors=FALSE, header=FALSE,col.names="Measurement")

remove incorrect "BodyBody", "()", "(", ")", "'","-"

df_val_names$Measurement<-gsub("\(\)","",df_val_names$Measurement) df_val_names$Measurement<-gsub("\(","",df_val_names$Measurement) df_val_names$Measurement<-gsub("\)","",df_val_names$Measurement) df_val_names$Measurement<-gsub(",","",df_val_names$Measurement) df_val_names$Measurement<-gsub("-","",df_val_names$Measurement) df_val_names$Measurement<-gsub("BodyBody","Body",df_val_names$Measurement) df_val_names$Measurement<-gsub("mean","Mean",df_val_names$Measurement) df_val_names$Measurement<-gsub("std","Std",df_val_names$Measurement) df_val_names$Measurement<-gsub("\\","",df_val_names$Measurement)

Load in the files with the correct column names

df_test_sub<-read.csv2(test_sub,stringsAsFactors=FALSE, header=FALSE, col.names="Subject") df_test_act<-read.csv2(test_act,stringsAsFactors=FALSE, header=FALSE, col.names ="Activity") df_test_val<-read.csv2(test_val,stringsAsFactors=FALSE,header=FALSE, dec=".",col.names=df_val_names$Measurement) df_train_sub<-read.csv2(train_sub,stringsAsFactors=FALSE, header=FALSE, col.names="Subject") df_train_act<-read.csv2(train_act,stringsAsFactors=FALSE, header=FALSE, col.names ="Activity") df_train_val<-read.csv2(train_val,stringsAsFactors=FALSE,header=FALSE, dec=".",col.names=df_val_names$Measurement)

df_act_labels <- read.csv2(act_labels, stringsAsFactors=FALSE, header=FALSE)

Tidy up the Measurements - df_val_names

Remove incorrect "BodyBody" in variable names 516:554

df_val_names<-df_val_names %>% separate(Measurement,c("Prefix","Measurement"),sep=" ") %>% select(Measurement)

Substitute numbered values in activity with the names from

activity_labels.txt file.

Tidy up variable names first.

Split the variable V1 into two - "Prefix" and "Activity".

df_act_labels<-df_act_labels %>% separate(V1,c("Prefix","Activity"),sep=" ") y<-nrow(df_act_labels)

Now loop through the df_act_labels performing the substitutions:

Prefix Activity

1 1 WALKING

2 2 WALKING_UPSTAIRS

3 3 WALKING_DOWNSTAIRS

4 4 SITTING

5 5 STANDING

6 6 LAYING

First for the test activities

for (i in 1:nrow(df_act_labels)){ df_test_act$Activity<-gsub(df_act_labels[[i,1]],df_act_labels[[i,2]],df_test_act$Activity) }

And then for the training activities

for (i in 1:nrow(df_act_labels)){ df_train_act$Activity<-gsub(df_act_labels[[i,1]],df_act_labels[[i,2]],df_train_act$Activity) }

1. Merge the training and test data sets

- load each of the datasets

tmp<-cbind(df_test_sub,df_test_act)

Add a column which notes that this are the Test data

tmp$DataSet<-"Test" tmp<-cbind(tmp,df_test_val)

Add a column which notes that this are the Train data

tmp2<-cbind(df_train_sub,df_train_act) tmp2$DataSet<-"Train" tmp2<-cbind(tmp2,df_train_val)

Now concatenate the rows

tmp3<-rbind(tmp,tmp2)

2. Extracts the mean and std deviation for each measurement

- keep only the 'mean' and 'std' variables from features.txt (column names for x)

3. Uses descriptive activity names to name the activities in the data set -

DONE

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.

- dplyr might not be best for this. Try aggregate() function.

- or summarize_each()