Wagner Pinheiro
February, 2017
In this CodeBook is demonstrated all the functions necessary to tidy the dataset and all the results after running the source code.
Before we can tidy the data, we need to download and unzip the dataset using the HAR.download function.
Source code:
source("run_analysis.R")
HAR.download## function (Config = HAR.Config)
## {
## file = paste0(Config$data_path, Config$file_zipped)
## if (!file.exists(file)) {
## if (!dir.exists(Config$data_path)) {
## dir.create(Config$data_path)
## }
## download.file(Config$url, file)
## }
## if (!dir.exists(paste0(Config$data_path, "dataset"))) {
## unzip(file, exdir = Config$data_path)
## file.rename(paste0(Config$data_path, "UCI HAR Dataset"),
## paste0(Config$data_path, "dataset"))
## }
## paste0("Dataset downloaded and unzipped in folder: ", Config$data_path,
## "dataset")
## }
Running the HAR.download():
HAR.download()## [1] "Dataset downloaded and unzipped in folder: ./data/dataset"
This function load a defined dataset passed as parameter ("train" or "test"),
HAR.load## function (dataset = "train", Config = HAR.Config)
## {
## if (!file.exists(paste0(Config$data_path, "dataset/features.txt"))) {
## HAR.download(Config)
## }
## feature_list <- read.table(paste0(Config$data_path, "dataset/features.txt"),
## col.names = c("id", "name"))
## activity_labels <- read.table(paste0(Config$data_path, "dataset/activity_labels.txt"),
## col.names = c("activity_id", "activity"))
## feature_labels <- feature_list[, "name"]
## feature_labels <- HAR.describeFeatures(feature_labels)
## x <- read.table(paste0(Config$data_path, "dataset/", dataset,
## "/X_", dataset, ".txt"), col.names = feature_labels)
## y <- read.table(paste0(Config$data_path, "dataset/", dataset,
## "/y_", dataset, ".txt"), col.names = c("activity_id"))
## y <- merge(y, activity_labels, by.x = "activity_id", by.y = "activity_id",
## all.x = TRUE)
## sub <- read.table(paste0(Config$data_path, "dataset/", dataset,
## "/subject_", dataset, ".txt"), col.names = c("subject"))
## data <- cbind(x, sub, y)
## data
## }
test_data <- HAR.load("test")
dim(test_data)## [1] 2947 564
In the HAR.load() function we merge the features with the activity names, to: 3. Uses descriptive activity names to name the activities in the data set:
y <- merge(y, activity_labels, by.x = "activity_id", by.y ="activity_id", all.x=TRUE)For load and merge the train and test data, we call the HAR.loadAndMerge function:
HAR.loadAndMerge## function (Config = HAR.Config)
## {
## train_data <- HAR.load(dataset = "train", Config = Config)
## test_data <- HAR.load(dataset = "test", Config = Config)
## data <- rbind(train_data, test_data)
## data
## }
Running the HAR.loadAndMerge to 1. Merges the training and the test sets to create one data set:
full_dataset <- HAR.loadAndMerge()
str(full_dataset)## 'data.frame': 10299 obs. of 564 variables:
## $ time.BodyAcc.mean.X : num 0.289 0.278 0.28 0.279 0.277 ...
## $ time.BodyAcc.mean.Y : num -0.0203 -0.0164 -0.0195 -0.0262 -0.0166 ...
## $ time.BodyAcc.mean.Z : num -0.133 -0.124 -0.113 -0.123 -0.115 ...
## $ time.BodyAcc.std.X : num -0.995 -0.998 -0.995 -0.996 -0.998 ...
## $ time.BodyAcc.std.Y : num -0.983 -0.975 -0.967 -0.983 -0.981 ...
## $ time.BodyAcc.std.Z : num -0.914 -0.96 -0.979 -0.991 -0.99 ...
## $ time.BodyAcc.mad.X : num -0.995 -0.999 -0.997 -0.997 -0.998 ...
## $ time.BodyAcc.mad.Y : num -0.983 -0.975 -0.964 -0.983 -0.98 ...
## $ time.BodyAcc.mad.Z : num -0.924 -0.958 -0.977 -0.989 -0.99 ...
## $ time.BodyAcc.max.X : num -0.935 -0.943 -0.939 -0.939 -0.942 ...
## $ time.BodyAcc.max.Y : num -0.567 -0.558 -0.558 -0.576 -0.569 ...
## $ time.BodyAcc.max.Z : num -0.744 -0.818 -0.818 -0.83 -0.825 ...
## $ time.BodyAcc.min.X : num 0.853 0.849 0.844 0.844 0.849 ...
## $ time.BodyAcc.min.Y : num 0.686 0.686 0.682 0.682 0.683 ...
## $ time.BodyAcc.min.Z : num 0.814 0.823 0.839 0.838 0.838 ...
## $ time.BodyAcc.sma : num -0.966 -0.982 -0.983 -0.986 -0.993 ...
## $ time.BodyAcc.energy.X : num -1 -1 -1 -1 -1 ...
## $ time.BodyAcc.energy.Y : num -1 -1 -1 -1 -1 ...
## $ time.BodyAcc.energy.Z : num -0.995 -0.998 -0.999 -1 -1 ...
## $ time.BodyAcc.iqr.X : num -0.994 -0.999 -0.997 -0.997 -0.998 ...
## $ time.BodyAcc.iqr.Y : num -0.988 -0.978 -0.965 -0.984 -0.981 ...
## $ time.BodyAcc.iqr.Z : num -0.943 -0.948 -0.975 -0.986 -0.991 ...
## $ time.BodyAcc.entropy.X : num -0.408 -0.715 -0.592 -0.627 -0.787 ...
## $ time.BodyAcc.entropy.Y : num -0.679 -0.501 -0.486 -0.851 -0.559 ...
## $ time.BodyAcc.entropy.Z : num -0.602 -0.571 -0.571 -0.912 -0.761 ...
## $ time.BodyAcc.arCoeff.X.1 : num 0.9293 0.6116 0.273 0.0614 0.3133 ...
## $ time.BodyAcc.arCoeff.X.2 : num -0.853 -0.3295 -0.0863 0.0748 -0.1312 ...
## $ time.BodyAcc.arCoeff.X.3 : num 0.36 0.284 0.337 0.198 0.191 ...
## $ time.BodyAcc.arCoeff.X.4 : num -0.0585 0.2846 -0.1647 -0.2643 0.0869 ...
## $ time.BodyAcc.arCoeff.Y.1 : num 0.2569 0.1157 0.0172 0.0725 0.2576 ...
## $ time.BodyAcc.arCoeff.Y.2 : num -0.2248 -0.091 -0.0745 -0.1553 -0.2725 ...
## $ time.BodyAcc.arCoeff.Y.3 : num 0.264 0.294 0.342 0.323 0.435 ...
## $ time.BodyAcc.arCoeff.Y.4 : num -0.0952 -0.2812 -0.3326 -0.1708 -0.3154 ...
## $ time.BodyAcc.arCoeff.Z.1 : num 0.279 0.086 0.239 0.295 0.44 ...
## $ time.BodyAcc.arCoeff.Z.2 : num -0.4651 -0.0222 -0.1362 -0.3061 -0.2691 ...
## $ time.BodyAcc.arCoeff.Z.3 : num 0.4919 -0.0167 0.1739 0.4821 0.1794 ...
## $ time.BodyAcc.arCoeff.Z.4 : num -0.191 -0.221 -0.299 -0.47 -0.089 ...
## $ time.BodyAcc.correlation.X.Y : num 0.3763 -0.0134 -0.1247 -0.3057 -0.1558 ...
## $ time.BodyAcc.correlation.X.Z : num 0.4351 -0.0727 -0.1811 -0.3627 -0.1898 ...
## $ time.BodyAcc.correlation.Y.Z : num 0.661 0.579 0.609 0.507 0.599 ...
## $ time.GravityAcc.mean.X : num 0.963 0.967 0.967 0.968 0.968 ...
## $ time.GravityAcc.mean.Y : num -0.141 -0.142 -0.142 -0.144 -0.149 ...
## $ time.GravityAcc.mean.Z : num 0.1154 0.1094 0.1019 0.0999 0.0945 ...
## $ time.GravityAcc.std.X : num -0.985 -0.997 -1 -0.997 -0.998 ...
## $ time.GravityAcc.std.Y : num -0.982 -0.989 -0.993 -0.981 -0.988 ...
## $ time.GravityAcc.std.Z : num -0.878 -0.932 -0.993 -0.978 -0.979 ...
## $ time.GravityAcc.mad.X : num -0.985 -0.998 -1 -0.996 -0.998 ...
## $ time.GravityAcc.mad.Y : num -0.984 -0.99 -0.993 -0.981 -0.989 ...
## $ time.GravityAcc.mad.Z : num -0.895 -0.933 -0.993 -0.978 -0.979 ...
## $ time.GravityAcc.max.X : num 0.892 0.892 0.892 0.894 0.894 ...
## $ time.GravityAcc.max.Y : num -0.161 -0.161 -0.164 -0.164 -0.167 ...
## $ time.GravityAcc.max.Z : num 0.1247 0.1226 0.0946 0.0934 0.0917 ...
## $ time.GravityAcc.min.X : num 0.977 0.985 0.987 0.987 0.987 ...
## $ time.GravityAcc.min.Y : num -0.123 -0.115 -0.115 -0.121 -0.122 ...
## $ time.GravityAcc.min.Z : num 0.0565 0.1028 0.1028 0.0958 0.0941 ...
## $ time.GravityAcc.sma : num -0.375 -0.383 -0.402 -0.4 -0.4 ...
## $ time.GravityAcc.energy.X : num 0.899 0.908 0.909 0.911 0.912 ...
## $ time.GravityAcc.energy.Y : num -0.971 -0.971 -0.97 -0.969 -0.967 ...
## $ time.GravityAcc.energy.Z : num -0.976 -0.979 -0.982 -0.982 -0.984 ...
## $ time.GravityAcc.iqr.X : num -0.984 -0.999 -1 -0.996 -0.998 ...
## $ time.GravityAcc.iqr.Y : num -0.989 -0.99 -0.992 -0.981 -0.991 ...
## $ time.GravityAcc.iqr.Z : num -0.918 -0.942 -0.993 -0.98 -0.98 ...
## $ time.GravityAcc.entropy.X : num -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ time.GravityAcc.entropy.Y : num -1 -1 -1 -1 -1 -1 -1 -1 -1 -1 ...
## $ time.GravityAcc.entropy.Z : num 0.114 -0.21 -0.927 -0.596 -0.617 ...
## $ time.GravityAcc.arCoeff.X.1 : num -0.59042 -0.41006 0.00223 -0.06493 -0.25727 ...
## $ time.GravityAcc.arCoeff.X.2 : num 0.5911 0.4139 0.0275 0.0754 0.2689 ...
## $ time.GravityAcc.arCoeff.X.3 : num -0.5918 -0.4176 -0.0567 -0.0858 -0.2807 ...
## $ time.GravityAcc.arCoeff.X.4 : num 0.5925 0.4213 0.0855 0.0962 0.2926 ...
## $ time.GravityAcc.arCoeff.Y.1 : num -0.745 -0.196 -0.329 -0.295 -0.167 ...
## $ time.GravityAcc.arCoeff.Y.2 : num 0.7209 0.1253 0.2705 0.2283 0.0899 ...
## $ time.GravityAcc.arCoeff.Y.3 : num -0.7124 -0.1056 -0.2545 -0.2063 -0.0663 ...
## $ time.GravityAcc.arCoeff.Y.4 : num 0.7113 0.1091 0.2576 0.2048 0.0671 ...
## $ time.GravityAcc.arCoeff.Z.1 : num -0.995 -0.834 -0.705 -0.385 -0.237 ...
## $ time.GravityAcc.arCoeff.Z.2 : num 0.996 0.834 0.714 0.386 0.239 ...
## $ time.GravityAcc.arCoeff.Z.3 : num -0.996 -0.834 -0.723 -0.387 -0.241 ...
## $ time.GravityAcc.arCoeff.Z.4 : num 0.992 0.83 0.729 0.385 0.241 ...
## $ time.GravityAcc.correlation.X.Y : num 0.57 -0.831 -0.181 -0.991 -0.408 ...
## $ time.GravityAcc.correlation.X.Z : num 0.439 -0.866 0.338 -0.969 -0.185 ...
## $ time.GravityAcc.correlation.Y.Z : num 0.987 0.974 0.643 0.984 0.965 ...
## $ time.BodyAccJerk.mean.X : num 0.078 0.074 0.0736 0.0773 0.0734 ...
## $ time.BodyAccJerk.mean.Y : num 0.005 0.00577 0.0031 0.02006 0.01912 ...
## $ time.BodyAccJerk.mean.Z : num -0.06783 0.02938 -0.00905 -0.00986 0.01678 ...
## $ time.BodyAccJerk.std.X : num -0.994 -0.996 -0.991 -0.993 -0.996 ...
## $ time.BodyAccJerk.std.Y : num -0.988 -0.981 -0.981 -0.988 -0.988 ...
## $ time.BodyAccJerk.std.Z : num -0.994 -0.992 -0.99 -0.993 -0.992 ...
## $ time.BodyAccJerk.mad.X : num -0.994 -0.996 -0.991 -0.994 -0.997 ...
## $ time.BodyAccJerk.mad.Y : num -0.986 -0.979 -0.979 -0.986 -0.987 ...
## $ time.BodyAccJerk.mad.Z : num -0.993 -0.991 -0.987 -0.991 -0.991 ...
## $ time.BodyAccJerk.max.X : num -0.985 -0.995 -0.987 -0.987 -0.997 ...
## $ time.BodyAccJerk.max.Y : num -0.992 -0.979 -0.979 -0.992 -0.992 ...
## $ time.BodyAccJerk.max.Z : num -0.993 -0.992 -0.992 -0.99 -0.99 ...
## $ time.BodyAccJerk.min.X : num 0.99 0.993 0.988 0.988 0.994 ...
## $ time.BodyAccJerk.min.Y : num 0.992 0.992 0.992 0.993 0.993 ...
## $ time.BodyAccJerk.min.Z : num 0.991 0.989 0.989 0.993 0.986 ...
## $ time.BodyAccJerk.sma : num -0.994 -0.991 -0.988 -0.993 -0.994 ...
## $ time.BodyAccJerk.energy.X : num -1 -1 -1 -1 -1 ...
## $ time.BodyAccJerk.energy.Y : num -1 -1 -1 -1 -1 ...
## $ time.BodyAccJerk.energy.Z : num -1 -1 -1 -1 -1 ...
## [list output truncated]
For 2. Extracts only the measurements on the mean and standard deviation for each measurement, we use the auxiliary function HAR.extractMeanAndStdFields to grep only the variables with 'mean' and 'std' in they names. This auxiliary function is used in the previous executed HAR.load function.
Source code:
HAR.extractMeanAndStdFields## function (data)
## {
## data[, grepl("mean|std|subject|activity", names(data))]
## }
Running HAR.extractMeanAndStdFields:
partial_dataset <- HAR.extractMeanAndStdFields(full_dataset)
str(partial_dataset)## 'data.frame': 10299 obs. of 82 variables:
## $ time.BodyAcc.mean.X : num 0.289 0.278 0.28 0.279 0.277 ...
## $ time.BodyAcc.mean.Y : num -0.0203 -0.0164 -0.0195 -0.0262 -0.0166 ...
## $ time.BodyAcc.mean.Z : num -0.133 -0.124 -0.113 -0.123 -0.115 ...
## $ time.BodyAcc.std.X : num -0.995 -0.998 -0.995 -0.996 -0.998 ...
## $ time.BodyAcc.std.Y : num -0.983 -0.975 -0.967 -0.983 -0.981 ...
## $ time.BodyAcc.std.Z : num -0.914 -0.96 -0.979 -0.991 -0.99 ...
## $ time.GravityAcc.mean.X : num 0.963 0.967 0.967 0.968 0.968 ...
## $ time.GravityAcc.mean.Y : num -0.141 -0.142 -0.142 -0.144 -0.149 ...
## $ time.GravityAcc.mean.Z : num 0.1154 0.1094 0.1019 0.0999 0.0945 ...
## $ time.GravityAcc.std.X : num -0.985 -0.997 -1 -0.997 -0.998 ...
## $ time.GravityAcc.std.Y : num -0.982 -0.989 -0.993 -0.981 -0.988 ...
## $ time.GravityAcc.std.Z : num -0.878 -0.932 -0.993 -0.978 -0.979 ...
## $ time.BodyAccJerk.mean.X : num 0.078 0.074 0.0736 0.0773 0.0734 ...
## $ time.BodyAccJerk.mean.Y : num 0.005 0.00577 0.0031 0.02006 0.01912 ...
## $ time.BodyAccJerk.mean.Z : num -0.06783 0.02938 -0.00905 -0.00986 0.01678 ...
## $ time.BodyAccJerk.std.X : num -0.994 -0.996 -0.991 -0.993 -0.996 ...
## $ time.BodyAccJerk.std.Y : num -0.988 -0.981 -0.981 -0.988 -0.988 ...
## $ time.BodyAccJerk.std.Z : num -0.994 -0.992 -0.99 -0.993 -0.992 ...
## $ time.BodyGyro.mean.X : num -0.0061 -0.0161 -0.0317 -0.0434 -0.034 ...
## $ time.BodyGyro.mean.Y : num -0.0314 -0.0839 -0.1023 -0.0914 -0.0747 ...
## $ time.BodyGyro.mean.Z : num 0.1077 0.1006 0.0961 0.0855 0.0774 ...
## $ time.BodyGyro.std.X : num -0.985 -0.983 -0.976 -0.991 -0.985 ...
## $ time.BodyGyro.std.Y : num -0.977 -0.989 -0.994 -0.992 -0.992 ...
## $ time.BodyGyro.std.Z : num -0.992 -0.989 -0.986 -0.988 -0.987 ...
## $ time.BodyGyroJerk.mean.X : num -0.0992 -0.1105 -0.1085 -0.0912 -0.0908 ...
## $ time.BodyGyroJerk.mean.Y : num -0.0555 -0.0448 -0.0424 -0.0363 -0.0376 ...
## $ time.BodyGyroJerk.mean.Z : num -0.062 -0.0592 -0.0558 -0.0605 -0.0583 ...
## $ time.BodyGyroJerk.std.X : num -0.992 -0.99 -0.988 -0.991 -0.991 ...
## $ time.BodyGyroJerk.std.Y : num -0.993 -0.997 -0.996 -0.997 -0.996 ...
## $ time.BodyGyroJerk.std.Z : num -0.992 -0.994 -0.992 -0.993 -0.995 ...
## $ time.BodyAccMag.mean : num -0.959 -0.979 -0.984 -0.987 -0.993 ...
## $ time.BodyAccMag.std : num -0.951 -0.976 -0.988 -0.986 -0.991 ...
## $ time.GravityAccMag.mean : num -0.959 -0.979 -0.984 -0.987 -0.993 ...
## $ time.GravityAccMag.std : num -0.951 -0.976 -0.988 -0.986 -0.991 ...
## $ time.BodyAccJerkMag.mean : num -0.993 -0.991 -0.989 -0.993 -0.993 ...
## $ time.BodyAccJerkMag.std : num -0.994 -0.992 -0.99 -0.993 -0.996 ...
## $ time.BodyGyroMag.mean : num -0.969 -0.981 -0.976 -0.982 -0.985 ...
## $ time.BodyGyroMag.std : num -0.964 -0.984 -0.986 -0.987 -0.989 ...
## $ time.BodyGyroJerkMag.mean : num -0.994 -0.995 -0.993 -0.996 -0.996 ...
## $ time.BodyGyroJerkMag.std : num -0.991 -0.996 -0.995 -0.995 -0.995 ...
## $ freq.BodyAcc.mean.X : num -0.995 -0.997 -0.994 -0.995 -0.997 ...
## $ freq.BodyAcc.mean.Y : num -0.983 -0.977 -0.973 -0.984 -0.982 ...
## $ freq.BodyAcc.mean.Z : num -0.939 -0.974 -0.983 -0.991 -0.988 ...
## $ freq.BodyAcc.std.X : num -0.995 -0.999 -0.996 -0.996 -0.999 ...
## $ freq.BodyAcc.std.Y : num -0.983 -0.975 -0.966 -0.983 -0.98 ...
## $ freq.BodyAcc.std.Z : num -0.906 -0.955 -0.977 -0.99 -0.992 ...
## $ freq.BodyAcc.meanFreq.X : num 0.252 0.271 0.125 0.029 0.181 ...
## $ freq.BodyAcc.meanFreq.Y : num 0.1318 0.0429 -0.0646 0.0803 0.058 ...
## $ freq.BodyAcc.meanFreq.Z : num -0.0521 -0.0143 0.0827 0.1857 0.5598 ...
## $ freq.BodyAccJerk.mean.X : num -0.992 -0.995 -0.991 -0.994 -0.996 ...
## $ freq.BodyAccJerk.mean.Y : num -0.987 -0.981 -0.982 -0.989 -0.989 ...
## $ freq.BodyAccJerk.mean.Z : num -0.99 -0.99 -0.988 -0.991 -0.991 ...
## $ freq.BodyAccJerk.std.X : num -0.996 -0.997 -0.991 -0.991 -0.997 ...
## $ freq.BodyAccJerk.std.Y : num -0.991 -0.982 -0.981 -0.987 -0.989 ...
## $ freq.BodyAccJerk.std.Z : num -0.997 -0.993 -0.99 -0.994 -0.993 ...
## $ freq.BodyAccJerk.meanFreq.X : num 0.8704 0.6085 0.1154 0.0358 0.2734 ...
## $ freq.BodyAccJerk.meanFreq.Y : num 0.2107 -0.0537 -0.1934 -0.093 0.0791 ...
## $ freq.BodyAccJerk.meanFreq.Z : num 0.2637 0.0631 0.0383 0.1681 0.2924 ...
## $ freq.BodyGyro.mean.X : num -0.987 -0.977 -0.975 -0.987 -0.982 ...
## $ freq.BodyGyro.mean.Y : num -0.982 -0.993 -0.994 -0.994 -0.993 ...
## $ freq.BodyGyro.mean.Z : num -0.99 -0.99 -0.987 -0.987 -0.989 ...
## $ freq.BodyGyro.std.X : num -0.985 -0.985 -0.977 -0.993 -0.986 ...
## $ freq.BodyGyro.std.Y : num -0.974 -0.987 -0.993 -0.992 -0.992 ...
## $ freq.BodyGyro.std.Z : num -0.994 -0.99 -0.987 -0.989 -0.988 ...
## $ freq.BodyGyro.meanFreq.X : num -0.2575 -0.0482 -0.2167 0.2169 -0.1533 ...
## $ freq.BodyGyro.meanFreq.Y : num 0.0979 -0.4016 -0.0173 -0.1352 -0.0884 ...
## $ freq.BodyGyro.meanFreq.Z : num 0.5472 -0.0682 -0.1107 -0.0497 -0.1622 ...
## $ freq.BodyAccMag.mean : num -0.952 -0.981 -0.988 -0.988 -0.994 ...
## $ freq.BodyAccMag.std : num -0.956 -0.976 -0.989 -0.987 -0.99 ...
## $ freq.BodyAccMag.meanFreq : num -0.0884 -0.0441 0.2579 0.0736 0.3943 ...
## $ freq.BodyBodyAccJerkMag.mean : num -0.994 -0.99 -0.989 -0.993 -0.996 ...
## $ freq.BodyBodyAccJerkMag.std : num -0.994 -0.992 -0.991 -0.992 -0.994 ...
## $ freq.BodyBodyAccJerkMag.meanFreq : num 0.347 0.532 0.661 0.679 0.559 ...
## $ freq.BodyBodyGyroMag.mean : num -0.98 -0.988 -0.989 -0.989 -0.991 ...
## $ freq.BodyBodyGyroMag.std : num -0.961 -0.983 -0.986 -0.988 -0.989 ...
## $ freq.BodyBodyGyroMag.meanFreq : num -0.129 -0.272 -0.2127 -0.0357 -0.2736 ...
## $ freq.BodyBodyGyroJerkMag.mean : num -0.992 -0.996 -0.995 -0.995 -0.995 ...
## $ freq.BodyBodyGyroJerkMag.std : num -0.991 -0.996 -0.995 -0.995 -0.995 ...
## $ freq.BodyBodyGyroJerkMag.meanFreq: num -0.0743 0.1581 0.4145 0.4046 0.0878 ...
## $ subject : int 1 1 1 1 1 1 1 1 1 1 ...
## $ activity_id : int 1 1 1 1 1 1 1 1 1 1 ...
## $ activity : Factor w/ 6 levels "LAYING","SITTING",..: 4 4 4 4 4 4 4 4 4 4 ...
This function replace abbreviated features names, and is used in the HAR.load() function, for 4. Appropriately labels the data set with descriptive variable names.
Source code:
HAR.describeFeatures## function (feature_labels)
## {
## feature_labels <- gsub("\\(\\)", "", feature_labels)
## feature_labels <- gsub("\\)", "", feature_labels)
## feature_labels <- gsub(",", "-", feature_labels)
## feature_labels <- gsub("\\(", "-", feature_labels)
## feature_labels <- gsub("^(t)", "time-", feature_labels)
## feature_labels <- gsub("^(f)", "freq-", feature_labels)
## feature_labels
## }
Calculate the averages values for each variable.
Source code:
HAR.calcAvg## function (data)
## {
## tbl_df(data) %>% group_by(activity, subject) %>% summarise_each(funs(mean),
## -subject, -activity, -activity_id) %>% gather(measurement,
## mean, -subject, -activity)
## }
Running HAR.calcAvg:
avgs_dataset <- HAR.calcAvg(partial_dataset)
str(avgs_dataset)## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame': 3160 obs. of 4 variables:
## $ activity : Factor w/ 6 levels "LAYING","SITTING",..: 1 1 1 1 1 1 2 2 2 2 ...
## $ subject : int 20 24 27 28 29 30 12 13 17 18 ...
## $ measurement: chr "time.BodyAcc.mean.X" "time.BodyAcc.mean.X" "time.BodyAcc.mean.X" "time.BodyAcc.mean.X" ...
## $ mean : num 0.268 0.277 0.278 0.278 0.279 ...
## - attr(*, "vars")=List of 1
## ..$ : symbol activity
## - attr(*, "drop")= logi TRUE
## - attr(*, "indices")=List of 6
## ..$ : int 0 1 2 3 4 5 40 41 42 43 ...
## ..$ : int 6 7 8 9 10 11 12 46 47 48 ...
## ..$ : int 13 14 15 16 17 18 19 53 54 55 ...
## ..$ : int 20 21 22 23 24 25 60 61 62 63 ...
## ..$ : int 26 27 28 29 30 31 66 67 68 69 ...
## ..$ : int 32 33 34 35 36 37 38 39 72 73 ...
## - attr(*, "group_sizes")= int 474 553 553 474 474 632
## - attr(*, "biggest_group_size")= int 632
## - attr(*, "labels")='data.frame': 6 obs. of 1 variable:
## ..$ activity: Factor w/ 6 levels "LAYING","SITTING",..: 1 2 3 4 5 6
## ..- attr(*, "vars")=List of 1
## .. ..$ : symbol activity
## ..- attr(*, "drop")= logi TRUE
Save the tidy dataset to a file.
Source code:
HAR.saveData## function (data, filename = "tidy_data_avgs.txt")
## {
## write.table(data, file = filename, row.name = FALSE)
## }
Running:
HAR.saveData(data=avgs_dataset, filename="./data/tidy_data_avgs.txt")
file.info("./data/tidy_data_avgs.txt")## size isdir mode mtime
## ./data/tidy_data_avgs.txt 190430 FALSE 664 2017-02-03 14:32:17
## ctime atime uid
## ./data/tidy_data_avgs.txt 2017-02-03 14:32:17 2017-02-03 14:25:43 1000
## gid uname grname
## ./data/tidy_data_avgs.txt 1000 wagner wagner
Click here to see the resulting tidy dataset
Plotting the results of averages coloured by subject:
HAR.plotAvgs(avgs_dataset)