This project is part of the Getting and Cleaning Data course from Johns Hopkins University on Coursera.org.
The purpose of this project is to demonstrate your ability to collect, work with, and clean a data set. The goal is to prepare tidy data that can be used for later analysis.
run_analysis.R performs the data preparation and
then followed by the 5 Parts required as described in the course
project’s definition:
One of the most exciting areas in all of data science right now is wearable computing - see for example this article . Companies like Fitbit, Nike, and Jawbone Up are racing to develop the most advanced algorithms to attract new users. The data linked to from the course website represent data collected from the accelerometers from the Samsung Galaxy S smartphone. A full description is available at the site where the data was obtained:
Description can be found here UCI Machine Learning Repository
Here are the data for the project: Data Set
library(dplyr)
library(data.table)
filename <- "Getting_Cleaning_Dataset.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if folder exists
if (!file.exists("UCI HAR Dataset")) {
unzip(filename)
}
Read .txt files into data frames
- This project will use six data, which are
x_train.txt,x_test.txt,y_train.txt,y_test.txt,subject_train.txtandsubject_test.txt, they can all be found inside the downloaded dataset, namely URI HAR Dataset. - The `
features.txt(561 rows, 2 columns) contains the correct variable name, which corresponds to each column ofx_train.txtwith 7352 rows, 561 columns contains recorded features train data andx_test.txtwith 2947 rows, 561 columns contains recorded features test data. Further explanation of each feature is in thefeatures_info.txt. - The
activity_labels.txt6 rows, 2 columns List of activities performed when the corresponding measurements were taken and its codes (labels) which corresponds to each number in they_train.txt(7352 rows, 1 columns) andy_test.txt(2947 rows, 1 columns). - The
README.txtis the overall desciption about the overall process of how publishers of this dataset did the experiment and got the data result.
Activity (1 to 6) (WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING)
#features and activities labels
features <- data.table::fread("UCI HAR Dataset/features.txt", col.names = c("n","functions"))
activities <- data.table::fread("UCI HAR Dataset/activity_labels.txt", col.names = c("code", "activity"))
Subjects group of 30 volunteers
subject_test <- data.table::fread("UCI HAR Dataset/test/subject_test.txt", col.names = "subject")
subject_train <- data.table::fread("UCI HAR Dataset/train/subject_train.txt", col.names = "subject")
Data where 70% of the volunteers was selected for generating the training data and 30% the test data, and Activity (1 to 6)
x_train <- data.table::fread("UCI HAR Dataset/train/X_train.txt", col.names = features$functions)
x_test <- data.table::fread("UCI HAR Dataset/test/X_test.txt", col.names = features$functions)
y_test <- data.table::fread("UCI HAR Dataset/test/y_test.txt", col.names = "code")
y_train <- data.table::fread("UCI HAR Dataset/train/y_train.txt", col.names = "code")
Combine test and train activities
X<-rbind(x_train,x_test)
Y<-rbind(y_train,y_test)
combine subject and combine all data
Subject<-rbind(subject_train,subject_test)
All_data<-cbind(Subject,X,Y)
TidyData<-select(All_data,subject,code,contains("mean()"),contains("std()"))
There are some ways to do that, the easy way is to use data.table
library We use rows from Tidydata$code and then we assign the value of
activity column from activities
TidyData$code<-activities[TidyData$code,activity]
There are a few things to denote: - “t” = time - “f” = frequency - “Acc” = Accelerometer - “Mag” = Magnitude - “Gyro” = Gyroscopic - “Freq” = Frequency - “stimed” = estimated
names(TidyData)[2] <- "activity"
#colnames(TidyData)[2]<-"activity"
names(TidyData)<-gsub("Acc", "Accelerometer", names(TidyData))
names(TidyData)<-gsub("Gyro", "Gyroscope", names(TidyData))
names(TidyData)<-gsub("BodyBody", "Body", names(TidyData))
names(TidyData)<-gsub("Mag", "Magnitude", names(TidyData))
names(TidyData)<-gsub("^t", "Time", names(TidyData))
names(TidyData)<-gsub("^f", "Frequency", names(TidyData))
names(TidyData)<-gsub("tBody", "TimeBody", names(TidyData))
names(TidyData)<-gsub("-mean()", "Mean", names(TidyData), ignore.case = TRUE)
names(TidyData)<-gsub("-std()", "STD", names(TidyData), ignore.case = TRUE)
names(TidyData)<-gsub("-freq()", "Frequency", names(TidyData), ignore.case = TRUE)
names(TidyData)<-gsub("angle", "Angle", names(TidyData))
names(TidyData)<-gsub("gravity", "Gravity", names(TidyData))
Part 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.
Using dplyr package and use summarise function, by adding across(everything())
tidyDataset <- TidyData %>% group_by(subject,activity) %>%
summarise(across(everything(),mean))
write.table(tidyDataset, file = "tidyDataset.txt", row.names = FALSE)
data.table::fwrite(x = tidyDataset, file = "tidyData.csv", quote = FALSE)
The final tidy data is produced inside the
run_analysis.R, which I simply named it
tidyDataset.txt and tidyData.csv
Both are the sameresult, the differences is the format.The tidy data
produced after going through all 5 steps of the course project. It
contains 180 observations and 68 variables. Where the first column is
the subject id, second column is the activity and the rest are the
average of each feature variables. To sum up tidyDataset (180 rows, 88
columns) is created by sumarizing TidyData taking the means of each
variable for each activity and each subject, after groupped by subject
and activity.
str(tidyDataset)
## tibble [180 x 68] (S3: grouped_df/tbl_df/tbl/data.frame)
## $ subject : int [1:180] 1 1 1 1 1 1 2 2 2 2 ...
## $ activity : chr [1:180] "LAYING" "SITTING" "STANDING" "WALKING" ...
## $ TimeBodyAccelerometerMean()-X : num [1:180] 0.222 0.261 0.279 0.277 0.289 ...
## $ TimeBodyAccelerometerMean()-Y : num [1:180] -0.04051 -0.00131 -0.01614 -0.01738 -0.00992 ...
## $ TimeBodyAccelerometerMean()-Z : num [1:180] -0.113 -0.105 -0.111 -0.111 -0.108 ...
## $ TimeGravityAccelerometerMean()-X : num [1:180] -0.249 0.832 0.943 0.935 0.932 ...
## $ TimeGravityAccelerometerMean()-Y : num [1:180] 0.706 0.204 -0.273 -0.282 -0.267 ...
## $ TimeGravityAccelerometerMean()-Z : num [1:180] 0.4458 0.332 0.0135 -0.0681 -0.0621 ...
## $ TimeBodyAccelerometerJerkMean()-X : num [1:180] 0.0811 0.0775 0.0754 0.074 0.0542 ...
## $ TimeBodyAccelerometerJerkMean()-Y : num [1:180] 0.003838 -0.000619 0.007976 0.028272 0.02965 ...
## $ TimeBodyAccelerometerJerkMean()-Z : num [1:180] 0.01083 -0.00337 -0.00369 -0.00417 -0.01097 ...
## $ TimeBodyGyroscopeMean()-X : num [1:180] -0.0166 -0.0454 -0.024 -0.0418 -0.0351 ...
## $ TimeBodyGyroscopeMean()-Y : num [1:180] -0.0645 -0.0919 -0.0594 -0.0695 -0.0909 ...
## $ TimeBodyGyroscopeMean()-Z : num [1:180] 0.1487 0.0629 0.0748 0.0849 0.0901 ...
## $ TimeBodyGyroscopeJerkMean()-X : num [1:180] -0.1073 -0.0937 -0.0996 -0.09 -0.074 ...
## $ TimeBodyGyroscopeJerkMean()-Y : num [1:180] -0.0415 -0.0402 -0.0441 -0.0398 -0.044 ...
## $ TimeBodyGyroscopeJerkMean()-Z : num [1:180] -0.0741 -0.0467 -0.049 -0.0461 -0.027 ...
## $ TimeBodyAccelerometerMagnitudeMean() : num [1:180] -0.8419 -0.9485 -0.9843 -0.137 0.0272 ...
## $ TimeGravityAccelerometerMagnitudeMean() : num [1:180] -0.8419 -0.9485 -0.9843 -0.137 0.0272 ...
## $ TimeBodyAccelerometerJerkMagnitudeMean() : num [1:180] -0.9544 -0.9874 -0.9924 -0.1414 -0.0894 ...
## $ TimeBodyGyroscopeMagnitudeMean() : num [1:180] -0.8748 -0.9309 -0.9765 -0.161 -0.0757 ...
## $ TimeBodyGyroscopeJerkMagnitudeMean() : num [1:180] -0.963 -0.992 -0.995 -0.299 -0.295 ...
## $ FrequencyBodyAccelerometerMean()-X : num [1:180] -0.9391 -0.9796 -0.9952 -0.2028 0.0382 ...
## $ FrequencyBodyAccelerometerMean()-Y : num [1:180] -0.86707 -0.94408 -0.97707 0.08971 0.00155 ...
## $ FrequencyBodyAccelerometerMean()-Z : num [1:180] -0.883 -0.959 -0.985 -0.332 -0.226 ...
## $ FrequencyBodyAccelerometerJerkMean()-X : num [1:180] -0.9571 -0.9866 -0.9946 -0.1705 -0.0277 ...
## $ FrequencyBodyAccelerometerJerkMean()-Y : num [1:180] -0.9225 -0.9816 -0.9854 -0.0352 -0.1287 ...
## $ FrequencyBodyAccelerometerJerkMean()-Z : num [1:180] -0.948 -0.986 -0.991 -0.469 -0.288 ...
## $ FrequencyBodyGyroscopeMean()-X : num [1:180] -0.85 -0.976 -0.986 -0.339 -0.352 ...
## $ FrequencyBodyGyroscopeMean()-Y : num [1:180] -0.9522 -0.9758 -0.989 -0.1031 -0.0557 ...
## $ FrequencyBodyGyroscopeMean()-Z : num [1:180] -0.9093 -0.9513 -0.9808 -0.2559 -0.0319 ...
## $ FrequencyBodyAccelerometerMagnitudeMean() : num [1:180] -0.8618 -0.9478 -0.9854 -0.1286 0.0966 ...
## $ FrequencyBodyAccelerometerJerkMagnitudeMean(): num [1:180] -0.9333 -0.9853 -0.9925 -0.0571 0.0262 ...
## $ FrequencyBodyGyroscopeMagnitudeMean() : num [1:180] -0.862 -0.958 -0.985 -0.199 -0.186 ...
## $ FrequencyBodyGyroscopeJerkMagnitudeMean() : num [1:180] -0.942 -0.99 -0.995 -0.319 -0.282 ...
## $ TimeBodyAccelerometerSTD()-X : num [1:180] -0.928 -0.977 -0.996 -0.284 0.03 ...
## $ TimeBodyAccelerometerSTD()-Y : num [1:180] -0.8368 -0.9226 -0.9732 0.1145 -0.0319 ...
## $ TimeBodyAccelerometerSTD()-Z : num [1:180] -0.826 -0.94 -0.98 -0.26 -0.23 ...
## $ TimeGravityAccelerometerSTD()-X : num [1:180] -0.897 -0.968 -0.994 -0.977 -0.951 ...
## $ TimeGravityAccelerometerSTD()-Y : num [1:180] -0.908 -0.936 -0.981 -0.971 -0.937 ...
## $ TimeGravityAccelerometerSTD()-Z : num [1:180] -0.852 -0.949 -0.976 -0.948 -0.896 ...
## $ TimeBodyAccelerometerJerkSTD()-X : num [1:180] -0.9585 -0.9864 -0.9946 -0.1136 -0.0123 ...
## $ TimeBodyAccelerometerJerkSTD()-Y : num [1:180] -0.924 -0.981 -0.986 0.067 -0.102 ...
## $ TimeBodyAccelerometerJerkSTD()-Z : num [1:180] -0.955 -0.988 -0.992 -0.503 -0.346 ...
## $ TimeBodyGyroscopeSTD()-X : num [1:180] -0.874 -0.977 -0.987 -0.474 -0.458 ...
## $ TimeBodyGyroscopeSTD()-Y : num [1:180] -0.9511 -0.9665 -0.9877 -0.0546 -0.1263 ...
## $ TimeBodyGyroscopeSTD()-Z : num [1:180] -0.908 -0.941 -0.981 -0.344 -0.125 ...
## $ TimeBodyGyroscopeJerkSTD()-X : num [1:180] -0.919 -0.992 -0.993 -0.207 -0.487 ...
## $ TimeBodyGyroscopeJerkSTD()-Y : num [1:180] -0.968 -0.99 -0.995 -0.304 -0.239 ...
## $ TimeBodyGyroscopeJerkSTD()-Z : num [1:180] -0.958 -0.988 -0.992 -0.404 -0.269 ...
## $ TimeBodyAccelerometerMagnitudeSTD() : num [1:180] -0.7951 -0.9271 -0.9819 -0.2197 0.0199 ...
## $ TimeGravityAccelerometerMagnitudeSTD() : num [1:180] -0.7951 -0.9271 -0.9819 -0.2197 0.0199 ...
## $ TimeBodyAccelerometerJerkMagnitudeSTD() : num [1:180] -0.9282 -0.9841 -0.9931 -0.0745 -0.0258 ...
## $ TimeBodyGyroscopeMagnitudeSTD() : num [1:180] -0.819 -0.935 -0.979 -0.187 -0.226 ...
## $ TimeBodyGyroscopeJerkMagnitudeSTD() : num [1:180] -0.936 -0.988 -0.995 -0.325 -0.307 ...
## $ FrequencyBodyAccelerometerSTD()-X : num [1:180] -0.9244 -0.9764 -0.996 -0.3191 0.0243 ...
## $ FrequencyBodyAccelerometerSTD()-Y : num [1:180] -0.834 -0.917 -0.972 0.056 -0.113 ...
## $ FrequencyBodyAccelerometerSTD()-Z : num [1:180] -0.813 -0.934 -0.978 -0.28 -0.298 ...
## $ FrequencyBodyAccelerometerJerkSTD()-X : num [1:180] -0.9642 -0.9875 -0.9951 -0.1336 -0.0863 ...
## $ FrequencyBodyAccelerometerJerkSTD()-Y : num [1:180] -0.932 -0.983 -0.987 0.107 -0.135 ...
## $ FrequencyBodyAccelerometerJerkSTD()-Z : num [1:180] -0.961 -0.988 -0.992 -0.535 -0.402 ...
## $ FrequencyBodyGyroscopeSTD()-X : num [1:180] -0.882 -0.978 -0.987 -0.517 -0.495 ...
## $ FrequencyBodyGyroscopeSTD()-Y : num [1:180] -0.9512 -0.9623 -0.9871 -0.0335 -0.1814 ...
## $ FrequencyBodyGyroscopeSTD()-Z : num [1:180] -0.917 -0.944 -0.982 -0.437 -0.238 ...
## $ FrequencyBodyAccelerometerMagnitudeSTD() : num [1:180] -0.798 -0.928 -0.982 -0.398 -0.187 ...
## $ FrequencyBodyAccelerometerJerkMagnitudeSTD() : num [1:180] -0.922 -0.982 -0.993 -0.103 -0.104 ...
## $ FrequencyBodyGyroscopeMagnitudeSTD() : num [1:180] -0.824 -0.932 -0.978 -0.321 -0.398 ...
## $ FrequencyBodyGyroscopeJerkMagnitudeSTD() : num [1:180] -0.933 -0.987 -0.995 -0.382 -0.392 ...
## - attr(*, "groups")= tibble [30 x 2] (S3: tbl_df/tbl/data.frame)
## ..$ subject: int [1:30] 1 2 3 4 5 6 7 8 9 10 ...
## ..$ .rows : list<int> [1:30]
## .. ..$ : int [1:6] 1 2 3 4 5 6
## .. ..$ : int [1:6] 7 8 9 10 11 12
## .. ..$ : int [1:6] 13 14 15 16 17 18
## .. ..$ : int [1:6] 19 20 21 22 23 24
## .. ..$ : int [1:6] 25 26 27 28 29 30
## .. ..$ : int [1:6] 31 32 33 34 35 36
## .. ..$ : int [1:6] 37 38 39 40 41 42
## .. ..$ : int [1:6] 43 44 45 46 47 48
## .. ..$ : int [1:6] 49 50 51 52 53 54
## .. ..$ : int [1:6] 55 56 57 58 59 60
## .. ..$ : int [1:6] 61 62 63 64 65 66
## .. ..$ : int [1:6] 67 68 69 70 71 72
## .. ..$ : int [1:6] 73 74 75 76 77 78
## .. ..$ : int [1:6] 79 80 81 82 83 84
## .. ..$ : int [1:6] 85 86 87 88 89 90
## .. ..$ : int [1:6] 91 92 93 94 95 96
## .. ..$ : int [1:6] 97 98 99 100 101 102
## .. ..$ : int [1:6] 103 104 105 106 107 108
## .. ..$ : int [1:6] 109 110 111 112 113 114
## .. ..$ : int [1:6] 115 116 117 118 119 120
## .. ..$ : int [1:6] 121 122 123 124 125 126
## .. ..$ : int [1:6] 127 128 129 130 131 132
## .. ..$ : int [1:6] 133 134 135 136 137 138
## .. ..$ : int [1:6] 139 140 141 142 143 144
## .. ..$ : int [1:6] 145 146 147 148 149 150
## .. ..$ : int [1:6] 151 152 153 154 155 156
## .. ..$ : int [1:6] 157 158 159 160 161 162
## .. ..$ : int [1:6] 163 164 165 166 167 168
## .. ..$ : int [1:6] 169 170 171 172 173 174
## .. ..$ : int [1:6] 175 176 177 178 179 180
## .. ..@ ptype: int(0)
## ..- attr(*, ".drop")= logi TRUE
tidyDataset
## # A tibble: 180 x 68
## # Groups: subject [30]
## subject activity `TimeBodyAccele~ `TimeBodyAccele~ `TimeBodyAccele~
## <int> <chr> <dbl> <dbl> <dbl>
## 1 1 LAYING 0.222 -0.0405 -0.113
## 2 1 SITTING 0.261 -0.00131 -0.105
## 3 1 STANDING 0.279 -0.0161 -0.111
## 4 1 WALKING 0.277 -0.0174 -0.111
## 5 1 WALKING~ 0.289 -0.00992 -0.108
## 6 1 WALKING~ 0.255 -0.0240 -0.0973
## 7 2 LAYING 0.281 -0.0182 -0.107
## 8 2 SITTING 0.277 -0.0157 -0.109
## 9 2 STANDING 0.278 -0.0184 -0.106
## 10 2 WALKING 0.276 -0.0186 -0.106
## # ... with 170 more rows, and 63 more variables:
## # `TimeGravityAccelerometerMean()-X` <dbl>,
## # `TimeGravityAccelerometerMean()-Y` <dbl>,
## # `TimeGravityAccelerometerMean()-Z` <dbl>,
## # `TimeBodyAccelerometerJerkMean()-X` <dbl>,
## # `TimeBodyAccelerometerJerkMean()-Y` <dbl>,
## # `TimeBodyAccelerometerJerkMean()-Z` <dbl>,
## # `TimeBodyGyroscopeMean()-X` <dbl>, `TimeBodyGyroscopeMean()-Y` <dbl>,
## # `TimeBodyGyroscopeMean()-Z` <dbl>, `TimeBodyGyroscopeJerkMean()-X` <dbl>,
## # `TimeBodyGyroscopeJerkMean()-Y` <dbl>,
## # `TimeBodyGyroscopeJerkMean()-Z` <dbl>,
## # `TimeBodyAccelerometerMagnitudeMean()` <dbl>,
## # `TimeGravityAccelerometerMagnitudeMean()` <dbl>,
## # `TimeBodyAccelerometerJerkMagnitudeMean()` <dbl>,
## # `TimeBodyGyroscopeMagnitudeMean()` <dbl>,
## # `TimeBodyGyroscopeJerkMagnitudeMean()` <dbl>,
## # `FrequencyBodyAccelerometerMean()-X` <dbl>,
## # `FrequencyBodyAccelerometerMean()-Y` <dbl>,
## # `FrequencyBodyAccelerometerMean()-Z` <dbl>,
## # `FrequencyBodyAccelerometerJerkMean()-X` <dbl>,
## # `FrequencyBodyAccelerometerJerkMean()-Y` <dbl>,
## # `FrequencyBodyAccelerometerJerkMean()-Z` <dbl>,
## # `FrequencyBodyGyroscopeMean()-X` <dbl>,
## # `FrequencyBodyGyroscopeMean()-Y` <dbl>,
## # `FrequencyBodyGyroscopeMean()-Z` <dbl>,
## # `FrequencyBodyAccelerometerMagnitudeMean()` <dbl>,
## # `FrequencyBodyAccelerometerJerkMagnitudeMean()` <dbl>,
## # `FrequencyBodyGyroscopeMagnitudeMean()` <dbl>,
## # `FrequencyBodyGyroscopeJerkMagnitudeMean()` <dbl>,
## # `TimeBodyAccelerometerSTD()-X` <dbl>, `TimeBodyAccelerometerSTD()-Y` <dbl>,
## # `TimeBodyAccelerometerSTD()-Z` <dbl>,
## # `TimeGravityAccelerometerSTD()-X` <dbl>,
## # `TimeGravityAccelerometerSTD()-Y` <dbl>,
## # `TimeGravityAccelerometerSTD()-Z` <dbl>,
## # `TimeBodyAccelerometerJerkSTD()-X` <dbl>,
## # `TimeBodyAccelerometerJerkSTD()-Y` <dbl>,
## # `TimeBodyAccelerometerJerkSTD()-Z` <dbl>, `TimeBodyGyroscopeSTD()-X` <dbl>,
## # `TimeBodyGyroscopeSTD()-Y` <dbl>, `TimeBodyGyroscopeSTD()-Z` <dbl>,
## # `TimeBodyGyroscopeJerkSTD()-X` <dbl>, `TimeBodyGyroscopeJerkSTD()-Y` <dbl>,
## # `TimeBodyGyroscopeJerkSTD()-Z` <dbl>,
## # `TimeBodyAccelerometerMagnitudeSTD()` <dbl>,
## # `TimeGravityAccelerometerMagnitudeSTD()` <dbl>,
## # `TimeBodyAccelerometerJerkMagnitudeSTD()` <dbl>,
## # `TimeBodyGyroscopeMagnitudeSTD()` <dbl>,
## # `TimeBodyGyroscopeJerkMagnitudeSTD()` <dbl>,
## # `FrequencyBodyAccelerometerSTD()-X` <dbl>,
## # `FrequencyBodyAccelerometerSTD()-Y` <dbl>,
## # `FrequencyBodyAccelerometerSTD()-Z` <dbl>,
## # `FrequencyBodyAccelerometerJerkSTD()-X` <dbl>,
## # `FrequencyBodyAccelerometerJerkSTD()-Y` <dbl>,
## # `FrequencyBodyAccelerometerJerkSTD()-Z` <dbl>,
## # `FrequencyBodyGyroscopeSTD()-X` <dbl>,
## # `FrequencyBodyGyroscopeSTD()-Y` <dbl>,
## # `FrequencyBodyGyroscopeSTD()-Z` <dbl>,
## # `FrequencyBodyAccelerometerMagnitudeSTD()` <dbl>,
## # `FrequencyBodyAccelerometerJerkMagnitudeSTD()` <dbl>,
## # `FrequencyBodyGyroscopeMagnitudeSTD()` <dbl>,
## # `FrequencyBodyGyroscopeJerkMagnitudeSTD()` <dbl>
Fine particulate matter (PM2.5) is an ambient air pollutant for which there is strong evidence that it is harmful to human health. In the United States, the Environmental Protection Agency (EPA) is tasked with setting national ambient air quality standards for fine PM and for tracking the emissions of this pollutant into the atmosphere. Approximatly every 3 years, the EPA releases its database on emissions of PM2.5. This database is known as the National Emissions Inventory (NEI). You can read more information about the NEI at the EPA National Emissions Inventory web site.
For each year and for each type of PM source, the NEI records how many tons of PM2.5 were emitted from that source over the course of the entire year. The data that you will use for this assignment are for 1999, 2002, 2005, and 2008.
- Data for Peer Assessment \[29Mb\]
The zip file contains two files:
PM2.5 Emissions (summarySCC_PM25.rds): This file contains a data
frame with all of the PM2.5 emissions data. Here are the first few rows.
## fips SCC Pollutant Emissions type year
## 4 09001 10100401 PM25-PRI 15.714 POINT 1999
## 8 09001 10100404 PM25-PRI 234.178 POINT 1999
## 12 09001 10100501 PM25-PRI 0.128 POINT 1999
## 16 09001 10200401 PM25-PRI 2.036 POINT 1999
## 20 09001 10200504 PM25-PRI 0.388 POINT 1999
## 24 09001 10200602 PM25-PRI 1.490 POINT 1999
## 28 09001 10200603 PM25-PRI 0.200 POINT 1999
## 32 09001 10300401 PM25-PRI 0.081 POINT 1999
## 36 09001 10300501 PM25-PRI 0.184 POINT 1999
## 40 09001 10300504 PM25-PRI 0.273 POINT 1999
fips: A five-digit number (represented as a string) indicating the U.S. countySCC: The name of the source as indicated by a digit string (see source code classification table)Pollutant: A string indicating the pollutantEmissions: Amount of PM2.5 emitted, in tonstype: The type of source (point, non-point, on-road, or non-road)year: The year of emissions recorded
Source Classification Code Table (Source_Classification_Code.rds):
This table provides a mapping from the SCC digit strings in the
Emissions table to the actual name of the PM2.5 source. The sources are
categorized in a few different ways from more general to more specific
and you may choose to explore whatever categories you think are most
useful. For example, source “10100101” is known as “Ext Comb /Electric
Gen /Anthracite Coal /Pulverized Coal”.
The overall goal of this assignment is to explore the National Emissions Inventory database and see what it say about fine particulate matter pollution in the United states over the 10-year period 1999–2008. You may use any R package you want to support your analysis.
Question 1 plot1.R
Have total emissions from PM2.5 decreased in the United States from 1999 to 2008? Using the base plotting system, make a plot showing the total PM2.5 emission from all sources for each of the years 1999, 2002, 2005, and 2008.
library(dplyr)
filename <- "exdata_NEI_PM2.5.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if folder exists
if (!file.exists("summarySCC_PM25.rds")) {
unzip(filename)
}
#Read data
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
#Differet ways to create the subset
Total<-with(NEI,tapply(NEI$Emissions, as.factor(NEI$year), sum))
total_annual_emissions <- aggregate(Emissions ~ year, NEI, FUN = sum)
Total_emision<-NEI%>%group_by(year)%>%
summarize(Emissions=sum(Emissions))%>%print
#Plotting
png(filename='plot1.png',width = 640,height = 480)
color_range <- colorRampPalette(c("blue","green"))
par(mar = c(4, 5.5, 2, 1), oma = c(0, 0, 2,0))
x<-barplot(height = Total/10^6
, names.arg = names(Total)
, xlab = "Years", ylab = expression("Emissions (10"^6*" Tons)")
, col = color_range(4), ylim=c(0,8.5)
, main = expression('Annual Emission PM'[2.5]*' from 1999 to 2008'))
text(x =x , y = round(Total/10^6,4)
, label = round(Total/10^6,4)
, pos = 3, cex = 0.8, col = "black")
dev.off()
Question 2 plot2.R
Have total emissions from PM2.5 decreased in the Baltimore City, Maryland (𝚏𝚒𝚙𝚜 == “𝟸𝟺𝟻𝟷𝟶”) from 1999 to 2008? Use the base plotting system to make a plot answering this question.
library(dplyr)
filename <- "exdata_NEI_PM2.5.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if folder exists
if (!file.exists("summarySCC_PM25.rds")) {
unzip(filename)
}
#Read Data
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
#create subset
Total_emision_baltimore<-NEI%>%filter(fips=="24510")%>%
group_by(year)%>%
summarize(Emissions=sum(Emissions))
#Plotting
color_range <- 2:5
png(filename='plot2.png',width = 640,height = 480)
x<-barplot(height = Total_emision_baltimore$Emissions
, names.arg = Total_emision_baltimore$year
, xlab = "Years", ylab = expression("Emissions (Tons)")
, col = color_range, ylim = c(0,3800)
, main = expression('Annual Emission PM'[2.5]*' in Baltimore City-MD'))
text(x =x , y = round(Total_emision_baltimore$Emissions,3)
, label = round(Total_emision_baltimore$Emissions,3)
, pos = 3, cex = 0.8, col = "black")
dev.off()
Question 3 plot3.R
Of the four types of sources indicated by the 𝚝𝚢𝚙𝚎 (point, nonpoint, onroad, nonroad) variable, which of these four sources have seen decreases in emissions from 1999–2008 for Baltimore City? Which have seen increases in emissions from 1999–2008? Use the ggplot2 plotting system to make a plot answer this question.
library(dplyr)
library(ggplot2)
filename <- "exdata_NEI_PM2.5.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if file exists
if (!file.exists("summarySCC_PM25.rds")) {
unzip(filename)
}
#read data
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
#subset in base a emission
Activity_emision_baltimore<-NEI%>%filter(fips=="24510")%>%
group_by(year,type)%>%
summarize(Emissions=sum(Emissions))%>%print
#plotting
png(filename='plot3.png',width = 640,height = 480)
i<-ggplot(Activity_emision_baltimore,aes(factor(year),Emissions,fill=type,label=round(Emissions,2))) +
geom_col() +
facet_grid(.~type,scales = "free",space="free") +
labs(x="year", y=expression("Total PM "[2.5]*" Emission (Tons)")) +
labs(title=expression("PM"[2.5]*" Emissions, Baltimore City 1999-2008 by Source Type"))#+
#geom_label(aes(fill = type), colour = "white", fontface = "bold")
print(i)
dev.off()
Question 4 plot4.R
Across the United States, how have emissions from coal combustion-related sources changed from 1999–2008?
library(dplyr)
library(ggplot2)
filename <- "exdata_NEI_PM2.5.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if folder exists
if (!file.exists("summarySCC_PM25.rds")) {
unzip(filename)
}
#read data
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
#differet ways to create subset
combustion_coal <- SCC[grep("Fuel Comb.*Coal", SCC$EI.Sector),"SCC"]
#selecciona todas las columnas
coal_SCC <- SCC[grep("[Cc][Oo][Aa][Ll]", SCC$EI.Sector),]
#generate a subset
Coal_NEI<-subset(NEI,NEI$SCC%in%combustion_coal)
Total_emision<-Coal_NEI%>%group_by(year)%>%
summarize(Emissions=sum(Emissions))%>%print
color_range <- colorRampPalette(c("red","yellow"))
#plotting
png(filename='plot4.png',width = 640,height = 480)
i<-ggplot(Total_emision,aes(factor(year),Emissions/10^5,label=round(Emissions/10^5,4))) +
geom_col(fill=color_range(4)) +
labs(x="year", y=expression("Total PM "[2.5]*" Emission (10 "^6*" Tons)")) +
labs(title=expression("PM"[2.5]*" Coal Combustion Source Emissions Across US from 1999-2008"))+
geom_label(colour = "Black", fontface = "bold")
print(i)
dev.off()
Question 5 plot5.R
How have emissions from motor vehicle sources changed from 1999–2008 in Baltimore City?
library(dplyr)
library(ggplot2)
filename <- "exdata_NEI_PM2.5.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if folder exists
if (!file.exists("summarySCC_PM25.rds")) {
unzip(filename)
}
#read data
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
#only Highway Vehicles
motor_vehicles <- SCC[grep("Vehicle", SCC$EI.Sector,ignore.case = TRUE),"SCC"]
motor_vehicles <- SCC[grep("[Vv]ehicle", SCC$EI.Sector),"SCC"]
motor_vehicles <- SCC[grep("Mobile.*Vehicles", SCC$EI.Sector),"SCC"]
#include Off-highway Vehicle
motor_vehicles <- SCC[grep("Vehicle", SCC$SCC.Level.Two,ignore.case = TRUE),"SCC"]
vehicles_NEI<-subset(NEI,NEI$SCC%in%motor_vehicles)
Vehicles_emision_baltimore<-vehicles_NEI%>%filter(fips=="24510")%>%
group_by(year)%>%
summarize(Emissions=sum(Emissions))%>%print
color_range <- colorRampPalette(c("blue","gray"))
#plotting
png(filename='plot5.png',width = 640,height = 480)
i<-ggplot(Vehicles_emision_baltimore,aes(factor(year),Emissions,label=round(Emissions,2))) +
geom_col(fill=color_range(4)) +
labs(x="year", y=expression("Total PM "[2.5]*" Emission (Tons)") ) +
labs(title=expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore from 1999-2008"))+
geom_label(colour = "Black", fontface = "bold")
print(i)
dev.off()
Question 6 plot6.R
Compare emissions from motor vehicle sources in Baltimore City with emissions from motor vehicle sources in Los Angeles County, California (𝚏𝚒𝚙𝚜 == “𝟶𝟼𝟶𝟹𝟽”). Which city has seen greater changes over time in motor vehicle emissions?
library(dplyr)
library(ggplot2)
filename <- "exdata_NEI_PM2.5.zip"
# Checking if archieve already exists.
if (!file.exists(filename)){
fileURL <- "https://d396qusza40orc.cloudfront.net/exdata%2Fdata%2FNEI_data.zip"
download.file(fileURL, filename, method="curl")
}
# Checking if folder exists
if (!file.exists("summarySCC_PM25.rds")) {
unzip(filename)
}
#read Data
NEI<-readRDS("summarySCC_PM25.rds")
SCC<-readRDS("Source_Classification_Code.rds")
#include Off-highway Vehicle
motor_vehicles <- SCC[grep("Vehicle", SCC$SCC.Level.Two,ignore.case = TRUE),"SCC"]
vehicles_NEI<-subset(NEI,NEI$SCC%in%motor_vehicles)
Vehicles_emision_baltimore<-vehicles_NEI%>%filter(fips=="24510")%>%
group_by(year)%>%
summarize(Emissions=sum(Emissions))%>%mutate(city="Baltimore City, MD")%>%
print
Vehicles_emision_losangeles<-vehicles_NEI%>%filter(fips=="06037")%>%
group_by(year)%>%
summarize(Emissions=sum(Emissions))%>%mutate(city="Los Angeles County, CA")%>%
print
vehicle_emissions <- rbind(Vehicles_emision_baltimore,Vehicles_emision_losangeles)
#plotting
png(filename='plot6.png',width = 640,height = 480)
i<-ggplot(vehicle_emissions,aes(factor(year),Emissions,fill=city,label=round(Emissions,2)))+
geom_col()+facet_grid(.~city)+
ylab(expression("total PM "[2.5]*" emissions in tons")) +
xlab("year") +
ggtitle(expression("PM"[2.5]*" Motor Vehicle Source Emissions in Baltimore & LA, 1999-2008"))#+
#geom_label(aes(fill = city),colour = "white", fontface = "bold")
print(i)
dev.off()
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