First set environment and load data
setwd("C:/Users/JosePortatil/Documents/GitHub/RepData_PeerAssessment1")
## Error: no es posible cambiar el directorio de trabajo
Sys.setlocale("LC_TIME", "C")
## [1] "C"
Data <- read.csv("./activity.csv")
See Data Structure
str(Data)
## 'data.frame': 17568 obs. of 3 variables:
## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
## $ date : Factor w/ 61 levels "2012-10-01","2012-10-02",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...
-Load library and apply method to calculate mean of steps taken per day
library(plyr)
## Warning: package 'plyr' was built under R version 2.15.3
StepsSummary contains the total number of steps taken that day
StepsSummary <- ddply(Data, ~date, summarise, sum_steps = sum(steps, na.rm = TRUE),
mean_steps = sum(steps, na.rm = TRUE)/288)
StepsSummary
## date sum_steps mean_steps
## 1 2012-10-01 0 0.0000
## 2 2012-10-02 126 0.4375
## 3 2012-10-03 11352 39.4167
## 4 2012-10-04 12116 42.0694
## 5 2012-10-05 13294 46.1597
## 6 2012-10-06 15420 53.5417
## 7 2012-10-07 11015 38.2465
## 8 2012-10-08 0 0.0000
## 9 2012-10-09 12811 44.4826
## 10 2012-10-10 9900 34.3750
## 11 2012-10-11 10304 35.7778
## 12 2012-10-12 17382 60.3542
## 13 2012-10-13 12426 43.1458
## 14 2012-10-14 15098 52.4236
## 15 2012-10-15 10139 35.2049
## 16 2012-10-16 15084 52.3750
## 17 2012-10-17 13452 46.7083
## 18 2012-10-18 10056 34.9167
## 19 2012-10-19 11829 41.0729
## 20 2012-10-20 10395 36.0938
## 21 2012-10-21 8821 30.6285
## 22 2012-10-22 13460 46.7361
## 23 2012-10-23 8918 30.9653
## 24 2012-10-24 8355 29.0104
## 25 2012-10-25 2492 8.6528
## 26 2012-10-26 6778 23.5347
## 27 2012-10-27 10119 35.1354
## 28 2012-10-28 11458 39.7847
## 29 2012-10-29 5018 17.4236
## 30 2012-10-30 9819 34.0938
## 31 2012-10-31 15414 53.5208
## 32 2012-11-01 0 0.0000
## 33 2012-11-02 10600 36.8056
## 34 2012-11-03 10571 36.7049
## 35 2012-11-04 0 0.0000
## 36 2012-11-05 10439 36.2465
## 37 2012-11-06 8334 28.9375
## 38 2012-11-07 12883 44.7326
## 39 2012-11-08 3219 11.1771
## 40 2012-11-09 0 0.0000
## 41 2012-11-10 0 0.0000
## 42 2012-11-11 12608 43.7778
## 43 2012-11-12 10765 37.3785
## 44 2012-11-13 7336 25.4722
## 45 2012-11-14 0 0.0000
## 46 2012-11-15 41 0.1424
## 47 2012-11-16 5441 18.8924
## 48 2012-11-17 14339 49.7882
## 49 2012-11-18 15110 52.4653
## 50 2012-11-19 8841 30.6979
## 51 2012-11-20 4472 15.5278
## 52 2012-11-21 12787 44.3993
## 53 2012-11-22 20427 70.9271
## 54 2012-11-23 21194 73.5903
## 55 2012-11-24 14478 50.2708
## 56 2012-11-25 11834 41.0903
## 57 2012-11-26 11162 38.7569
## 58 2012-11-27 13646 47.3819
## 59 2012-11-28 10183 35.3576
## 60 2012-11-29 7047 24.4688
## 61 2012-11-30 0 0.0000
steps_taken_per_day <- StepsSummary$sum_steps
head(steps_taken_per_day)
## [1] 0 126 11352 12116 13294 15420
Histogram of the total number of steps taken each day
hist(steps_taken_per_day, col = "blue", xlab = "sum_steps", main = " ")
Mean total number of steps taken per day
mean(steps_taken_per_day)
## [1] 9354
Median total number of steps taken per day
median(steps_taken_per_day)
## [1] 10395
The average number of steps taken, averaged across all days of 5-minute interval
Intervalmean <- ddply(Data, ~interval, summarise, mean_steps = mean(steps, na.rm = TRUE))
Intervalmean
## interval mean_steps
## 1 0 1.71698
## 2 5 0.33962
## 3 10 0.13208
## 4 15 0.15094
## 5 20 0.07547
## 6 25 2.09434
## 7 30 0.52830
## 8 35 0.86792
## 9 40 0.00000
## 10 45 1.47170
## 11 50 0.30189
## 12 55 0.13208
## 13 100 0.32075
## 14 105 0.67925
## 15 110 0.15094
## 16 115 0.33962
## 17 120 0.00000
## 18 125 1.11321
## 19 130 1.83019
## 20 135 0.16981
## 21 140 0.16981
## 22 145 0.37736
## 23 150 0.26415
## 24 155 0.00000
## 25 200 0.00000
## 26 205 0.00000
## 27 210 1.13208
## 28 215 0.00000
## 29 220 0.00000
## 30 225 0.13208
## 31 230 0.00000
## 32 235 0.22642
## 33 240 0.00000
## 34 245 0.00000
## 35 250 1.54717
## 36 255 0.94340
## 37 300 0.00000
## 38 305 0.00000
## 39 310 0.00000
## 40 315 0.00000
## 41 320 0.20755
## 42 325 0.62264
## 43 330 1.62264
## 44 335 0.58491
## 45 340 0.49057
## 46 345 0.07547
## 47 350 0.00000
## 48 355 0.00000
## 49 400 1.18868
## 50 405 0.94340
## 51 410 2.56604
## 52 415 0.00000
## 53 420 0.33962
## 54 425 0.35849
## 55 430 4.11321
## 56 435 0.66038
## 57 440 3.49057
## 58 445 0.83019
## 59 450 3.11321
## 60 455 1.11321
## 61 500 0.00000
## 62 505 1.56604
## 63 510 3.00000
## 64 515 2.24528
## 65 520 3.32075
## 66 525 2.96226
## 67 530 2.09434
## 68 535 6.05660
## 69 540 16.01887
## 70 545 18.33962
## 71 550 39.45283
## 72 555 44.49057
## 73 600 31.49057
## 74 605 49.26415
## 75 610 53.77358
## 76 615 63.45283
## 77 620 49.96226
## 78 625 47.07547
## 79 630 52.15094
## 80 635 39.33962
## 81 640 44.01887
## 82 645 44.16981
## 83 650 37.35849
## 84 655 49.03774
## 85 700 43.81132
## 86 705 44.37736
## 87 710 50.50943
## 88 715 54.50943
## 89 720 49.92453
## 90 725 50.98113
## 91 730 55.67925
## 92 735 44.32075
## 93 740 52.26415
## 94 745 69.54717
## 95 750 57.84906
## 96 755 56.15094
## 97 800 73.37736
## 98 805 68.20755
## 99 810 129.43396
## 100 815 157.52830
## 101 820 171.15094
## 102 825 155.39623
## 103 830 177.30189
## 104 835 206.16981
## 105 840 195.92453
## 106 845 179.56604
## 107 850 183.39623
## 108 855 167.01887
## 109 900 143.45283
## 110 905 124.03774
## 111 910 109.11321
## 112 915 108.11321
## 113 920 103.71698
## 114 925 95.96226
## 115 930 66.20755
## 116 935 45.22642
## 117 940 24.79245
## 118 945 38.75472
## 119 950 34.98113
## 120 955 21.05660
## 121 1000 40.56604
## 122 1005 26.98113
## 123 1010 42.41509
## 124 1015 52.66038
## 125 1020 38.92453
## 126 1025 50.79245
## 127 1030 44.28302
## 128 1035 37.41509
## 129 1040 34.69811
## 130 1045 28.33962
## 131 1050 25.09434
## 132 1055 31.94340
## 133 1100 31.35849
## 134 1105 29.67925
## 135 1110 21.32075
## 136 1115 25.54717
## 137 1120 28.37736
## 138 1125 26.47170
## 139 1130 33.43396
## 140 1135 49.98113
## 141 1140 42.03774
## 142 1145 44.60377
## 143 1150 46.03774
## 144 1155 59.18868
## 145 1200 63.86792
## 146 1205 87.69811
## 147 1210 94.84906
## 148 1215 92.77358
## 149 1220 63.39623
## 150 1225 50.16981
## 151 1230 54.47170
## 152 1235 32.41509
## 153 1240 26.52830
## 154 1245 37.73585
## 155 1250 45.05660
## 156 1255 67.28302
## 157 1300 42.33962
## 158 1305 39.88679
## 159 1310 43.26415
## 160 1315 40.98113
## 161 1320 46.24528
## 162 1325 56.43396
## 163 1330 42.75472
## 164 1335 25.13208
## 165 1340 39.96226
## 166 1345 53.54717
## 167 1350 47.32075
## 168 1355 60.81132
## 169 1400 55.75472
## 170 1405 51.96226
## 171 1410 43.58491
## 172 1415 48.69811
## 173 1420 35.47170
## 174 1425 37.54717
## 175 1430 41.84906
## 176 1435 27.50943
## 177 1440 17.11321
## 178 1445 26.07547
## 179 1450 43.62264
## 180 1455 43.77358
## 181 1500 30.01887
## 182 1505 36.07547
## 183 1510 35.49057
## 184 1515 38.84906
## 185 1520 45.96226
## 186 1525 47.75472
## 187 1530 48.13208
## 188 1535 65.32075
## 189 1540 82.90566
## 190 1545 98.66038
## 191 1550 102.11321
## 192 1555 83.96226
## 193 1600 62.13208
## 194 1605 64.13208
## 195 1610 74.54717
## 196 1615 63.16981
## 197 1620 56.90566
## 198 1625 59.77358
## 199 1630 43.86792
## 200 1635 38.56604
## 201 1640 44.66038
## 202 1645 45.45283
## 203 1650 46.20755
## 204 1655 43.67925
## 205 1700 46.62264
## 206 1705 56.30189
## 207 1710 50.71698
## 208 1715 61.22642
## 209 1720 72.71698
## 210 1725 78.94340
## 211 1730 68.94340
## 212 1735 59.66038
## 213 1740 75.09434
## 214 1745 56.50943
## 215 1750 34.77358
## 216 1755 37.45283
## 217 1800 40.67925
## 218 1805 58.01887
## 219 1810 74.69811
## 220 1815 85.32075
## 221 1820 59.26415
## 222 1825 67.77358
## 223 1830 77.69811
## 224 1835 74.24528
## 225 1840 85.33962
## 226 1845 99.45283
## 227 1850 86.58491
## 228 1855 85.60377
## 229 1900 84.86792
## 230 1905 77.83019
## 231 1910 58.03774
## 232 1915 53.35849
## 233 1920 36.32075
## 234 1925 20.71698
## 235 1930 27.39623
## 236 1935 40.01887
## 237 1940 30.20755
## 238 1945 25.54717
## 239 1950 45.66038
## 240 1955 33.52830
## 241 2000 19.62264
## 242 2005 19.01887
## 243 2010 19.33962
## 244 2015 33.33962
## 245 2020 26.81132
## 246 2025 21.16981
## 247 2030 27.30189
## 248 2035 21.33962
## 249 2040 19.54717
## 250 2045 21.32075
## 251 2050 32.30189
## 252 2055 20.15094
## 253 2100 15.94340
## 254 2105 17.22642
## 255 2110 23.45283
## 256 2115 19.24528
## 257 2120 12.45283
## 258 2125 8.01887
## 259 2130 14.66038
## 260 2135 16.30189
## 261 2140 8.67925
## 262 2145 7.79245
## 263 2150 8.13208
## 264 2155 2.62264
## 265 2200 1.45283
## 266 2205 3.67925
## 267 2210 4.81132
## 268 2215 8.50943
## 269 2220 7.07547
## 270 2225 8.69811
## 271 2230 9.75472
## 272 2235 2.20755
## 273 2240 0.32075
## 274 2245 0.11321
## 275 2250 1.60377
## 276 2255 4.60377
## 277 2300 3.30189
## 278 2305 2.84906
## 279 2310 0.00000
## 280 2315 0.83019
## 281 2320 0.96226
## 282 2325 1.58491
## 283 2330 2.60377
## 284 2335 4.69811
## 285 2340 3.30189
## 286 2345 0.64151
## 287 2350 0.22642
## 288 2355 1.07547
The average number of steps taken, averaged across all days(y-axis) of 5-minute interval(x-axis) PLOT
plot(Intervalmean$interval, Intervalmean$mean_steps, type = "l", ylab = "Mean_steps",
xlab = "Interval")
maximum number of steps
maximo <- max(Intervalmean$mean_steps)
maximo
## [1] 206.2
Find that interval
a <- which(Intervalmean$mean_steps == maximo)
Intervalo <- Intervalmean[a, ]
Intervalo[1, 1]
## [1] 835
Count the number of NA values in the data frame Data.
table(is.na(Data$steps))
##
## FALSE TRUE
## 15264 2304
Create another DataFrame with the same data but this one NA values will be filled with the mean for that day
Data2 <- Data # dataset that will be filled-in missing values
listado <- which(is.na(Data2[, 1]))
Fill Na values with a foor loop
for (i in listado) {
x <- Data2[i, 2]
Nuevo_valor <- (subset(StepsSummary, date == x)$mean_steps)
Data2[i, 1] <- as.integer(Nuevo_valor)
}
Data2 has no NA values
table(is.na(Data2$steps))
##
## FALSE
## 17568
StepsSummary2 contains the total number of steps taken that day in data frame Data2
StepsSummary2 <- ddply(Data2, ~date, summarise, sum_steps = sum(steps, na.rm = TRUE))
steps_taken_per_day2 <- StepsSummary2$sum_steps
head(steps_taken_per_day2)
## [1] 0 126 11352 12116 13294 15420
Histogram of the total number of steps taken each day
hist(steps_taken_per_day2, col = "blue", xlab = "sum_steps", main = " ")
Mean total number of steps taken per day
mean(steps_taken_per_day2)
## [1] 9354
Median total number of steps taken per day
median(steps_taken_per_day2)
## [1] 10395
The median and mean didnt change with this aproach.
The first thing to do its convert the column Date to Date format
Data2$date <- as.Date(Data2$date, "%Y-%m-%d")
str(Data2$date)
## Date[1:17568], format: "2012-10-01" "2012-10-01" "2012-10-01" "2012-10-01" ...
Create a vector of booleans checking if a specific day in the data frame is a weekday or weekend.
WeekDay <- (weekdays(Data2$date) == "Sunday" | weekdays(Data2$date) == "Saturday")
summary(WeekDayD)
## Error: objeto 'WeekDayD' no encontrado
Add a boolean column to DataFrame checking if it is weekend or not
Data2$WeekDay <- WeekDay
To do this part of the assignment i subset the data into two subdata frames
One Data Frame for Weekkdays
DataWeekDay <- subset(Data2, WeekDay == TRUE)
Another Data Frame for Weekends
DataWeekEnd <- subset(Data2, WeekDay == FALSE)
Calculate average number of steps taken, averaged across all weekday days
IntervalmeanWeekDay <- ddply(DataWeekDay, ~interval, summarise, mean_steps = mean(steps,
na.rm = TRUE))
# IntervalmeanWeekDay
IntervalmeanWeekDay$Day <- as.factor("weekday")
Calculate average number of steps taken, averaged across all weekends days
IntervalmeanWeekEnd <- ddply(DataWeekEnd, ~interval, summarise, mean_steps = mean(steps,
na.rm = TRUE))
# IntervalmeanWeekEnd
IntervalmeanWeekEnd$Day <- as.factor("weekend")
Combine average number of steps taken in weekends and weekdays
IntervalMeanTotalbyDay <- rbind(IntervalmeanWeekDay, IntervalmeanWeekEnd)
PLOT
library(lattice)
## Warning: package 'lattice' was built under R version 2.15.3
xyplot(mean_steps ~ interval | Day, data = IntervalMeanTotalbyDay, type = "l",
layout = c(1, 2), ylab = "steps")
