+
+
+
+
+
+
+Reproducible Research: Peer Assessment +1
+Thomas Frank
+25-09-2023
+ +
+
+Loading and preprocessing the data
+rm(list=ls()) # clean workspace
+unzip(zipfile="activity.zip")
+data <- read.csv("activity.csv")
+head(data)## steps date interval
+## 1 NA 2012-10-01 0
+## 2 NA 2012-10-01 5
+## 3 NA 2012-10-01 10
+## 4 NA 2012-10-01 15
+## 5 NA 2012-10-01 20
+## 6 NA 2012-10-01 25str(data)## 'data.frame': 17568 obs. of 3 variables:
+## $ steps : int NA NA NA NA NA NA NA NA NA NA ...
+## $ date : chr "2012-10-01" "2012-10-01" "2012-10-01" "2012-10-01" ...
+## $ interval: int 0 5 10 15 20 25 30 35 40 45 ...total.steps <-data%>%
+ dplyr::filter(!is.na(steps))%>%
+ dplyr::group_by(date)%>%
+ dplyr::summarise(stepsum =sum(steps))
+
+What is mean total number of steps taken per day?
+ggplot(total.steps, aes(x=stepsum)) +
+ geom_histogram(binwidth=1000) +
+ xlab("total number of steps taken each day") +
+ geom_vline(data=total.steps, xintercept=mean(total.steps[["stepsum"]])) +
+ geom_vline(data=total.steps, xintercept=median(total.steps[["stepsum"]]), color="red", linetype="dashed")
The mean number of steps per day is 10766 (black vertical line). The +median number of steps per day is 10765 (red dashed vertical line).
+
+
+What is the average daily activity pattern?
+average.d <-data%>%
+ dplyr::group_by(interval)%>%
+ dplyr::summarise(meansteps =mean(steps, na.rm = TRUE))
+
+ggplot(data=average.d, aes(x=interval, y=meansteps)) +
+ geom_line() +
+ xlab("5-minute interval") +
+ ylab("average number of steps taken")+
+ scale_x_continuous(limits=c(0,2400), breaks=c(seq(0,2400,200)))
The 5-minute interval “835” contains the maximum number of steps +(206.2) on average across all the days in the dataset.
+
+
+
+
+Imputing missing values
+The number of missing values is shown in the following table:
+missing <- is.na(data[["steps"]])
+kable(table(missing), col.names=c("Missing values", "Frequency (n)"), caption = "Number of missing values in the dataset:")| Missing values | +Frequency (n) | +
|---|---|
| FALSE | +15264 | +
| TRUE | +2304 | +
As substitution rule, all of the missing values are filled in with +mean value for that 5-minute interval.
+repl.na <- function(steps, interval) {
+ replaced <- NA
+ if (!is.na(steps))
+ replaced <- c(steps)
+ else
+ replaced <- (average.d[average.d[["interval"]]==interval, "meansteps"])
+ return(replaced)
+}
+repl.d <- data
+repl.d[["steps"]] <- mapply(repl.na, repl.d[["steps"]], repl.d[["interval"]])
+repl.d<- unnest(repl.d, steps)total.steps2 <-repl.d%>%
+ dplyr::group_by(date)%>%
+ dplyr::summarise(stepsum =sum(steps))
+
+ggplot(total.steps2, aes(x=stepsum)) +
+ geom_histogram(binwidth=1000) +
+ xlab("total number of steps taken each day") +
+ geom_vline(data=total.steps2, xintercept=mean(total.steps2[["stepsum"]])) +
+ geom_vline(data=total.steps2, xintercept=median(total.steps2[["stepsum"]]), color="red", linetype="dashed")
The mean number of steps per day is 10766 (black vertical line). The +median number of steps per day is 10766 (red dashed vertical line). +These values do not differ from the estimates from the first part of the +assignment. Thus, the impact of imputing missing data on the estimates +of the total daily number of steps is low.
+
+
+Are there differences in activity patterns between weekdays and +weekends?
+repl.d2 <- repl.d%>%
+ dplyr::mutate(date = ymd(date),
+ date2 = weekdays(date),
+ is.wke = ifelse(date2%in%c("Saturday","Sunday"), "weekend", "weekday"))average.d2 <-repl.d2%>%
+ dplyr::group_by(interval,is.wke)%>%
+ dplyr::summarise(meansteps =mean(steps, na.rm = TRUE))## `summarise()` has grouped output by 'interval'. You can override using the
+## `.groups` argument.ggplot(data=average.d2, aes(x=interval, y=meansteps)) +
+ geom_line() +
+ xlab("5-minute interval") +
+ ylab("average number of steps taken")+
+ scale_x_continuous(limits=c(0,2400), breaks=c(seq(0,2400,200)))+
+ facet_wrap(~is.wke, ncol = 1, nrow=2)
There is a difference in the activity pattern in that the average +number of steps on the weekday is higher in the morning and lower in the +afternoon/evening than a weekend day. Weekend days in the graph shown +above include Saturday and Sunday.
+
+
+
+
+
+
+R environment used for this report
+## R version 4.2.0 (2022-04-22)
+## Platform: x86_64-pc-linux-gnu (64-bit)
+## Running under: openSUSE Tumbleweed
+##
+## Matrix products: default
+## BLAS: /software/content/R/4.2.0_tumbleweed/lib/libRblas.so
+## LAPACK: /software/content/R/4.2.0_tumbleweed/lib/libRlapack.so
+##
+## Random number generation:
+## RNG: Inversion
+## Normal: Mersenne-Twister
+## Sample: Rejection
+##
+## locale:
+## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
+## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
+## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
+## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
+## [9] LC_ADDRESS=C LC_TELEPHONE=C
+## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
+##
+## attached base packages:
+## [1] base datasets graphics grDevices methods stats utils
+##
+## other attached packages:
+## [1] dplyr_1.1.3 ggplot2_3.4.2 knitr_1.43 lubridate_1.9.2
+## [5] tidyr_1.3.0
+##
+## loaded via a namespace (and not attached):
+## [1] bslib_0.5.0 cachem_1.0.8 cli_3.6.1 colorspace_2.1-0
+## [5] compiler_4.2.0 digest_0.6.33 evaluate_0.21 fansi_1.0.4
+## [9] farver_2.1.1 fastmap_1.1.1 generics_0.1.3 glue_1.6.2
+## [13] grid_4.2.0 gtable_0.3.3 highr_0.10 htmltools_0.5.6
+## [17] jquerylib_0.1.4 jsonlite_1.8.7 labeling_0.4.2 lifecycle_1.0.3
+## [21] magrittr_2.0.3 munsell_0.5.0 pillar_1.9.0 pkgconfig_2.0.3
+## [25] purrr_1.0.2 R6_2.5.1 rlang_1.1.1 rmarkdown_2.23
+## [29] rstudioapi_0.15.0 sass_0.4.7 scales_1.2.1 tibble_3.2.1
+## [33] tidyselect_1.2.0 timechange_0.2.0 tools_4.2.0 utf8_1.2.3
+## [37] vctrs_0.6.3 withr_2.5.0 xfun_0.40 yaml_2.3.7