Final Visual

Theme, Font and Color Settings

Theme and font settings

theme_blue <- function(base_size = 11, base_family = "") { theme_grey(base_size = base_size, base_family = base_family) %+replace% theme(line = element_line(colour = "white", size = I(1.5), linetype = "solid", lineend = "round", arrow = NULL, inherit.blank = FALSE), rect = element_rect(fill = "#00010F", colour = "#00010F", size = 0, linetype = "solid", inherit.blank = FALSE), text = element_text(family = "Helvetica", face = "plain", colour = "white", size = base_size,hjust = I(.5), vjust = I(.5), angle = 0, lineheight = base_size,color = NULL,margin = margin(I(.2),I(.2),I(.2),I(.2),unit="pt"),debug=F, inherit.blank = FALSE), title = element_text(family = "AvantGarde", face = "plain", colour = "white", size = I(base_size),hjust = I(.5), vjust = I(1), angle = 0, lineheight = I(base_size),color = NULL,margin = margin(I(1),I(1),I(1),I(1),unit="pt"), inherit.blank = FALSE), axis.line = element_line(colour = "#3A42B0", size = 1, linetype = "solid", lineend = "round", inherit.blank = FALSE), axis.text.x = element_text(size = base_size*0.6, color = "white", lineheight = 0.6,hjust = I(1), vjust = I(1),angle=60), axis.text.y = element_text(size = base_size*0.6, color = "white", lineheight = 0.9,hjust = I(.5), vjust = I(1),angle=60), axis.ticks = element_line(colour = "#3A42B0", size = 0.2), axis.title = element_text(family = "Helvetica", face = "italic", colour = "white", size = I(base_size*1),hjust = I(.5), vjust = I(.5), lineheight = I(base_size*1), inherit.blank = FALSE), axis.title.x = element_text(size = base_size, color = "white", margin = margin(0, .5, 0, 0)), axis.title.y = element_text(size = base_size, color = "white", angle = 90, margin = margin(0, .5, 0, 0)), axis.ticks.length = unit(0.3, "lines"), # Specify legend options legend.background = element_rect(color = NA, fill = "#060713"), legend.key = element_rect(color = "white", fill = "black"), legend.key.size = unit(1.2, "lines"), legend.key.height = NULL, legend.key.width = NULL, legend.text = element_text(size = base_size*0.8, color = "white"), legend.title = element_text(size = base_size*0.8, face = "bold", hjust = 0, color = "white"), legend.position = "right", legend.text.align = NULL, legend.title.align = NULL, legend.direction = "vertical", legend.box = NULL, # Specify panel options panel.background = element_rect(fill = "#060713",color="#060713", inherit.blank = FALSE), panel.border = element_rect(fill = NA, color = NA), panel.grid.major = element_line(colour = "#34415B", size = I(.1), linetype = "solid",lineend = "butt", inherit.blank = FALSE), panel.grid.minor = element_line(colour = "#1C2D52", size = I(.1), linetype = "dotted",lineend = "butt", inherit.blank = FALSE), panel.spacing = margin(rep(.5,4),unit="pt"), panel.spacing.x = unit(.5,"pt"), panel.spacing.y = unit(.5,"pt"), # Specify facetting options strip.background = element_rect(fill = "#060713", color = "#060713"), strip.text = element_text(family = "Helvetica", face = "plain", colour = "white", size = I(base_size*.8),hjust = .5,lineheight = I(base_size*1),margin = margin(rep(1,4), "pt"),inherit.blank = FALSE), strip.text.x = element_text(color = "white"), strip.text.y = element_text(color = "white",angle = -90), # Specify plot options plot.background = element_rect(fill = "#00010F",colour = "#00010F", linetype = "solid", inherit.blank = FALSE), plot.title = element_text(family=base_family,size = base_size, color = "white", lineheight = I(1.2),vjust=1), plot.subtitle = element_text( size = I(base_size*.8),hjust = I(.5),vjust=1, lineheight = I(1.2), inherit.blank = FALSE), plot.margin = margin(.2,.2,.2,.2,unit="cm"), strip.switch.pad.grid = unit(0, "cm") ) } update_geom_defaults("bar", list(fill = "#0686DB")) theme_set(theme_blue(base_family = "AvantGarde")) update_geom_defaults("text",list(colour="white",size=rel(2.5))) windowsFonts(AvantGarde=windowsFont("TT AvantGarde-Book")) windowsFonts(Helvetica=windowsFont("TT Helvetica")) options(scipen=12)

Color Schema

Color Schema

# http://colorschemedesigner.com/csd-3.5/ ColorKey <- c("Base", "Darker", "Darkest", "LightBold", "LightLight") MonoDarkBlue <- c("#00021B", "#060713", "#00010F", "#3A42B0", "#686DB0") CompGold <- c("#B48B18", "#1B1608", "#161000", "#B4943B", "#B4A06A") AnalogicViolet <- c("#230159", "#0B0612", "#06000F", "#6839B0", "#8467B0") AnalogicTurquoise <- c("#00395B", "#050D11", "#00090E", "#3983AF", "#6794AF") Actcolors <- c(Meditation = "#6839B0", Productivity = "#357AA2", Distraction = "#FF7549", RStudio = "#75AADB", Yoga = "#DEF83F", Plyometric = "#E03974") ColorMatrix <- matrix(data = c(MonoDarkBlue, CompGold, AnalogicViolet, AnalogicTurquoise), nrow = 4, ncol = 5, byrow = T, dimnames = list(c("MonoDarkBlue", "CompGold", "AnalogicViolet", "AnalogicTurquoise"), ColorKey)) write_csv(as.data.frame(ColorMatrix), path = "ColorMatrix.csv")

Data Wrangling

Load Data

Meditation <- read.csv("~/Northeastern/Git/ppua5302/Project 1/Data/insight_sessions_export.csv", header = T, sep = ",") library(lubridate) names(Meditation) <- c("Time", "Duration", "Activity", "Preset") Meditation$Time <- mdy_hms(Meditation$Time) Meditation$Duration <- as.numeric(as.character(Meditation$Duration)) tagList(tags$h4("Types of Health Activity by Week number for the Year 2017"), tags$div(style = "width:100%", DT::datatable(HlthLine <- Meditation %>% filter(year(Time) == "2017") %>% mutate(Weeknum = week(Time)) %>% group_by(Weeknum, Activity) %>% summarise(Sum = sum(Duration)) %>% ungroup %>% mutate(Hrs = { Sum/60 }) %>% select(-Sum) %>% spread(key = Activity, value = Hrs) %>% replace_na(list(Meditation = 0, Walking = 0, Yoga = 0, Breathing = 0)) %>% mutate(Meditation = { Meditation + Breathing }) %>% select(-c(Breathing, Healing)), width = "100%")))

Types of Health Activity by Week number for the Year 2017

Filter Data and add to Sheet

GsheetUpdate <- Meditation %>% filter(Time > mdy("07-13-2017")) names(GsheetUpdate) <- c("Started At", "Duration (minutes)", "Activity", "Preset") library(googlesheets) gs_auth() MedTrack <- gs_url("https://docs.google.com/spreadsheets/d/19Pz41A0dq0DsJIBuFO0VOw0fXAmzxO7UOa7bHKcIgYQ/edit#gid=1807004634") gs_edit_cells(MedTrack, ws = 2, input = GsheetUpdate, anchor = "A4095")

D Fs of Health Activities

Med17 <- Meditation %>% filter(year(Time) == "2017" & Activity == "Meditation") %>% mutate(Weeknum = week(Time)) %>% select(Weeknum, everything()) Yoga17 <- Meditation %>% filter(year(Time) == "2017" & Activity == "Yoga") %>% mutate(Weeknum = week(Time)) %>% select(Weeknum, everything()) Plyo17 <- Meditation %>% filter(year(Time) == "2017" & Activity == "Walking") %>% mutate(Weeknum = week(Time)) %>% select(Weeknum, everything())

Import Rescuetime Data

RT <- read_csv(file = "~/Northeastern/Git/ppua5302/Project 1/Data/RescueTime_Report_All_Activities__by_week_2017-01-01.csv", col_names = T) RT$Date <- ymd(RT$Date) class(RT$Productivity)

## [1] "integer"

unique(RT$Overview)

## [1] "Reference & Learning" "Communication & Scheduling" ## [3] "Social Networking" "Business" ## [5] "Utilities" "Design & Composition" ## [7] "Entertainment" "Software Development" ## [9] "Uncategorized" "News & Opinion" ## [11] "Shopping" "Miscellaneous"

RT$Overview <- factor(RT$Overview, levels = unique(RT$Overview), labels = c("Ref & Learn", "Com & Sch", "Social Net", "Business", "Utilities", "Design", "Entertain", "Software Dev", "Uncat.", "News & Op", "Shopping", "Misc")) htmltools::tags$p("Sum of time and percent of total time in each category of productivity for 2017")

Sum of time and percent of total time in each category of productivity for 2017

knitr::kable(RTSums <- RT %>% group_by(Productivity) %>% summarise(sum = { sum(`Time Spent (seconds)`)/3600 }) %>% mutate(P = sum/sum(sum)))

Productivity sum P

-2 84.27556 0.0311079

-1 489.65000 0.1807400

0 934.40833 0.3449095

1 527.00972 0.1945302

2 673.79722 0.2487125

htmltools::tags$p("Total % of time spent in productive activities during 2017:", sum(RTSums$P[4:5]))

Total % of time spent in productive activities during 2017: 0.443242717273937

Totals

RTOV <- RT %>% group_by(Overview, Category) %>% summarize(`Total Time` = sum(`Time Spent (seconds)`)/3600) %>% arrange(desc(`Total Time`)) %>% filter(`Total Time` > 1) TotalHrs <- sum(RTOV$`Total Time`[!is.na(RTOV$`Total Time`)]) tags$p("Total Hours of Digital Activity in 2017: ", TotalHrs)

Total Hours of Digital Activity in 2017: 2701.25611111111

TtlProd <- RT %>% filter(Productivity > 0) %>% select(`Time Spent (seconds)`) %>% colSums() TtlDist <- RT %>% filter(Productivity < 0) %>% select(`Time Spent (seconds)`) %>% colSums() tags$p("Ratio of Productive:Distracting Digital Activity in 2017: ", (TtlProd/3600)/(TtlDist/3600))

Ratio of Productive:Distracting Digital Activity in 2017: 2.09226951617806

htmltools::tagList(tags$p("Total Hours of Meditation in 2017:", sum(Med17 %>% group_by(Weeknum) %>% summarize(TotalHrs = sum(Duration)/60) %>% .$TotalHrs)))

Total Hours of Meditation in 2017: 424.233333333333

htmltools::tagList(tags$p("Total Hours of Yoga in 2017:", sum(Yoga17 %>% group_by(Weeknum) %>% summarize(TotalHrs = sum(Duration)/60) %>% .$TotalHrs)))

Total Hours of Yoga in 2017: 289.25

TotalR <- RT %>% mutate(R = str_detect(Activity, "rstudio")) %>% filter(R == T) %>% select(-R) %>% select(`Time Spent (seconds)`) %>% colSums() htmltools::tagList(tags$p("Total Hours of R Usage in 2017:", TotalR/3600))

Total Hours of R Usage in 2017: 188.762222222222

tagList(tags$h4("Mean, Max, and Total Time in Hrs + Total # of Events per Productivity level for Each week of 2017"), tags$div(DT::datatable((RT %>% filter(Date > "2017-01-01") %>% group_by(Date, Productivity) %>% select(-`Number of People`) %>% summarise_if(.predicate = is.numeric, .funs = c(MeanTime.s = mean, MaxTime.s = max, TotalTime.s = sum, TotalEntries = n_distinct)) %>% ungroup() %>% mutate_at(.vars = vars(ends_with("s")), .funs = funs(./3600))), width = "100%")))

Mean, Max, and Total Time in Hrs + Total # of Events per Productivity level for Each week of 2017

Visualization

Health Activities

Graph of Health Activities

ggplot(data = HlthLine, mapping = aes(x = Weeknum)) + geom_line(mapping = aes(y = Meditation, colour = "Meditation")) + geom_line(mapping = aes(y = Yoga, colour = "Yoga")) + geom_line(mapping = aes(y = Walking, colour = "Plyometric")) + geom_point(mapping = aes(y = Meditation, colour = "Meditation"), shape = 24) + geom_point(mapping = aes(y = Yoga, colour = "Yoga"), shape = 25) + geom_point(mapping = aes(y = Walking, colour = "Plyometric")) + scale_x_discrete(limits = seq(0, 54, 2)) + scale_y_continuous(minor_breaks = seq(0, 50, 1), breaks = seq(0, 50, 4)) + theme(legend.position = "right", legend.direction = "vertical") + guides(colour = guide_legend("Activity")) + scale_colour_manual(name = "Lines", values = Actcolors, breaks = c("Meditation", "Yoga", "Plyometric")) + labs(title = "Health Activities by Week", subtitle = "Hours spent per Activity by Week Number\nActivities:Meditation,Yoga,Plyometric", caption = "", x = "Week Number", y = "Hours")

# ggsave(last_plot(), filename = 'Health.pdf', path='Images/Plots',device = # 'pdf',width = 10, height = 2, units = 'in')

Rescuetime (Digital Activities)

Graph 1 Timeseries

MedbyWeek <- Med17 %>% group_by(Weeknum) %>% summarize(Sum = round(sum(Duration)/60, 2)) ProbyWeek <- RT %>% filter(Productivity > 0) %>% mutate(Weeknum = week(Date)) %>% group_by(Weeknum) %>% summarize(Sum = round(sum(`Time Spent (seconds)`)/3600, 2)) DisbyWeek <- RT %>% filter(Productivity < 0) %>% mutate(Weeknum = week(Date)) %>% group_by(Weeknum) %>% summarize(Sum = round(sum(`Time Spent (seconds)`)/3600, 2)) RbyWeek <- RT %>% filter(Activity == "rstudio") %>% mutate(Weeknum = week(Date)) %>% group_by(Weeknum) %>% summarize(Sum = round(sum(`Time Spent (seconds)`)/3600, 2)) # https://stackoverflow.com/questions/7549694/adding-regression-line-equation-and-r2-on-graph lm_eqn = function(m) { l <- list(a = format(coef(m)[1], digits = 2), b = format(abs(coef(m)[2]), digits = 2), r2 = format(summary(m)$r.squared, digits = 3)) if (coef(m)[2] >= 0) { eq <- substitute(italic(y) == a + b %.% italic(x) * "," ~ ~italic(r)^2 ~ "=" ~ r2, l) } else { eq <- substitute(italic(y) == a - b %.% italic(x) * "," ~ ~italic(r)^2 ~ "=" ~ r2, l) } as.character(as.expression(eq)) } RStudioLM <- lm(Sum ~ Weeknum, RbyWeek) LM <- RbyWeek %>% mutate(LM = predict(RStudioLM, newdata = RbyWeek)) %>% filter(LM > 0) htmltools::tags$p("The rate at which RStudio Usage increases by week in hrs:", RStudioLM$coefficients["Weeknum"])

The rate at which RStudio Usage increases by week in hrs: 0.500887190402477

ggplot(mapping = aes(x = Weeknum)) + geom_line(data = ProbyWeek, mapping = aes(y = Sum, colour = "Productivity")) + geom_line(data = DisbyWeek, mapping = aes(y = Sum, colour = "Distraction")) + geom_line(data = RbyWeek, mapping = aes(y = Sum, colour = "RStudio")) + geom_line(data = LM, mapping = aes(y = LM, colour = "RStudio"), size = 0.5) + geom_text(aes(x = 45, y = 1, label = lm_eqn(RStudioLM)), colour = "#75AADB", size = 4, parse = TRUE) + geom_point(data = ProbyWeek, mapping = aes(y = Sum, colour = "Productivity"), shape = 24, fill = "#357AA2") + geom_point(data = DisbyWeek, mapping = aes(y = Sum, colour = "Distraction"), shape = 25, fill = "#FF7549") + geom_point(data = RbyWeek, mapping = aes(y = Sum, colour = "RStudio"), colour = "#75AADB") + scale_x_discrete(limits = seq(0, 54, 2)) + scale_y_continuous(minor_breaks = seq(0, 50, 1), breaks = seq(0, 50, 4)) + theme(legend.position = "bottom", legend.direction = "horizontal") + guides(colour = guide_legend("Activity")) + scale_colour_manual(name = "Lines", values = Actcolors, breaks = c("Productivity", "Distraction", "RStudio")) + labs(title = "Key Activities by Week", subtitle = "Hours spent per Activity by Week Number\nActivities:Productivity,Distraction,RStudio", caption = "", x = "Week Number", y = "Hours")

# ggsave(last_plot(), filename = '2017TS.pdf', path='Images/Plots',device = # 'pdf',width = 10, height = 5, units = 'in')

Graph 1 Time by Category

RTCats <- RT %>% group_by(Overview, Category, Productivity) %>% summarize(`Total Time` = sum(`Time Spent (seconds)`)/3600) %>% arrange(desc(Productivity)) %>% filter(`Total Time` > 1) CatSums <- RTCats %>% group_by(Overview, Productivity) %>% summarize(Sum = sum(`Total Time`)) %>% arrange(desc(Overview), desc(Productivity)) %>% mutate(ProSum = Sum * Productivity) CatSums$Productivity <- factor(CatSums$Productivity, levels = c(2, 1, 0, -1, -2)) CatTotals <- RTCats %>% group_by(Overview) %>% summarize(Total = sum(`Total Time`)) %>% arrange(desc(Total)) fillcolors <- c(`2` = "#357AA2", `1` = "#67A9D1", `0` = "#FEF0ED", `-1` = "#FF9777", `-2` = "#FF7549") # fix(CatSums) ggplot() + geom_bar(data = CatSums, stat = "identity", mapping = aes(x = reorder(Overview, ProSum), y = Sum, fill = Productivity)) + scale_fill_manual(values = fillcolors) + geom_text(data = CatTotals, aes(Overview, Total, label = paste(round(Total/TotalHrs, 2) * 100, "%", sep = "")), position = position_dodge(0.9), vjust = 0.5, hjust = 0) + labs(title = "Total Time per Activity Category", subtitle = "Rescuetime Primary Categories", caption = "", x = "Category", y = "Total Time (HH:MM:SS)") + coord_flip() + theme(axis.text.y = element_text(angle = 0, hjust = 1))

ggsave(last_plot(), filename = "CategoryTotals.pdf", path = "Images/Plots", device = "pdf", width = 5.0435, height = 3.5, units = "in")

RT Top Activities by Cat

RTCatActs <- RT %>% group_by(Overview, Activity) %>% summarise(sum = { sum(`Time Spent (seconds)`)/3600 }) %>% ungroup %>% group_by(Overview) %>% top_n(n = 3, wt = sum) %>% ungroup RTCatActs <- inner_join(RTCatActs, RT %>% select(Activity, Productivity) %>% distinct, by = "Activity") RTCatActs$Productivity <- as.factor(RTCatActs$Productivity) # RTCatActs <- editData::editData(RTCatActs) fix(RTCatActs) actsums <- sum(RTCatActs$sum) ggplot(data = RTCatActs, mapping = aes(x = reorder(Activity, sum), y = sum)) + geom_bar(stat = "identity", mapping = aes(fill = Productivity)) + scale_fill_manual(values = fillcolors) + facet_wrap(~Overview, scale = "free") + geom_label(mapping = aes(label = paste(round({ sum/actsums }, 2) * 100, "%", sep = "")), color = "#00010F", label.padding = unit(0.15, "lines"), size = rel(2)) + # geom_text(mapping=aes(label = # paste(round({sum/actsums},2)*100,'%',sep='')),size=rel(1.2),color='white')+ theme(axis.text.x = element_text(vjust = 1, hjust = 0.8), axis.text.y = element_text(vjust = 0.1, hjust = 0.9)) + coord_flip() + labs(title = "Top 3 Activities Per Category", subtitle = "Total time by Activity with % Total Labels", caption = "Note: scale for each facet is set to free", x = "Hrs", y = "Activity") + theme(plot.title = element_text(hjust = 0.5), plot.subtitle = element_text(hjust = 0.5), axis.title.x = element_text(hjust = 0.3), plot.caption = element_text(hjust = 0))

# ggsave(last_plot(), filename = 'Top3.pdf', path='Images/Plots',device = # 'pdf',width = 10, height = 4, units = 'in') # ------------------- Sat Feb 10 23:23:28 2018 --------------------# TODO Make # Productivity Discrete as.factor and add consistent color schema as other graph.

Initialize API

library(httr) RTURL <- parse_url("https://www.rescuetime.com/anapi/data") RTep <- oauth_endpoint(authorize = NULL, access = "https://www.rescuetime.com/api/oauth/data", base_url = "https://www.rescuetime.com/anapi/data") oauth2.0_authorize_url() RTURL$query$pv <- "interval" RTURL$query$rb <- "2017-01-01" RTURL$query$re <- "2017-12-31" RTURL$query$rs <- "hour" RTURL$query$rk <- "productivity" RTURL$query$format <- "csv" RTHrs <- GET(build_url(RTURL))

Read and Prepare Data from Rescuetime API

RTHrs <- read.csv("~/Northeastern/Git/ppua5302/Project 1/Data/RTHourly.csv") RTHrs <- RTHrs[, c(1, 2, 4)] colnames(RTHrs) <- c("Date", "Time", "Productivity") RTHrTotal <- RTHrs %>% mutate(Hour = as.numeric(str_extract(Date, "\\d{2}(?=\\:\\d{2}\\:\\d{2})"))) %>% group_by(Hour, Productivity) %>% summarize(Total = sum(Time)/3600) RTHrTotal$Productivity <- factor(RTHrTotal$Productivity, levels = c(2, 1, 0, -1, -2)) RTHrPD <- RTHrTotal %>% filter(Productivity != "0") %>% mutate(Actual = Total * sign(as.numeric(as.character(Productivity)))) RTHrPD <- plyr::ddply(RTHrPD, .variables = "Total", .fun = transform, percent = Total/sum(RTHrPD$Total) * 100) RTHrPD <- plyr::ddply(RTHrPD, .variables = "Total", .fun = transform, pos = (cumsum(Actual) + 0.5 * abs(Actual)))

Density by Hour of Day Graph

# Stack bars with percentages # https://stackoverflow.com/questions/22231124/how-to-draw-stacked-bars-in-ggplot2-that-show-percentages-based-on-group fillcolors <- c(`2` = "#357AA2", `1` = "#67A9D1", `0` = "#FEF0ED", `-1` = "#FF9777", `-2` = "#FF7549") ggplot(data = RTHrPD, mapping = aes(x = Hour)) + geom_bar(stat = "identity", mapping = aes(y = Actual, fill = Productivity)) + scale_fill_manual(values = fillcolors) + geom_text(stat = "identity", aes(label = round(percent, 0), y = Actual), position = position_stack(vjust = 1)) + labs(title = "Total Productivity for Year 2017 by Hour of Day", subtitle = "Percentage of Total by Hour in Labels", caption = "", x = "Hour of Day", y = "Total Time (Hrs)") + scale_x_continuous(breaks = seq(1, 23))

# ggsave(last_plot(), filename = 'Hourly.pdf', path='Images/Plots',device = # 'pdf',width = 5.0435, height = 3.5, units = 'in')

Productivity by Day of Week

RTDay <- RTHrs %>% mutate(Day = wday(ymd(str_extract(Date, "\\d{4}\\-\\d{2}\\-\\d{2}")), label = T)) %>% group_by(Day, Productivity) %>% summarize(Total = sum(Time)/3600) %>% filter(Productivity != 0) %>% ungroup() %>% mutate(Actual = Total * sign(as.numeric(as.character(Productivity)))) RTDay <- plyr::ddply(RTDay, .variables = "Day", transform, percent = Total/sum(Total) * 100) RTDay$Productivity <- factor(RTDay$Productivity, levels = c(2, 1, 0, -1, -2))

Graph of Productivity by Day of Week

ggplot(data = RTDay, mapping = aes(x = Day)) + geom_bar(stat = "identity", mapping = aes(y = Actual, fill = Productivity)) + scale_fill_manual(values = fillcolors) + geom_text(stat = "identity", aes(label = round(percent, 0), y = Actual), position = position_stack(vjust = 1)) + labs(title = "Total Productivity for Year 2017 by Day of Week", subtitle = "Percentage of Total by Day in Labels", caption = "", x = "Day of Week", y = "Total Time (Hrs)")

# ggsave(last_plot(), filename = 'Daily.pdf', path='Images/Plots',device = # 'pdf',width = 5.0435, height = 3.5, units = 'in')

Linear Correlation Meditation and Productivity

MedPro <- left_join(ProbyWeek, MedbyWeek, by = "Weeknum") MedPro[, c(2, 3)] <- apply(MedPro[, c(2, 3)], 2, FUN = "scale") tags$h4("Is there a correlation between Meditation and Productivity in 2017?")

Is there a correlation between Meditation and Productivity in 2017?

lmMedPro <- lm(Sum.x ~ Sum.y, data = MedPro) summary(lmMedPro)

## ## Call: ## lm(formula = Sum.x ~ Sum.y, data = MedPro) ## ## Residuals: ## Min 1Q Median 3Q Max ## -2.4204 -0.4679 -0.0238 0.4647 3.5432 ## ## Coefficients: ## Estimate Std. Error t value Pr(>|t|) ## (Intercept) 3.212e-16 1.360e-01 0.000 1.000 ## Sum.y 1.948e-01 1.373e-01 1.418 0.162 ## ## Residual standard error: 0.9904 on 51 degrees of freedom ## Multiple R-squared: 0.03795, Adjusted R-squared: 0.01909 ## F-statistic: 2.012 on 1 and 51 DF, p-value: 0.1621

plot(lmMedPro)

tags$strong("There does not appear to be a correlation")

There does not appear to be a correlation

Initialize Parallel

library(parallel) cl <- makeCluster(detectCores(), type = "PSOCK") clusterEvalQ(cl, { library(rvest) library(stringr) library(lubridate) }) clusterExport(cl, "Test") clusterEvalQ(cl, { library(parallel) FBAct <- read_html("~/Northeastern/Git/ppua5302/Project 1/Data/FB Activity Feed/(3) Stephen Synchronicity.html") Test <- FBAct %>% html_nodes(xpath = "tr._51mx") }) Dates <- system.time(sapply(Test, function(x) { x %>% html_nodes(css = "td._51m-.vTop._5ep5 > div > div > span") %>% html_text() %>% mdy_hm() })) stopCluster(cl)

Facebook Activities

Scrape Facebook Activity

FBAct <- read_html("~/Northeastern/Git/ppua5302/Project 1/Data/FB Activity Feed/(3) Stephen Synchronicity.html") Items <- FBAct %>% html_nodes(xpath = "//tr[contains(@class,'_51mx')]") Time <- FBAct %>% html_nodes(css = "td._51m-.vTop._5ep5 > div > div > span") %>% html_text() %>% mdy_hm() Test <- FBAct %>% html_nodes(css = "tr._51mx") Dates <- lapply(Items, function(x) { x %>% html_nodes(css = "td._51m-.vTop._5ep5 > div > div > span") %>% html_text() %>% mdy_hm() }) Action <- lapply(Items, function(x) { x %>% html_nodes(css = "td._51m-.vTop._5ep5 > div > div > div") %>% html_text() }) Content <- lapply(Items, function(x) { x %>% html_nodes(css = "td._51m-.vTop._5ep6") %>% html_text() }) Activity <- data.frame(Dates = map_call(Dates, "c"), Action = { Action %>% unlist() }, Content = { Content %>% unlist() }) # write_csv(Activity,path='~/Northeastern/Git/ppua5302/Project 1/Data/FB Activity # Feed/Activity.csv')

Clean FB Activity

Activity <- read.csv(file = "~/Northeastern/Git/ppua5302/Project 1/Data/FB Activity Feed/Activity.csv", stringsAsFactors = F) Activity <- Activity %>% filter(year("2018-01-01") > year(Dates) & year(Dates) > year("2016-01-01")) Activity$Weeknum <- sapply(Activity$Dates, FUN = "week", simplify = T) Activity$SAction <- sapply(Activity$Action, FUN = function(x) { str_match(x, "^Stephen\\sSynchronicity\\s(\\w+)") %>% .[, 2] }) ActTotals <- Activity %>% group_by(SAction) %>% summarize(Sum = n()) %>% arrange(desc(Sum)) %>% filter(!is.na(SAction)) ActTotals <- ActTotals %>% mutate(P = paste(round({ Sum/sum(Sum) * 100 }, 2), "%", sep = "")) tagList(tags$p("Types of FB activities, the sum of total occurences, and the percent of total activities that sum represents:"))

Types of FB activities, the sum of total occurences, and the percent of total activities that sum represents:

knitr::kable(ActTotals)

SAction Sum P

likes 2046 36.98%

liked 897 16.21%

reacted 662 11.97%

commented 510 9.22%

saved 437 7.9%

shared 290 5.24%

replied 282 5.1%

added 106 1.92%

posted 65 1.17%

became 49 0.89%

wrote 49 0.89%

is 42 0.76%

updated 35 0.63%

reached 26 0.47%

was 18 0.33%

followed 11 0.2%

recommended 4 0.07%

created 2 0.04%

poked 1 0.02%

Graph Activity

ggplot(data = ActTotals, mapping = aes(x = reorder(SAction, order(Sum, decreasing = T)), y = Sum)) + geom_bar(stat = "identity", fill = "#FF7549") + geom_text(aes(label = Sum), position = position_dodge(0.9), vjust = 0) + labs(title = "Facebook Activity", subtitle = "Totals by type of Action", caption = "", x = "Action", y = "Count")

# ggsave(last_plot(), filename = 'FBActivityPlot.pdf', path='Images/Plots',device # = 'pdf',width = 5.0435, height = 3.5, units = 'in')

Project 1: The Digital Shadow Illuminated

Stephen Synchronicity

2018-04-30

Final Visual

Theme, Font and Color Settings

Color Schema

Data Wrangling

Types of Health Activity by Week number for the Year 2017

Mean, Max, and Total Time in Hrs + Total # of Events per Productivity level for Each week of 2017

Visualization

Health Activities

Rescuetime (Digital Activities)

Is there a correlation between Meditation and Productivity in 2017?

Facebook Activities

Productivity	sum	P
-2	84.27556	0.0311079
-1	489.65000	0.1807400
0	934.40833	0.3449095
1	527.00972	0.1945302
2	673.79722	0.2487125

SAction	Sum	P
likes	2046	36.98%
liked	897	16.21%
reacted	662	11.97%
commented	510	9.22%
saved	437	7.9%
shared	290	5.24%
replied	282	5.1%
added	106	1.92%
posted	65	1.17%
became	49	0.89%
wrote	49	0.89%
is	42	0.76%
updated	35	0.63%
reached	26	0.47%
was	18	0.33%
followed	11	0.2%
recommended	4	0.07%
created	2	0.04%
poked	1	0.02%