In maczokni/selfquant: This package is for creating a quantified self tracker using google sheets, and for pulling, displaying, and archiving the data collected with it.

library(googlesheets)
library(tidyverse)
library(plotly)
library(ohicore) # devtools::install_github('ohi-science/ohicore')
library(selfquant) # devtools::install_github('maczokni/selfquant')

#This markdown template allows you to generate a monthly report from your selfquant results. It will automatically generate everything, but you will need to read in your data first. 

#Set the title of the google sheet (eg: Reka Quant), and the name of the specific worksheet (eg: July_2017)

sq_title <- "Chelsea Quant"
sq_worksheet <- "July 2017"


#To read your in from google sheets, run the following:

(my_sheets <- gs_ls()) #you might need to run this outside the markdown doc to create oauth token for the doc to knit...
sq_data <- getQuant(title=sq_title, workSheet = sq_worksheet)
#calculate summary stats
sq_data[is.na(sq_data)] <- 0
sq_summary <- calcSummary(filter(sq_data, X1 != "Notables"))

petalDf <- sq_summary %>%
  dplyr::select( X1, net_w1, net_w2, net_w3, net_w4) %>%
  tidyr::gather("week", "net", 2:5) %>%
  dplyr::group_by(X1) %>%
  dplyr::summarise(score=sum(net)) %>%
  dplyr::arrange(-score)

names(petalDf)[1]<-"Metric"

topMetric <- head(petalDf$Metric, n=1)
bottomMetric <- tail(petalDf$Metric, n=1)

r I(sq_worksheet) Selfquant Report

This report summarises the scores from your selfquant point scoring system for r I(sq_worksheet).

Your highest scoring category was r I(head(petalDf$Metric, n=1)) with a net score of r I(head(petalDf$mean_net, n=1)) over the month, while your lowest score was r I(tail(petalDf$mean_net, n=1)) in the r I(tail(petalDf$Metric, n=1)) category.

This plot shows your scores in each category across the 4 weeks of the r I(sq_worksheet) space:

plotWeek(sq_summary)

And this plot shows you your overall net scores in each category:

This plot shows your scores in each category across the 4 weeks of the r I(sq_worksheet) space:

plotFlower <- function(sq_summary){

  #first subset the dataframe to contain only the metric variable (x1) and the net scores for each week
  netCols <- dplyr::select(sq_summary, X1, net_w1, net_w2, net_w3, net_w4)

  #then gather to get week as one variable
  netColsSum <- tidyr::gather(netCols, "week", "net", 2:5)

  #rename the metric column
  names(netColsSum)[1]<-"Metric"

  #get the mean good to bad net score over all 4 weeks for each metric
  petalDf <- netColsSum %>%
              dplyr::group_by(Metric) %>%
              dplyr::summarise(score=sum(net))

  #make the flower plot
  PlotFlower(
    petalDf$score, rep(1, nrow(petalDf)), petalDf$Metric, center=sum(petalDf$score),
    main = "Score This Month", fill.col = RColorBrewer::brewer.pal(nrow(petalDf), 'Spectral'), disk = 0.33,
    xlim = c(-2,2), ylim = c(-2,2))


}

plotFlower(sq_summary)

The score in the middle represents the total score for the month. Your current average monthly score is 43 and your best score is 55.

maczokni/selfquant documentation built on May 30, 2019, 9:54 p.m.