Graphing My Daily Phone Use

How many times do I look at my phone? I set up a small program on my phone to count the screen activations and logged to a file. In this post I show what went wrong and how to plot the results.

The data

I set up a small program on my phone that counts every day how many times I use my phone (to be specific, it counts the times the screen has been activated).

My data looks like this:

"screen_log";"1-19-19";"17.30";"7"
"screen_log";"1-19-19";"17.33";"8"
"screen_log";"1-19-19";"17.36";"9"
"screen_log";"1-19-19";"17.38";"10"

To account for comma use and possible problems I set up the program on my phone to write a “;”-seperated file that records screen_log, the date, the time and the current value of screen_count. Every day around 12 o clock it reset the counter to 0. I thought it would be cool to compare different days.

The problems

I started the data collection on januari 19th around 17:00h, so the first day is already halfway through. For reasons I cannot fathom, sometimes the system date is recorded in the USA style MONTH-DAY-YEAR and sometimes in the rest-of-the-world style of DAY-MONTH-YEAR. I wish I could set it to YEAR-MONTH-DAY (ISO 8601).

Reading in the data

I use read_csv2, which expects “;” as a seperator and never converts text to factor. This particular textfile has no headers, so I tell R what to call the columns.

library(tidyverse) # what else
screenlog <- read_csv2("data/screenlog.csv",col_names = c("type","date","time","counter"))

Data cleaning

I have to deal with the different time formats, so I set up a regex that works only with Januari, if it detects -01-19 it pulls out the numbers before that, if it detects the other variant it takes the second part. I combine the date and time into a timestamp and pull out the hours and minutes, before combining the hours and minutes into HMS time class. Finally I remove anything over 23 hours, because in that period the counter is reset.

screenlog <-   
  screenlog %>% 
    mutate(
        day = case_when( 
            str_detect(date, "[0-9]{1,2}-01-19") ~ 
                str_replace(date, "([0-9]{1,2})-01-19","\\1"),
            str_detect(date, "1-[0-9]{1,2}-19") ~ 
              str_replace(date, "1-([0-9]{1,2})-19", "\\1") ,
            TRUE ~  NA_character_
            ),
        timestamp = paste0("2019-01-",day, " ",time),
        timestamp = as.POSIXct(timestamp,tz = "GMT", format = "%Y-%m-%d %H.%M"),
        hours = str_replace(time,"\\.[0-9]{1,2}", "") %>% as.numeric(),
        minutes = str_replace(time,"[0-9]{1,2}\\.", "") %>% as.numeric(),
        time = hms::hms(hours = hours, minutes = minutes)
        ) %>% 
    filter(hours < 23)

How does it look?

First an overview:

screenlog %>% 
    ggplot(aes(timestamp, counter, color = day))+
    geom_step()+
    ggtitle("Times I looked at my screen during vacation", subtitle = "daily values")+
    theme_light()
Daily cumulative screen looking values

Daily cumulative screen looking values

But that is difficult to compare, so I also show them overlayed:

screenlog %>% 
    ggplot(aes(time, counter, group = day, color = day))+
    geom_step()+
    ggtitle("Times I looked at my screen during vacation", subtitle = "overlay plot")+
    theme_light()
overlay of cumulative screen lookings every day on the same hourly scale

overlay of cumulative screen lookings every day on the same hourly scale

Fin

The only remaining question is: what did I do on the 25th that I looked soooo (326 times) many times on my screen? Is there a bug in the counting? Was I really bored, did I take a lot of photo’s? I was in the Botanical Gardens of Malaga and did take a lot of pictures with my phone.