The last day of the #30DayChartChallenge- woot! As predicted I did not manage to make a chart every day; the latter half of the challenge clashed with school holidays and a trip away, but 19/30 days is not too bad.
I initially planned to focus on Our World in Data charts and that worked well initially, but it became more difficult to find prompt-relevant data there as the challenge went on. Once I let myself look more broadly, I had a lot of fun to making plots about everything from egg prices to hollywood star age differences to measles vax rates and Puteketeke controversies.
For the last of the challenge I wanted to look at my own data about how much time I spent on the challenge in April. I pulled data from my Timing app and learned some useful things about working with date and time data in the process.
Here I am reading my timing data in to RStudio and miraculously R is recognising my dates as dates and time values as time- that should make things easier right?
library(tidyverse) # tidyverse now includes the `lubridate` package
library(here)
library(gt)
library(plotly)
library(ggeasy)
library(Manu)
chart_time <- read_csv(here("charts/2025-04-30_natgeo/chart_time.csv"))
glimpse(chart_time)Rows: 35
Columns: 2
$ date <date> 2025-03-27, 2025-03-28, 2025-03-29, 2025-03-30, 2025-03-31, 2025…
$ time <time> 02:06:00, 02:13:00, 01:05:00, 00:00:00, 01:58:00, 02:19:00, 01:4…If you are less lucky and your dates have read in as characters, you can make them into dates using functions from the lubridate package. If you have 2025-04-01 format dates, df$date <- ymd(df$date), tells R that your date variable is a date in year-month-day format.
I am interested in how much time I spent during each week of the challenge, so here make a new variable using case_when() and the day and month values, to make another variable with challenge week values Week0 to Week4.
chart_time <- chart_time %>%
mutate(day = day(date)) %>%
mutate(month = month(date)) %>%
mutate(challenge_week = case_when(month == 3 ~ "Week0",
day %in% 1:7 ~ "Week1",
day %in% 8:14 ~ "Week2",
day %in% 15:21 ~ "Week3",
day %in% 22:30 ~ "Week4"))
glimpse(chart_time)Rows: 35
Columns: 5
$ date <date> 2025-03-27, 2025-03-28, 2025-03-29, 2025-03-30, 2025-0…
$ time <time> 02:06:00, 02:13:00, 01:05:00, 00:00:00, 01:58:00, 02:1…
$ day <int> 27, 28, 29, 30, 31, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, …
$ month <dbl> 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4…
$ challenge_week <chr> "Week0", "Week0", "Week0", "Week0", "Week0", "Week1", "…How much time did I spent across the month? Here I am summing all time values in the dataset. I was initially surprised to find that when you sum a variable in time format, the output is in seconds (156000 seconds!). It took me a little time to work out how to convert those values into meaningful units.
Initially I tried dividing by 60 a couple of times to get minutes and hours but the values were still seconds (turns out that only works when the data is numeric). A bit of lubridate googling and I found that you can use the seconds_to_period() function will reformat values so that they display as hours, minutes, and seconds.
# total time spent
sum_total <- chart_time %>%
summarise(total_seconds = sum(time)) %>%
mutate(total_period = seconds_to_period(total_seconds))
gt::gt(sum_total)| total_seconds | total_period |
|---|---|
| 156000 | 1d 19H 20M 0S |
How many hours did I spend each week? On average about 8 hours per week, a few more than that in the first week when I did 7 charts in 7 days, and the week before we headed away during the school holidays so that I could schedule some posts in advance.
# time per week
week_total <- chart_time %>%
group_by(challenge_week) %>%
summarise(total_seconds = sum(time)) %>%
mutate(total_period = seconds_to_period(total_seconds))
gt::gt(week_total)| challenge_week | total_seconds | total_period |
|---|---|---|
| Week0 | 26520 | 7H 22M 0S |
| Week1 | 43380 | 12H 3M 0S |
| Week2 | 23580 | 6H 33M 0S |
| Week3 | 37560 | 10H 26M 0S |
| Week4 | 24960 | 6H 56M 0S |
Expressing the time as a “period” displays nicely when put in a gt() table as above, but when I tried plotting the same data, the y axis is STILL seconds.
week_total %>%
ggplot(aes(x = challenge_week, y = total_period)) +
geom_col(fill = "#7d9fc2") +
theme_minimal()
My first instinct was to use the hours() function to pull the hours out of the total period variable, but that was unsuccessful.
week_total %>%
ggplot(aes(x = challenge_week, y = hours(total_period))) +
geom_col(fill = "#7d9fc2") +
theme_minimal()
So I went back to basics and turned the time period back to numeric and worked out the minutes and hours by dividing by 60.
week_total <- week_total %>%
mutate(total_period_numeric = as.numeric(total_period)) %>%
mutate(total_minutes = total_period_numeric / 60) %>%
mutate(total_hours = round(total_minutes / 60))
glimpse(week_total)Rows: 5
Columns: 6
$ challenge_week <chr> "Week0", "Week1", "Week2", "Week3", "Week4"
$ total_seconds <drtn> 26520 secs, 43380 secs, 23580 secs, 37560 secs, 2…
$ total_period <Period> 7H 22M 0S, 12H 3M 0S, 6H 33M 0S, 10H 26M 0S, 6H …
$ total_period_numeric <dbl> 26520, 43380, 23580, 37560, 24960
$ total_minutes <dbl> 442, 723, 393, 626, 416
$ total_hours <dbl> 7, 12, 7, 10, 7week_total %>%
ggplot(aes(x = challenge_week, y = total_hours)) +
geom_col(fill = "#7d9fc2") +
theme_minimal() +
labs(x = "Challenge week", y = "Hours", title = "30 Day Chart Challenge April 2025", subtitle = "Time each week spent making plots") +
theme_minimal() +
scale_y_continuous(limits = c(0,15))
p <- chart_time %>%
ggplot(aes(x = date, y = time, colour = challenge_week)) +
geom_point() +
geom_line() +
theme_minimal() +
scale_colour_manual(values = get_pal("Kereru")) +
labs(x = "Date", y = "Hours", title = "30 Day Chart Challenge April 2025", subtitle = "Daily time spent making plots") +
scale_y_time(labels = c(0,1,2,3)) +
easy_remove_legend_title()
p
ggplotly(p)