data2020 <- read_csv("Toggl_time_entries_2020-01-01_to_2020-12-31.csv", col_types = cols())
data2021 <- read_csv("Toggl_time_entries_2021-01-01_to_2021-12-31.csv", col_types = cols())
# glimpse(data2020)
data2020 <- data2020 %>%
select(Project,
Description,
`Start date`,
`Start time`,
`End date`,
`End time`,
Duration,
Tags)
data2021 <- data2021 %>%
select(Project,
Description,
`Start date`,
`Start time`,
`End date`,
`End time`,
Duration,
Tags)
# glimpse(data2020)
grouped_data <- data2020 %>%
group_by(Project) %>%
summarise(
n = n(),
hms = sum(Duration) %>% hms::as_hms(),
sec = sum(Duration)
)
timeWorkHMS <- data2020 %>%
pull(Duration) %>%
sum() %>%
hms::as_hms()
timeWork <- data2020 %>%
pull(Duration) %>%
sum()
# fraction of entire time spent working
foo <- timeWork / dyears(1)
# estimated sleep fraction if I sleep 7 hours each night
bar <- 365 * dhours(7) / dyears(1)
# 8 hours
time <- 365 * dhours(8) / dyears(1)
# data2020 %>%
# slice_min(`Start date`)
#
# data2020 %>%
# slice_max(`Start date`)
Basic grouping by category
grouped <- data2020 %>%
group_by(Project) %>%
summarise(
n = n(),
hms = sum(Duration) %>% hms::as_hms(),
sec = sum(Duration)
)
grouped %>%
ggplot(aes(y = reorder(Project, n), weight=n, fill = Project)) +
geom_bar() +
xlab("Hours") +
ylab("Group") +
ggtitle("Time Spent per Group")
Heat map of when during the day I worked Heat map of sorts of type of work I did when Possible heatmap of what times I work at (and what type when)
See if you can distinguish schedules that were established at any point in time. Bonus points if I do this numerically with differences or something.
Productivty over time vs what I was doing (ie time spent working during semester and summer and weekend and weekday)
It’s interesting I independently (two times) came up with the same questions - I am interested in seeing this.