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work_data_eda.R
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work_data_eda.R
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library(skimr)
library(tidyverse)
library(wesanderson)
library(DT)
# work_data check
skim_with(integer = list(min = partial(min, na.rm = TRUE), max = partial(max, na.rm = TRUE)), append = TRUE)
work_data %>%
skim() %>%
filter(stat %in% c('missing', 'min', 'max')) %>%
select(variable, stat, formatted) %>%
spread(stat, formatted) %>%
select(variable, missing, min, max)
# pomodoros per weekday
colores <- wes_palette(name = 'GrandBudapest1', type = 'continuous')
day_levels <- c('pon', 'wt', 'œr', 'czw', 'pt', 'sob', 'niedz')
work_data %>%
group_by(day = wday(date, label = TRUE)) %>%
summarize(mean_pomodoros = round(mean(pomodoros, na.rm = TRUE), 1),
sd = round(sd(pomodoros, na.rm = TRUE), 1)) %>%
mutate(day = gsub('\\\\.', '', day),
is_workday = (day!='niedz' & day!='sob')) %>%
mutate(day = factor(day, day_levels)) %>%
ggplot(aes(x = day, y = mean_pomodoros, fill = is_workday)) +
scale_fill_manual(values = colores) +
labs(x = 'weekday', y = 'pomodoros', title = 'Mean pomodoros per weekday') +
geom_col(aes(x = day, y = mean_pomodoros + sd, fill = !is_workday), alpha = 0.2) +
geom_col(aes(x = day, y = mean_pomodoros, fill = is_workday)) +
geom_label(aes(label = mean_pomodoros), vjust = 1.5) +
geom_col(aes(x = day, y = mean_pomodoros - sd, fill = !is_workday), alpha = 0.2) +
guides(fill = 'none')
# scale_colour_continuous(name ="value",
# breaks=c("min", "mean", "max"),
# labels=c("min", "mean", "max"))
# pomodoros per month
work_data %>%
filter(wday(date)!=1 & wday(date)!=7) %>%
group_by(month = month(date, label = TRUE)) %>%
summarize(monthly_pomodoroes = sum(pomodoros, na.rm = TRUE)) %>%
mutate(hours = monthly_pomodoroes/2) %>%
ggplot(aes(x = month, y = monthly_pomodoroes)) +
geom_col(fill = colores[1]) +
geom_hline(yintercept = 160, linetype="dashed", color = colores[2]) +
geom_text(aes(3, 160, label = '80 hours', vjust = -.5, color = colores[2])) +
scale_fill_manual(values = colores) +
theme(legend.position="none")
# pomodoros per week
avg_pomodoros_weekly <- work_data %>%
group_by(week_no = week(date)) %>%
summarize(weekly_pomodores = sum(pomodoros, na.rm = TRUE)) %>%
summarize(mean(weekly_pomodores)) %>%
.[[1]] %>%
round()
work_data %>%
group_by(week_no = week(date)) %>%
summarize(weekly_pomodoroes = sum(pomodoros, na.rm = TRUE),
avg_weekly_pomodores = round(mean(pomodoros, na.rm = TRUE), 2)) %>%
mutate(hours = weekly_pomodoroes/2) %>%
ggplot(aes(x = week_no, y = weekly_pomodoroes)) +
geom_col(fill = colores[1]) +
geom_hline(yintercept = 40, linetype="dashed", color = colores[2]) +
geom_text(aes(45, 40, label = '20 hours', vjust = -.5, color = colores[2])) +
scale_fill_manual(values = colores) +
geom_hline(yintercept = avg_pomodoros_ever, linetype = "dashed", color = colores[3]) +
geom_text(aes(45, avg_pomodoros_weekly, label = paste0(avg_pomodoros_weekly/2, ' hours'), vjust = -.2)) +
geom_text(aes(45, avg_pomodoros_weekly, label = 'average', vjust = 1.5)) +
theme(legend.position="none") +
labs(x = 'week', y = 'sum of pomodoros', title = 'Sum of pomodoros per week')
# pomodors per day
avg_pomodoros_daily = round(mean(work_data[wday(work_data$date)!=1 & wday(work_data$date)!=7, ]$pomodoros, na.rm = TRUE))
work_data %>%
mutate(is_workday = (wday(date)!=1 & wday(date)!=7)) %>%
ggplot(aes(x = date, y = pomodoros)) +
geom_col(aes(fill = is_workday), show.legend = TRUE, width = 1) +
scale_fill_manual(values = colores) +
geom_hline(yintercept = avg_pomodoros_daily, linetype = 'dashed', color = colores[3]) +
geom_text(aes(as.Date('2018-11-09'), y = avg_pomodoros_daily, label = paste0(avg_pomodoros_daily/2, ' hours'), vjust = -.5)) +
geom_text(aes(as.Date('2018-11-09'), y = avg_pomodoros_daily, label = 'average', vjust = 1.3)) +
labs(x = 'month', y = 'pomodoros', title = 'Pomodoros in time')
# time_up vs weekday
work_data %>%
group_by(day = wday(date, label = TRUE)) %>%
summarize(mean_timeup = round(mean(time_up, na.rm = TRUE)/3600, 1),
sd = round(sd(time_up, na.rm = TRUE)/3600, 1)) %>%
mutate(day = gsub('\\\\.', '', day),
is_workday = (day!='niedz' & day!='sob')) %>%
mutate(day = factor(day, day_levels)) %>%
ggplot(aes(x = day, y = mean_timeup, fill = is_workday)) +
scale_fill_manual(values = colores) +
labs(x = 'weekday', y = 'time up [hours]', title = 'Mean time up per weekday') +
geom_col(aes(x = day, y = mean_timeup + sd, fill = !is_workday), alpha = 0.2) +
geom_col(aes(x = day, y = mean_timeup, fill = is_workday)) +
geom_label(aes(label = mean_timeup), vjust = 1.5) +
geom_col(aes(x = day, y = mean_timeup - sd, fill = !is_workday), alpha = 0.2) +
guides(fill = 'none')