Let's say I have a data.frame consisting of industry type and starting and ending dates (e.g. for an employee).
mydf <- data.frame(industry = c("Government", "Education", "Military", "Private Sector", "Government", "Private Sector"),
start_date = c("2014-01-01", "2016-02-01", "2012-11-01", "2013-03-01", "2012-12-01", "2011-12-01"),
end_date = c("2020-12-01", "2016-10-01", "2014-01-01", "2016-10-01", "2015-10-01", "2014-09-01"))
> mydf
industry start_date end_date
1 Government 2014-01-01 2020-12-01
2 Education 2016-02-01 2016-10-01
3 Military 2012-11-01 2014-01-01
4 Private Sector 2013-03-01 2016-10-01
5 Government 2012-12-01 2015-10-01
6 Private Sector 2011-12-01 2014-09-01
I'd like to create a stacked ggplot bar chart in which each unique year in the start_date column is on the X axis (e.g. 2011-2016) and the y axis represents the total number of observations (the row count) represented in a given industry for that year.
I'm not sure what the right way to manipulate the data.frame to allow for this. Presumably I'd need to manipulate the data to have columns for industry year and count. But I'm not sure how to produce the year columns from a date range. Any ideas?
Convert the date columns to Date, create the 'date' sequence from the 'start_date' to 'end_date' for each row with map2 (from purrr), unnest the list output, count the year and plot with geom_bar
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
mydf %>%
mutate(across(c(start_date, end_date), as.Date)) %>%
transmute(industry, date = map2(start_date, end_date, seq, by = 'day')) %>%
unnest(c(date)) %>%
count(industry, year = factor(year(date))) %>%
ggplot(aes(x = year, y = n, fill = industry)) +
geom_col() +
theme_bw()
If the plot should be separate for each 'industry'
mydf %>%
mutate(across(c(start_date, end_date), as.Date)) %>%
transmute(industry, date = map2(start_date, end_date, seq, by = 'day')) %>%
unnest(c(date)) %>%
count(industry, year = factor(year(date))) %>%
ggplot(aes(x = year, y = n, fill = industry)) +
geom_col() +
facet_wrap(~ industry) +
theme_bw()
-output
As #IanCampbell suggested, the by for seq can be 'year'
mydf %>%
mutate(across(c(start_date, end_date), as.Date)) %>%
transmute(industry, date = map2(start_date, end_date, seq, by = 'year')) %>%
unnest(c(date)) %>%
count(industry, year = factor(year(date))) %>%
ggplot(aes(x = year, y = n, fill = industry)) +
geom_col() +
facet_wrap(~ industry) +
theme_bw()
Is this what you're looking for?
I would recommend using purrr::pmap to create a new data frame with one row for each year based on each row of the original data.
We can use the purrr::pmap_dfr to automatically return a single data frame bound by row.
We can use the ~with(list(...), ) trick to be able to reference columns by name.
Then we can use dplyr::count to count by combinations of columns. Then it's easy.
library(dplyr)
library(purrr)
library(lubridate)
library(ggplot)
mydf %>%
mutate(across(c(start_date, end_date), as.Date),
start_year = year(start_date),
end_year = year(end_date)) %>%
pmap_dfr(~with(list(...),data.frame(industry,
year = seq(start_year, end_year)))) %>%
count(year, industry) %>%
ggplot(aes(x = year, y = n, fill = industry)) +
geom_bar(stat="identity")
Related
I am trying to compare different years' variables but I am having trouble plotting them together.
The time series is a temperature series which can be found in https://github.com/gonzalodqa/timeseries as temp.csv
I would like to plot something like the image but I find it difficult to subset the months between the years and then combine the lines in the same plot under the same months
If someone can give some advice or point me in the right direction I would really appreciate it
You can try this way.
The first chart shows all the available temperatures, the second chart is aggregated by month.
In the first chart, we force the same year so that ggplot will plot them aligned, but we separate the lines by colour.
For the second one, we just use month as x variable and year as colour variable.
Note that:
with scale_x_datetime we can hide the year so that no one can see that we forced the year 2020 to every observation
with scale_x_continous we can show the name of the months instead of the numbers
[just try to run the charts with and without scale_x_... to understand what I'm talking about]
month.abb is a useful default variable for months names.
# read data
df <- readr::read_csv2("https://raw.githubusercontent.com/gonzalodqa/timeseries/main/temp.csv")
# libraries
library(ggplot2)
library(dplyr)
# line chart by datetime
df %>%
# make datetime: force unique year
mutate(datetime = lubridate::make_datetime(2020, month, day, hour, minute, second)) %>%
ggplot() +
geom_line(aes(x = datetime, y = T42, colour = factor(year))) +
scale_x_datetime(breaks = lubridate::make_datetime(2020,1:12), labels = month.abb) +
labs(title = "Temperature by Datetime", colour = "Year")
# line chart by month
df %>%
# average by year-month
group_by(year, month) %>%
summarise(T42 = mean(T42, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = month, y = T42, colour = factor(year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Temperature by Month", colour = "Year")
In case you want your chart to start from July, you can use this code instead:
months_order <- c(7:12,1:6)
# line chart by month
df %>%
# average by year-month
group_by(year, month) %>%
summarise(T42 = mean(T42, na.rm = TRUE), .groups = "drop") %>%
# create new groups starting from each July
group_by(neworder = cumsum(month == 7)) %>%
# keep only complete years
filter(n() == 12) %>%
# give new names to groups
mutate(years = paste(unique(year), collapse = " / ")) %>%
ungroup() %>%
# reorder months
mutate(month = factor(month, levels = months_order, labels = month.abb[months_order], ordered = TRUE)) %>%
# plot
ggplot() +
geom_line(aes(x = month, y = T42, colour = years, group = years)) +
labs(title = "Average Temperature by Month", colour = "Year")
EDIT
To have something similar to the first plot but starting from July, you could use the following code:
# libraries
library(ggplot2)
library(dplyr)
library(lubridate)
# custom months order
months_order <- c(7:12,1:6)
# fake dates for plot
# note: choose 4 to include 29 Feb which exist only in leap years
dates <- make_datetime(c(rep(3,6), rep(4,6)), months_order)
# line chart by datetime
df %>%
# create date time
mutate(datetime = make_datetime(year, month, day, hour, minute, second)) %>%
# filter years of interest
filter(datetime >= make_datetime(2018,7), datetime < make_datetime(2020,7)) %>%
# create increasing group after each july
group_by(year, month) %>%
mutate(dummy = month(datetime) == 7 & datetime == min(datetime)) %>%
ungroup() %>%
mutate(dummy = cumsum(dummy)) %>%
# force unique years and create custom name
group_by(dummy) %>%
mutate(datetime = datetime - years(year - 4) - years(month>=7),
years = paste(unique(year), collapse = " / ")) %>%
ungroup() %>%
# plot
ggplot() +
geom_line(aes(x = datetime, y = T42, colour = years)) +
scale_x_datetime(breaks = dates, labels = month.abb[months_order]) +
labs(title = "Temperature by Datetime", colour = "Year")
To order month differently and sum up the values in couples of years, you've to work a bit with your data before plotting them:
library(dplyr) # work data
library(ggplot2) # plots
library(lubridate) # date
library(readr) # fetch data
# your data
df <- read_csv2("https://raw.githubusercontent.com/gonzalodqa/timeseries/main/temp.csv")
df %>%
mutate(date = make_date(year, month,day)) %>%
# reorder month
group_by(month_2 = factor(as.character(month(date, label = T, locale = Sys.setlocale("LC_TIME", "English"))),
levels = c('Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun')),
# group years as you like
year_2 = ifelse( year(date) %in% (2018:2019), '2018/2019', '2020/2021')) %>%
# you can put whatever aggregation function you need
summarise(val = mean(T42, na.rm = T)) %>%
# plot it!
ggplot(aes(x = month_2, y = val, color = year_2, group = year_2)) +
geom_line() +
ylab('T42') +
xlab('month') +
theme_light()
A slightly different solution without the all dates to 2020 trick.
library(tidyverse)
library(lubridate)
df <- read_csv2("https://raw.githubusercontent.com/gonzalodqa/timeseries/main/temp.csv")
df <- df |>
filter(year %in% c(2018, 2019, 2020)) %>%
mutate(year = factor(year),
month = ifelse(month<10, paste0(0,month), month),
day = paste0(0, day),
month_day = paste0(month, "-", day))
df |> ggplot(aes(x=month_day, y=T42, group=year, col=year)) +
geom_line() +
scale_x_discrete(breaks = c("01-01", "02-01", "03-01", "04-01", "05-01", "06-01", "07-01", "08-01", "09-01", "10-01", "11-01", "12-01"))
For a population of individuals I have a regular time series of what category they fall into. I would like to summarise the composition of this population over time, by the categories, as a stacked bar chart in R. For example:
set.seed(1)
id <- seq(1:25)
t1 <- sample(LETTERS[1:5], 25, replace=TRUE)
t2 <- sample(LETTERS[1:5], 25, replace=TRUE, prob=c(0.1,0.1,0.1,0.1,0.6))
t3 <- sample(LETTERS[1:5], 25, replace=TRUE, prob=c(0.2,0.1,0.2,0.1,0.4))
df <- data.frame(cbind(id, t1, t2, t3))
with frequencies:
> table(df$t1)
A B C D E
7 6 3 2 7
> table(df$t2)
B C D E
3 4 5 13
> table(df$t3)
A B C D E
4 2 5 4 10
So, at time period 1, 7 of the 25 are category A, 6 category B, whilst at time period 2, none are category A, 3 category B, etc. The chart will look like this (from EXCEL):
Can this be made in ggplot? Thanks.
Here is an option with data.table
library(dplyr)
library(data.table)
library(ggplot2)
melt(setDT(df), id.var = "id")[, .N, .(variable, value)][, perc := N / sum(N), variable] %>%
ggplot(aes(x = variable, y = perc, fill = value)) +
geom_bar(stat = "identity") +
scale_y_continuous(labels = scales::percent)
This can be done by first reshaping into 'long' format with pivot_longer, then get the frequency count and use the summarised 'n' as 'y' in ggplot aes while specifying the 'x' as 'name' and the fill as 'value' column created from pivot_longer
library(dplyr)
library(tidyr)
library(ggplot2)
df %>%
pivot_longer(cols = everything()) %>%
count(name, value) %>%
ggplot(aes(x = name, y = n, fill = value)) +
geom_col()
If we need proportion instead of count,
df %>%
pivot_longer(cols = everything()) %>%
count(name, value) %>%
group_by(name) %>%
mutate(prop = n/sum(n)) %>%
ggplot(aes(x = name, y = prop, fill = value)) +
geom_col() +
scale_y_continuous(labels= scales::percent)
I have a list of interviews conducted by two survey institutes A + B over a long period of time (several years) and a corresponding date variable:
date_of_interview institute
--------------------------
2021-04-01 A
2021-04-01 A
2021-04-02 A
2021-04-02 A
2021-04-02 A
2021-04-02 B
2021-04-02 B
2021-04-02 B
etc.
All interviews should be evenly distributed over the weekdays (monday to friday). In order to check this, I would like to create the following graphic with a time variable on the x-axis (calendar weeks from 1-52):
library(tidyverse)
df <- df %>% mutate(weekday = format(date_of_interview, "%u"),
week = format(date_of_interview, "%V"))
However, I am struggleing with calculating the percentages of the weekdays within the week-groups. All weekdays should be around 20% (mo-fr).
ggplot(aes(x = week, fill = weekday, group = weekday)) +
geom_bar(position = "stack") +
facet_wrap(institute ~.)
From what I understand you want each facet to be an institute, each group per facet to be a weekday, and the filling to be the weekdays themselves. You can shuffle them around to suit your requirement if I have misunderstood.
library(dplyr)
library(ggplot2)
df <- df %>%
mutate(
week = format(date_of_interview, "%V"),
weekday = format(date_of_interview, "%u"),
.keep='unused'
) %>%
group_by(institute, week, weekday) %>%
summarise(n = n()) %>%
mutate(freq = n / sum(n), .keep='unused') %>%
ungroup()
ggplot(df, aes(x=week, y=freq, fill=weekday)) +
geom_bar(stat='identity') +
facet_wrap(institute ~.)
I tested on this dataframe:
df <- data.frame(
date_of_interview = as.Date(c(
'2021-04-01', '2021-04-01', '2021-04-02', '2021-04-02',
'2021-04-02', '2021-04-02', '2021-04-02', '2021-04-02',
'2021-04-09', '2021-04-10', '2021-04-11')),
institute = c('A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'A', 'A', 'A')
)
Hi and apologies if this was asked already, I could not find anything since an hour so I'll ask:
I have data in this style (sorry for the horrible formatting, how can I make these prettier?):
person start_time end_time amount
A 2019-10-04 2020-04-21 10
A 2019-12-10 2020-01-09 20
B 2019-11-04 2020-08-21 30
B 2019-12-10 2020-01-20 15
C 2019-12-20 2020-03-19 5
So, I want to be able to plot the sum of the amount per person with ggplot2 over time until today (or sys_date).
This means, for person A, the plot should show 10 from 2019-10-04 until 2019-12-10, and afterwards it should jump to 30 (10+20). This is until 2020-01-09 (since this is in the past), where the amount should go back to 10.
Similarly, for person B the amount should be 30 between 2019-11-04 and 2019-12-10, afterwards it should be 45, and fall back to 30 on 2020-01-20.
I tried something along:
SumAmount <- data %>%
group_by(person,start_time,end_time) %>%
summarise(cumulatedAmount = sum(amount))
But this isn't what I need...
Thanks a lot and apologies again for the poor formatting.
Here is one idea. We can calculate the total amount of each date and then plot the total amount.
library(tidyverse)
library(lubridate)
dat2 <- dat %>%
# Convert to date class
mutate_at(vars(ends_with("time")), ymd) %>%
# Create a date sequence and expand it
mutate(Date = map2(start_time, end_time, seq.Date, by = 1)) %>%
unnest(cols = Date) %>%
# Calculate the total amount for each date
group_by(person, Date) %>%
summarize(amount = sum(amount))
ggplot(dat2, aes(x = Date, y = amount, color = person)) +
geom_point() +
geom_line()
Here is another option. This is the same way to expand the data frame based on date. After that, we can use stat_summary to plot the data.
library(tidyverse)
library(lubridate)
dat2 <- dat %>%
# Convert to date class
mutate_at(vars(ends_with("time")), ymd) %>%
# Create a date sequence and expand it
mutate(Date = map2(start_time, end_time, seq.Date, by = 1)) %>%
unnest(cols = Date)
ggplot(dat2, aes(x = Date, y = amount, color = person)) +
stat_summary(fun.y = sum, geom = "path")
Update
This solution add 0 to the next date of the last date for each person
library(tidyverse)
library(lubridate)
dat2 <- dat %>%
# Convert to date class
mutate_at(vars(ends_with("time")), ymd) %>%
# Create a date sequence and expand it
mutate(Date = map2(start_time, end_time, seq.Date, by = 1)) %>%
unnest(cols = Date) %>%
# Calculate the total amount for each date
group_by(person, Date) %>%
summarize(amount = sum(amount))
dat3 <- dat2 %>%
# Find the last date for each person
filter(Date == max(Date)) %>%
# Add one day to the last date for each person
# Set amount to be 0
mutate(Date = Date + 1, amount = 0)
# Combine data frames
dat4 <- bind_rows(dat2, dat3)
ggplot(dat4, aes(x = Date, y = amount, color = person)) +
geom_point() +
geom_line()
DATA
dat <- read.table(text = "person start_time end_time amount
A '2019-10-04' '2020-04-21' 10
A '2019-12-10' '2020-01-09' 20
B '2019-11-04' '2020-08-21' 30
B '2019-12-10' '2020-01-20' 15
C '2019-12-20' '2020-03-19' 5",
stringsAsFactors = FALSE, header = TRUE)
I feel like this should be an easy task for ggplot, tidyverse, lubridate, but I cannot seem to find an elegant solution.
GOAL: Create a bar graph of my data aggregated/summarized/grouped_by year and month.
#Libraries
library(tidyverse)
library(lubridate)
# Data
date <- sample(seq(as_date('2013-06-01'), as_date('2014-5-31'), by="day"), 10000, replace = TRUE)
value <- rnorm(10000)
df <- tibble(date, value)
# Summarise
df2 <- df %>%
mutate(year = year(date), month = month(date)) %>%
unite(year_month,year,month) %>%
group_by(year_month) %>%
summarise(avg = mean(value),
cnt = n())
# Plot
ggplot(df2) +
geom_bar(aes(x=year_month, y = avg), stat = 'identity')
When I create the year_month variable, it naturally becomes a character variable instead of a date variable. I have also tried grouping by year(date), month(date) but then I can't figure out how to use two variables as the x-axis in ggplot. Perhaps this could be solved by flooring the dates to the first day of the month...?
You were really close. The missing pieces are floor_date() and scale_x_date():
library(tidyverse)
library(lubridate)
date <- sample(seq(as_date('2013-06-01'), as_date('2014-5-31'), by = "day"),
10000, replace = TRUE)
value <- rnorm(10000)
df <- tibble(date, value) %>%
group_by(month = floor_date(date, unit = "month")) %>%
summarize(avg = mean(value))
ggplot(df, aes(x = month, y = avg)) +
geom_bar(stat = "identity") +
scale_x_date(NULL, date_labels = "%b %y", breaks = "month")