how can I make one graph from different dates? For example I have data from 2019 and 2020 and would like to display the results in one chart only for months. How can I limit data from a given time period? I want to have one line for 2019 year and the second line for 2020 year.
Date
Microsoft Teams
2019-01-06
3
2019-03-10
10
2019-06-09
15
2019-12-29
10
2020-01-06
25
2020-03-10
35
2020-06-09
43
2020-12-29
39
On this graph I want to make another line for year 2020. For this I use this command:
ggplot() + geom_line(data=trendy, aes(x=date, y=`Microsoft Teams`), color="blue")
+ labs(title="Popularność wyszukiwania hasła Microsoft Teams", x="Data", y="Popularność", caption = "")
+ scale_x_date(date_labels = "%B", limit=c(as.Date("2019-01-01"),as.Date("2019-12-31")))
Can someone help me if it's possible?
I am not sure which one do you prefer, but here are two options for you.
manipulate data
trendy <- data %>%
mutate(Date = as.Date(Date),
year = year(Date),
date = paste('2000', month(Date), day(Date), sep = '-'),
date = as.Date(date))
plot 1
ggplot(data=trendy, aes(x=Date, y=`Microsoft Teams`, color = year)) +
geom_line() +
labs(title="Popularność wyszukiwania hasła Microsoft Teams", x="Data", y="Popularność", caption = "") +
scale_x_date(date_labels = "%B") +
theme_bw()
plot 2
ggplot(data=trendy, aes(x=date, y=`Microsoft Teams`, color = factor(year))) +
geom_line() +
labs(title="Popularność wyszukiwania hasła Microsoft Teams", x="Data", y="Popularność", caption = "") +
scale_x_date(date_labels = "%B") +
theme_bw()
library(tidyverse)
library(lubridate)
Preparing the data:
dat <- tribble(~Date, ~Teams,
"2019-01-06", 3,
"2019-03-10", 10,
"2019-06-09", 15,
"2019-12-29", 10,
"2020-01-06", 25,
"2020-03-10", 35,
"2020-06-09", 43,
"2020-12-29", 39)
dat <- mutate(dat, Date = parse_date(Date))
The trick is to separate the dates into years and months, and then map years as the colour dimension in the chart:
dat %>%
mutate(years = as.character(year(Date)), months = month(Date, label = TRUE)) %>%
ggplot(aes(x = months, y = Teams, colour = years, group = years)) +
geom_line()
Use lubridate package ymd to extract year and month from date with month, year and
Make both factor with as.factor
Then plot with ggplot
library(tidyverse)
library(lubridate)
df1 <- df %>%
mutate(year = as.factor(year(ymd(Date))),
month = as.factor(month(Date))
)
ggplot(df1, aes(x = month, y = Microsoft.Teams, colour = year, group=year)) +
geom_point()+
geom_line()
Related
Similar to this question: Split up time series per year for plotting which has done in Python, I want to display the daily time series as multiple lines by year. How can I achieve this in R?
library(ggplot2)
library(dplyr)
# Dummy data
df <- data.frame(
day = as.Date("2017-06-14") - 0:364,
value = runif(365) + seq(-140, 224)^2 / 10000
)
# Most basic bubble plot
p <- ggplot(df, aes(x=day, y=value)) +
geom_line() +
xlab("")
p
Out:
One solution is using ggplot2, but date_labels are displayed incorrectly:
library(tidyverse)
library(lubridate)
p <- df %>%
# mutate(date = ymd(date)) %>%
mutate(date=as.Date(date)) %>%
mutate(
year = factor(year(date)), # use year to define separate curves
date = update(date, year = 1) # use a constant year for the x-axis
) %>%
ggplot(aes(date, value, color = year)) +
scale_x_date(date_breaks = "1 month", date_labels = "%b")
# Raw daily data
p + geom_line()
Out:
Alternative solution is to use gg_season from feasts package:
library(feasts)
library(tsibble)
library(dplyr)
tsibbledata::aus_retail %>%
filter(
State == "Victoria",
Industry == "Cafes, restaurants and catering services"
) %>%
gg_season(Turnover)
Out:
References:
Split up time series per year for plotting
R - How to create a seasonal plot - Different lines for years
If you want your x axis to represent the months from January to February, then perhaps getting the yday of the date and adding it to the first of January on a random year would be simplest:
library(tidyverse)
library(lubridate)
df <- data.frame(
day = as.Date("2017-06-14") - 0:364,
value = runif(365) + seq(-140, 224)^2 / 10000
)
df %>%
mutate(year = factor(year(day)), date = yday(day) + as.Date('2017-01-01')) %>%
ggplot(aes(date, value, color = year)) +
geom_line() +
scale_x_date(breaks = seq(as.Date('2017-01-01'), by = 'month', length = 12),
date_labels = '%b')
Created on 2023-02-07 with reprex v2.0.2
I tend to think simple is better:
transform(df, year = format(day, "%Y")) |>
ggplot(aes(x=day, y=value, group=year, color=year)) +
geom_line() +
xlab(NULL)
optionally removing the year legend with + guides(colour = "none").
I am trying to plot daily data with days of the week (Monday:Sunday) on the y-axis, week of the year on the x-axis with monthly labels (January:December), and facet by year with each facet as its own row. I want the week of the year to align between the facets. I also want each tile to be square.
Here is a toy dataset to work with:
my_data <- tibble(Date = seq(
as.Date("1/11/2013", "%d/%m/%Y"),
as.Date("31/12/2014", "%d/%m/%Y"),
"days"),
Value = runif(length(VectorofDates)))
One solution I came up with is to use lubridate::week() to number the weeks and plot by week. This correctly aligns the x-axis between the facets. The problem is, I can't figure out how to label the x-axis with monthly labels.
my_data %>%
mutate(Week = week(Date)) %>%
mutate(Weekday = wday(Date, label = TRUE, week_start = 1)) %>%
mutate(Year = year(Date)) %>%
ggplot(aes(fill = Value, x = Week, y = Weekday)) +
geom_tile() +
theme_bw() +
facet_grid(Year ~ .) +
coord_fixed()
Alternatively, I tried plotting by the first day of the week using lubridate::floor_date and lubridate::round_date. In this solution, the x-axis is correctly labeled, but the weeks don't align between the two years. Also, the tiles aren't perfectly square, though I think this could be fixed by playing around with the coord_fixed ratio.
my_data %>%
mutate(Week = floor_date(Date, week_start = 1),
Week = round_date(Week, "week", week_start = 1)) %>%
mutate(Weekday = wday(Date, label = TRUE, week_start = 1)) %>%
mutate(Year = year(Date)) %>%
ggplot(aes(fill = Value, x = Week, y = Weekday)) +
geom_tile() +
theme_bw() +
facet_grid(Year ~ .) +
scale_x_datetime(name = NULL, labels = label_date("%b")) +
coord_fixed(7e5)
Any suggestions of how to get the columns to align correctly by week of the year while labeling the months correctly?
The concept is a little flawed, since the same week of the year is not guaranteed to fall in the same month. However, you can get a "close enough" solution by using the labels and breaks argument of scale_x_continuous. The idea here is to write a function which takes a number of weeks, adds 7 times this value as a number of days onto an arbitrary year's 1st January, then format it as month name only using strftime:
my_data %>%
mutate(Week = week(Date)) %>%
mutate(Weekday = wday(Date, label = TRUE, week_start = 1)) %>%
mutate(Year = year(Date)) %>%
ggplot(aes(fill = Value, x = Week, y = Weekday)) +
geom_tile() +
theme_bw() +
facet_grid(Year ~ .) +
coord_fixed() +
scale_x_continuous(labels = function(x) {
strftime(as.Date("2000-01-01") + 7 * x, "%B")
}, breaks = seq(1, 52, 4.2))
Another option if you're sick of reinventing the wheel is to use the calendarHeat function in the Github-only makeR package:
install_github("jbryer/makeR")
library(makeR)
calendarHeat(my_data$Date, my_data$Value)
I am plotting router statistics (collected from merlin speed monitoring tool).
The graphs are faceted by year-month, and I want each month's x axis to expand to the entire month, even when I only have part of a months data.
In the example below, the data for January 2022 is incomplete (just 6 hours or
so of data).
The code I have tried:
library(tidyverse)
library(scales)
X.df <- read.csv(url("https://pastebin.com/raw/sGAzEDe6")) %>%
mutate(date = as.POSIXct(date, origin="1970-01-01"))
ggplot(X.df , aes(date, Download, colour = Download)) +
geom_line()+
facet_wrap(~ month, scale="free_x", ncol = 1) +
scale_colour_gradient(low="red",high="green", limits=c(0.0, 50), oob = squish) +
scale_x_datetime(date_labels = "%d/%m", breaks = "7 day", minor_breaks = "1 day") +
coord_cartesian(ylim = c(0, 60))
Again, I want the range of the x axis in each facet to cover the entire month. Thus, I want the X axis for the 2021-12 facet to run from 1st Dec 2021 to 31st Dec 2021, and the X axis for the 2022-01 facet to run from 1st Jan 2022 to 31st Jan 2022.
Is there some way of forcing this within ggplot2?
An additional, smaller self-contained example to try your code on:
X.df <- tribble(
~date, ~month, ~Download,
"2021-12-01T00:30:36Z","2021-12",20.13,
"2021-12-07T06:30:31Z","2021-12",38.95,
"2021-12-14T08:00:31Z","2021-12",38.44,
"2021-12-21T09:30:29Z","2021-12",28.57,
"2021-12-28T16:00:31Z","2021-12",30.78,
"2021-12-31T13:00:28Z","2021-12",55.45,
"2022-01-01T00:00:28Z","2022-1",55.44,
"2022-01-01T02:30:29Z","2022-1",55.63,
"2022-01-01T03:00:29Z","2022-1",55.75,
"2022-01-01T05:00:29Z","2022-1",55.8,
"2022-01-07T03:00:29Z","2022-1",53.6,
"2022-01-07T05:00:29Z","2022-1",51.8
)
As always, thanks in advance. Pete
Updat II: Removed prior versions:
In your database there is only one january 2022 date
in the dataframe we complete the dates of januare of 2022 using complete from tidyr package.
library(tidyverse)
library(lubridate)
X.df %>%
mutate(date = ymd(date)) %>%
group_by(month(date)) %>%
complete(date = seq(min(date), max(ceiling_date(date, unit = "month") - ddays(1)), by = 'day')) %>%
fill(month) %>%
ggplot(aes(x = date, Download, colour = Download)) +
geom_line()+
facet_wrap(~ month, scale="free_x", ncol = 1) +
scale_colour_gradient(low="red",high="green", limits=c(0.0, 50), oob = squish) +
scale_x_date(date_breaks = "1 day", date_labels = "%d/%m", expand = c(0, 0)) +
coord_cartesian(ylim = c(0, 60))
I am trying to do a faceted plot of a grouped dataframe with ggplot2, using geom_line(). My dataframe has a Date column and I would like to have dates on the horizontal axis. If I just use Date in aes(x=Date, ...) I get nice labels on the horizontal axis. However, the line has an almost horizontal section where the date jumps from the end of one group to the beginning of the next group. This code and chart shows that:
dts <- seq.Date(as.Date("2020-01-01"), as.Date("2021-12-31"), by="day")
mos <- sapply(dts, month)
df <- data.frame(Date=dts, Month=mos)
nr <- nrow(df)
df$X <- rep(1, nr)
df %>%
group_by(Month) -> dfgrp
dfgrp %>%
group_by(Month) %>%
mutate(Time = Date[1:n()],
Z = cumsum(X)) %>%
ggplot(aes(x=Date, y=Z)) +
geom_line(color="darkgreen", size=0.5) +
facet_grid(. ~ Month, scale="free_x") +
theme(axis.text.x = element_text(angle=45, size=7))
I would not like my chart to have those almost-horizontal lines when the date changes by a large amount. I was able to generate a chart without those lines using integers on aes() as follows:
dfgrp %>%
mutate(Time = 1:n() %>% as.integer(),
Z = cumsum(X)) %>%
ggplot(aes(x=Time, y=Z)) +
geom_line(color="darkgreen", size=0.5) +
facet_grid(. ~ Month, scale="free_x") +
scale_x_continuous(breaks = seq(from=1, to=nr, by=10) %>% as.integer(),
labels = function(x) as.character(dfgrp$Date[x])) +
theme(axis.text.x = element_text(angle=45, size=7))
The line on the chart looks like I want it but the dates on the horizontal axis are not correct: they end in February 2020 in every facet while the dates in the dataframe end in December 2021 and the dates in the first chart begin and end on different months in different facets.
I tried many things but nothing worked. Any suggestions on how to have a chart with dates like in the first chart above and lines like in the second chart above?
Help will be much appreciated.
You may want to adjust the dates to be in the same year, but noting the original year as a variable:
library(lubridate)
dfgrp %>%
group_by(Month) %>%
mutate(year = year(Date),
adj_date = ymd(paste(2020, month(Date), day(Date)))) %>%
# 2020 was leap year so 2/29 won't be lost
mutate(Time = Date[1:n()],
Z = cumsum(X)) %>%
ggplot(aes(x=adj_date, y=Z, color = year, group = year)) +
geom_line(size=0.5) +
facet_grid(. ~ Month, scale="free_x") +
theme(axis.text.x = element_text(angle=45, size=7))
I have a data frame like this (in date order):
freq date
3 Jan-18
2 Feb-18
42 Mar-18
2 Apr-18
4 May-18
However, when I plot this with the following code, it doesn't order by the order saved in the data frame. Instead it plots them in alphabetical order (see x-axis). How can this be fixed so that the plot is done in the order saved in the data frame?
Note that the date column is of type character which is likely why, but changing this to date format is tricky since there is no day, and when you do so it changes e.g. Jun-18 to 01-1918-06, which doesn't look nice on a graph. So, I'm trying to do this without changing it to date format if possible.
ggplot(df, aes(x = date, y = freq)) +
geom_point()
1) Assuming the data shown reproducibly in the Note at the end convert the data to a zoo series with yearmon index (which can represent a year and month without a day) in which case it is straight-forward using autoplot.zoo . Omit the geom argument if you want a line plot.
library(ggplot2)
library(zoo)
z <- read.zoo(df, index = "date", FUN = as.yearmon, format = "%b-%y")
autoplot(z, geom = "point") + scale_x_yearmon()
2) This also works:
library(dplyr)
library(ggplot2)
library(zoo)
df %>%
mutate(date = as.yearmon(date, format = "%b-%y")) %>%
ggplot(aes(date, freq)) + geom_point() + scale_x_yearmon()
Note
Lines <- "
freq date
3 Jan-18
2 Feb-18
42 Mar-18
2 Apr-18
4 May-18"
df <- read.table(text = Lines, header = TRUE)
Another way and if data is showed as in example could be:
library(dplyr)
#Code
df %>%
mutate(date=factor(date,levels = unique(date),ordered = T)) %>%
ggplot(aes(x=date,y=freq))+
geom_point()
Output:
Or formatting the date variable:
#Code2
df %>%
mutate(date=as.Date(paste0(date,'-01'),'%b-%y-%d')) %>%
ggplot(aes(x=date,y=freq))+
geom_point()+
scale_x_date(date_labels = '%b-%y')+
ggtitle('My title')
Output:
Some data used:
#Data
structure(list(freq = c(3L, 2L, 42L, 2L, 4L), date = c("Jan-18",
"Feb-18", "Mar-18", "Apr-18", "May-18")), class = "data.frame", row.names = c(NA,
-5L))
If you don't want to rely on the zoo package, you could simply pick a year (e.g. 2021) and the conversion of the date column in your example works fine. You can then specify how the date is displayed in ggplot2's scale_x_date(). Here is how it looks like.
library(ggplot2)
df <- read.table(header = T, text = "
freq date
3 Jan-18
2 Feb-18
42 Mar-18
2 Apr-18
4 May-18")
df$date <- as.Date(paste0(df$date, "-2021"), format = "%B-%d-%Y")
ggplot(df, aes(date, y = freq)) +
geom_point() +
theme_bw() +
labs(x = "Date", y = "Frequency") +
scale_x_date(date_breaks = "2 weeks", date_labels = "%d-%b") +
theme(axis.text.x = element_text(angle = 45, vjust = 0.5))