I am trying to develop a weather plot like that appears in weather data - something like.
I want to plot daily value (although average value can appear in circle). I am using ggplot2 as it need multifaceted (for each month and year).
st <- as.Date ("2009-1-1")
en <- as.Date ("2011-12-28")
date1 <- seq(st, en, "1 day")
year <- format(date1, "%Y")
month <- format (date1, "%b")
day <- as.numeric (format(date1, "%d"))
avgtm <- round (rnorm (length(date1), 50,5), 1)
maxtm <- avgtm + abs(rnorm (length (avgtm), 0, 5))
mintm <- avgtm - abs(rnorm (length (avgtm), 0, 5))
myd <- data.frame ( year, month, day, avgtm, maxtm, mintm)
require(ggplot2)
qplot(day, avgtm, data = myd, geom = "line", col = "red") +
facet_grid(year ~ month) + theme_bw()
There is one major problem here, line will connect between months.
Each month is plotted to maximum (although one month can end in 28, leaving blank at the month).
Is there a smart way to achieve what I want to achieve. I tried ggplot2 but there might be other nice options.
Edit:
I am trying to add vertical line at the first day of month to demark the months. Here is I tried to find the first day of month:
td = as.Date (seq(as.Date("2009/1/1"), as.Date("2011/12/28"), "months"))
I tried to use this to plot line:
qplot(date, avgtm, data = myd, geom = "line", col = "red") +
facet_wrap(~year, scales='free_x', ncol=1, nrow=3) +
geom_vline(xintercept=td, linetype="dotted") + theme_bw()
But running an error:
Error : Invalid intercept type: should be a numeric vector, a function, or a name of a function
How can plot the vertical line with the date ?
There is a solution with panel.xblocks from latticeExtra:
st <- as.Date("2009-1-1")
en <- as.Date("2011-12-28")
date1 <- seq(st, en, "1 day")
avgtm <- round (rnorm (length(date1), 50,5), 1)
myd <- data.frame(date1, avgtm)
I define two functions to extract month and year values instead of
including them in the data.frame. This approach is useful with
panel.xblocks in the panel function of xyplot:
month <- function(x)format(x, '%m')
year <- function(x)format(x, '%Y')
I use year(date1) as conditioning variable to produce three
panels. Each of these panels will display the time series for that
year (panel.xyplot) and a sequence of contiguous blocks with
alternating colors to highlight months (panel.xblocks). You
should note that the y argument in panel.xblocks is the
function month previously defined:
xyplot(avgtm ~ date1 | year(date1), data=myd,
type='l', layout=c(1, 3),
scales=list(x=list(relation='free')),
xlab='', ylab='',
panel=function(x, y, ...){
panel.xblocks(x, month,
col = c("lightgray", "white"),
border = "darkgray")
panel.xyplot(x, y, lwd = 1, col='black', ...)
})
How about making a date column, then faceting on year only
myd$date <- as.Date(paste(myd$year, myd$month, myd$day), format='%Y %b %d')
qplot(date, avgtm, data = myd, geom = "line", col = "red") +
facet_wrap(~year, scales='free_x', ncol=1, nrow=3)
You could add scales='free_x' to your plot as well, but will find it makes interpretation difficult.
By faceting on month and year you are telling the viewer and the plotting tool that the variables plotted are not continuous. This is incorrect as you've pointed out in your question. Thus, no faceting... You can add tick marks for each month or each day if you want.
library(scales)
qplot(date, avgtm, data = myd, geom = "line", col = "red") +
facet_wrap(~year, scales='free_x', ncol=1, nrow=3) +
scale_x_date(breaks=date_breaks("month"), labels=date_format("%b"))
Alternatively you could extract day of year and plot everything on one plot, coloring by year:
myd$doy <- format(myd$date, '%j')
p <- ggplot(myd, aes(x=doy, y=avgtm, color=year, group=year))
p + geom_line()
or
p + geom_smooth()
Related
I have a data set of Standardized Precipitation Index values from 1980 to 2005. There is one value for each month, so altogether there are 312 (26 years * 12 months) values. The SPI values range between -3 and +3. Here is an easy reproducible example, since the exact values are not important for my question:
vec1 <- rep(seq(1980, 2005), each= 12)
vec2 <- sample(x = -3:3, size = 312, replace = TRUE)
df <- data.frame(vec1, vec2)
colnames(df) <- c("Year", "SPI")
Now I would like to plot the SPI values with the years being the x-axis.
When I try to plot it using ggplot2:
ggplot() +
geom_line(aes(x=df$Year, y=df$SPI))
Something like this comes out:
So the problem is, there is no continuous line.
I can plot it with a continuous line with Base R for example:
plot(vec2, type="l")
But then the problem is that the x-axis only shows the values 1:312 and I need the years as the x-values.
Anybody with a hint?
EDIT after the answer of marcguery:
It turned out that I cannot use a line plot for my purpose. Instead, I need to do a column plot with many single columns when using ggplot2 since I need to color the areas above/below zero.
marcguery's answer works for a geom_line() plot, but unfortunately not for a geom_col() plot. I have no idea why.
Here is the modified code:
vec1 <- seq(as.Date("1980-01-01"),
by = "month",
to = as.Date("2005-12-01"))
vec2 <- sample(x = -3:3, size = 312, replace = TRUE)
vec3 <- 1:312
df <- data.frame(vec1, vec2, vec3)
colnames(df) <- c("Date", "SPI", "ID")
library(data.table)
df <- as.data.table(df)
This is what unfortunately does not work with the dates as x-axis, there is a strange output:
library(ggplot2)
# with Date as x-axis
ggplot(data= df, aes(x= Date, y= SPI, width= 1)) +
geom_col(data = df[SPI <= 0], fill = "red") +
geom_col(data = df[SPI >= 0], fill = "blue") +
theme_bw()
This is what works with the simple rownumber as x-axis:
# with ID as x-axis
ggplot(data= df, aes(x= ID, y= SPI, width= 1)) +
geom_col(data = df[SPI <= 0], fill = "red") +
geom_col(data = df[SPI >= 0], fill = "blue") +
theme_bw()
I need something like the last example, just with the dates as the x-axis.
Your observations per month of each year have all the same value in your column Year, hence why ggplot cannot assign them different x values. Since you are working with dates, you could use Date format for your time points so that each month is assigned a different value.
#Seed for reproducibility
set.seed(123)
#Data
vec1 <- seq(as.Date("1980-01-01"),
by = "month",
to = as.Date("2005-12-01"))
vec2 <- sample(x = -3:3, size = 312, replace = TRUE)
df <- data.frame(vec1, vec2)
colnames(df) <- c("Date", "SPI")
#Plot
library(ggplot2)
ggplot(df) +
geom_line(aes(x = Date, y = SPI))+
scale_x_date(breaks = "5 years", date_labels = "%Y",
limits = c(as.Date("1979-12-01"),
as.Date("2006-01-01")),
expand = c(0,0))
Edit after you added your question about coloring the area between your values and 0 based on the sign of the values:
You can definitely use a geom_line plot for that purpose. Using a geom_col plot is a possibility but you would loose visual information about change between your x variables which are continuously related as they represent dates.
To plot a nice geom_line, I will base my approach on the answer here https://stackoverflow.com/a/18009173/14027775. You will have to adapt your data by transforming your dates to numerical values, for instance number of days since a given date (typically 1970/01/01).
#Colored plot
#Numerical format for dates (number of days after 1970-01-01)
df$numericDate <- difftime(df$Date,
as.Date("1970-01-01", "%Y-%m-%d"),
units="days")
df$numericDate <- as.numeric(df$Date)
rx <- do.call("rbind",
sapply(1:(nrow(df)-1), function(i){
f <- lm(numericDate~SPI, df[i:(i+1),])
if (f$qr$rank < 2) return(NULL)
r <- predict(f, newdata=data.frame(SPI=0))
if(df[i,]$numericDate < r & r < df[i+1,]$numericDate)
return(data.frame(numericDate=r,SPI=0))
else return(NULL)
}))
#Get back to Date format
rx$Date <- as.Date(rx$numericDate, origin = "1970-01-01")
d2 <- rbind(df,rx)
ggplot(d2,aes(Date,SPI)) +
geom_area(data=subset(d2, SPI<=0), fill="red") +
geom_area(data=subset(d2, SPI>=0), fill="blue") +
geom_line()+
scale_x_date(breaks = "5 years", date_labels = "%Y",
limits = c(as.Date("1979-12-01"),
as.Date("2006-01-01")),
expand = c(0,0))
Now if you want to keep using geom_col, the reason why you don't see all the bars using dates for the x axis is that they are too thin to be filled as they represent one single day over a long period of time. By filling and coloring them, you should be able to see all of them.
ggplot(data= df, aes(x= Date, y= SPI)) +
geom_col(data = df[df$SPI <= 0,],
fill = "red", color="red", width= 1) +
geom_col(data = df[df$SPI >= 0,],
fill = "blue", color="blue", width= 1) +
scale_x_date(breaks = "5 years", date_labels = "%Y",
limits = c(as.Date("1979-12-01"),
as.Date("2006-01-01")),
expand = c(0,0))
I need to visualize and compare the difference in two equally long sales periods. 2018/2019 and 2019/2020. Both periods begin at week 44 and end at week 36 of the following year. If I create a graph, both periods are continuous and line up. If I use only the week number, the values are sorted as continuum and the graph does not make sense. Can you think of a solution?
Thank You
Data:
set.seed(1)
df1 <- data.frame(sells = runif(44),
week = c(44:52,1:35),
YW = yearweek(seq(as.Date("2018-11-01"), as.Date("2019-08-31"), by = "1 week")),
period = "18/19")
df2 <- data.frame(sells = runif(44),
week = c(44:52,1:35),
YW = yearweek(seq(as.Date("2019-11-01"), as.Date("2020-08-31"), by = "1 week")),
period = "19/20")
# Yearweek on x axis, when both period are separated
ggplot(df1, aes(YW, sells)) +
geom_line(aes(color="Period 18/19")) +
geom_line(data=df2, aes(color="Period 19/20")) +
labs(color="Legend text")
# week on x axis when weeks are like continuum and not splited by year
ggplot(df1, aes(week, sells)) +
geom_line(aes(color="Period 18/19")) +
geom_line(data=df2, aes(color="Period 19/20")) +
labs(color="Legend text")
Another alternative is to facet it. This'll require combining the two sets into one, preserving the data source. (This is commonly a better way of dealing with it in general, anyway.)
(I don't have tstibble, so my YW just has seq(...), no yearweek. It should translate.)
ggplot(dplyr::bind_rows(tibble::lst(df1, df2), .id = "id"), aes(YW, sells)) +
geom_line(aes(color = id)) +
facet_wrap(id ~ ., scales = "free_x", ncol = 1)
In place of dplyr::bind_rows, one might also use data.table::rbindlist(..., idcol="id"), or do.call(rbind, ...), though with the latter you will need to assign id externally.
One more note: the default formatting of the x-axis is obscuring the "year" of the data. If this is relevant/important (and not apparent elsewhere), then use ggplot2's normal mechanism for forcing labels, e.g.,
... +
scale_x_date(labels = function(z) format(z, "%Y-%m"))
While unlikely that you can do this without having tibble::lst available, you can replace that with list(df1=df1, df2=df2) or similar.
If you want to keep the x axis as a numeric scale, you can do:
ggplot(df1, aes((week + 9) %% 52, sells)) +
geom_line(aes(color="Period 18/19")) +
geom_line(data=df2, aes(color="Period 19/20")) +
scale_x_continuous(breaks = 1:52,
labels = function(x) ifelse(x == 9, 52, (x - 9) %% 52),
name = "week") +
labs(color="Legend text")
Try this. You can format your week variable as a factor and keep the desired order. Here the code:
library(ggplot2)
library(tsibble)
#Data
df1$week <- factor(df1$week,levels = unique(df1$week),ordered = T)
df2$week <- factor(df2$week,levels = unique(df2$week),ordered = T)
#Plot
ggplot(df1, aes(week, sells)) +
geom_line(aes(color="Period 18/19",group=1)) +
geom_line(data=df2, aes(color="Period 19/20",group=1)) +
labs(color="Legend text")
Output:
I am practicing with R and have hit a speedbump while trying to create a graph of airline passengers per month.
I want to show a separate monthly line graph for each year from 1949 to 1960 whereby data has been recorded. To do this I have used ggplot to create a line graph with the values per month. This works fine, however when I try to separate this by year using facet_wrap() and formatting the current month field: facet_wrap(format(air$month[seq(1, length(air$month), 12)], "%Y")); it returns this:
Graph returned
I have also tried to format the facet by inputting my own sequence for the years: rep(c(1949:1960), each = 12). This returns a different result which is better but still wrong:
Second graph
Here is my code:
air = data.frame(
month = seq(as.Date("1949-01-01"), as.Date("1960-12-01"), by="months"),
air = as.vector(AirPassengers)
)
ggplot(air, aes(x = month, y = air)) +
geom_point() +
labs(x = "Month", y = "Passengers (in thousands)", title = "Total passengers per month, 1949 - 1960") +
geom_smooth(method = lm, se = F) +
geom_line() +
scale_x_date(labels = date_format("%b"), breaks = "12 month") +
facet_wrap(format(air$month[seq(1, length(air$month), 12)], "%Y"))
#OR
facet_wrap(rep(c(1949:1960), each = 12))
So how do I make an individual graph per year?
Thanks!
In the second try you were really close. The main problem with the data is that you are trying to make a facetted plot with different x-axis values (dates including the year). An easy solution to fix that would be to transform the data to a "common" x axis scale and then do the facetted plot. Here is the code that should output the desired plot.
library(tidyverse)
library(lubridate)
air %>%
# Get the year value to use it for the facetted plot
mutate(year = year(month),
# Get the month-day dates and set all dates with a dummy year (2021 in this case)
# This will get all your dates in a common x axis scale
month_day = as_date(paste(2021,month(month),day(month), sep = "-"))) %>%
# Do the same plot, just change the x variable to month_day
ggplot(aes(x = month_day,
y = air)) +
geom_point() +
labs(x = "Month",
y = "Passengers (in thousands)",
title = "Total passengers per month, 1949 - 1960") +
geom_smooth(method = lm,
se = F) +
geom_line() +
# Set the breaks to 1 month
scale_x_date(labels = scales::date_format("%b"),
breaks = "1 month") +
# Use the year variable to do the facetted plot
facet_wrap(~year) +
# You could set the x axis in an 90° angle to get a cleaner plot
theme(axis.text.x = element_text(angle = 90,
vjust = 0.5,
hjust = 1))
I have a time series of monthly data for 10 years:
myts <- ts(rnorm(12*10), frequency = 12, start = 2001)
Now, I'd like to plot the data but with the x-axis restricted to a range/ticks from Jan - Dec (generic year). Thus, the whole time series should be broken in ten lines where each line starts at Jan and ends at Dec. So multiple lines should be overplotted each other which I'd like to use to visually compare different years. Is there a straight forward command to do that in R?
So far I came up with following solution using matplot which might not be the most sophisticated one:
mydf <- as.data.frame(matrix(myts, 12))
matplot(mydf,type="l")
Or even better would be a way to calculate an average value and the corresponding CI/standard deviation for each month and plot then the average from Jan - Dec as a line and the corresponding CI/standard deviation as a band around the line for the average.
Consider using ggplot2.
library(ggplot2)
library(ggfortify)
d <- fortify(myts)
d$year <- format(d$Index, "%Y")
d$month <- format(d$Index, "%m")
It's useful to start by reshaping the ts object into a long dataframe. Given the dataframe, it's straightforward to create the plots you have in mind:
ggplot(d, aes(x = month, y = Data, group = year, colour = year)) +
geom_line()
ggplot(d, aes(x = month, y = Data, group = month)) +
stat_summary(fun.data = mean_se, fun.args = list(mult = 1.96))
Result:
You can also summarise the data yourself, then plot it:
d_sum <- do.call(rbind, (lapply(split(d$Data, d$month), mean_se, mult = 1.96)))
d_sum$month <- rownames(d_sum)
ggplot(d_sum, aes(x = month, y = y, ymin = ymin, ymax = ymax)) +
geom_errorbar() +
geom_point() +
geom_line(aes(x = as.numeric(month)))
Result:
I want the x-axis in the following graph to start at 06:00 and end at 22:00, with breaks at every 4 hours. I can't figure out the following, however.
a) How to make the x-axis start at 06:00 without any empty space before 06:00.
b) How to make the x-axis end at 22:00 without any empty space after 22:00. Right now it doesn't even show 22:00
c) How to have breaks at every 4 hours.
d) How to assign a label to the y-axis (currently it's simply X4, the column name).
I've tried several things, but without success. Some example data:
range <- seq(as.POSIXct("2015/4/18 06:00"),as.POSIXct("2015/4/18 22:00"),"mins")
df <- data.frame(matrix(nrow=length(range),ncol=4))
df[,1] <- c(1:length(range))
df[,2] <- 2*c(1:length(range))
df[,3] <- 3*c(1:length(range))
df[,4] <- range
Reshape:
library(reshape2)
df2 <- melt(df,id="X4")
Graph:
library(ggplot2)
ggplot(data=df2,aes(x=X4,y=value,color=variable)) + geom_line()+
scale_y_continuous(expand=c(0,0)) +
coord_cartesian(xlim=c(as.POSIXct("2015/4/18 06:00:00"),as.POSIXct("2015/4/18 22:00:00")))
Which makes the graph look like this:
Any ideas?
Here is some code that should help you. This can easily be done using scale_x_datetime.
## desired start and end points
st <- as.POSIXct("2015/4/18 06:00:00")
nd <- as.POSIXct("2015/4/18 22:00:00")
## display data for given time range
ggplot(data = df2, aes(x = X4, y = value, color = variable)) +
geom_line() +
scale_y_continuous("Some name", expand = c(0, 0)) +
scale_x_datetime("Some name", expand = c(0, 0), limits = c(st, nd),
breaks = seq(st, nd, "4 hours"),
labels = strftime(seq(st, nd, "4 hours"), "%H:%S"))