I'm trying to make a simple Date * temperature heatmap (?raster graph?) that shows temperature over time based on binned temperature groups. Something like this but only along the date axis (no time variable. I'd prefer to use ggplot, but keep runnning astray. The graph the data produces is headed in the right direction, but I can't figure out how to get rid of the y-axis. I'd appreciate any help
dat <- data.frame(temp = sample(20,100, replace=TRUE), date=seq(as.Date("2011-07-01"), by=1, len=100))
p <- ggplot(dat, aes(date, temp)) + geom_tile(aes(fill = temp)) + scale_fill_gradient(low = "blue", high = "red")
Thanks!
So you don't want to map temp on the y axis?
Well then you could use a fixed value for y and remove the rest of the y-axis:
dat <- data.frame(temp = sample(20,100, replace=TRUE),
date=seq(as.Date("2011-07-01"), by=1, len=100))
require(ggplot2)
ggplot(dat, aes(x = date, y = 1)) +
geom_tile(aes(fill = temp)) +
scale_fill_gradient(low = "blue", high = "red") +
labs(y = NULL) +
scale_y_continuous(breaks = NULL)
You could also try doing something like the plot below with the metvurst package.
http://i.imgur.com/8Js1Uz7.png
dat <- data.frame(temp = sample(20,60, replace=TRUE),
date=seq(as.POSIXct("2011-01-01 00:00"), by=3600, len=8760))
dat$year <- as.numeric(format(dat$date,"%Y"))
dat$month <- as.numeric(format(dat$date,"%m"))
# Install and load metvurst library
install_github('metvurst', 'tim-salabim')
library(metvurst)
plot.air.temp <- strip(x = dat$temp,
date = dat$date,
cond = dat$year,
arrange = "long",
colour = colorRampPalette(rev(brewer.pal(11, "Spectral"))),
main = "Daily Air Temperatures\n\nTemperature [°C]")
plot.air.temp
Related
I have a data set of Standardized Precipitation Index values from 1980 to 2005. There is one value for each month, so altogether there are 312 (26 years * 12 months) values. The SPI values range between -3 and +3. Here is an easy reproducible example, since the exact values are not important for my question:
vec1 <- rep(seq(1980, 2005), each= 12)
vec2 <- sample(x = -3:3, size = 312, replace = TRUE)
df <- data.frame(vec1, vec2)
colnames(df) <- c("Year", "SPI")
Now I would like to plot the SPI values with the years being the x-axis.
When I try to plot it using ggplot2:
ggplot() +
geom_line(aes(x=df$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$SPI >= 0,],
fill = "blue", color="blue", width= 1) +
scale_x_date(breaks = "5 years", date_labels = "%Y",
limits = c(as.Date("1979-12-01"),
as.Date("2006-01-01")),
expand = c(0,0))
I'm really new to R and I'm trying to plot data from air polution with NOx from 5 different locations (having a data of monthly averages from every location from 01-1996 to 12-2019). Each plot line should represent different location.
I've created a ggplot but I find it really unclear. I would like to ask you about your tips to make that plot better to read (It will be no bigger than A4, because it will be included in my work and printed). I would also like to have more years on X axis (1996, 1997, 1998)
ALIBA <- read_csv("ALIBA_Praha/NOx/all_sorted.csv")
BMISA <- read_csv("BMISA_Mikulov/NOx/all_sorted.csv")
CCBDA <- read_csv("CCBDA_CB/NOx/all_sorted.csv")
TKARA <- read_csv("TKARA_Karvina/NOx/all_sorted.csv")
UULKA <- read_csv("UULKA_UnL/NOx/all_sorted.csv")
ggplot() +
geom_line(data = ALIBA, aes(x = START_TIME, y = VALUE), color = "blue") +
geom_line(data = BMISA, aes(x = START_TIME, y = VALUE), color = "red") +
geom_line(data = CCBDA, aes(x = START_TIME, y = VALUE), color = "yellow") +
geom_line(data = TKARA, aes(x = START_TIME, y = VALUE), color = "green") +
geom_line(data = UULKA, aes(x = START_TIME, y = VALUE), color = "pink")
all csv files are in format:
START_TIME,VALUE
1996-01-01T00:00:00Z,61.3049451304964
1996-02-01T00:00:00Z,47.7234010245664
1996-03-01T00:00:00Z,33.083512309072
1996-04-01T00:00:00Z,47.771166691758
1996-05-01T00:00:00Z,24.7022422574005
1996-06-01T00:00:00Z,25.4495954480684
1996-07-01T00:00:00Z,23.301224242488
...
Thanks
First, I would paste all data sets together:
ALIBA <- read_csv("ALIBA_Praha/NOx/all_sorted.csv")
ALIBA$Location <- "ALIBA" # and so on
BMISA <- read_csv("BMISA_Mikulov/NOx/all_sorted.csv")
CCBDA <- read_csv("CCBDA_CB/NOx/all_sorted.csv")
TKARA <- read_csv("TKARA_Karvina/NOx/all_sorted.csv")
UULKA <- read_csv("UULKA_UnL/NOx/all_sorted.csv")
df <- rbind(ALIBA, BMISA, ...) # and so on
ggplot(data = df, aes(x = START_TIME, y = VALUE, color = Location) +
geom_line(size = 1) + # play with the stroke thickness
scale_color_brewer(palette = "Set1") + # here you can choose from a wide variety of palettes, just google
How would you like to add more years? In the same graph (everything will be tiny) or in seperate "windows" (= facets, better)?
I have measurement data from the past months:
Variables
x <- df$DatoTid
y <- df$Partikler
color <- df$Opgave
I'm trying to plot my data based on the timestamp, so that I have the hours of the day in the x-axis, instead of the specific POSIXct datetime.
I would like the labels and ticks of the x-axis to be fx "00:00", "01:00",..."24:00".
So that noon is in the middle of the x-axis.
So far I tried to convert the datetime values into characters.
Doesn't look good yet (as you can see the axis ticks and labels are gone. Possibly other things are wrong as well).
Can someone help me?
And please let me know how to upload the data for you. I don't know how to add a huge .csv-file....
# Rounding up to nearest 10 min:
head(df)
df$Tid2 <- format(strptime("1970-01-01", "%Y-%m-%d", tz="CET") +
round(as.numeric(df$DatoTid)/300)*300 + 3600, "%Y-%m-%d %H:%M:%S")
head(df)
df$Tid2 <- as.character(df$Tid2)
str(df)
x <- df$Tid2
y <- df$Partikler
color <- df$Opgave
plot2 <- ggplot(data = df, aes(x = x, y = y, color = color)) +
geom_point(shape=16, alpha=0.6, size=1.8) +
scale_y_continuous(labels=function(x) format(x, big.mark = ".", decimal.mark = ",", scientific = FALSE)) +
scale_x_discrete(breaks=c("00:00:00", "06:00:00", "09:00:00", "12:00:00", "18:00:00", "21:00:00")) +
scale_color_discrete(name = "Case") +
xlab(" ") +
ylab(expression(paste("Partikelkoncentration [pt/cc]"))) +
myTheme +
theme(legend.text=element_text(size=8), legend.title=element_text(size=8))
plot2
I would approach this by making a new time stamp that uses a single day, but the hours/minutes/seconds of your existing time stamp.
First, here's a made-up version of your data, here using a linear trend in Partikler:
library(tidyverse); library(lubridate)
df <- data_frame(Tid2 = seq.POSIXt(from = ymd_h(2019010100),
to = ymd_h(2019011500), by = 60*60),
Partikler = seq(from = 0, to = 2.5E5, along.with = Tid2),
Opgave = as.factor(floor_date(Tid2, "3 days")))
# Here's a plot that's structurally similar to yours:
ggplot(df, aes(Tid2, Partikler, col = Opgave)) +
geom_point() +
scale_color_discrete(name = "Case")
Now, if we change the timestamps to be in the same day, we can control them like usual in ggplot, but with them collapsed into a single day of timing. We can also change the x axis so it doesn't mention the date component of the time stamp:
df2 <- df %>%
mutate(Tid2_sameday = ymd_hms(paste(Sys.Date(),
hour(Tid2), minute(Tid2), second(Tid2))))
ggplot(df2, aes(Tid2_sameday, Partikler, col = Opgave)) +
geom_point() +
scale_color_discrete(name = "Case") +
scale_x_datetime(date_labels = "%H:%M")
I need to plot hourly data for different days using ggplot, and here is my dataset:
The data consists of hourly observations, and I want to plot each day's observation into one separate line.
Here is my code
xbj1 = bj[c(1:24),c(1,6)]
xbj2 = bj[c(24:47),c(1,6)]
xbj3 = bj[c(48:71),c(1,6)]
ggplot()+
geom_line(data = xbj1,aes(x = Date, y= Value), colour="blue") +
geom_line(data = xbj2,aes(x = Date, y= Value), colour = "grey") +
geom_line(data = xbj3,aes(x = Date, y= Value), colour = "green") +
xlab('Hour') +
ylab('PM2.5')
Please advice on this.
I'll make some fake data (I won't try to transcribe yours) first:
set.seed(2)
x <- data.frame(
Date = rep(Sys.Date() + 0:1, each = 24),
# Year, Month, Day ... are not used here
Hour = rep(0:23, times = 2),
Value = sample(1e2, size = 48, replace = TRUE)
)
This is a straight-forward ggplot2 plot:
library(ggplot2)
ggplot(x) +
geom_line(aes(Hour, Value, color = as.factor(Date))) +
scale_color_discrete(name = "Date")
ggplot(x) +
geom_line(aes(Hour, Value)) +
facet_grid(Date ~ .)
I highly recommend you find good tutorials for ggplot2, such as http://www.cookbook-r.com/Graphs/. Others exist, many quite good.
Here is my sample data:
Singer <- c("A","B","C","A","B","C")
Rank <- c(1,2,3,3,2,1)
Episode <- c(1,1,1,2,2,2)
Votes <- c(0.3,0.28,0.11,0.14,0.29,0.38)
data <- data_frame(Episode,Singer,Rank,Votes)
data$Episode <- as.character(data$Episode)
I would like to make a line graph to show the performance of each singer.
I tried to use ggplot2 like below:
ggplot(data,aes(x=Episode,y=Votes,group = Singer)) + geom_line()
I have two questions:
How can I format the y-axis as percentage?
How can I label each dot in this line graph as the values of "Rank", which allows me to show rank and votes in the same graph?
To label each point use:
geom_label(aes(label = Rank))
# or
geom_text(aes(label = Rank), nudge_y = .01, nudge_x = 0)
To format the axis labels use:
scale_y_continuous(labels = scales::percent_format())
# or without package(scales):
scale_y_continuous(breaks = (seq(0, .4, .2)), labels = sprintf("%1.f%%", 100 * seq(0, .4, .2)), limits = c(0,.4))
Complete code:
library(ggplot2)
library(scales)
ggplot(data, aes(x = factor(Episode), y = Votes, group = Singer)) +
geom_line() +
geom_label(aes(label = Rank)) +
scale_y_continuous(labels = scales::percent_format())
Data:
Singer <- c("A","B","C","A","B","C")
Rank <- c(1,2,3,3,2,1)
Episode <- c(1,1,1,2,2,2)
Votes <- c(0.3,0.28,0.11,0.14,0.29,0.38)
data <- data_frame(Episode,Singer,Rank,Votes)
# no need to transform to character bc we use factor(Episode) in aes(x=..)