I want to make bar charts where the bar minimum can be specified (much like the box in a box and whisker plot). Can barplot do that? I suspect the answer's in ggplot, but I can't find an example.
Here's some data:
X Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 Highest recorded 31.5 31.8 30.3 28.0 24.9 24.4 21.7 20.9 24.5 25.4 26.0 28.7
2 Mean monthly maximum 27.8 28.6 27.0 24.8 22.0 20.0 18.9 18.8 20.4 22.4 23.9 26.8
3 Mean daily maximum 24.2 24.8 23.1 20.9 18.4 16.3 15.5 15.7 16.9 18.3 20.0 22.4
4 Mean 19.1 19.8 18.1 16.2 13.8 11.9 11.2 11.6 12.7 14.1 15.7 17.7
5 Mean daily minimum 14.0 14.7 13.1 11.4 9.2 7.5 6.9 7.4 8.4 10.0 11.4 13.0
6 Mean monthly minimum 7.6 9.1 6.8 3.8 2.3 -0.5 -0.2 1.0 2.3 3.7 5.3 6.7
7 Lowest recorded 4.0 5.6 4.1 -1.3 0.0 -3.1 -2.6 -1.4 -0.8 2.0 2.7 4.1
xaxis =c("J" ,"F" ,"M" ,"A" ,"M" ,"J","J","A", "S", "O","N","D")
So ideally, I end up with a stacked bar for each month, that starts at the 'Lowest recorded' value, rather than at zero.
I've also had a try with superbarplot from the UsingR package. I can get the bars to start where I want, but can't move the x axis down out of the centre of the plot. Thanks in advance.
You can use geom_boxplot in ggplot2 to get what (I think) you want specifying the precomputed values and stat = 'identity' and use geom_crossbar to put in the other
# first, your data
weather <- read.table(text = 'X Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 "Highest recorded" 31.5 31.8 30.3 28.0 24.9 24.4 21.7 20.9 24.5 25.4 26.0 28.7
2 "Mean monthly maximum" 27.8 28.6 27.0 24.8 22.0 20.0 18.9 18.8 20.4 22.4 23.9 26.8
3 "Mean daily maximum" 24.2 24.8 23.1 20.9 18.4 16.3 15.5 15.7 16.9 18.3 20.0 22.4
4 "Mean" 19.1 19.8 18.1 16.2 13.8 11.9 11.2 11.6 12.7 14.1 15.7 17.7
5 "Mean daily minimum" 14.0 14.7 13.1 11.4 9.2 7.5 6.9 7.4 8.4 10.0 11.4 13.0
6 "Mean monthly minimum" 7.6 9.1 6.8 3.8 2.3 -0.5 -0.2 1.0 2.3 3.7 5.3 6.7
7 "Lowest recorded" 4.0 5.6 4.1 -1.3 0.0 -3.1 -2.6 -1.4 -0.8 2.0 2.7 4.1', header =T)
library(reshape2)
library(ggplot2)
# reshape to wide format (basically transposing the data.frame)
w <- dcast(melt(weather), variable~X)
ggplot(w, aes(x=variable,ymin = `Lowest recorded`,
ymax = `Highest recorded`, lower = `Lowest recorded`,
upper = `Highest recorded`, middle = `Mean daily maximum`)) +
geom_boxplot(stat = 'identity') +
xlab('month') +
ylab('Temperature') +
geom_crossbar(aes(y = `Mean monthly maximum` ))+
geom_crossbar(aes(y = `Mean monthly minimum`)) +
geom_crossbar(aes(y = `Mean daily maximum` ))+
geom_crossbar(aes(y = `Mean daily minimum`))
This is partially described in an example in the help for geom_boxplot
Related
I have the following data:
CET <- url("http://www.metoffice.gov.uk/hadobs/hadcet/cetml1659on.dat")
cet <- read.table(CET, sep = "", skip = 6, header = TRUE,
fill = TRUE, na.string = c(-99.99, -99.9))
names(cet) <- c(month.abb, "Annual")
cet <- cet[-nrow(cet), ]
rn <- as.numeric(rownames(cet))
Years <- rn[1]:rn[length(rn)]
annCET <- data.frame(Temperature = cet[, ncol(cet)],Year = Years)
cet <- cet[, -ncol(cet)]
cet <- stack(cet)[,2:1]
names(cet) <- c("Month","Temperature")
cet <- transform(cet, Year = (Year <- rep(Years, times = 12)),
nMonth = rep(1:12, each = length(Years)),
Date = as.Date(paste(Year, Month, "15", sep = "-"),format = "%Y-%b-%d"))
cet <- cet[with(cet, order(Date)), ]
idx <- cet$Year > 1900
cet <- cet[idx,]
cet <- cet[,c('Date','Temperature')]
plot(cet, type = 'l')
This demonstrates the monthly temperature cycle from 1900 to 2014 in England, UK.
I would like to evaluate the phase and amplitude of the seasonal cycle of temperature follwowing the methods outlined in this paper. Specifically, they describe that given 12 monthly values (as we have here) we can estimate the yearly component as:
where X(t) represents 12 monthly values of surface temperature, x(t+t0), t = 0.5,...,11.5, are 12 monthly values of the de-meaned monthly temperature, where the factor of two is to account for both positive and negative frequencies.
Then the amplitude and phase of the seasonal cycle can be calculated as
and
They specify, that each year of data, they calculate the yearly (one cycle per year) sinusoidal component using the Fourier transform, as the equation shown above.
I'm a bit stuck on how to generate the time series they demonstrate here. Can anyone please provide some guidance as to how I can reproduce these methods. Note, I also work in matlab - in case anyone has some suggestions as to how this would be achieved in that environment.
Here is a subset of the data.
Date Temperature
1980-01-15 2.3
1980-02-15 5.7
1980-03-15 4.7
1980-04-15 8.8
1980-05-15 11.2
1980-06-15 13.8
1980-07-15 14.7
1980-08-15 15.9
1980-09-15 14.7
1980-10-15 9
1980-11-15 6.6
1980-12-15 5.6
1981-01-15 4.9
1981-02-15 3
1981-03-15 7.9
1981-04-15 7.8
1981-05-15 11.2
1981-06-15 13.2
1981-07-15 15.5
1981-08-15 16.2
1981-09-15 14.5
1981-10-15 8.6
1981-11-15 7.8
1981-12-15 0.3
1982-01-15 2.6
1982-02-15 4.8
1982-03-15 6.1
1982-04-15 8.6
1982-05-15 11.6
1982-06-15 15.5
1982-07-15 16.5
1982-08-15 15.7
1982-09-15 14.2
1982-10-15 10.1
1982-11-15 8
1982-12-15 4.4
1983-01-15 6.7
1983-02-15 1.7
1983-03-15 6.4
1983-04-15 6.8
1983-05-15 10.3
1983-06-15 14.4
1983-07-15 19.5
1983-08-15 17.3
1983-09-15 13.7
1983-10-15 10.5
1983-11-15 7.5
1983-12-15 5.6
1984-01-15 3.8
1984-02-15 3.3
1984-03-15 4.7
1984-04-15 8.1
1984-05-15 9.9
1984-06-15 14.5
1984-07-15 16.9
1984-08-15 17.6
1984-09-15 13.7
1984-10-15 11.1
1984-11-15 8
1984-12-15 5.2
1985-01-15 0.8
1985-02-15 2.1
1985-03-15 4.7
1985-04-15 8.3
1985-05-15 10.9
1985-06-15 12.7
1985-07-15 16.2
1985-08-15 14.6
1985-09-15 14.6
1985-10-15 11
1985-11-15 4.1
1985-12-15 6.3
1986-01-15 3.5
1986-02-15 -1.1
1986-03-15 4.9
1986-04-15 5.8
1986-05-15 11.1
1986-06-15 14.8
1986-07-15 15.9
1986-08-15 13.7
1986-09-15 11.3
1986-10-15 11
1986-11-15 7.8
1986-12-15 6.2
1987-01-15 0.8
1987-02-15 3.6
1987-03-15 4.1
1987-04-15 10.3
1987-05-15 10.1
1987-06-15 12.8
1987-07-15 15.9
1987-08-15 15.6
1987-09-15 13.6
1987-10-15 9.7
1987-11-15 6.5
1987-12-15 5.6
1988-01-15 5.3
1988-02-15 4.9
1988-03-15 6.4
1988-04-15 8.2
1988-05-15 11.9
1988-06-15 14.4
1988-07-15 14.7
1988-08-15 15.2
1988-09-15 13.2
1988-10-15 10.4
1988-11-15 5.2
1988-12-15 7.5
1989-01-15 6.1
1989-02-15 5.9
1989-03-15 7.5
1989-04-15 6.6
1989-05-15 13
1989-06-15 14.6
1989-07-15 18.2
1989-08-15 16.6
1989-09-15 14.7
1989-10-15 11.7
1989-11-15 6.2
1989-12-15 4.9
1990-01-15 6.5
1990-02-15 7.3
1990-03-15 8.3
1990-04-15 8
1990-05-15 12.6
1990-06-15 13.6
1990-07-15 16.9
1990-08-15 18
1990-09-15 13.2
1990-10-15 11.9
1990-11-15 6.9
1990-12-15 4.3
1991-01-15 3.3
1991-02-15 1.5
1991-03-15 7.9
1991-04-15 7.9
1991-05-15 10.8
1991-06-15 12.1
1991-07-15 17.3
1991-08-15 17.1
1991-09-15 14.7
1991-10-15 10.2
1991-11-15 6.8
1991-12-15 4.7
1992-01-15 3.7
1992-02-15 5.4
1992-03-15 7.5
1992-04-15 8.7
1992-05-15 13.6
1992-06-15 15.7
1992-07-15 16.2
1992-08-15 15.3
1992-09-15 13.4
1992-10-15 7.8
1992-11-15 7.4
1992-12-15 3.6
1993-01-15 5.9
1993-02-15 4.6
1993-03-15 6.7
1993-04-15 9.5
1993-05-15 11.4
1993-06-15 15
1993-07-15 15.2
1993-08-15 14.6
1993-09-15 12.4
1993-10-15 8.5
1993-11-15 4.6
1993-12-15 5.5
1994-01-15 5.3
1994-02-15 3.2
1994-03-15 7.7
1994-04-15 8.1
1994-05-15 10.7
1994-06-15 14.5
1994-07-15 18
1994-08-15 16
1994-09-15 12.7
1994-10-15 10.2
1994-11-15 10.1
1994-12-15 6.4
1995-01-15 4.8
1995-02-15 6.5
1995-03-15 5.6
1995-04-15 9.1
1995-05-15 11.6
1995-06-15 14.3
1995-07-15 18.6
1995-08-15 19.2
1995-09-15 13.7
1995-10-15 12.9
1995-11-15 7.7
1995-12-15 2.3
1996-01-15 4.3
1996-02-15 2.5
1996-03-15 4.5
1996-04-15 8.5
1996-05-15 9.1
1996-06-15 14.4
1996-07-15 16.5
1996-08-15 16.5
1996-09-15 13.6
1996-10-15 11.7
1996-11-15 5.9
1996-12-15 2.9
1997-01-15 2.5
1997-02-15 6.7
1997-03-15 8.4
1997-04-15 9
1997-05-15 11.5
1997-06-15 14.1
1997-07-15 16.7
1997-08-15 18.9
1997-09-15 14.2
1997-10-15 10.2
1997-11-15 8.4
1997-12-15 5.8
1998-01-15 5.2
1998-02-15 7.3
1998-03-15 7.9
1998-04-15 7.7
1998-05-15 13.1
1998-06-15 14.2
1998-07-15 15.5
1998-08-15 15.9
1998-09-15 14.9
1998-10-15 10.6
1998-11-15 6.2
1998-12-15 5.5
1999-01-15 5.5
1999-02-15 5.3
1999-03-15 7.4
1999-04-15 9.4
1999-05-15 12.9
1999-06-15 13.9
1999-07-15 17.7
1999-08-15 16.1
1999-09-15 15.6
1999-10-15 10.7
1999-11-15 7.9
1999-12-15 5
2000-01-15 4.9
2000-02-15 6.3
2000-03-15 7.6
2000-04-15 7.8
2000-05-15 12.1
2000-06-15 15.1
2000-07-15 15.5
2000-08-15 16.6
2000-09-15 14.7
2000-10-15 10.3
2000-11-15 7
2000-12-15 5.8
2001-01-15 3.2
2001-02-15 4.4
2001-03-15 5.2
2001-04-15 7.7
2001-05-15 12.6
2001-06-15 14.3
2001-07-15 17.2
2001-08-15 16.8
2001-09-15 13.4
2001-10-15 13.3
2001-11-15 7.5
2001-12-15 3.6
2002-01-15 5.5
2002-02-15 7
2002-03-15 7.6
2002-04-15 9.3
2002-05-15 11.8
2002-06-15 14.4
2002-07-15 16
2002-08-15 17
2002-09-15 14.4
2002-10-15 10.1
2002-11-15 8.5
2002-12-15 5.7
2003-01-15 4.5
2003-02-15 3.9
2003-03-15 7.5
2003-04-15 9.6
2003-05-15 12.1
2003-06-15 16.1
2003-07-15 17.6
2003-08-15 18.3
2003-09-15 14.3
2003-10-15 9.2
2003-11-15 8.1
2003-12-15 4.8
2004-01-15 5.2
2004-02-15 5.4
2004-03-15 6.5
2004-04-15 9.4
2004-05-15 12.1
2004-06-15 15.3
2004-07-15 15.8
2004-08-15 17.6
2004-09-15 14.9
2004-10-15 10.5
2004-11-15 7.7
2004-12-15 5.4
2005-01-15 6
2005-02-15 4.3
2005-03-15 7.2
2005-04-15 8.9
2005-05-15 11.4
2005-06-15 15.5
2005-07-15 16.9
2005-08-15 16.2
2005-09-15 15.2
2005-10-15 13.1
2005-11-15 6.2
2005-12-15 4.4
2006-01-15 4.3
2006-02-15 3.7
2006-03-15 4.9
2006-04-15 8.6
2006-05-15 12.3
2006-06-15 15.9
2006-07-15 19.7
2006-08-15 16.1
2006-09-15 16.8
2006-10-15 13
2006-11-15 8.1
2006-12-15 6.5
2007-01-15 7
2007-02-15 5.8
2007-03-15 7.2
2007-04-15 11.2
2007-05-15 11.9
2007-06-15 15.1
2007-07-15 15.2
2007-08-15 15.4
2007-09-15 13.8
2007-10-15 10.9
2007-11-15 7.3
2007-12-15 4.9
2008-01-15 6.6
2008-02-15 5.4
2008-03-15 6.1
2008-04-15 7.9
2008-05-15 13.4
2008-06-15 13.9
2008-07-15 16.2
2008-08-15 16.2
2008-09-15 13.5
2008-10-15 9.7
2008-11-15 7
2008-12-15 3.5
2009-01-15 3
2009-02-15 4.1
2009-03-15 7
2009-04-15 10
2009-05-15 12.1
2009-06-15 14.8
2009-07-15 16.1
2009-08-15 16.6
2009-09-15 14.2
2009-10-15 11.6
2009-11-15 8.7
2009-12-15 3.1
2010-01-15 1.4
2010-02-15 2.8
2010-03-15 6.1
2010-04-15 8.8
2010-05-15 10.7
2010-06-15 15.2
2010-07-15 17.1
2010-08-15 15.3
2010-09-15 13.8
2010-10-15 10.3
2010-11-15 5.2
2010-12-15 -0.7
2011-01-15 3.7
2011-02-15 6.4
2011-03-15 6.7
2011-04-15 11.8
2011-05-15 12.2
2011-06-15 13.8
2011-07-15 15.2
2011-08-15 15.4
2011-09-15 15.1
2011-10-15 12.6
2011-11-15 9.6
2011-12-15 6
2012-01-15 5.4
2012-02-15 3.8
2012-03-15 8.3
2012-04-15 7.2
2012-05-15 11.7
2012-06-15 13.5
2012-07-15 15.5
2012-08-15 16.6
2012-09-15 13
2012-10-15 9.7
2012-11-15 6.8
2012-12-15 4.8
2013-01-15 3.5
2013-02-15 3.2
2013-03-15 2.7
2013-04-15 7.5
2013-05-15 10.4
2013-06-15 13.6
2013-07-15 18.3
2013-08-15 16.9
2013-09-15 13.7
2013-10-15 12.5
2013-11-15 6.2
2013-12-15 6.3
2014-01-15 5.7
2014-02-15 6.2
2014-03-15 7.6
2014-04-15 10.2
2014-05-15 12.2
2014-06-15 15.1
2014-07-15 17.7
2014-08-15 14.9
2014-09-15 15.1
2014-10-15 12.5
2014-11-15 8.6
2014-12-15 5.2
Literally, the formula for Y can be represented in MATLAB as:
t=0.5:0.5:11.5; %//make sure the step size is indeed 0.5
Y = 1/6.*sum(exp(2*pi*i.*t/12).*X(t0-t); %// add the function for X
phi = atan2(imag(Y)/real(Y)); %// seasonal phase
without knowing the function for X I can't be sure this can indeed be vectorised, or whether you'd have to loop, which can be done like:
t=0.5:0.5:11.5; %//make sure the step size is indeed 0.5
Ytmp(numel(t),1)=0; %// initialise output
for ii = 1:numel(t)
Ytmp(ii,1) = exp(2*pi*i.*t(ii)/12).*X(t0-t(ii));
end
Y = 1/6 * sum(Ytmp)
Just slot in any t0 you want, loop over the codes above and you have your time series.
I need to read the data from this link http://www.bls.gov/cpi/cpifiles/cpiai.txt into a data frame. Sadly the data is not in a csv file. What is the best way of doing it?
I tried
cpiai <- read.table("http://www.bls.gov/cpi/cpifiles/cpiai.txt")
Thanks!
Sticking to read.table:
path<-"http://www.bls.gov/cpi/cpifiles/cpiai.txt"
cpiai <- read.table(path,header=T,fill=TRUE,skip=16)
>head(cpiai)
Year Jan. Feb. Mar. Apr. May June July Aug. Sep. Oct. Nov. Dec. Avg. Dec Avg
1 1913 9.8 9.8 9.8 9.8 9.7 9.8 9.9 9.9 10.0 10.0 10.1 10.0 9.9 NA NA
2 1914 10.0 9.9 9.9 9.8 9.9 9.9 10.0 10.2 10.2 10.1 10.2 10.1 10.0 1.0 1.0
3 1915 10.1 10.0 9.9 10.0 10.1 10.1 10.1 10.1 10.1 10.2 10.3 10.3 10.1 2.0 1.0
4 1916 10.4 10.4 10.5 10.6 10.7 10.8 10.8 10.9 11.1 11.3 11.5 11.6 10.9 12.6 7.9
5 1917 11.7 12.0 12.0 12.6 12.8 13.0 12.8 13.0 13.3 13.5 13.5 13.7 12.8 18.1 17.4
6 1918 14.0 14.1 14.0 14.2 14.5 14.7 15.1 15.4 15.7 16.0 16.3 16.5 15.1 20.4 18.0
The answer by J.R. gives you all you need to know, but just to be complete, also consider access via Quandl which curates the data and also gives you a unified (free) API across the millions of series they track.
For this CPI the page is https://www.quandl.com/FRED/CPIAUCSL-Consumer-Price-Index-for-All-Urban-Consumers-All-Items-USA-Inflation and access is as easy as
R> library(Quandl)
R> dat <- Quandl("FRED/CPIAUCSL")
R> head(dat)
Date Value
1 2014-09-01 237.633
2 2014-08-01 237.428
3 2014-07-01 237.909
4 2014-06-01 237.693
5 2014-05-01 237.083
6 2014-04-01 236.254
R>
I have a dataset of timeseries (30 years). I did a subset for the month and the date I want (shown below in the code). Is there a way to do a loop for each month and the days in those month? Also, is there a way to save the plots automatically, in different folders corresponding to each month? Right now I am doing it manually by changing the month and date which corresponds to dfOct31all <- df [ which(df$Month==10 & df$Day==31), ]in the code below then plotting and saving it. By the way, I'm using RStudio.
Can someone please guide me?
Thanks!
setwd("WDir")
df <- read.csv("Velocity.csv", header = TRUE)
attach(df)
#Day 31
dfOct31all <- df [ which(df$Month==10 & df$Day==31), ]
dfall31Mbs <- dfOct31all[c(-1,-2,-3)]
densities <- lapply(dfall31Mbs, density)
par(mfcol=c(5,5), oma=c(1,1,0,0), mar=c(1,1,1,0), tcl=-0.1, mgp=c(0,0,0))
plot(densities[[1]], col="black",main = "1000mb",xlab=NA,ylab=NA)
plot(densities[[2]], col="black",main="925mb",xlab=NA,ylab=NA)
plot(densities[[3]], col="black",main="850mb",xlab=NA,ylab=NA)
plot(densities[[4]], col="black",main="700mb",xlab=NA,ylab=NA)
plot(densities[[5]], col="black",main="600mb",xlab=NA,ylab=NA)
plot(densities[[6]], col="black",main="500mb",xlab=NA,ylab=NA)
plot(densities[[7]], col ="black",main="400mb",xlab=NA,ylab=NA)
plot(densities[[8]], col="black",main="300mb",xlab=NA,ylab=NA)
plot(densities[[9]], col="black",main="250mb",xlab=NA,ylab=NA)
plot(densities[[10]], col="black",main="200mb",xlab=NA,ylab=NA)
plot(densities[[11]], col= "black",main="150mb",xlab=NA,ylab=NA)
plot(densities[[12]], col= "black",main="100mb",xlab=NA,ylab=NA)
plot(densities[[13]], col = "black",main="70mb",xlab=NA,ylab=NA)
plot(densities[[14]], col="black",main="50mb",xlab=NA,ylab=NA)
plot(densities[[15]], col="black",main="30mb",xlab=NA,ylab=NA)
plot(densities[[16]], col = "black",main="20mb",xlab=NA,ylab=NA)
plot(densities[[17]], col="black",main="10mb",xlab=NA,ylab=NA)
Snippet of data is shown as well
Year Month Day 1000mb 925mb 850mb 700mb 600mb 500mb 400mb 300mb 250mb 200mb 150mb 100mb 70mb 50mb 30mb 20mb 10mb
1984 10 31 6 6.6 7.9 11.5 14.6 17 20.8 25.8 26.4 25.3 24.4 22.7 19.9 19.2 20.4 24.8 30.8
1985 10 31 5.8 7.1 7.7 11.5 14.7 17.3 25.3 32.6 32.9 32.4 27.1 20.9 14.2 9.7 6.4 7.3 7.4
1986 10 31 4.3 6.1 7.7 11.3 18.4 26.3 34.4 44.5 48.9 46.2 34.5 20.4 13.8 13.2 21.7 31 46.4
1987 10 31 2.2 2.9 4 7 9 13.9 19.9 25.8 26.6 23.7 17.3 12 7 3.1 1.7 5.8 14.1
1988 10 31 2.5 2.1 2.3 6.5 6.4 5.1 7.4 12.1 13.4 16.1 16.7 15.2 8.8 5 2.8 6.2 8.9
1989 10 31 3.4 4 4.7 4.4 4.1 4 4.6 4.8 5.9 5.6 10.9 13.9 12.3 10.4 8.1 8 8
1990 10 31 4 4.9 7.5 14.6 19 21.9 25.7 28.3 29.4 29.2 27.3 18 12.6 10.1 9 12 19.9
1991 10 31 2.8 3.2 4 10.8 12.1 11.2 9.9 9.1 9.9 12.8 18 17.5 10.4 6.3 4.2 7.6 11.7
1992 10 31 5.9 6.9 7.9 13.1 17.9 25.2 34.6 47.3 53.3 53 42.4 21.3 11.6 6 4.6 8.5 12.8
1993 10 31 2.3 1.5 0.4 3.6 6.3 10.1 14.3 19.1 21.6 21.8 18.4 13.6 12.3 9.5 6.9 11 18.1
1994 10 31 2 2.2 3.8 11.6 17 19.8 23.6 24.9 25.5 26.2 28.4 25.2 16.7 13.6 9.3 8.3 9.8
1995 10 31 1.5 2 3.4 7.6 9.1 11.2 13.7 17.9 20.3 21.7 21.1 16.7 13 12.1 14.9 21.4 27.3
1996 10 31 1.9 2.4 3.5 8 11.7 17.4 26.4 35.6 33.3 24.6 12.4 4.1 0.5 3.4 7.2 9.4 11.6
1997 10 31 3.7 4.8 7.8 19.2 24.6 29.6 35.6 41 41.8 42 37.9 23.7 11.2 8.6 4.2 3.8 7
1998 10 31 0.7 1.1 0.9 4.8 8.4 11.4 14 25.3 29.7 25.2 15.9 6.6 2.1 1 4.5 8.9 6.1
1999 10 31 1.9 1.6 2.4 10.7 15.3 19 23.2 29 32.4 31.9 28 20.3 10.8 9.4 12 14.5 16.9
2000 10 31 5.1 5.8 6.7 12.8 18.2 23.9 29.9 40.7 42.2 33.7 23.5 12.7 2.6 1.6 3.8 4.7 5.1
2001 10 31 5.7 6.1 7.1 10.1 10.8 14.7 18.3 22.8 22.3 22.2 22 14 9.5 6.6 5.2 6.5 8.6
2002 10 31 1.4 1.6 1.8 9.2 14.5 19.5 24.8 30 30.5 27.6 22.2 13.9 9.1 7.1 8.5 16.1 23.8
2003 10 31 1.5 1.3 0.7 1 3.5 6 11.7 21.5 21.9 22.9 23 20.7 15.8 12.5 14.5 20.1 26
2004 10 31 5.4 5.6 6.9 14.4 23.3 33.3 46.1 60.9 62.1 54.6 42.9 28 17.3 12.3 10.1 13.6 13.3
2005 10 31 1.7 1.3 3 10.3 15.8 19.5 21.1 22.8 24.1 24.5 24.5 20.6 13.5 10.7 10 10.7 10.4
2006 10 31 2.3 1.5 1.7 8.7 12.5 15.9 18.7 20.5 21.8 24.3 29.9 25.3 18.3 12.8 7.7 8.8 12.4
2007 10 31 3.7 2.7 2.3 2.2 2.6 4.2 6.5 11.9 15.9 19.6 17.2 9.5 6.9 5.7 4.9 5.8 11.7
2008 10 31 7.7 10.8 14.3 20.3 23 25.8 27.4 32.1 35.4 34.8 25.8 13.2 7.1 2.9 2.6 3.4 6
2009 10 31 0.5 0.2 2 9.3 13.5 17.6 18.8 20.8 21.4 21.2 18.9 14.2 11.1 6.4 1.9 3 8
2010 10 31 5.6 6.8 8.5 13.4 16.5 20.3 23.8 26.8 31 28.1 24 15.7 9.9 7 4.8 3.9 1.8
2011 10 31 5.9 6.7 5.6 7.9 10.3 11.8 12.5 16.2 19.5 21.4 17.9 13.2 9.6 7.9 8 8.3 10.8
2012 10 31 4.8 6.3 9.4 19.5 24.2 27.2 27.5 27.3 27.7 30.7 27.5 16.7 10 7.6 8 13.8 19.7
2013 10 31 1.4 1.9 3.9 9.1 13.1 17.3 22.9 29.7 30.4 27.3 23.5 18.2 13.1 6.3 4.4 2.4 9.4
I wrote it out for each day rather than doing a loop.
I want to make bar charts where the bar minimum can be specified (much like the box in a box and whisker plot). Can barplot do that? I suspect the answer's in ggplot, but I can't find an example.
Here's some data:
X Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 Highest recorded 31.5 31.8 30.3 28.0 24.9 24.4 21.7 20.9 24.5 25.4 26.0 28.7
2 Mean monthly maximum 27.8 28.6 27.0 24.8 22.0 20.0 18.9 18.8 20.4 22.4 23.9 26.8
3 Mean daily maximum 24.2 24.8 23.1 20.9 18.4 16.3 15.5 15.7 16.9 18.3 20.0 22.4
4 Mean 19.1 19.8 18.1 16.2 13.8 11.9 11.2 11.6 12.7 14.1 15.7 17.7
5 Mean daily minimum 14.0 14.7 13.1 11.4 9.2 7.5 6.9 7.4 8.4 10.0 11.4 13.0
6 Mean monthly minimum 7.6 9.1 6.8 3.8 2.3 -0.5 -0.2 1.0 2.3 3.7 5.3 6.7
7 Lowest recorded 4.0 5.6 4.1 -1.3 0.0 -3.1 -2.6 -1.4 -0.8 2.0 2.7 4.1
xaxis =c("J" ,"F" ,"M" ,"A" ,"M" ,"J","J","A", "S", "O","N","D")
So ideally, I end up with a stacked bar for each month, that starts at the 'Lowest recorded' value, rather than at zero.
I've also had a try with superbarplot from the UsingR package. I can get the bars to start where I want, but can't move the x axis down out of the centre of the plot. Thanks in advance.
You can use geom_boxplot in ggplot2 to get what (I think) you want specifying the precomputed values and stat = 'identity' and use geom_crossbar to put in the other
# first, your data
weather <- read.table(text = 'X Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1 "Highest recorded" 31.5 31.8 30.3 28.0 24.9 24.4 21.7 20.9 24.5 25.4 26.0 28.7
2 "Mean monthly maximum" 27.8 28.6 27.0 24.8 22.0 20.0 18.9 18.8 20.4 22.4 23.9 26.8
3 "Mean daily maximum" 24.2 24.8 23.1 20.9 18.4 16.3 15.5 15.7 16.9 18.3 20.0 22.4
4 "Mean" 19.1 19.8 18.1 16.2 13.8 11.9 11.2 11.6 12.7 14.1 15.7 17.7
5 "Mean daily minimum" 14.0 14.7 13.1 11.4 9.2 7.5 6.9 7.4 8.4 10.0 11.4 13.0
6 "Mean monthly minimum" 7.6 9.1 6.8 3.8 2.3 -0.5 -0.2 1.0 2.3 3.7 5.3 6.7
7 "Lowest recorded" 4.0 5.6 4.1 -1.3 0.0 -3.1 -2.6 -1.4 -0.8 2.0 2.7 4.1', header =T)
library(reshape2)
library(ggplot2)
# reshape to wide format (basically transposing the data.frame)
w <- dcast(melt(weather), variable~X)
ggplot(w, aes(x=variable,ymin = `Lowest recorded`,
ymax = `Highest recorded`, lower = `Lowest recorded`,
upper = `Highest recorded`, middle = `Mean daily maximum`)) +
geom_boxplot(stat = 'identity') +
xlab('month') +
ylab('Temperature') +
geom_crossbar(aes(y = `Mean monthly maximum` ))+
geom_crossbar(aes(y = `Mean monthly minimum`)) +
geom_crossbar(aes(y = `Mean daily maximum` ))+
geom_crossbar(aes(y = `Mean daily minimum`))
This is partially described in an example in the help for geom_boxplot
I have the following data.
HEIrank1
HEI.ID X2007 X2008 X2009 X2010 X2011 X2012
1 OP 41.8 147.6 90.3 82.9 106.8 63.0
2 MO 20.0 20.8 21.1 20.9 12.6 20.6
3 SD 21.2 32.3 25.7 23.9 25.0 40.1
4 UN 51.8 39.8 19.9 20.9 21.6 22.5
5 WS 18.0 19.9 15.3 13.6 15.7 15.2
6 BF 11.5 36.9 20.0 23.2 18.2 23.8
7 ME 34.2 30.3 28.4 30.1 31.5 25.6
8 IM 7.7 18.1 20.5 14.6 17.2 17.1
9 OM 11.4 11.2 12.2 11.1 13.4 19.2
10 DC 14.3 28.7 20.1 17.0 22.3 16.2
11 OC 28.6 44.0 24.9 27.9 34.0 30.7
12 TH 7.4 10.0 5.8 8.8 8.7 8.6
13 CC 12.1 11.0 12.2 12.1 14.9 15.0
14 MM 11.7 24.2 18.4 18.6 31.9 31.7
15 MC 19.0 13.7 17.0 20.4 20.5 12.1
16 SH 11.4 24.8 26.1 12.7 19.9 25.9
17 SB 13.0 22.8 15.9 17.6 17.2 9.6
18 SN 11.5 18.6 22.9 12.0 20.3 11.6
19 ER 10.8 13.2 20.0 11.0 14.9 14.2
20 SL 44.9 21.6 21.3 26.5 17.0 8.0
I try following commends to draw regression line for each HEIs.
year <- c(2007 , 2008 , 2009 , 2010 , 2011, 2012)
op <- as.numeric(HEIrank1[1,])
lm.r <- lm(op~year)
plot(year, op)
abline(lm.r)
I want to draw to draw regression line for each college in one graph and I do not how.can you help me.
Here's my approach with ggplot2 but the graph is uninterpretable with that many lines.
library(ggplot2);library(reshape2)
mdat <- melt(HEIrank1, variable.name="year")
mdat$year <- as.numeric(substring(mdat$year, 2))
ggplot(mdat, aes(year, value, colour=HEI.ID, group=HEI.ID)) +
geom_point() + stat_smooth(se = FALSE, method="lm")
Faceting may be a better way to got:
ggplot(mdat, aes(year, value, group=HEI.ID)) +
geom_point() + stat_smooth(se = FALSE, method="lm") +
facet_wrap(~HEI.ID)