i am trying to plot the time series x_t = A + (-1)^t B
To do this i am using the following code. The problem is, that the ggplot is wrong.
require (ggplot2)
set.seed(42)
N<-2
A<-sample(1:20,N)
B<-rnorm(N)
X<-c(A+B,A-B)
dat<-sapply(1:N,function(n) X[rep(c(n,N+n),20)],simplify=FALSE)
dat<-data.frame(t=rep(1:20,N),w=rep(A,each=20),val=do.call(c,dat))
ggplot(data=dat,aes(x=t, y=val, color=factor(w)))+
geom_line()+facet_grid(w~.,scale = "free")
looking at the head of dat everything looks right:
> head(dat)
t w val
1 1 12 10.5533
2 2 12 13.4467
3 3 12 10.5533
4 4 12 13.4467
5 5 12 10.5533
6 6 12 13.4467
So the lower (blue) line should only have values 10.5533 and 13.4467. But it also takes different values. What is wrong in my code?
Thanks in advance for any help
You really should be more careful before asserting that something is "wrong". The way you are creating dat the rows are not ordered by dat$t, so head(...) is not displaying the extra values:
head(dat[order(dat$w,dat$t),],10)
# t w val
# 21 1 18 18.43530
# 61 1 18 18.36313
# 22 2 18 19.56470
# 62 2 18 17.63687
# 23 3 18 18.43530
# 63 3 18 18.36313
# 24 4 18 19.56470
# 64 4 18 17.63687
# 25 5 18 18.43530
# 65 5 18 18.36313
Note the row numbers.
Related
I am newbie at R. Now I want to plot data (two variables) and showing regression line including the boxplot. I am able to show those data except the r square value and equation chart.
Below is my script in showing the graph
library (car)
scatterplot(FIRST_S2A_NDVI, MEAN_DRONE_NDVI,
main = "NDVI Value from Sentinel and Drone",
xlab = "NDVI Value from Sentinel",
ylab = "NDVI Value from Drone",
pch = 15, col = "black",
regLine = list(col="green"), smooth = FALSE)
The figure is like this.
Now, the final touch is to add the equation and r square value on my figure. What script do I need to write. I tried this script from Add regression line equation and R^2 on graph but still no idea how to show them.
Thanks for read and hopefully helping me in this.
p.s.
Content of my data
OBJECTID SAMPLE_GRID FIRST_S2A_NDVI MEAN_DRONE_NDVI
1 1 1 0.6411405 0.8676092
2 2 2 0.4335293 0.5697814
3 3 3 0.7350439 0.7321858
4 4 4 0.7268013 0.8271566
5 5 5 0.3638939 0.5682631
6 6 6 0.1953890 0.3168246
7 7 7 0.4841993 0.7380627
8 8 8 0.4137447 0.3239288
9 9 9 0.8219178 0.8676065
10 10 10 0.2647872 0.2296441
11 11 11 0.8126657 0.8519964
12 12 12 0.2648504 0.2465738
13 13 13 0.5992035 0.8016030
14 14 14 0.2420299 0.3933670
15 15 15 0.5059137 0.7593807
16 16 16 0.7713419 0.8026068
17 17 17 0.3762540 0.5941540
18 18 18 0.5876435 0.7763927
19 19 19 0.2491609 0.5095306
20 20 20 0.3213648 0.4456958
21 21 21 0.2101466 0.1960858
22 22 22 0.3749034 0.4956361
23 23 23 0.5712630 0.7350484
24 24 24 0.8444895 0.8577550
25 25 25 0.3331450 0.4390229
26 26 26 0.1851611 0.4573663
27 27 27 0.4914998 0.2750837
28 28 28 0.7121390 0.7780228
For adding the equation and the R squared value to your current plot. You can simply create a model with the y and x variables and format a equation and paste in over the plot using mtext function.
m <- lm(MEAN_DRONE_NDVI~FIRST_S2A_NDVI)
eq <- paste0("y = ",round(coef(m)[2],3),"x ",
ifelse(coef(m)[1]<0,round(coef(m)[1],3),
paste("+",round(coef(m)[1],3))))
mtext(eq, 3,-1)
mtext(paste0("R^2 = ",round(as.numeric(summary(m)[8]),3)), 3, -3)
You can change the variables in your model and also change the position of the text with the 2nd and 3rd arguments in the mtext function
1st DF:
t.d
V1 V2 V3 V4
1 1 6 11 16
2 2 7 12 17
3 3 8 13 18
4 4 9 14 19
5 5 10 15 20
names(t.d) <- c("ID","A","B","C")
t.d$FinalTime <- c("7/30/2009 08:18:35","9/30/2009 19:18:35","11/30/2009 21:18:35","13/30/2009 20:18:35","15/30/2009 04:18:35")
t.d$InitTime <- c("6/30/2009 9:18:35","6/30/2009 9:18:35","6/30/2009 9:18:35","6/30/2009 9:18:35","6/30/2009 9:18:35")
>t.d
ID A B C FinalTime InitTime
1 1 6 11 16 7/30/2009 08:18:35 6/30/2009 9:18:35
2 2 7 12 17 9/30/2009 19:18:35 6/30/2009 9:18:35
3 3 8 13 18 11/30/2009 21:18:35 6/30/2009 9:18:35
4 4 9 14 19 13/30/2009 20:18:35 6/30/2009 9:18:35
5 5 10 15 20 15/30/2009 04:18:35 6/30/2009 9:18:35
2nd DF:
> s.d
F D E Time
1 10 19 28 6/30/2009 08:18:35
2 11 20 29 8/30/2009 19:18:35
3 12 21 30 9/30/2009 21:18:35
4 13 22 31 01/30/2009 20:18:35
5 14 23 32 10/30/2009 04:18:35
6 15 24 33 11/30/2009 04:18:35
7 16 25 34 12/30/2009 04:18:35
8 17 26 35 13/30/2009 04:18:35
9 18 27 36 15/30/2009 04:18:35
Output to be:
From DF "t.d" I have to calculate the time interval for each row between "FinalTime" and "InitTime" (InitTime will always be less than FinalTime).
Another DF "temp" from "s.d" has to be formed having data only within the above time interval, and then the most recent values of "F","D","E" have to be taken and attached to the 'ith' row of "t.d" from which the time interval was calculated.
Also we have to see if the newly formed DF "temp" has the following conditions true:
here 'j' represents value for each row:
if(temp$F[j] < 35.5) + (temp$D[j] >= 100) >= 1)
{
temp$Flag <- 1
} else{
temp$Flag <- 0
}
Originally I have 3 million rows in the dataframe and 20 columns in each DF.
I have solved the above problem using "for loop" but it obviously takes 2 to 3 days as there are a lot of rows.
(Also if I have to add new columns to the resultant DF if multiple conditions get satisfied on each row?)
Can anybody suggest a different technique? Like using apply functions?
My suggestion is:
use lapply over row indices
handle in the function call your if branches
return either your dataframe or NULL
combine everything with rbind
by replacing lapply with mclapply from the 'parallel' package, your code gets executed in parallel.
resultList <- lapply(1:nrow(t.d), function(i){
do stuff
if(condition){
return(df)
}else{
return(NULL)
}
resultDF <- do.call(rbind, resultList)
I'm doing some cluster analysis on the MLTobs from the LifeTables package and have come across a tricky problem with the Year variable in the mlt.mx.info dataframe. Year contains the period that the life table was taken, in intervals. Here's a table of the data:
1751-1754 1755-1759 1760-1764 1765-1769 1770-1774 1775-1779 1780-1784 1785-1789 1790-1794
1 1 1 1 1 1 1 1 1
1795-1799 1800-1804 1805-1809 1810-1814 1815-1819 1816-1819 1820-1824 1825-1829 1830-1834
1 1 1 1 1 2 3 3 3
1835-1839 1838-1839 1840-1844 1841-1844 1845-1849 1846-1849 1850-1854 1855-1859 1860-1864
4 1 5 3 8 1 10 11 11
1865-1869 1870-1874 1872-1874 1875-1879 1876-1879 1878-1879 1880-1884 1885-1889 1890-1894
11 11 1 12 2 1 15 15 15
1895-1899 1900-1904 1905-1909 1908-1909 1910-1914 1915-1919 1920-1924 1921-1924 1922-1924
15 15 15 1 16 16 16 2 1
1925-1929 1930-1934 1933-1934 1935-1939 1937-1939 1940-1944 1945-1949 1947-1949 1948-1949
19 19 1 20 1 22 22 3 1
1950-1954 1955-1959 1956-1959 1958-1959 1960-1964 1965-1969 1970-1974 1975-1979 1980-1984
30 30 2 1 40 40 41 41 41
1983-1984 1985-1989 1990-1994 1991-1994 1992-1994 1995-1999 2000-2003 2000-2004 2005-2006
1 42 42 1 1 44 3 41 22
2005-2007
14
As you can see, some of the intervals sit within other intervals. Thankfully none of them overlap. I want to simplify the intervals so intervals such as 1992-1994 and 1991-1994 all go into 1990-1994.
An idea might be to get the modulo of each interval and sort them into their new intervals that way but I'm unsure how to do this with the interval data type. If anyone has any ideas I'd really appreciate the help. Ultimately I want to create a histogram or barplot to illustrate the nicely.
If I understand your problem, you'll want something like this:
bottom <- seq(1750, 2010, 5)
library(dplyr)
new_df <- mlt.mx.info %>%
arrange(Year) %>%
mutate(year2 = as.numeric(substr(Year, 6, 9))) %>%
mutate(new_year = paste0(bottom[findInterval(year2, bottom)], "-",(bottom[findInterval(year2, bottom) + 1] - 1)))
View(new_df)
So what this does, it creates bins, and outputs a new column (new_year) that is the bottom of the bin. So everything from 1750-1754 will correspond to a new value of 1750-1754 (in string form; the original is an integer type, not sure how to fix that). Does this do what you want? Double check the results, but it looks right to me.
Column data$form contains 170 unique different values, (numbers from 1 to ~800).
I would like to merge some values (e.g with a 10 radius/step).
I need to do this in order to use:
colors = rainbow(length(unique(data$form)))
In a plot and provide a better visual result.
Thank you in advance for your help.
you can use %/% to group them and mean to combine them and normalize to scale them.
# if you want specifically 20 groups:
groups <- sort(form) %/% (800/20)
x <- c(by(sort(form), groups, mean))
x <- normalize(x, TRUE) * 19 + 1
0 1 2 3 4
1.000000 1.971781 2.957476 4.103704 4.948560
5 6 7 8 9
5.950617 7.175309 7.996914 8.953086 9.952263
10 11 12 13 14
10.800705 11.901235 12.888889 13.772291 14.888889
15 16 17 18 19
15.927984 16.864198 17.918519 18.860082 20.000000
You could also use cut. If you use the argument labels=FALSE, you get an integer value:
form <- runif(170, min=1,max=800)
> cut(form, breaks=20)
[1] (518,558] (280,320] (240,280] (121,160] (757,797]
[6] (160,200] (320,359] (598,638] (80.8,121] (359,399]
[7] (121,160] (200,240] ...
20 Levels: (1.18,41] (41,80.8] (80.8,121] (121,160] (160,200] (200,240] (240,280] (280,320] (320,359] (359,399] (399,439] ... (757,797]
> cut(form, breaks=20, labels=FALSE)
[1] 14 8 7 4 20 5 9 16 3 10 4 6 5 18 18 6 2 12
[19] 2 19 13 11 13 11 14 12 17 5 ...
On a side-note, I want you to re-consider plotting with rainbow colours, as it distorts reading the data, cf. Rainbow Color Map (Still) Considered Harmful.
This question already has answers here:
How to plot all the columns of a data frame in R
(11 answers)
Closed 12 days ago.
Here is the type of data that I'm importing as a csv file:
RPID mm ID Time Freq Freq.1 Freq.2
RPO483 1 B6AC 5 23301 30512
RPO483 1 B6AC 25 19 17
RPO244 1 B6C 5 14889 20461
RPO244 1 B6C 25 81 86
RPO876 1 G3G3A 5 106760 59950 103745
RPO876 1 G3G3A 25 4578 38119 37201
RPO876 7 F3G3A 5 205803 148469 173580
RPO876 7 F3G3A 25 28648 30321 26454
RPO939 7 F3E324A 5 242285
RPO939 7 F3E324A 25 42837
RPO934 7 F3E325A 5 242001 129272 112371
RPO934 7 F3E325A 25 73057 58685 66582
For each "ID", I'd like to generate a boxplot for values in columns "Freq", "Freq.1" and "Freq.2". However, currently I'm only able to successfully plot one Y value -- for example:
dataset <- read.csv("~/R/dataset.csv")
library(ggplot2)
p <- ggplot(dataset)
p + geom_boxplot(aes(x=ID, y=Freq, color=mm))
I've tried something like y=c(Freq,Freq.1,Freq.2), but this results in the following:
Error: Aesthetics must either be length one, or the same length as the dataProblems:ID
I'm sure there is a simple solution to this, but as I am very new to R, I can't tell
if it is a problem of wrong data format, wrong syntax, wrong package or something else entirely.
Any help would be greatly appreciated !
You need to reshape the data in order to plot.
First I read your data. Note that you have some NA values.
dat <- read.table(text = '
RPID mm ID Time Freq Freq.1 Freq.2
RPO483 1 B6AC 5 23301 30512
RPO483 1 B6AC 25 19 17
RPO244 1 B6C 5 14889 20461
RPO244 1 B6C 25 81 86
RPO876 1 G3G3A 5 106760 59950 103745
RPO876 1 G3G3A 25 4578 38119 37201
RPO876 7 F3G3A 5 205803 148469 173580
RPO876 7 F3G3A 25 28648 30321 26454
RPO939 7 F3E324A 5 242285
RPO939 7 F3E324A 25 42837
RPO934 7 F3E325A 5 242001 129272 112371
RPO934 7 F3E325A 25 73057 58685 66582',head=T, fill=T)
Using reshape2 for example
library(reshape2)
dat.m <- melt(dat,id.vars='ID', measure.vars=c('Freq','Freq.1','Freq.2'))
library(ggplot2)
p <- ggplot(dat.m) +
geom_boxplot(aes(x=ID, y=value, color=variable))