I am working on a map, where the color of each point is proportional to one response variable, and the size of the point is proportional to another. I've noticed that when I try to plot the points using formula notation things go haywire, while default notation performs as expected. I have used formula notation to plot maps many times before, and thought that the notations were nearly interchangeable. Why would these produce different results? I have read through the plot.formula and plot.default documentation and haven't been able to figure it out. Based on this I am wondering if it has to do with the columns of dat being coerced to factors, but I'm not sure why that would be happening. Any ideas?
Consider the following example data frame, dat:
latitude <- c(runif(10, min = 45, max = 48))
latitude[9] <- NA
longitude <- c(runif(10, min = -124.5, max = -122.5))
longitude[9] <- NA
color <- c("#00FFCCCC", "#99FF00CC", "#FF0000CC", "#3300FFCC", "#00FFCCCC",
"#00FFCCCC", "#3300FFCC", "#00FFCCCC", NA, "#3300FFCC")
size <- c(4.916667, 5.750000, 7.000000, 2.000000, 5.750000,
4.500000, 2.000000, 4.500000, NA, 2.000000)
dat <- as.data.frame(cbind(longitude, latitude, color, size))
Plotting according to formula notation
plot(latitude ~ longitude, data = dat, type = "p", pch = 21, col = 1, bg = color, cex = size)
produces
this mess and the following error: graphical parameter "type" is obsolete.
Plotting according to the default notation
plot(longitude, latitude, type = "p", pch = 21, col = 1, bg = color, cex = size)
works as expected, though with the same error.
There are a couple of problems with this. First is that your use of cbind is turning this into a matrix, albeit temporarily, which is converting your numbers to character. See:
dat <- as.data.frame(cbind(longitude, latitude, color, size))
str(dat)
# 'data.frame': 10 obs. of 4 variables:
# $ longitude: Factor w/ 9 levels "-122.855375511572",..: 6 8 9 1 4 3 2 7 NA 5
# $ latitude : Factor w/ 9 levels "45.5418886151165",..: 6 2 4 1 3 7 5 9 NA 8
# $ color : Factor w/ 4 levels "#00FFCCCC","#3300FFCC",..: 1 3 4 2 1 1 2 1 NA 2
# $ size : Factor w/ 5 levels "2","4.5","4.916667",..: 3 4 5 1 4 2 1 2 NA 1
If instead you just use data.frame, you'll get:
dat <- data.frame(longitude, latitude, color, size)
str(dat)
# 'data.frame': 10 obs. of 4 variables:
# $ longitude: num -124 -124 -124 -123 -124 ...
# $ latitude : num 47.3 45.9 46.3 45.5 46 ...
# $ color : Factor w/ 4 levels "#00FFCCCC","#3300FFCC",..: 1 3 4 2 1 1 2 1 NA 2
# $ size : num 4.92 5.75 7 2 5.75 ...
plot(latitude ~ longitude, data = dat, pch = 21, col = 1, bg = color, cex = size)
But now the colors are all dorked. Okay, the problem is likely because your $color is a factor, which is being interpreted internally as integers. Try stringsAsFactors=F:
dat <- data.frame(longitude, latitude, color, size, stringsAsFactors=FALSE)
str(dat)
# 'data.frame': 10 obs. of 4 variables:
# $ longitude: num -124 -124 -124 -123 -124 ...
# $ latitude : num 47.3 45.9 46.3 45.5 46 ...
# $ color : chr "#00FFCCCC" "#99FF00CC" "#FF0000CC" "#3300FFCC" ...
# $ size : num 4.92 5.75 7 2 5.75 ...
plot(latitude ~ longitude, data = dat, pch = 21, col = 1, bg = color, cex = size)
Related
I'm scaling one column in a dataset with the intention of fitting a linear model. However, when I try to write the dataframe (with scaled column) to a csv, it doesn't work because the scaled column became complex with center and scale attributes.
Can someone please indicate how to convert the scaled column to something that can write to a csv? (and maybe why scale() needs to do it this way.)
# make a data frame
testDF <- data.frame(x1 = c(1,2,2,3,2,4,4,5,6,15,36,42,11,12,23,24,25,66,77,18,9),
x2 = c(1,4,5,9,4,15,17,25,35,200,1297,1764,120,150,500,500,640,4200,6000,365,78))
# scale the x1 attribute
testDF <- testDF %>%
mutate(x1_scaled = scale(x1, center = TRUE, scale = TRUE))
# write to csv doesn't work
write_csv(as.matrix(testDF), "testDF.csv")
# but plotting and lm do work
ggplot(testDF, aes(x1_scaled)) +
geom_histogram(aes(y = ..density..),binwidth = 1)
Lm_scaled <- lm(x2 ~ x1_scaled, data = testDF)
plot(Lm_scaled)
scale returns a matrix output. We could extract the column or use as.vector to remove the dim attribute
testDF <- testDF %>%
mutate(x1_scaled = as.vector(scale(x1, center = TRUE, scale = TRUE)))
Check the structure of the output without as.vector and with as.vector
> testDF %>%
+ mutate(x1_scaled = scale(x1, center = TRUE, scale = TRUE)) %>% str
'data.frame': 21 obs. of 3 variables:
$ x1 : num 1 2 2 3 2 4 4 5 6 15 ...
$ x2 : num 1 4 5 9 4 15 17 25 35 200 ...
$ x1_scaled: num [1:21, 1] -0.824 -0.776 -0.776 -0.729 -0.776 ...
..- attr(*, "scaled:center")= num 18.4
..- attr(*, "scaled:scale")= num 21.2
> testDF %>%
+ mutate(x1_scaled = as.vector(scale(x1, center = TRUE, scale = TRUE))) %>% str
'data.frame': 21 obs. of 3 variables:
$ x1 : num 1 2 2 3 2 4 4 5 6 15 ...
$ x2 : num 1 4 5 9 4 15 17 25 35 200 ...
$ x1_scaled: num -0.824 -0.776 -0.776 -0.729 -0.776 ...
You can simply convert the scale column to numeric in base R and write out the dataframe:
testDF$x1_scaled <- as.numeric(testDF$x1_scaled)
write_csv(testDF, "testDF.csv")
I have a data set that has missing data from about July 7th to July 19th. Graph of my dataset. You can see the data gap pretty easily. I would like to truncate it so that the gap isnt there and the before and after data butt up against each other. Something like this . I did try to follow the linked example but I dont understand how they set up xseq. I also tried just removing the offending dates and creating a dataframe without them but that didnt solve the problem.
Im not sure if its helpful but here is the existing code for the graph:
together <- ggplot() +
stat_summary(data = grid_pad, aes(x = DTT, y = grid_value, fill = 'Ambient'), geom='ribbon', fun.data = mean_cl_quantile, alpha = 0.25) +
stat_summary(data = grid_pad, aes(x = DTT, y = grid_value, color = 'Ambient'), geom='line', fun = mean, size = 0.9) +
stat_summary(data = turtle_pad, aes(x = DTT, y = turtle_value, fill = 'Turtle'), geom='ribbon', fun.data = mean_cl_quantile, alpha = 0.25) +
stat_summary(data = turtle_pad, aes(x = DTT, y = turtle_value, color = 'Turtle'), geom='line', fun = mean, size = 0.9) +
labs(x = "Date", y = "Temperature")+
scale_color_manual("Legend", values = c('Ambient' = '#1b9e77', 'Turtle' = '#d95f02'), labels = c(Ambient = 'Ambient Temp', Turtle = 'Turtle Temp')) +
scale_fill_manual("Legend", values = c('Ambient' = '#1b9e77', 'Turtle' = '#d95f02'), labels = c(Ambient = 'Ambient Temp', Turtle = 'Turtle Temp')) +
theme_classic() +
ggtitle("Ambient and Turtle Temperatures")+
ggeasy::easy_center_title()+
easy_remove_legend_title()
together
and here is the structure of my data:
> str(grid_pad)
grouped_df [142,800 x 3] (S3: grouped_df/tbl_df/tbl/data.frame)
$ Logger : Factor w/ 50 levels "TL1","TL11","TL12",..: 1 1 1 1 1 1 1 1 1 1 ...
$ DTT : POSIXct[1:142800], format: "2021-05-28 00:00:00" "2021-05-28 01:00:00" "2021-05-28 02:00:00" "2021-05-28 03:00:00" ...
$ grid_value: num [1:142800] NA NA NA NA NA 19.5 19.5 19.5 20 22 ...
- attr(*, "groups")= tibble [50 x 2] (S3: tbl_df/tbl/data.frame)
..$ Logger: Factor w/ 50 levels "TL1","TL11","TL12",..: 1 2 3 4 5 6 7 8 9 10 ...
> str(turtle_pad)
grouped_df [57,120 x 3] (S3: grouped_df/tbl_df/tbl/data.frame)
$ Name : Factor w/ 20 levels "F1","F11","F12",..: 1 1 1 1 1 1 1 1 1 1 ...
$ DTT : POSIXct[1:57120], format: "2021-05-28 00:00:00" "2021-05-28 01:00:00" "2021-05-28 02:00:00" "2021-05-28 03:00:00" ...
$ turtle_value: num [1:57120] NA NA NA NA NA NA NA NA NA NA ...
- attr(*, "groups")= tibble [20 x 2] (S3: tbl_df/tbl/data.frame)
..$ Name : Factor w/ 20 levels "F1","F11","F12",..: 1 2 3 4 5 6 7 8 9 10 ...
with base R, verbose:
df_with_gap <- data.frame(Name = gl(41, 1),
DTT = as.Date(Sys.Date()) + (-20:20),
turtle_value = c(runif(20), rep(NA, 5), runif(16))
)
rows_to_keep <- !is.na(df_with_gap$turtle_value)
## remove NAs
df_without_gap <- df_with_gap[rows_to_keep,]
## create some index to use for x-values ggplot
df_without_gap$pseudo_date <- rownames(df)
Please note:
while you could use DTT of the remaining values to label your axis (see label argument in ?scale_x_continuous`, the plot will be misleading as it covers up missing information)
a scatter plot would be the way to go if you want to show the association between ambient and turtle temperature.
to show seasonality of instead, consider adding a smoother (?geom_smooth for ggplot)
to convey variability, a boxplot might be more instructive
helpful chart pickers on the web
So briefly and without further ado - is it possible to retrieve only a name of element in list and use it as a main title of plot?
Let me explain - example:
Let's create a random df:
a <- c(1,2,3,4)
b <- runif(4)
c <- runif(4)
d <- runif(4)
e <- runif(4)
f <- runif(4)
df <- data.frame(a,b,c,d,e,f)
head(df)
a b c d e f
1 1 0.9694204 0.9869154 0.5386678 0.39331278 0.15054698
2 2 0.8949330 0.9910894 0.1009689 0.03632476 0.15523628
3 3 0.4930752 0.7179144 0.6957262 0.36579883 0.32006026
4 4 0.4850141 0.5539939 0.3196953 0.14348259 0.05292068
Then I want to create a list of data frame (based on this above) with specific columns to make a plot. In other words I'd like to make plot where first column of df (a) will be x axis on the plot and columns b,c,d,e and gonna represent values on y axis on the plot. Yes there'll be 5 plots - that's the point!
So my idea was to write some simple function which be able to create a list of df's based on that created above so:
my_fun <- function(x){
a <- df[1]
b <- x
aname <- "x_label"
bname <- "y_label"
df <- data.frame(a,b)
names(df) <- c(aname,bname)
return(df)
}
Run it for all (specified) columns:
df_s <- apply(df[,2:6], 2, function(x) my_fun(x))
So I have now:
class(df_s)
[1] "list"
str(df_s)
List of 5
$ b:'data.frame': 4 obs. of 2 variables:
..$ x_label: num [1:4] 1 2 3 4
..$ y_label: num [1:4] 0.969 0.895 0.493 0.485
$ c:'data.frame': 4 obs. of 2 variables:
..$ x_label: num [1:4] 1 2 3 4
..$ y_label: num [1:4] 0.987 0.991 0.718 0.554
$ d:'data.frame': 4 obs. of 2 variables:
..$ x_label: num [1:4] 1 2 3 4
..$ y_label: num [1:4] 0.539 0.101 0.696 0.32
$ e:'data.frame': 4 obs. of 2 variables:
..$ x_label: num [1:4] 1 2 3 4
..$ y_label: num [1:4] 0.3933 0.0363 0.3658 0.1435
$ f:'data.frame': 4 obs. of 2 variables:
..$ x_label: num [1:4] 1 2 3 4
..$ y_label: num [1:4] 0.1505 0.1552 0.3201 0.0529
Something that I wanted, but here's the question. I'd like to create a plot for every df in my list... As a result I want 5 plots with main titles b, c, d, e, f respectively Axis labels are the same name of the plot isn't... So I tried:
lapply(df_s, function(x) plot(x[2] ~ x[1], data = x, main = ???))
What should be instead of question marks? I tried main = names(df_s)[x] however it didin't work...
I think the following works. However, I think it might be best to use ggplot2 instead of the plot function (unless you are saving the plots inside inside lapply).
lapply(1 : length(df_s), function(x)
plot(df_s[[x]][,2] ~ df_s[[x]][,1],
xlab = names(df_s[[x]])[1],
ylab = names(df_s[[x]])[1],
main = names(df_s[x])))
With ggplot2
plot_lst <- lapply(seq_along(df_s), function(i) {
ggplot(df_s[[i]], aes(x=x_label, y=y_label)) +
geom_point() +
theme(plot.title = element_text(hjust = 0.5)) +
ggtitle(names(df_s)[i]) })
I have 9 plots with 3 time series in each plot, one of these plots contains only one curve and it's the reference plot which I would like to place in between the two rows that contain the other 8 plots. Is there an easy way to do so?
I use facet_wrap(~density,nrow=2) but I get one row with 5 and another with 4 plots. I am sure other people had this problem, is there an easy way around to organize the position of this reference plot, or do I have to create two separate plots and overlay them? Otherwise I might have to move this reference plot in all the other plots but it seems redundant information.
This is my current result, but as you can see it's not very well laid out.
The graphic you are looking for can be generated with gridArrange from the
gridExtra package. Here is
an example using the storms data set from the
dplyr.
library(ggplot2)
library(gridExtra)
library(dplyr)
data(storms, package = 'dplyr')
str(storms)
## Classes 'tbl_df', 'tbl' and 'data.frame': 10010 obs. of 13 variables:
## $ name : chr "Amy" "Amy" "Amy" "Amy" ...
## $ year : num 1975 1975 1975 1975 1975 ...
## $ month : num 6 6 6 6 6 6 6 6 6 6 ...
## $ day : int 27 27 27 27 28 28 28 28 29 29 ...
## $ hour : num 0 6 12 18 0 6 12 18 0 6 ...
## $ lat : num 27.5 28.5 29.5 30.5 31.5 32.4 33.3 34 34.4 34 ...
## $ long : num -79 -79 -79 -79 -78.8 -78.7 -78 -77 -75.8 -74.8 ...
## $ status : chr "tropical depression" "tropical depression" "tropical depression" "tropical depression" ...
## $ category : Ord.factor w/ 7 levels "-1"<"0"<"1"<"2"<..: 1 1 1 1 1 1 1 1 2 2 ...
## $ wind : int 25 25 25 25 25 25 25 30 35 40 ...
## $ pressure : int 1013 1013 1013 1013 1012 1012 1011 1006 1004 1002 ...
## $ ts_diameter: num NA NA NA NA NA NA NA NA NA NA ...
## $ hu_diameter: num NA NA NA NA NA NA NA NA NA NA ...
Let's create two graphics. The first graphic will be only form category == -1
storms (this would be the control group in your question). The second
graphic will be a facteted graphic for the category > -1 storm
First, we'll build a generic ggplot object for the graphics.
graphic <-
ggplot() +
aes(x = long, y = lat, color = category) +
geom_point() +
facet_wrap( ~ category) +
scale_color_hue(breaks = levels(storms$category),
labels = levels(storms$category),
drop = FALSE)
Next we build the two graphics as needed.
g1 <- graphic %+% dplyr::filter(storms, category == -1) + theme(legend.position = "none")
g2 <- graphic %+% dplyr::filter(storms, category != -1)
gridExtra::grid.arrange can take a layout matrix where the numbers 1 and 2
denote the first and second graphics passed to the function. (This works for
a lot more than just two graphics, by the way.) By repeating the values of 1
and 2 in the matrix we can control the relative size of the two graphics in
the graphics device.
gridExtra::grid.arrange(g1, g2,
layout_matrix =
matrix(c(1, 1, 1, 2, 2, 2, 2, 2,
1, 1, 1, 2, 2, 2, 2, 2,
1, 1, 1, 2, 2, 2, 2, 2),
byrow = TRUE, nrow = 3)
)
If I understand the question correctly you could reformat your data with appropriate facetting variables to introduce a new row of reference panels
library(ggplot2)
d <- data.frame(x=rep(1:10, 8), y = rnorm(80),
f=gl(8,10, ordered = TRUE))
d$f1 <- factor(d$f <= 4, labels=c(1,3))
d$f2 <- as.numeric(d$f) %% 4
d2 <- data.frame(x=1:10, y=0, f1 = 2)
ggplot(d, aes(x,y)) +
geom_point(aes(colour=f)) +
geom_point(data=d2, colour="black") +
facet_grid(f1~f2)
I am failry new to R and recently used it to make some Boxplots. I also added the mean and standard deviation in my boxplot. I was wondering if i could add some kind of tick mark or circle in different percentile as well. Let's say if i want to mark the 85th, $ 90th percentile in each HOUR boxplot, is there a way to do this? My data consist of a year worth of loads in MW in each hour & My output consist of 24 boxplots for each hour for each month. I am doing each month at a time because i am not sure if there is a way to run all 96(Each month, weekday/weekend , for 4 different zones) boxplots at once. Thanks in advance for help.
JANWD <-read.csv("C:\\My Directory\\MWBox2.csv")
JANWD.df<-data.frame(JANWD)
JANWD.sub <-subset(JANWD.df, MONTH < 2 & weekend == "NO")
KeepCols <-c("Hour" , "Houston_Load")
HWD <- JANWD.sub[ ,KeepCols]
sd <-tapply(HWD$Houston_Load, HWD$Hour, sd)
means <-tapply(HWD$Houston_Load, HWD$Hour, mean)
boxplot(Houston_Load ~ Hour, data=HWD, xlab="WEEKDAY HOURS", ylab="MW Differnce", ylim= c(-10, 20), smooth=TRUE ,col ="bisque", range=0)
points(sd, pch = 22, col= "blue")
points(means, pch=23, col ="red")
#Output of the subset of data used to run boxplot for month january in Houston
str(HWD)
'data.frame': 504 obs. of 2 variables:
`$ Hour : int 1 2 3 4 5 6 7 8 9 10 ...'
`$ Houston_Load: num 1.922 2.747 -2.389 0.515 1.922 ...'
#OUTPUT of the original data
str(JANWD)
'data.frame': 8783 obs. of 9 variables:
$ Date : Factor w/ 366 levels "1/1/2012","1/10/2012",..: 306 306 306 306 306 306 306 306 306 306 ...
`$ Hour : int 1 2 3 4 5 6 7 8 9 10 ...'
` $ MONTH : int 8 8 8 8 8 8 8 8 8 8 ...'
`$ weekend : Factor w/ 2 levels "NO","YES": 1 1 1 1 1 1 1 1 1 1 ...'
`$ TOTAL_LOAD : num 0.607 5.111 6.252 7.607 0.607 ...'
`$ Houston_Load: num -2.389 0.515 1.922 2.747 -2.389 ...'
`$ North_Load : num 2.95 4.14 3.55 3.91 2.95 ...'
`$ South_Load : num -0.108 0.267 0.54 0.638 -0.108 ...'
`$ West_Load : num 0.154 0.193 0.236 0.311 0.154 ...'
Here is one way, using quantile() to compute the relevant percentiles for you. I add the marks using rug().
set.seed(1)
X <- rnorm(200)
boxplot(X, yaxt = "n")
## compute the required quantiles
qntl <- quantile(X, probs = c(0.85, 0.90))
## add them as a rgu plot to the left hand side
rug(qntl, side = 2, col = "blue", lwd = 2)
## add the box and axes
axis(2)
box()
Update: In response to the OP providing str() output, here is an example similar to the data that the OP has to hand:
set.seed(1) ## make reproducible
HWD <- data.frame(Hour = rep(0:23, 10),
Houston_Load = rnorm(24*10))
Now get I presume you want ticks at 85th and 90th percentiles for each Hour? If so we need to split the data by Hour and compute via quantile() as I showed earlier:
quants <- sapply(split(HWD$Houston_Load, list(HWD$Hour)),
quantile, probs = c(0.85, 0.9))
which gives:
R> quants <- sapply(split(HWD$Houston_Load, list(HWD$Hour)),
+ quantile, probs = c(0.85, 0.9))
R> quants
0 1 2 3 4 5 6
85% 0.3576510 0.8633506 1.581443 0.2264709 0.4164411 0.2864026 1.053742
90% 0.6116363 0.9273008 2.109248 0.4218297 0.5554147 0.4474140 1.366114
7 8 9 10 11 12 13 14
85% 0.5352211 0.5175485 1.790593 1.394988 0.7280584 0.8578999 1.437778 1.087101
90% 0.8625322 0.5969672 1.830352 1.519262 0.9399476 1.1401877 1.763725 1.102516
15 16 17 18 19 20 21
85% 0.6855288 0.4874499 0.5493679 0.9754414 1.095362 0.7936225 1.824002
90% 0.8737872 0.6121487 0.6078405 1.0990935 1.233637 0.9431199 2.175961
22 23
85% 1.058648 0.6950166
90% 1.145783 0.8436541
Now we can draw marks at the x locations of the boxplots
boxplot(Houston_Load ~ Hour, data = HWD, axes = FALSE)
xlocs <- 1:24 ## where to draw marks
tickl <- 0.15 ## length of marks used
for(i in seq_len(ncol(quants))) {
segments(x0 = rep(xlocs[i] - 0.15, 2), y0 = quants[, i],
x1 = rep(xlocs[i] + 0.15, 2), y1 = quants[, i],
col = c("red", "blue"), lwd = 2)
}
title(xlab = "Hour", ylab = "Houston Load")
axis(1, at = xlocs, labels = xlocs - 1)
axis(2)
box()
legend("bottomleft", legend = paste(c("0.85", "0.90"), "quantile"),
bty = "n", lty = "solid", lwd = 2, col = c("red", "blue"))
The resulting figure should look like this: