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.
Related
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$Location <- "ALIBA" # and so on
BMISA <- read_csv("BMISA_Mikulov/NOx/all_sorted.csv")
CCBDA <- read_csv("CCBDA_CB/NOx/all_sorted.csv")
TKARA <- read_csv("TKARA_Karvina/NOx/all_sorted.csv")
UULKA <- read_csv("UULKA_UnL/NOx/all_sorted.csv")
df <- rbind(ALIBA, BMISA, ...) # and so on
ggplot(data = df, aes(x = START_TIME, y = VALUE, color = Location) +
geom_line(size = 1) + # play with the stroke thickness
scale_color_brewer(palette = "Set1") + # here you can choose from a wide variety of palettes, just google
How would you like to add more years? In the same graph (everything will be tiny) or in seperate "windows" (= facets, better)?
Say I have two datasets. One that contains two months of data:
units_sold <- data.frame(date = seq(as.Date("2017-05-01"), as.Date("2017-07-01"), 1),
units = rep(20,62),
category = "units_sold")
And one that contains just a week:
forecast <- data.frame(date = seq(as.Date("2017-06-12"), as.Date("2017-06-18"), 1),
units = 5,
category = "forecast")
I can put them on the same plot. I.e.,
joined <- rbind(units_sold, forecast)
ggplot(data = joined, aes(x=date, y=units, colour = category)) + geom_line()
However, I can't seem to figure out how to put a ribbon between the two lines.
This is what I'm trying:
library(dplyr)
ribbon_dat <- left_join(forecast, units_sold, by = "date") %>%
rename(forecast = units.x) %>%
rename(units_sold = units.y) %>%
select(-c(category.x, category.y))
ggplot(data = joined, aes(x=date, y=units, colour = category)) +
geom_line() +
geom_ribbon(aes(x=ribbon_dat$date, ymin=ribbon_dat$forecast, ymax=ribbon_dat$units_sold))
I get this error: Error: Aesthetics must be either length 1 or the same as the data (69): x, ymin, ymax, y, colour
You are very close, you need to pass the second dataset to the data argument in geom_ribbon().
ggplot(data = joined, aes(x = date)) +
geom_line(aes(y = units, colour = category)) +
geom_ribbon(
data = ribbon_dat,
mapping = aes(ymin = forecast, ymax = units_sold)
)
In R with ggplot, I want to create a spaghetti plot (2 quantitative variables) grouped by a third variable to specify line color. Secondly, I want to aggregate that grouping variable with the line type or width.
Here's an example using the airquality dataset. I want the line's color to represent the month, and the summer months to have a different line width from non-summer months.
First, I created an indicator variable for the aggregated groups:
airquality$Summer <- with(airquality, ifelse(Month >= 6 & Month < 9, 1, 0))
I would like something like this, but with differing line widths:
However, this fails:
library(ggplot2)
ggplot(data = airquality, aes(x=Wind, y = Temp, color = as.factor(Month), group = Summer)) +
geom_point() +
geom_line(linetype = as.factor(Summer))
This also fails (specifying airquality$Summer):
ggplot(data = airquality, aes(x=Wind, y = Temp,
color = as.factor(Month), group = airquality$Summer)) +
geom_point() +
geom_line(linetype = as.factor(airquality$Summer))
I attempted this solution, but get another error:
lty <- setNames(c(0, 1), levels(airquality$Summer))
ggplot(data = airquality, aes(x=Wind, y = Temp,
color = as.factor(Month), group = airquality$Summer)) +
geom_point() +
geom_line(linetype = as.factor(airquality$Summer)) +
scale_linetype_manual(values = lty)
Any ideas?
EDIT:
My actual data show very clear trends, and I want to differentiate the top line from all the others below. My goal is to convince people they should make more than just the minimum payment on their student loans:
You just need to change the group to Month and putlinetype in aes:
ggplot(data = airquality, aes(x=Wind, y = Temp, color = as.factor(Month), group = Month)) +
geom_point() +
geom_line(aes(linetype = factor(Summer)))
If you want to specify the linetype you can use a few methods. Here is one way:
lineT <- c("solid", "dotdash")
names(lineT) <- c("1","0")
ggplot(data = airquality, aes(x=Wind, y = Temp, color = as.factor(Month))) +
geom_point() +
geom_line(aes(linetype = factor(Summer))) +
scale_linetype_manual(values = lineT)
I have the following dataset:
year <- as.factor(c(1999,2000,2001))
era <- c(0.4,0.6,0.7)
player_id <- as.factor(c(2,2,2))
df <- data.frame(year, era, player_id)
Using this data I created the following graph:
ggplot(data = df, aes(x = year, y=era, colour = player_id))+
geom_line() +
geom_text(aes(label = player_id), hjust=0.7)
Thing is however that I do now get a label at every datapoint. I only want to have a label at the end of each datapoint.
Any thoughts on what I should change to I get only one label?
If I understand correctly, you want label at end of data point. You could do this using directlabels library, as below:
library(ggplot2)
library(directlabels)
ggplot(data = df, aes(x = year, y=era, group = player_id, colour = player_id))+
geom_line() +
scale_colour_discrete(guide = 'none') +
scale_x_discrete(expand=c(0, 1)) +
geom_dl(aes(label = player_id), method = list(dl.combine("last.points"), cex = 0.8))
Output:
If I am understanding correctly what you want, then you can replace the geom_text(...) with geom_point()
Here is an example from the geom_boxplot man page:
p = ggplot(mpg, aes(class, hwy))
p + geom_boxplot(aes(colour = drv))
which looks like this:
I would like to make a very similar plot, but with (yearmon formatted) dates where the class variable is in the example, and a factor variable where drv is in the example.
Here is some sample data:
df_box = data_frame(
Date = sample(
as.yearmon(seq.Date(from = as.Date("2013-01-01"), to = as.Date("2016-08-01"), by = "month")),
size = 10000,
replace = TRUE
),
Source = sample(c("Inside", "Outside"), size = 10000, replace = TRUE),
Value = rnorm(10000)
)
I have tried a bunch of different things:
Put an as.factor around the date variable, then I no longer have the nicely spaced out date scale for the x-axis:
df_box %>%
ggplot(aes(
x = as.factor(Date),
y = Value,
# group = Date,
color = Source
)) +
geom_boxplot(outlier.shape = NA) +
theme_bw() +
xlab("Month Year") +
theme(
axis.text.x = element_text(hjust = 1, angle = 50)
)
On the other hand, if I use Date as an additional group variable as suggested here, adding color no longer has any additional impact:
df_box %>%
ggplot(aes(
x = Date,
y = Value,
group = Date,
color = Source
)) +
geom_boxplot() +
theme_bw()
Any ideas as to how achieve the output of #1 while still maintaining a yearmon scale x-axis?
Since you need separate boxes for each combination of Date and Source, use interaction(Source, Date) as the group aesthetic:
ggplot(df_box, aes(x = Date, y = Value,
colour = Source,
group = interaction(Source, Date))) +
geom_boxplot()