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Plotting two variables as lines using ggplot2 on the same graph
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How can I add a legend to my plot with this dataset?
I would like to plot all variables in the same plot and identify the lines by names.
Data NO2 SO2 O3
2004-01-01 24.49864 2.756818 30.17857
2 2004-01-02 33.40000 3.912609 22.45514
3 2004-01-03 27.55435 5.654783 24.33342
4 2004-01-04 24.87391 8.910000 30.64569
5 2004-01-05 41.96348 10.893478 44.92825
6 2004-01-06 48.80913 13.417391 44.91305
7 2004-01-07 49.10217 16.808696 28.34968
8 2004-01-08 49.14217 26.273913 16.61955
9 2004-01-09 34.52261 8.438261 17.80235
10 2004-01-10 45.33087 7.955217 36.34493
You can use gather from tidyr to reshape your data frame to the long format before plotting.
library(tidyr)
library(ggplot2)
# reshape to long format
datL <- gather(dat, Var, value, -Data)
# plot
ggplot(datL, aes(x = Data, y = value, colour = Var, group = Var)) +
geom_line()
Here, dat is the name of your data frame.
library(reshape2)
library(ggplot2)
s <-
"Date NO2 SO2 O3
2004-01-01 24.49864 2.756818 30.17857
2004-01-02 33.40000 3.912609 22.45514
2004-01-03 27.55435 5.654783 24.33342
2004-01-04 24.87391 8.910000 30.64569
2004-01-05 41.96348 10.893478 44.92825
2004-01-06 48.80913 13.417391 44.91305
2004-01-07 49.10217 16.808696 28.34968
2004-01-08 49.14217 26.273913 16.61955
2004-01-09 34.52261 8.438261 17.80235
2004-01-10 45.33087 7.955217 36.34493
"
df <- read.delim(textConnection(s), sep="")
df$Date <- as.Date(df$Date, "%Y-%m-%d")
df <- melt(df, id.vars="Date")
ggplot(df, aes(Date, value, col=variable)) +
geom_line()
This question is duplicated from plot multiple columns on the same graph in R
Related
This question has been asked multiple times but I cannot find any that fit my needs.
My goal is to plot timeseries for one month over multiple years. The following JAN dataframe is created by sub-setting from data frame containing daily rainfall for the entire year.
> head(JAN)
DATE RCM GPM TRI
1: 2000-01-01 0.012182957 NA NA
2: 2000-01-02 0.001769934 NA NA
3: 2000-01-03 0.007916438 NA NA
4: 2000-01-04 0.008227825 NA NA
5: 2000-01-05 0.005192382 NA NA
6: 2000-01-06 0.065458169 NA NA
The dataframe is for the month of January containing daily records over 20 years.
I got the following plot.
dfmelt<-melt(JAN,id.vars="DATE")
ggplot(dfmelt,aes(x=DATE,y=value,
col=variable,group = lubridate::year(DATE)))+
labs(title='JANUARY')+
geom_line()
I'm assuming it's because my data consists only January months and while plotting breaks are there for February to December.
I want to avoid this to see the trend of precipitation over the years for the month january.
introducing breaks give the following
breaks <- unique(as.Date(cut(dfmelt$DATE, "month")))
ba2 <- transform(dfmelt, year = as.integer(format(DATE, "%Y")))
p <- ggplot(ba2, aes(x=DATE,y=value,
col=variable)) +
geom_line() +
facet_grid(cols = vars(year), scales = "free_x", space = "free_x")
p + scale_x_date(breaks = breaks, date_labels = "%b")
Is there any way to get a continuous plot basically joining the lines together? using any other package or language?
Suppose we have the data frame df1 shown in the Note at the end which has a values column with 22 * 31 = 682 rows, one for each of the 31 dates in January for each of the 22 years from 2000 to 2021.
Then convert to ts with frequency 31 and plot.
tt <- ts(df1$values, start = 2000, freq = 31)
plot(tt)
or to use ggplot2
library(ggplot2)
library(zoo)
z <- as.zoo(tt)
autoplot(z)
Note
set.seed(123)
date <- seq(as.Date("2000-01-01"), as.Date("2021-12-31"), 1)
values <- seq_along(date)
df1 <- subset(data.frame(date, values), months(date) == "January")
I'm trying to plot a ggplot graph of a time serie dataframe. However, the resultant plot is empty.
nombre_ERA5 <- format(as.Date(gsub(x=names(ERA5_prom),pattern="X",replacement="",fixed=T), format = "%Y.%m.%d"),format="%Y-%m-%d")
names(ERA5_prom) <- nombre_ERA5
#plot(ERA5_prom[366:731],type="l")
df <- data.frame(names(ERA5_prom))
colnames(df)[1]<-c("Fecha")
df$Valor <- ERA5_prom
ggplot()+geom_line(data = df,aes(x=Fecha,y=Valor))
df
Fecha Valor
1 2015-01-01 284.0547
2 2015-01-02 283.5696
3 2015-01-03 284.7942
4 2015-01-04 287.0302
5 2015-01-05 286.3637
Please try this one, please specify group
ggplot(data = df, aes(x=Fecha,y=Valor, group = 1)) + geom_line()+geom_point()
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I have the following dataframe where each user has 288 observations:
User5 User8 User10
2015-01-01 00:00:00 12.3 10.3 17.5
2015-01-01 00:30:00 20.1 12.7 20.9
2015-01-01 01:00:00 12.8 9.2 17.8
2015-01-01 01:30:00 11.5 6.9 12.5
2015-01-01 02:00:00 12.2 9.2 7.5
2015-01-01 02:30:00 9.2 14.2 9.0
.................... .... .... ....
2015-01-01 23:30:00 11.2 10.7 16.8
How can I make a graph with multiple graphs of each time series?
Another option is to convert from wide to long format then plot everything in the same graph. Below is the code that use the DF posted by #G. Grothendieck
library(tidyverse)
library(scales)
# Convert Time from factor to Date/Time
DF$Time <- as.POSIXct(DF$Time)
# Convert from wide to long format (`tidyr::gather`)
df_long <- DF %>% gather(key = "user", value = "value", -Time)
# Plot all together, color based on User
# We use pretty_breaks() from scales package for automatic Date/Time labeling
ggplot(df_long, aes(Time, value, group = user, color = user)) +
geom_line() +
scale_x_datetime(breaks = pretty_breaks()) +
theme_bw()
Edit: to plot each user in a separated panel, use facet_grid
ggplot(df_long, aes(Time, value, group = user, color = user)) +
geom_line() +
scale_x_datetime(breaks = pretty_breaks()) +
theme_bw() +
facet_grid(user ~ .)
Assuming the data frame DF in the Note at the end read it into a zoo object and then plot. Assuming that "multiple graphs" means one panel per user, any of the following 3 options can be used. If "mulitple graphs" means one panel with three lines in it, one per user, then add the screen = 1 argument to the first two and facet = NULL to the third.
library(zoo)
z <- read.zoo(DF, tz = "")
# 1
plot(z)
# 2
library(lattice)
xyplot(z)
# 3
library(ggplot2)
autoplot(z)
Note
Lines <- "
Time,User5,User8,User10
2015-01-01 00:00:00,12.3,10.3,17.5
2015-01-01 00:30:00,20.1,12.7,20.9
2015-01-01 01:00:00,12.8,9.2,17.8
2015-01-01 01:30:00,11.5,6.9,12.5
2015-01-01 02:00:00,12.2,9.2,7.5
2015-01-01 02:30:00,9.2,14.2,9
2015-01-01 23:30:00,11.2,10.7,16.8"
DF <- read.csv(text = Lines)
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I am trying to recreate a bar graph that I created in Excel using data that lists inventory and sales throughout the year. Here is my graph in Excel:
Note: Average sales rate is total sales / total inventory for the 13 months in the bar graph.
I am doing this through R and the ggplot package. I am quite new at this but this was what I managed so far:
library(lubridate)
library(ggplot2)
library(scales)
library(reshape2)
COdata <- read.csv("C:/.../CenterOne.csv")
# Grab related data
# VIN refers to a unique inventory identifier for the item
# First Launch Date is what I use to count my inventory for the month
# Sale Date is what I use to count my sales for the month
DFtest <- COdata[, c("VIN", "First.Launch.Date", "Sale.Date")]
Here is a snapshot of what the data looks like:
> head(DFtest)
VIN First.Launch.Date Sale.Date
1 4T1BF1FK4CU048373 22/04/2015 0:00
2 2T3KF4DVXCW108677 16/03/2015 0:00
3 4T1BF1FKXCU035935 19/03/2015 0:00 20/03/2015 0:00
4 JTDKN3DU3B1465796 16/04/2015 0:00
5 2T3YK4DV8CW015050
6 4T1BF1FK5CU599556 30/04/2015 0:00
I convert the dates to a proper format removing the hours/seconds and breaking them up into monthly intervals:
DFtest$First.Launch.Date <- as.Date(DFtest$First.Launch.Date, format = "%d/%m/%Y")
DFtest$Sale.Date <- as.Date(DFtest$Sale.Date, format = "%d/%m/%Y")
DFtest$month.listings <- as.Date(cut(DFtest$First.Launch.Date, breaks = "month"))
DFtest$month.sales <- as.Date(cut(DFtest$Sale.Date, breaks = "month"))
> head(DFtest)
VIN First.Launch.Date Sale.Date month.listings month.sales
1 4T1BF1FK4CU048373 2015-04-22 <NA> 2015-04-01 <NA>
2 2T3KF4DVXCW108677 2015-03-16 <NA> 2015-03-01 <NA>
3 4T1BF1FKXCU035935 2015-03-19 2015-03-20 2015-03-01 2015-03-01
4 JTDKN3DU3B1465796 2015-04-16 <NA> 2015-04-01 <NA>
5 2T3YK4DV8CW015050 <NA> <NA> <NA> <NA>
6 4T1BF1FK5CU599556 2015-04-30 <NA> 2015-04-01 <NA>
Avg line graph - my attempt at creating one
DF_Listings = data.frame(table(format(DFtest$month.listings)))
DF_Sales = data.frame(table(format(DFtest$month.sales)))
DF_Merge <- merge(DF_Listings, DF_Sales, by = "Var1", all = TRUE)
> head(DF_Listings)
Var1 Freq
1 2014-12-01 77
2 2015-01-01 886
3 2015-02-01 930
4 2015-03-01 1167
5 2015-04-01 1105
6 2015-05-01 1279
DF_Merge$Avg <- DF_Merge$Freq.y / DF_Merge$Freq.x
> head(DF_Merge)
Var1 Freq.x Freq.y Avg
1 2014-12-01 77 NA NA
2 2015-01-01 886 277 0.3126411
3 2015-02-01 930 383 0.4118280
4 2015-03-01 1167 510 0.4370180
5 2015-04-01 1105 309 0.2796380
6 2015-05-01 1279 319 0.2494136
ggplot(DF_Merge, aes(x=Var1, y=Avg, group = 1)) +
stat_smooth(aes(x = seq(length(unique(Var1)))),
se = F, method = "lm", formula = y ~ poly(x, 11))
Bar Graph
dfm <- melt(DFtest[ , c("VIN", "First.Launch.Date", "Sale.Date")], id.vars = 1)
dfm$value <- as.Date(cut(dfm$value, breaks = "month"))
ggplot(dfm, aes(x= value, width = 0.4)) +
geom_bar(aes(fill = variable), position = "dodge") +
scale_x_date(date_breaks = "months", labels = date_format("%m-%Y")) +
theme(axis.text.x=element_text(hjust = 0.5)) +
xlab("Date") + ylab("")
So I managed to make some of the plots which brings me to several questions:
How would I combine them into all a single graph using ggplot?
Notice how my bar graph has blanks for the first and last month? How do I remove that (precisely, how do I remove 11-2014 and 01-2016 from the x-axis)?
In my bar graph, January 2014 had no sales and as a result, the inventory bar takes up a larger space. How do I reduce its size to fit with the rest of the graph?
What could I do to change the x-axis from using dates as numbers (i.e. 12-2014) to using month-year in words (i.e. December-2014). I've tried using as.yearmon but that doesn't work with the scale_x_date portion of my ggplot function.
There's also the issue with the average sales rate line which I can safely assume I would be using geom_hline() but I am not sure how to approach this.
Using mtoto's suggestion of utilizing googleVis, I took a crack at recreating the graph:
# Testing Google Vis
mytest <- DF_Merge
library(zoo)
library(plyr) # to rename columns
library(googleVis)
mytest$Var1 <- as.yearmon(mytest$Var1)
mytest$Var1 <- as.factor(mytest$Var1) # googleVis cannot understand yearmon "class" so change it to factor
# Rename columns to ensure comprehension
mytest <- rename(mytest, c("Var1"="Date", "Freq.x"="Listings", "Freq.y"="Sales", "Avg"="Sales Rate"))
# Prepare for values to be displayed right on the plot
mytest$Listings.annotation <- mytest$Listings
mytest$Sales.annotation <- mytest$Sales
mytest$`Sales Rate.annotation` <- percent(mytest$`Sales Rate`) #Googlevis automatically understands that .annotation is used to display values in the graph
# Create average rate line
mytest$`Sales Rate` <- as.numeric(mytest$`Sales Rate`)
mytest$AvgRate <- (sum(mytest$Sales) / sum(mytest$Listings))
mytest <- rename(mytest, c("AvgRate"="Average Sales Rate"))
# Create the annotation for the average line
mytest$`Average Sales Rate.annotation` <- mytest$`Average Sales Rate`
x = nrow(mytest) - 1
mytest$`Average Sales Rate.annotation`[1:x] = "" # Ensures only the last row in this column has a value
mytest$`Average Sales Rate.annotation` <- as.numeric(mytest$`Average Sales Rate.annotation`, na.rm = TRUE)
mytest$`Average Sales Rate.annotation`[nrow(mytest)] <- percent(mytest$`Average Sales Rate.annotation`[nrow(mytest)]) # Transforms only the last row to a proper percentage!
# Plot the graph
column <- gvisComboChart(mytest, xvar= "Date",
yvar=c("Listings", "Listings.annotation", "Sales", "Sales.annotation", "Sales Rate", "Sales Rate.annotation", "Average Sales Rate",
"Average Sales Rate.annotation"),
options=list(seriesType="bars",
series="[{type: 'bars', targetAxisIndex:0, color:'orange'},
{type: 'bars', targetAxisIndex:0, color:'green'},
{type: 'line', targetAxisIndex:1, color:'red'},
{type: 'line', targetAxisIndex:1, color:'purple', lineDashStyle:[2,2,20,2,20,2]}]",
vAxes="[{format:'decimal', textPosition: 'out', viewWindow:{min:0, max:200}},
{format:'percent', textPosition: 'out', viewWindow:{min:0, max:1}}]",
hAxes="[{textPosition: 'out'}]",
legend = "bottom",
curveType="function",
width=1500,
height=800))
plot(column)
The variables could have been named better but I was able to get what I was looking for with my final result:
With ggplot2, I would like to create a multiplot (facet_grid) where each plot is the weekly count values for the month.
My data are like this :
day_group count
1 2012-04-29 140
2 2012-05-06 12595
3 2012-05-13 12506
4 2012-05-20 14857
I have created for this dataset two others colums the Month and the Week based on day_group :
day_group count Month Week
1 2012-04-29 140 Apr 17
2 2012-05-06 12595 May 18
3 2012-05-13 12506 May 19
4 2012-05-20 14857 May 2
Now I would like for each Month to create a barplot where I have the sum of the count values aggregated by week. So for example for a year I would have 12 plots with 4 bars (one per week).
Below is what I use to generate the plot :
ggplot(data = count_by_day, aes(x=day_group, y=count)) +
stat_summary(fun.y="sum", geom = "bar") +
scale_x_date(date_breaks = "1 month", date_labels = "%B") +
facet_grid(facets = Month ~ ., scales="free", margins = FALSE)
So far, my plot looks like this
https://dl.dropboxusercontent.com/u/96280295/Rplot.png
As you can see the x axes is not as I'm looking for. Instead of showing only week 1, 2, 3 and 4, it displays all the month.
Do you know what I must change to get what I'm looking for ?
Thanks for your help
Okay, now that I see what you want, I wrote a small program to illustrate it. The key to your order of month problem is making month a factor with the levels in the right order:
library(dplyr)
library(ggplot2)
#initialization
set.seed(1234)
sday <- as.Date("2012-01-01")
eday <- as.Date("2012-07-31")
# List of the first day of the months
mfdays <- seq(sday,length.out=12,by="1 month")
# list of months - this is key to keeping the order straight
mlabs <- months(mfdays)
# list of first weeks of the months
mfweek <- trunc((mfdays-sday)/7)
names(mfweek) <- mlabs
# Generate a bunch of event-days, and then months, then week numbs in our range
n <- 1000
edf <-data.frame(date=sample(seq(sday,eday,by=1),n,T))
edf$month <- factor(months(edf$date),levels=mlabs) # use the factor in the right order
edf$week <- 1 + as.integer(((edf$date-sday)/7) - mfweek[edf$month])
# Now summarize with dplyr
ndf <- group_by(edf,month,week) %>% summarize( count = n() )
ggplot(ndf) + geom_bar(aes(x=week,y=count),stat="identity") + facet_wrap(~month,nrow=1)
Yielding:
(As an aside, I am kind of proud I did this without lubridate ...)
I think you have to do this but I am not sure I understand your question:
ggplot(data = count_by_day, aes(x=Week, y=count, group= Month, color=Month))