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$Valor <- ERA5_prom
ggplot()+geom_line(data = df,aes(x=Fecha,y=Valor))
df
Fecha Valor
1 2015-01-01 284.0547
2 2015-01-02 283.5696
3 2015-01-03 284.7942
4 2015-01-04 287.0302
5 2015-01-05 286.3637
Please try this one, please specify group
ggplot(data = df, aes(x=Fecha,y=Valor, group = 1)) + geom_line()+geom_point()
Related
This question has been asked multiple times but I cannot find any that fit my needs.
My goal is to plot timeseries for one month over multiple years. The following JAN dataframe is created by sub-setting from data frame containing daily rainfall for the entire year.
> head(JAN)
DATE RCM GPM TRI
1: 2000-01-01 0.012182957 NA NA
2: 2000-01-02 0.001769934 NA NA
3: 2000-01-03 0.007916438 NA NA
4: 2000-01-04 0.008227825 NA NA
5: 2000-01-05 0.005192382 NA NA
6: 2000-01-06 0.065458169 NA NA
The dataframe is for the month of January containing daily records over 20 years.
I got the following plot.
dfmelt<-melt(JAN,id.vars="DATE")
ggplot(dfmelt,aes(x=DATE,y=value,
col=variable,group = lubridate::year(DATE)))+
labs(title='JANUARY')+
geom_line()
I'm assuming it's because my data consists only January months and while plotting breaks are there for February to December.
I want to avoid this to see the trend of precipitation over the years for the month january.
introducing breaks give the following
breaks <- unique(as.Date(cut(dfmelt$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$values, start = 2000, freq = 31)
plot(tt)
or to use ggplot2
library(ggplot2)
library(zoo)
z <- as.zoo(tt)
autoplot(z)
Note
set.seed(123)
date <- seq(as.Date("2000-01-01"), as.Date("2021-12-31"), 1)
values <- seq_along(date)
df1 <- subset(data.frame(date, values), months(date) == "January")
I currently have a plot of my data that looks like this:
However because of the negative spike in around 2017, the graph shows values above and below the x axis. How do I make it so the graph only shows values above the x axis?
This is the code I am currently using to produce my graph
plot(dandpw)
addLegend(lty = 1)
mydata
> head(dandpw)
QLD1.Price NSW1.Price VIC1.Price SA1.Price TAS1.Price
2008-01-07 10:30:00 33.81019 36.52777 49.66935 216.45379 30.88968
2008-01-14 10:30:00 45.09321 37.55887 49.04155 248.33518 51.16057
2008-01-21 10:30:00 27.22551 29.57798 31.28935 31.56158 45.99226
2008-01-28 10:30:00 26.14283 27.32113 30.20470 31.90042 53.48170
2008-02-04 10:30:00 91.86961 36.77000 37.09027 37.57167 56.28464
2008-02-11 10:30:00 62.60607 28.83509 34.95866 35.18217 55.78961
dput(head(dandpw
You can do this in two ways. Since there is no usable dput (only the picture), I assume your data is in a data frame.
You can remove negative numbers from your dataset
You can put limits on the y-axis shown in the chart (using ggplot2)
Method 1 (not recommended as it alters your data):
#remove negatives and replace with NA. Can also replace with 0 if desired
dandpw[dandpw < 0] <- NA
Method 2:
#assume dandpw is data frame
library(tidyverse)
names(dandpw)[1] <- "date" #looks like your date column might not be named
#ggplot prefers long format
dandpw <- dandpw %>% gather(variables, values, -date)
ggplot(data = dandpw, aes(x = date, y = values, color = variables)) +
geom_line() +
coord_cartesian(ylim = c(0, max(dandpw$values, na.rm = T) * 1.1 ))
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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))
I want to plot the graph for the time series data for one week, like:
Day1 Time Day2 Time Day3 Time.......Day7 Time
123 00:00hr 7897 00:00hr 4662 00:00hr 1235 00:00hr
4562 01:00hr 4645 01:00hr 4564 01:00hr....7898 01:00hr
..... ....... .... ....... .... ............... ......
..... ....... .... ...... .... ............... .....
.... ..... ....... ...............................................
4653 23:00 46456 23:00hr 7895 23:00hr 7892 23:00hr
I want to plot the graph for above time series data in same graph.The graph should be like it first plot the graph for Day1(of all the 24 hrs) next of it Day2,Day3...consecutively.
The X-axis should be of time index,like (day1,00),(day1,01).......(day2,00)...
Please help me out of these problem
Data generation:
set.seed(1)
df <- data.frame(matrix(round(runif(24*7,0,1000), 0), ncol=7))
colnames(df) <- paste0("Day", 1:7)
df$Time <- c(sprintf("0%d:00", 0:9), sprintf("%d:00", 10:23))
df
Here, I believe, you don't need seven Time columns with same values.
Now we can use common reshape and ggplot routine:
require(reshape2)
require(ggplot2)
mdf <- melt(df, id.vars="Time")
g <- ggplot(mdf, aes(x=1:(24*7))) + geom_line(aes(y=value))
g
Which produces the plot:
Some aesthetics for x axis:
g + scale_x_continuous(breaks=seq(0, 24*7, 24), labels=0:7, name="Days")
Basic graphics solution:
plot(mdf$value, type="l")