I already asked the same question yesterday, but I didnt get any suggestions until now, so I decided to delete the old one and ask again, giving additional infos.
So here again:
I have a dataframe like this:
Link to the original dataframe: https://megastore.uni-augsburg.de/get/JVu_V51GvQ/
Date DENI011
1 1993-01-01 9.946
2 1993-01-02 13.663
3 1993-01-03 6.502
4 1993-01-04 6.031
5 1993-01-05 15.241
6 1993-01-06 6.561
....
....
6569 2010-12-26 44.113
6570 2010-12-27 34.764
6571 2010-12-28 51.659
6572 2010-12-29 28.259
6573 2010-12-30 19.512
6574 2010-12-31 30.231
I want to create a plot that enables me to compare the monthly values in the DENI011 over the years. So I want to have something like this:
http://r-statistics.co/Top50-Ggplot2-Visualizations-MasterList-R-Code.html#Seasonal%20Plot
Jan-Dec on the x-scale, values on the y-scale and the years displayed by different colored lines.
I found several similar questions here, but nothing works for me. I tried to follow the instructions on the website with the example, but the problem is that I cant create a ts-object.
Then I tried it this way:
Ref_Data$MonthN <- as.numeric(format(as.Date(Ref_Data$Date),"%m")) # Month's number
Ref_Data$YearN <- as.numeric(format(as.Date(Ref_Data$Date),"%Y"))
Ref_Data$Month <- months(as.Date(Ref_Data$Date), abbreviate=TRUE) # Month's abbr.
g <- ggplot(data = Ref_Data, aes(x = MonthN, y = DENI011, group = YearN, colour=YearN)) +
geom_line() +
scale_x_discrete(breaks = Ref_Data$MonthN, labels = Ref_Data$Month)
That also didnt work, the plot looks horrible. I dont need to put all the years in 1 plot from 1993-2010. Actually only a few years would be ok, like from 1998-2006 maybe.
And suggestions, how to solve this?
As others have noted, in order to create a plot such as the one you used as an example, you'll have to aggregate your data first. However, it's also possible to retain daily data in a similar plot.
reprex::reprex_info()
#> Created by the reprex package v0.1.1.9000 on 2018-02-11
library(tidyverse)
library(lubridate)
# Import the data
url <- "https://megastore.uni-augsburg.de/get/JVu_V51GvQ/"
raw <- read.table(url, stringsAsFactors = FALSE)
# Parse the dates, and use lower case names
df <- as_tibble(raw) %>%
rename_all(tolower) %>%
mutate(date = ymd(date))
One trick to achieve this would be to set the year component in your date variable to a constant, effectively collapsing the dates to a single year, and then controlling the axis labelling so that you don't include the constant year in the plot.
# Define the plot
p <- df %>%
mutate(
year = factor(year(date)), # use year to define separate curves
date = update(date, year = 1) # use a constant year for the x-axis
) %>%
ggplot(aes(date, deni011, color = year)) +
scale_x_date(date_breaks = "1 month", date_labels = "%b")
# Raw daily data
p + geom_line()
In this case though, your daily data are quite variable, so this is a bit of a mess. You could hone in on a single year to see the daily variation a bit better.
# Hone in on a single year
p + geom_line(aes(group = year), color = "black", alpha = 0.1) +
geom_line(data = function(x) filter(x, year == 2010), size = 1)
But ultimately, if you want to look a several years at a time, it's probably a good idea to present smoothed lines rather than raw daily values. Or, indeed, some monthly aggregate.
# Smoothed version
p + geom_smooth(se = F)
#> `geom_smooth()` using method = 'loess'
#> Warning: Removed 117 rows containing non-finite values (stat_smooth).
There are multiple values from one month, so when plotting your original data, you got multiple points in one month. Therefore, the line looks strange.
If you want to create something similar to the example your provided, you have to summarize your data by year and month. Below I calculated the mean of each year and month for your data. In addition, you need to convert your year and month to factors if you want to plot it as discrete variables.
library(dplyr)
Ref_Data2 <- Ref_Data %>%
group_by(MonthN, YearN, Month) %>%
summarize(DENI011 = mean(DENI011)) %>%
ungroup() %>%
# Convert the Month column to factor variable with levels from Jan to Dec
# Convert the YearN column to factor
mutate(Month = factor(Month, levels = unique(Month)),
YearN = as.factor(YearN))
g <- ggplot(data = Ref_Data2,
aes(x = Month, y = DENI011, group = YearN, colour = YearN)) +
geom_line()
g
If you don't want to add in library(dplyr), this is the base R code. Exact same strategy and results as www's answer.
dat <- read.delim("~/Downloads/df1.dat", sep = " ")
dat$Date <- as.Date(dat$Date)
dat$month <- factor(months(dat$Date, TRUE), levels = month.abb)
dat$year <- gsub("-.*", "", dat$Date)
month_summary <- aggregate(DENI011 ~ month + year, data = dat, mean)
ggplot(month_summary, aes(month, DENI011, color = year, group = year)) +
geom_path()
Related
i have a dataset given with:
Country Time Value
1 USA 1999-Q1 292929
2 USA 1999-Q2 392023
3. USA 1999-Q3 9392992
4
.... and so on. Now I would like to plot this dataframe with Time being on the x-axis and y being the Value. But the problem I face is I dont know how to plot the Time. Because it is not given in month/date/year. If that would be the case I would just code as.Date( format = "%m%d%y"). I am not allowed to change the quarterly name. So when I plot it, it should stay that way. How can I do this?
Thank you in advance!
Assuming DF shown in the Note at the end, convert the Time column to yearqtr class which directly represents year and quarter (as opposed to using Date class) and use scale_x_yearqtr. See ?scale_x_yearqtr for more information.
library(ggplot2)
library(zoo)
fmt <- "%Y-Q%q"
DF$Time <- as.yearqtr(DF$Time, format = fmt)
ggplot(DF, aes(Time, Value, col = Country)) +
geom_point() +
geom_line() +
scale_x_yearqtr(format = fmt)
(continued after graphics)
It would also be possible to convert it to a wide form zoo object with one column per country and then use autoplot. Using DF from the Note below:
fmt <- "%Y-Q%q"
z <- read.zoo(DF, split = "Country", index = "Time",
FUN = as.yearqtr, format = fmt)
autoplot(z) + scale_x_yearqtr(format = fmt)
Note
Lines <- "
Country Time Value
1 USA 1999-Q1 292929
2 USA 1999-Q2 392023
3 USA 1999-Q3 9392992"
DF <- read.table(text = Lines)
Using ggplot2:
library(ggplot2)
ggplot(df, aes(Time, Value, fill = Country)) + geom_col()
I know other people have already answered, but I think this more general answer should also be here.
When you do as.Date(), you can only do the beginning. I tried it on your data frame (I called it df), and it worked:
> as.Date(df$Time, format = "%Y")
[1] "1999-11-28" "1999-11-28" "1999-11-28"
Now, I don't know if you want to use plot(), ggplot(), the ggplot2 library... I don't know that, and it doesn't matter. However you want to specify the y axis, you can do it this way.
SO!
I am trying to create a plot of monthly deviations from annual means for temperature data using a bar chart. I have data across many years and I want to show the seasonal behavior in temperatures between months. The bars should represent the deviation from the annual average, which is recalculated for each year. Here is an example that is similar to what I want, only it is for a single year:
My data is sensitive so I cannot share it yet, but I made a reproducible example using the txhousing dataset (it comes with ggplot2). The salesdiff column is the deviation between monthly sales (averaged acrross all cities) and the annual average for each year. Now the problem is plotting it.
library(ggplot2)
df <- aggregate(sales~month+year,txhousing,mean)
df2 <- aggregate(sales~year,txhousing,mean)
df2$sales2 <- df2$sales #RENAME sales
df2 <- df2[,-2] #REMOVE sales
df3<-merge(df,df2) #MERGE dataframes
df3$salesdiff <- df3$sales - df3$sales2 #FIND deviation between monthly and annual means
#plot deviations
ggplot(df3,aes(x=month,y=salesdiff)) +
geom_col()
My ggplot is not looking good at the moment-
Somehow it is stacking the columns for each month with all of the data across the years. Ideally the date would be along the x-axis spanning many years (I think the dataset is from 2000-2015...), and different colors depending on if salesdiff is higher or lower. You are all awesome, and I would welcome ANY advice!!!!
Probably the main issue here is that geom_col() will not take on different aesthetic properties unless you explicitly tell it to. One way to get what you want is to use two calls to geom_col() to create two different bar charts that will be combined together in two different layers. Also, you're going to need to create date information which can be easily passed to ggplot(); I use the lubridate() package for this task.
Note that we combine the "month" and "year" columns here, and then useymd() to obtain date values. I chose not to convert the double valued "date" column in txhousing using something like date_decimal(), because sometimes it can confuse February and January months (e.g. Feb 1 gets "rounded down" to Jan 31).
I decided to plot a subset of the txhousing dataset, which is a lot more convenient to display for teaching purposes.
Code:
library("tidyverse")
library("ggplot2")
# subset txhousing to just years >= 2011, and calculate nested means and dates
housing_df <- filter(txhousing, year >= 2011) %>%
group_by(year, month) %>%
summarise(monthly_mean = mean(sales, na.rm = TRUE),
date = first(date)) %>%
mutate(yearmon = paste(year, month, sep = "-"),
date = ymd(yearmon, truncated = 1), # create date column
salesdiff = monthly_mean - mean(monthly_mean), # monthly deviation
higherlower = case_when(salesdiff >= 0 ~ "higher", # for fill aes later
salesdiff < 0 ~ "lower"))
ggplot(data = housing_df, aes(x = date, y = salesdiff, fill = as.factor(higherlower))) +
geom_col() +
scale_x_date(date_breaks = "6 months",
date_labels = "%b-%Y") +
scale_fill_manual(values = c("higher" = "blue", "lower" = "red")) +
theme_bw()+
theme(legend.position = "none") # remove legend
Plot:
You can see the periodic behaviour here nicely; an increase in sales appears to occur every spring, with sales decreasing during the fall and winter months. Do keep in mind that you might want to reverse the colours I assigned if you want to use this code for temperature data! This was a fun one - good luck, and happy plotting!
Something like this should work?
Basically you need to create a binary variable that lets you change the color (fill) if salesdiff is positive or negative, called below factordiff.
Plus you needed a date variable for month and year combined.
library(ggplot2)
library(dplyr)
df3$factordiff <- ifelse(df3$salesdiff>0, 1, 0) # factor variable for colors
df3 <- df3 %>%
mutate(date = paste0(year,"-", month), # this builds date like "2001-1"
date = format(date, format="%Y-%m")) # here we create the correct date format
#plot deviations
ggplot(df3,aes(x=date,y=salesdiff, fill = as.factor(factordiff))) +
geom_col()
Of course this results in a hard to read plot because you have lots of dates, you can subset it and show only a restricted time:
df3 %>%
filter(date >= "2014-1") %>% # we filter our data from 2014
ggplot(aes(x=date,y=salesdiff, fill = as.factor(factordiff))) +
geom_col() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) # adds label rotation
I have been trying to plot a graph between two columns from a data frame which I had created. The data values stored in the first column is daily time data named "Time"(format- YYYY-MM-DD) and the second column contains precipitation magnitude, which is a numeric value named "data1".
This data is taken from an excel file "St Lucia3" which has a total 11598 data points and stores daily precipitation data from 1981 to 2018 in two columns:
YearMonthDay (format- "YYYYMMDD", example "19810501")
Rainfall (mm)
The code for importing data into R:
StLucia <- read_excel("C:/Users/hp/Desktop/St Lucia3.xlsx")
The code for time data "Time" :
Time <- as.Date(as.character(StLucia$YearMonthDay), format= "%Y%m%d")
The code for precipitation data "data1" :
library("imputeTS")
data1 <- na_ma(StLucia$`Rainfall (mm)`, k = 4, weighting = "exponential")
The code for data frame "Pecip1" :
Precip1 <- data.frame(Time, data1, check.rows=TRUE)
The code for ggplot is:
ggplot(data = Precip1, mapping= aes(x= Time, y= data1)) + geom_line()
Using ggplot for plotting the graph between "Time" and "data1" results as:
Can someone please explain to me why there is an "unusual kink" like behavior at the right end of the graph, even though there are no such values in the column "data1".
The plot of "data1" data against its index is as shown:
The code for this plot is:
plot(data1, type = "l")
Any help would be highly appreciated. Thanks!
By using pad we can make up for those lost values an assign an NA value as to
avoid plotting in the region of missing data.
library(padr)
library(zoo)
YearMonthDay<-c(19810501,19810502,19810504,19810505)
Data<-c(1,2,3,4)
StLucia<-data.frame(YearMonthDay,Data)
StLucia$YearMonthDay <- as.Date(as.character(StLucia$YearMonthDay), format=
"%Y%m%d")
> StLucia
YearMonthDay Data
1 1981-05-01 1
2 1981-05-02 2
3 1981-05-04 3
4 1981-05-05 4
Note: you can see we are missing a date, but still there is no gap between position 2 and 3, thus plotting versus indexing you would not see a gap.
So lets add the missing date:
StLucia<-pad(StLucia,interval="day")
> StLucia
YearMonthDay Data
1 1981-05-01 1
2 1981-05-02 2
3 1981-05-03 NA
4 1981-05-04 3
5 1981-05-05 4
plot(StLucia, type = "l")
If you want to fill in those NA values, use na.locf() from package(zoo)
Here is a reproducible example - change the names to match your data.
# create sample data
set.seed(47)
dd = data.frame(t = Sys.Date() + c(0:5, 30:32), y = runif(9))
# demonstrate problem
ggplot(dd, aes(t, y)) +
geom_point() +
geom_line()
The easiest solution, as Tung points out, is to use a more appropriate geom, like geom_col:
ggplot(dd, aes(t, y)) +
geom_col()
If you really want to use lines, you should fill in the missing dates with NA for rainfall. H
# calculate all days
all_days = data.frame(t = seq.Date(from = min(dd$t), to = max(dd$t), by = "day"))
# join to original data
library(dplyr)
dd_complete = left_join(all_days, dd, by = "t")
# ggplot won't connect lines across missing values
ggplot(dd_complete, aes(t, y)) +
geom_point() +
geom_line()
Alternately, you could replace the missing values with 0s to have the line just go along the axis, but I think it's nicer to not plot the line, which implies no data/missing data, rather than plot 0s which implies no rainfall.
I am struggling (due to lack of knowledge and experience) to create a plot in R with time series from three different years (2009, 2013 and 2017). Failing to solve this problem by searching online has led me here.
I wish to create a plot that shows change in nitrate concentrations over the course of May to October for all years, but keep failing since the x-axis is defined by one specific year. I also receive errors because the x-axis lengths differ (due to different number of samples). To solve this I have tried making separate columns for month and year, with no success.
Data example:
date NO3.mg.l year month
2009-04-22 1.057495 2009 4
2013-05-08 1.936000 2013 5
2017-05-02 2.608000 2017 5
Code:
ggplot(nitrat.all, aes(x = date, y = NO3.mg.l, colour = year)) + geom_line()
This code produces a plot where the lines are positioned next to one another, whilst I want a plot where they overlay one another. Any help will be much appreciated.
Nitrate plot
Probably, that will be helpful for plotting:
library("lubridate")
library("ggplot2")
# evample of data with some points for each year
nitrat.all <- data.frame(date = c(ymd("2009-03-21"), ymd("2009-04-22"), ymd("2009-05-27"),
ymd("2010-03-15"), ymd("2010-04-17"), ymd("2010-05-10")), NO3.mg.l = c(1.057495, 1.936000, 2.608000,
3.157495, 2.336000, 3.908000))
nitrat.all$year <- format(nitrat.all$date, format = "%Y")
ggplot(data = nitrat.all) +
geom_point(mapping = aes(x = format(date, format = "%m-%d"), y = NO3.mg.l, group = year, colour = year)) +
geom_line(mapping = aes(x = format(date, format = "%m-%d"), y = NO3.mg.l, group = year, colour = year))
As for selecting of the dates corresponding to a certain month, you may subset your data frame by a condition using basic R-functions:
n_month1 <- 3 # an index of the first month of the period to select
n_month2 <- 4 # an index of the first month of the period to select
test_for_month <- (as.numeric(format(nitrat.all$date, format = "%m")) >= n_month1) &
(as.numeric(format(nitrat.all$date, format = "%m")) <= n_month2)
nitrat_to_plot <- nitrat.all[test_for_month, ]
Another quite an elegant approach is to use filter() from dplyr package
nitrat.all$month <- as.numeric(format(nitrat.all$date, format = "%m"))
library("dplyr")
nitrat_to_plot <- filter(nitrat.all, ((month >= n_month1) & (month <= n_month2)))
I am trying to plot a line graph with multiple lines in different colors, but not having much luck. My data set consists of 10 states and the voting turnout rates for each state from 9 elections (so the states are listed in the left column, and each subsequent column is an election year from 1980-2012 with the voting turnout rate for each of the 10 states). I would like to have a graph with the year on the X axis and the voting turnout rate on the Y axis, with a line for each state.
I found this previous answer (Plotting multiple lines from a data frame in R) to a similar question but cannot seem to replicate it using my data. Any ideas/suggestions would be immensely appreciated!
Use tidyr::gather or reshape::melt to transform the data to a long form.
## Simulate data
d <- data.frame(state=letters[1:10],
'1980'=runif(10,0,100),
'1981'=runif(10,0,100),
'1982'=runif(10,0,100))
library(dplyr)
library(tidyr)
library(ggplot2)
## Transform to a long df
e <- d %>% gather(., key, value, -state) %>%
mutate(year = as.numeric(substr(as.character(key), 2, 5))) %>%
select(-key)
## Plot
ggplot(data=e,aes(x=year,y=value,color=state)) +
geom_point() +
geom_line()
Please include your data, or sample data, in your question so that we can answer your question directly and help you get to the root of the problem. Pasting your data is simplified by using dput().
Here's another solution to your problem, using scoa's sample data and the reshape2 package instead of the tidyr package:
# Sample data
d <- data.frame(state = letters[1:10],
'1980' = runif(10,0,100),
'1981' = runif(10,0,100),
'1982' = runif(10,0,100))
library(reshape2)
library(ggplot2)
# Melt data and remove X introduced into year name
melt.d <- melt(d, id = "state")
melt.d[["variable"]] <- gsub("X", "", melt.td[["variable"]])
# Plot melted data
ggplot(data = melt.d,
aes(x = variable,
y = value,
group = state,
color = state)) +
geom_point() +
geom_line()
Produces:
Note that I left out the as.numeric() conversion for year from scoa's example, and this is why the graph above does not include the extra x-axis ticks that scoa's does.