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.
Related
I am new to coding in R, when I was using ggplot2 to make a line graph, I get vertical lines. This is my code:
all_trips_v2 %>%
group_by(Month_Name, member_casual) %>%
summarise(average_duration = mean(length_of_ride))%>%
ggplot(aes(x = Month_Name, y = average_duration)) + geom_line()
And I'm getting something like this:
This is a sample of my data:
(Not all the cells in the Month_Name is August, it's just sorted)
Any help will be greatly appreciated! Thank you.
I added a bit more code just for the mere example. the data i chose is probably not the best choice to display a proper timer series.
I hope the features of ggplot i displayed will be benficial for you in the future
library(tidyverse)
library(lubridate)
mydat <- sample_frac(storms,.4)
# setting the month of interest as the current system's month
month_of_interest <- month(Sys.Date(),label = TRUE)
mydat %>% group_by(year,month) %>%
summarise(avg_pressure = mean(pressure)) %>%
mutate(month = month(month,label = TRUE),
current_month = month == month_of_interest) %>%
# the mutate code is just for my example.
ggplot(aes(x=year, y=avg_pressure,
color=current_month,
group=month,
size=current_month
))+geom_line(show.legend = FALSE)+
## From here its not really important,
## just ideas for your next plots
scale_color_manual(values=c("grey","red"))+
scale_size_manual(values = c(.4,1))+
ggtitle(paste("Averge yearly pressure,\n
with special interest in",month_of_interest))+
theme_minimal()
## Most important is that you notice the group argument and also,
# in most cases you will want to color your different lines.
# I added a logical variable so only October will be colored,
# but that is not mandatory
You should add a grouping argument.
see further info here:
https://ggplot2.tidyverse.org/reference/aes_group_order.html
# Multiple groups with one aesthetic
p <- ggplot(nlme::Oxboys, aes(age, height))
# The default is not sufficient here. A single line tries to connect all
# the observations.
p + geom_line()
# To fix this, use the group aesthetic to map a different line for each
# subject.
p + geom_line(aes(group = Subject))
I have several datasets and my end goal is to do a graph out of them, with each line representing the yearly variation for the given information. I finally joined and combined my data (as it was in a per month structure) into a table that just contains the yearly means for each item I want to graph (column depicting year and subsequent rows depicting yearly variation for 4 different elements)
I have one factor that is the year and 4 different variables that read yearly variations, thus I would like to graph them on the same space. I had the idea to joint the 4 columns into one by factor (collapse into one observation per row and the year or factor in the subsequent row) but seem unable to do that. My thought is that this would give a structure to my y axis. Would like some advise, and to know if my approach to the problem is effective. I am trying ggplot2 but does not seem to work without a defined (or a pre defined range) y axis. Thanks
I would suggest next approach. You have to reshape your data from wide to long as next example. In that way is possible to see all variables. As no data is provided, this solution is sketched using dummy data. Also, you can change lines to other geom you want like points:
library(tidyverse)
set.seed(123)
#Data
df <- data.frame(year=1990:2000,
v1=rnorm(11,2,1),
v2=rnorm(11,3,2),
v3=rnorm(11,4,1),
v4=rnorm(11,5,2))
#Plot
df %>% pivot_longer(-year) %>%
ggplot(aes(x=factor(year),y=value,group=name,color=name))+
geom_line()+
theme_bw()
Output:
We could use melt from reshape2 without loading multiple other packages
library(reshape2)
library(ggplot2)
ggplot(melt(df, id.var = 'year'), aes(x = factor(year), y = value,
group = variable, color = variable)) +
geom_line()
-output plot
Or with matplot from base R
matplot(as.matrix(df[-1]), type = 'l', xaxt = 'n')
data
set.seed(123)
df <- data.frame(year=1990:2000,
v1=rnorm(11,2,1),
v2=rnorm(11,3,2),
v3=rnorm(11,4,1),
v4=rnorm(11,5,2))
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()
i've decadal time series from 1700 to 1900 (21 time slices) and for each decade i've got 7 categories that represent a quantity; see here
As you can see, only 5 of the decades actually have data.
I can plot a nice little stacked area chart in R, with the help of this very nice example, which retains only the 5 time slices that have data.
My problem is that i want an x-axis that retains all 21 times slices but still plots a stacked area chart using only the 5 time slices. The idea is that the stacked areas will still only be plotted against the correct year but simply connect up to the next point, 10 ticks down the x-axis, ignoring the no-data in between. i can achieve something in excel but i dont like it.
My reasoning is i want to plot lines on the top of the stacked area that are much more complete, for example from 1700 to 1850, or 1800 to 1900, for visual comparison purposes.
This post suggests how to connect dots in a line chart when you want to ignore NAs but it doesnt work for me in this instance.
a <- 1700:1900
b <- a[seq(1, length(a), 10)]
df <- data.frame("Year"=b,replicate(7,sample(1:21)))
rows <- c(2:10,11:15,17,19,21)
df[rows,2:8] <- NA
df
thanks a lot
If you wish to transform your year to factor, on the lines of the code below:
# Transform the data to long
library(reshape2)
df <- melt(data = df, na.rm = FALSE, id.vars = "Year")
df$Year <- as.factor(df$Year)
# Chart
require(ggplot2)
ggplot(df, aes(Year, value)) +
geom_area(aes(colour = variable, fill= variable), position = 'stack')
It will generate the chart below:
I wasn't sure if you are interested in mapping all of the X variables. I was thinking that this is the case so I reshaped your data. Presumably, it is wiser not to change the Year to factor. The code below:
a <- 1700:1900
b <- a[seq(1, length(a), 10)]
df <- data.frame("Year"=b,replicate(7,sample(1:21)))
rows <- c(2:10,11:15,17,19,21)
df[rows,2:8] <- NA
# Transform the data to long
library(reshape2)
df <- melt(data = df, na.rm = FALSE, id.vars = "Year")
# Leave it as int.
# df$Year <- as.factor(df$Year)
# Chart
require(ggplot2)
ggplot(df, aes(Year, value)) +
geom_area(aes(colour = variable, fill= variable), position = 'stack')
would generate much more meaningful chart:
Potentially, if you decide to use years as factors you may group them and have one category for a number of missing years so the x-axis is more readable. I would say it's a matter of presentation to great extent.
I have been trying to plot a line plot with ggplot.
My data looks something like this:
I04 F04 I05 F05 I06 F06
CAT 3 12 2 6 6 20
DOG 0 0 0 0 0 0
BIEBER 1 0 0 1 0 0
and can be found here.
Basically, we have a certain number of CATs (or other creatures) initially in a year (this is I04), and a certain number of CATs at the end of the year (this is F04). This goes on for some time.
I can plot something like this fairly simply using the code below, and get this:
This is fantastic, but doesn't work very well for me. After all, I have these staring and ending inventory for each year. So I am interested in seeing how the initial values (I04, I05, I06) change over time. So, for each animal, I would like to create two different lines, one for initial quantity and one for final quantity (F01, F05, F06). This seems to me like now I have to consider two factors.
This is really difficult given the way my data is set up. I'm not sure how to tell ggplot that all the I prefixed years are one factor, and all the F prefixed years are another factor. When the dataframe gets melted, it's too late. I'm not sure how to control this situation.
Any advice on how I can separate these values or perhaps another, better way to tackle this situation?
Here is the code I have:
library(ggplot2)
library(reshape2)
DF <- read.csv("mydata.csv", stringsAsFactors=FALSE)
## cleaning up, converting factors to numeric, etc
text_names <- data.frame(as.character(DF$animals))
names(text_names) <- c("animals")
numeric_cols <- DF[, -c(1)]
numeric_cols <- sapply(numeric_cols, as.numeric)
plot_me <- data.frame(cbind(text_names, numeric_cols))
plot_me$animals <- as.factor(plot_me$animals)
meltedDF <- melt(plot_me)
p <- ggplot()
p <- p + geom_line(aes(seq(1:36), meltedDF$value, group=meltedDF$animals, color=meltedDF$animals))
p
Using your original data from the link:
nd <- reshape(mydata, idvar = "animals", direction = "long", varying = names(mydata)[-1], sep = "")
ggplot(nd, aes(x = time, y = I, group = animals, colour = animals)) + geom_line() + ggtitle("Development of initial inventories")
ggplot(nd, aes(x = time, y = F, group = animals, colour = animals)) + geom_line() + ggtitle("Development of final inventories")
I think from a data analyst perspective the following approach might provide better insight.
For each animal we visualize the initial and the final quantity in a separate panel. Moreover, each subplot has its own y scale because the values of the different animal types are radically different. Like this, differences within and across animal types are easier to spot.
Given the current structure of your data, we do not need two different factors. After the gather call the indicator column includes data like I04, F04, etc. We just need to separate the first character from the rest resulting in two columns type and time. We can use type as the argument for color in the ggplot call. time provides a unified x-axis across all animal types.
library(tidyr)
library(dplyr)
library(ggplot2)
data %>% gather(indicator, value, -animals) %>%
separate(indicator, c('type', 'time'), sep = 1) %>%
mutate(
time = as.numeric(time)
) %>% ggplot(aes(time, value, color = type)) +
geom_line() +
facet_grid(animals ~ ., scales = "free_y")
Of course, you might also do it the other way round, namely using a subplot for the initial and the final quantities like this:
data %>% gather(indicator, value, -animals) %>%
separate(indicator, c('type', 'time'), sep=1) %>%
mutate(
time = as.numeric(time)
) %>% ggplot(aes(time, value, color = animals)) +
geom_line() +
facet_grid(type ~ ., scales = "free_y")
But as described above, I would not recommend that because the y scale varies too much across animal types.