I have a data frame with data for max 2 years period on different objects:
ISBN Date Quantity
3457 2004-06-15 10
3457 2004-08-16 6
3457 2004-08-19 10
3457 2005-04-19 7
3457 2005-04-20 12
9885 2013-01-15 10
9885 2013-03-16 6
9855 2013-08-19 10
9885 2014-09-19 7
9885 2014-09-20 12
How can I plot Jan to Dec for the 1st year, continued by Jan to Dec for the 2nd year?
I guess the idea is to normalize the years (to have 1st, 2nd), but not the months. (here's an example)
Number of Items Sold over 2 Years Period Since Release
I'd use the lubridate package for something like this. Note I am calling for dataframe df because you didn't give it a name.
So for example:
library(lubridate)
First format the date like so:
df$Date <- ymd(df$Date)
Then extract the month and the year:
df$Month <- month(df$Date, label=TRUE, abbr=TRUE)
df$Year <- year(df$Date)
From there you can plot your results with ggplot2:
library(ggplot2)
ggplot(df, aes(x=Month, y=Quantity, colour=Year)) +
geom_point()
Note your question could be asked better here as you haven't provided a reproducible example.
You could try:
data <- df %>%
group_by(ISBN) %>%
arrange(Date) %>%
mutate(Year = year(Date),
Month = month(Date, label = TRUE),
Rank = paste(sapply(cumsum(Year != lag(Year,default=0)), toOrdinal), "Year")) %>%
group_by(Rank, Month, add = TRUE) %>%
summarise(Sum = sum(Quantity))
ggplot(data = data, aes(x = Month, y = Sum,
group = factor(ISBN),
colour = factor(ISBN))) +
geom_line(stat = "identity") +
facet_grid(. ~ Rank) +
scale_colour_discrete(name = "ISBN") +
theme(panel.margin = unit(0, "lines"),
axis.text.x = element_text(angle = 90))
Aussming the following df:
df <- data.frame(
ISBN = sample(c(3457, 9885), 1000, replace = TRUE),
Date = sample(seq(as.Date('2004/01/01'),
as.Date('2011/12/31'), by = "month"),
1000, replace = TRUE),
Quantity = sample(1:12, 1000, replace = TRUE)
)
This would produce:
Related
I'm trying to represent the movements of patients between several treatment groups measured in 3 different years. However, there're dropouts where some patients from 1st year are missing in the 2nd year or there are patients in the 2nd year who weren't in the 1st. Same for 3rd year. I have a label called "none" for these combinations, but I don't want it to be in the plot.
An example plot with only 2 years:
EDIT
I have tried with geom_sankey as well (https://rdrr.io/github/davidsjoberg/ggsankey/man/geom_sankey.html).
Although it is more accurate to what I'm looking for. I don't know how to omit the stratum groups without labels (NA). In this case, I'm using my full data, not a dummy example. I can't share it but I can try to create an example if needed. This is the code I've tried:
data = bind_rows(data_2015,data_2017,data_2019) %>%
select(sip, Year, Grp) %>%
mutate(Grp = factor(Grp), Year = factor(Year)) %>%
arrange(sip) %>%
pivot_wider(names_from = Year, values_from = Grp)
df_sankey = data %>% make_long(`2015`,`2017`,`2019`)
ggplot(df_sankey, aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = factor(node),
label = node,
color=factor(node) )) +
geom_sankey(flow.alpha = 0.5, node.color = 1) +
geom_sankey_label(size = 3.5, color = 1, fill = "white") +
scale_fill_viridis_d() +
scale_colour_viridis_d() +
theme_sankey(base_size = 16) +
theme(legend.position = "none") + xlab('')
Figure:
Any idea how to omit the missing groups every year as stratum (without omitting them in the alluvium) will be super helpful. Thanks!
Solved! The solution was much easier I though. I'll leave here the solution in case someone else struggles with a similar problem.
Create a wide table of counts per every group / cohort.
# Data with 3 cohorts for years 2015, 2017 and 2019
# Grp is a factor with 3 levels: 1 to 6
# sip is a unique ID
library(tidyverse)
data_wide = data %>%
select(sip, Year, Grp) %>%
mutate(Grp = factor(Grp, levels=c(1:6)), Year = factor(Year)) %>%
arrange(sip) %>%
pivot_wider(names_from = Year, values_from = Grp)
Using ggsankey package we can transform it as the specific type the package expects. There's already an useful function for this.
df_sankey = data %>% make_long(`2015`,`2017`,`2019`)
# The tibble accounts for every change in X axis and Y categorical value (node):
> head(df_sankey)
# A tibble: 6 × 4
x node next_x next_node
<fct> <chr> <fct> <chr>
1 2015 3 2017 2
2 2017 2 2019 2
3 2019 2 NA NA
4 2015 NA 2017 1
5 2017 1 2019 1
6 2019 1 NA NA
Looks like using the pivot_wider() to pass it to make_long() created a situation where each combination for every value was completed, including missings as NA. Drop NA values in 'node' and create the plot.
df_sankey %>% drop_na(node) %>%
ggplot(aes(x = x,
next_x = next_x,
node = node,
next_node = next_node,
fill = factor(node),
label = node,
color=factor(node) )) +
geom_sankey(flow.alpha = 0.5, node.color = 1) +
geom_sankey_label(size = 3.5, color = 1, fill = "white") +
scale_fill_viridis_d() +
scale_colour_viridis_d() +
theme_sankey(base_size = 16) +
theme(legend.position = "none") + xlab('')
Solved!
I am trying to compare different years' variables but I am having trouble plotting them together.
The time series is a temperature series which can be found in https://github.com/gonzalodqa/timeseries as temp.csv
I would like to plot something like the image but I find it difficult to subset the months between the years and then combine the lines in the same plot under the same months
If someone can give some advice or point me in the right direction I would really appreciate it
You can try this way.
The first chart shows all the available temperatures, the second chart is aggregated by month.
In the first chart, we force the same year so that ggplot will plot them aligned, but we separate the lines by colour.
For the second one, we just use month as x variable and year as colour variable.
Note that:
with scale_x_datetime we can hide the year so that no one can see that we forced the year 2020 to every observation
with scale_x_continous we can show the name of the months instead of the numbers
[just try to run the charts with and without scale_x_... to understand what I'm talking about]
month.abb is a useful default variable for months names.
# read data
df <- readr::read_csv2("https://raw.githubusercontent.com/gonzalodqa/timeseries/main/temp.csv")
# libraries
library(ggplot2)
library(dplyr)
# line chart by datetime
df %>%
# make datetime: force unique year
mutate(datetime = lubridate::make_datetime(2020, month, day, hour, minute, second)) %>%
ggplot() +
geom_line(aes(x = datetime, y = T42, colour = factor(year))) +
scale_x_datetime(breaks = lubridate::make_datetime(2020,1:12), labels = month.abb) +
labs(title = "Temperature by Datetime", colour = "Year")
# line chart by month
df %>%
# average by year-month
group_by(year, month) %>%
summarise(T42 = mean(T42, na.rm = TRUE), .groups = "drop") %>%
ggplot() +
geom_line(aes(x = month, y = T42, colour = factor(year))) +
scale_x_continuous(breaks = 1:12, labels = month.abb, minor_breaks = NULL) +
labs(title = "Average Temperature by Month", colour = "Year")
In case you want your chart to start from July, you can use this code instead:
months_order <- c(7:12,1:6)
# line chart by month
df %>%
# average by year-month
group_by(year, month) %>%
summarise(T42 = mean(T42, na.rm = TRUE), .groups = "drop") %>%
# create new groups starting from each July
group_by(neworder = cumsum(month == 7)) %>%
# keep only complete years
filter(n() == 12) %>%
# give new names to groups
mutate(years = paste(unique(year), collapse = " / ")) %>%
ungroup() %>%
# reorder months
mutate(month = factor(month, levels = months_order, labels = month.abb[months_order], ordered = TRUE)) %>%
# plot
ggplot() +
geom_line(aes(x = month, y = T42, colour = years, group = years)) +
labs(title = "Average Temperature by Month", colour = "Year")
EDIT
To have something similar to the first plot but starting from July, you could use the following code:
# libraries
library(ggplot2)
library(dplyr)
library(lubridate)
# custom months order
months_order <- c(7:12,1:6)
# fake dates for plot
# note: choose 4 to include 29 Feb which exist only in leap years
dates <- make_datetime(c(rep(3,6), rep(4,6)), months_order)
# line chart by datetime
df %>%
# create date time
mutate(datetime = make_datetime(year, month, day, hour, minute, second)) %>%
# filter years of interest
filter(datetime >= make_datetime(2018,7), datetime < make_datetime(2020,7)) %>%
# create increasing group after each july
group_by(year, month) %>%
mutate(dummy = month(datetime) == 7 & datetime == min(datetime)) %>%
ungroup() %>%
mutate(dummy = cumsum(dummy)) %>%
# force unique years and create custom name
group_by(dummy) %>%
mutate(datetime = datetime - years(year - 4) - years(month>=7),
years = paste(unique(year), collapse = " / ")) %>%
ungroup() %>%
# plot
ggplot() +
geom_line(aes(x = datetime, y = T42, colour = years)) +
scale_x_datetime(breaks = dates, labels = month.abb[months_order]) +
labs(title = "Temperature by Datetime", colour = "Year")
To order month differently and sum up the values in couples of years, you've to work a bit with your data before plotting them:
library(dplyr) # work data
library(ggplot2) # plots
library(lubridate) # date
library(readr) # fetch data
# your data
df <- read_csv2("https://raw.githubusercontent.com/gonzalodqa/timeseries/main/temp.csv")
df %>%
mutate(date = make_date(year, month,day)) %>%
# reorder month
group_by(month_2 = factor(as.character(month(date, label = T, locale = Sys.setlocale("LC_TIME", "English"))),
levels = c('Jul','Aug','Sep','Oct','Nov','Dec','Jan','Feb','Mar','Apr','May','Jun')),
# group years as you like
year_2 = ifelse( year(date) %in% (2018:2019), '2018/2019', '2020/2021')) %>%
# you can put whatever aggregation function you need
summarise(val = mean(T42, na.rm = T)) %>%
# plot it!
ggplot(aes(x = month_2, y = val, color = year_2, group = year_2)) +
geom_line() +
ylab('T42') +
xlab('month') +
theme_light()
A slightly different solution without the all dates to 2020 trick.
library(tidyverse)
library(lubridate)
df <- read_csv2("https://raw.githubusercontent.com/gonzalodqa/timeseries/main/temp.csv")
df <- df |>
filter(year %in% c(2018, 2019, 2020)) %>%
mutate(year = factor(year),
month = ifelse(month<10, paste0(0,month), month),
day = paste0(0, day),
month_day = paste0(month, "-", day))
df |> ggplot(aes(x=month_day, y=T42, group=year, col=year)) +
geom_line() +
scale_x_discrete(breaks = c("01-01", "02-01", "03-01", "04-01", "05-01", "06-01", "07-01", "08-01", "09-01", "10-01", "11-01", "12-01"))
I want to plot the average annual value of the stream flow data using
WATER YEAR which starts at October and ends at September (say 10/01/1983 to 09/30/1984, this is defined as 1984 water year)
I tried to find solutions elsewhere but I have failed.
Now I'm using the following script to plot the annual average flow
library(tidyverse)
library(lubridate)
library(ggplot2)
#df <- read_csv('dataframe.csv')
df <- df %>%
mutate(date = mdy(df$date))
df <- df %>%
mutate(year = floor_date(date, "year")) %>%
group_by(year) %>%
summarize(avg = mean(flow))
y <- df$avg
x <- as.Date(df$year, format = "Y")
d <- data.frame(x = x, y = y)
# interpolate values from zero to y and create corresponding number of x values
vals <- lapply(d$y, function(y) seq(0, y, by = 0.1))
y <- unlist(vals)
mid <- rep(d$x, lengths(vals))
d2 <- data.frame(x = mid - 100,
xend = mid + 100,
y = y,
yend = y)
ggplot(data = d2, aes(x = x, xend = xend, y = y, yend = yend, color = y)) +
geom_segment(size = 2) +
scale_color_gradient2(low = "midnightblue", mid = "deepskyblue", high = "aquamarine",
midpoint = max(d2$y)/2)+
scale_x_date(date_breaks = "1 year",date_labels = "%Y", expand = c(0,0)) +
theme(axis.text.x = element_text(angle=90, vjust=.5))+
labs(x = "Years", y = "Mean Annual Flow (cms)")+
ggtitle("Mean Annual Flow, Rancho River at ELdorado (1983-2020)")+
theme(plot.title = element_text(hjust = 0.5))
For this I got the following results using calendar year
If I used Water Year there will be no results for 1983
The data frame can be found in the following link
https://drive.google.com/file/d/11PVub9avzMFhUz02cHfceGh9DrlVQDbD/view?usp=sharing
Kindly assist.
If date is superior to 10/01/year(date) it means that this is the next year (in water years):
df %>%
mutate(date=mdy(date), year=year(date), year = year + (date >= mdy(paste0("10/01/", year))))
# A tibble: 5,058 x 3
date flow year
<date> <dbl> <dbl>
1 1983-10-01 3.31 1984
2 1983-10-02 3.19 1984
3 1983-10-03 3.7 1984
4 1983-10-04 3.83 1984
5 1983-10-05 3.44 1984
6 1983-10-06 4.37 1984
7 1983-10-07 6.78 1984
8 1983-10-08 6.3 1984
9 1983-10-09 6.46 1984
10 1983-10-10 6.62 1984
# … with 5,048 more rows
I am looking at data from Nov to April and would like to have a plot starting from Nov to April. Below is my sample code to screen out month of interests.
library(tidyverse)
mydata = data.frame(seq(as.Date("2010-01-01"), to=as.Date("2011-12-31"),by="days"), A = runif(730,10,50))
colnames(mydata) = c("Date", "A")
DF = mydata %>%
mutate(Year = year(Date), Month = month(Date), Day = day(Date)) %>%
filter(Month == 11 | Month == 12 | Month == 01 | Month == 02 | Month == 03 | Month == 04)
I tried to re-order the data starting at month 11 followed by month 12 and then month 01,02,03,and,04. I used the code factor(Month, levels = c(11,12,01,02,03,04)) along with the code above but it didn't work.
I wanted a plot that starts at month Nov and ends on April. The following code gave me attached plot
ggplot(data = DF, aes(Month,A))+
geom_bar(stat = "identity")+ facet_wrap(~Year, ncol = 2)
Right now, the plot is starting at January all the way to December- I dont want this. I want the plot starting at November, and all the way to April. I tried to label the plot using scale_x_date(labels = date_format("%b", date_breaks = "month", name = "Month") which didn't work. Any help would
I converted Month to character before applying factor() and it worked.
DF = mydata %>%
mutate(Year = year(Date), Month = month(Date), Day = day(Date)) %>%
filter(Month %in% c(11, 12, 1, 2, 3, 4)) %>%
mutate(Month = sprintf("%02d", Month)) %>%
mutate(Month = factor(Month, levels = c("11","12","01","02","03","04")))
ggplot(data = DF, aes(Month,A))+
geom_bar(stat = "identity")+ facet_wrap(~Year, ncol = 2)
Output:
user2332849 answer is close but does introduce an error. The bar are not in the correct order. For example for 2010, it plot is showing November and December's data prior to the beginning of the year's data. In order to plot in the proper order the year will need adjustment so that the calendar starts on month 11 and goes to month 4.
#Convert month to Factor and set desired order
DF$Month<- factor(DF$Month, levels=c(11, 12, 1, 2, 3, 4))
#Adjust the year to match the year of the beginning of series
#For example assign Jan, Feb, Mar and April to prior year
DF$Year<-ifelse(as.integer(as.character(DF$Month)) <6, DF$Year-1, DF$Year)
#plot
ggplot(data = DF, aes(Month,A))+
geom_bar(stat = "identity") +
facet_wrap(~Year, ncol = 3)
In the plot below the first 4 months of 2010 is shifted to become the last 4 periods of the prior year. And the last 2 months of 2011 is ready for the first 4 months of 2012.
I made a dataframe with columns year,month,temp,upper and lower
upper and lower are the max temperature by year and lower is the minimum
I have two questions:
first is why for some values in the end of dataframe the upper and lower are not correctly computed but in the rest of the dataframe they are fine?
And why am I getting weird axes when I am using ggplot
the dataframe is this
as you can see upper and lower for 2017 is wrong
Year Month Temp upper lower
1 1880 Jan -.29 -.29 -.09
2 1880 Feb -.18 -.29 -.09
3 1880 Mar -.11 -.29 -.09
......
1655 2017 Nov .84 .96 1.12
1656 2017 Dec .88 .96 1.12
the code is:
newDF <- df %>%
group_by(Year) %>%
mutate(upper = max(Temp), # identify max value for month day
lower = min(Temp) # identify min value for month day
) %>%
ungroup()
p <- ggplot(newDF, aes(Month, Temp)) +
geom_linerange(newDF, mapping=aes(x=Year, ymin=lower, ymax=upper), colour = "wheat2", alpha=.1)
print(p)
the graph seems fine but the axis are messed up
I think you're very close -- it's just the second part that needs a tweak. ggplot can work with a date field as the x axis, but the Month field is text (and it doesn't include the Year). Here I make a new column called date that combines them. lubridate is a handy package for that, since it does some smart parsing of date formats.
# Fake data
library(dplyr)
df <- data_frame(
Year = rep(1880:2017, each = 12),
Month = rep(month.abb, times = (2017-1880+1)),
Temp = rnorm(n = 1656, mean = 0, sd = 1)
)
newDF = df %>%
# This line adds a date field based on Year and Month
mutate(date = lubridate::ymd(paste(Year, Month, 1))) %>%
group_by(Year) %>%
mutate(upper = max(Temp), # identify max value for month day
lower = min(Temp), # identify min value for month day
) %>%
ungroup()
library(ggplot2)
p <- ggplot(newDF, aes(date, Temp)) +
geom_linerange(newDF, mapping=aes(x=Year, ymin=lower, ymax=upper), colour = "wheat2", alpha=.1)
print(p)