here's my data:
head(df)
FY Analyte Value
<fct> <fct> <dbl>
1 2007-08 CONF(G) 634
2 2007-08 PH(G) 7.8
3 2007-08 TEMP(G) 24.8
4 2007-08 UHS(G) 2.5
5 2007-08 FC(G) 0.5
6 2007-08 CBOD(C) 1
My dataset is a long df, spanning 10 years. I want to create multiple ggplots (of each Analyte) where the x axis is FY (financial year) and the y axis is Value. Ideally the Y axis title would also change based on the variable being plotted.
I've seen a few reproducible chunks of code in my search to do this but none of them seem to apply to a long dataframe (where I want to loop through each level of the Analyte variable). I also want it to save to my working directory (possibly using the png and dev.off() functions).
Anyone know a solution?
Thanks!
Split the data for each Analyte and use map to save the plot as separate image.
library(tidyverse)
df %>%
group_split(Analyte) %>%
map(~{
analyte_name <- .$Analyte[1]
tmp <- ggplot(., aes(FY, Value)) + geom_boxplot() + ggtitle(analyte_name)
ggsave(paste0(analyte_name, '.png'), tmp)
})
Related
This seems so simple. I can easily do it in Excel but I want to automate the process through R. I have installed ggplot2. Using RStudio I have read in my CSV file.
The resulting data frame has over 200 rows, each a town in New Hampshire. The first column is titled "Town" and each row below that has the text name of the town, (e.g., "Concord" or "Lancaster"). Column 2 contains a number for each town (spending per elementary school pupil) and the title of that column in the CSV file is "01/02 Elem PPE" - but it shows as "X01.02.Elem.PPE" when using View(). Column 3 has similar numbers for each town and its title in View() is "X02.03.Elem.PPE". Columns 4 through 11 are similar.
I just want to plot a line graph of the numbers in columns 2-11 for one row (one town). It will show how the spending per pupil has changed in that town over time. There must be a simple way to do this, but I can't find it.
Please help. I am a 77 year old with some programming experience 3-5 decades ago but new to R and Rstudio only yesterday.
First, I'll make some new data that mimics yours. It should have more or less the same properties.
library(glue)
library(tidyverse)
set.seed(4314)
mat <- matrix(rpois(40, 5000), ncol=10)
colnames(mat) <- glue("X{sprintf('%2.0f', 1:10)}.{sprintf('%2.0f', 2:11)}.Elem.PPE", sep="") %>%
gsub(". ", ".0", ., fixed=TRUE) %>%
gsub("X ", "X0", ., fixed=TRUE)
df <- tibble(town = c("Concord", "Lancaster", "Manchester", "Nashua"))
df <- bind_cols(df, as_tibble(mat))
Now, this is where you would start. I'm going to assume that you read your csv into an object called df. The first thing you should do to make plotting easier is to pivot the data from wide-form (one-row and 10 columns per observation) to long-form with 1 column and 10 rows per observation. I'm going to save this in an object called df2. The pivot_longer function is in the tidyr package. The first argument is the columns that you want to change from wide- to long-form, in this case, it's everything except town. Then you tell it a variable name for the column names and a variable name for the values. Then, I'm just using a couple of regular expressions to go from X01.02.Elem.PPE to 01/02 for plotting purposes.
df2 <- df %>%
pivot_longer(-town, names_to="time", values_to="val") %>%
mutate(time = gsub("X(.*)\\.Elem\\.PPE", "\\1", time),
time = gsub("\\.", "/", time))
The resulting data frame looks like this:
# # A tibble: 40 x 3
# town time val
# <chr> <chr> <int>
# 1 Concord 01/02 4965
# 2 Concord 02/03 4953
# 3 Concord 03/04 5066
# 4 Concord 04/05 5100
# 5 Concord 05/06 4979
# 6 Concord 06/07 5090
# 7 Concord 07/08 5136
# 8 Concord 08/09 5076
# 9 Concord 09/10 5079
# 10 Concord 10/11 4945
Next, we can make a plot for a single place (before we think about automation). Let's try Concord. First, we'll save the values that we want to put on the x-axis:
xlabs <- unique(df2$time)
Next, we can use ggplot() to make the plot. In the code below, we're first piping the data frame to a filter that will pull out the values for a single town. The filtered data frame is piped into the ggplot() function. Since time in the data frame is a character vector, we need to turn it into a factor and then into a numeric to make the line plot. We add the line geometry to plot the line. Then we change the x-axis labels with scale_x_continuous(). The labs() function changes the axis labels for the x- and y-axes. Finally, ggtitle() puts the title at the top of the plot. I also like theme_bw() rather than the gray background, but that's entirely a matter of personal preference. The resulting plot looks like this:
df2 %>% filter(town == "Concord") %>%
ggplot(aes(x=as.numeric(as.factor(time)), y=val)) +
geom_line() +
scale_x_continuous(breaks=1:10, labels = xlabs) +
labs(x="Time", y="Spending per Pupil") +
ggtitle("Concord") +
theme_bw()
Now, the next part you mentioned was automation - you want to do this for every row of the original data frame. We could do that as follows. First, untown grabs the unique values of town from the data. The for() loop loops from 1 to the number of values in untown. Then you can see where "Concord" was in the previous plot, we now have untown[i]. We also use ggsave() at the end and we paste together the town name and .png. This will make a different plot for each town in R's working directory.
untown <- unique(df2$town)
for(i in 1:length(untown)){
df2 %>% filter(town == untown[i]) %>%
ggplot(aes(x=as.numeric(as.factor(time)), y=val)) +
geom_line() +
scale_x_continuous(breaks=1:10, labels = xlabs) +
labs(x="Time", y="Spending per Pupil") +
ggtitle(untown[i]) +
theme_bw()
ggsave(glue("{untown[i]}.png"), width=9, height=6)
}
I have a set of categorical variables listed by date. The desired outcome is a plot of counts of the categorical variables selected by a particular date range. I can produce a plot of the entire set but no variations that I have found (or people have suggested I use) produces that outcome. Date is formatted as date and libloc is a character. The end result desired is plot of the number of instructions we do in different locations by semester.
I understand this is an unimportant/uninteresting question to most of you -- but I am a 62 year old classics librarian stuck at home because of the pandemic having to learn to program so I can keep my job - so can people please be kind. I realize I am not phrasing my question the way you might want but I am doing the best I can trying to do this.
library(ggplot)
library(lubridate)
library(readr)
df <- read_excel("C:/Users/12083/Desktop/instructions/datasetd.xlsx")
df %>%
select(date,Location) %>%
filter(date >= as.Date("2017-01-05") & date <= as.Date("2018-01-10"))%>%
group_by(Location) %>%
summarise(count=n())
g <- ggplot(df, aes(Location))
g + geom_bar()
Salve!
You might find that my santoku package helps. It can chop dates into intervals:
library(santoku)
library(dplyr)
df_summary <- df %>%
select(date,Location) %>%
filter(date >= as.Date("2017-01-05") & date <= as.Date("2018-01-10")) %>%
mutate(semester = chop(date, as.Date(c("2017-01-05", "2017-01-09")))) %>%
group_by(Location, semester) %>%
summarise(count=n())
Obviously you will want to pick your semester dates appropriately.
Then you can print with something like:
ggplot(df_summary, aes(semester, count)) + geom_col() + facet_wrap(vars(location))
Hope this helps:
#### Filtering using R to a specific date range ####
# From: https://stackoverflow.com/questions/62926802/filtering-using-r-to-a-specific-date-range
# First, I downloaded a sample dataset with dates and categorical data from here:
# https://vincentarelbundock.github.io/Rdatasets/datasets.html
# Specifically, I got weather.csv
setwd("F:/Home Office/R")
data = read.csv("weather.csv") # Read the data into R
head(data) # Quality control, looks good
data = data[,2:3] # For this example, I cut it to only take the relevant columns
data$date = as.Date(data$date) # This formats the date as dates for R
library(tidyverse) # This will import some functions that you need, spcifically %>% and ggplot
# Step 0: look that the data makes sense to you
summary(data$date)
summary(data$city)
# Step 1: filter the right data
filtered = data %>%
filter(date > as.Date("2016-07-01") & date < as.Date("2017-07-01")) # This will only take rows between those dates
# Step 2: Plot the filtered data
# Using a bar plot:
plot = ggplot(filtered, aes(x=city, fill = city)) + geom_bar() # You don't really need the fill, but I like it
plot
# Quality control: look at the numbers before and after the filtering:
summary(data$city)
summary(filtered$city)
Outputs:
> summary(short.data$city)
Auckland Beijing Chicago Mumbai San Diego
731 731 731 731 731
> summary(filtered$city)
Auckland Beijing Chicago Mumbai San Diego
364 364 364 364 364
You might be able to make it more elegant... but I think it works well
EDIT TO MAKE IT INTO A LINE PLOT
This edit is following your request in the comments:
# Line plot
# The major difference between geom_bar() and geom_line() is that
# geom_line() requires both an X and Y values.
# So first I created a new data frame which has these values:
summarised.data = filtered %>%
group_by(city) %>%
tally()
# Now you can create the plot with ggplot:
# Notes:
# 1. group = 1 is necessary
# 2. I added geom_point() so that each X value gets a point. I think it's easier to read. You can remove this if you like
plot.line = ggplot(summarised.data, aes(x=city, y=n, group = 1)) + geom_line() + geom_point()
plot.line
Outputs:
> summarised.data
# A tibble: 5 x 2
city n
<fct> <int>
1 Auckland 364
2 Beijing 364
3 Chicago 364
4 Mumbai 364
5 San Diego 364
This is a new answer because the approach is different
#### Filtering using R to a specific date range ####
# From: https://stackoverflow.com/questions/62926802/filtering-using-r-to-a-specific-date-range
# First, the data I took by copy and pasting from here:
# https://stackoverflow.com/questions/63006201/mapping-sample-data-to-actual-csv-data
# and saved it as bookdata.csv with Excel
setwd("C:/Users/di58lag/Documents/scratchboard/Scratchboard")
data = read.csv("bookdata.csv") # Read the data into R
head(data) # Quality control, looks good
data$dates = as.Date(data$dates, format = "%d/%m/%Y") # This formats the date as dates for R
library(tidyverse) # This will import some functions that you need, spcifically %>% and ggplot
# Step 0: look that the data makes sense to you
summary(data$dates)
summary(data$city)
# Step 1: filter the right data
start.date = as.Date("2020-01-02")
end.date = as.Date("2020-01-04")
filtered = data %>%
filter(dates >= start.date &
dates <= end.date) # This will only take rows between those dates
# Step 2: Plotting
# Now you can create the plot with ggplot:
# Notes:
# I added geom_point() so that each X value gets a point.
# I think it's easier to read. You can remove this if you like
# Also added color, because I like it, feel free to delete
Plot = ggplot(filtered, aes(x=dates, y=classes, group = city)) + geom_line(aes(linetype=city, color = city)) + geom_point(aes(color=city))
Plot
# For a clean version of the plot:
clean.plot = ggplot(filtered, aes(x=dates, y=classes, group = city)) + geom_line(aes(linetype=city))
clean.plot
Outputs:
Plot:
Clean.plot:
EDIT: ADDED A TABLE FUNCTION!
After reading your comments I think I figured out what you're trying to do.
You asked for:
"counts of location of instructors on the vertical and dates on the horizontal."
The problem is that the original data doesn't actually give you the number of counts - ie "how many times a specific location apears in a specific date".
Therefore, I had to add another line using the table function to calculate this:
data.table = as.data.frame(table(filtered))
this calculates how many times each combination of date+location apears and give a value called "Freq".
Now you can plot this Freq as the count as follows:
# Step 1.5: Counting the values
data.table = as.data.frame(table(filtered)) # This calculates the frequency of each date+location combination
data.table = data.table %>% filter(Freq>0) # This is used to cut out any Freq=0 values (you don't want to plot cases where no event occured)
data.table$dates = as.Date(data.table$dates) # You need to rerun the "as.Date" func because it formats the dates back to "Factors"
#Quality control:
dim(filtered) # Gives you the size of the dataframe before the counting
dim(data.table) # Gives the size after the counting
summary(data.table) # Will give you a summary of how many values are for each city, what is the date range and what is the Frequency range
# Now you can create the plot with ggplot:
# Notes:
# I added geom_point() so that each X value gets a point.
# I think it's easier to read. You can remove this if you like
# Also added color, because I like it, feel free to delete
Plot = ggplot(data.table, aes(x=dates, y=Freq, group = city)) + geom_line(aes(linetype=city, color = city)) + geom_point(aes(color=city))
Plot
# For a clean version of the plot:
clean.plot = ggplot(filtered, aes(x=dates, y=classes, group = city)) + geom_line(aes(linetype=city))
clean.plot
I have a feeling it's not exactly what you wanted becuase the numbers are quite low (ranging between 1-12 counts) but this is what I understand.
OUTPUTS:
> summary(data.table)
city dates Freq
Pocatello :56 Min. :2015-01-12 Min. :1.000
Idaho Falls:10 1st Qu.:2015-02-10 1st Qu.:1.000
Meridian : 8 Median :2015-03-04 Median :1.000
: 0 Mean :2015-03-11 Mean :1.838
8 : 0 3rd Qu.:2015-04-06 3rd Qu.:2.000
Boise : 0 Max. :2015-06-26 Max. :5.000
(Other) : 0
I have a very big dataset that I'd like to illustrate using plotly in R.
A sample of my dataset is shown below:
> new_data_2
# Groups: newdatum [8]
date activity totaal
<date> <fct> <int>
1 2019-11-21 N11 144
2 2019-09-22 N11 129
3 2019-05-15 N22 117
4 2019-01-23 N22 12
5 2019-07-04 N22 12
6 2019-07-18 N22 12
...
For every activity I want to display the amount (totaal) per date (date) in a time series plot.
Somehow I don't get it right in R. Somehow I need to group my activity to display, but I can't figure it out.
new_data_2 %>%
group_by(activity) %>%
plot_ly(x=new_data_2$newdatum) %>%
add_lines(y=~new_data_2$totaal, color = ~factor(newdatum))
It does display an empty plot and not with the 'activity' on the left side.
What i want to achieve is:
You're on the right track, but after the group_by() you need to tell R to do something to the groups.
new_data_2 %>%
group_by(activity, date) %>% # use two groupings since you want by activity & date
summarise(totaal_2 = sum(totaal))
That should get to the dataframe you're looking for. You can use ggplot & plotly on it from there.
I would recommend reshaping the data first (as above), saving it as a new object, and then graphing it. Doing it this way helps you see each step along the way. Pipes %>% are great, but can make each step difficult to see.
This might not be very obvious at first, but the structure of your data is ideal for plot with multiple time series. You don't even need to worry with the group_by function. Your dataset seems to hava a long format where the dates in the date column and the names in activity column are not unique. But you will have only one variable per activity and date.
Given the correct specifications, plot_ly() will group your data using color=~activity like this: p <- plot_ly(new_data2, x = ~date, y = ~totall, color = ~activity) %>% add_lines(). Since you haven't provided a data sample that is large enough, I'll use the built-in dataset economics_long to show you how you can do this. First of all, notice how the structure of my sampled dataset matches yours:
date variable value
1 1967-07-01 psavert 12.5
2 1967-08-01 psavert 12.5
3 1967-09-01 psavert 11.7
4 1967-10-01 psavert 12.5
5 1967-11-01 psavert 12.5
6 1967-12-01 psavert 12.1
...
Plot:
Code:
library(plotly)
library(dplyr)
# data
data("economics_long")
df <- data.frame(economics_long)
# keep only some variables that have values on a comparable level
df <- df %>% filter(!(variable %in% c('pop', 'pce', 'unemploy')))
# plotly time series
p <- plot_ly(df, x = ~date, y = ~value, color = ~variable) %>%
add_lines()
# show plot
p
Is is possible to create something like this in R?
I have 7 different variables that i want to include for product A and the same 7 for the rest of the products, B, C...
However I also want to include the summaries vales (min, mean and max).
How can I create this?
I already have all the different variables as a "Value".
I was trying with something like
protein~product
but i want for all variables inside the Product AAA. If possible, the same for all products ( i don't know it that will be possible due to the amount of the variables).
this is a part of the data..
product protein fat moisture ash fiber starch sugar
AAA 49 1.0 NA NA 10 7.4 6.1
BBB 35 1.6 NA NA 10.6 8.5 10.0
AVF 40 1.2 NA NA 6 7.8 6.3
Thank you!
You can start your adventure with this example.
EDIT: I added some info, how to get from your data format to a long data format, required for the plot.
Also find more info at similar questions:
Plot multiple boxplot in one graph
# simulate the data
set.seed(314)
id <- rep(1:100, each = 3)
prod <- paste("product",rep(letters[1:3], each=300))
ing <- rep(c('protein','fat','starch'), 300)
mg <- rnorm(900, 5, 2)
df <- data.frame(prod, ing, mg, id)
#reconstruct your data format
yourdata <- df %>% group_by(id, prod) %>% spread(ing, mg)
library(ggplot2)
library(dplyr)
library(tidyr)
# get your format in long format
pd <- yourdata %>% gather(ing, mg, -id, -prod)
# use the long format for the plot
ggplot(pd, aes(x = ing, y = mg, fill = ing)) + geom_boxplot() +
facet_grid(~prod)
This seems simple, but I've tried multiple variations of matplot, ggplot2, regular old plot...I can't get any to do what I need.
I have a gigantic dataframe of years, months, and observations. I simplified it down to number of observations per month, per year, see below. I'm not sure why it read in with the "X" in front of each column heading, but if it's not going to affect the code, right now I don't care.
head(storms)
X Month X1992 X1993 X1994
1 1 1 2 1
2 2 2 4 1
3 3 3 26 10
4 4 4 47 26
5 5 5 969 615
The full (simplified) set is 10 columns of years (1992-2001), each with 12 months/rows of totals (1 storm in Jan 1992, 26 storms in March 1993...). I need simply to plot these all on an x-axis 120 months long, # of observations per month on the y-axis. It could be a line or bars or vertical lines. I've seen many ways to plot 20 lines with 12 months on the x-axis; that is not what I'm going for. I also need to label the years every 12 months, but I think I can figure that out after I get this block out of the way.
In other words (I hope this is more clear if the previous is not):
y axis: # of storms, ylim=c(0-1000)
x axis: 10 sets of months (Jan-Dec, 1992-2001, 120 months total). The only labels will be the years, every 12 months of course.
I know I'm just thinking about it wrong, could someone please set my head straight?
(first post; please also tell me if I'm not formatting or inquiring properly!)
is this something you are looking for? If I am not mistaken, you may want to rearrange your data frame. You wanna make your data frame longer rather than wider. Then, you can draw a figure. The thing is that you have 120 month. So you may need to think plot space issue. But at least this example let you move forward. I hope this helps you.
library(tidyr)
library(ggplot2)
# Create a sample data
month <- rep(c(1:12), each = 1, times = 2)
nintytwo <- runif(24, 0, 20)
nintythree <- runif(24, 0, 20)
# Crate a data frame
ana <- data.frame(month, nintytwo, nintythree)
# Make the data longer rather than wider.
bob <- gather(ana, year, value, -month)
bob$month <- as.factor(bob$month)
# Draw a firure
cathy <- ggplot(bob, aes(x= year,y = value, fill = month)) + geom_bar(stat="identity", position="dodge")
cathy
Here's an example using base R :
# create an example data
set.seed(123)
df <- data.frame(Month=1:12)
for(y in 1992:2001){
tmp <- data.frame(X=as.integer(abs(rnorm(12,mean=2,sd=10))))
colnames(tmp) <- paste("X",y,sep="")
df <- cbind(df,tmp)
}
# reshape to long format (one column with n.of storms, and period columns)
long <- reshape(df[,-1], idvar="Month", ids=df$Month,
times=names(df[,-1]), timevar="Year",
varying = list(names(df[,-1])),
direction = "long",v.names="Storms")
# remove the "X" from the year
long$Year <- substr(long$Year,2,nchar(long$Year))
nYears <- length(unique(long$Year))
# plot the line
plot(x=1:nrow(long),y=long$Storms,type="l",
xaxt="n",main="Monthly Storms",
xlab="Period",ylab="Storms",col="RoyalBlue")
# add custom labels
axis(1,at=((1:nYears)*12)-6,labels=unique(long$Year))
# add vertical lines
abline(v=c(0.5,((1:nYears)*12)+0.5),col="Gray80",lty=2)
Result :