Looking for some help with what I am assuming is a very simple task. From my data below, I want to create a stacked bar graph, with the fill = colnames(df_Consumers)[2,4]. I'm trying to get the x-axis to be df_Consumers$Month, the y-axis as df_Consumers$Referrals with the 2nd and 4th columns being the stacked bar graphs. I hope this makes sense. Apologies in advance if I am too vague. My ggplot code and data are below. Thanks in advance!
ggplot(df_Consumers, aes(x = Month, y = Referrals)) +
geom_col(aes(fill = df_Consumers[2, 4]))
ggplot likes long data frames. I'd suggest the following:
library(tidyverse)
df_Consumers %>%
select(-Referrals) %>%
pivot_longer(c(New.Consumers, No.Fill), names_to = "type", values_to = "value") %>%
ggplot() +
aes(x = Month, y = value, fill = type) +
geom_col()
Related
I made an upset plot using the ggupset package and added a break to the y axis with scale_y_break from the ggbreakpackage.
However, when I add scale_y_break, the combination matrix under the bar plot disappears.
Is there a way to combine the combination matrix of the plot made without scale_y_break with the bar plot portion of a plot made with scale_y_break? I can't seem to be able to access the grobs of these plots or use any other workaround. If anyone could help, I would greatly appreciate it!
Example with scale_x_upset and scale_y_break:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
I would like to combine the barplot portion of the plot created with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)+ scale_y_break(breaks = c(750,1000))
with the combination matrix portion of the plot made with:
df = tidy_movies %>% distinct(title, year, length, .keep_all=TRUE)
ggplot(df, aes(x=Genres)) + geom_bar() + scale_x_upset(n_intersections = 20)
Thanks!
Here is come basic code for a column plot:
library(tidyverse)
diamonds %>%
group_by(cut) %>%
summarise(
count = n()
) %>%
ggplot(
aes(
x = cut,
y = count,
fill = count
)
) +
geom_col() +
scale_fill_viridis_c(
option = "plasma"
)
I could not find any examples of what I would like to do so I will try and explain it as best I can. I have applied a colour gradient to the fill aesthetic which colours the whole column plot one colour. Is it possible to have it such that each column of the plot contains the full colour spectrum up until it's respective value?
By which I mean the "Ideal" column of my plot would look exactly like the key in the legend. Then the "Premium" column would look like the key in the legend but cut off ~2/3 of the way up.
Thanks
You can do this fairly easily with a bit of data manipulation. You need to give each group in your original data frame a sequential number that you can associate with the fill scale, and another column the value of 1. Then you just plot using position_stack
library(ggplot2)
library(dplyr)
diamonds %>%
group_by(cut) %>%
mutate(fill_col = seq_along(cut), height = 1) %>%
ggplot(aes(x = cut, y = height, fill = fill_col)) +
geom_col(position = position_stack()) +
scale_fill_viridis_c(option = "plasma")
I'm having some trouble with this conceptually.
Using the 'Decathlon' dataset from library(GDAdata) and ggplot2;
I want to make boxplots using all of the point variables, which is essentially the last half of the dataset; columns = 15:24 or P100m:P1500.
On a single plot, I want a boxplot for each of those columns on the x axis, and Totalpoints on the y axis.
Any help is appreciated! Thank you.
Here is one way to do it:
Load GDAdata::Decathlon
Make long format with pivot_longer
then use ggplot with geom_boxplot()
library(tidyverse)
library(GDAdata)
df <- GDAdata::Decathlon %>%
select(15:24) %>%
pivot_longer(
cols = everything(),
names_to = "Names",
values_to = "Values"
)
p <- ggplot(df, aes(x=Names, y=Values)) +
geom_boxplot()
p
I have made a relatively simple boxplot with ggplot
ggplot(l8tc.df_17_18,aes(x=landcover,y= tcw_17, group=landcover))+
geom_boxplot()+
geom_boxplot(aes(y= tcw_18),position_dodge(1))
A screenshot to get an idea of the data used:
This is the output:
I want the different boxplots to be next to each other and not in one vertical line. I have looked through all related questions and tried out a couple of options, however I could not find a solution so far.
I am still a ggplot beginner though.
Any ideas?
You should use in this case different data format and melt it.
require(reshape2)
require(tidyverse)
# format data
melted_data <- l8tc.df_17_18 %>%
select(landcover, tcw_17, tcw_18) %>%
melt('landcover', variable.name = 'tcw')
# plot
ggplot(melted_data, aes(x = as.factor(landcover), y = value)) + geom_boxplot(aes(fill = tcw))
a dodge should be automatic but if you want ot experiment use geom_boxplot(aes(fill = tcw), position = position_dodge())
https://ggplot2.tidyverse.org/reference/position_dodge.html
you can write it in one line without creating temp file
l8tc.df_17_18 %>%
select(landcover, tcw_17, tcw_18) %>%
melt('landcover', variable.name = 'tcw') %>%
ggplot(aes(x = as.factor(landcover), y = value)) + geom_boxplot(aes(fill = tcw))
Here is a snapshot of data:
restaurant_change_sales = c(3330.443, 3122.534)
restaurant_change_labor = c(696.592, 624.841)
restaurant_change_POS = c(155.48, 139.27)
rest_change = data.frame(restaurant_change_sales, restaurant_change_labor, restaurant_change_POS)
I want two bars for each of the columns indicating the change. One graph for each of the columns.
I tried:
ggplot(aes(x = rest_change$restaurant_change_sales), data = rest_change) + geom_bar()
This is not giving the result the way I want. Please help!!
So ... something like:
library(ggplot2)
library(dplyr)
library(tidyr)
restaurant_change_sales = c(3330.443, 3122.534)
restaurant_change_labor = c(696.592, 624.841)
restaurant_change_POS = c(155.48, 139.27)
rest_change = data.frame(restaurant_change_sales,
restaurant_change_labor,
restaurant_change_POS)
cbind(rest_change,
change = c("Before", "After")) %>%
gather(key,value,-change) %>%
ggplot(aes(x = change,
y = value)) +
geom_bar(stat="identity") +
facet_grid(~key)
Which will produce:
Edit:
To be extra fancy e.g. make it so that the order of x-axis labels goes from "Before" to "After", you can add this line: scale_x_discrete(limits = c("Before", "After")) to the end of the ggplot function
Your data are not formatted properly to work well with ggplot2, or really any of the plotting packages in R. So we'll fix your data up first, and then use ggplot2 to plot it.
library(tidyr)
library(dplyr)
library(ggplot2)
# We need to differentiate between the values in the rows for them to make sense.
rest_change$category <- c('first val', 'second val')
# Now we use tidyr to reshape the data to the format that ggplot2 expects.
rc2 <- rest_change %>% gather(variable, value, -category)
rc2
# Now we can plot it.
# The category that we added goes along the x-axis, the values go along the y-axis.
# We want a bar chart and the value column contains absolute values, so no summation
# necessary, hence we use 'identity'.
# facet_grid() gives three miniplots within the image for each of the variables.
ggplot2(rc2, aes(x=category, y=value, facet=variable)) +
geom_bar(stat='identity') +
facet_grid(~variable)
You have to melt your data:
library(reshape2) # or library(data.table)
rest_change$rowN <- 1:nrow(rest_change)
rest_change <- melt(rest_change, id.var = "rowN")
ggplot(rest_change,aes(x = rowN, y = value)) + geom_bar(stat = "identity") + facet_wrap(~ variable)