Hi I want to make this kind of graphs. I know it is bar charts plot. And can be used with bar_charts. But the only I can do is to make a simple bar charts with the maximum value above the bars. I suppose it is more complicated and I am not sure whether ggplot can make it. Any idea ?
[
tab1<read.csv("/Users/vladalexandru/Documente/vlad_R/R_markdown/Rmrkd/Tab/Tx_tn.csv")
tab1$dat <- as.Date(tab1$dat)
tab2 <- aggregate(tab1, by = list(format(tab1$dat, "%m")), FUN = "mean")
ggplot(data=tab2, aes(x=Group.1, y= tx)) +
geom_bar(stat="identity", fill="yellow")+
geom_text(aes(label=round(tx,0)), vjust=-0.3, size=3.5)+
labs(x = "month")+
scale_y_continuous(name = "°C")+
theme_minimal()
It is actually possible to make a gradient-filled boxplot like this in ggplot. It's a hassle though. You have to add a transparent bar at the bottom of a stacked bar chart, then construct the gradient by slicing the bar into thin pieces and coloring them:
set.seed(123)
a <- sample(10:20, 12, TRUE)
b <- sample(1:10, 12, TRUE)
data.frame(vals = c(sapply(1:12, function(i) c(rep(a[i]/39, 39), 20 - b[i]))),
month =factor( rep(month.abb, each = 40), levels = month.abb),
fills = rep(c(1:39, "top"), 12)) %>%
ggplot(aes(x = month, y = vals, fill = fills)) +
geom_col(fill = "gray95", aes(y = Inf), width = 0.7) +
geom_col(position = position_stack(), width = 0.5) +
scale_fill_manual(values = c("#00000000",
colorRampPalette(colors = c("forestgreen",
"gold", "orange"))(38),
"#00000000"),
guide = guide_none()) +
theme_classic()
Related
With this data:
df <- data.frame(value =c(20, 50, 90),
group = c(1, 2,3))
I can get a bar chart:
df %>% ggplot(aes(x = group, y = value, fill = value)) +
geom_col() +
coord_flip()+
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
But I would like to have the colors of those bars to vary according to their corresponding values in value.
I have managed to change them using geom_raster:
ggplot() +
geom_raster(aes(x = c(0:20), y = .9, fill = c(0:20)),
interpolate = TRUE) +
geom_raster(aes(x = c(0:50), y = 2, fill = c(0:50)),
interpolate = TRUE) +
geom_raster(aes(x = c(0:90), y = 3.1, fill = c(0:90)),
interpolate = TRUE) +
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
This approach is not efficient when I have many groups in real data. Any suggestions to get it done more efficiently would be appreciated.
I found the accepted answer to a previous similar question, but "These numbers needs to be adjusted depending on the number of x values and range of y". I was looking for an approach that I do not have to adjust numbers based on data. David Gibson's answer fits my purpose.
It does not look like this is supported natively in ggplot. I was able to get something close by adding additional rows, ranging from 0 to value) to the data. Then use geom_tile and separating the tiles by specifying width.
library(tidyverse)
df <- data.frame(value = c(20, 50, 90),
group = c(1, 2, 3))
df_expanded <- df %>%
rowwise() %>%
summarise(group = group,
value = list(0:value)) %>%
unnest(cols = value)
df_expanded %>%
ggplot() +
geom_tile(aes(
x = group,
y = value,
fill = value,
width = 0.9
)) +
coord_flip() +
scale_fill_viridis_c(option = "C") +
theme(legend.position = "none")
If this is too pixilated you can increase the number of rows generated by replacing list(0:value) with seq(0, value, by = 0.1).
This is a real hack using ggforce. This package has a geom that can take color gradients but it is for a line segment. I've just increased the size to make the line segment look like a bar. I made all the bars the same length to get the correct gradient, then covered a portion of each bar over with the same color as the background color to make them appear to be the correct length. Had to hide the grid lines, however. :-)
df %>%
ggplot() +
geom_link(aes(x = 0, xend = max(value), y = group, yend = group, color = stat(index)), size = 30) +
geom_link(aes(x = value, xend = max(value), y = group, yend = group), color = "grey", size = 31) +
scale_color_viridis_c(option = "C") +
theme(legend.position = "none", panel.background = element_rect(fill = "grey"),
panel.grid = element_blank()) +
ylim(0.5, max(df$group)+0.5 )
In ggplot2/geom_tile, how to change fill color whice exceed the limits？
As the image, Region_4/5 are out of limis(1,11) , so the fill color is default grey, how to change 'Region_4' to 'darkblue', 'Region_5' to 'black' . Thanks!
library(tidyverse)
library(RColorBrewer)
tile_data <- data.frame(category=letters[1:5],
region=paste0('region_',1:5),
sales=c(1,2,5,0.1,300))
tile_data %>% ggplot(aes(x=category,
y=region,
fill=sales))+
geom_tile()+
scale_fill_gradientn(limits=c(1,11),
colors=brewer.pal(12,'Spectral'))+
theme_minimal()
If you want to keep the gradient scale and have two additional discrete values for off limits above and below, I think the easiest way would be to have separate fill scales for "in-limit" and "off-limit" values. This can be done with separate calls to geom_tile on subsets of your data and with packages such as {ggnewscale}.
I think it then would make sense to place the discrete "off-limits" at the respective extremes of your gradient color bar. You need then three geom_tile calls and three scale_fill calls, and you will need to specify the guide order within each scale_fill call. You will then need to play around with the legend margins, but it's not a big problem to make it look OK.
library(tidyverse)
library(RColorBrewer)
tile_data <- data.frame(
category = letters[1:5],
region = paste0("region_", 1:5),
sales = c(1, 2, 5, 0.1, 300)
)
ggplot(tile_data, aes(
x = category,
y = region,
fill = sales
)) +
geom_tile(data = filter(tile_data, sales <= 11 & sales >=1)) +
scale_fill_gradientn(NULL,
limits = c(1, 11),
colors = brewer.pal(11, "Spectral"),
guide = guide_colorbar(order = 2)
) +
ggnewscale::new_scale_fill() +
geom_tile(data = filter(tile_data, sales > 11), mapping = aes(fill = sales > 11)) +
scale_fill_manual("Sales", values = "black", labels = "> 11", guide = guide_legend(order = 1)) +
ggnewscale::new_scale_fill() +
geom_tile(data = filter(tile_data, sales < 1), mapping = aes(fill = sales < 1)) +
scale_fill_manual(NULL, values = "darkblue", labels = "< 1", guide = guide_legend(order = 3)) +
theme_minimal() +
theme(legend.spacing.y = unit(-6, "pt"),
legend.title = element_text(margin = margin(b = 10)))
Created on 2021-11-22 by the reprex package (v2.0.1)
You can try scales::squish, define the limits, and put the out of bound (oob) values into the scalw:
p = tile_data %>% ggplot(aes(x=category,y=region,fill=sales))+ geom_tile()
p + scale_fill_gradientn(colors = brewer.pal(11,"Spectral"),
limit = c(1,11),oob=scales::squish)
I am trying to obtain a back-to-back bar plot (or pyramid plot) similar to the ones shown here:
Population pyramid with gender and comparing across two time periods with ggplot2
Basically, a pyramid plot of a quantitative variable whose values have to be displayed for combinations of three categorical variables.
library(ggplot2)
library(dplyr)
df <- data.frame(Gender = rep(c("M", "F"), each = 20),
Age = rep(c("0-10", "11-20", "21-30", "31-40", "41-50",
"51-60", "61-70", "71-80", "81-90", "91-100"), 4),
Year = factor(rep(c(2009, 2010, 2009, 2010), each= 10)),
Value = sample(seq(50, 100, 5), 40, replace = TRUE)) %>%
mutate(Value = ifelse(Gender == "F", Value *-1 , Value))
ggplot(df) +
geom_col(aes(fill = interaction(Gender, Year, sep = "-"),
y = Value,
x = Age),
position = "dodge") +
scale_y_continuous(labels = abs,
expand = c(0, 0)) +
scale_fill_manual(values = hcl(h = c(15,195,15,195),
c = 100,
l = 65,
alpha=c(0.4,0.4,1,1)),
name = "") +
coord_flip() +
facet_wrap(.~ Gender,
scale = "free_x",
strip.position = "bottom") +
theme_minimal() +
theme(legend.position = "bottom",
panel.spacing.x = unit(0, "pt"),
strip.background = element_rect(colour = "black"))
example of back-to-back barplot I want to mimick
Trying to mimick this example on my data, things go wrong from the first ggplot function call as the bars are not dodged on both sides of the axis:
mydf = read.table("https://raw.githubusercontent.com/gilles-guillot/IPUMS_R/main/tmp/df.csv",
header=TRUE,sep=";")
ggplot(mydf) +
geom_col(aes(fill = interaction(mig,ISCO08WHO_yrstud, sep = "-"),
x = country,
y = f),
position = "dodge")
failed attempt to get a back-to-back bar plot
as I was expected from:
ggplot(df) +
geom_col(aes(fill = interaction(Gender, Year, sep = "-"),
y = Value,
x = Age),
position = "dodge")
geol_col plot with bar dodged symmetrically around axis
In the example you are following, df$Value is made negative if Gender == 'F'. You need to do similar to achieve "bar dodged symmetrically around axis".
One of the value in my dataset is zero, I think because of that I am not able to adjust labels correctly in my pie chart.
#Providing you all a sample dataset
Averages <- data.frame(Parameters = c("Cars","Motorbike","Bicycle","Airplane","Ships"), Values = c(15.00,2.81,50.84,51.86,0.00))
mycols <- c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF","#FF9999")
duty_cycle_pie <- Averages %>% ggplot(aes(x = "", y = Values, fill = Parameters)) +
geom_bar(width = 1, stat = "identity", color = "white") +
coord_polar("y", start = 0)+
geom_text(aes(y = cumsum(Values) - 0.7*Values,label = round(Values*100/sum(Values),2)), color = "white")+
scale_fill_manual(values = mycols)
Labels are not placed in the correct way. Please tell me how can get 3D piechart.
Welcome to stackoverflow. I am happy to help, however, I must note that piecharts are highly debatable and 3D piecharts are considered bad practice.
https://www.darkhorseanalytics.com/blog/salvaging-the-pie
https://en.wikipedia.org/wiki/Misleading_graph#3D_Pie_chart_slice_perspective
Additionally, if the names of your variables reflect your actual dataset (Averages), a piechart would not be appropriate as the pieces do not seem to be describing parts of a whole. Ex: avg value of Bicycle is 50.84 and avg value of Airplane is 51.86. Having these result in 43% and 42% is confusing; a barchart would be easier to follow.
Nonetheless, the answer to your question about placement can be solved with position_stack().
library(tidyverse)
Averages <-
data.frame(
Parameters = c("Cars","Motorbike","Bicycle","Airplane","Ships"),
Values = c(15.00,2.81,50.84,51.86,0.00)
) %>%
mutate(
# this will ensure the slices go biggest to smallest (a best practice)
Parameters = fct_reorder(Parameters, Values),
label = round(Values/sum(Values) * 100, 2)
)
mycols <- c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF","#FF9999")
Averages %>%
ggplot(aes(x = "", y = Values, fill = Parameters)) +
geom_bar(width = 1, stat = "identity", color = "white") +
coord_polar("y", start = 0) +
geom_text(
aes(y = Values, label = label),
color = "black",
position = position_stack(vjust = 0.5)
) +
scale_fill_manual(values = mycols)
To move the pieces towards the outside of the pie, you can look into ggrepel
https://stackoverflow.com/a/44438500/4650934
For my earlier point, I might try something like this instead of a piechart:
ggplot(Averages, aes(Parameters, Values)) +
geom_col(aes(y = 100), fill = "grey70") +
geom_col(fill = "navyblue") +
coord_flip()
I have a test dataset like this:
df_test <- data.frame(
proj_manager = c('Emma','Emma','Emma','Emma','Emma','Alice','Alice'),
proj_ID = c(1, 2, 3, 4, 5, 6, 7),
stage = c('B','B','B','A','C','A','C'),
value = c(15,15,20,20,20,70,5)
)
Preparation for viz:
input <- select(df_test, proj_manager, proj_ID, stage, value) %>%
filter(proj_manager=='Emma') %>%
do({
proj_value_by_manager = sum(distinct(., proj_ID, value)$value);
mutate(., proj_value_by_manager = proj_value_by_manager)
}) %>%
group_by(stage) %>%
do({
sum_value_byStage = sum(distinct(.,proj_ID,value)$value);
mutate(.,sum_value_byStage= sum_value_byStage)
}) %>%
mutate(count_proj = length(unique(proj_ID)))
commapos <- function(x, ...) {
format(abs(x), big.mark = ",", trim = TRUE,
scientific = FALSE, ...) }
Visualization:
ggplot (input, aes(x=stage, y = count_proj)) +
geom_bar(stat = 'identity')+
geom_bar(aes(y=-proj_value_by_manager),
stat = "identity", fill = "Blue") +
scale_y_continuous(labels = commapos)+
coord_flip() +
ylab('') +
geom_text(aes(label= sum_value_byStage), hjust = 5) +
geom_text(aes(label= count_proj), hjust = -1) +
labs(title = "Emma: 4 projects| $90M Values \n \n Commitment|Projects") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept = 0, linetype =1)
My questions are:
Why is the y-values not showing up right? e.g. C is labeled 20, but nearing hitting 100 on the scale.
How to adjust the position of labels so that it sits on the top of its bar？
How to re-scale the y axis so that both the very short bar of 'count of project' and long bar of 'Project value' can be well displayed?
Thank you all for the help!
I think your issues are coming from the fact that:
(1) Your dataset has duplicated values. This causes geom_bar to add all of them together. For example there are 3 obs for B where proj_value_by_manager = 90 which is why the blue bar extends to 270 for that group (they all get added).
(2) in your second geom_bar you use y = -proj_value_by_manager but in the geom_text to label this you use sum_value_byStage. That's why the blue bar for A is extending to 90 (since proj_value_by_manager is 90) but the label reads 20.
To get you what I believe the chart you want is you could do:
#Q1: No dupe dataset so it doesnt erroneous add columns
input2 <- input[!duplicated(input[,-c(2,4)]),]
ggplot (input2, aes(x=stage, y = count_proj)) +
geom_bar(stat = 'identity')+
geom_bar(aes(y=-sum_value_byStage), #Q1: changed so this y-value matches your label
stat = "identity", fill = "Blue") +
scale_y_continuous(labels = commapos)+
coord_flip() +
ylab('') +
geom_text(aes(label= sum_value_byStage, y = -sum_value_byStage), hjust = 1) + #Q2: Added in y-value for label and hjust so it will be on top
geom_text(aes(label= count_proj), hjust = -1) +
labs(title = "Emma: 4 projects| $90M Values \n \n Commitment|Projects") +
theme(plot.title = element_text(hjust = 0.5)) +
geom_hline(yintercept = 0, linetype =1)
For your last question, there is no good way to display both of these. One option would be to rescale the small data and still label it with a 1 or 3. However, I didn't do this because once you scale down the blue bars the other bars look OK to me.