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I have data with large degrees of separation between "clusters/groups" of values that I hope to make a histogram with, but dividing the bins into equal sized groups has been difficult. I'd like for zero (0) to have it's own bin, the total number of equally spaced bins be < 8 (ideally, to avoid crowding the plot) with an extra empty bin for "..." signifying the large gaps in-between the data values. The actual dataset has 800+ zeros with maybe 5% data >0. Naturally the zeros will over-shadow the rest of the data, but a log transform will fix that. I just can't figure out the best way to break-up the data...
Data looks like this:
set.seed(123)
zero <- runif(50, min=0, max=0)
small <- runif(7, min=0, max=0.1)
medium <- runif(5, min=0, max=0.5)
high <- runif(3, min=1.5, max=2.5)
f <- function(x){
return(data.frame(ID=deparse(substitute(x)), value=x))
}
all <- bind_rows(f(zero), f(small), f(medium), f(high))
all <- as.data.frame(all[,-1])
names(all)[1] <- "value"
My attempt:
bins <- all %>% mutate(bin = cut(all$value, breaks = c(0, seq(0.01:0.4), Inf), right = FALSE)) %>%
count(bin, name = "freq") %>%
add_row(bin = "...", freq = NA_integer_) %>%
mutate(bin = fct_relevel(bin, "...", after = 0.4))
But I get this error:
Error in `mutate()`:
! Problem while computing `bin = fct_relevel(bin, "...", after = 0.5)`.
Caused by error:
! `idx` must contain one integer for each level of `f`
This is not equally spaced, but I'm looking for something like this as labels for my plot:
levels(bins$bin) <- c("0", "0.01-0.05", "0.05-0.1", "0.1-0.2", "0.2-0.3", "0.3-0.4", "...", "2.0+")
ggplot(bins, aes(x = bin, y = freq, fill = bin)) +
geom_histogram(stat = "identity", colour = "black")
You can use cut directly inside ggplot
ggplot(all, aes(cut(value, breaks = c(0, 0.25, 0.5, 3), inc = TRUE))) +
geom_bar() +
scale_y_log10() +
labs(x = "value")
This worked for me (using my own data):
bins <- WET %>% mutate(bin = cut(den, breaks = c(0, seq(0.001, 0.225, 0.15), 0.255, 0.3, Inf), right = FALSE)) %>%
count(bin, name = "freq") %>% # build frequency table, frequency = freq
add_row(bin = "...", freq = NA_integer_) %>% # add empty row for NA
mutate(bin = fct_relevel(bin, "...", after = 3)) # Put factor level "..." after 3! (the 3rd position)
levels(bins$bin) <- c("0", "0.001-0.15", "0.15-0.255", "...", "0.3+")
# fct_relevel(f, "a", after = 2), "..., after = x, x must be an integer! (2nd position)
ggplot(bins, aes(x = bin, y = freq, fill = bin)) +
geom_bar(stat = "identity", colour = "black") +
geom_text(aes(label = freq), vjust = -0.5) +
scale_y_continuous(limits = c(0, 800), expand = expansion(mult = c(0, 0.05))) +
scale_fill_brewer(name = "Density", palette="Greys", breaks = c("0", "0.001-0.15", "0.15-0.255", "0.3+")) +
# Only show these legend values (exclude "...")
labs(title = "Wet seasons - Pink shrimp density (no./m2)",x = "Density range", y = "Frequency") +
theme(plot.title = element_text(hjust = 0.5)) +
theme(axis.text = element_text(size = 9, face = "bold")) +
theme(axis.title = element_text(size = 13, face = "bold")) + # Axis titles
theme(axis.title.x = element_text(vjust = -3)) +
theme(panel.border = element_rect(color = "black", fill = NA, size = 1)) +
# Adjust distance of x-axis title from plot
theme(plot.margin = margin(t = 20, # Top margin
r = 50, # Right margin
b = 40, # Bottom margin
l = 10)) # Left margin
I am trying to reproduce this figure (without the Portugal highlight):
The data (and figure) can be found in this link: https://stat.link/uz49al.
I imported and reshaped the data into a long format, but then I got stuck on how it would be possible to rearrange the legend entries in the same order as in the original.
I would very much appreciate your help!
Thanks!
Here is where I got:
# load data
f5_5_data_before <-
read_excel("uz49al.xlsx", sheet = "Figure1.20", range = "A32:E68")
names(f5_5_data_before)[1] <- "Country"
names(f5_5_data_before)[2] <- "Odds_ratio"
names(f5_5_data_before)[3] <- "SE"
names(f5_5_data_before)[4] <- "sig"
names(f5_5_data_before)[5] <- "non_sig"
f5_5_data_before$Country <- as.factor(f5_5_data_before$Country)
f5_5_data_before <- f5_5_data_before %>%
mutate(
category = case_when(
is.na(sig) ~ "Non-significant",
!is.na(sig) ~ "Significant"
),
value = case_when(
category == "Non-significant" ~ non_sig,
category == "Significant" ~ sig
)
)
f5_5_data_before$group2 <- "Before accounting for reading performance"
f5_5_data_after <-
read_excel("uz49al.xlsx", sheet = "Figure1.20", range = "A32:I68")
f5_5_data_after <- f5_5_data_after[, c(1, 6:9)]
names(f5_5_data_after)[1] <- "Country"
names(f5_5_data_after)[2] <- "Odds_ratio"
names(f5_5_data_after)[3] <- "SE"
names(f5_5_data_after)[4] <- "sig"
names(f5_5_data_after)[5] <- "non_sig"
f5_5_data_after$Country <- as.factor(f5_5_data_after$Country)
f5_5_data_after <- f5_5_data_after %>%
mutate(
category = case_when(
is.na(sig) ~ "Non-significant",
!is.na(sig) ~ "Significant"
),
value = case_when(
category == "Non-significant" ~ non_sig,
category == "Significant" ~ sig
)
)
f5_5_data_after$group2 <- "After accounting for reading performance"
# appending in long format
f5_5_data <- rbind(f5_5_data_after, f5_5_data_before)
# shaded rectangle
rect1 <- data.frame(
xmin = 14.5,
xmax = 15.5,
ymin = -Inf,
ymax = Inf
)
# figure
f5_5 <- ggplot() +
geom_col(data = f5_5_data %>% filter(group2 == "After accounting for reading performance"),
aes(x = reorder(Country,-Odds_ratio),
y = value,
fill = category,
colour = group2),
width=0.5,
) +
geom_point(
data = f5_5_data %>% filter(group2 == "Before accounting for reading performance"),
aes(x = Country,
y = value,
fill = category,
colour = group2),
shape = 23,
size = 3,
) +
geom_rect(
data = rect1,
aes(
xmin = xmin,
xmax = xmax,
ymin = ymin,
ymax = ymax
),
alpha = 0.5,
inherit.aes = FALSE
) +
scale_y_continuous(breaks = pretty_breaks(),
limits = c(0, 25),
expand = c(0, 0)) +
labs(x = NULL,
y = "Odds ratio") +
theme(axis.text.x = element_text(angle = 90))
print(f5_5)
This yields the following output:
As you can see, the legend looks substantially different and essentially I got stuck.
One option to achieve your desired result would be via the ggnewscale package which allows for multiple scales for the same aesthetic. Doing so we could map category on the fill aes in both the geom_col and the geom_point but have two different legends:
Note: I simplified your data wrangling code a bit.
library(readxl)
library(dplyr)
library(ggplot2)
library(ggnewscale)
url <- "https://stat.link/uz49al"
download.file(url, destfile = "uz49al.xlsx")
dat <- read_excel("uz49al.xlsx", sheet = "Figure1.20", range = "A32:I68")
dat <- list(
before = setNames(dat[, 1:5], c("Country", "Odds_ratio", "SE", "sig", "non_sig")),
after = setNames(dat[, c(1, 6:9)], c("Country", "Odds_ratio", "SE", "sig", "non_sig"))
) %>%
bind_rows(.id = "group2")
dat <- dat %>%
mutate(
category = if_else(is.na(sig), "nonsig", "sig"),
value = if_else(is.na(sig), non_sig, sig)
) %>%
select(-sig, -non_sig)
group2_labels <- c(after = "After accounting for reading performance", before = "Before accounting for reading performance")
rect1 <- data.frame(xmin = 14.5, xmax = 15.5, ymin = -Inf, ymax = Inf)
ggplot(dat, aes(x = reorder(Country,-Odds_ratio), y = value)) +
geom_col(data = ~filter(.x, group2 == "after"), aes(fill = category), width = 0.5) +
scale_fill_manual(labels = NULL, values = c(sig = "darkblue", nonsig = "steelblue"),
name = group2_labels[["after"]], guide = guide_legend(title.position = "right")) +
new_scale_fill() +
geom_point(data = ~filter(.x, group2 == "before"), aes(fill = category), size = 3, shape = 23, color = "lightblue") +
geom_rect(data = rect1, aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax),
alpha = 0.5, inherit.aes = FALSE) +
scale_fill_manual(labels = NULL, values = c(nonsig = "white", sig = "lightblue"), breaks = c("sig", "nonsig"),
name = group2_labels[["before"]], guide = guide_legend(title.position = "right")) +
scale_y_continuous(breaks = scales::pretty_breaks(), limits = c(0, 25), expand = c(0, 0)) +
labs(x = NULL, y = "Odds ratio") +
theme(axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "top")
This question already has an answer here:
Circular barchart customization from r-graph-gallery
(1 answer)
Closed 8 months ago.
I am trying to run this code from this link https://www.r-graph-gallery.com/299-circular-stacked-barplot.html.
# library
library(tidyverse)
library(viridis)
# Create dataset
data <- data.frame(
individual=paste( "Mister ", seq(1,60), sep=""),
group=c( rep('A', 10), rep('B', 30), rep('C', 14), rep('D', 6)) ,
value1=sample( seq(10,100), 60, replace=T),
value2=sample( seq(10,100), 60, replace=T),
value3=sample( seq(10,100), 60, replace=T)
)
# Transform data in a tidy format (long format)
data <- data %>% gather(key = "observation", value="value", -c(1,2))
# Set a number of 'empty bar' to add at the end of each group
empty_bar <- 2
nObsType <- nlevels(as.factor(data$observation))
to_add <- data.frame( matrix(NA, empty_bar*nlevels(data$group)*nObsType, ncol(data)) )
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each=empty_bar*nObsType )
data <- rbind(data, to_add)
data <- data %>% arrange(group, individual)
data$id <- rep( seq(1, nrow(data)/nObsType) , each=nObsType)
# Get the name and the y position of each label
label_data <- data %>% group_by(id, individual) %>% summarize(tot=sum(value))
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id-0.5) /number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse( angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle+180, angle)
# prepare a data frame for base lines
base_data <- data %>%
group_by(group) %>%
summarize(start=min(id), end=max(id) - empty_bar) %>%
rowwise() %>%
mutate(title=mean(c(start, end)))
# prepare a data frame for grid (scales)
grid_data <- base_data
grid_data$end <- grid_data$end[ c( nrow(grid_data), 1:nrow(grid_data)-1)] + 1
grid_data$start <- grid_data$start - 1
grid_data <- grid_data[-1,]
# Make the plot
p <- ggplot(data) +
# Add the stacked bar
geom_bar(aes(x=as.factor(id), y=value, fill=observation), stat="identity", alpha=0.5) +
scale_fill_viridis(discrete=TRUE) +
# Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
geom_segment(data=grid_data, aes(x = end, y = 0, xend = start, yend = 0), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 50, xend = start, yend = 50), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 150, xend = start, yend = 150), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
geom_segment(data=grid_data, aes(x = end, y = 200, xend = start, yend = 200), colour = "grey", alpha=1, size=0.3 , inherit.aes = FALSE ) +
# Add text showing the value of each 100/75/50/25 lines
ggplot2::annotate("text", x = rep(max(data$id),5), y = c(0, 50, 100, 150, 200), label = c("0", "50", "100", "150", "200") , color="grey", size=6 , angle=0, fontface="bold", hjust=1) +
ylim(-150,max(label_data$tot, na.rm=T)) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1,4), "cm")
) +
coord_polar() +
# Add labels on top of each bar
geom_text(data=label_data, aes(x=id, y=tot+10, label=individual, hjust=hjust), color="black", fontface="bold",alpha=0.6, size=5, angle= label_data$angle, inherit.aes = FALSE ) +
# Add base line information
geom_segment(data=base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha=0.8, size=0.6 , inherit.aes = FALSE ) +
geom_text(data=base_data, aes(x = title, y = -18, label=group), hjust=c(1,1,0,0), colour = "black", alpha=0.8, size=4, fontface="bold", inherit.aes = FALSE)
# Save at png
ggsave(p, file="output.png", width=10, height=10)
However, I am not sure why I am not getting the gaps and the scales in my figure (see below). As depicted, the numbers are printed inside the figure and the gaps between different groups of data are not there.
The original figure should be as follows:
There is a bug in the code. group has to be a factor to make the code adding the gaps work. To fix this add data$group <- factor(data$group).
Note: My guess is that the reason for this bug is that as of version 4.0.0 R treats strings in data frames as strings rather than factors. Hence, for versions < 4.0.0 the code worked fine as is.
# library
library(tidyverse)
library(viridis)
#> Loading required package: viridisLite
# Create dataset
data <- data.frame(
individual = paste("Mister ", seq(1, 60), sep = ""),
group = c(rep("A", 10), rep("B", 30), rep("C", 14), rep("D", 6)),
value1 = sample(seq(10, 100), 60, replace = T),
value2 = sample(seq(10, 100), 60, replace = T),
value3 = sample(seq(10, 100), 60, replace = T)
)
# Convert to factor
data$group <- factor(data$group)
# Transform data in a tidy format (long format)
data <- data %>% gather(key = "observation", value = "value", -c(1, 2))
# Set a number of 'empty bar' to add at the end of each group
empty_bar <- 2
nObsType <- nlevels(as.factor(data$observation))
to_add <- data.frame(matrix(NA, empty_bar * nlevels(data$group) * nObsType, ncol(data)))
colnames(to_add) <- colnames(data)
to_add$group <- rep(levels(data$group), each = empty_bar * nObsType)
data <- rbind(data, to_add)
data <- data %>% arrange(group, individual)
data$id <- rep(seq(1, nrow(data) / nObsType), each = nObsType)
# Get the name and the y position of each label
label_data <- data %>%
group_by(id, individual) %>%
summarize(tot = sum(value))
#> `summarise()` has grouped output by 'id'. You can override using the `.groups`
#> argument.
number_of_bar <- nrow(label_data)
angle <- 90 - 360 * (label_data$id - 0.5) / number_of_bar # I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0)
label_data$hjust <- ifelse(angle < -90, 1, 0)
label_data$angle <- ifelse(angle < -90, angle + 180, angle)
# prepare a data frame for base lines
base_data <- data %>%
group_by(group) %>%
summarize(start = min(id), end = max(id) - empty_bar) %>%
rowwise() %>%
mutate(title = mean(c(start, end)))
# prepare a data frame for grid (scales)
grid_data <- base_data
grid_data$end <- grid_data$end[c(nrow(grid_data), 1:nrow(grid_data) - 1)] + 1
grid_data$start <- grid_data$start - 1
grid_data <- grid_data[-1, ]
# Make the plot
ggplot(data) +
# Add the stacked bar
geom_bar(aes(x = as.factor(id), y = value, fill = observation), stat = "identity", alpha = 0.5) +
scale_fill_viridis(discrete = TRUE) +
# Add a val=100/75/50/25 lines. I do it at the beginning to make sur barplots are OVER it.
geom_segment(data = grid_data, aes(x = end, y = 0, xend = start, yend = 0), colour = "grey", alpha = 1, size = 0.3, inherit.aes = FALSE) +
geom_segment(data = grid_data, aes(x = end, y = 50, xend = start, yend = 50), colour = "grey", alpha = 1, size = 0.3, inherit.aes = FALSE) +
geom_segment(data = grid_data, aes(x = end, y = 100, xend = start, yend = 100), colour = "grey", alpha = 1, size = 0.3, inherit.aes = FALSE) +
geom_segment(data = grid_data, aes(x = end, y = 150, xend = start, yend = 150), colour = "grey", alpha = 1, size = 0.3, inherit.aes = FALSE) +
geom_segment(data = grid_data, aes(x = end, y = 200, xend = start, yend = 200), colour = "grey", alpha = 1, size = 0.3, inherit.aes = FALSE) +
# Add text showing the value of each 100/75/50/25 lines
ggplot2::annotate("text", x = rep(max(data$id), 5), y = c(0, 50, 100, 150, 200), label = c("0", "50", "100", "150", "200"), color = "grey", size = 6, angle = 0, fontface = "bold", hjust = 1) +
ylim(-150, max(label_data$tot, na.rm = T)) +
theme_minimal() +
theme(
legend.position = "none",
axis.text = element_blank(),
axis.title = element_blank(),
panel.grid = element_blank(),
plot.margin = unit(rep(-1, 4), "cm")
) +
coord_polar() +
# Add labels on top of each bar
geom_text(data = label_data, aes(x = id, y = tot + 10, label = individual, hjust = hjust), color = "black", fontface = "bold", alpha = 0.6, size = 5, angle = label_data$angle, inherit.aes = FALSE) +
# Add base line information
geom_segment(data = base_data, aes(x = start, y = -5, xend = end, yend = -5), colour = "black", alpha = 0.8, size = 0.6, inherit.aes = FALSE) +
geom_text(data = base_data, aes(x = title, y = -18, label = group), hjust = c(1, 1, 0, 0), colour = "black", alpha = 0.8, size = 4, fontface = "bold", inherit.aes = FALSE)
#> Warning: Removed 24 rows containing missing values (position_stack).
#> Warning: Removed 9 rows containing missing values (geom_text).
This is my first question to StackExchange, and I've searched for answers that have been helpful, but haven't really gotten me to where I'd like to be.
This is a stacked bar chart, combined with a point chart, combined with a line.
Here's my code:
theme_set(theme_light())
library(lubridate)
FM <- as.Date('2018-02-01')
x.range <- c(FM - months(1) - days(1) - days(day(FM) - 1), FM - days(day(FM) - 1) + months(1))
x.ticks <- seq(x.range[1] + days(1), x.range[2], by = 2)
#populate example data
preds <- data.frame(FM = FM, DATE = seq(x.range[1] + days(1), x.range[2] - days(1), by = 1))
preds <- data.frame(preds, S_O = round(seq(1, 1000000, by = 1000000/nrow(preds))))
preds <- data.frame(preds, S = round(ifelse(month(preds$FM) == month(preds$DATE), day(preds$DATE) / 30.4, 0) * preds$S_O))
preds <- data.frame(preds, O = preds$S_O - preds$S)
preds <- data.frame(preds, pred_sales = round(1000000 + rnorm(nrow(preds), 0, 10000)))
preds$ma <- with(preds, stats::filter(pred_sales, rep(1/5, 5), sides = 1))
y.max <- ceiling(max(preds$pred_sales) / 5000) * 5000 + 15000
line.cols <- c(O = 'palegreen4', S = 'steelblue4',
P = 'maroon', MA = 'blue')
fill.cols <- c(O = 'palegreen3', S = 'steelblue3',
P = 'red')
p <- ggplot(data = preds,
mapping = aes(DATE, pred_sales))
p <- p +
geom_bar(data = reshape2::melt(preds[,c('DATE', 'S', 'O')], id.var = 'DATE'),
mapping = aes(DATE, value, group = 1, fill = variable, color = variable),
width = 1,
stat = 'identity',
alpha = 0.5) +
geom_point(mapping = aes(DATE, pred_sales, group = 2, fill = 'P', color = 'P'),
shape = 22, #square
alpha = 0.5,
size = 2.5) +
geom_line(data = preds[!is.na(preds$ma),],
mapping = aes(DATE, ma, group = 3, color = 'MA'),
alpha = 0.8,
size = 1) +
geom_text(mapping = aes(DATE, pred_sales, label = formatC(pred_sales / 1000, format = 'd', big.mark = ',')),
angle = 90,
size = 2.75,
hjust = 1.25,
vjust = 0.4) +
labs(title = sprintf('%s Sales Predictions - %s', 'Overall', format(FM, '%b %Y')),
x = 'Date',
y = 'Volume in MMlbs') +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 8),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
legend.position = 'bottom',
legend.text = element_text(size = 8),
legend.margin = margin(t = 0.25, unit = 'cm')) +
scale_x_date(breaks = x.ticks,
date_labels = '%b %e',
limits = x.range) +
scale_y_continuous(limits = c(0, y.max),
labels = function(x) { formatC(x / 1000, format='d', big.mark=',') }) +
scale_color_manual(values = line.cols,
breaks = c('MA'),
labels = c(MA = 'Mvg Avg (5)')) +
scale_fill_manual(values = fill.cols,
breaks = c('P', 'O', 'S'),
labels = c(O = 'Open Orders', S = 'Sales', P = 'Predictions'))
p
The chart it generates is this:
As you can see, the legend does a couple of funky things. It's close, but not quite there. I only want boxes with exterior borders for Predictions, Open Orders, and Sales, and only a blue line for the Mvg Avg (5).
Any advice would be appreciated.
Thanks!
Rather late, but if you are still interested to understand this problem, the following should work. Explanations are included as comments within the code:
library(dplyr)
preds %>%
# scale the values for ALL numeric columns in the dataset, before
# passing the dataset to ggplot()
mutate_if(is.numeric, ~./1000) %>%
# since x / y mappings are stated in the top level ggplot(), there's
# no need to repeat them in the subsequent layers UNLESS you want to
# override them
ggplot(mapping = aes(x = DATE, y = pred_sales)) +
# 1. use data = . to inherit the top level data frame, & modify it on
# the fly for this layer; this is neater as you are essentially
# using a single data source for the ggplot object.
# 2. geom_col() is a more succinct way to say geom_bar(stat = "identity")
# (I'm using tidyr rather than reshape package, since ggplot2 is a
# part of the tidyverse packages, & the two play together nicely)
geom_col(data = . %>%
select(S, O, DATE) %>%
tidyr::gather(variable, value, -DATE),
aes(y = value, fill = variable, color = variable),
width = 1, alpha = 0.5) +
# don't show legend for this layer (o/w the fill / color legend would
# include a square shape in the centre of each legend key)
geom_point(aes(fill = 'P', color = 'P'),
shape = 22, alpha = 0.5, size = 2.5, show.legend = FALSE) +
# use data = . %>% ... as above.
# since the fill / color aesthetic mappings from the geom_col layer would
# result in a border around all fill / color legends, avoid it all together
# here by hard coding the line color to "blue", & map its linetype instead
# to create a separate linetype-based legend later.
geom_line(data = . %>% na.omit(),
aes(y = ma, linetype = 'MA'),
color = "blue", alpha = 0.8, size = 1) +
# scales::comma is a more succinct alternative to formatC for this use case
geom_text(aes(label = scales::comma(pred_sales)),
angle = 90, size = 2.75, hjust = 1.25, vjust = 0.4) +
labs(title = sprintf('%s Sales Predictions - %s', 'Overall', format(FM, '%b %Y')),
x = 'Date',
y = 'Volume in MMlbs') +
theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1, size = 8),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
legend.title = element_blank(),
legend.position = 'bottom',
legend.text = element_text(size = 8),
legend.margin = margin(t = 0.25, unit = 'cm')) +
scale_x_date(breaks = x.ticks,
date_labels = '%b %e',
limits = x.range) +
# as above, scales::comma is more succinct
scale_y_continuous(limits = c(0, y.max / 1000),
labels = scales::comma) +
# specify the same breaks & labels for the manual fill / color scales, so that
# a single legend is created for both
scale_color_manual(values = line.cols,
breaks = c('P', 'O', 'S'),
labels = c(O = 'Open Orders', S = 'Sales', P = 'Predictions')) +
scale_fill_manual(values = fill.cols,
breaks = c('P', 'O', 'S'),
labels = c(O = 'Open Orders', S = 'Sales', P = 'Predictions')) +
# create a separate line-only legend using the linetype mapping, with
# value = 1 (i.e. unbroken line) & specified alpha / color to match the
# geom_line layer
scale_linetype_manual(values = 1,
label = 'Mvg Avg (5)',
guide = guide_legend(override.aes = list(alpha = 1,
color = "blue")))
I have a ggplot which is doing exactly as I want till I add an overlay to it. The data to replicate are below, here's the ggplot:
timeline <- ggplot(dataset, aes(x = Month, y = Sessions,fill = Channel, group = Channel)) +
geom_area(alpha = 0.3) +
stat_summary(aes(group = 2), fun.y = sum, geom = 'line', size = 2, alpha = 0.5) +
theme(axis.text.x=element_text(angle=90, hjust=1),
axis.title.x = element_blank())
Results:
So far so good. Note the x-axis showing months in order. Then I add an overlay (see example data below to generate the variables in the code):
# make overlay representing TV
tv_begin <- sample(mts, 4)
tv_end <- tv_begin %m+% months(1)
tv_overlay <- data.frame(start = format(tv_begin, "%b-%Y"), end = format(tv_end, "%b-%Y"))
Then:
timeline + geom_rect(data = tv_overlay, inherit.aes = FALSE,
aes(xmin = start, xmax = end,
ymin = -Inf, ymax = Inf,
alpha = "TV On"),
fill = "black")
Results:
Now my x axis months are not ordered and I don't know how to prevent this happening? How can I maintain the x axis OR how can I have the new overlay inherit from them so that adding the overlay does not change the x-axis?
----Here's the data to replicate variable "dataset"-----
## Build dummy data
library(dplyr)
# dimensions
channels <- c("Facebook", "Youtube", "SEM", "Organic", "Direct", "Email")
last_month <- Sys.Date() %m+% months(-1) %>% floor_date("month")
mts <- seq(from = last_month %m+% months(-23), to = last_month, by = "1 month")
yr_month <- format(mts, "%b-%Y")
dimvars <- expand.grid(Month = yr_month, Channel = channels)
# metrics
rws <- nrow(dimvars)
set.seed(42)
# generates variablility in the random data
randwalk <- function(initial_val, ...){
initial_val + cumsum(rnorm(...))
}
Sessions <- ceiling(randwalk(3000, n = rws, mean = 8, sd = 1500)) %>% abs()
Transactions <- ceiling(randwalk(200, n = rws, mean = 0, sd = 75)) %>% abs()
Revenue <- ceiling(randwalk(10000, n = rws, mean = 0, sd = 3500)) %>% abs()
Spend <- ceiling(randwalk(6000, n = rws, mean = 0, sd = 3500)) %>% abs()
# make primary df
dataset <- cbind(dimvars, Sessions, Transactions, Revenue, Spend) %>%
mutate(Spend = ifelse(Channel %in% c("Direct", "Organic"), NA, Spend))
Remove the format() calls from your code. It turns everything to strings/factors.
Here I have converted x-axis data with as.Date(), and formatted the x-Axis in the plot with scale_x_date():
library(tidyverse)
library(lubridate)
# dimensions
channels <- c("Facebook", "Youtube", "SEM", "Organic", "Direct", "Email")
last_month <- Sys.Date() %m+% months(-1) %>% floor_date("month") %>% as.Date()
mts <- seq(from = last_month %m+% months(-23), to = last_month, by = "1 month") %>% as.Date()
#yr_month <- format(mts, "%b-%Y")
yr_month <- mts # format(mts, "%b-%Y")
dimvars <- expand.grid(Month = yr_month, Channel = channels, stringsAsFactors = FALSE)
rws <- nrow(dimvars)
set.seed(42)
# generates variablility in the random data
randwalk <- function(initial_val, ...){
initial_val + cumsum(rnorm(...))
}
Sessions <- ceiling(randwalk(3000, n = rws, mean = 8, sd = 1500)) %>% abs()
Transactions <- ceiling(randwalk(200, n = rws, mean = 0, sd = 75)) %>% abs()
Revenue <- ceiling(randwalk(10000, n = rws, mean = 0, sd = 3500)) %>% abs()
Spend <- ceiling(randwalk(6000, n = rws, mean = 0, sd = 3500)) %>% abs()
# make primary df
dataset <- cbind(dimvars, Sessions, Transactions, Revenue, Spend) %>%
mutate(Spend = ifelse(Channel %in% c("Direct", "Organic"), NA, Spend))
glimpse(dataset)
# make overlay representing TV
tv_begin <- sample(mts, 4)
tv_end <- tv_begin %m+% months(1)
tv_overlay <- data.frame(start = tv_begin, end = tv_end)
glimpse(tv_overlay)
timeline <- ggplot(dataset, aes(x = Month, y = Sessions,fill = Channel, group = Channel)) +
geom_area(alpha = 0.3) +
stat_summary(aes(group = 2), fun.y = sum, geom = 'line', size = 2, alpha = 0.5) +
theme(axis.text.x=element_text(angle=90, hjust=1),
axis.title.x = element_blank()) +
scale_x_date(date_labels = "%b-%d", date_breaks = "1 month")
timeline + geom_rect(data = tv_overlay, inherit.aes = FALSE,
aes(xmin = start, xmax = end,
ymin = -Inf, ymax = Inf,
alpha = "TV On"),
fill = "black")