Related
I have dataframe which represents sales by model within 2 different years. 'change' column stands for absolute change by models from 2020 to 2021 while 'chng.percent' measures this change in percentages.
However, I am struggling to apply the given Code of slope plot to my data.
df <- data.frame (model = c("A", "A", "B","B"),
year = c(2020,2021,2020,2021),
sale =c(105,190,110,180),
chang = c(85,NA,70,NA),
chng.percent = c(80.9,NA, 63.6,NA))
Expected outcome (Like this)
Here's a way to do it all within ggplot using your existing data:
ggplot(df, aes(year, sale, color = model)) +
geom_line(arrow = arrow(type = "closed", angle = 20),
key_glyph = draw_key_point) +
geom_vline(aes(xintercept = year)) +
geom_text(aes(label = sale, hjust = ifelse(year == 2020, 1.3, -0.3)),
color = "black",
size = 6) +
geom_text(aes(x = min(df$year) + 0.25, y = 105,
label = paste0("+", chang[1], "; ", chng.percent[1], "%"),
color = "A"), size = 5) +
geom_text(aes(x = max(df$year) - 0.25, y = 150,
label = paste0("+", chang[3], "; ", chng.percent[3], "%"),
color = "B"), size = 5) +
theme_void(base_size = 16) +
coord_cartesian(clip = "off") +
scale_x_continuous(breaks = c(2020, 2021)) +
guides(color = guide_legend(override.aes = list(size = 5))) +
scale_color_brewer(palette = "Set1") +
theme(plot.margin = margin(30, 30, 30, 30),
aspect.ratio = 1.5,
axis.text.x = element_text(size = 20))
you can try something like this :
df <- data.frame(model = c("A", "B"),
sale_2020 =c(105,110),
sale_2021 =c(190,180),
chang = c(85,70),
chng.percent = c(80.9, 63.6))
df %>%
ggplot() +
geom_segment(aes(x = 1, xend = 2,
y = sale_2020,
yend = sale_2021,
group = model,
col = model),
size = 1.2) +
# set the colors
scale_color_manual(values = c("#468189", "#9DBEBB"), guide = "none") +
# remove all axis stuff
theme_classic() +
theme(axis.line = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.ticks = element_blank()) +
geom_text(aes(x = x, y = y, label = label),
data = data.frame(x = 1:2,
y = 10 + max(df$sale_2021),
label = c("2020", "2021")),
col = "grey30",
size = 6) +
# add vertical lines that act as axis for 2020
geom_segment(x = 1, xend = 1,
y = min(df$sale_2020) -10,
yend = max(df$sale_2020) + 81,
col = "grey70", size = 1.5) +
# add vertical lines that act as axis for 2021
geom_segment(x = 2, xend = 2,
y = min(df$sale_2021) - 80,
yend = max(df$sale_2021) + 1,
col = "grey70", size = 1.5) +
# add the success rate next to each point on 2021 axis
geom_text(aes(x = 2 + 0.08,
y = sale_2021,
label = paste0(round(sale_2021, 1))),
col = "grey30") +
# add the success rate next to each point on 2021 axis
geom_text(aes(x = 1 - 0.08,
y = sale_2020,
label = paste0(round(sale_2020, 1))),
col = "grey30") +
# add the success rate next to each point on 2020 axis
geom_text(aes(x = 2 - 0.5,
y = c(156, 135),
label = paste0(round(chng.percent, 1), "%")),
col = "grey30")
I am trying to reproduce this kind of Figure, with two densities, a first one pointing upwards and a second one pointing downwards. I would also like to have some blank space between the two densities.
Here is the code I am currently using.
library(hrbrthemes)
library(tidyverse)
library(RWiener)
# generating data
df <- rwiener(n = 1e2, alpha = 2, tau = 0.3, beta = 0.5, delta = 0.5)
df %>%
ggplot(aes(x = q) ) +
geom_density(
data = . %>% filter(resp == "upper"),
aes(y = ..density..),
colour = "steelblue", fill = "steelblue",
outline.type = "upper", alpha = 0.8, adjust = 1, trim = TRUE
) +
geom_density(
data = . %>% filter(resp == "lower"),
aes(y = -..density..), colour = "orangered", fill = "orangered",
outline.type = "upper", alpha = 0.8, adjust = 1, trim = TRUE
) +
# stimulus onset
geom_vline(xintercept = 0, lty = 1, col = "grey") +
annotate(
geom = "text",
x = 0, y = 0,
# hjust = 0,
vjust = -1,
size = 3, angle = 90,
label = "stimulus onset"
) +
# aesthetics
theme_ipsum_rc(base_size = 12) +
theme(axis.text.y = element_blank() ) +
labs(x = "Reaction time (in seconds)", y = "") +
xlim(0, NA)
Which results in something like...
How could I add some vertical space between the two densities to reproduce the above Figure?
If you want to try without faceting, you're probably best to just plot the densities as polygons with adjusted y values according to your desired spacing:
s <- 0.25 # set to change size of the space
ud <- density(df$q[df$resp == "upper"])
ld <- density(df$q[df$resp == "lower"])
x <- c(ud$x[1], ud$x, ud$x[length(ud$x)],
ld$x[1], ld$x, ld$x[length(ld$x)])
y <- c(s, ud$y + s, s, -s, -ld$y - s, -s)
df2 <- data.frame(x = x, y = y,
resp = rep(c("upper", "lower"), each = length(ud$x) + 2))
df2 %>%
ggplot(aes(x = x, y = y, fill = resp, color = resp) ) +
geom_polygon(alpha = 0.8) +
scale_fill_manual(values = c("steelblue", "orangered")) +
scale_color_manual(values = c("steelblue", "orangered"), guide = guide_none()) +
geom_vline(xintercept = 0, lty = 1, col = "grey") +
annotate(
geom = "text",
x = 0, y = 0,
# hjust = 0,
vjust = -1,
size = 3, angle = 90,
label = "stimulus onset"
) +
# aesthetics
theme_ipsum_rc(base_size = 12) +
theme(axis.text.y = element_blank() ) +
labs(x = "Reaction time (in seconds)", y = "")
you can try facetting
set.seed(123)
q=rbeta(100, 0.25, 1)
df_dens =data.frame(gr=1,
x=density(df$q)$x,
y=density(df$q)$y)
df_dens <- rbind(df_dens,
data.frame(gr=2,
x=density(df$q)$x,
y=-density(df$q)$y))
ggplot(df_dens, aes(x, y, fill = factor(gr))) +
scale_x_continuous(limits = c(0,1)) +
geom_area(show.legend = F) +
facet_wrap(~gr, nrow = 2, scales = "free_y") +
theme_minimal() +
theme(strip.background = element_blank(),
strip.text.x = element_blank(),
axis.text.y = element_blank(),
axis.title.y = element_blank())
The space between both plots can be increased using panel.spacing = unit(20, "mm"). Instead of facet_grid you can also try facet_grid(gr~., scales = "free_y")
I want to plot a graph. Several of my x-axis labels have a common label. So I want to add common text as label instead of several separate labels on x-axis as shown in the attached images. How can this be done?
library(dplyr)
library(forcats)
library(ggplot2)
df <- data.frame(conc = c(0, 10, 50, 100, "Positive Control"),
values = c(3, 3, 4, 5, 10),
name = c("TiO2 NP", "TiO2 NP", "TiO2 NP", "TiO2 NP", "Cyclophosamide"))
df$conc <- as.factor(df$conc)
labels2 <- paste0(df$conc, "\n", df$name)
df %>%
mutate(conc = fct_reorder(conc, values)) %>%
ggplot(aes(x = conc, y=values, fill = conc))+
geom_bar(stat = "identity",show.legend = FALSE, width = 0.6)+
scale_x_discrete(labels = labels2)+
labs(x = "\n Dose (mg/kg BW)")
I don't think there's a simple way. You have to play with ggplot2 for some time to make something really custom. Here's my example:
df %>%
mutate(
conc = fct_reorder(conc, values),
labels2 = if_else(
name == 'TiO2 NP',
as.character(conc),
paste0(conc, '\n', name)
)
) %>%
ggplot(aes(x=conc, y=values, fill = conc)) +
geom_bar(
stat = "identity",
show.legend = FALSE,
width = 0.6
) +
geom_rect(aes(
xmin = .4,
xmax = 5.6,
ymin = -Inf,
ymax = 0
),
fill = 'white'
) +
geom_text(aes(
y = -.4,
label = labels2
),
vjust = 1,
size = 3.4,
color = rgb(.3, .3, .3)
) +
geom_line(data = tibble(
x = c(.9, 4.1),
y = c(-1.2, -1.2)
),
aes(
x = x,
y = y
),
color = rgb(.3, .3, .3),
inherit.aes = FALSE
) +
geom_curve(data = tibble(
x1 = c(.8, 4.1),
x2 = c(.9, 4.2),
y1 = c(-.8, -1.2),
y2 = c(-1.2, -.8)
),
aes(
x = x1,
y = y1,
xend = x2,
yend = y2
),
color = rgb(.3, .3, .3),
inherit.aes = FALSE
) +
geom_text(aes(
x = 2.5,
y = -1.7,
label = 'TiO2 NP'
),
size = 3.4,
color = rgb(.3, .3, .3),
check_overlap = TRUE
) +
geom_text(aes(
x = 3,
y = -2.4,
label = '\n Dose (mg/kg BW)'
),
show.legend = FALSE,
check_overlap = TRUE
) +
theme_minimal() +
theme(
axis.text.x = element_blank(),
axis.title.x = element_blank()
) +
scale_y_continuous(
breaks = seq(0, 10, 2.5),
limits = c(-2.5, 10)
)
For a more automated approach, you can try placing the common variable in facet_grid with scales = "free", space = "free", to simulate a 2nd x-axis line. The rest of the code below are for aesthetic tweaks:
df %>%
mutate(conc = fct_reorder(conc, values)) %>%
ggplot(aes(x = conc, y = values, fill = conc)) +
geom_col(show.legend = F, width = 0.6) + #geom_col() is equivalent to geom_bar(stat = "identity")
facet_grid(~ fct_rev(name),
scales = "free", space = "free",
switch = "x") + #brings the facet label positions from top (default) to bottom
scale_x_discrete(expand = c(0, 0.5)) + #adjusts the horizontal space at the ends of each facet
labs(x = "\n Dose (mg/kg BW)") +
theme(axis.line.x = element_line(arrow = arrow(ends = "both")), #show line (with arrow ends) to
#indicate facet label's extent
panel.spacing = unit(0, "cm"), #adjusts space between the facets
strip.placement = "outside", #positions facet labels below x-axis labels
strip.background = element_blank()) #transparent background for facet labels
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 wrote the following code to produce graphs like the one at the end. The thing is that I need to modify the dates shown in the x axis to make the image more understandable (ideally showing a point every two quarters)
Here is the dataset
And here is the code, which works fin until I try to modify scale_x_date. I tried to change in several ways the way in which the dates are introduced in the plot without success. I'd appreciate any help.
#rm(list=ls())
library(urca)
library(ggplot2)
library(ggrepel)
library(reshape2)
library(pracma)
library(extrafont)
library(dplyr)
library(lubridate)
library(zoo)
loadfonts(device = "win")
### Data set
info <- read.csv("base_completa_frame.csv",header=TRUE,dec=",", sep = ";")
info <- ts(info,frequency =4, c(1982,1))
info <- window(info, start=c(2000,4))
### Transf.
data_var <- diff(info,4)/ts(head(info,dim(info)[1]-4), start = c(2001,4), frequency = 4)
data_var <- ts(data_var,frequency =4, c(2001,4))
data_var <- window(data_var, start = c(2002,4))
data_var[,c(25:27)] <- window(info[,c(25:27)], start = c(2002,4))
data_var[,c(7,8,13,14)] <- window(diff(info[,c(7,8,13,14)]), start = c(2002,4))
data_var[,c(25:27,48:50)] <- window(diff(info[,c(25:27,48:50)],4), start = c(2002,4))
colnames(data_var) <- colnames(info)
data_var <- data_var[,-11:-12]
### Graphs
# Growth
time_ref <- time(data_var)
time_rec <- format(date_decimal(as.numeric(time_ref)),"%Y-%m-%d")
time_rec <- seq(as.Date(time_rec[1]), length = length(time_rec)[1], by = "quarter")
time_rec <- na.omit(time_rec[2*(1:length(time_rec))])
label_rec <- as.yearqtr(time_rec)
data_plot <- data.frame(data_var)
data_plot[,"time_ref"] <- time_ref
data_melt <- melt(data_plot, id = "time_ref")
for (i in nomb_melt){
ts_ref <- data_melt[which(data_melt$variable == i),]
ts_ref[,"value"] <- 100*ts_ref[,"value"]
sd_ref <- sd(ts_ref[,"value"])
t_ref <- qt(0.975,dim(ts_ref)[1]-5)*sd_ref/sqrt(dim(ts_ref)[1]-4)
test_L <- tail(ts_ref[,"value"],dim(ts_ref)[1]-4) < head(ts_ref[,"value"],dim(ts_ref)[1]-4) - t_ref
test_L <- which(test_L == TRUE)
test_U <- tail(ts_ref[,"value"],dim(ts_ref)[1]-4) > head(ts_ref[,"value"],dim(ts_ref)[1]-4) + t_ref
test_U <- which(test_U == TRUE)
ts_ref <- tail(ts_ref,dim(ts_ref)[1]-4)
ind_test <- 1:dim(ts_ref)[1]
ind_test[test_L] <- "Menor"
ind_test[test_U] <- "Mayor"
ind_test[-c(test_L,test_U)] <- "Igual"
ts_ref[,"ind_test"] <- ind_test
peaks <- findpeaks(ts_ref[,"value"], sortstr=TRUE)[1:4,2]
mins <- findpeaks(-ts_ref[,"value"], sortstr=TRUE)[1:4,2]
p <- ggplot(ts_ref, aes(x = time_ref, y = value, color = variable)) +
geom_rect(aes(xmin = time_ref,xmax = dplyr::lead(time_ref),
ymin = -Inf, ymax = Inf, fill = factor(ind_test)),
alpha = .2, linetype=0) +
scale_fill_manual(values = alpha(c("yellow","green", "red"), .2)) +
geom_line() + scale_color_manual(values="black") +
labs(x =" ", y = "Porcentaje") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
axis.line = element_line(colour = "black"),
strip.text = element_text(size=14),
text=element_text(family="Calibri"),
axis.text.x = element_text(angle=0)) +
geom_label_repel(
data = ts_ref[peaks,],
aes(label = format(round(ts_ref[peaks,"value"],2), 2)),
size = 3,
nudge_y = 1,
arrow = arrow(length = unit(0.01, 'npc')),
point.padding = unit(0.02, 'npc'),
label.size = NA, fill = "white") +
geom_label_repel(
data = ts_ref[mins,],
aes(label = format(round(ts_ref[mins,"value"],2), 2)),
size = 3,
nudge_y = -1,
arrow = arrow(length = unit(0.01, 'npc')),
point.padding = unit(0.02, 'npc'),
label.size = NA, fill = "white") +
scale_x_date(breaks = as.Date(time_rec),
labels = label_rec)
print(p)
}
Finally, here is one of the almost ready plots
I also didn't find a way to solve using scale_x_date. However, since you're using as.yearqtr to create the labels, I tried scale_x_yearqtr and it worked. For simplicity, I'm going to plot for PIB_Colombia and will only include here the code for the plot:
ggplot(ts_ref, aes(x = time_ref, y = value, color = variable)) +
geom_rect(aes(xmin = time_ref,xmax = dplyr::lead(time_ref),
ymin = -Inf, ymax = Inf, fill = factor(ind_test)),
alpha = .2, linetype=0) +
scale_fill_manual(values = alpha(c("yellow","green", "red"), .2)) +
geom_line() + scale_color_manual(values="black") +
labs(x =" ", y = "Porcentaje") +
theme_bw() +
theme(plot.title = element_text(hjust = 0.5),
legend.position="none",
panel.border = element_blank(),
axis.line = element_line(colour = "black"),
strip.text = element_text(size=14),
#text=element_text(family="Calibri"),
axis.text.x = element_text(angle=0)) +
geom_label_repel(
data = ts_ref[peaks,],
aes(label = format(round(ts_ref[peaks,"value"],2), 2)),
size = 3,
nudge_y = 1,
arrow = arrow(length = unit(0.01, 'npc')),
point.padding = unit(0.02, 'npc'),
label.size = NA, fill = "white") +
geom_label_repel(
data = ts_ref[mins,],
aes(label = format(round(ts_ref[mins,"value"],2), 2)),
size = 3,
nudge_y = -1,
arrow = arrow(length = unit(0.01, 'npc')),
point.padding = unit(0.02, 'npc'),
label.size = NA, fill = "white") +
scale_x_yearqtr(format = "%Y Q%q", n=length(time_rec))
This yielded the plot:
I used exactly the number of breaks you wanted to include, but you can control that by changing n within scale_x_yearqtr.