Here is the codes and the present outplot
df <- data.frame(state = c('0','1'),
male = c(26287942,9134784),
female = c(16234000,4406645))
#output
> df
state male female
1 0 26287942 16234000
2 1 9134784 4406645
library(ggplot2)
library(tidyr)
df_long <- pivot_longer(df, cols = c("female","male"))
names(df_long) <- c('state','sex','observations')
ggplot(data = df_long) +
geom_col(aes(x = sex, y =observations, fill = state)) +
theme(legend.position = c(0.1,0.9),
legend.background = element_rect(fill='lightgrey') )
I want to adjust the plots like this. (I marked what I want to change.)
Simplify the scientific records in y-axis.
Count the ratio (the number of state 1)/(the number of state 0 + state 1) and plot like this.
It may be a little complicated, and I don't know which functions to use. If possible, can anyone tell me some related functions or examples?
You can set options(scipen = 99) to disable scientific notation on y-axis. We can create a separate dataset for label data.
library(tidyverse)
options(scipen = 99)
long_data <- df %>%
pivot_longer(cols = c(male, female),
names_to = "sex",
values_to = "observations")
label_data <- long_data %>%
group_by(sex) %>%
summarise(perc = observations[match(1, state)]/sum(observations),
total = sum(observations), .groups = "drop")
ggplot(long_data) +
geom_col(aes(x = sex, y = observations, fill = state)) +
geom_text(data = label_data,
aes(label = round(perc, 2), x = sex, y = total),
vjust = -0.5) +
theme(legend.position = c(0.1,0.9),
legend.background = element_rect(fill='lightgrey'))
By searching the Internet for about two days, I have finished the work!
sex <- c('M','F')
y0 <- c(26287942,16234000)
y1 <- c(9134784, 4406645)
y0 <- y0*10^{-7}
y1 <- y1*10^{-7}
ratio <- y1/(y0+y1)
ratio <- round(ratio,2)
m <- t(matrix(c(y0,y1),ncol=2))
colnames(m) <- c(as.character(sex))
df <- as.data.frame(m)
df <- cbind(c('0','1'),df)
colnames(df)[1] <- 'observations'
df
df_long <- pivot_longer(df, cols = as.character(sex))
names(df_long) <- c('state','sex','observations')
df_r <- as.data.frame(df_long)
df_r <- data.frame(df_r,ratio=rep(ratio,2))
ggplot(data = df_r) +
geom_col(aes(x =sex, y = observations, fill = state))+
theme(legend.position = c(0.1,0.9),
legend.background = element_rect(fill=NULL) )+
geom_line(aes(x=sex,y=ratio*10),group=1)+
geom_point(aes(x=sex,y=ratio*10))+
geom_text(aes(x=sex,y=ratio*10+0.35),label=rep(ratio,2))+
scale_y_continuous(name =expression(paste('observations(','\u00D7', 10^7,')')),
sec.axis = sec_axis(~./10,name='ratio'))
The output:
Given two monthly time series data sample from this link.
I will need to create one plot containing 3 subplots: plot1 for the original values, plot2 for month over month changes, and plot3 for year over year changes.
I'm able to draw the plot with code below, but the code is too redundant. So my question is how could achieve that in a concise way? Thanks.
library(xlsx)
library(ggplot2)
library(reshape)
library(dplyr)
library(tidyverse)
library(lubridate)
library(cowplot)
library(patchwork)
df <- read.xlsx('./sample_data.xlsx', 'Sheet1')
colnames(df)
# df
cols <- c('food_index', 'energy_index')
df <- df %>% mutate(date=as.Date(date)) %>%
mutate(across(-contains('date'), as.numeric)) %>%
mutate(date= floor_date(date, 'month')) %>%
group_by(date) %>%
summarise_at(vars(cols), funs(mean(., na.rm=TRUE))) %>%
mutate(across(cols, list(yoy = ~(. - lag(., 12))/lag(., 12)))*100) %>%
mutate(across(cols, list(mom = ~(. - lag(., 1))/lag(., 1)))*100) %>%
filter(date >= '2018-01-01' & date <= '2021-12-31') %>%
as.data.frame()
df1 <- df %>%
select(!grep('mom|yoy', names(df)))
df1_long <- melt(df1, id.vars = 'date')
plot1 <- ggplot(df1_long[!is.na(df1_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: $'
)
# MoM changes
df2 <- df %>%
select(grep('date|mom', names(df)))
df2_long <- melt(df2, id.vars = 'date')
plot2 <- ggplot(df2_long[!is.na(df2_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: %'
)
# YoY changes
df3 <- df %>%
select(grep('date|yoy', names(df)))
df3_long <- melt(df3, id.vars = 'date')
plot3 <- ggplot(df3_long[!is.na(df3_long$value), ],
aes(x = date,
y = value,
col = variable)) +
geom_line(size=0.6, alpha=0.5) +
geom_point(size=1, alpha=0.8) +
labs(
x='',
y='Unit: %'
)
plot <- plot1 + plot2 + plot3 + plot_layout(ncol=1)
# plot <- plot_grid(plot1, plot2, plot3, labels = c('Value', 'MoM', 'YoY'), label_size = 12)
plot
Out:
The expected result will be similar to the plot below (the upper plot will display the original data, the middle plot will display the mom changes data, and the lower plot will display the yoy changes data):
References:
https://waterdata.usgs.gov/blog/beyond-basic-plotting/
http://www.sthda.com/english/articles/24-ggpubr-publication-ready-plots/81-ggplot2-easy-way-to-mix-multiple-graphs-on-the-same-page/
Side-by-side plots with ggplot2
Maybe this is what you are looking for? By reshaping your data to the right shape, using a plot function and e.g. purrr::map2 you could achieve your desired result without duplicating your code like so.
Using some fake random example data to mimic your true data:
library(tidyr)
library(dplyr)
library(ggplot2)
df_long <- df |>
rename(food_index_raw = food_index, energy_index_raw = energy_index) |>
pivot_longer(-date, names_to = c("variable", ".value"), names_pattern = "^(.*?_index)_(.*)$")
plot_fun <- function(x, y, ylab) {
x <- x |>
select(date, variable, value = .data[[y]]) |>
filter(!is.na(value))
ggplot(
x,
aes(
x = date,
y = value,
col = variable
)
) +
geom_line(size = 0.6, alpha = 0.5) +
geom_point(size = 1, alpha = 0.8) +
labs(
x = "",
y = ylab
)
}
yvars <- c("raw", "mom", "yoy")
ylabs <- paste0("Unit: ", c("$", "%", "%"))
plots <- purrr::map2(yvars, ylabs, plot_fun, x = df_long)
library(patchwork)
wrap_plots(plots) + plot_layout(ncol = 1)
DATA
set.seed(123)
date <- seq.POSIXt(as.POSIXct("2017-01-31"), as.POSIXct("2022-12-31"), by = "month")
food_index <- runif(length(date))
energy_index <- runif(length(date))
df <- data.frame(date, food_index, energy_index)
EDIT Adding subtitles to each plot when using patchwork is (as of the moment) a bit tricky. What I would do in this case would be to use a faceting "hack". To this end I slightly adjusted the function to take a subtitle argument and switched to purrr::pmap:
library(tidyr)
library(dplyr)
library(ggplot2)
df_long <- df |>
rename(food_index_raw = food_index, energy_index_raw = energy_index) |>
pivot_longer(-date, names_to = c("variable", ".value"), names_pattern = "^(.*?_index)_(.*)$")
plot_fun <- function(x, y, ylab, subtitle) {
x <- x |>
select(date, variable, value = .data[[y]]) |>
filter(!is.na(value))
ggplot(
x,
aes(
x = date,
y = value,
col = variable
)
) +
geom_line(size = 0.6, alpha = 0.5) +
geom_point(size = 1, alpha = 0.8) +
facet_wrap(~.env$subtitle) +
labs(
x = "",
y = ylab
) +
theme(strip.background = element_blank(), strip.text.x = element_text(hjust = 0))
}
yvars <- c("raw", "mom", "yoy")
ylabs <- paste0("Unit: ", c("$", "%", "%"))
subtitle <- c("Original", "Month-to-Month", "Year-to-Year")
plots <- purrr::pmap(list(y = yvars, ylab = ylabs, subtitle = subtitle), plot_fun, x = df_long)
library(patchwork)
wrap_plots(plots) + plot_layout(ncol = 1)
The target output is done with facets rather than stitching plots together. You could do this too if you like, but it requires reshaping your data in a different way. Which approach you take is really a matter of taste.
library(ggplot2)
library(dplyr)
yoy <- function(x) 100 * (x - lag(x, 13)) / lag(x, 12)
mom <- function(x) 100 * (x - lag(x)) / lag(x)
df %>%
mutate(date = as.Date(date, origin = "1899-12-30"),
`Actual value (Dollars).Food Index` = food_index,
`Month-on-month change (%).Food Index` = mom(food_index),
`Year-on-year change (%).Food Index` = yoy(food_index),
`Actual value (Dollars).Energy Index` = energy_index,
`Month-on-month change (%).Energy Index` = mom(energy_index),
`Year-on-year change (%).Energy Index` = yoy(energy_index)) %>%
select(-food_index, -energy_index) %>%
tidyr::pivot_longer(-1) %>%
filter(date > as.Date("2018-01-01")) %>%
tidyr::separate(name, into = c("series", "index"), sep = "\\.") %>%
ggplot(aes(date, value, color = index)) +
geom_point(na.rm = TRUE) +
geom_line() +
facet_grid(series~., scales = "free_y") +
theme_bw(base_size = 16)
Reproducible data taken from link in question
df <- structure(list(date = c(42766, 42794, 42825, 42855, 42886, 42916,
42947, 42978, 43008, 43039, 43069, 43100, 43131, 43159, 43190,
43220, 43251, 43281, 43312, 43343, 43373, 43404, 43434, 43465,
43496, 43524, 43555, 43585, 43616, 43646, 43677, 43708, 43738,
43769, 43799, 43830, 43861, 43890, 43921, 43951, 43982, 44012,
44043, 44074, 44104, 44135, 44165, 44196, 44227, 44255, 44286,
44316, 44347, 44377, 44408, 44439, 44469, 44500, 44530, 44561
), food_index = c(58.53, 61.23, 55.32, 55.34, 61.73, 56.91, 54.27,
59.08, 60.11, 66.01, 60.11, 63.41, 69.8, 72.45, 81.11, 89.64,
88.64, 88.62, 98.27, 111.11, 129.39, 140.14, 143.44, 169.21,
177.39, 163.88, 135.07, 151.28, 172.81, 143.82, 162.13, 172.22,
176.67, 179.3, 157.27, 169.12, 192.51, 194.2, 179.4, 169.1, 193.17,
174.92, 181.92, 188.41, 192.14, 203.41, 194.19, 174.3, 174.86,
182.33, 182.82, 185.36, 192.41, 195.59, 202.6, 201.51, 225.01,
243.78, 270.67, 304.57), energy_index = c(127.36, 119.87, 120.96,
112.09, 112.19, 109.24, 109.56, 106.89, 109.35, 108.35, 112.39,
117.77, 119.52, 122.24, 120.91, 125.41, 129.72, 135.25, 139.33,
148.6, 169.62, 184.23, 204.38, 198.55, 189.29, 202.47, 220.23,
240.67, 263.12, 249.74, 240.84, 243.42, 261.2, 256.76, 258.69,
277.98, 289.63, 293.46, 310.81, 318.68, 310.04, 302.17, 298.62,
260.92, 269.29, 258.84, 241.68, 224.18, 216.36, 226.57, 235.98,
253.86, 267.37, 261.99, 273.37, 280.91, 291.84, 297.88, 292.78,
289.79)), row.names = c(NA, 60L), class = "data.frame")
I am trying to label 4 lines grouped by the value of variable cc. To label the lines I use ggrepel but I get all the 4 labels instead of 2 for each graph. How to correct this error?
The location of the labels is in this example at the last date but I want something more flexible: I want to locate each of the 4 labels in specific points that I chose (e.g. b at date 1, a at date 2, etc.). How to do that?
library(tidyverse)
library(ggrepel)
library(cowplot)
set.seed(1234)
df <- tibble(date = c(rep(1,4), rep(2,4), rep(3,4), rep(4,4)),
country = rep(c('a','b','c','d'),4),
value = runif(16),
cc = rep(c(1,1,2,2),4))
df$cc <- as.factor(df$cc)
# make list of plots
ggList <- lapply(split(df, df$cc), function(i) {
ggplot(i, aes(x = date, y = value, color = country)) +
geom_line(lwd = 1.1) +
geom_text_repel(data = subset(df, date == 4),
aes(label = country)) +
theme(legend.position = "none")
})
# plot as grid in 1 columns
cowplot::plot_grid(plotlist = ggList, ncol = 1,
align = 'v', labels = levels(df$cc))
Created on 2021-08-18 by the reprex package (v2.0.0)
Here I make a tibble to hold color and position preferences, and join that to df.
The geom_text_repel line should probably use i instead of df so that it's split the same way as the line. The only trouble is this forces us to specify that we want four colors up front, since otherwise each chart would just use the two it needs.
set.seed(1234)
df <- tibble(date = c(rep(1,4), rep(2,4), rep(3,4), rep(4,4)),
country = rep(c('a','b','c','d'),4),
value = runif(16),
cc = rep(c(1,1,2,2),4))
label_pos <- tibble(country = letters[1:4],
label_pos = c(2, 1, 3, 2),
color = RColorBrewer::brewer.pal(4, "Set2")[1:4])
df <- df %>% left_join(label_pos)
df$cc <- as.factor(df$cc)
# make list of plots
ggList <- lapply(split(df, df$cc), function(i) {
ggplot(i, aes(x = date, y = value, color = color)) +
geom_line(lwd = 1.1) +
geom_text_repel(data = subset(i, date == label_pos),
aes(label = country), box.padding = unit(0.02, "npc"), direction = "y") +
scale_color_identity() +
theme(legend.position = "none")
})
# plot as grid in 1 columns
cowplot::plot_grid(plotlist = ggList, ncol = 1,
align = 'v', labels = levels(df$cc))
How can I scale/normalize my data per row (Observations)? Something like [-1:1] like a z score?
I have seen previous post which involve normalization of the whole dataset like this https://stats.stackexchange.com/questions/178626/how-to-normalize-data-between-1-and-1
, but id like to normalise per row so they can be plotted in a same box plot as they all show same pattern across x-axis.
Obs <- c("A", "B", "C")
count1 <- c(100,15,3)
count2 <- c(250, 30, 5)
count3 <- c(290, 20, 8)
count4<- c(80,12, 2 )
df <- data.frame(Obs, count1, count2, count3, count4)
dff<- df %>% pivot_longer(cols = !Obs, names_to = 'count', values_to = 'Value')
ggplot(dff, aes(x = count, y = Value)) +
geom_jitter(alpha = 0.1, color = "tomato") +
geom_boxplot()
Based on the link you shared, you can use apply to use the corresponding function to rescale dataframe over [-1,1].
library(scales)
library(ggplot2)
library(tidyr)
Obs <- c("A", "B", "C")
count1 <- c(100,15,3)
count2 <- c(250, 30, 5)
count3 <- c(290, 20, 8)
count4<- c(80,12, 2 )
df <- data.frame(count1, count2, count3, count4)
df <- as.data.frame(t(apply(df, 1, function(x)(2*(x-min(x))/(max(x)-min(x)))- 1)))
df <- cbind(Obs, df)
dff<- df %>%
tidyr::pivot_longer(cols = !Obs, names_to = 'count', values_to = 'Value')
ggplot(dff, aes(x = count, y = Value)) +
geom_jitter(alpha = 0.1, color = "tomato") +
geom_boxplot()
Console output:
If you pivot it longer, you can group by your observations and scale:
df %>%
pivot_longer(cols = !Obs, names_to = 'count', values_to = 'Value') %>% group_by(Obs) %>%
mutate(z=as.numeric(scale(Value))) %>%
ggplot(aes(x=count,y=z))+geom_boxplot()
Or in base R, just do:
boxplot(t(scale(t(df[,-1]))))