I want to display the IDs that have extreme values on a boxplot but I have no idea how to do it.
For example the IDs corresponding to the values 10, 98 and 120
Poids<-c(round(rnorm(100,65,10),1),10,53,120,98)
ID<-c(paste("A",1:26,sep = ""),paste("B",1:26,sep = ""),paste("C",1:26,sep = ""),
paste("D",1:26,sep = ""))
mydata<-data.frame(ID=ID,Poids=Poids)
Using tidyverse packages you can create a subset inside geom_text, here how:
Data
Poids <- c(round(rnorm(100,65,10),1),10,53,120,98)
ID <- c(paste("A",1:26,sep = ""),paste("B",1:26,sep = ""),paste("C",1:26,sep = ""),
paste("D",1:26,sep = ""))
mydata <- data.frame(ID=ID,Poids=Poids)
Setting values manually
Code
library(dplyr)
library(ggplot2)
mydata %>%
ggplot(aes(x = Poids))+
geom_boxplot()+
geom_text(
data = mydata %>% filter(Poids %in% c(10,98,120)),
mapping = aes(y = 0,label = ID),
nudge_y = .05
)
Output
Using boxplot outlier criteria
Code
# remotes::install_github("vbfelix/relper")
library(relper)
mydata %>%
ggplot(aes(x = Poids))+
geom_boxplot()+
geom_text(
data = mydata %>% filter(is_outlier(Poids)),
mapping = aes(y = 0,label = ID),
nudge_y = .05
)
Output
I have a large dataset in which I want to group similar resistance patterns together. A plot to visualize similarity of resistance pattern is needed.
dat <- read.table(text="Id Resistance.Pattern
A SSRRSSSSR
B SSSRSSSSR
C RRRRSSRRR
D SSSSSSSSS
E SSRSSSSSR
F SSSRRSSRR
G SSSSR
H SSSSSSRRR
I RRSSRRRSS", header=TRUE)
I would separate out the values into a wider dataframe and then make a heatmap and dendrogram to compare sillimanites in patterns:
library(tidyverse)
library(ggdendro)
recode_dat <- dat |>
mutate(pat = str_split(Resistance.Pattern, "")) |>
unnest_wider(pat, names_sep = "_") |>
select(starts_with("pat_")) |>
mutate(across(everything(), ~case_when(. == "S" ~ 1, . == "R" ~ 2, is.na(.) ~0)))
rownames(recode_dat) <- dat$Id
dendro <- as.dendrogram(hclust(d = dist(x = scale(recode_dat))))
dendro_plot <- ggdendrogram(data = dendro, rotate = TRUE)
heatmap_plot <- dat |>
mutate(pat = str_split(Resistance.Pattern, "")) |>
unnest_wider(pat, names_sep = "_") |>
pivot_longer(cols = starts_with("pat_"), names_to = "pattern_position") |>
mutate(Id = factor(Id, levels = dat$Id[order.dendrogram(dendro)])) |>
ggplot(aes(pattern_position, Id))+
geom_tile(aes(fill = value))+
scale_x_discrete(labels = \(x) sub(".*_(\\d+$)", "\\1", x))+
theme(legend.position = "top")
cowplot::plot_grid(heatmap_plot, dendro_plot,nrow = 1, align = "h", axis = "tb")
It sounds as though the second column of your data frame represents sensitivity (S) and resistance (R), presumably to antibiotics (though this is not clear in your question). That being the case, you are presumably looking for something like this:
library(tidyverse)
p <- strsplit(dat$Resistance.Pattern, "")
do.call(rbind, lapply(p, \(x) c(x, rep(NA, max(lengths(p)) - length(x))))) %>%
as.data.frame() %>%
cbind(Id = dat$Id) %>%
mutate(Id = factor(Id, rev(Id))) %>%
pivot_longer(V1:V9) %>%
ggplot(aes(name, Id, fill = value)) +
geom_tile(col = "white", size = 2) +
coord_equal() +
scale_fill_manual(values = c("#e02430", "#d8d848"),
labels = c("Resistant", "Sensitive"),
na.value = "gray95") +
scale_x_discrete(name = "Antibiotic", position = "top",
labels = 1:9) +
labs(fill = "Resistance", y = "ID") +
theme_minimal(base_size = 20) +
theme(text = element_text(color = "gray30"))
I'd separate the entries by character, convert the binary data to numeric and plot the matrix as a heatmap and show the character string as rownames.
Whether to use a row and/or column clustering depends on whats desired.
library(dplyr)
library(tidyr) # for unnest_wider
library(gplots) # for heatmap.2
mm <-
dat %>%
group_by(Resistance.Pattern) %>%
summarize(Id, Resistance.Pattern) %>%
mutate(binary = strsplit(Resistance.Pattern, "")) %>%
unnest_wider(binary, names_sep="") %>%
mutate(across(starts_with("binary"), ~ as.numeric(c(R = 1, S = 0)[.x])))
mm2 <- as.matrix(mm[, -c(1,2)]) |> unname() # the numeric part
rownames(mm2) <- apply(as.matrix(mm[,1:2]), 1, paste, collapse=" ")
heatmap.2(mm2, trace="none", Colv="none", dendrogram="row",
col=c("green", "darkgreen"), margins=c(10,10))
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")
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]))))