I have a question related to the histograms in R using ggplot2. I have been working trying to represent some values in a histogram from two different variables. After trying and looking for some solutions in Stackoverflow I got it but...does somebody know how to print NAs count as a new column just to compare the missings in the two variables?
Here is the R code:
i<-"ADL_1_bathing"
j<-"ADL_1_T2_bathing"
t1<-data.frame(datosMedicos[,i])
colnames(t1)<-"datos"
t2<-data.frame(datosMedicos[,j])
colnames(t2)<-"datos"
t1$time<-"t1"
t2$time<-"t2"
juntarParaGrafico<-rbind(t1,t2)
ggplot(juntarParaGrafico, aes(datos, fill = time) ) +
geom_histogram(col="darkblue",alpha = 0.5, aes(y = ..count..), binwidth = 0.2, position = 'dodge', na.rm = F) +
theme(legend.justification = c(1, 1), legend.position=c(1, 1))+
labs(title=paste0("Distribution of ",i), x=i, y="Count")
And this is the output:
Image about the two variables values but without the missing bars:
you could try to summarise the number of NAs b4 plotting. How about this?
library(ggplot2)
library(dplyr)
df1 = data.frame(a = rnorm(1:20))
df1[sample(1:20, 5),] = NA
df2 = data.frame(a = rnorm(1:20))
df2[sample(1:20, 3),] = NA
df2$time = "t2"
df1$time = "t1"
df = rbind(df1, df2)
df %>% group_by(time) %>% summarise(numNAs = sum(is.na(a)))
histogramDF= df %>% group_by(time) %>% summarise(numNAs = sum(is.na(a)))
qplot(x=time, y = numNAs, fill=time, data = histogramDF, stat='identity', geom="histogram")
Related
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
Let me first share a dummy data, from which I want to prepare ggplot graphs.
library(tidyverse)
set.seed(1)
sample_size <- 1200
dates <- sample(seq(1,31),sample_size,replace = TRUE)
Monthss <- sample(seq(1,12),sample_size,replace = TRUE)
hrs <- sample(seq(1,23),sample_size,replace = TRUE)
minutes <- sample(seq(1,59),sample_size,replace = TRUE)
date_time_vector <- paste0(dates,"-",Monthss,"-",2022," ",hrs,":",minutes) |> lubridate::parse_date_time("dmy HM")
Conversion <- sample(c(TRUE,FALSE),sample_size, prob = c(0.25,0.75), replace = TRUE)
df <- data.frame(Date = date_time_vector, Conversion_Status = Conversion)
df <- df |> mutate(Leads = round(runif(sample_size, min = 0,max = 10),digits = 0))
df <- df[complete.cases(df), ]
The code above gives me a data.frame with columns Date, Leads and Conversion_Status. I want to prepare Monthly column chart of total leads per day. (For example, daily leads in January, daily leads in February, etc.) So, basically, I will need to split the data on the basis of Month, and prepare one chart for each month. How can I prepare such charts?
I have tried following way:
bar_function <- function(df, col1, col2, title) {
df %>%
ggplot2::ggplot(aes(x = {{col1}}, y = {{col2}})) +
ggplot2::geom_col(fill = "steelblue") +
theme(plot.background = element_rect(fill = "white")) +theme(plot.title = element_text(hjust = 0.5))+coord_flip() +
ggplot2::labs(title = title)
}
mycharts <- df |> dplyr::nest_by(Month) |> dplyr::mutate(plot = bar_function(df,Date,Leads,"Daily Leads by Month"))
But it is giving me errors.
You can split according to month(year) and plot that.
library(ggplot2)
library(lubridate)
set.seed(1)
sample_size <- 1200
dates <- sample(seq(1,31),sample_size,replace = TRUE)
Monthss <- sample(seq(1,12),sample_size,replace = TRUE)
hrs <- sample(seq(1,23),sample_size,replace = TRUE)
minutes <- sample(seq(1,59),sample_size,replace = TRUE)
date_time_vector <- paste0(dates,"-",Monthss,"-",2022," ",hrs,":",minutes) |> lubridate::parse_date_time("dmy HM")
Conversion <- sample(c(TRUE,FALSE),sample_size, prob = c(0.25,0.75), replace = TRUE)
df <- data.frame(Date = date_time_vector, Conversion_Status = Conversion)
df$Leads <- round(runif(sample_size, min = 0,max = 10),digits = 0)
df <- df[complete.cases(df), ]
df$month_year <- strftime(df$Date, format = "%m-%Y")
df.split <- split(df, f = df$month_year)
out <- vector("list", length(df.split))
names(out) <- names(df.split)
for (i in seq_along(df.split)) {
out[[i]] <- ggplot(data = df.split[[i]], mapping = aes(x = Date, y = Leads)) +
geom_col(fill = "steelblue") +
theme(plot.background = element_rect(fill = "white")) +
theme(plot.title = element_text(hjust = 0.5))+
coord_flip() +
labs(title = "Daily leads by month")
}
To plot you can just print e.g. out[[1]].
If you want to change the desired columns dynamically, you can use aes_string for mapping. This can naturally be wrapped into sapply and there are probably other ways of approaching the problem. The for loop is pretty agnostic and I find that it's readable even by people who do not dabble in R (compared to say sapply).
There are some issues with your code. First, your dataset has no Month column, i.e. you have to add it for which I use lubridate::month. Second, you are passing the dataset df to your bar function instead of the splitted data column from your nested df. Third, in the mutate step you have to wrap the result in list():
library(ggplot2)
library(dplyr, warn=FALSE)
mycharts <- df |>
nest_by(Month = lubridate::month(Date)) |>
mutate(plot = list(bar_function(data, Date, Leads, "Daily Leads by Month")))
mycharts$plot[[1]]
mycharts$plot[[5]]
I finally found an answer. I used following code:
lapply(split(df, df$Month),
function(x)
ggplot(x, aes(x=Date, y=Leads)) +
geom_col(fill = "steelblue") + coord_flip()+
ggtitle(x$Month[1]))
Thank you all for your support.
I have built a stacked bar chart showing the relative proportions of response to different questions. Now I want to show a particular response ontop of that barchart, to show how an individuals response relates to the overall proportions of responses.
I created a toy example here:
library(ggplot2)
n = 1000
n_groups = 5
overall_df = data.frame(
state = sample(letters[1:8], n, replace = TRUE),
frequency = runif(n, min = 0, max = 1),
var_id = rep(LETTERS[1:n_groups], each = 1000 / n_groups)
)
row = data.frame(
A = "a", B = "b", C = "c", D = "h", E = "b"
)
ggplot(overall_df,
aes(fill=state, y=frequency, x=var_id)) +
geom_bar(position="fill", stat="identity")
The goal here is to have the responses in the object row plotted as a point in the corresponding barchart box, with a line connecting the points.
Here is a (poorly drawn) example of the desired result. Thanks for your help.
This was trickier than I thought. I'm not sure there's any way round manually calculating the x/y co-ordinates of the line.
library(dplyr)
library(ggplot2)
df <- overall_df %>% group_by(state, var_id) %>%
summarize(frequency = sum(frequency))
freq <- unlist(Map(function(d, val) {
(sum(d$frequency[d$state > val]) + 0.5 * d$frequency[d$state == val]) /
sum(d$frequency)
}, d = split(df, df$var_id), val = row))
line_df <- data.frame(state = unlist(row),
frequency = freq,
var_id = names(row))
ggplot(df, aes(fill=state, y=frequency, x=var_id)) +
geom_col(position="fill") +
geom_line(data = line_df, aes(group = 1)) +
geom_point(data = line_df, aes(group = 1))
Created on 2022-03-08 by the reprex package (v2.0.1)
Here's an automated approach using dplyr. I prepare the summary by joining the label data to the original data, and then using group_by + summarize to get those.
library(dplyr)
row_df <- data.frame(state = letters[1:n_groups], var_id = LETTERS[1:n_groups])
line_df <- row_df %>%
left_join(overall_df, by = "var_id") %>%
group_by(var_id) %>%
summarize(state = last(state.x),
frequency = (sum(frequency[state.x < state.y]) +
sum(frequency[state.x == state.y])/2) / sum(frequency))
ggplot(overall_df, aes(fill=state, y=frequency, x=var_id)) +
geom_bar(position="fill", stat="identity") +
geom_point(data = line_df) +
geom_line(data = line_df, aes(group = 1))
I have been trying to figure out how to group 9 datasets into 3 different groups (1, 2, and 3).
I have 3 different data frames that look like this:
ID1 ID2 dN dS Omega Label_ID1 Label_ID2 Group
QJY77946 NP_073551 0.0293 0.0757 0.3872 229E-CoV 229E-CoV Intra
QJY77954 NP_073551 0.0273 0.0745 0.3668 229E-CoV 229E-CoV Intra
...
So, the only columns that I´m interested in are three: dN, dS, and Omega.
My main goal is to take these three columns from my data frames and plots in a boxplot using Rstudio.
To do that, first I take the 3 columns of each data frame with these lines:
dN_1 <- df_1$dN
dS_1 <- df_1$dS
Omega_1 <- df_1$Omega
Then, to generate the plot I use this line (option 1):
boxplot(dN_S, dS_S, Omega_S, dN_M, dS_M, Omega_M, dN_E, dS_E, Omega_E,
main = "Test",
xlab = "Frames",
ylab = "Distribution",
col = "red")
My goal is to group these 9 boxes into 3 separate groups:
I know that using ggplot2 could be easier, so my option 2 is to use these lines (option 2):
df_1 %>%
ggplot(aes(y=dN_S)) +
geom_boxplot(
color = "blue",
fill = "blue",
alpha = 0.2,
notch = T,
notchwidth = 0.8)
However, you can see that I couldn´t find a way to plot all groups in the same plot.
So how can I group my data in the boxplot using option 1 or option 2? Maybe the second option is less development but perhaps someone could help with that too.
library(dplyr)
library(purrr)
library(tidyr)
library(ggplot2)
set.seed(123)
df_s <- data.frame(dN = runif(20),
dS = runif(20),
Omega = runif(20))
df_m <- data.frame(dN = runif(20),
dS = runif(20),
Omega = runif(20))
df_e <- data.frame(dN = runif(20),
dS = runif(20),
Omega = runif(20))
df <-
list(df_s, df_m, df_e) %>%
set_names(c("S", "M", "E")) %>%
map_dfr(bind_rows, .id = "df") %>%
pivot_longer(-df)
ggplot(df)+
geom_boxplot(aes(x = name, y = value))+
facet_wrap(~df, nrow = 1)
Created on 2021-09-24 by the reprex package (v2.0.0)
One way to accomplish this is by providing ggplot() another aesthetic, like fill. Here's a small reproducible example:
library(tidyverse)
df <- tibble(category = rep(letters[1:4], 5),
time = c(rep("before", 10), rep("after", 10)),
num = rnorm(20))
df %>%
ggplot() +
geom_boxplot(aes(x=category, y=num, fill = time))
Let me know if you're looking for something else.
I have several sets of data that I calculate binned normalized differences for. The results I want to plot within a single line plot using ggplot. The lines representing different combinations of the paired differences are supposed to be distinguished by colors and line types.
I am stuck on taking the computed values from the bins (would be y-axis values now), and plotting these onto an x-axis.
Below is the code I use for importing the data and calculating the normalized differences.
# Read data from column 3 as data table for different number of rows
# you could use replicate here for test
# dat1 <- data.frame(replicate(1,sample(25:50,10000,rep=TRUE)))
# dat2 <- data.frame(replicate(1,sample(25:50,9500,rep=TRUE)))
dat1 <- fread("/dir01/a/dat01.txt", header = FALSE, data.table=FALSE, select=c(3))
dat2 <- fread("/dir02/c/dat02.txt", header = FALSE, data.table=FALSE, select=c(3))
# Change column names
colnames(dat1) <- c("Dat1")
colnames(dat2) <- c("Dat2")
# Perhaps there is a better way to compute the following as all-in-one? I have broken these down step by step.
# 1) Sum for each bin
bin1 = cut(dat1$Dat1, breaks = seq(25, 50, by = 2))
sum1 = tapply(dat1$Dat1, bin1, sum)
bin2 = cut(dat2$Dat2, breaks = seq(25, 50, by = 2))
sum2 = tapply(dat2$Dat2, bin2, sum)
# 2) Total sum of all bins
sumt1 = sum(sum1)
sumt2 = sum(sum2)
# 3) Divide each bin by total sum of all bins
sumn1 = lapply(sum1, `/`, sumt1)
sumn2 = lapply(sum2, `/`, sumt2)
# 4) Convert to data frame as I'm not sure how to difference otherwise
df_sumn1 = data.frame(sumn1)
df_sumn2 = data.frame(sumn2)
# 5) Difference between the two as percentage
dbin = (df_sumn1 - df_sumn2)*100
How can I plot those results using ggplot() and geom_line()?
I want
dbin values on the x-axis ranging from 25-50
different colors and line types for the lines
Here is what I tried:
p1 <- ggplot(dbin, aes(x = ?, color=Data, linetype=Data)) +
geom_line() +
scale_linetype_manual(values=c("solid")) +
scale_x_continuous(limits = c(25, 50)) +
scale_color_manual(values = c("#000000"))
dput(dbin) outputs:
structure(list(X.25.27. = -0.0729132928804117, X.27.29. = -0.119044772581772,
X.29.31. = 0.316016473225017, X.31.33. = -0.292812782147632,
X.33.35. = 0.0776336591308158, X.35.37. = 0.0205584754637611,
X.37.39. = -0.300768421159599, X.39.41. = -0.403235174844081,
X.41.43. = 0.392510458816457, X.43.45. = 0.686758883448307,
X.45.47. = -0.25387105113263, X.47.49. = -0.0508324553382303), class = "data.frame", row.names = c(NA,
-1L))
Edit
The final piece of code that works, using only the dbin and plots multiple dbins:
dat1 <- data.frame(a = replicate(1,sample(25:50,10000,rep=TRUE, prob = 25:0/100)))
dat2 <- data.frame(a = replicate(1,sample(25:50,9500,rep=TRUE, prob = 0:25/100)))
dat3 <- data.frame(a = replicate(1,sample(25:50,9500,rep=TRUE, prob = 12:37/100)))
dat4 <- data.frame(a = replicate(1,sample(25:50,9500,rep=TRUE, prob = 37:12/100)))
calc_bin_props <- function(data) {
as_tibble(data) %>%
mutate(bin = cut(a, breaks = seq(25, 50, by = 2))) %>%
group_by(bin) %>%
summarise(sum = sum(a), .groups = "drop") %>%
filter(!is.na(bin)) %>%
ungroup() %>%
mutate(sum = sum / sum(sum))
}
diff_data <-
full_join(
calc_bin_props(data = dat1),
calc_bin_props(dat2),
by = "bin") %>%
separate(bin, c("trsh", "bin", "trshb", "trshc")) %>%
mutate(dbinA = (sum.x - sum.y * 100)) %>%
select(-starts_with("trsh"))
diff_data2 <-
full_join(
calc_bin_props(data = dat3),
calc_bin_props(dat4),
by = "bin") %>%
separate(bin, c("trsh", "bin", "trshb", "trshc")) %>%
mutate(dbinB = (sum.x - sum.y * 100)) %>%
select(-starts_with("trsh"))
# Combine two differences, and remove sum.x and sum.y
full_data <- cbind(diff_data, diff_data2[,4])
full_data <- full_data[,-c(2:3)]
# Melt the data to plot more than 1 variable on a plot
m <- melt(full_data, id.vars="bin")
theme_update(plot.title = element_text(hjust = 0.5))
ggplot(m, aes(as.numeric(bin), value, col=variable, linetype = variable)) +
geom_line() +
scale_linetype_manual(values=c("solid", "longdash")) +
scale_color_manual(values = c("black", "black"))
dev.off()
library(tidyverse)
Creating example data as shown in question, but adding different probabilities to the two sample() calls, to create so visible difference
between the two sets of randomized data.
dat1 <- data.frame(a = replicate(1,sample(25:50,10000,rep=TRUE, prob = 25:0/100))) %>% as_tibble()
dat2 <- data.frame(a = replicate(1,sample(25:50,9500,rep=TRUE, prob = 0:25/100))) %>% as_tibble()
Using dplyr we can handle this within data.frames (tibbles) without
the need to switch to other datatypes.
Let’s define a function that can be applied to both datasets to get
the preprocessing done.
We use base::cut() to create
a new column that pairs each value with its bin. We then group the data
by bin, calculate the sum for each bin and finally divide the bin sums
by the total sum.
calc_bin_props <- function(data) {
as_tibble(data) %>%
mutate(bin = cut(a, breaks = seq(25, 50, by = 2), labels = seq(25, 48, by = 2))) %>%
group_by(bin) %>%
summarise(sum = sum(a), .groups = "drop") %>%
filter(!is.na(bin)) %>%
ungroup() %>%
mutate(sum = sum / sum(sum))
}
Now we call calc_bin_props() on both datasets and join them by bin.
This gives us a dataframe with the columns bin, sum.x and sum.y.
The latter two are correspond to the bin sums derived from dat1 and
dat2. With the mutate() line we calculate the differences between the
two columns.
diff_data <-
full_join(
calc_bin_props(data = dat1),
calc_bin_props(dat2),
by = "bin") %>%
mutate(dbin = (sum.x - sum.y),
bin = as.numeric(as.character(bin))) %>%
select(-starts_with("trsh"))
Before we feed the data into ggplot() we convert it to the long
format using pivot_longer() this allows us to instruct ggplot() to
plot the results for sum.x, sum.y and dbin as separate lines.
diff_data %>%
pivot_longer(-bin) %>%
ggplot(aes(as.numeric(bin), value, color = name, linetype = name)) +
geom_line() +
scale_linetype_manual(values=c("longdash", "solid", "solid")) +
scale_color_manual(values = c("black", "purple", "green"))