Ggplot for Loop Outputs All the Same Graph

ggplot for loop outputs all the same graph

Here's a more concise way to produce your example:

df <- data.frame(
  Person = paste0("Person", 1:5),
  var1 = c(1,2,3,4,5),
  var2 = c(2,2,2,2,2),
  var3 = c(1,3,5,3,1),
  var4 = c(5,4,3,2,1)
)

Now, about your plots.

Best solution

Reshape the data frame to 'long' format, and then use facets:

library(ggplot2)
library(tidyr)
gather(df, var, value, -Person) %>%
  ggplot(aes(Person, value)) +
    geom_bar(stat = "identity") +
    facet_wrap(~ var)

Otherwise…

If you gotta stick with a data structure that looks like what you posted, then use aes_string:

library(ggplot2)
library(gridExtra)

g <- lapply(1:4, function(i) {

  ggplot(df, aes_string("Person", paste0("var", i))) +
    geom_bar(stat = "identity")

})
grid.arrange(grobs = g, ncol = 2)

Save multiple ggplots using a for loop

Here is a fully reproducible example of creating ggplots in a loop.

# Plot separate ggplot figures in a loop.
library(ggplot2)

# Make list of variable names to loop over.
var_list = combn(names(iris)[1:3], 2, simplify=FALSE)

# Make plots.
plot_list = list()
for (i in 1:3) {
    p = ggplot(iris, aes_string(x=var_list[[i]][1], y=var_list[[i]][2])) +
        geom_point(size=3, aes(colour=Species))
    plot_list[[i]] = p
}

# Save plots to tiff. Makes a separate file for each plot.
for (i in 1:3) {
    file_name = paste("iris_plot_", i, ".tiff", sep="")
    tiff(file_name)
    print(plot_list[[i]])
    dev.off()
}

# Another option: create pdf where each page is a separate plot.
pdf("plots.pdf")
for (i in 1:3) {
    print(plot_list[[i]])
}
dev.off()

ggplot2: output multiple plots by looping

Following this answer:

library(ggplot2)
point1 <- c(1,2)
point2 <- c(2,2)

points <-as.data.frame(rbind(point1,point2))
point_plots_list <- vector('list', ncol(points))
point_plots_list <-  list()
for (i in 1:nrow(points)) {
  message(i)
  point_plots_list[[i]] <- local({
    i <- i
  p <- ggplot(data = points) +
    geom_point(aes(x = points[i, 1], y = points[i, 2])) +
    xlim(-3, 3) +
    ylim(-3, 3) +
    theme_bw()
  })
}
#> 1
#> 2
point_plots_list
#> [[1]]

#> 
#> [[2]]

Using lapply instead of a for loop and displaying plots with ggarrange or multiplot:

library(ggplot2)
library(ggpubr)
point1 <- c(1, 2)
point2 <- c(2, 2)

points <- as.data.frame(rbind(point1, point2))

plot_data <- function(data) {
  ggplot(data) +
    geom_point(aes(x = data[, 1], y = data[, 2])) +
    xlim(-3, 3) +
    ylim(-3, 3) +
    theme_bw()
}
myplots <- list()
myplots <- lapply(1:nrow(points), function(i) plot_data(points[i, ]))

ggarrange(myplots[[1]], myplots[[2]], 
          
          labels = c("A", "B"),
          ncol = 2, nrow = 1)

source("http://peterhaschke.com/Code/multiplot.R") #load multiplot function
multiplot(plotlist = myplots, cols = 2)
#> Loading required package: grid

^{Created on 2021-04-09 by the reprex package (v0.3.0)}

Loop to save same plot with different highlights using ggplot2

Using patchwork and lapply to loop over your samples one approach to achieve your desired result may look like so:

1. Use a first loop to create your plots and store them in a list
2. Use a second loop to glue the plots together in grids of 2 rows and 2 columns using patchwork::wrap_plots

Making use of some fake random example data:

library(ggplot2)
library(gghighlight)
library(patchwork)

plot_fun <- function(highlight) {
  ggplot(Dta, aes(x=value, group=Sample, 
                color = Sample)) +
  geom_density()+
  xlab("value")+
  ylab("Density")+
  theme_classic()+
  gghighlight(Sample == highlight,
              unhighlighted_params = list(colour = "grey90"))
}

Samples <- unique(Dta$Sample)

# Make plots
p <- lapply(Samples, plot_fun)

nrow <- 2
ncol <- 2

# Page counter
idx_page <- rep(seq(ceiling(length(p) / nrow / ncol)), each = nrow * ncol, length.out = length(p))

# Export plots as grids of 2 x 2
pdf('Allplots.pdf', width = 8, height = 11)
lapply(unique(idx_page), function(x) {
  wrap_plots(p[idx_page == x], ncol = ncol, nrow = nrow)
})
#> [[1]]
#> 
#> [[2]]
#> 
#> [[3]]
#> 
#> [[4]]
#> 
#> [[5]]
dev.off()
#> quartz_off_screen 
#>                 2

DATA

# Random example data
set.seed(123)
n <- 20
Dta <- data.frame(
  Sample = rep(LETTERS[seq(n)], each = 100),
  value = rnorm(100 * n)
)

How to use for loop across various years and get multiple plots together?

Here is an example using mtcars to split by a variable into separate plots. What I created is a scatter plot of vs and mpg by splitting the dataset by cyl. First create an empty list. Then I use lapply to loop through the values of cyl (4,6,8) and then filter the data by that value. After that I plot the scatter plot for the subset and save it to the empty list. Each segment of the list will represent a plot and you can pull them out as you see fit.

library(dplyr)
library(ggplot2)
gglist <- list()

gglist <- lapply(c(4,6,8), function(x){

ggplot(filter(mtcars, cyl == x))+
    geom_point(aes(x=vs,y=mpg))
})

Ggplot over multiple dataframes with loops

Your problem wasn't in the iteration over the list of dataframes, it was in the use of b1 within the annotate(). Here, I've created a new dataframe within each loop, and called the column name specifically. There is probably a nicer way of doing this, though. Also, the ggsave() needed to call the names of the items in the list, specifically.

library(tidyverse)

#Create dataframes(In this example n = 3)
df_1 <- data.frame(a1 = 1:1000,
                   b1 = 1:1000)  
df_2 <- data.frame(a1 = 1:1000,
                   b1 = 1:1000)
df_3 <- data.frame(a1 = 1:1000,
                   b1 = 1:1000)

##Store dataframes in list
example.list<-lapply(1:3, function(x) eval(parse(text=paste0("df_", x)))) #In order to store all datasets in one list using their name
names(example.list)<-lapply(1:3, function(x) paste0("df_", x))

#Graph and save for each dataframe

for (i in 1:length(example.list)){
  df_i <- example.list[[i]]
  benp <-  
    df_i %>%
    ggplot(aes(x=b1)) + 
    geom_histogram(fill="steelblue", aes(y=..density.., alpha=..count..), bins=60) + 
    labs(title="Beneficios", subtitle="") + ylab("Densidad") + 
    xlab("Beneficios ($millones)") + 
    geom_vline(aes(xintercept=mean(b1)), color="red4",linetype="dashed") +
    theme(legend.position = "none") + 
    annotate("text", x= mean(df_i$b1), y=0, label=round(mean(df_i$b1), digits = 2), 
             colour="red4", size=3.5, vjust=-1.5, hjust=-0.5) 
  ggsave(benp, file=paste0(names(example.list)[i],"_histogram.png"))
}

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