I am trying to plot one graph with multiple trials (from separate text files). In the below case, I am plotting the "place" variable with the "firing rate" variable, and it works when I use ggplot on its own:
a <- read.table("trial1.txt", header = T)
library(ggplot2)
ggplot(a, aes(x = place, y = firing_rate)) + geom_point() + geom_path()
But when I try to create a for loop to go through each trial file in the folder and plot it on the same graph, I am having issues. This is what I have so far:
files <- list.files(pattern=".txt")
for (i in files){
p <- lapply(i, read.table)
print(ggplot(p, aes(x = place, y = firing_rate)) + geom_point() + geom_path())
}
It gives me a "Error: data must be a data frame, or other object coercible by fortify(), not a list" message. I am a novice in R so I am not sure what to make of that.
Thank you in advance for the help!
In general avoiding loops is the best adivce in R. Since you are using ggplot you may be interested in using the map_df function from tidyverse:
First create a read function and include the filename as a trial lable:
readDataFile = function(x){
a <- read.table(x, header = T)
a$trial = x
return(a)
}
Next up map_df:
dataComplete = map_df(files, readDataFile)
This runs our little function on each file and combines them all to a single data frame (of course assuming they are compatible in format).
Finally, you can plot almost as before but can distinguish based on the trial variable:
ggplot(dataComplete, aes(x = place, y = firing_rate, color=trial, group=trial)) + geom_point() + geom_path()
Related
I converted https://users.stat.ufl.edu/~winner/data/sexlierel.dat into a csv file. How would I do a histogram like this https://d33wubrfki0l68.cloudfront.net/73b1aa01f56fdebb5829f8bb9efefd2d424165dd/0799c/eda_files/figure-html/unnamed-chunk-6-1.png? When I knit it gives me a data must be in data frame error.
data_set <- read.csv("project_data.csv", header = TRUE)
names(data_set)
summary(data_set)
summary(data_set$Gender)
data=data.frame("Gender","Count")
```
```{r}
plot_density(data_set)
ggplot(data = "Gender") + geom_histogram(mapping = aes(x = "Count"), binwidth = 1)
scatter=ggplot(data=data, aes("Gender", "Count")) + geom_point()
```
[1]: https://users.stat.ufl.edu/~winner/data/sexlierel.dat
[2]: https://d33wubrfki0l68.cloudfront.net/73b1aa01f56fdebb5829f8bb9efefd2d424165dd/0799c/eda_files/figure-html/unnamed-chunk-6-1.png
The following is probably closer to what you want:
data_set <- read.csv("project_data.csv", header = TRUE)
ggplot(data = data_set, aes(Gender)) + geom_histogram(binwidth = 1)
scatter = ggplot(data = data_set, aes(Gender, Count)) + geom_point()
This assumes there is a Count column in your data set. I don't know anything about plot_density() or what package it's from, so I have no advice about that.
Explanation of the other bits:
You don't need to call data.frame() to get your data into a data frame. read.csv() has already done that for you.
data.frame() doesn't work that way anyway; passing it "Gender" and "Count" just makes it construct an otherwise empty data frame with those two strings as cells. In my experience, calling data.frame() directly never does what I want it to, so I avoid it and use other functions that do that work for me.
ggplot(data = "Gender")... this part doesn't work because you are just giving ggplot a string instead of a data set.
scatter = ggplot(data = data ... this part would be fine, but data doesn't contain what you want because of #2 above.
I am using an NS3 based simulator called NDNsim. I can generate certain trace files that can be used to analyze performance, etc. However I need to visualize the data generated.
I am a complete Novice with R, and would like a way to visualize. Here is how the output looks from which I would to plot. Any help is appreciated.
It's pretty difficult to know what you're looking for, since you have almost 50,000 measurements across 9 variables. Here's one way of getting a lot of that information on the screen:
df <- read.table(paste0("https://gist.githubusercontent.com/wuodland/",
"9b2c76650ea37459f869c59d5f5f76ea/raw/",
"6131919c105c95f8ba6967457663b9c37779756a/rate.txt"),
header = TRUE)
library(ggplot2)
ggplot(df, aes(x = Time, y = Kilobytes, color = Type)) +
geom_line() +
facet_wrap(~FaceDescr)
You could look into making sub structures from your input file and then graphing that by node, instead of trying to somehow invoke the plotter in just the right way.
df <- read.table(paste0("https://gist.githubusercontent.com/wuodland/",
"9b2c76650ea37459f869c59d5f5f76ea/raw/",
"6131919c105c95f8ba6967457663b9c37779756a/rate.txt"),
header = TRUE)
smaller_df <- df[which(df$Type=='InData'), names(df) %in% c("Time", "Node",
"FaceId", "FaceDescr", "Type", "Packets", "Kilobytes",
"PacketRaw", "KilobyteRaw")]
ggplot(smaller_df, aes(x = Time, y = Kilobytes, color = Type))
+ geom_line()
+ facet_wrap (~ Node)
The above snippet makes a smaller data frame from your original text data using only the "InData" Type, and then plots that by nodes.
I am trying to loop a ggplot2 plot with a linear regression line over it. It works when I type the y column name manually, but the loop method I am trying does not work. It is definitely not a dataset issue.
I've tried many solutions from various websites on how to loop a ggplot and the one I've attempted is the simplest I could find that almost does the job.
The code that works is the following:
plots <- ggplot(Everything.any, mapping = aes(x = stock_VWRETD, y = stock_10065)) +
geom_point() +
labs(x = 'Market Returns', y = 'Stock Returns', title ='Stock vs Market Returns') +
geom_smooth(method='lm',formula=y~x)
But I do not want to do this another 40 times (and then 5 times more for other reasons). The code that I've found on-line and have tried to modify it for my means is the following:
plotRegression <- function(z,na.rm=TRUE,...){
nm <- colnames(z)
for (i in seq_along(nm)){
plots <- ggplot(z, mapping = aes(x = stock_VWRETD, y = nm[i])) +
geom_point() +
labs(x = 'Market Returns', y = 'Stock Returns', title ='Stock vs Market Returns') +
geom_smooth(method='lm',formula=y~x)
ggsave(plots,filename=paste("regression1",nm[i],".png",sep=" "))
}
}
plotRegression(Everything.any)
I expect it to be the nice graph that I'd expect to get, a Stock returns vs Market returns graph, but instead on the y-axis, I get one value which is the name of the respective column, and the Market value plotted as normally, but as if on a straight number-line across the one y-axis value. Please let me know what I am doing wrong.
Desired Plot:
Actual Plot:
Sample Data is available on Google Drive here:
https://drive.google.com/open?id=1Xa1RQQaDm0pGSf3Y-h5ZR0uTWE-NqHtt
The problem is that when you assign variables to aesthetics in aes, you mix bare names and strings. In this example, both X and Y are supposed to be variables in z:
aes(x = stock_VWRETD, y = nm[i])
You refer to stock_VWRETD using a bare name (as required with aes), however for y=, you provide the name as a character vector produced by colnames. See what happens when we replicate this with the iris dataset:
ggplot(iris, aes(Petal.Length, 'Sepal.Length')) + geom_point()
Since aes expects variable names to be given as bare names, it doesn't interpret 'Sepal.Length' as a variable in iris but as a separate vector (consisting of a single character value) which holds the y-values for each point.
What can you do? Here are 2 options that both give the proper plot
1) Use aes_string and change both variable names to character:
ggplot(iris, aes_string('Petal.Length', 'Sepal.Length')) + geom_point()
2) Use square bracket subsetting to manually extract the appropriate variable:
ggplot(iris, aes(Petal.Length, .data[['Sepal.Length']])) + geom_point()
you need to use aes_string instead of aes, and double-quotes around your x variable, and then you can directly use your i variable. You can also simplify your for loop call. Here is an example using iris.
library(ggplot2)
plotRegression <- function(z,na.rm=TRUE,...){
nm <- colnames(z)
for (i in nm){
plots <- ggplot(z, mapping = aes_string(x = "Sepal.Length", y = i)) +
geom_point()+
geom_smooth(method='lm',formula=y~x)
ggsave(plots,filename=paste("regression1_",i,".png",sep=""))
}
}
myiris<-iris
plotRegression(myiris)
I'm quite new in R, trying to find my why around. I have created a new data frame based on the "original" data frame.
library(dplyr)
prdgrp <- as.vector(mth['MMITCL'])
prdgrp %>% distinct(MMITCL)
When doing this, then the result is a list of Unique values of the column MMITCL. I would like to use this data in a loop sequence that first creates a new subset of the original data and the prints a graph based on this:
#START LOOP
for (i in 1:length(prdgrp))
{
# mth[c(MMITCL==prdgrp[i],]
mth_1 <- mth[c(mth$MMITCL==prdgrp[i]),]
# Development of TPC by month
library(ggplot2)
library(scales)
ggplot(mth_1, aes(Date, TPC_MTD))+ geom_line()
}
# END LOOP
Doing this gives me the following error message:
Error in mth$MMITCL == prdgrp[i] :
comparison of these types is not implemented
In addition: Warning:
I `[.data.frame`(mth, c(mth$MMITCL == prdgrp[i]), ) :
Incompatible methods ("Ops.factor", "Ops.data.frame") for "=="
What am I doing wrong.
If you just want to plot the outputs there is no need to subset the dataframe, it is simpler to just put ggplot in a loop (or more likely use facet_wrap). Without seeing your data it is a bit hard to give you a precise answer. However there are two generic iris examples below - hopefully these will also show where you made the error in sub setting your dataframe. Please let me know if you have any questions.
library(ggplot2)
#looping example
for(i in 1:length(unique(iris$Species))){
g <- ggplot(data = iris[iris$Species == unique(iris$Species)[i], ],
aes(x = Sepal.Length,
y = Sepal.Width)) +
geom_point()
print(g)
}
#facet_wrap example
g <- ggplot(data = iris, aes(x = Sepal.Length, y = Sepal.Width)) +
geom_point() +
facet_wrap(~Species)
g
However if you need to save the data frames for later use, one option is to put them into a list. If you only need to save the data frame with in the loop you can just remove the list and use whatever variable name you wish.
myData4Later <- list()
for(i in 1:length(unique(iris$Species))){
myData4Later[[i]] <- iris[iris$Species == unique(iris$Species)[i], ]
g <- ggplot(data = myData4Later[[i]],
aes(x = Sepal.Length,
y = Sepal.Width)) +
geom_point()
print(g)
}
I hope I can get a contextual clue as to what may be wrong here without providing data frame, but can if necessary, but ultimately I want to utilize lapply to create multiple boxplots across multiple Ys and same X, but get the following error, but Termed is definitely in my CMrecruitdat data.frame:
Error in aes_string(x = Termed, y = RecVar, fill = Termed) :
object 'Termed' not found
RecVar <- CMrecruitdat[,c("Req.Open.To.System.Entry", "Req.Open.To.Hire", "Tenure")]
BP <- function (RecVar){
require(ggplot2)
ggplot(CMrecruitdat, aes_string(x=Termed, y=RecVar, fill=Termed))+
geom_boxplot()+
guides(fill=false)
}
lapply(RecVar, FUN=BP)
If you use aes_string, you should pass strings rather than vectors and use strings for all your fields.
RecVar <- CMrecruitdat[,c("Termed", "Req.Open.To.System.Entry", "Req.Open.To.Hire", "Tenure")]
BP <- function (RecVar){
require(ggplot2)
ggplot(RecVar, aes_string(x="Termed", y=RecVar, fill="Termed"))+
geom_boxplot()+
guides(fill=false)
}
lapply(names(RecVar), FUN=BP)