I have a dataset with 99 observations and I need to create boxplots for ones with a specific string in them. However, when I run this code I get 57 of the exact same plots from the original function instead of the loop. I was wondering how to prevent the plots from being overwritten but still create all 57. Here is the code and a picture of the plot.
Thanks!
Boxplot Format
#starting boxplot function
myboxplot <- function(mydata=ivf_dataset, myexposure =
"ART_CURRENT", myoutcome = "MEG3_DMR_mean")
{bp <- ggplot(ivf_dataset, aes(ART_CURRENT, MEG3_DMR_mean))
bp <- bp + geom_boxplot(aes(group =ART_CURRENT))
}
#pulling out variables needed for plots
outcomes = names(ivf_dataset)[grep("_DMR_", names(ivf_dataset),
ignore.case = T)]
#creating loop for 57 boxplots
allplots <- list()
for (i in seq_along(outcomes))
{
allplots[[i]]<- myboxplot (myexposure = "ART_CURRENT", myoutcome =
outcomes[i])
}
allplots
I recommend reading about standard and non-standard evaluation and how this works with the tidyverse. Here are some links
http://adv-r.had.co.nz/Functions.html#function-arguments
http://adv-r.had.co.nz/Computing-on-the-language.html
I also found this useful
https://rstudio-pubs-static.s3.amazonaws.com/97970_465837f898094848b293e3988a1328c6.html
Also, you need to produce an example so that it is possible to replicate your problem. Here is the data that I created.
df <- data.frame(label = rep(c("a","b","c"), 5),
x = rnorm(15),
y = rnorm(15),
x2 = rnorm(15, 10),
y2 = rnorm(15, 5))
I kept most of your code the same and only changed what needed to be changed.
myboxplot2 <- function(mydata = df, myexposure, myoutcome){
bp <- ggplot(mydata, aes_(as.name(myexposure), as.name(myoutcome))) +
geom_boxplot()
print(bp)
}
myboxplot2(myexposure = "label", myoutcome = "y")
Because aes() uses non-standard evaluation, you need to use aes_(). Again, read the links above.
Here I am getting all the columns that start with x. I am assuming that your code gets the columns that you want.
outcomes <- names(df)[grep("^x", names(df), ignore.case = TRUE)]
Here I am looping through in the same way that you did. I am only storing the plot object though.
allplots <- list()
for (i in seq_along(outcomes)){
allplots[[i]]<- myboxplot2(myexposure = "label", myoutcome = outcomes[i])$plot
}
allplots
Related
I have a dataset with numeric and factor variables. I want to do one page with numeric and other with factor var. First of all, i select factor var with his index.
My df is IRIS dataset.
df<-iris
df$y<-sample(0:1,nrow(iris),replace=TRUE)
fact<-colnames(df)[sapply(df,is.factor)]
index_fact<-which(names(df)%in%fact)
Then i calculate rest of it (numerics)
nm<-ncol(df)-length(fact)
Next step is create loop
i_F=1
i_N=1
list_plotN<- list()
list_plotF<- list()
for (i in 1:length(df)){
plot <- ggplot(df,aes(x=df[,i],color=y,fill=y))+xlab(names(df)[i])
if (is.factor(df[,i])){
p_factor<-plot+geom_bar()
list_plotF[[i_F]]<-p_factor
i_F=i_F+1
}else{
p_numeric <- plot+geom_histogram()
list_plotN[[i_N]]<-p_numeric
i_N=i_N+1
}
}
When i see list_plotF and list_plot_N,it didn't well. It always have same vars. i don't know what i'm doing wrong.
thanks!!!
I don't really follow your for loop code all that well. But from what I see it seems to be saving the last plot in every loop you make. I've reconstructed what I think you need using lapply. I generally prefer lapply to for loops whenever I can.
Lapply takes a list of values and a function and applies that function to every value. you can define your function separately like I have so everything looks cleaner. Then you just mention the function in the lapply command.
In our case the list is a list of columns from your dataframe df. The function it applies first creates our base plot. Then it does a quick check to see if the column it is looking at is a factor.. If it's a factor it creates a bar graph, else it creates a histogram.
histOrBar <- function(var) {
basePlot <- ggplot(df, aes_string(var))
if ( is.factor(df[[var]]) ) {
basePlot + geom_bar()
} else {
basePlot + geom_histogram()
}
}
loDFs <- lapply(colnames(df), histOrBar)
Consider passing column names with aes_string to better align x with df:
for (i in 1:length(df)){
plot <- ggplot(df, aes_string(x=names(df)[i], color="y", fill="y")) +
xlab(names(df)[i])
...
}
To demonstrate the problem using aes() and solution using aes_string() in OP's context, consider the following random data frame with columns of different data types: factor, char, int, num, bool, date.
Data
library(ggplot2)
set.seed(1152019)
alpha <- c(LETTERS, letters, c(0:9))
data_tools <- c("sas", "stata", "spss", "python", "r", "julia")
random_df <- data.frame(
group = sample(data_tools, 500, replace=TRUE),
int = as.numeric(sample(1:15, 500, replace=TRUE)),
num = rnorm(500),
char = replicate(500, paste(sample(LETTERS[1:2], 3, replace=TRUE), collapse="")),
bool = as.numeric(sample(c(TRUE, FALSE), 500, replace=TRUE)),
date = as.Date(sample(as.integer(as.Date('2019-01-01', origin='1970-01-01')):as.integer(Sys.Date()),
500, replace=TRUE), origin='1970-01-01')
)
Graph
fact <- colnames(random_df)[sapply(random_df,is.factor)]
index_fact <- which(names(random_df) %in% fact)
i_F=1
i_N=1
list_plotN <- list()
list_plotF <- list()
plot <- NULL
for (i in 1:length(random_df)){
# aes() VERSION
#plot <- ggplot(random_df, aes(x=random_df[,i], color=group, fill=group)) +
# xlab(names(random_df)[i])
# aes_string() VERSION
plot <- ggplot(random_df, aes_string(x=names(random_df)[i], color="group", fill="group")) +
xlab(names(random_df)[i])
if (is.factor(random_df[,i])){
p_factor <- plot + geom_bar()
list_plotF[[i_F]] <- p_factor
i_F=i_F+1
}else{
p_numeric <- plot + geom_histogram()
list_plotN[[i_N]] <- p_numeric
i_N=i_N+1
}
}
Problem (using aes() where graph outputs DO NOT change according to type)
Solution (using aes_string() where graphs DO change according to type)
I have a data with 8 variables (x1,y1,x2,y2,x3,y3,x4,y4), and i should do a function that generate a 4 plots x1vsy1, x2vsy2, x3vsy3 and x4vsy4.
So i was trying to do that one by one, doing a new data with the variables and after generate de plot.
minidata<-select(alldata,x1,y1)
ggplot(minidata,aes(x1,y1))+geom_point()+ggtitle("m VS n")
This works, but when i try to put that in the function
graph<-function(m,n){
minidata<-select(alldata,m,n)
ggplot(minidata,aes(x=m,y=n))+geom_point()+ggtitle("m VS n")
}
graph(y1,x1)
This doesnt work say "Error in FUN(X[[i]], ...) : object 'y1' not found"
what i could do to generate a function that creates the 4 plots?
There are a number of ways doing this. One approach is:
minidata <- data.frame( x1 = 1:20,
y1 = rnorm(20),
x2 = 1:20,
y2 = runif(20))
myGraph <- function( df, x, y ){
mdf <- df[ ,c(x,y)]
names(mdf) <- c("x","y")
ggplot(mdf,aes(x=x,y=y))+geom_point() + ggtitle(paste(y,"~",x)) + labs (x =x, y = y)
}
# call function by passing names of the column using names() function
myGraph (minidata, names(minidata)[1], names(minidata)[2])
# or simply giving a name
myGraph (minidata, "x2", "y2")
I'm trying to make multiple ggplot charts from multiple data frames. I have developed the code below but the final loop doesn't work.
df1 <- tibble(
a = rnorm(10),
b = rnorm(10)
)
df2 <- tibble(
a = rnorm(20),
b = rnorm(20)
)
chart_it <- function(x) {
x %>% ggplot() +
geom_line(mapping = aes(y=a,x=b)) +
ggsave(paste0(substitute(x),".png"))
}
ll <- list(df1,df2)
for (i in seq_along(ll)) {
chart_it(ll[[i]])
}
I know its something to do with
ll[[i]]
but I dont understand why because when I put that in the console it gives the dataframe I want. Also, is there a way do this the tidyverse way with the map functions instead of a for loop?
I assume you want to see two files called df1.png and df2.png at the end.
You need to somehow pass on the names of the dataframes to the function. One way of doing it would be through named list, passing the name along with the content of the list element.
library(ggplot2)
library(purrr)
df1 <- tibble(
a = rnorm(10),
b = rnorm(10)
)
df2 <- tibble(
a = rnorm(20),
b = rnorm(20)
)
chart_it <- function(x, nm) {
p <- x %>% ggplot() +
geom_line(mapping = aes(y=a,x=b))
ggsave(paste0(nm,".png"), p, device = "png")
}
ll <- list(df1=df1,df2=df2)
for (i in seq_along(ll)) {
chart_it(ll[[i]], names(ll[i]))
}
In tidyverse you could just replace the loop with the following command without modifying the function.
purrr::walk2(ll, names(ll),chart_it)
or simply
purrr::iwalk(ll, chart_it)
There's also imap and lmap, but they will leave some output in the console, which is not what you would like to do, I guess.
The problem is in your chart_it function. It doesn't return a ggplot. Try saving the result of the pipe into a variable and return() that (or place it as the last statement in the function).
Something along the lines of
chart_it <- function(x) {
chart <- x %>% ggplot() +
geom_line(mapping = aes(y=a,x=b))
ggsave(paste0(substitute(x),".png")) # this will save the last ggplot figure
return(chart)
}
I would like to create a function that creates different kinds of plotly plots based on the parameters that are passed into it. If I create the following data
library(plotly)
#### test data
lead <- rep("Fred Smith", 30)
lead <- append(lead, rep("Terry Jones", 30))
lead <- append(lead, rep("Henry Sarduci", 30))
proj_date <- seq(as.Date('2017-11-01'), as.Date('2017-11-30'), by = 'day')
proj_date <- append(proj_date, rep(proj_date, 2))
set.seed(1237)
actHrs <- runif(90, 1, 100)
cummActHrs <- cumsum(actHrs)
forHrs <- runif(90, 1, 100)
cummForHrs <- cumsum(forHrs)
df <- data.frame(Lead = lead, date_seq = proj_date,
cActHrs = cummActHrs,
cForHrs = cummForHrs)
I could plot it using:
plot_ly(data = df, x = ~date_seq, y = ~cActHrs, split = ~Lead)
If I made a makePlot function like the one shown below, how would I make it do something like this:
makePlot <- function(plot_data = df, x_var = date_seq, y_var, split_var) {
plot <- plot_ly(data = df, x = ~x_var, y = ~y_var, split = ~split_var)
return(plot)
}
?
Is there a function I can wrap x_var, y_var, and split_var with so that plotly will recognize them as x, y, and split parameters?
Eventually got around to figuring this out and hope this little follow up takes some of the mystery of these types of tasks. Although this question is focused on plotting, it's important to first build an understanding of how the functions in various R packages (e.g. dplyr and plotly) evaluate expressions and how to manipulate the way those expressions are evaluated. A great reference to build this understanding is Hadley's article on programming in dplyr here or alternatively here.
Once that's under your belt, this turns out to be pretty easy. The trick is to simply pass your variable arguments like you do when you call dplyr functions and be sure to quote those parameters inside your plotting function. For the question above, this function worked for me:
makePlot <- function(plot_data = df, x_var, y_var, split_var,
type_var="scatter",
mode_var="lines+markers") {
quo_x <- enquo(x_var)
quo_y <- enquo(y_var)
quo_split <- enquo(split_var)
# print(c(quo_x, quo_y, quo_split))
plot <- plot_ly(data = plot_data, x = quo_x, y = quo_y, split = quo_split,
type=type_var, mode=mode_var)
return(plot)
}
# using df created in question, pass col's as args like dplyr functions
p1 <- makePlot2(df, date_seq, cActHrs, Lead)
p2 <- makePlot2(df, date_seq, cForHrs, Lead)
I am trying to automatize plots (fitted vs model input variable, fitted vs model output variable) from models and would like to isolate the input and output variable names from the nlme() results.
I managed with something looking like a very dirty solution. Do you have anything more elegant to share?
Thanks!
here is an example:
df <- data.frame(foot = c(18,49.5,36.6,31.55,8.3,17,30.89,13.39,23.04,34.88,35.9,47.8,23.9,31,29.7,25.5,10.8,36,6.3,46.5,9.2,29,5.4,7.5,34.7,16.8,45.5,28,30.50955414,30.2866242,65.9,26.6,12.42038217,81.8,6.8,35.44585987,7,45.8,29,16.7,19.6,46.3,32.9,20.9,40.6,10,21.3,18.6,41.4,6.6),
leg = c(94.3588,760.9818,696.9112,336.64,12.43,69.32,438.9675,31.8159,153.6262,473.116,461.66276,897.7088,131.6944,395.909156,633.1044,179.772,41.3292,457.62,9.072,870.74,18.6438,356.64,5.3486,8.802,452.425561,82.618,839.649888,276.73016,560.63,655.83,2287.6992,234.1807,63,3475.649195,14.098,837.35,10.01,1149.87,615.03,124.35,184.33,1418.66,707.25,123.62,687.87,24.9696,192.416,181.5872,954.158,10.1716),
region=c(rep("a",13), rep("b", 17), rep("c", 20)),
disease = "healthy")
df$g <- "a" #No random effect wanted
m1 <- nlme(leg~a*foot^b,
data = df,
start= c(1,1),
fixed=a+b~1,
groups=~g,
weights=varPower(form=~foot))
I want to do data$output <- data$leg but automatised:
output_var <- eval(parse(text=paste(m1$call$data, as.character(m1$call$model)[2], sep="$")))
df$output <- output_var
I want to do data$input <- data$foot but automatised:
input_var <- eval(parse(text=paste(m1$call$data, gsub('a|b| |\\*|\\/|\\+|\\-|\\^', '', as.character(m1$call$model)[3]), sep="$")))
df$input <- input_var
df$fit_m1 <- fitted.values(m1)
So that I can use generic varaibles in my ggplot:
ggplot(df)+
geom_point(aes(x=input, y=output, colour = region))+
geom_line(aes(x=input, y=fit_m1))
Here is a solution using broom::augment
library(nlme)
library(ggplot)
library(broom)
# Get the fitted values along with the input and output
augmented <- augment(m1, data=df)
# Change the names of the dataframe so you have our standard input and output
new_names <- names(augmented)
new_names[c(1, 2)] <- c('input', 'output')
names(augmented) <- new_names
# Then you can plot using your standard names
ggplot(augmented, aes(input)) +
geom_point(aes(y = output, color = region)) +
geom_line(aes(y = .fitted))