Solution for R: Adding Progress Bars in R
is Given Below:
I am using the R programming language. I am trying to learn how to add “progress bars” to estimate how much time is remaining while a function is running (https://www.rdocumentation.org/packages/progress/versions/1.2.2/topics/progress_bar).
For example:
library(progress)
pb <- progress_bar$new(total = 100)
for (i in 1:100) {
pb$tick()
Sys.sleep(1 / 100)
}
Suppose I have a function called “grid_function” and a dataset called “DF_1”. I am taking each individual row from “DF_1” and feeding this row into “grid_function”. “grid_function” performs some calculations using this row, and stores it into a “list” called “resultdf1”. Finally, “resultdf1” is converted into a data frame called “final_output”. The “feeding process” can be seen below:
resultdf1 <- apply(DF_1,1, # 1 means rows
FUN=function(x){
do.call(
# Call Function grid_function with the arguments in
# a list
grid_function,
# force list type for the arguments
c(list(train_data_new), as.list(
# make the row to a named vector
unlist(x)
)
))
}
)
l = resultdf1
final_output = rbindlist(l, fill = TRUE)
Question: I would like to add a “progress bar” to the above code.
What I tried: I tried to do this as follows:
library(doParallel)
library(future)
cl <- makePSOCKcluster(6) # 6 cpu cores out of 8
registerDoParallel(cl)
pb <- progress_bar$new(total = 100)
for (i in 1:100) {
resultdf1 <- apply(DF_1,1, # 1 means rows
FUN=function(x){
do.call(
# Call Function grid_function2 with the arguments in
# a list
grid_function,
# force list type for the arguments
c(list(train_data_new), as.list(
# make the row to a named vector
unlist(x)
)
))
}
)
l = resultdf1
final_output = rbindlist(l, fill = TRUE)
pb$tick()
Sys.sleep(1 / 100)
}
stopCluster(cl)
This appears to be working, but I am not sure if I did everything correctly. Can someone please tell me if I have done this correctly? Is there any chance that adding this “progress bar” will actually result in the function taking more time to run?
Thanks
UPDATE: @Serkan : Here is the full code:
library(dplyr)
library(data.table)
results_table <- data.frame()
grid_function <- function(train_data, random_1, random_2, random_3, random_4, split_1, split_2, split_3) {
#bin data according to random criteria
train_data <- train_data %>% mutate(cat = ifelse(a1 <= random_1 & b1 <= random_3, "a", ifelse(a1 <= random_2 & b1 <= random_4, "b", "c")))
train_data$cat = as.factor(train_data$cat)
#new splits
a_table = train_data %>%
filter(cat == "a") %>%
select(a1, b1, c1, cat)
b_table = train_data %>%
filter(cat == "b") %>%
select(a1, b1, c1, cat)
c_table = train_data %>%
filter(cat == "c") %>%
select(a1, b1, c1, cat)
#calculate random quantile ("quant") for each bin
table_a = data.frame(a_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_1)))
table_b = data.frame(b_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_2)))
table_c = data.frame(c_table%>% group_by(cat) %>%
mutate(quant = quantile(c1, prob = split_3)))
#create a new variable ("diff") that measures if the quantile is bigger tha the value of "c1"
table_a$diff = ifelse(table_a$quant > table_a$c1,1,0)
table_b$diff = ifelse(table_b$quant > table_b$c1,1,0)
table_c$diff = ifelse(table_c$quant > table_c$c1,1,0)
#group all tables
final_table = rbind(table_a, table_b, table_c)
#create a table: for each bin, calculate the average of "diff"
final_table_2 = data.frame(final_table %>%
group_by(cat) %>%
summarize(
mean = mean(diff)
))
#add "total mean" to this table
final_table_2 = data.frame(final_table_2 %>% add_row(cat = "total", mean = mean(final_table$diff)))
#format this table: add the random criteria to this table for reference
final_table_2$random_1 = random_1
final_table_2$random_2 = random_2
final_table_2$random_3 = random_3
final_table_2$random_4 = random_4
final_table_2$split_1 = split_1
final_table_2$split_2 = split_2
final_table_2$split_3 = split_3
results_table <- rbind(results_table, final_table_2)
final_results = dcast(setDT(results_table), random_1 + random_2 + random_3 + random_4 + split_1 + split_2 + split_3 ~ cat, value.var="mean")
}
# create some data for this example
a1 = rnorm(1000,100,10)
b1 = rnorm(1000,100,5)
c1 = sample.int(1000, 1000, replace = TRUE)
train_data = data.frame(a1,b1,c1)
#grid
random_1 <- seq(80,100,5)
random_2 <- seq(85,120,5)
random_3 <- seq(85,120,5)
random_4 <- seq(90,120,5)
split_1 = seq(0,1,0.1)
split_2 = seq(0,1,0.1)
split_3 = seq(0,1,0.1)
DF_1 <- expand.grid(random_1 , random_2, random_3, random_4, split_1, split_2, split_3)
#reduce the size of the grid for this example
DF_1 = DF_1[1:100,]
colnames(DF_1) <- c("random_1" , "random_2", "random_3", "random_4", "split_1", "split_2", "split_3")
train_data_new <- copy(train_data)
resultdf1 <- apply(DF_1,1, # 1 means rows
FUN=function(x){
do.call(
# Call Function grid_function2 with the arguments in
# a list
grid_function,
# force list type for the arguments
c(list(train_data_new), as.list(
# make the row to a named vector
unlist(x)
)
))
}
)
l = resultdf1
final_output = rbindlist(l, fill = TRUE)
And here is how the final output should look like:
head(final_output)
random_1 random_2 random_3 random_4 split_1 split_2 split_3 b c total a
1: 80 85 85 90 0 0 0 0 0 0 NA
2: 85 85 85 90 0 0 0 0 0 0 NA
3: 90 85 85 90 0 0 0 0 0 0 NA
4: 95 85 85 90 0 0 0 0 0 0 NA
5: 100 85 85 90 0 0 0 0 0 0 NA
6: 80 90 85 90 0 0 0 0 0 0 NA
I would opt for .progress = TRUE in furrr:future_map, an example R Script below;
library(furrr)
library(magrittr)
plan(multisession, workers = 2)
1:10 %>% future_map(
~ Sys.sleep(2),
.progress = TRUE
)
It adds a progress-bar automatically,
Progress: ─────────────────────────────────────── 100%
Without your data.frame and specific function, I cant replicate your problem to show you how it is implemented, but the apply-family are easily translated to the map-family of functions.