Hey all I apologise if this is very simple and mediocre but I can't seem to create a function that turns a time variable (00:00:00) into a numeric AND creates a new column to put the result in.
I can turn the time into a numeric, I just cannot complete the 'new column' part. Any help is appreciated.
Time <- function(x) {
begin <- x$avg..wait.time
x$Num.wait.time <- as.numeric(as.POSIXct(strptime(begin, "%H:%M:%S")))
}
(NOTE: avg..wait.time is the time cell and
Num.wait.time is the new variable/column I want to create)
If your purpose is not in writing the function per se, with dplyr you can directly tackle the problem with existing wheels, and not have to write a separate function.
library(dplyr)
df <- data.frame(avg.wait.time = c("01:02:03", "03:02:01"))
df <- df %>%
dplyr::mutate(
avg.wait.numeric = as.numeric(as.POSIXct(strptime(avg.wait.time, "%H:%M:%S")))
)
If you wish to write a separate function, I would do as follows:
Time <- function(x,
input_var = "avg.wait.time",
output_var = "avg.wait.numeric") {
x[[output_var]] <-
as.numeric(as.POSIXct(strptime(x[[input_var]], "%H:%M:%S")))
return(x)
}
This allows the input variable name and output variable name to be specified, currently set with some arbitrary default values (you can kick these out, of course).
Related
I feel stupid for asking such a simple question, but I am hitting my head in the wall.
Why does the paste0() create a string that cannot be not interpreted as name for an empty object ? Is there a different way of create the LHS that would be better?
As input I have a dataframe. As an output I want to have a new filtered dataframe. This works fine as long as I manually type all the code. However, I am trying to reduce repetition, and therefore I want to create a function that does the same thing, but then it is not working anymore.
library(magrittr)
df <- data.frame(
var_a = round(runif(20), digits = 1),
var_b = sample(letters, 20)
)
### Find duplicates
df$duplicate_num <- duplicated(df$var_a)
df$duplicate_txt <- duplicated(df$var_b)
df # a check
### Create two lists of duplicates
list_of_duplicate_num <-
df %>%
filter(duplicate_num)
list_of_duplicate_num # a check
list_of_duplicate_txt <-
df %>%
filter(duplicate_txt)
list_of_duplicate_txt # a check '
So far everything works as expected.
I would like to simplify the code and make this to a function that takes the arguments "num" or "txt". But I am having problems with creating the LHS.
The below should, in my mind, do the same as the code above.
paste0("list_of_duplicate_", "num") <-
df %>%
filter(duplicate_num)
I do get an error message:
Error in paste0("list_of_duplicate_", "num") <- df %>%
filter(duplicate_num) :
target of assignment expands to non-language object
My goal is to create a function with something like this:
make_list_of_duplicates <- function(criteria = "num") {
paste0("list_of_duplicate_", criteria) <-
df %>%
filter(paste0("duplicate_", criteria))
paste0("list_of_duplicate_", criteria) # a check
}
### Create two lists of duplicates
make_list_of_duplicates("num")
make_list_of_duplicates("txt")
and then continue with some joins etc.
I have been looking to tidy evaluation, assignments, rlang::enexpr(), base::substitute(), get(), mget() and many other things, but after two day of reading and trial and error, I am convinced that there must be a an other direction to look at that I am not seeing.
I am running MS Open R 4.0.2.
I am grateful for any suggestions.
Sincerely,
Eero
I found the solution to my question, when I understood that it was a case of indirection. Because I was on a wrong track, I created lots of complications and made it more difficult than necessary. Thanks to #r2evans who pointed me in the right direction. I have in the mean time decided that I will use loops, instead of functions, but here is the working function:
## Example of using paste inside a function to refer to an object.
library(magrittr)
library(dplyr)
df <- data.frame(
var_a = round(runif(20), digits = 1),
var_b = sample(letters, 20)
)
# Find duplicates
df$duplicate_num <- duplicated(df$var_a)
df$duplicate_txt <- duplicated(df$var_b)
# SEE https://dplyr.tidyverse.org/articles/programming.html#indirection-2
make_list_of_duplicates_f2 <- function(criteria = "num") {
df %>%
filter(.data[[paste0("duplicate_", {{criteria}})]])
}
# Create two lists of duplicates
list_of_duplicates_f2_num <-
make_list_of_duplicates_f2("num")
list_of_duplicates_f2_txt <-
make_list_of_duplicates_f2("txt")
I currently have a list which is made up of around 80+ data frames, what I would like to do is to loop a chunk of code for each individual data frame within the list, without naming each one individually, or splitting them into individual data frames to work on.
Currently I split the list into each individual data frame using the below code:
dat5split <- setNames(split(dat5, dat5$CODE), paste0("df", unique(dat5$CODE)))
list2env(dat5split, globalenv())
I then work through each data frame individually:
# call in SPC function and write to 'results10000'
results10000<-SPC_XBAR(df10000,vol_n,seasonality)
results10000 = results10000 %>%
cbind(Spec = df10000$CODE) %>%
subset(`table_n` == 1)
results10000 <- results10000[order(results10000$tpd),]
results10000$Date <- as.Date(cbind(Date = df10000$CENSUS_DATE))
# call in SPC function and write to 'results10001'
results10001<-SPC_XBAR(df10001,vol_n,seasonality)
results10001 = results10001 %>%
cbind(Spec = df10001$CODE) %>%
subset(`table_n` == 1)
results10001 <- results10001[order(results10001$tpd),]
results10001$Date <- as.Date(cbind(Date = df10001$CENSUS_DATE))
Currently I call in the function 'SPC_XBAR' to where vol_n and seasonality are set earlier in the code. The script then passes the values to the function which then assigns the results to 'results10000, results10001' etc etc. Upon which I do a small bit of data wrangling on each newly created data frame before feeding the results back into sql server at the end.
As you can see each one is being individually hard coded which is not efficient.
What I would like to do is to loop a chunk of code for each individual data frame within the list, without naming each one individually.
I believe a loop would solve this issue but I am a little inexperienced when it comes to the ability to create a loop around it. Any advice would be much appreciated.
Cheers
Have you considered using lapply instead of a loop throughout the list? Check it here...
EDIT: I try to elaborate a bit more... What happens if you do this:
myFunction <- function(x) {
results<-SPC_XBAR(x,vol_n,seasonality)
results = results %>%
cbind(Spec = x$CODE) %>%
subset(`table_n` == 1)
results <- results[order(results$tpd),]
results$Date <- as.Date(cbind(Date = x$CENSUS_DATE))
results
}
lapply(dat5split, myFunction)
I would expect it to return a list of the resulting datasets
When I run this Loop I can print the results and I want to create a data frame with this data but I cant. Until now I have this:
filenames <- list.files(path=getwd())
numfiles <- length(filenames)
for (i in 1:numfiles) {
file <- read.table(filenames[i],header = TRUE)
ts = subset(file, file$name == "plantNutrientUptake")
tss = subset (ts, ts$path == "//plants/nitrate")
tssc = tss[,2:3]
d40 = tssc[41,2]
print(d40)
print(filenames[i])
}
This is not the most efficient way to do this, but it takes advantage of what code you've already written. First, you'll create an empty data frame with the columns you want, but filled with NA. Then, in each iteration of the loop, you'll fill one row of the data frame.
filenames <- list.files(path=getwd())
numfiles <- length(filenames)
# Create an empty data.frame
df <- data.frame(filename = rep(NA, numfiles), d40 = rep(NA, numfiles))
for (i in 1:numfiles){
file <- read.table(filenames[i],header = TRUE)
ts = subset(file, file$name == "plantNutrientUptake")
tss = subset (ts, ts$path == "//plants/nitrate")
tssc = tss[,2:3]
d40 = tssc[41,2]
# Fill row i of the data frame
df[i,"filename"] = filenames[i]
df[i,"d40"] = d40
}
Hope that does it! Good luck :)
There are a lot of ways to do what you are asking. Also, without a reproducible example it is difficult to validate that code will run. I couldn't tell what type of data was in each of your variable so I just guessed that they were mostly characters with one numeric. You'll need to change the code if that's not true.
The following method is using base R (no other packages). It builds off of what you have done. There are other ways to do this using map, do.call, or apply. But it's important to be able to run through a loop.
As someone commented, your code is just re-writing itself every loop. Luckily you have the variable i that you can use to specify where things go.
filenames <- list.files(path=getwd())
numfiles <- length(filenames)
# Declare an empty dataframe for efficiency purposes
df <- data.frame(
ts = rep(NA_character_,numfiles),
tss = rep(NA_character_,numfiles),
tssc = rep(NA_character_,numfiles),
d40 = rep(NA_real_,numfiles),
stringsAsFactors = FALSE
)
# Loop through the files and fill in the data
for (i in 1:numfiles){
file <- read.table(filenames[i],header = TRUE)
df$ts[i] <- subset(file, file$name == "plantNutrientUptake")
df$tss[i] <- subset (ts, ts$path == "//plants/nitrate")
df$tssc[i] <- tss[,2:3]
df$d40[i] <- tssc[41,2]
print(d40)
print(filenames[i])
}
You'll notice a few things about this code that are extra.
First, I'm declaring the variable type for each column explicitly. You can use rep(NA,numfiles) but that leave R to guess what the column should be. This may not be a problem for you if all of your variables are obviously of the same type. But imagine you have a variable a = c("1","A","B") of all characters. R will go through the first iteration of the loop and guess that the column is numeric. Then on the second run of the loop will crash when it runs into a character.
Next, I'm declaring the entire dataframe before entering the loop. When people tell you that loops in [modern] R are slow it is often because you are re-allocating memory every loop. By declaring the entire dataframe up front you speed up the loop significantly. This also allows you to reference any cell in the dataframe...which is exactly what you want to do in the loop.
Finally, I'm using the $ syntax to make things clear. Writing df[i,"d40"] <- d40 is the same as writing df$d40[i] <- d40. I just think it is clear to use the second method. This is a matter of personal preference.
I am trying to build a data frame with book id, title, author, rating, collection, start and finish date from the LibraryThing api with my personal data. I am able to get a nested list fairly easily, and I have figured out how to build a data frame with everything but the dates (perhaps in not the best way but it works). My issue is with the dates.
The list I'm working with normally has 20 elements, but it adds the startfinishdates element only if I added dates to the book in my account. This is causing two issues:
If it was always there, I could extract it like everything else and it would have NA most of the time, and I could use cbind to get it lined up correctly with the other information
When I extract it using the name, and get an object with less elements, I don't have a way to join it back to everything else (it doesn't have the book id)
Ultimately, I want to build this data frame and an answer that tells me how to pull out the book id and associate it with each startfinishdate so I can join on book id is acceptable. I would just add that to the code I have.
I'm also open to learning a better approach from the jump and re-designing the entire thing as I have not worked with lists much in R and what I put together was after much trial and error. I do want to use R though, as ultimately I am going to use this to create an R Markdown page for my web site (for instance, a plot that shows finish dates of books).
You can run the code below and get the data (no api key required).
library(jsonlite)
library(tidyverse)
library(assertr)
data<-fromJSON("http://www.librarything.com/api_getdata.php?userid=cau83&key=392812157&max=450&showCollections=1&responseType=json&showDates=1")
books.lst<-data$books
#create df from json
create.df<-function(item){
df<-map_df(.x=books.lst,~.x[[item]])
df2 <- t(df)
return(df2)
}
ids<-create.df(1)
titles<-create.df(2)
ratings<-create.df(12)
authors<-create.df(4)
#need to get the book id when i build the date df's
startdates.df<-map_df(.x=books.lst,~.x$startfinishdates) %>% select(started_stamp,started_date)
finishdates.df<-map_df(.x=books.lst,~.x$startfinishdates) %>% select(finished_stamp,finished_date)
collections.df<-map_df(.x=books.lst,~.x$collections)
#from assertr: will create a vector of same length as df with all values concatenated
collections.v<-col_concat(collections.df, sep = ", ")
#assemble df
books.df<-as.data.frame(cbind(ids,titles,authors,ratings,collections.v))
names(books.df)<-c("ID","Title","Author","Rating","Collections")
books.df<-books.df %>% mutate(ID=as.character(ID),Title=as.character(Title),Author=as.character(Author),
Rating=as.character(Rating),Collections=as.character(Collections))
This approach is outside the tidyverse meta-package. Using base-R you can make it work using the following code.
Map will apply the user defined function to each element of data$books which is provided in the argument and extract the required fields for your data.frame. Reduce will take all the individual dataframes and merge them (or reduce) to a single data.frame booksdf.
library(jsonlite)
data<-fromJSON("http://www.librarything.com/api_getdata.php?userid=cau83&key=392812157&max=450&showCollections=1&responseType=json&showDates=1")
booksdf=Reduce(function(x,y){rbind(x,y)},
Map(function(x){
lenofelements = length(x)
if(lenofelements>20){
if(!is.null(x$startfinishdates$started_date)){
started_date = x$startfinishdates$started_date
}else{
started_date=NA
}
if(!is.null(x$startfinishdates$started_stamp)){
started_stamp = x$startfinishdates$started_date
}else{
started_stamp=NA
}
if(!is.null(x$startfinishdates$finished_date)){
finished_date = x$startfinishdates$finished_date
}else{
finished_date=NA
}
if(!is.null(x$startfinishdates$finished_stamp)){
finished_stamp = x$startfinishdates$finished_stamp
}else{
finished_stamp=NA
}
}else{
started_stamp = NA
started_date = NA
finished_stamp = NA
finished_date = NA
}
book_id = x$book_id
title = x$title
author = x$author_fl
rating = x$rating
collections = paste(unlist(x$collections),collapse = ",")
return(data.frame(ID=book_id,Title=title,Author=author,Rating=rating,
Collections=collections,Started_date=started_date,Started_stamp=started_stamp,
Finished_date=finished_date,Finished_stamp=finished_stamp))
},data$books))
I have a list of locally saved html files. I want to extract multiple nodes from each html and save the results in a vector. Afterwards, I would like to combine them in a dataframe. Now, I have a piece of code for 1 node, which works (see below), but it seems quite long and inefficient if I apply it for ~ 20 variables. Also, something really strange with the saving to vector (XXX_name) it starts with the last observation and then continues with the first, second, .... Do you have any suggestions for simplifying the code/ making it more efficient?
# Extracts name variable and stores in a vector
XXX_name <- c()
for (i in 1:216) {
XXX_name <- c(XXX_name, name)
mydata <- read_html(files[i], encoding = "latin-1")
reads_name <- html_nodes(mydata, 'h1')
name <- html_text(reads_name)
#print(i)
#print(name)
}
Many thanks!
You can put the workings inside a function then apply that function to each of your variables with map
First, create the function:
read_names <- function(var, node) {
mydata <- read_html(files[var], encoding = "latin-1")
reads_name <- html_nodes(mydata, node)
name <- html_text(reads_name)
}
Then we create a df with all possible combinations of inputs and apply the function to that
library(tidyverse)
inputs <- crossing(var = 1:216, node = vector_of_nodes)
output <- map2(inputs$var, inputs$node, read_names)