I'm a noob in R.
I'm working with a dataframe of like 150 rows and 21 columns. I want to change from column 5 to 20 the character "-" to "0.00".
I'm using this code and it works individually:
datos$max52sem<-str_replace(datos$max52sem,"-","0.00")
datos$min52sem<-str_replace(datos$min52sem,"-","0.00")
I'm trying to use a "for" to change it from all columns, instead of writing all my variables' names 15 times.
This is what I'm writing:
mis_vars<-c("max52sem","min52sem","cierre_prev","cierre_hoy","max_hoy","min_hoy","ret_hoy","ret_sem","ret_mes","ret_año","ret_ytd","vol","upa","vla","pvla","pu")
for(x in mis_vars)
datos$x<-str_replace(datos$x,"-","0")
"mis_vars" are the names of my columns (variables) I want to change in my dataframe, but I get this answer from R and I don't know what I'm doing wrong.
Error in $<-.data.frame(*tmp*, "x", value = character(0)) :
replacement has 0 rows, data has 1220>
With dplyr, we can use mutate_at
library(dplyr)
library(stringr)
datos <- datos %>%
mutate_at(vars(mis_vars), ~ str_replace(., "-", "0"))
In the OP's for loop, instead of datos$x <-, it should be datos[[x]] <- as it will be creating a column named 'x' instead of the variable in 'mis_vars'
Or using only base R
datos[mis_vars] <- lapply(datos[mis_vars], sub, pattern = "-", replacement = "0")
In base R, we can use lapply to change multiple columns
datos[mis_vars] <- lapply(datos[mis_vars], function(x) sub("-", "0.00", x))
Related
I'm trying to do a string search and replace across multiple columns in R. My code:
# Get columns of interest
selected_columns <- c(368,370,372,374,376,378,380,382,384,386,388,390,392,394)
#Perform grepl across multiple columns
df[,selected_columns][grepl('apples',df[,selected_columns],ignore.case = TRUE)] <- 'category1'
However, I'm getting the error:
Error: undefined columns selected
Thanks in advance.
grep/grepl works on vectors/matrix and not on data.frame/list. According to the?grep`
x - a character vector where matches are sought, or an object which can be coerced by as.character to a character vector.
We can loop over the columns (lapply) and replace the values based on the match
df[, selected_columns] <- lapply(df[, selected_columns],
function(x) replace(x, grepl('apples', x, ignore.case = TRUE), 'category1'))
Or with dplyr
library(dplyr)
library(stringr)
df %>%
mutate_at(selected_columns, ~ replace(., str_detect(., 'apples'), 'category1'))
Assuming you want to partially match a cell and replace it, you could use rapply() and replace cell contents that have "apples" with "category1" using gsub():
df[selected_columns] <- rapply(df[selected_columns], function(x) gsub("apples", "category1", x), how = "replace")
Just keep in mind the difference between grepl()/gsub() (with and without boundaries in your regex), and %in%/match() when searching for strings.
I have a very large dataset with some columns formatted as currency, some numeric, some character. When reading in the data all currency columns are identified as factor and I need to convert them to numeric. The dataset it too wide to manually identify the columns. I am trying to find a programmatic way to identify if a column contains currency data (ex. starts with '$') and then pass that list of columns to be cleaned.
name <- c('john','carl', 'hank')
salary <- c('$23,456.33','$45,677.43','$76,234.88')
emp_data <- data.frame(name,salary)
clean <- function(ttt){
as.numeric(gsub('[^a-zA-z0-9.]','', ttt))
}
sapply(emp_data, clean)
The issue in this example is that this sapply works on all columns resulting in the name column being replaced with NA. I need a way to programmatically identify just the columns that the clean function needs to be applied to.. in this example salary.
Using dplyr and stringr packages, you can use mutate_if to identify columns that have any string starting with a $ and then change the accordingly.
library(dplyr)
library(stringr)
emp_data %>%
mutate_if(~any(str_detect(., '^\\$'), na.rm = TRUE),
~as.numeric(str_replace_all(., '[$,]', '')))
Taking advantage of the powerful parsers the readr package offers out of the box:
my_parser <- function(col) {
# Try first with parse_number that handles currencies automatically quite well
res <- suppressWarnings(readr::parse_number(col))
if (is.null(attr(res, "problems", exact = TRUE))) {
res
} else {
# If parse_number fails, fall back on parse_guess
readr::parse_guess(col)
# Alternatively, we could simply return col without further parsing attempt
}
}
library(dplyr)
emp_data %>%
mutate(foo = "USD13.4",
bar = "£37") %>%
mutate_all(my_parser)
# name salary foo bar
# 1 john 23456.33 13.4 37
# 2 carl 45677.43 13.4 37
# 3 hank 76234.88 13.4 37
A base R option is to use startsWith to detect the dollar columns, and gsub to remove "$" and "," from the columns.
doll_cols <- sapply(emp_data, function(x) any(startsWith(as.character(x), '$')))
emp_data[doll_cols] <- lapply(emp_data[doll_cols],
function(x) as.numeric(gsub('\\$|,', '', x)))
imported tibble from textfile. Many numeric columns are imported as "chr". I guess it's because they contain a "," instead of a ".".
My goal is to write a loop which runs through the names of desired columns, replaces "," with "." and converts columns into "num".
Little example:
data <- data.frame("A1" =c("2,1","2,1","2,1"), "A2" =c("1,3","1,3","1,3"),
stringsAsFactors = F) %>% as.tibble() #example data
colname <- c("A1", "A2") #creating variable for loop
for(i in colname) {
nam <- paste0("data$", i)
assign(nam, as.numeric(gsub(",",".", eval(parse(text = paste0("data$",i))))) )
}
Instead of overwriting the existing column, R creates a new variable:
data$A1 # that's the existing column as part of the tibble
[1] "2,1" "2,1" "2,1"
`data$A1` # thats just a new variable. mind the little``
[1] 2.1 2.1 2.1
I also tried to assign (<-) the new numeric values via eval, but that does not work either.
eval(parse(text = paste0("data$", i))) <- as.numeric(
gsub(",",".", eval(parse(text = paste0("data$",i)))))
Error: target of assignment expands to non-language object
Any suggestions on how to transform? I have the same issue with other columns that I want to aggregate to a new variable. This variable should also be part of the existing tibble. I could do it by hand. This would take lots of time and probably produce many mistakes.
Thanks a lot!
Sam
As you are already working with the tidyverse, you can use dplyr::mutate_at and the colname variable you have already defined.
data %>%
mutate_at(.vars = colname,
.funs = function(x) { as.numeric(gsub(",", ".", x)) })
My dataframe, dat, has two columns which look like this:
value condition
2 learning/cat
4 learning/dog
1 naming/cat
6 naming/dog
I would like to 'trim' the data frame to only include rows in which condition contains "naming".
I've tried to do this with grep:
dat = dat[grep("naming", dat$condition, value = T)]
which causes the following error:
Error in `[.data.frame`(dat, grep("naming", dat$condition, value = T)) :
undefined columns selected
Can anyone suggest a fix? Any help would be greatly appreciated!
You can split up condition using separate from tidyr:
df = input_df %>% separate( condition, into = c("condition1", "condition2"), sep = "/")
Then just use filter:
only_naming_df = df %>% filter(condition1 == "naming")
The error is easy to fix once adding a comma after the parenthesis. But I want to have a list of available options to achieve this task. Belows are solution and comments from others and mine.
Use grep or grepl
grep returns the index (row number), while grepl returns a logical vector (TRUE or FALSE). Notice that when using grep in this case, value = T should not be added because it will return the string, which is not helpful for subsetting.
dat[grep("naming", dat$condition), ]
dat[grepl("naming", dat$condition), ]
Functions from dplyr and stringr
str_detect is equivalent to grepl(pattern, x), while str_which is equivalent to grep(pattern, x).
library(dplyr)
library(stringr)
dat %>% filter(str_detect(condition, "naming"))
dat %>% slice(str_which(condition, "naming"))
Data Preparation
# Create example dataframes
dat <- read.table(text = "value condition
2 learning/cat
4 learning/dog
1 naming/cat
6 naming/dog",
header = TRUE, stringsAsFactors = FALSE)
I've been trying to remove the white space that I have in a data frame (using R). The data frame is large (>1gb) and has multiple columns that contains white space in every data entry.
Is there a quick way to remove the white space from the whole data frame? I've been trying to do this on a subset of the first 10 rows of data using:
gsub( " ", "", mydata)
This didn't seem to work, although R returned an output which I have been unable to interpret.
str_replace( " ", "", mydata)
R returned 47 warnings and did not remove the white space.
erase_all(mydata, " ")
R returned an error saying 'Error: could not find function "erase_all"'
I would really appreciate some help with this as I've spent the last 24hrs trying to tackle this problem.
Thanks!
A lot of the answers are older, so here in 2019 is a simple dplyr solution that will operate only on the character columns to remove trailing and leading whitespace.
library(dplyr)
library(stringr)
data %>%
mutate_if(is.character, str_trim)
## ===== 2020 edit for dplyr (>= 1.0.0) =====
df %>%
mutate(across(where(is.character), str_trim))
You can switch out the str_trim() function for other ones if you want a different flavor of whitespace removal.
# for example, remove all spaces
df %>%
mutate(across(where(is.character), str_remove_all, pattern = fixed(" ")))
If i understood you correctly then you want to remove all the white spaces from entire data frame, i guess the code which you are using is good for removing spaces in the column names.I think you should try this:
apply(myData, 2, function(x)gsub('\\s+', '',x))
Hope this works.
This will return a matrix however, if you want to change it to data frame then do:
as.data.frame(apply(myData, 2, function(x) gsub('\\s+', '', x)))
EDIT In 2020:
Using lapply and trimws function with both=TRUE can remove leading and trailing spaces but not inside it.Since there was no input data provided by OP, I am adding a dummy example to produce the results.
DATA:
df <- data.frame(val = c(" abc", " kl m", "dfsd "),
val1 = c("klm ", "gdfs", "123"),
num = 1:3,
num1 = 2:4,
stringsAsFactors = FALSE)
#situation: 1 (Using Base R), when we want to remove spaces only at the leading and trailing ends NOT inside the string values, we can use trimws
cols_to_be_rectified <- names(df)[vapply(df, is.character, logical(1))]
df[, cols_to_be_rectified] <- lapply(df[, cols_to_be_rectified], trimws)
# situation: 2 (Using Base R) , when we want to remove spaces at every place in the dataframe in character columns (inside of a string as well as at the leading and trailing ends).
(This was the initial solution proposed using apply, please note a solution using apply seems to work but would be very slow, also the with the question its apparently not very clear if OP really wanted to remove leading/trailing blank or every blank in the data)
cols_to_be_rectified <- names(df)[vapply(df, is.character, logical(1))]
df[, cols_to_be_rectified] <- lapply(df[, cols_to_be_rectified],
function(x) gsub('\\s+', '', x))
## situation: 1 (Using data.table, removing only leading and trailing blanks)
library(data.table)
setDT(df)
cols_to_be_rectified <- names(df)[vapply(df, is.character, logical(1))]
df[, c(cols_to_be_rectified) := lapply(.SD, trimws), .SDcols = cols_to_be_rectified]
Output from situation1:
val val1 num num1
1: abc klm 1 2
2: kl m gdfs 2 3
3: dfsd 123 3 4
## situation: 2 (Using data.table, removing every blank inside as well as leading/trailing blanks)
cols_to_be_rectified <- names(df)[vapply(df, is.character, logical(1))]
df[, c(cols_to_be_rectified) := lapply(.SD, function(x) gsub('\\s+', '', x)), .SDcols = cols_to_be_rectified]
Output from situation2:
val val1 num num1
1: abc klm 1 2
2: klm gdfs 2 3
3: dfsd 123 3 4
Note the difference between the outputs of both situation, In row number 2: you can see that, with trimws we can remove leading and trailing blanks, but with regex solution we are able to remove every blank(s).
I hope this helps , Thanks
One possibility involving just dplyr could be:
data %>%
mutate_if(is.character, trimws)
Or considering that all variables are of class character:
data %>%
mutate_all(trimws)
Since dplyr 1.0.0 (only strings):
data %>%
mutate(across(where(is.character), trimws))
Or if all columns are strings:
data %>%
mutate(across(everything(), trimws))
Picking up on Fremzy and the comment from Stamper, this is now my handy routine for cleaning up whitespace in data:
df <- data.frame(lapply(df, trimws), stringsAsFactors = FALSE)
As others have noted this changes all types to character. In my work, I first determine the types available in the original and conversions required. After trimming, I re-apply the types needed.
If your original types are OK, apply the solution from MarkusN below https://stackoverflow.com/a/37815274/2200542
Those working with Excel files may wish to explore the readxl package which defaults to trim_ws = TRUE when reading.
Picking up on Fremzy and Mielniczuk, I came to the following solution:
data.frame(lapply(df, function(x) if(class(x)=="character") trimws(x) else(x)), stringsAsFactors=F)
It works for mixed numeric/charactert dataframes manipulates only character-columns.
You could use trimws function in R 3.2 on all the columns.
myData[,c(1)]=trimws(myData[,c(1)])
You can loop this for all the columns in your dataset. It has good performance with large datasets as well.
If you're dealing with large data sets like this, you could really benefit form the speed of data.table.
library(data.table)
setDT(df)
for (j in names(df)) set(df, j = j, value = df[[trimws(j)]])
I would expect this to be the fastest solution. This line of code uses the set operator of data.table, which loops over columns really fast. There is a nice explanation here: Fast looping with set.
R is simply not the right tool for such file size. However have 2 options :
Use ffdply and ff base
Use ff and ffbase packages:
library(ff)
library(ffabse)
x <- read.csv.ffdf(file=your_file,header=TRUE, VERBOSE=TRUE,
first.rows=1e4, next.rows=5e4)
x$split = as.ff(rep(seq(splits),each=nrow(x)/splits))
ffdfdply( x, x$split , BATCHBYTES=0,function(myData)
apply(myData,2,function(x)gsub('\\s+', '',x))
Use sed (my preference)
sed -ir "s/(\S)\s+(/S)/\1\2/g;s/^\s+//;s/\s+$//" your_file
If you want to maintain the variable classes in your data.frame - you should know that using apply will clobber them because it outputs a matrix where all variables are converted to either character or numeric. Building upon the code of Fremzy and Anthony Simon Mielniczuk you can loop through the columns of your data.frame and trim the white space off only columns of class factor or character (and maintain your data classes):
for (i in names(mydata)) {
if(class(mydata[, i]) %in% c("factor", "character")){
mydata[, i] <- trimws(mydata[, i])
}
}
I think that a simple approach with sapply, also works, given a df like:
dat<-data.frame(S=LETTERS[1:10],
M=LETTERS[11:20],
X=c(rep("A:A",3),"?","A:A ",rep("G:G",5)),
Y=c(rep("T:T",4),"T:T ",rep("C:C",5)),
Z=c(rep("T:T",4),"T:T ",rep("C:C",5)),
N=c(1:3,'4 ','5 ',6:10),
stringsAsFactors = FALSE)
You will notice that dat$N is going to become class character due to '4 ' & '5 ' (you can check with class(dat$N))
To get rid of the spaces on the numeic column simply convert to numeric with as.numeric or as.integer.
dat$N<-as.numeric(dat$N)
If you want to remove all the spaces, do:
dat.b<-as.data.frame(sapply(dat,trimws),stringsAsFactors = FALSE)
And again use as.numeric on col N (ause sapply will convert it to character)
dat.b$N<-as.numeric(dat.b$N)