Creating another column in R - r

This is my current data set
I want to take the numbers after "narrow" (e.g. 20) and make another vector. Any idea how I can do that?

We can use sub to remove the substring "Narrow", followed by a , and zero or more spaces (\\s+), replace with blank ("") and convert to numeric
df1$New <- as.numeric(sub("Narrow,\\s*", "", df1$Stimulus))

You could use separate to separate the stimulus column into two vectors.
library(tidyr)
df %>%
separate(col = stimulus,
sep = ", ",
into = c("Text","Number"))

Maybe you can try the code below, using regmatches
df$new <- with(df, as.numeric(unlist(regmatches(stimulus,gregexpr("\\d+",stimulus)))))

You want separate from the tidyr package.
library(dplyr)
df <- data.frame(x = c(NA, "a.b", "a.d", "b.c"))
df %>% separate(x, c("A", "B"))
#> A B
#> 1 <NA> <NA>
#> 2 a b
#> 3 a d
#> 4 b c

Related

Remove a number of character from string in a column

I have a data frame with a column of strings and I would like to remove the first three characters in each of the strings. As in the following example:
From this:
df <- data_frame(col1 = c('01_A','02_B', '03_C'))
To this:
df <- data_frame(col1 = c('A','B', 'C'))
I have been trying to use the dplyr transmute function but I can't really get it to work.
Any help would be super appreciated!
I think this will work:
library(dplyr)
library(stringr)
df %>%
mutate(col1 = str_remove(col1, "\\d+(_)"))
col1
1 A
2 B
3 C
We could also use substring from base R as the OP mentioned above position based substring extraction
df$col1 <- substring(df$col1, 4)
df$col1
#[1] "A" "B" "C"
You can use sub like below
> df %>%
+ mutate(col1 = sub("^.{3}", "", col1))
# A tibble: 3 x 1
col1
<chr>
1 A
2 B
3 C

How do I get the column number from a dataframe which contains specific strings?

I have a data frame df with 7 columns and I have a list z containing multiple strings.
I want a dataframe containing only the columns in df which contain the sting from z.
df <- data.frame("a_means","b_means","c_means","d_means","e_mean","f_means","g_means")
z <- c("a_m","c_m","f_m")
How do I get the column number of the z strings in df? Or how do I get a dataframe with only the columns which contains the z strings.
What I want is:
print(df)
"a_means" "c_m" "f_m"
What I tried:
match(a, names(df)
and
df[,which(colnames(df) %in% colnames(df[ ,grepl(z,names(df)])]
You can use:
df[,match(z, substring(colnames(df), 1, 3))]
With base R:
z <- paste(z, collapse = "|")
df[, grepl(z, names(df))] # you could use grep as well
Combine the search patterns and use that as a pattern for stringr::str_detect() function.
library(dplyr)
library(stringr)
df <- data.frame(a_means = "a_means",
b_means = "b_means",
c_means = "c_means",
d_means = "d_means",
e_means = "e_means",
f_means = "f_means",
g_means = "g_means"
)
z <- c("a_m","c_m","f_m")
z <- paste(z, collapse = "|")
df %>% select_if(str_detect(names(df), z))
#> a_means c_means f_means
#> 1 a_means c_means f_means
You can simply do this:
library(dplyr)
df %>%
select(contains(z))
Check out help("starts_with"). You can also match to a starting prefix with starts_with() among other things.
You can use select and matches to subest the columns based on z
library(dplyr)
df <- data.frame("a_means","b_means","c_means","d_means","e_mean","f_means","g_means")
z <- c("a_m","c_m","f_m")
df %>%
select(matches(z))
#> X.a_means. X.c_means. X.f_means.
#> 1 a_means c_means f_means

How to rename individual column in dataframe [duplicate]

I know if I have a data frame with more than 1 column, then I can use
colnames(x) <- c("col1","col2")
to rename the columns. How to do this if it's just one column?
Meaning a vector or data frame with only one column.
Example:
trSamp <- data.frame(sample(trainer$index, 10000))
head(trSamp )
# sample.trainer.index..10000.
# 1 5907862
# 2 2181266
# 3 7368504
# 4 1949790
# 5 3475174
# 6 6062879
ncol(trSamp)
# [1] 1
class(trSamp)
# [1] "data.frame"
class(trSamp[1])
# [1] "data.frame"
class(trSamp[,1])
# [1] "numeric"
colnames(trSamp)[2] <- "newname2"
# Error in names(x) <- value :
# 'names' attribute [2] must be the same length as the vector [1]
This is a generalized way in which you do not have to remember the exact location of the variable:
# df = dataframe
# old.var.name = The name you don't like anymore
# new.var.name = The name you want to get
names(df)[names(df) == 'old.var.name'] <- 'new.var.name'
This code pretty much does the following:
names(df) looks into all the names in the df
[names(df) == old.var.name] extracts the variable name you want to check
<- 'new.var.name' assigns the new variable name.
colnames(trSamp)[2] <- "newname2"
attempts to set the second column's name. Your object only has one column, so the command throws an error. This should be sufficient:
colnames(trSamp) <- "newname2"
colnames(df)[colnames(df) == 'oldName'] <- 'newName'
This is an old question, but it is worth noting that you can now use setnames from the data.table package.
library(data.table)
setnames(DF, "oldName", "newName")
# or since the data.frame in question is just one column:
setnames(DF, "newName")
# And for reference's sake, in general (more than once column)
nms <- c("col1.name", "col2.name", etc...)
setnames(DF, nms)
This can also be done using Hadley's plyr package, and the rename function.
library(plyr)
df <- data.frame(foo=rnorm(1000))
df <- rename(df,c('foo'='samples'))
You can rename by the name (without knowing the position) and perform multiple renames at once. After doing a merge, for example, you might end up with:
letterid id.x id.y
1 70 2 1
2 116 6 5
3 116 6 4
4 116 6 3
5 766 14 9
6 766 14 13
Which you can then rename in one step using:
letters <- rename(letters,c("id.x" = "source", "id.y" = "target"))
letterid source target
1 70 2 1
2 116 6 5
3 116 6 4
4 116 6 3
5 766 14 9
6 766 14 13
I think the best way of renaming columns is by using the dplyr package like this:
require(dplyr)
df = rename(df, new_col01 = old_col01, new_col02 = old_col02, ...)
It works the same for renaming one or many columns in any dataset.
I find that the most convenient way to rename a single column is using dplyr::rename_at :
library(dplyr)
cars %>% rename_at("speed",~"new") %>% head
cars %>% rename_at(vars(speed),~"new") %>% head
cars %>% rename_at(1,~"new") %>% head
# new dist
# 1 4 2
# 2 4 10
# 3 7 4
# 4 7 22
# 5 8 16
# 6 9 10
works well in pipe chaines
convenient when names are stored in variables
works with a name or an column index
clear and compact
I like the next style for rename dataframe column names one by one.
colnames(df)[which(colnames(df) == 'old_colname')] <- 'new_colname'
where
which(colnames(df) == 'old_colname')
returns by the index of the specific column.
Let df be the dataframe you have with col names myDays and temp.
If you want to rename "myDays" to "Date",
library(plyr)
rename(df,c("myDays" = "Date"))
or with pipe, you can
dfNew <- df %>%
plyr::rename(c("myDays" = "Date"))
Try:
colnames(x)[2] <- 'newname2'
This is likely already out there, but I was playing with renaming fields while searching out a solution and tried this on a whim. Worked for my purposes.
Table1$FieldNewName <- Table1$FieldOldName
Table1$FieldOldName <- NULL
Edit begins here....
This works as well.
df <- rename(df, c("oldColName" = "newColName"))
You can use the rename.vars in the gdata package.
library(gdata)
df <- rename.vars(df, from = "oldname", to = "newname")
This is particularly useful where you have more than one variable name to change or you want to append or pre-pend some text to the variable names, then you can do something like:
df <- rename.vars(df, from = c("old1", "old2", "old3",
to = c("new1", "new2", "new3"))
For an example of appending text to a subset of variables names see:
https://stackoverflow.com/a/28870000/180892
You could also try 'upData' from 'Hmisc' package.
library(Hmisc)
trSamp = upData(trSamp, rename=c(sample.trainer.index..10000. = 'newname2'))
If you know that your dataframe has only one column, you can use:
names(trSamp) <- "newname2"
The OP's question has been well and truly answered. However, here's a trick that may be useful in some situations: partial matching of the column name, irrespective of its position in a dataframe:
Partial matching on the name:
d <- data.frame(name1 = NA, Reported.Cases..WHO..2011. = NA, name3 = NA)
## name1 Reported.Cases..WHO..2011. name3
## 1 NA NA NA
names(d)[grepl("Reported", names(d))] <- "name2"
## name1 name2 name3
## 1 NA NA NA
Another example: partial matching on the presence of "punctuation":
d <- data.frame(name1 = NA, Reported.Cases..WHO..2011. = NA, name3 = NA)
## name1 Reported.Cases..WHO..2011. name3
## 1 NA NA NA
names(d)[grepl("[[:punct:]]", names(d))] <- "name2"
## name1 name2 name3
## 1 NA NA NA
These were examples I had to deal with today, I thought might be worth sharing.
I would simply change a column name to the dataset with the new name I want with the following code:
names(dataset)[index_value] <- "new_col_name"
I found colnames() argument easier
https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/row%2Bcolnames
select some column from the data frame
df <- data.frame(df[, c( "hhid","b1005", "b1012_imp", "b3004a")])
and rename the selected column in order,
colnames(df) <- c("hhid", "income", "cost", "credit")
check the names and the values to be sure
names(df);head(df)
I would simply add a new column to the data frame with the name I want and get the data for it from the existing column. like this:
dataf$value=dataf$Article1Order
then I remove the old column! like this:
dataf$Article1Order<-NULL
This code might seem silly! But it works perfectly...
We can use rename_with to rename columns with a function (stringr functions, for example).
Consider the following data df_1:
df_1 <- data.frame(
x = replicate(n = 3, expr = rnorm(n = 3, mean = 10, sd = 1)),
y = sample(x = 1:2, size = 10, replace = TRUE)
)
names(df_1)
#[1] "x.1" "x.2" "x.3" "y"
Rename all variables with dplyr::everything():
library(tidyverse)
df_1 %>%
rename_with(.data = ., .cols = everything(.),
.fn = str_replace, pattern = '.*',
replacement = str_c('var', seq_along(.), sep = '_')) %>%
names()
#[1] "var_1" "var_2" "var_3" "var_4"
Rename by name particle with some dplyr verbs (starts_with, ends_with, contains, matches, ...).
Example with . (x variables):
df_1 %>%
rename_with(.data = ., .cols = contains('.'),
.fn = str_replace, pattern = '.*',
replacement = str_c('var', seq_along(.), sep = '_')) %>%
names()
#[1] "var_1" "var_2" "var_3" "y"
Rename by class with many functions of class test, like is.integer, is.numeric, is.factor...
Example with is.integer (y):
df_1 %>%
rename_with(.data = ., .cols = is.integer,
.fn = str_replace, pattern = '.*',
replacement = str_c('var', seq_along(.), sep = '_')) %>%
names()
#[1] "x.1" "x.2" "x.3" "var_1"
The warning:
Warning messages:
1: In stri_replace_first_regex(string, pattern, fix_replacement(replacement), :
longer object length is not a multiple of shorter object length
2: In names[cols] <- .fn(names[cols], ...) :
number of items to replace is not a multiple of replacement length
It is not relevant, as it is just an inconsistency of seq_along(.) with the replace function.
library(dplyr)
rename(data, de=de.y)

Remove special characters from entire dataframe in R

Question:
How can you use R to remove all special characters from a dataframe, quickly and efficiently?
Progress:
This SO post details how to remove special characters. I can apply the gsub function to single columns (images 1 and 2), but not the entire dataframe.
Problem:
My dataframe consists of 100+ columns of integers, string, etc. When I try to run the gsub on the dataframe, it doesn't return the output I desire. Instead, I get what's shown in image 3.
df <- read.csv("C:/test.csv")
dfa <- gsub("[[:punct:]]", "", df$a) #this works on a single column
dfb <- gsub("[[:punct:]]", "", df$b) #this works on a single column
df_all <- gsub("[[:punct:]]", "", df) #this does not work on the entire df
View(df_all)
df - This is the original dataframe:
dfa - This is gsub applied to column b. Good!
df_all - This is gsub applied to the entire dataframe. Bad!
Summary:
Is there a way to gsub an entire dataframe? Else, should an apply function be used instead?
Here is a possible solution using dplyr:
# Example data
bla <- data.frame(a = c(1,2,3),
b = c("fefa%^%", "fes^%#$%", "gD%^E%Ewfseges"),
c = c("%#%$#^#", "%#$#%#", ",.,gdgd$%,."))
# Use mutate_all from dplyr
bla %>%
mutate_all(funs(gsub("[[:punct:]]", "", .)))
a b c
1 1 fefa
2 2 fes
3 3 gDEEwfseges gdgd
Update:
mutate_all has been superseded, and funs is deprecated as of dplyr 0.8.0. Here is an updated solution using mutate and across:
# Example data
df <- data.frame(a = c(1,2,3),
b = c("fefa%^%", "fes^%#$%", "gD%^E%Ewfseges"),
c = c("%#%$#^#", "%#$#%#", ",.,gdgd$%,."))
# Use mutate_all from dplyr
df %>%
mutate(across(everything(), ~gsub("[[:punct:]]", "", .x)))
Another solution is to convert the data frame to a matrix first then run the gsub and then convert back to a data frame as follows:
as.data.frame(gsub("[[:punct:]]", "", as.matrix(df)))
I like Ryan's answer using dplyr. As mutate_all and funs are now deprecated, here is my suggested updated solution using mutate and across:
# Example data
df <- data.frame(a = c(1,2,3),
b = c("fefa%^%", "fes^%#$%", "gD%^E%Ewfseges"),
c = c("%#%$#^#", "%#$#%#", ",.,gdgd$%,."))
# Use across() from dplyr
df %>%
mutate(across(everything(), ~gsub("[[:punct:]]", "", .x)))
a b c
1 1 fefa
2 2 fes
3 3 gDEEwfseges gdgd

How to rename a single column in a data.frame?

I know if I have a data frame with more than 1 column, then I can use
colnames(x) <- c("col1","col2")
to rename the columns. How to do this if it's just one column?
Meaning a vector or data frame with only one column.
Example:
trSamp <- data.frame(sample(trainer$index, 10000))
head(trSamp )
# sample.trainer.index..10000.
# 1 5907862
# 2 2181266
# 3 7368504
# 4 1949790
# 5 3475174
# 6 6062879
ncol(trSamp)
# [1] 1
class(trSamp)
# [1] "data.frame"
class(trSamp[1])
# [1] "data.frame"
class(trSamp[,1])
# [1] "numeric"
colnames(trSamp)[2] <- "newname2"
# Error in names(x) <- value :
# 'names' attribute [2] must be the same length as the vector [1]
This is a generalized way in which you do not have to remember the exact location of the variable:
# df = dataframe
# old.var.name = The name you don't like anymore
# new.var.name = The name you want to get
names(df)[names(df) == 'old.var.name'] <- 'new.var.name'
This code pretty much does the following:
names(df) looks into all the names in the df
[names(df) == old.var.name] extracts the variable name you want to check
<- 'new.var.name' assigns the new variable name.
colnames(trSamp)[2] <- "newname2"
attempts to set the second column's name. Your object only has one column, so the command throws an error. This should be sufficient:
colnames(trSamp) <- "newname2"
colnames(df)[colnames(df) == 'oldName'] <- 'newName'
This is an old question, but it is worth noting that you can now use setnames from the data.table package.
library(data.table)
setnames(DF, "oldName", "newName")
# or since the data.frame in question is just one column:
setnames(DF, "newName")
# And for reference's sake, in general (more than once column)
nms <- c("col1.name", "col2.name", etc...)
setnames(DF, nms)
This can also be done using Hadley's plyr package, and the rename function.
library(plyr)
df <- data.frame(foo=rnorm(1000))
df <- rename(df,c('foo'='samples'))
You can rename by the name (without knowing the position) and perform multiple renames at once. After doing a merge, for example, you might end up with:
letterid id.x id.y
1 70 2 1
2 116 6 5
3 116 6 4
4 116 6 3
5 766 14 9
6 766 14 13
Which you can then rename in one step using:
letters <- rename(letters,c("id.x" = "source", "id.y" = "target"))
letterid source target
1 70 2 1
2 116 6 5
3 116 6 4
4 116 6 3
5 766 14 9
6 766 14 13
I think the best way of renaming columns is by using the dplyr package like this:
require(dplyr)
df = rename(df, new_col01 = old_col01, new_col02 = old_col02, ...)
It works the same for renaming one or many columns in any dataset.
I find that the most convenient way to rename a single column is using dplyr::rename_at :
library(dplyr)
cars %>% rename_at("speed",~"new") %>% head
cars %>% rename_at(vars(speed),~"new") %>% head
cars %>% rename_at(1,~"new") %>% head
# new dist
# 1 4 2
# 2 4 10
# 3 7 4
# 4 7 22
# 5 8 16
# 6 9 10
works well in pipe chaines
convenient when names are stored in variables
works with a name or an column index
clear and compact
I like the next style for rename dataframe column names one by one.
colnames(df)[which(colnames(df) == 'old_colname')] <- 'new_colname'
where
which(colnames(df) == 'old_colname')
returns by the index of the specific column.
Let df be the dataframe you have with col names myDays and temp.
If you want to rename "myDays" to "Date",
library(plyr)
rename(df,c("myDays" = "Date"))
or with pipe, you can
dfNew <- df %>%
plyr::rename(c("myDays" = "Date"))
Try:
colnames(x)[2] <- 'newname2'
This is likely already out there, but I was playing with renaming fields while searching out a solution and tried this on a whim. Worked for my purposes.
Table1$FieldNewName <- Table1$FieldOldName
Table1$FieldOldName <- NULL
Edit begins here....
This works as well.
df <- rename(df, c("oldColName" = "newColName"))
You can use the rename.vars in the gdata package.
library(gdata)
df <- rename.vars(df, from = "oldname", to = "newname")
This is particularly useful where you have more than one variable name to change or you want to append or pre-pend some text to the variable names, then you can do something like:
df <- rename.vars(df, from = c("old1", "old2", "old3",
to = c("new1", "new2", "new3"))
For an example of appending text to a subset of variables names see:
https://stackoverflow.com/a/28870000/180892
You could also try 'upData' from 'Hmisc' package.
library(Hmisc)
trSamp = upData(trSamp, rename=c(sample.trainer.index..10000. = 'newname2'))
If you know that your dataframe has only one column, you can use:
names(trSamp) <- "newname2"
The OP's question has been well and truly answered. However, here's a trick that may be useful in some situations: partial matching of the column name, irrespective of its position in a dataframe:
Partial matching on the name:
d <- data.frame(name1 = NA, Reported.Cases..WHO..2011. = NA, name3 = NA)
## name1 Reported.Cases..WHO..2011. name3
## 1 NA NA NA
names(d)[grepl("Reported", names(d))] <- "name2"
## name1 name2 name3
## 1 NA NA NA
Another example: partial matching on the presence of "punctuation":
d <- data.frame(name1 = NA, Reported.Cases..WHO..2011. = NA, name3 = NA)
## name1 Reported.Cases..WHO..2011. name3
## 1 NA NA NA
names(d)[grepl("[[:punct:]]", names(d))] <- "name2"
## name1 name2 name3
## 1 NA NA NA
These were examples I had to deal with today, I thought might be worth sharing.
I would simply change a column name to the dataset with the new name I want with the following code:
names(dataset)[index_value] <- "new_col_name"
I found colnames() argument easier
https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/row%2Bcolnames
select some column from the data frame
df <- data.frame(df[, c( "hhid","b1005", "b1012_imp", "b3004a")])
and rename the selected column in order,
colnames(df) <- c("hhid", "income", "cost", "credit")
check the names and the values to be sure
names(df);head(df)
I would simply add a new column to the data frame with the name I want and get the data for it from the existing column. like this:
dataf$value=dataf$Article1Order
then I remove the old column! like this:
dataf$Article1Order<-NULL
This code might seem silly! But it works perfectly...
We can use rename_with to rename columns with a function (stringr functions, for example).
Consider the following data df_1:
df_1 <- data.frame(
x = replicate(n = 3, expr = rnorm(n = 3, mean = 10, sd = 1)),
y = sample(x = 1:2, size = 10, replace = TRUE)
)
names(df_1)
#[1] "x.1" "x.2" "x.3" "y"
Rename all variables with dplyr::everything():
library(tidyverse)
df_1 %>%
rename_with(.data = ., .cols = everything(.),
.fn = str_replace, pattern = '.*',
replacement = str_c('var', seq_along(.), sep = '_')) %>%
names()
#[1] "var_1" "var_2" "var_3" "var_4"
Rename by name particle with some dplyr verbs (starts_with, ends_with, contains, matches, ...).
Example with . (x variables):
df_1 %>%
rename_with(.data = ., .cols = contains('.'),
.fn = str_replace, pattern = '.*',
replacement = str_c('var', seq_along(.), sep = '_')) %>%
names()
#[1] "var_1" "var_2" "var_3" "y"
Rename by class with many functions of class test, like is.integer, is.numeric, is.factor...
Example with is.integer (y):
df_1 %>%
rename_with(.data = ., .cols = is.integer,
.fn = str_replace, pattern = '.*',
replacement = str_c('var', seq_along(.), sep = '_')) %>%
names()
#[1] "x.1" "x.2" "x.3" "var_1"
The warning:
Warning messages:
1: In stri_replace_first_regex(string, pattern, fix_replacement(replacement), :
longer object length is not a multiple of shorter object length
2: In names[cols] <- .fn(names[cols], ...) :
number of items to replace is not a multiple of replacement length
It is not relevant, as it is just an inconsistency of seq_along(.) with the replace function.
library(dplyr)
rename(data, de=de.y)

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