Creating a column out of quartiles from another column - r

I want to make a new column out of quartiles from another column
df <- within(df, quartile <- as.integer(cut(df$x, quantile(df$x, probs=0:4/4), include.lowest=TRUE)))
When I use this code I get
Error in `[<-.data.frame`(`*tmp*`, del, value = NULL) : missing values are not allowed in subscripted assignments of data frames
df$x is numeric and doesn't have any NAs.
> sum(is.na(df$x))
[1] 0
Although in my df there are some NAs in some columns, I do not use those columns in the function mentioned above. I can't figure out where the problem is.
This function actually works.
df$Quartile <-cut(df$x,quantile(df$x),include.lowest=TRUE,labels=FALSE)
But the assigned variable df$Quartile doesn't appear in the dataframe.

I would just use dplyr::mutate to add a new column.
library(dplyr)
df = df %>%
mutate(q_col = cut(x, quantile(x), include.lowest=TRUE, labels=FALSE))
mpg cyl disp hp drat wt qsec vs am gear carb q_col
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 3
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 3
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 3
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 3
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 2
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 1

Related

Replace all other values in R

I have a column including lots of "0" and other values (f.i. 2 or 2,3 etc). Is there any possibility to rename the columns with 0 to "None" and all other values to "others"? I wanted to use fct_recode or fct_collapse but cant figure out how to include all other values. Do you have any idea? I must not be necessarily include the fct_recode function.
Thanks a lot
Philipp
I tried to use fct_recode, fct_collapse
Here is a way to do it using mtcars and the vs column as an example:
cars <- mtcars
head(cars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
cars$vs <- ifelse(cars$vs == 0, "none", "other")
head(cars)
mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 none 1 4 4
Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 none 1 4 4
Datsun 710 22.8 4 108 93 3.85 2.320 18.61 other 1 4 1
Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 other 0 3 1
Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 none 0 3 2
Valiant 18.1 6 225 105 2.76 3.460 20.22 other 0 3 1
Note that R coerces the vs column from numeric to character. But you could do that explicitly first for clarity:
cars$vs <- as.character(cars$vs)
Using dplyr, we can do this on multiple colums as
library(dplyr)
df1 <- df1 %>%
mutate(across(everything(), ~ case_when(.x == 0 ~ "none", TRUE ~ "other")))
Or in base R
df1[] <- c("other", "none")[1 + (df1 == 0)]

RStudio: colnames() function not showing name of very first column

When I run colnames(), it never shows the name of this first column.
For example, after wasting a lot of time researching online, I discovered the name of the first column in mtcars is das_Auto.
Why doesn't this name show when I run this code?
[colnames(mtcars)][1]
What's the easiest way to determine the name of the first column in a data set?
This is because the first 'column' of mtcars is not actually a column but an index. If you want to convert it to a column you can run the below:
df <- cbind(das_Auto = rownames(mtcars), mtcars)
rownames(df) <- 1:nrow(mtcars)
head(df)
das_Auto mpg cyl disp hp drat wt qsec vs am gear carb
1 Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
2 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
3 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
4 Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
5 Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
6 Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

Looping through rows and columns in R does not work

I am just trying to fill gaps but in a loop. It is a monthly data, and fill_gaps produces NAs for every day. I am not sure why.
for (x in 2:length(differencing)){
for(micky in 1:length(differencing$`d_ BA`)){
if(is.na(differencing[micky,x])== T){
differencing[micky,x] = differencing[micky-1,x]
}
}
}
here is the error that I am getting:
Error: Assigned data `differencing[(micky - 1), x]` must be compatible with row subscript `micky`.
x 1 row must be assigned.
x Assigned data has 0 rows.
i Row updates require a list value. Do you need `list()` or `as.list()`?
Run `rlang::last_error()` to see where the error occurred.
This can be easily done using fill
library(tidyr)
library(dplyr)
differencing %>%
fill(everything())
Or we can use na.locf from zoo
library(zoo)
na.locf(differencing)
In the OP's loop, in the first line, it would be
for (x in 2:length(differencing$`d_ BA`)
...
as length of a data.frame will be the number of columns (as mentioned in the comments) and is different from length of a column i.e. vector
As the OP mentioned none of them works (OP didn't provide any example), using a small reproducible example ('tmp')
tmp %>%
fill(everything())
# mpg cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#Hornet Sportabout 18.7 6 258 110 3.15 3.440 17.02 0 0 3 2
#Valiant 18.1 6 258 110 2.76 3.460 20.22 1 0 3 1
or using na.locf
na.locf(tmp)
# mpg cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#Hornet Sportabout 18.7 6 258 110 3.15 3.440 17.02 0 0 3 2
#Valiant 18.1 6 258 110 2.76 3.460 20.22 1 0 3 1
data
tmp <- head(mtcars)
tmp[c(2, 5, 6), c(3, 4, 2)] <- NA

write r function to modify value in data frame

I have a set a variables say Var1, Var2 to Varn. They all take three possible values 0, 1, and 2. I want to replace all 2 as 1
like so
df$Var1[df$Var1 >= 1] <- 1
This does the job. But when I try to write a function to do this
MakeBinary <- function(varName dfName){dfName$varName[dfName$varNAme > = 1] <- 1}
and use this function like:
MakeBinary(Var2, df)
I got an error message: Error in $<-.data.frame(*tmp*, "varName", value = numeric(0)) :
replacement has 0 rows, data has 512.
I just want to know why I got this message. Thanks. My sample size is 512.
If we are passing column name as string, then use [[ instead of $ and return the dataset
MakeBinary <- function(varName, dfName){
dfName[[varName]][dfName[[varName]] >= 1] <- 1
dfName
}
MakeBinary("Var2", df)
example with mtcars
MakeBinary("carb", head(mtcars))
# mpg cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 1
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 1
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 1
#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Unquoted arguments for variable names can be passed as well, but it needs to be converted to string
MakeBinary <- function(varName, dfName){
varName <- deparse(substitute(varName))
dfName[[varName]][dfName[[varName]] >= 1] <- 1
dfName
}
MakeBinary(Var2, df)
Using a reproducible example with mtcars
MakeBinary(carb, head(mtcars))
# mpg cyl disp hp drat wt qsec vs am gear carb
#Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 1
#Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 1
#Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
#Hornet 4 Drive 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
#Hornet Sportabout 18.7 8 360 175 3.15 3.440 17.02 0 0 3 1
#Valiant 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1

Adding row in dplyr across a selected number of columns

While within dplyr workflow I would like to append a row across a selected number of columns.
Desired results
Starting with the mtcarsdata and applying function(s) with the goal of adding string "A" to columns 2:5 the one should arrive at the following results:
mpg cyl disp hp drat wt qsec vs am gear carb
NA A A A A NA NA NA NA NA NA
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
The following criteria were met:
For the columns with available index in vars() call the "A" string was added
For the remaining columns the NA value was provided
Approach
require(dplyr)
mtcars %>%
mutate_at(.cols = vars(2:5),
.funs = add_row(. = "A", .before = 1))
Naturally, this results in an error message:
Error: Unsupported index type: NULL
Hence my question: how can I utilise add_row, or a similar approach, to force value across a set of columns initially passed via vars()?
Side notes
I don't mind doing this via rbind but I would like to keep my %>% workflow:
%>% - receive object
Add something across first row to columns x:y %>%
Add something across first row to columns m:n %>%
Other manipulations
Add the row then update:
mtcars %>%
head %>%
add_row(.before = 1) %>%
mutate_at(.cols = vars(2:5),
funs(ifelse(is.na(.), "A", .)))
# mpg cyl disp hp drat wt qsec vs am gear carb
# 1 NA A A A A NA NA NA NA NA NA
# 2 21.0 6 160 110 3.9 2.620 16.46 0 1 4 4
# 3 21.0 6 160 110 3.9 2.875 17.02 0 1 4 4
# 4 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1
# 5 21.4 6 258 110 3.08 3.215 19.44 1 0 3 1
# 6 18.7 8 360 175 3.15 3.440 17.02 0 0 3 2
# 7 18.1 6 225 105 2.76 3.460 20.22 1 0 3 1
Note: This will add "A" to any row that has NAs.

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