This question already has answers here:
Limit result of subtraction to a minimum of zero
(1 answer)
Constrain lower limit of the result of a subtraction
(1 answer)
Closed 2 years ago.
I am a beginner in working with functions in R.
I would like to help to construct a simple function with the following example below:
database 1
a b
1 70
3 74
4 76
6 68
I would like to create a new column in this dataset with the following condition:
column c: I want to generate values base on (threshold 73) 73 column b values - 73 = difference (70 - 73 first row) but if the value is negative I want to put 0.
Like this:
database 2
a b c
1 70 0
3 74 1
4 76 3
6 68 0
please, does someone could show me any function?
Thanks!
You can try pmax
df$c <- pmax(df$b-73,0)
or
df$c <- (df$b-73)*(df$b>73)
df$c <- ifelse(df$b-73<0,0,df$b-73)
Related
I am applying this function to my dataset column DL1 on another vector as below and receiving the results expected
table(df$DL1[df$DL1 %in% undefined_dl_codes])
Result:
0 10 30 3B 4 49 54 5A 60 7 78 8 90
24 366 4 3 665 40 1 1 14 8 4 87 1
however I do have columns DL2, DL3 and DL4 which have same data, how can I apply the function to multiple columns and receive the result of all. I would need to go through all 4 required columns and receive 1 result as summary.
Any help highly appreciated!
May not be the best of the methods, however you could do the following
table(c(df$DL1[df$DL1 %in% undefined_dl_codes],
df$DL2[df$DL2 %in% undefined_dl_codes],
df$DL3[df$DL3 %in% undefined_dl_codes],
df$DL4[df$DL4 %in% undefined_dl_codes]
)
)
Using Raghuveer solution I further simplified,
attach(df)
table(c(DL1,DL2,DL3,DL4)[c(DL1,DL2,DL3,DL4) %in% undefined_dl_codes])
detach(df)
This question already has answers here:
Convert *some* column classes in data.table
(2 answers)
Closed 4 years ago.
I am trying to write a "for" loop that iterates through each column in a data.table and return a frequency table. However, I keep getting an error saying:
library(datasets)
data(cars)
cars <- as.data.table(cars)
for (i in names(cars)){
print(table(cars[,i]))
}
Error in `[.data.table`(cars, , i) :
j (the 2nd argument inside [...]) is a single symbol but column name 'i' is not found. Perhaps you intended DT[, ..i]. This difference to data.frame is deliberate and explained in FAQ 1.1.
When I use each column individually like below, I do not have any problem:
> table(cars[,dist])
2 4 10 14 16 17 18 20 22 24 26 28 32 34 36 40 42 46 48 50 52 54 56 60 64 66
1 1 2 1 1 1 1 2 1 1 4 2 3 3 2 2 1 2 1 1 1 2 2 1 1 1
68 70 76 80 84 85 92 93 120
1 1 1 1 1 1 1 1 1
My data is quite large (8921483x52), that is why I want to use the "for" loop and run everything at once then look at the result.
I included the cars dataset (which is easier to run) to demonstrate my code.
If I convert the dataset to data.frame, there is no problem running the "for" loop. But I just want to know why this does not work with data.table because I am learning it, which work better with large dataset in my belief.
If by chance, someone saw a post with an answer already, please let me know because I have been trying for several hours to look for one.
Some solution found here
My personal preference is the apply function though
library(datasets)
data(cars)
cars <- as.data.table(cars)
apply(cars,2,table)
To make your loop work you tweak the i
library(datasets)
data(cars)
cars <- as.data.table(cars)
for (i in names(cars)){
print(table(cars[,(i) := as.character(get(i))]))
}
This question already has answers here:
How to add leading zeros?
(8 answers)
Closed 4 years ago.
I am trying to concatenate some data in a column of a df, with "0000"
I tried to use paste() in a loop, but it becomes very performance heavy, as I have +2.000.000 rows. Thus, it takes forever.
Is there a smart, less performance heavy way to do it?
#DF:
CUSTID VALUE
103 12
104 10
105 15
106 12
... ...
#Desired result:
#DF:
CUSTID VALUE
0000103 12
0000104 10
0000105 15
0000106 12
... ...
How can this be achieved?
paste is vectorized so it'll work with a vector of values (i.e. a column in a data frame. The following should work:
DF <- data.frame(
CUSTID = 103:107,
VALUE = 13:17
)
DF$CUSTID <- paste0('0000', DF$CUSTID)
Should give you
CUSTID VALUE
1 0000103 13
2 0000104 14
3 0000105 15
4 0000106 16
5 0000107 17
This question already has answers here:
How to convert a factor to integer\numeric without loss of information?
(12 answers)
Closed 4 years ago.
I am trying to divide all rows of my dataframe column by a number (say 10). I thought it to be a trivial problem until I tried it. In the example below, I am trying to get the 'mm' column to result in values 8100, 3222.2 and 5433.3
test <- data.frame(locations=c("81000","32222","54333"), value=c(87,54,43))
test$mm <- as.numeric(test$locations) / 10
head(test)
locations value mm
1 81000 87 0.3
2 32222 54 0.1
3 54333 43 0.2
What am I doing wrong?
Change factors to be character, then apply as.numeric
> test$mm <- as.numeric(as.character(test$locations)) / 10
> test
locations value mm
1 81000 87 8100.0
2 32222 54 3222.2
3 54333 43 5433.3
This question already has answers here:
Aggregate / summarize multiple variables per group (e.g. sum, mean)
(10 answers)
Closed 6 years ago.
Hey I have some data looks like this:
ExpNum Compound Peak Tau SS
1 a 100 30 50
2 a 145 23 45
3 b 78 45 56
4 b 45 43 23
5 c 344 23 56
Id like to fund the mean based on Compound name
What I have
Norm_Table$Norm_Peak = (aggregate(data[[3]],by=list(Compound),FUN=normalization))
This is fine and I have this coding repeating 3 times just changing the data[[x]] number. Would lapply work here? or a for loop?
A dplyr solution:
library(dplyr)
data %>%
group_by(Compound) %>%
summarize_each(funs(mean), -ExpNum)