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I have a dataframe in R that I loaded from a CSV file. One of the variables is called "Amount" and is meant to contain positive and negative numbers.
When I looked at the dataframe, this variable's datatype is listed as a factor, and I need it in a numeric format (Not sure which kind though - integer - numeric, umm...?). So, I tried to convert it to one of those two formats but saw some interesting behavior.
Initial dataframe:
str(df)
Amount : Factor w/ 11837 levels "","-1","-10",..: 2 2 1664 4 6290 6290 6290 6290 6290 6290 ...
As I mentioned above, I saw something weird when I tried to convert it to either numeric or integer. To show this, I put together this comparison:
df2 <- data.frame(df$Amount, as.numeric(df$Amount), as.integer(df$Amount))
str(df2)
'data.frame': 2620276 obs. of 3 variables:
$ df.Amount : Factor w/ 11837 levels "","-1","-10",..: 2 2 1664 4 6290 6290 6290 6290 6290 6290 ...
$ as.numeric.df.Amount.: num 2 2 1664 4 6290 ...
$ as.integer.df.Amount.: int 2 2 1664 4 6290 6290 6290 6290 6290 6290 ...
> head(df2, 20)
df.Amount as.numeric.df.Amount. as.integer.df.Amount.
1 -1 2 2
2 -1 2 2
3 -201 1664 1664
4 -100 4 4
5 1 6290 6290
6 1 6290 6290
7 1 6290 6290
8 1 6290 6290
9 1 6290 6290
10 1 6290 6290
11 1 6290 6290
12 1 6290 6290
13 1 6290 6290
14 1 6290 6290
15 1 6290 6290
16 1 6290 6290
17 1 6290 6290
18 2 7520 7520
19 2 7520 7520
20 2 7520 7520
The as.numeric and as.integer functions are taking the Amount variable and doing something to it, but I don't know that that is. My goal is to get the Amount variable into some sort of a number data type so I can perform sum/mean/etc on it.
What I am I doing incorrectly that's causing the weird numbers, and what can I do to fix it?
302
There are two steps for converting factor to numeric: Step 1: Convert the data vector into a factor. The factor() command is used to create and modify factors in R. Step 2: The factor is converted into a numeric vector using as. numeric().
Factors are stored as integers, and have labels associated with these unique integers. While factors look (and often behave) like character vectors, they are actually integers under the hood, and you need to be careful when treating them like strings.
To convert category variables to dummy variables in tidyverse, use the spread() method. To do so, use the spread() function with three arguments: key, which is the column to convert into categorical values, in this case, “Reporting Airline”; value, which is the value you want to set the key to (in this case “dummy”);
To convert columns of an R data frame from integer to numeric we can use lapply function. For example, if we have a data frame df that contains all integer columns then we can use the code lapply(df,as. numeric) to convert all of the columns data type into numeric data type.
The root of the problem is likely some funky value in your imported csv. If it came from excel, this is not uncommon. It can be a percent symbol, a "comment" character from excel or any of a long list of things. I would look at the csv in your editor of choice and see what you can see.
Aside from that, you have a few options.
read.csv takes an optional argument stringsAsFactors which you can set to FALSE
A factor is stored as integer levels which map to values. When you convert directly with as.numeric you wind up with those integer levels rather than the initial values:
> x<-10:20
> as.numeric(factor(x))
[1] 1 2 3 4 5 6 7 8 9 10 11
>
otherwise look at ?factor:
In particular, as.numeric applied to a factor is meaningless, and may happen by implicit coercion. To transform a factor
fto approximately its original numeric values,as.numeric(levels(f))[f]is recommended and slightly more efficient thanas.numeric(as.character(f)).
However, I suspect this will error because the input has something in it besides a number.
115
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