How to select columns conditionally in a data frame in R
How can I find the mean/median (any other such thing) of women? I have tried a few piece of code to access the women data in particular but was unsuccessful. Any help is really appreciated.
> jalal <- read.csv("jalal.csv", header=TRUE,sep=",")
> which(jalal$sex==F)
integer(0)
> jalal
age sex weight eye.color hair.color
1 23 F 93.8 blue black
2 21 M 180.8 amber gray
3 22 F 196.5 hazel gray
4 22 M 256.2 amber black
5 21 M 219.6 blue gray
6 16 F 152.1 blue gray
7 21 F 183.3 gray chestnut
8 18 M 179.1 brown blond
9 15 M 206.1 blue white
10 19 M 211.6 brown blond
11 20 F 209.4 blue white
12 21 M 194.0 brown auburn
13 22 F 204.1 green black
14 21 F 157.4 hazel red
15 15 F 238.0 green gray
16 20 F 154.8 gray gray
17 16 F 245.8 gray gray
18 23 M 198.2 gray red
19 19 M 169.1 green brown
20 24 M 198.0 green gray
> subset(jalal, subset=(sex =F)) -> females
> females
[1] age sex weight eye.color hair.color
<0 rows> (or 0-length row.names)
> subset(jalal, subset=(sex ==F)) -> females
> females
[1] age sex weight eye.color hair.color
<0 rows> (or 0-length row.names)
Here's what's in jalal.csv:
"age","sex","weight","eye.color","hair.color"
23,"F",93.8,"blue","black"
21,"M",180.8,"amber","gray"
22,"F",196.5,"hazel","gray"
22,"M",256.2,"amber","black"
21,"M",219.6,"blue","gray"
16,"F",152.1,"blue","gray"
21,"F",183.3,"gray","chestnut"
18,"M",179.1,"brown","blond"
15,"M",206.1,"blue","white"
19,"M",211.6,"brown","blond"
20,"F",209.4,"blue","white"
21,"M",194,"brown","auburn"
22,"F",204.1,"green","black"
21,"F",157.4,"hazel","red"
15,"F",238,"green","gray"
20,"F",154.8,"gray","gray"
16,"F",245.8,"gray","gray"
23,"M",198.2,"gray","red"
19,"M",169.1,"green","brown"
24,"M",198,"green","gray"
Answer
You're looking for aggregate. Here is a forumla that returns the median age and weight by sex:
aggregate(cbind(age, weight) ~ sex, data=jalal, FUN=median)
## sex age weight
## 1 F 20.5 189.9
## 2 M 21.0 198.1
To get a data frame containing just the women, here is the syntax for [:
jalal[jalal$sex == 'F',]
Note the quotes around 'F'. A bare F means FALSE. That's why your second subset expression fails.
subset(jalal, subset=(sex =='F'))
## age sex weight eye.color hair.color
## 1 23 F 93.8 blue black
## 3 22 F 196.5 hazel gray
## 6 16 F 152.1 blue gray
...
In the comment, it is requested for a method for the mean values for women with blue eyes. The first approach is to filter the data frame to just blue-eyed people:
aggregate(cbind(age, weight) ~ sex, data=jalal[jalal$eye.color == 'blue',], FUN=mean)
## sex age weight
## 1 F 19.66667 151.7667
## 2 M 18.00000 212.8500
But this seems hackish, after all, we're not filtering the data frame on women. So here is a formula that gives the mean age and weight, by sex and eye color. From this, you can find the mean of blue-eyed women, green-eyed men, etc.:
aggregate(cbind(age, weight) ~ sex + eye.color, data=jalal, FUN=mean)
## sex eye.color age weight
## 1 M amber 21.50000 218.5000
## 2 F blue 19.66667 151.7667
## 3 M blue 18.00000 212.8500
## 4 M brown 19.33333 194.9000
## 5 F gray 19.00000 194.6333
## 6 M gray 23.00000 198.2000
## 7 F green 18.50000 221.0500
## 8 M green 21.50000 183.5500
## 9 F hazel 21.50000 176.9500
Note rows 2 and 3 here match the results in the prior expression.