I have a simple data set in R -- 2 conditions called "COND", and within those conditions adults chose between one of 2 pictures, we call house or car. This variable is called "SAW"
I have 69 people, and 69 rows of data
FOR SOME Reason -- R is adding an empty factor to both, How do I get rid of it?
When I type table to see how many are in each-- this is the output
table(MazeData$SAW)
car house
2 9 59
table(MazeData$COND)
Apples No_Apples
2 35 33
Where the heck are these 2 mystery rows coming from? it wont let me make my simple box plots and bar plots or run t.test because of this error - can someone help? thanks!!
I have a data set and I want to find the rows which include a specific word "result". I used the following function but it seems it doesn't work correctly. Any suggestion?
data$new<-data.frame(grepl("result",col1))
data:
col1 col2
ABC result VDCbvdc home 22
fgc school 34
university result home exam 45
exam math stat 65
try data$new <- grepl("result",data$col1)
data$new should be assigned to a vector, but you're trying to feed it a data frame. also, col1 only exists inside data, so you'll need data$col1.
I have a csv file with multiple columns. I want to print the values of one column if the values of two other columns are above a certain number. I then want to output this to a plain .txt file. I know how to do this on linux using awk but I don't think I can do this with R? (I am new)
Example data-
Height Age Name
145.2 13 David
170.3 20 Emma
100.1 8 Bob
200.5 23 Ben
176.6 19 Jim
180.7 20 James
165.8 25 Helen
So in this example, I am looking to output the name to a .txt file, if height is greater than or equal to (>=) 170 and also if the age is greater or equal to 19.
So it should output Emma, Ben, Jim and James to a new .txt file.
Any help would be great!
I have tried the code Jean suggested, I get this sort of output-
Emma, Ben, Jim ... Henry
Where it is just showing some of them and not a complete list in one column, just written across the console. I can't see the complete list on R or linux.
I have edited my data, in the real thing there is decimal points in the data which I think is causing the issue. Is there away around this?
You can print your output to a file this way.
sink(file='somefile.txt')
#print(x[x$Height>=170 & x$Age>=19, "Name"])
cat(paste((x[x$Height>=170 & x$Age>=19, "Name"]), collapse="\n"))
sink()
I'm new to R and I am having some trouble iterating through the unique element of a vector. I have a dataframe "School" with 700 different teachers. Each teacher has around 40 students.
I want to be able to loop through each teacher, create a graphs for the mean score of his/her students' over time, save the graphs in a folder and automatically email that folder to that teacher.
I'm just getting started and am having trouble setting up the for-loop. In Stata, I know how to loop through each unique element in a list, but am having trouble doing that in R. Any help would be appreciated.
School$Teacher School$Student School$ScoreNovember School$ScoreDec School$TeacherEmail
A 1 35 45 A#school.org
A 2 43 65 A#school.org
B 1 66 54 B#school.org
A 3 97 99 A#school.org
C 1 23 45 C#school.org
Your question seems a bit vague and it looks like you want us to write your whole project. Could you share what you have done so far and where exactly you are struggling?
see ?subset
School=data.frame(Teacher=c("A","B"), ScoreNovember=10:11, ScoreDec=13:14)
for (teacher in unique(School$Teacher)) {
teacher_df=subset(School, Teacher==teacher)
MeanScoreNovember=mean(teacher_df$ScoreNovember)
MeanScoreDec =mean(teacher_df$ScoreDec)
# do your plot
# send your email
}
I think you have 3 questions, which will need separate questions, how do I:
Create graphs
Automatically email output
Compute a subset mean based on group
For the 3rd one, I like using the plyr package, other people will recommend data.table or dplyrpackages. You can also use aggregate from base. To get a teacher's mean:
library(plyr)
ddply(School,.(Teacher),summarise,Nov_m=mean(ScoreNovember))
If you want per student per teacher, etc. just add between the columns, like:
library(plyr)
ddply(School,.(Teacher,Student),summarise,Nov_m=mean(ScoreNovember))
You could do that for each score column (and then chart it) if your data was long rather than wide you could also add the date ('November', 'Dec') as a group in the brackets, or:
library(plyr)
ddply(School,.(Teacher,Student),summarise,Nov_m=mean(ScoreNovember),Dec_m=mean(ScoreDec))
See if that helps with the 3rd, but look at splitting your questions up too.
I am stumped. Normally, read.csv works as expected, but I have come across an issue where the behavior is unexpected. It most likely is user error on my part, but any help will be appreciated.
Here is the URL for the file
http://nces.ed.gov/ipeds/datacenter/data/SFA0910.zip
Here is my code to get the file, unzip, and read it in:
URL <- "http://nces.ed.gov/ipeds/datacenter/data/SFA0910.zip"
download.file(URL, destfile="temp.zip")
unzip("temp.zip")
tmp <- read.table("sfa0910.csv",
header=T, stringsAsFactors=F, sep=",", row.names=NULL)
Here is my problem. When I open the data csv data in Excel, the data look as expected. When I read the data into R, the first column is actually named row.names. R is reading in one extra row of data, but I can't figure out where the "error" occurs that is causing row.names to be a column. Simply, it looks like the data shifted over.
However, what is strange is that the last column in R does appear to contain the proper data.
Here are a few rows from the first few columns:
tmp[1:5,1:7]
row.names UNITID XSCUGRAD SCUGRAD XSCUGFFN SCUGFFN XSCUGFFP
1 100654 R 4496 R 1044 R 23
2 100663 R 10646 R 1496 R 14
3 100690 R 380 R 5 R 1
4 100706 R 6119 R 774 R 13
5 100724 R 4638 R 1209 R 26
Any thoughts on what I could be doing wrong?
My tip: use count.fields() as a quick diagnostic when delimited files do not behave as expected.
First, count the number of fields using table():
table(count.fields("sfa0910.csv", sep = ","))
# 451 452
# 1 6852
That tells you that all but one of the lines contains 452 fields. So which is the aberrant line?
which(count.fields("sfa0910.csv", sep = ",") != 452)
# [1] 1
The first line is the problem. On inspection, all lines except the first are terminated by 2 commas.
The question now is: what does that mean? Is there supposed to be an extra field in the header row which was omitted? Or were the 2 commas appended to the other lines in error? It may be best to contact whoever generated the data, if possible, to clarify the ambiguity.
I have a fix maybe based on mnel's comments
dat<-readLines(paste("sfa", '0910', ".csv", sep=""))
ncommas<-sapply(seq_along(dat),function(x){sum(attributes(gregexpr(',',dat[x])[[1]])$match.length)})
> head(ncommas)
[1] 450 451 451 451 451 451
all columns after the first have an extra seperator which excel ignores.
for(i in seq_along(dat)[-1]){
dat[i]<-gsub('(.*),','\\1',dat[i])
}
write(dat,'temp.csv')
tmp<-read.table('temp.csv',header=T, stringsAsFactors=F, sep=",")
> tmp[1:5,1:7]
UNITID XSCUGRAD SCUGRAD XSCUGFFN SCUGFFN XSCUGFFP SCUGFFP
1 100654 R 4496 R 1044 R 23
2 100663 R 10646 R 1496 R 14
3 100690 R 380 R 5 R 1
4 100706 R 6119 R 774 R 13
5 100724 R 4638 R 1209 R 26
the moral of the story .... listen to Joshua Ulrich ;)
Quick fix. Open the file in excel and save it. This will also delete the extra seperators.
Alternatively
dat<-readLines(paste("sfa", '0910', ".csv", sep=""),n=1)
dum.names<-unlist(strsplit(dat,','))
tmp <- read.table(paste("sfa", '0910', ".csv", sep=""),
header=F, stringsAsFactors=F,col.names=c(dum.names,'XXXX'),sep=",",skip=1)
tmp1<-tmp[,-dim(tmp)[2]]
I know you've found an answer but as your answer helped me to find out this, I'll share:
If you read into R a file with different amount of columns for different rows, like this:
1,2,3,4,5
1,2,3,4
1,2,3
it would be read-in filling the missing columns with NAs, like this:
1,2,3,4,5
1,2,3,4,NA
1,2,3,NA,NA
BUT!
If the row with the biggest columns is not the first row, like this:
1,2,3,4
1,2,3,4,5
1,2,3
then it would be read in a bit confusing way:
1,2,3,4
1,2,3,4
5,NA,NA,NA
1,2,3,NA
(overwhelming before you figure out the problem and quite simple after!)
Just hope it may help someone!
If you using local data, also make sure that it's in the right place. To be sure put it for instance in your working directory and change it via
setwd("C:/[User]/[MyFolder]")
directly in your R-console.