I am trying to read data out of a csv-file.
The data consists of small integer numbers (53, 98 ...)
The csv was made with OpenOffice, the data stood there in the first column
one number in each row.
reading data was simple (no problem at all):
BirthNumbers <- read.csv(“/Users/.../RawData.csv”, header=FALSE)
Now I try to calculate mean(BirthNumbers) (for example),
but it is not possible, the error message:
x is not numeric
Where is my mistake?
Thanks for all help
Norbert
It's probably being read in as characters.
Try mean(as.numeric(BirthNumbers))
As per https://stat.ethz.ch/R-manual/R-devel/library/utils/html/read.table.html (see Value section), read.csv returns a data frame.
You should be calling mean on the column of the data frame. Since you have no headers (given your header = FALSE), most likely the column is called V1 (verify by doing head(BirthNumbers) or colnames(BirthNumbers)), so you should do mean(BirthNumbers$V1).
Related
I have a data frame like this I read in from a .csv (or .xlsx, I've tried both), and one of the variables in the data frame is a vector of dates.
Generate the data with this
Name <- rep("Date", 15)
num <- seq(1:15)
Name <- paste(Name, num, sep = "_")
data1 <- data.frame(
Name,
Due.Date = seq(as.Date("2020/09/24", origin = "1900-01-01"),
as.Date("2020/10/08", origin = "1900-01-01"), "days")
)
When I reference one of the cells specifically, like this: str(project_dates$Due.Date[241]) it reads the date as normal.
However, the exact position of the important dates varies from project to project, so I wrote a command that identifies where the important dates are in the sheet, like this: str(project_dates[str_detect(project_dates$Name, "Date_17"), "Due.Date"])
This code worked on a few projects, but on the current project it now returns a character vector of length 2. One of the values is the date, and the other value is NA. And to make matters worse, the location of the date and the NA is not fixed across dates--the date is the first value in some cells and the second in others (otherwise I would just reference, e.g., the first item in the vector).
What is going on here, but more importantly, how do I fix this?!
Clarification on the second command:
When I was originally reading from an Excel file, the command was project_dates[str_detect(project_dates$Name, "Date_17"), "Due.Date"]$Due.Date because it was returning a 1x1 tibble, and I needed the value in the tibble.
When I switched to reading in data as a csv, I had to remove the $Due.Date because the command was now reading the value as an atomic vector, so the $ operator was no longer valid.
Help me, Oh Blessed 1's (with) Knowledge! You're my only hope!
Edited to include an image of the data like the one that generates the error
I feel sheepish.
I was able to remove the NAs with
data1<- data1[!is.na(data1$Due.Date), ].
I assumed that command would listwise delete the rows with any missing values, so if the cell contained the 2-length vector, then I would lose the whole row of data. Instead, it removed the NA from the cell, leaving only the date.
Thank you to everyone who commented and offered help!
I was just going through a tremendous headache caused by read_csv messing up my data by substituting content with NA while reading simple and clean csv files.
I’m iterating over multiple large csv files that add up to millions of observations. Some columns contain quite some NA for some variables.
When reading a csv that contains NA in a certain column for the first 1000 + x observations, read_csv populates the entire column with NA and thus, the data is lost for further operations.
The warning message “Warning: x parsing failure” is shown, but as I’m reading multiple files I cannot check this file by file. Still, I would not know an automated fix for the parsing problem indicated also with problems(x)
Using read.csv instead of read_csv does not cause the problem, but it is slow and I run into encoding issues (using different encodings requires too much memory for large files).
An option to overcome this bug is to add a first observation (first row) to your data that contains something for each column, but still I need to read the file first somehow.
See a simplified example below:
##create a dtafrane
df <- data.frame( id = numeric(), string = character(),
stringsAsFactors=FALSE)
##poluate columns
df[1:1500,1] <- seq(1:1500)
df[1500,2] <- "something"
# variable string contains the first value in obs. 1500
df[1500,]
## check the numbers of NA in variable string
sum(is.na(df$string)) # 1499
##write the df
write_csv(df, "df.csv")
##read the df with read_csv and read.csv
df_readr <- read_csv('df.csv')
df_read_standard <- read.csv('df.csv')
##check the number of NA in variable string
sum(is.na(df_readr$string)) #1500
sum(is.na(df_read_standard$string)) #1499
## the read_csv files is all NA for variable string
problems(df_readr) ##What should that tell me? How to fix it?
Thanks to MrFlick for giving the answering comment on my questions:
The whole reason read_csv can be faster than read.csv is because it can make assumptions about your data. It looks at the first 1000 rows to guess the column types (via guess_max) but if there is no data in a column it can't guess what's in that column. Since you seem to know what's supposed to be in the columns, you should use the col_types= parameter to tell read_csv what to expect rather than making it guess. See the ?readr::cols help page to see how to tell read_csv what it needs to know.
Also guess_max = Inf overcomes the problem, but the speed advantage of read_csv seems to be lost.
I am trying to do Hartigan's diptest in R, however, I get the following error: 'x' must be numeric.
Apologies for such a basic question, but how do I ensure that the data that I load is numeric?
If I make up a set of values as follows (code below), the diptest works without problems:
library(diptest)
x = c(1,2,3,4,4,4,4,4,4,4,5,6,7,8,9,9,9,9,9,9,9,9,9)
hist(x)
dip.test(x)
But for example, when the same values are saved in an Excel file/tab delimited .txt file (saved as one column of values), and imported into R, when I run the diptest the 'x' must be numeric error occurs.
x <- read.csv("x.csv") # comes back in as a data frame
hist(x)
dip.test(x)
Is there a way to check what format the imported data from an Excel/.txt file is in R, and subsequently change it to be numeric? Thanks again.
Any help will be much appreciated thank you.
Here's what's happening. If you run the code that you know works, it's working because the data class is numeric as it should be. When you read it back in it's a data.frame, however. So you need to point to the numeric element of the data.frame:
library(diptest)
x = c(1,2,3,4,4,4,4,4,4,4,5,6,7,8,9,9,9,9,9,9,9,9,9)
write.csv(x, "x.csv", row.names=F)
x <- read.csv("x.csv") # comes back in as a data frame
hist(x$x)
dip.test(x$x)
Hartigans' dip test for unimodality / multimodality
data: x$x
D = 0.15217, p-value = 2.216e-05
alternative hypothesis: non-unimodal, i.e., at least bimodal
If you were to save the file to a .RDS instead of .csv then you could avoid this problem.
You could also check if your data frame contains any non-numeric characters as follows:
which(!grepl('^[0-9]',your_data_frame[[1]]))
I have a script that is working perfectly except that in my R cbind operation, adjacent to the numerical value that I require in the first row, is an 'X'.
Here is my script:
library(ncdf)
library(Kendall)
library(forecast)
library(zoo)
setwd("/home/cohara/RainfallData")
files=list.files(pattern="*.nc")
j=81
for (i in seq(1,9))
{
file<-open.ncdf(sprintf("/home/cohara/RainfallData/%s.nc",i))
year<-get.var.ncdf(file,"time")
data<-get.var.ncdf(file,"var61")
fit<-lm(data~year) #least sqaures regression
mean=rollmean(data,4,fill=NA)
kendall<-Kendall(data,year)
write.table(kendall[[2]],file="/home/cohara/RainfallAnalysis/Kendall_p-value_for_10%_increase_over_81_-_89_years.csv",append=TRUE,quote=FALSE,row.names=FALSE,col.names=FALSE)
write.table(kendall[[1]],file="/home/cohara/RainfallAnalysis/Kendall_tau_for_10%_increase_over_81_-_89_years.csv",append=TRUE,quote=FALSE,row.names=FALSE,col.names=FALSE)
png(sprintf("./10 percent increase over %s years.png",j))
par(family="serif",mar=c(4,6,4,1),oma=c(1,1,1,1))
plot(year,data,pch="*",col=4,ylab="Precipitation (mm)",main=(sprintf("10 percent increase over %s years",j)),cex.lab=1.5,cex.main=2,ylim=c(800,1400),abline(fit,col="red",lty=1.5))
par(new=T)
plot(year,mean,type="l",xlab="year",ylab="Precipitation (mm)",cex.lab=1.5,ylim=c(800,1400),lty=1.5)
legend("bottomright",legend=c("Kendall tau = ",kendall[[1]]))
legend("bottomleft",legend=c("Kendall 2-tailed p-value = ",kendall[[2]]))
legend(x="topright",c("4 year moving average","Simple linear trend"),lty=1.5,col=c("black","red"),cex=1.2)
legend("topleft",c("Annual total"),pch="*",col="blue",cex=1.2)
dev.off()
j=j+1
}
tmp<-read.csv("/home/cohara/RainfallAnalysis/Kendall_p-value_for_10%_increase_over_81_to_89_years.csv")
tmp2<-read.csv("/home/cohara/RainfallAnalysis/Kendall_p-value_for_10%_increase_over_81_-_89_years.csv")
tmp<-cbind(tmp,tmp2)
tmp3<-read.csv("/home/cohara/RainfallAnalysis/Kendall_tau_for_10%_increase_over_81_to_89_years.csv")
tmp4<-read.csv("/home/cohara/RainfallAnalysis/Kendall_tau_for_10%_increase_over_81_-_89_years.csv")
tmp3<-cbind(tmp3,tmp4)
write.table(tmp,"/home/cohara/RainfallAnalysis/Kendall_p-value_for_10%_increase_over_81_to_89_years.csv",sep="\t",row.names=FALSE)
write.table(tmp3,"/home/cohara/RainfallAnalysis/Kendall_tau_for_10%_increase_over_81_to_89_years.csv",sep="\t",row.names=FALSE)
The output looks like this, from the .csv files created:
X0.0190228056162596 X0.000701081415172666
0.0395622998 0.00531819
0.0126547674 0.0108218994
0.0077754743 0.0015568719
0.0001407317 0.002680057
0.0096391216 0.012719159
0.0107234037 0.0092436085
0.0503448173 0.0103918528
0.0167525802 0.0025036721
I want to be able to use excel functions on the data, so, for simplicity, I don't want row names (I'll be running this loop maybe a hundred times), but I need column names because otherwise the first set of values is cut off.
Can anyone tell me where the 'X' is coming from and how to get rid of it?
Thanks in advance,
Ciara
Here is what I think is going on. Start by running these small examples:
df1 <- read.csv(text = "0.0190228056162596, 0.000701081415172666
0.0395622998, 0.00531819
0.0126547674, 0.0108218994")
df2 <- read.csv(text = "0.0190228056162596, 0.000701081415172666
0.0395622998, 0.00531819
0.0126547674, 0.0108218994", header = FALSE)
df1
df2
str(df1)
str(df2)
names(df1)
names(df2)
make.names(c(0.0190228056162596, 0.000701081415172666))
Please read ?read.csv and about the header argument. As you will find, header = TRUE is default in read.csv. Thus, if the csv file you read lacks header, read.csv will still 'assume' that the file has a header, and use the values in the first row as a header. Another argument in read.csv is check.names, which defaults to TRUE:
If TRUE then the names of the variables in the data frame are checked to ensure that they are syntactically valid variable names. If necessary they are adjusted (by make.names).
In your case, it seems that the data you read lack a header and that the first row is numbers only. read.csv will default treat this row as a header. make.names takes values in the first row (here numbers 0.0190228056162596, 0.000701081415172666), and spits out the 'syntactically valid variable names' X0.0190228056162596 and X0.000701081415172666. Which is not what you want.
Thus, you need to explicitly set header = FALSE to avoid that read.csvconvert the first row to (valid) variable names.
For next time, please provide a minimal, self contained example. Check these links for general ideas, and how to do it in R: here, here, here, and here
I am aware that there are similar questions on this site, however, none of them seem to answer my question sufficiently.
This is what I have done so far:
I have a csv file which I open in excel. I manipulate the columns algebraically to obtain a new column "A". I import the file into R using read.csv() and the entries in column A are stored as factors - I want them to be stored as numeric. I find this question on the topic:
Imported a csv-dataset to R but the values becomes factors
Following the advice, I include stringsAsFactors = FALSE as an argument in read.csv(), however, as Hong Ooi suggested in the page linked above, this doesn't cause the entries in column A to be stored as numeric values.
A possible solution is to use the advice given in the following page:
How to convert a factor to an integer\numeric without a loss of information?
however, I would like a cleaner solution i.e. a way to import the file so that the entries of column entries are stored as numeric values.
Cheers for any help!
Whatever algebra you are doing in Excel to create the new column could probably be done more effectively in R.
Please try the following: Read the raw file (before any excel manipulation) into R using read.csv(... stringsAsFactors=FALSE). [If that does not work, please take a look at ?read.table (which read.csv wraps), however there may be some other underlying issue].
For example:
delim = "," # or is it "\t" ?
dec = "." # or is it "," ?
myDataFrame <- read.csv("path/to/file.csv", header=TRUE, sep=delim, dec=dec, stringsAsFactors=FALSE)
Then, let's say your numeric columns is column 4
myDataFrame[, 4] <- as.numeric(myDataFrame[, 4]) # you can also refer to the column by "itsName"
Lastly, if you need any help with accomplishing in R the same tasks that you've done in Excel, there are plenty of folks here who would be happy to help you out
In read.table (and its relatives) it is the na.strings argument which specifies which strings are to be interpreted as missing values NA. The default value is na.strings = "NA"
If missing values in an otherwise numeric variable column are coded as something else than "NA", e.g. "." or "N/A", these rows will be interpreted as character, and then the whole column is converted to character.
Thus, if your missing values are some else than "NA", you need to specify them in na.strings.
If you're dealing with large datasets (i.e. datasets with a high number of columns), the solution noted above can be manually cumbersome, and requires you to know which columns are numeric a priori.
Try this instead.
char_data <- read.csv(input_filename, stringsAsFactors = F)
num_data <- data.frame(data.matrix(char_data))
numeric_columns <- sapply(num_data,function(x){mean(as.numeric(is.na(x)))<0.5})
final_data <- data.frame(num_data[,numeric_columns], char_data[,!numeric_columns])
The code does the following:
Imports your data as character columns.
Creates an instance of your data as numeric columns.
Identifies which columns from your data are numeric (assuming columns with less than 50% NAs upon converting your data to numeric are indeed numeric).
Merging the numeric and character columns into a final dataset.
This essentially automates the import of your .csv file by preserving the data types of the original columns (as character and numeric).
Including this in the read.csv command worked for me: strip.white = TRUE
(I found this solution here.)
version for data.table based on code from dmanuge :
convNumValues<-function(ds){
ds<-data.table(ds)
dsnum<-data.table(data.matrix(ds))
num_cols <- sapply(dsnum,function(x){mean(as.numeric(is.na(x)))<0.5})
nds <- data.table( dsnum[, .SD, .SDcols=attributes(num_cols)$names[which(num_cols)]]
,ds[, .SD, .SDcols=attributes(num_cols)$names[which(!num_cols)]] )
return(nds)
}
I had a similar problem. Based on Joshua's premise that excel was the problem I looked at it and found that the numbers were formatted with commas between every third digit. Reformatting without commas fixed the problem.
So, I had the similar situation here in my data file when I readin as a csv. All the numeric value were turned into char. But in my file there was a value with a word "Filtered" instead of NA. I converted "Filtered" to NA in vim editor of linux terminal with a command <%s/Filtered/NA/g> and saved this file and later used it and read it in R, all the values were num type and not char type any more.
Looks like character value "Filtered" was inducing all values to be char format.
Charu
Hello #Shawn Hemelstrand here are the steps in detail below:
example matrix file.csv having 'Filtered' word in it
I opened the file.csv in linux command terminal
vi file.csv
then press "Esc shift:"
and type the following command at the bottom
"%s/Filtered/NA/g"
press enter
then press "Esc shift:"
write "wq" at the bottom (this save the file and quit vim editor)
then in R script I read the file
data<- read.csv("file.csv", sep = ',', header = TRUE)
str(data)
All columns were num type which were earlier char type.
In case you need more help, it would be easier to share your txt or csv file.