I have a data frame called mydata with multiple columns, one of which is Benefits, which contains information about samples whether they are CB (complete responding), ICB (Intermediate) or NCB (Non-responding at all).
So basically the Benefit column is a vector with three values:
Benefit <- c("CB" , "ICB" , "NCB")
I want to make a histogram/barplot based on the number of each one of those. So basically it's not a numeric column. I tried solving this by the following code :
hist(as.numeric(metadata$Benefit))
tried also
barplot(metadata$Benefit)
didn't work obviously.
The second thing I want to do is to find a relation between the Age column of the same data frame and the Benefit column, like for example do the younger patients get more benefit ? Is there anyway to do that ?
THANKS!
Hi and welcome to the site :)
One nice way to find issues with code is to only run one command at the time.
# lets create some data
metadata <- data.frame(Benefit = c("ICB", "CB", "CB", "NCB"))
now the command 'as.numeric' does not work on character-data
as.numeric(metadata$Benefit) # returns NA's
Instead what we want is to count the number of instances of each unique value of the column Benefit, we do this with 'table'
tabledata <- table(metadata$Benefit)
and then it is the barplot function we want to create the plot
barplot(tabledata)
Related
I'm in a very basic class that introduces R for genetic purposes. I'm encountering a rather peculiar problem in trying to follow the instructions given. Here is what I have along with the instructor's notes:
MangrovesRaw<-read.csv("C:/Users/esteb/Documents/PopGen/MangrovesSites.csv")
#i'm going to make a new dataframe now, with one column more than the mangrovesraw dataframe but the same number of rows.
View(MangrovesRaw)
Mangroves<-data.frame(matrix(nrow = 528, ncol = 23))
#next I want you to name the first column of Mangroves "pop"
colnames(Mangroves)<-c(col1="pop")
#i'm now assigning all values of that column to be 1
Mangroves$pop<-1
#assign the rest of the columns (2 to 23) to the entirety of the MangrovesRaw dataframe
#then change the names to match the mangroves raw names
colnames(Mangroves)[2:23]<-colnames(MangrovesRaw)
I'm not really sure how to assign columns that haven't been named used the $ as we have in the past. A friend suggested I first run
colnames(Mangroves)[2:23]<-colnames(MangrovesRaw)
Mangroves$X338<-MangrovesRaw
#X338 is the name of the first column from MangrovesRaw
But while this does transfer the data from MangrovesRaw, it comes at the cost of having my column names messed up with X338. added to every subsequent column. In an attempt to modify this I found the following "fix"
colnames(Mangroves)[2:23]<-colnames(MangrovesRaw)
Mangroves$X338<-MangrovesRaw[,2]
#Mangroves$X338<-MangrovesRaw[,2:22]
#MangrovesRaw has 22 columns in total
While this transferred all the data I needed for the X338 Column, it didn't transfer any data for the remaining 21 columns. The code in # just results in the same problem of having X388. show up in all my column names.
What am I doing wrong?
There are a few ways to solve this problem. It may be that your instructor wants it done a certain way, but here's one simple solution: just cbind() the Mangroves$pop column with the real data. Then the data and column names are already added.
Mangroves <- cbind(Mangroves$pop, MangrovesRaw)
Here's another way:
Mangroves[, 2:23] <- MangrovesRaw
colnames(Mangroves)[2:23] <- colnames(MangrovesRaw)
good day
I don´t understand a topic here, is like it works but I can´t understand why
I have this database
# planets_df is pre-loaded in your workspace
# Use order() to create positions
positions <- order(planets_df$diameter)
positions
# Use positions to sort planets_df
planets_df[positions,]
I don´t understand why if u take the column diameter, then if u want to order it why u put it in a row of the dataframe like for me it should be [ rows, colum] but u put a column in a row and it changes, I really don´t get that.Why it´s not planets_df[,positions].
The exercise is solved I just don´t get it, is a data camp exercise btw.
Sorry if my English is wrong, it is not my native language.
I believe that I have created an example that matches your description. For the mtcars data set, which is pre-loaded in any R session, we can sort based on the variable mpg.
The function order returns the row indices sorted by mpg in this case. The ordering variable indicates the order that the rows should be presented in by storing the row indices based on mpg.
ordering <- order(mtcars$mpg)
This next step indicates that we want the rows of mtcars as specified by ordering. Essentially ordering is the order of the rows we want and so we pass that object to the row portion the call to mtcars.
mtcars[ordering,]
If we instead passed ordering as the columns, we would be reordering the columns of mtcars instead of the rows.
So I have two columns. I need to add a third column. However this third column needs to have A for the first amount of rows, and B for the second specified amount of rows.
I tried adding this data_exercise_3 ["newcolumn"] <- (1:6)
but it didn't work. Can someone tell me what I'm doing wrong please?
Looks like you're having a problem with subsetting a data frame correctly. I'd recommend reviewing this concept before you proceed much further, either via a Coursera course or on a website like this UCLA R learning module on subsetting data frames. Subsetting is a crucial component of data wrangling with R, and you'll go much faster with a solid foundation of the basics!
You can assign values to a subset of a data frame by using [row, column] notation. Since your data frame is called data_exercise_3 and the column you'd like to assign values to is called 'newcolumn', then assuming you want the first 6 rows as 'A' and the next 3 as 'B', you could write it like this:
data_exercise_3[1:6,'newcolumn'] <- 'A'
data_exercise_3[7:9,'newcolumn'] <- 'B'
data_exercise_3$category <- c(rep("A",6),rep("B",6))
I am very new to R and I am struggling to understand how to omit NA values in a specific way.
I have a large dataframe with several columns (up to 40) and rows (up to 200ish). I want to use data from one of the columns to do simple stats (wilcox.test, boxplot, etc): one column will have a continuous variable (V1), while the other has a binary variable (V2; 0 or 1), which divides 2 groups. I want to do this for the continuous variable using different V2 binary variables, which are unrelated. I organized this data in Excel, saved it as CSV and am using R Studio.
All these columns have interspersed NA values and when I use omit.na, it just takes off every single row where a NA value is present, which takes away an awful load of data. Is there any simple solution to do this? I have seen some answers to similar topics, but none seems quite exactly what I need to do.
Many thanks for any answer. Again, I am a baby-level newbie to R and may have overlooked something in other topics!
If I understand, you want to apply to function to a pair of column each time.
wilcox.test(V1,V2)
wilcox.test(V1,V3)...
Where Vi have no missing values. I would do something like this :
## use complete.cases to assert that you have no missing values
## for the selected pair
apply_clean <-
function(x,y){
ok <- complete.cases(x, y)
wilcox.test(x[ok],dat$V1[ok])
}
## apply this function to all columns after removing the continuous column
lapply(subset(dat,select=-V1),apply_clean,y=dat$V1)
You can manipulate the data.frame to omit based on any rules you like. For example:
dirty.frame <- data.frame(col1 = c(1,2,3,4,5,6,7,NA,9,10), col2 = c(10, 9, 8, 7,6,5,4,3,2,1))
cleaned.frame <- dirty.frame[!is.na(dirty.frame$col1),]
This code used is.na() to test if a row in a specific column is na. The ! means not, and will omit that row.
I have a df with over 30 columns and over 200 rows, but for simplicity will use an example with 8 columns.
X1<-c(sample(100,25))
B<-c(sample(4,25,replace=TRUE))
C<-c(sample(2,25,replace =TRUE))
Y1<-c(sample(100,25))
Y2<-c(sample(100,25))
Y3<-c(sample(100,25))
Y4<-c(sample(100,25))
Y5<-c(sample(100,25))
df<-cbind(X1,B,C,Y1,Y2,Y3,Y4,Y5)
df<-as.data.frame(df)
I wrote a function that melts the data generates a plot with X1 giving the x-axis values and faceted using the values in B and C.
plotdata<-function(l){
melt<-melt(df,id.vars=c("X1","B","C"),measure.vars=l)
plot<-ggplot(melt,aes(x=X1,y=value))+geom_point()
plot2<-plot+facet_grid(B ~ C)
ggsave(filename=paste("X_vs_",l,"_faceted.jpeg",sep=""),plot=plot2)
}
I can then manually input the required Y variable
plotdata("Y1")
I don't want to generate plots for all columns. I could just type the column of interest into plotdata and then get the result, but this seems quite inelegant (and time consuming). I would prefer to be able to manually specify the columns of interest e.g. "Y1","Y3","Y4" and then write a loop function to do all those specified.
However I am new to writing for loops and can't find a way to loop in the specific column names that are required for my function to work. A standard for(i in 1:length(df)) wouldn't be appropriate because I only want to loop the user specified columns
Apologies if there is an answer to this is already in stackoverflow. I couldn't find it if there was.
Thanks to Roland for providing the following answer:
Try
for (x in c("Y1","Y3","Y4")) {plotdata(x)}
The index variable doesn't have to be numeric