I have a dataframe which looks like below.
data <- data.frame(Var_1 = c("A","B","C","A","B"))
Var_1
A
B
C
A
B
Need to do count like below.
Var_1 Count
A 2
B 2
C 1
A 2
B 2
# sample data
df <- data.frame(Var_1 = c("A","B","C","A","B"))
# make a frequency table to determine the "count"
countsDF <- table(df$Var_1)
# use names to match the Var_1 in the countsDF, then assign
# the corresponding count
df$count <- countsDF[match(df$Var_1, names(countsDF))]
I believe you can try using the table and storing it as a dataframe, from this data frame you can access the frequency of your data.
> data <- data.frame(Var_1 = c("A","B","C","A","B"))
> df <- as.data.frame(table(data))
> df$data
[1] A B C
Levels: A B C
> df$Freq
[1] 2 2 1
> df
data Freq
1 A 2
2 B 2
3 C 1
PS: I am not sure if you mean to repeat the 'levels' of your data as mentioned in your question, but unless it is case specific (which is not mentioned) I would take a repetitive class or level into consideration.
Related
I currently have two .csv files that look like this:
File 1:
Attempt
Result
Intervention 1
B
Intervention 2
H
and File 2:
Name
Outcome 1
Outcome 2
Outcome 3
Sample 1
A
B
C
Sample 2
D
E
F
Sample 3
G
H
I
I would like to merge and align the two .csvs such that the result each row of File 1 is aligned by its "result" cell, against any of the three "outcome" columns in File 2, leaving blanks or "NA"s if there are no similarities.
Ideally, would look like this:
Attempt
Result
Name
Outcome 1
Outcome 2
Outcome 3
Intervention 1
B
Sample 1
A
B
C
Sample 2
D
E
F
Intervention 2
H
Sample 3
G
H
I
I've looked and only found answers when merging two .csv files with one common column. Any help would be very appreciated.
I will assume that " Result " in File 1 is unique, since more File 1 rows with same result value (i.e "B") will force us to consider new columns in the final data frame.
By this way,
Attempt <- c("Intervention 1","Intervention 2")
Result <- c("B","H")
df1 <- as.data.frame(cbind(Attempt,Result))
one <- c("Sample 1","A","B","C")
two <- c("Sample 2","D","E","F")
three <- c("Sample 3","G","H","I")
df2 <- as.data.frame(rbind(one,two,three))
row.names(df2) <- 1:3
colnames(df2) <- c("Name","Outcome 1","Outcome 2","Outcome 3")
vec_at <- rep(NA,nrow(df2));vec_res <- rep(NA,nrow(df2)); # Define NA vectors
for (j in 1:nrow(df2)){
a <- which(is.element(df1$Result,df2[j,2:4])==TRUE) # Row names which satisfy same element in two dataframes?
if (length(a>=1)){ # Don't forget that "a" may not be a valid index if no element satify the condition
vec_at[j] <- df1$Attempt[a] #just create a vector with wanted information
vec_res[j] <- df1$Result[a]
}
}
desired_df <- as.data.frame(cbind(vec_at,vec_res,df2)) # define your wanted data frame
Output:
vec_at vec_res Name Outcome 1 Outcome 2 Outcome 3
1 Intervention 1 B Sample 1 A B C
2 <NA> <NA> Sample 2 D E F
3 Intervention 2 H Sample 3 G H I
I wonder if you could use fuzzyjoin for something like this.
Here, you can provide a custom function for matching between the two data.frames.
library(fuzzyjoin)
fuzzy_left_join(
df2,
df1,
match_fun = NULL,
multi_by = list(x = paste0("Outcome_", 1:3), y = "Result"),
multi_match_fun = function(x, y) {
y == x[, "Outcome_1"] | y == x[, "Outcome_2"] | y == x[, "Outcome_3"]
}
)
Output
Name Outcome_1 Outcome_2 Outcome_3 Attempt Result
1 Sample_1 A B C Intervention_1 B
2 Sample_2 D E F <NA> <NA>
3 Sample_3 G H I Intervention_2 H
Very simple concept. I have a dataframe, df and would like to sort it from largest to smallest value from a column and then get the first two columns in a new table. But I run into a small issue it seems.
df<-df[-order(df$col2),]
but the error I get is that object of type 'closure' is not subsettable.
In theory once I get the data sorted I was going to use cbind to pull out the two columns I needed and put into a new dataframe. That much I can do.
Sample:
v2<-c(1,2,3,4,5,6)
v1<-c('A','B','C','D','E','F')
v3<-c(11,12,12.5,11.5,11.75,13)
df<-cbind(v1,v2,v3)
colnames(df)<-c("Number","Letter","Age")
Output:
df
6 F
5 E
4 D
3 C
2 B
1 A
You can use the arrange function in dplyr to sort a column (or multiple if you'd like).
library(dplyr)
df <- data.frame(
"Letter" = c('A','B','C','D','E','F'),
"Number" = c(1,2,3,4,5,6),
"Age" = c(11,12,12.5,11.5,11.75,13)
)
new_df <- df %>%
# desc orders from largest to smallest
arrange(desc(Number)) %>%
# select subsets the columns you want
select(Letter, Number)
new_df
Letter Number
1 F 6
2 E 5
3 D 4
4 C 3
5 B 2
6 A 1
Could you please try following and let me know if this helps you.
library(dplyr)
df[with(df, order(-Letter)), ] %>% select (Number)
Output will be as follows.
6 F
5 E
4 D
3 C
2 B
1 A
>
Data created by as follows:
df <- data.frame(
v1 = c('A','B','C','D','E','F'),
v2 = c(1,2,3,4,5,6),
v3 = c(11,12,12.5,11.5,11.75,13)
)
colnames(df)<-c("Number","Letter","Age")
I the following dataframes:
a <- c(1,1,1)
b<- c(10,8,2)
c<- c(2,2)
d<- c(3,5)
AB<- data.frame(a,b)
CD<- data.frame(c,d)
I would like to join AB and CD, where the first column of CD is equal to the second column of AB. Please note that my actual data will have a varying number of columns, with varying names, so I am really looking for a way to join based on position only. I have been trying this:
#Get the name of the last column in AB
> colnames(AB)[ncol(AB)]
[1] "b"
#Get the name of the first column in CD
> colnames(CD)[1]
[1] "c"
Then I attempt to join like this:
> abcd <- full_join(AB, CD, by = c(colnames(AB)[ncol(AB)]=colnames(CD)[1]))
Error: unexpected '=' in "abcd <- full_join(AB, CD, by = c(colnames(AB)[ncol(AB)]="
The behavior I am looking for is essentially this:
> abcd<- full_join(AB, CD, by = c("b" = "c"))
> abcd
a b d
1 1 10 NA
2 1 8 NA
3 1 2 3
4 1 2 5
We can do setNames
full_join(AB, CD, setNames(colnames(CD)[1], colnames(AB)[ncol(AB)]))
# a b d
#1 1 10 NA
#2 1 8 NA
#3 1 2 3
#4 1 2 5
We can replace the target column names with a common name, such as "Target", and then do full_join. Finally, replace the "Target" name with the original column name.
library(dplyr)
AB_name <- names(AB)
target_name <- AB_name[ncol(AB)] # Store the original column name
AB_name[ncol(AB)] <- "Target" # Set a common name
names(AB) <- AB_name
CD_name <- names(CD)
CD_name[1] <- "Target" # Set a common name
names(CD) <- CD_name
abcd <- full_join(AB, CD, by = "Target") %>% # Merge based on the common name
rename(!!target_name := Target) # Replace the common name with the original name
abcd
# a b d
# 1 1 10 NA
# 2 1 8 NA
# 3 1 2 3
# 4 1 2 5
I would like to replace the values in the last three columns in my data.frame with the three values in a vector.
Example of data.frame
df
A B C D
5 3 8 9
Vector
1 2 3
what I would like the data.frame to look like.
df
A B C D
5 1 2 3
Currently I am doing:
df$B <- Vector[1]
df$C <- Vector[2]
df$D <- Vector[3]
I would like to not replace the values one by one. I would like to do it all at once.
Any help will be appreciated. Please let me know if any further information is needed.
We can subset the last three columns of the dataset with tail, replicate the 'Vector' to make the lengths similar and assign the values to those columns
df[,tail(names(df),3)] <- Vector[col(df[,tail(names(df),3)])]
df
# A B C D
#1 5 1 2 3
NOTE: I replicated the 'Vector' assuming that there will be more rows in the 'df' in the original dataset.
Try this:
df[-1] <- 1:3
giving:
> df
A B C D
1 5 1 2 3
Alternately, we could do it non-destructively like this:
replace(df, -1, 1:3)
Note: The input df in reproducible form is:
df <- data.frame(A = 5, B =3, C = 8, D = 9)
I would like to convert the values in a column of an existing data frame into row names. Is is possible to do this without exporting the data frame and then reimporting it with a row.names = call?
For example I would like to convert:
> samp
names Var.1 Var.2 Var.3
1 A 1 5 0
2 B 2 4 1
3 C 3 3 2
4 D 4 2 3
5 E 5 1 4
Into:
> samp.with.rownames
Var.1 Var.2 Var.3
A 1 5 0
B 2 4 1
C 3 3 2
D 4 2 3
E 5 1 4
This should do:
samp2 <- samp[,-1]
rownames(samp2) <- samp[,1]
So in short, no there is no alternative to reassigning.
Edit: Correcting myself, one can also do it in place: assign rowname attributes, then remove column:
R> df<-data.frame(a=letters[1:10], b=1:10, c=LETTERS[1:10])
R> rownames(df) <- df[,1]
R> df[,1] <- NULL
R> df
b c
a 1 A
b 2 B
c 3 C
d 4 D
e 5 E
f 6 F
g 7 G
h 8 H
i 9 I
j 10 J
R>
As of 2016 you can also use the tidyverse.
library(tidyverse)
samp %>% remove_rownames %>% column_to_rownames(var="names")
in one line
> samp.with.rownames <- data.frame(samp[,-1], row.names=samp[,1])
It looks like the one-liner got even simpler along the line (currently using R 3.5.3):
# generate original data.frame
df <- data.frame(a = letters[1:10], b = 1:10, c = LETTERS[1:10])
# use first column for row names
df <- data.frame(df, row.names = 1)
The column used for row names is removed automatically.
With a one-row dataframe
Beware that if the dataframe has a single row, the behaviour might be confusing. As the documentation mentions:
If row names are supplied of length one and the data frame has a single row, the row.names is taken to specify the row names and not a column (by name or number).
This mean that, if you use the same command as above, it might look like it did nothing (when it actually named the first row "1", which won't look any different in the viewer).
In that case, you will have to stick to the more verbose:
df <- data.frame(a = "a", b = 1)
df <- data.frame(df, row.names = df[,1])
... but the column won't be removed. Also remember that, if you remove a column to end up with a single-column dataframe, R will simplify it to an atomic vector. In that case, you will want to use the extra drop argument:
df <- data.frame(df[,-1, drop = FALSE], row.names = df[,1])
You can execute this in 2 simple statements:
row.names(samp) <- samp$names
samp[1] <- NULL