How would one automate the control of columns and rows in facet_wrap such that there will never be any empty spaces? For example, my facet_wrap() code displays 24 plots with 5 columns and rows, but would like to have 4 columns and 6 rows. I know one can control the output within facet_wrap, but would like a solution that can automate this when producing multiple ggplots.
A bit long-winded, but it should give you the most "balanced" pair of rows and columns for a given n_plots (n_plots being the number of distinct values in the variable you are using to facet on):
n_plots <- 24
possible_arrangements <- data.frame()
ind = 1
for (i in 1:n_plots) {
if (n_plots%%i == 0){
j = n_plots/i
print(paste(i,j)) #for debugging purposes
possible_arrangements[ind,1] <- i
possible_arrangements[ind,2] <- j
ind = ind + 1
}
}
colnames(possible_arrangements) <- c("rows", "cols")
possible_arrangements <- possible_arrangements %>%
mutate(balance = abs(rows-cols))
solution <- possible_arrangements %>%
filter(balance == min(balance))
Output:
> solution
rows cols balance
1 4 6 2
2 6 4 2
If you want an unique solution then you can further require that cols>=rows or rows>=cols depending on how you like it.
Related
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Divide each data frame row by vector in R
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Closed 2 years ago.
I'm new to R and I've done my best googling for the answer to the question below, but nothing has come up so far.
In Excel you can keep a specific column or row constant when using a reference by putting $ before the row number or column letter. This is handy when performing operations across many cells when all cells are referring to something in a single other cell. For example, take a dataset with grades in a course: Row 1 has the total number of points per class assignment (each column is an assignment), and Rows 2:31 are the raw scores for each of 30 students. In Excel, to calculate percentage correct, I take each student's score for that assignment and refer it to the first row, holding row constant in the reference so I can drag down and apply that operation to all 30 rows below Row 1. Most importantly, in Excel I can also drag right to do this across all columns, without having to type a new operation.
What is the most efficient way to perform this operation--holding a reference row constant while performing an operation to all other rows, then applying this across columns while still holding the reference row constant--in R? So far I had to slice the reference row to a new dataframe, remove that row from the original dataframe, then type one operation per column while manually going back to the new dataframe to look up the reference number to apply for that column's operation. See my super-tedious code below.
For reference, each column is an assignment, and Row 1 had the number of points possible for that assignment. All subsequent rows were individual students and their grades.
# Extract number of points possible
outof <- slice(grades, 1)
# Now remove that row (Row 1)
grades <- grades[-c(1),]
# Turn number correct into percentage. The divided by
# number is from the sliced Row 1, which I had to
# look up and type one-by-one. I'm hoping there is
# code to do this automatically in R.
grades$ExamFinal < (grades$ExamFinal / 34) * 100
grades$Exam3 <- (grades$Exam3 / 26) * 100
grades$Exam4 <- (grades$Exam4 / 31) * 100
grades$q1.1 <- grades$q1.1 / 6
grades$q1.2 <- grades$q1.2 / 10
grades$q1.3 < grades$q1.3 / 6
grades$q2.2 <- grades$q2.2 / 3
grades$q2.4 <- grades$q2.4 / 12
grades$q3.1 <- grades$q3.1 / 9
grades$q3.2 <- grades$q3.2 / 8
grades$q3.3 <- grades$q3.3 / 12
grades$q4.1 <- grades$q4.1 / 13
grades$q4.2 <- grades$q4.2 / 5
grades$q6.1 <- grades$q6.1 / 5
grades$q6.2 <- grades$q6.2 / 6
grades$q6.3 <- grades$q6.3 / 11
grades$q7.1 <- grades$q7.1 / 7
grades$q7.2 <- grades$q7.2 / 8
grades$q8.1 <- grades$q8.1 / 7
grades$q8.3 <- grades$q8.3 / 13
grades$q9.2 <- grades$q9.2 / 13
grades$q10.1 <- grades$q10.1 / 8
grades$q12.1 <- grades$q12.1 / 12
You can use sweep
100*sweep(grades, 2, outof, "/")
# ExamFinal EXam3 EXam4
#1 100.00 76.92 32.26
#2 88.24 84.62 64.52
#3 29.41 100.00 96.77
Data:
grades
ExamFinal EXam3 EXam4
1 34 20 10
2 30 22 20
3 10 26 30
outof
[1] 34 26 31
grades <- data.frame(ExamFinal=c(34,30,10),
EXam3=c(20,22,26),
EXam4=c(10,20,30))
outof <- c(34,26,31)
You can use mapply on the original grades dataframe (don't remove the first row) to divide rows by the first row. Then convert the result back to a dataframe.
as.data.frame(mapply("/", grades[2:31, ], grades[1, ]))
The easiest way is to use some type of loop. In this case I am using the sapply function. To all of the elements in each column by the corresponding total score.
#Example data
outof<-data.frame(q1=c(3), q2=c(5))
grades<-data.frame(q1=c(1,2,3), q2=c(4,4, 5))
answermatrix <-sapply(1:ncol(grades), function(i) {
#grades[,i]/outof[i] #use this if "outof" is a vector
grades[,i]/outof[ ,i]
})
answermatrix
A loop would probably be your best bet.
The first part you would want to extract the most amount of points possible, as is listed in the first row, then use that number to calculate the percentage in the remaining rows per column:
`
j = 2 #sets the first row to 2 for later
for (i in 1:ncol(df) {
a <- df[1,] #this pulls the total points into a
#then we compute using that number
while(j <= nrow(df)-1){ #subtract the number of rows from removing the first
#row
b <- df[j,i] #gets the number per row per column that corresponds with each
#student
df[j,i] <- ((a/b)*100) #replaces that row,column with that percentage
j <- j+1 #goes to next row
}
}
`
The only drawback to this approach is data-frames produced in functions aren't copied to the global environment, but that can be fixed by introducing a function like so:
f1 <- function(x = <name of df> ,y= <name you want the completed df to be
called>) {
j = 2
for (i in 1:ncol(x) {
a <- x[1,]
while(j <= nrow(x)-1){
b <- df[j,i]
x[j,i] <- ((a/b)*100)
i <- i+1
}
}
arg_name <- deparse(substitute(y)) #gets argument name
var_name <- paste(arg_name) #construct the name
assign(var_name, x, env=.GlobalEnv) #produces global dataframe
}
I have a data frame with 159 obs and 27 variables, and I want to correlate all 159 obs from column 4 (variable 4) with each one of the following columns (variables), this is, correlate column 4 with 5, then column 4 with 6 and so on... I've been unsuccessfully trying to create a loop, and since I'm a beginner in R, it turned out harder than I thought. The reason why I want to turn it more simple is that I would need to do the same thing for a couple more data frames and if I had a function that could do that, it would be so much easier and less time-consuming. Thus, it would be wonderful if anyone could help me.
df <- ZEB1_23genes # CHANGE ZEB1_23genes for df (dataframe)
for (i in colnames(df)){ # Check the class of the variables
print(class(df[[i]]))
}
print(df)
# Correlate ZEB1 with each of the 23 genes accordingly to Pearson's method
cor.test(df$ZEB1, df$PITPNC1, method = "pearson")
### OR ###
cor.test(df[,4], df[,5])
So I can correlate individually but I cannot create a loop to go back to column 4 and correlate it to the next column (5, 6, ..., 27).
Thank you!
If I've understood your question correctly, the solution below should work well.
#Sample data
df <- data.frame(matrix(data = sample(runif(100000), 4293), nrow = 159, ncol = 27))
#Correlation function
#Takes data.frame contains columns with values to be correlated as input
#The column against which other columns must be correlated cab be specified (start_col; default is 4)
#The number of columns to be correlated against start_col can also be specified (end_col; default is all columns after start_col)
#Function returns a data.frame containing start_col, end_col, and correlation value as rows.
my_correlator <- function(mydf, start_col = 4, end_col = 0){
if(end_col == 0){
end_col <- ncol(mydf)
}
#out_corr_df <- data.frame(start_col = c(), end_col = c(), corr_val = c())
out_corr <- list()
for(i in (start_col+1):end_col){
out_corr[[i]] <- data.frame(start_col = start_col, end_col = i, corr_val = as.numeric(cor.test(mydf[, start_col], mydf[, i])$estimate))
}
return(do.call("rbind", out_corr))
}
test_run <- my_correlator(df, 4)
head(test_run)
# start_col end_col corr_val
# 1 4 5 -0.027508521
# 2 4 6 0.100414199
# 3 4 7 0.036648608
# 4 4 8 -0.050845418
# 5 4 9 -0.003625019
# 6 4 10 -0.058172227
The function basically takes a data.frame as an input and spits out (as output) another data.frame containing correlations between a given column from the original data.frame against all subsequent columns. I do not know the structure of your data, and obviously, this function will fail if it runs into unexpected conditions (for instance, a column of characters in one of the columns).
Trying to using %in% operator in r to find an equivalent SAS Code as below:
If weather in (2,5) then new_weather=25;
else if weather in (1,3,4,7) then new_weather=14;
else new_weather=weather;
SAS code will produce variable "new_weather" with values 25, 14 and as defined in variable "weather".
R code:
GS <- function(df, col, newcol){
# Pass a dataframe, col name, new column name
df[newcol] = df[col]
df[df[newcol] %in% c(2,5)]= 25
df[df[newcol] %in% c(1,3,4,7)] = 14
return(df)
}
Result: output values of "col" and "newcol" are same, when passing a data frame through a function "GS". Syntax is not picking up the second or more values for a variable "newcol"? Appreciated your time explaining the reason and possible fix.
Is this what you are trying to do?
df <- data.frame(A=seq(1:4), B=seq(1:4))
add_and_adjust <- function(df, copy_column, new_column_name) {
df[new_column_name] <- df[copy_column] # make copy of column
df[,new_column_name] <- ifelse(df[,new_column_name] %in% c(2,5), 25, df[,new_column_name])
df[,new_column_name] <- ifelse(df[,new_column_name] %in% c(1,3,4,7), 14, df[,new_column_name])
return(df)
}
Usage:
add_and_adjust(df, 'B', 'my_new_column')
df[newcol] is a data frame (with one column), df[[newcol]] or df[, newcol] is a vector (just the column). You need to use [[ here.
You also need to be assigning the result to df[[newcol]], not to the whole df. And to be perfectly consistent and safe you should probably test the col values, not the newcol values.
GS <- function(df, col, newcol){
# Pass a dataframe, col name, new column name
df[[newcol]] = df[[col]]
df[[newcol]][df[[col]] %in% c(2,5)] = 25
df[[newcol]][df[[col]] %in% c(1,3,4,7)] = 14
return(df)
}
GS(data.frame(x = 1:7), "x", "new")
# x new
# 1 1 14
# 2 2 25
# 3 3 14
# 4 4 14
# 5 5 25
# 6 6 6
# 7 7 14
#user9231640 before you invest too much time in writing your own function you may want to explore some of the recode functions that already exist in places like car and Hmisc.
Depending on how complex your recoding gets your function will get longer and longer to check various boundary conditions or to change data types.
Just based upon your example you can do this in base R and it will be more self documenting and transparent at one level:
df <- data.frame(A=seq(1:30), B=seq(1:30))
df$my_new_column <- df$B
df$my_new_column <- ifelse(df$my_new_column %in% c(2,5), 25, df$my_new_column)
df$my_new_column <- ifelse(df$my_new_column %in% c(1,3,4,7), 14, df$my_new_column)
i have a code using R language, i want to sum all data frame (df$number is unlist result in 'res')
total result is = [1] 1 3 5 7 9 20 31 42
digits <- function(x){as.integer(substring(x, seq(nchar(x)), seq(nchar(x))))}
generated <- function(x){ x + sum(digits(x))}
digitadition <- function(x,N) { c(x, replicate(N-1, x <<- generated(x))) }
res <- NULL
for(i in 0:50){
for(j in 2:50){
tmp <- digitadition(i,j)
IND <- 50*(i-1) + (j-1) - (i-1) #to index results
res[IND] <- tmp[length(tmp)]
}
}
df <- data.frame(number = unlist(res), generator=rep(1:50, each=49), N=2:50)
total <- table(df$number)[as.numeric(names(table(df$number)))<=50]
setdiff(1:50, as.numeric(names(total)))
sum(total)
i'm using sum(total) but the result of summary is '155' it is not the right answer, cause the right answer is '118'
what the spesific code to sum the 'total'?
thank you.
I ran your code and I think you may be confused on what you want to sum.
You setdiff contains the values 1 3 5 7 9 20 31 42 which sum is 118.
So, if you do sum(setdiff(1:50, as.numeric(names(total)))), you'll get the 118 you are looking for.
Your total variable is different from this. Let me explain what you are doing and what I think you should do.
Your code: total <- table(df$number)[as.numeric(names(table(df$number)))<=50]]
When you table(), you get each unique value from the vector, and the number of how many times this number appears on your vector.
And when you get the names() of this table, you get each of these unique values as a character, that's why you are setting as.numeric.
But the function unique() do this job for you, he extracts uniques values from a vector.
Here's what you can do: total <- unique(df$number[which(df$number <= 50)])
Where which() get the ID's of values <= 50, and unique extracts unique values of these ID's.
And finally: sum(setdiff(1:50, total)) that sums all the values from 1 to 50 that are not in your total vector.
And in my opinion, sum(setdiff(total, 1:50)) its more intuitive.
I want to subset a dataframe by applying two conditions to it. When I attach the dataframe, apply the first condition, detach the dataframe, attach it again, apply the second condition, and detach again, I get the expected result, a dataframe with 9 observations.
Of course, you wouldn't normally detach/attach before applying the second condition. So I attach, apply the two conditions after one another, and then detach. But the result is different now: It's a dataframe with 24 observations. All but 5 of these observations consist exclusively of NA-values.
I know there's lots of advice against using attach, and I understand the point that it's dangerous, because it's easy to loose track of an attach statement still being active. My point here is a different one; I see a behaviour in attach that I just can't understand. I'm using R Studio 0.99.465 with 64-bit-R 3.2.1.
So here's the code, first the version that is clumsy, but produces the correct result (df with 9 observations, all non-NA):
df <- expand.grid(early_vvl=c(0,1), inter_churn=c(0,1), inter_new_contract=c(0,1), late_vvl=c(0,1), late_no_reaction=c(0,1))
attach(df)
df <- df[(1-early_vvl) >= inter_churn + inter_new_contract + late_vvl,]
detach(df)
attach(df)
df <- df[early_vvl <= late_no_reaction,]
detach(df)
Now the one that produces the dataframe with 24 observations, most of which consist only of NA values:
df <- expand.grid(early_vvl=c(0,1), inter_churn=c(0,1), inter_new_contract=c(0,1), late_vvl=c(0,1), late_no_reaction=c(0,1))
attach(df)
df <- df[(1-early_vvl) >= inter_churn + inter_new_contract + late_vvl,]
df <- df[early_vvl <= late_no_reaction,]
detach(df)
I'm puzzled. Does anybody understand why the second version produces a different result?
Have a look at what happens here:
attach(df)
df <- df[(1-early_vvl) >= inter_churn + inter_new_contract + late_vvl,]
length(early_vvl <= late_no_reaction)
## [1] 32
df <- df[early_vvl <= late_no_reaction,]
detach(df)
So your logical vector early_vvl <= late_no_reaction still uses the original df, the one that you attached. When you subset the data.frame the second time, the logical is longer than the data.frame has rows and it behaves like this:
df <- data.frame(x=1:5, y = letters[1:5])
df[rep(c(TRUE, FALSE), 5), ]
## x y
## 1 1 a
## 3 3 c
## 5 5 e
## NA NA <NA>
## NA.1 NA <NA>
You could just use & to avoid the problem:
df <- expand.grid(early_vvl=c(0,1), inter_churn=c(0,1), inter_new_contract=c(0,1), late_vvl=c(0,1), late_no_reaction=c(0,1))
attach(df)
df <- df[(1-early_vvl) >= inter_churn + inter_new_contract + late_vvl & early_vvl <= late_no_reaction,]
detach(df)