I'm having a hard time to describe this so it's best explained with an example (as can probably be seen from the poor question title).
Using dplyr I have the result of a group_by and summarize I have a data frame that I want to do some further manipulation on by factor.
As an example, here's a data frame that looks like the result of my dplyr operations:
> df <- data.frame(run=as.factor(c(rep(1,3), rep(2,3))),
group=as.factor(rep(c("a","b","c"),2)),
sum=c(1,8,34,2,7,33))
> df
run group sum
1 1 a 1
2 1 b 8
3 1 c 34
4 2 a 2
5 2 b 7
6 2 c 33
I want to divide sum by a value that depends on run. For example, if I have:
> total <- data.frame(run=as.factor(c(1,2)),
total=c(45,47))
> total
run total
1 1 45
2 2 47
Then my final data frame will look like this:
> df
run group sum percent
1 1 a 1 1/45
2 1 b 8 8/45
3 1 c 34 34/45
4 2 a 2 2/47
5 2 b 7 7/47
6 2 c 33 33/47
Where I manually inserted the fraction in the percent column by hand to show the operation I want to do.
I know there is probably some dplyr way to do this with mutate but I can't seem to figure it out right now. How would this be accomplished?
(In base R)
You can use total as a look-up table where you get a total for each run of df :
total[df$run,'total']
[1] 45 45 45 47 47 47
And you simply use it to divide the sum and assign the result to a new column:
df$percent <- df$sum / total[df$run,'total']
run group sum percent
1 1 a 1 0.02222222
2 1 b 8 0.17777778
3 1 c 34 0.75555556
4 2 a 2 0.04255319
5 2 b 7 0.14893617
6 2 c 33 0.70212766
If your "run" values are 1,2...n then this will work
divisor <- c(45,47) # c(45,47,...up to n divisors)
df$percent <- df$sum/divisor[df$run]
first you want to merge in the total values into your df:
df2 <- merge(df, total, by = "run")
then you can call mutate:
df2 %<>% mutate(percent = sum / total)
Convert to data.table in-place, then merge and add new column, again in-place:
library(data.table)
setDT(df)[total, on = 'run', percent := sum/total]
df
# run group sum percent
#1: 1 a 1 0.02222222
#2: 1 b 8 0.17777778
#3: 1 c 34 0.75555556
#4: 2 a 2 0.04255319
#5: 2 b 7 0.14893617
#6: 2 c 33 0.70212766
Related
I am having the hardest time coming up with a code that lets me match a topic (Column B) to a name (Column A) and create a frequency column for the times B has matched with A (or how many times both have appeared together). Col A and B are codes for longer names.
I thought maybe using the count function from plyr but cant make it work. Maybe you can give me an idea of what I could use for a code?
For example I have a table:
**Col A
Col B**
1
38
1
6
1
38
2
38
2
7
2
7
2
8
2
7
The result that I am looking for is
**Col A
Col B
freq**
1
38
2
1
6
1
2
38
1
2
7
3
2
8
1
So the number 38 has appeared in "1" two times. 6 has appeared one time. and so on.
I have 600 rows of data and cant come up with a useful or even a close call code.
Thank you so much for your help!
Summarise and count using dplyr:
library(dplyr)
df2 <- df %>%
group_by(col1, col2) %>%
summarise(count = n()) %>%
ungroup()
returns:
col1 col2 count
<dbl> <dbl> <int>
1 1 6 1
2 1 38 2
3 2 7 3
4 2 8 1
5 2 38 1
I want to populate an existing column with values that continually add onto the row above.
This is easy in Excel, but I haven't figured out a good way to automate it in R.
If we had 2 columns in Excel, A and B, we want cell B2 to =B1+A2, and cell B3 would = B2+A3. How can I do this in R?
#example dataframe
df <- data.frame(A = 0:9, B = c(50,0,0,0,0,0,0,0,0,0))
#desired output
desired <- data.frame(A = 0:9, B = c("NA",51,53,56,60,65,71,78,86,95))
I tried using the lag() function, but it didn't give the correct output.
df <- df %>%
mutate(B = B + lag(A))
So I made a for loop that works, but I feel like there's a better solution.
for(i in 2:nrow(df)){
df$B[i] <- df$B[i-1] + df$A[i]
}
Eventually, I want to iterate this function over every n rows of the whole dataframe, essentially so the summation resets every n rows. (any tips on how to do that would be greatly appreciated!)
This might be close to what you need, and uses tidyverse. Specifically, it uses accumulate from purrr.
Say you want to reset to zero every n rows, you can also use group_by ahead of time.
It was not entirely clear how you'd like to handle the first row; here, it will just use the first B value and ignore the first A value, which looked similar to what you had in the post.
n <- 5
library(tidyverse)
df %>%
group_by(grp = ceiling(row_number() / n)) %>%
mutate(B = accumulate(A[-1], sum, .init = B[1]))
Output
A B grp
<int> <dbl> <dbl>
1 0 50 1
2 1 51 1
3 2 53 1
4 3 56 1
5 4 60 1
6 5 0 2
7 6 6 2
8 7 13 2
9 8 21 2
10 9 30 2
cumsum() can be used to get the result you need.
df$B <- cumsum(df$B + df$A)
df
A B
1 0 50
2 1 51
3 2 53
4 3 56
5 4 60
6 5 65
7 6 71
8 7 78
9 8 86
10 9 95
I have a long data frame with players' decisions who worked in groups.
I need to convert the data in such a way that each row (individual observation) would contain all group members decisions (so we basically can see whether they are interdependent).
Let's say the generating code is:
group_id <- c(rep(1, 3), rep(2, 3))
player_id <- c(rep(seq(1, 3), 2))
player_decision <- seq(10,60,10)
player_contribution <- seq(6,1,-1)
df <-
data.frame(group_id, player_id, player_decision, player_contribution)
So the initial data looks like:
group_id player_id player_decision player_contribution
1 1 1 10 6
2 1 2 20 5
3 1 3 30 4
4 2 1 40 3
5 2 2 50 2
6 2 3 60 1
But I need to convert it to wide per each group, but only for some of these variables, (in this example specifically for player_contribution, but in such a way that the rest of the data remains. So the head of the converted data would be:
data.frame(group_id=c(1,1),
player_id=c(1,2),
player_decision=c(10,20),
player_1_contribution=c(6,6),
player_2_contribution=c(5,5),
player_3_contribution=c(4,6)
)
group_id player_id player_decision player_1_contribution player_2_contribution player_3_contribution
1 1 1 10 6 5 4
2 1 2 20 6 5 6
I suspect I need to group_by in dplyr and then somehow gather per group but only for player_contribution (or a vector of variables). But I really have no clue how to approach it. Any hints would be welcome!
Here is solution using tidyr and dplyr.
Make a dataframe with the columns for the players contributions. Then join this dataframe back onto the columns of interest from the original Dataframe.
library(tidyr)
library(dplyr)
wide<-pivot_wider(df, id_cols= - player_decision,
names_from = player_id,
values_from = player_contribution,
names_prefix = "player_contribution_")
answer<-left_join(df[, c("group_id", "player_id", "player_decision") ], wide)
answer
group_id player_id player_decision player_contribution_1 player_contribution_2 player_contribution_3
1 1 1 10 6 5 4
2 1 2 20 6 5 4
3 1 3 30 6 5 4
4 2 1 40 3 2 1
5 2 2 50 3 2 1
6 2 3 60 3 2 1
I have a data frame which contains data relating to a score of different events. There can be a number of scoring events for one game. What I would like to do, is to subset the occasions when the score goes above 5 or below -5. I would also like to get the last row for each ID. So for each ID, I would have one or more rows depending on whether the score goes above 5 or below -5. My actual data set contains many other columns of information, but if I learn how to do this then I'll be able to apply it to anything else that I may want to do.
Here is a data set
ID Score Time
1 0 0
1 3 5
1 -2 9
1 -4 17
1 -7 31
1 -1 43
2 0 0
2 -3 15
2 0 19
2 4 25
2 6 29
2 9 33
2 3 37
3 0 0
3 5 3
3 2 11
So for this data set, I would hopefully get this output:
ID Score Time
1 -7 31
1 -1 43
2 6 29
2 9 33
2 3 37
3 2 11
So at the very least, for each ID there will be one line printed with the last score for that ID regardless of whether the score goes above 5 or below -5 during the event( this occurs for ID 3).
My attempt can subset when the value goes above 5 or below -5, I just don't know how to write code to get the last line for each ID:
Data[Data$Score > 5 | Data$Score < -5]
Let me know if you need anymore information.
You can use rle to grab the last row for each ID. Check out ?rle for more information about this useful function.
Data2 <- Data[cumsum(rle(Data$ID)$lengths), ]
Data2
# ID Score Time
#6 1 -1 43
#13 2 3 37
#16 3 2 11
To combine the two conditions, use rbind.
Data2 <- rbind(Data[Data$Score > 5 | Data$Score < -5, ], Data[cumsum(rle(Data$ID)$lengths), ])
To get rid of rows that satisfy both conditions, you can use duplicated and rownames.
Data2 <- Data2[!duplicated(rownames(Data2)), ]
You can also sort if desired, of course.
Here's a go at it in data.table, where df is your original data frame.
library(data.table)
setDT(df)
df[df[, c(.I[!between(Score, -5, 5)], .I[.N]), by = ID]$V1]
# ID Score Time
# 1: 1 -7 31
# 2: 1 -1 43
# 3: 2 6 29
# 4: 2 9 33
# 5: 2 3 37
# 6: 3 2 11
We are grouping by ID. The between function finds the values between -5 and 5, and we negate that to get our desired values outside that range. We then use a .I subset to get the indices per group for those. Then .I[.N] gives us the row number of the last entry, per group. We use the V1 column of that result as our row subset for the entire table. You can take unique values if unique rows are desired.
Note: .I[c(which(!between(Score, -5, 5)), .N)] could also be used in the j entry of the first operation. Not sure if it's more or less efficient.
Addition: Another method, one that uses only logical values and will never produce duplicate rows in the output, is
df[df[, .I == .I[.N] | !between(Score, -5, 5), by = ID]$V1]
# ID Score Time
# 1: 1 -7 31
# 2: 1 -1 43
# 3: 2 6 29
# 4: 2 9 33
# 5: 2 3 37
# 6: 3 2 11
Here is another base R solution.
df[as.logical(ave(df$Score, df$ID,
FUN=function(i) abs(i) > 5 | seq_along(i) == length(i))), ]
ID Score Time
5 1 -7 31
6 1 -1 43
11 2 6 29
12 2 9 33
13 2 3 37
16 3 2 11
abs(i) > 5 | seq_along(i) == length(i) constructs a logical vector that returns TRUE for each element that fits your criteria. ave applies this function to each ID. The resulting logical vector is used to select the rows of the data.frame.
Here's a tidyverse solution. Not as concise as some of the above, but easier to follow.
library(tidyverse)
lastrows <- Data %>% group_by(ID) %>% top_n(1, Time)
scorerows <- Data %>% group_by(ID) %>% filter(!between(Score, -5, 5))
bind_rows(scorerows, lastrows) %>% arrange(ID, Time) %>% unique()
# A tibble: 6 x 3
# Groups: ID [3]
# ID Score Time
# <int> <int> <int>
# 1 1 -7 31
# 2 1 -1 43
# 3 2 6 29
# 4 2 9 33
# 5 2 3 37
# 6 3 2 11
I am essentially trying to get disorganized data into long form for linear modeling.
I have 2 data.frames "rec" and "book"
Each row in "book" needs to be pasted onto the end of several of the rows of "rec" according to two variables in the row: "MRN" and "COURSE" which match.
I have tried the following and variations thereon to no avail:
i=1
newlist=list()
colnames(newlist)=colnames(book)
for ( i in 1:dim(rec)[1]) {
mrn=as.numeric(as.vector(rec$MRN[i]));
course=as.character(rec$COURSE[i]);
get.vector<-as.vector(((as.numeric(as.vector(book$MRN))==mrn) & (as.character(book$COURSE)==course)))
newlist[i]<-book[get.vector,]
i=i+1;
}
If anyone has any suggestions on
1)getting this to work
2) making it more elegant (or perhaps just less clumsy)
If I have been unclear in any way I beg your pardons.
I do understand I haven't combined any data above, I think if I can generate a long-format data.frame I can combine them all on my own
Sounds like you need to merge the two data-frames. Try this:
merge(rec, book, by = c('MRN', 'COURSE'))
and do read the help for merge (by doing ?merge at the R console) for more options on how to merge these.
I've created a simple example that may help you. In my case i wanted to paste the 'value' column from df1 in each row of df2, according to variables x1 and x2:
df1 <- read.table(textConnection("
x1 x2 value
1 2 12
1 3 56
2 1 35
2 2 68
"),header=T)
df2 <- read.table(textConnection("
test x1 x2
1 1 2
2 1 3
3 2 1
4 2 2
5 1 2
6 1 3
7 2 1
"),header=T)
library(sqldf)
sqldf("select df2.*, df1.value from df2 join df1 using(x1,x2)")
test x1 x2 value
1 1 1 2 12
2 2 1 3 56
3 3 2 1 35
4 4 2 2 68
5 5 1 2 12
6 6 1 3 56
7 7 2 1 35