I have data from several hundred participants who each provided between 1 and 6 sentences. They then rated their sentence(s) on 4 dimensions, as did two external raters.
I'd like to create a table, grouped by participant, with columns showing these values:
Participants' rate of agreement with rater 1 (par1), with rater 2 (par2) and overall (paro)
Participants' rate of agreement for each dimension with rater 1 (pad1.1, pad2.1 etc.), with rater 2 (pad1.2, pad2.2 etc.) and overall (pad1.o, pad2.o etc.)
Mean difference in rating between participant and rater 1 (mdrp1), rater 2 (mdrp2) and both raters (mdrpo)
Mean difference in rating for each dimension between participant and rater 1 (mdr1p1, mdr2p1 etc.), rater 2 (mdr1p2, mdr2p2 etc.) and both raters (mdr1po, mdr2po etc.)
(So with 4 dimensions there should be 30 values per participant)
Due to the size and structure of the data, I'm not sure where to start on this. I'm guessing that a loop would be necessary, but I've struggled to get my head around how to do that as well.
For agreement I'm considering adding TRUE/FALSE variables and then replacing them with 1 and 0 to eventually calculate agreement:
df <- df %>% mutate(par1 = (df$d1 == df$r1.1)
df <- df %>% mutate(par2 = (df$d1 == df$r2.1)
df <- df %>% mutate(paro = (df$d1 == df$r1.1 & df$d1 == df$r2.1)
And similarly for mean differences, adding variables with rating difference for each dimension...
df <- df %>% mutate(mdr1p1 = (df$d1 - df$r1.1))
df <- df %>% mutate(mdr1p2 = (df$d1 - df$r2.1))
df <- df %>% mutate(mdr1po = (df$d1 - ((df$r1.1 + df$r2.1)/2)))
...But these seem to be quite inefficient approaches!
My data looks like this:
ID Ans d1 d2 d3 d4 r1.1 r1.2 r1.3 r1.4 r2.1 r2.2 r2.3 r2.4
1 53 abc 3 3 3 3 3 2 4 3 3 2 4 3
2 a4 def 3 3 3 3 3 1 2 3 3 1 3 3
3 a4 ghi 4 4 4 4 3 2 5 1 3 1 5 2
4 hj jkl 3 3 3 3 3 1 3 3 3 1 5 3
5 32 mno 2 3 3 3 3 1 3 2 3 1 3 3
6 32 pqr 3 3 3 2 3 2 5 3 4 2 3 3
ID = participant
Ans = participants' written answer
d = dimension rated by participant
r1 = dimensions rated by external rater 1
r2 = dimensions rated by external rater 2
Example data:
structure(list(ID = c(1L, 2L, 2L, 3L, 4L, 4L, 5L),
Ans = c("abc", "def", "ghi", "jkl", "mno", "pqr", "stu"),
d1 = c(3L, 3L, 4L, 3L, 2L, 3L, 3L), d2 = c(3L, 3L, 4L, 3L, 3L, 3L, 1L),
d3 = c(3L, 3L, 4L, 3L, 3L, 3L, 1L), d4 = c(3L, 3L, 4L, 3L, 3L, 2L, 3L),
r1.1 = c(3L, 3L, 3L, 3L, 3L, 3L, 3L), r1.2 = c(2L, 1L, 2L, 1L, 1L, 2L, 3L),
r1.3 = c(4L, 2L, 5L, 3L, 3L, 5L, 3L), r1.4 = c(3L, 3L, 1L, 3L, 2L, 3L, 2L),
r2.1 = c(3L, 3L, 3L, 3L, 3L, 4L, 3L), r2.2 = c(2L, 1L, 1L, 1L, 1L, 2L, 1L),
r2.3 = c(4L, 3L, 5L, 5L, 3L, 3L, 5L), r2.4 = c(3L, 3L, 2L, 3L, 3L, 3L, 2L)),
row.names = c(1L, 2L, 3L, 4L, 5L, 6L), class = "data.frame")
Related
How can i remove rows with any same value that is in another column of the same row? For example,
df<-structure(list(V1 = c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L), V2 = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L,
2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L), V3 = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L)), row.names = c(NA, -27L
), class = "data.frame")
##Top three rows
V1 V2 V3
1 1 1 1
2 2 1 1
3 3 1 1
4 1 2 1
5 2 2 1
6 3 2 1
7 1 3 1
8 2 3 1
In the following case (only showing 8 rows), I would remove every row accept rows 6 and 8 since they do not have any duplicate values in any column of the same row. I'm preferably looking for a data.table solution since I have a much larger dataframe.
You may use anyDuplicated for each row.
library(data.table)
setDT(df)
df[apply(df, 1, anyDuplicated) == 0]
# V1 V2 V3
#1: 3 2 1
#2: 2 3 1
#3: 3 1 2
#4: 1 3 2
#5: 2 1 3
#6: 1 2 3
An option using pairwise combn on the columns to check if there are equal values
df[!Reduce(`|`, combn(df, 2, FUN = function(x)
x[[1]] == x[[2]], simplify = FALSE))]
V1 V2 V3
1: 3 2 1
2: 2 3 1
3: 3 1 2
4: 1 3 2
5: 2 1 3
6: 1 2 3
How would you go about creating the graph below in R? I want to show the duration of different treatments for different patients.
Mock data here:
Start Day Stop Day
Patient 1 Drug 1 1 3
Drug 2 2 5
Drug 3 3 8
Patient 2 Drug 1 2 4
Drug 2 2 5
Drug 3 1 6
Patient 3 Drug 1 4 7
Drug 2 3 8
Drug 3 5 6
Your graph can be generated using geom_segment in the ggplot2 package:
df <- structure(list(Patient = structure(c(1L, 1L, 1L, 2L, 2L, 2L,
3L, 3L, 3L), .Label = c("Patient1", "Patient2", "Patient3"), class = "factor"),
Drug = structure(c(1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L), .Label = c("Drug1",
"Drug2", "Drug3"), class = "factor"), StartDay = c(1L, 2L,
3L, 2L, 2L, 1L, 4L, 3L, 5L), StopDay = c(3L, 5L, 8L, 4L,
5L, 6L, 7L, 8L, 6L)), .Names = c("Patient", "Drug", "StartDay",
"StopDay"), class = "data.frame", row.names = c(NA, -9L))
df$Drug <- factor(df$Drug, levels(df$Drug)[c(3,2,1)])
library(ggplot2)
ggplot(data=df, aes(color=Drug))+
geom_segment(aes(x=StartDay, xend=StopDay, y=Drug, yend=Drug),lwd=12)+
facet_grid(Patient~.)+xlab("Days")
Suppose I have data which looks like this
Id Name Price sales Profit Month Category Mode Supplier
1 A 2 5 8 1 X K John
1 A 2 6 9 2 X K John
1 A 2 5 8 3 X K John
2 B 2 4 6 1 X L Sam
2 B 2 3 4 2 X L Sam
2 B 2 5 7 3 X L Sam
3 C 2 5 11 1 X M John
3 C 2 5 11 2 X L John
3 C 2 5 11 3 X K John
4 D 2 8 10 1 Y M John
4 D 2 8 10 2 Y K John
4 D 2 5 7 3 Y K John
5 E 2 5 9 1 Y M Sam
5 E 2 5 9 2 Y L Sam
5 E 2 5 9 3 Y M Sam
6 F 2 4 7 1 Z M Kyle
6 F 2 5 8 2 Z L Kyle
6 F 2 5 8 3 Z M Kyle
if I apply table function, it will just combines are the rows and result will be
K L M
X 4 4 1
Y 2 1 3
Z 0 1 2
Now what if I want not the sum of all rows but only sum of those rows with Unique Id
so it looks like
K L M
X 2 2 1
Y 1 1 2
Z 0 1 1
Thanks
If df is your data.frame:
# Subset original data.frame to keep columns of interest
df1 <- df[,c("Id", "Category", "Mode")]
# Remove duplicated rows
df1 <- df1[!duplicated(df1),]
# Create table
with(df1, table(Category, Mode))
# Mode
# Category K L M
# X 2 2 1
# Y 1 1 2
# Z 0 1 1
Or in one line using unique
table(unique(df[c("Id", "Category", "Mode")])[-1])
df <- structure(list(Id = c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L,
4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L), Name = structure(c(1L, 1L, 1L,
2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L), .Label = c("A",
"B", "C", "D", "E", "F"), class = "factor"), Price = c(2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), sales = c(5L, 6L, 5L, 4L, 3L, 5L, 5L, 5L, 5L, 8L, 8L, 5L,
5L, 5L, 5L, 4L, 5L, 5L), Profit = c(8L, 9L, 8L, 6L, 4L, 7L, 11L,
11L, 11L, 10L, 10L, 7L, 9L, 9L, 9L, 7L, 8L, 8L), Month = c(1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("X", "Y", "Z"
), class = "factor"), Mode = structure(c(1L, 1L, 1L, 2L, 2L,
2L, 3L, 2L, 1L, 3L, 1L, 1L, 3L, 2L, 3L, 3L, 2L, 3L), .Label = c("K",
"L", "M"), class = "factor"), Supplier = structure(c(1L, 1L,
1L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L, 2L, 2L, 2L
), .Label = c("John", "Kyle", "Sam"), class = "factor")), .Names = c("Id",
"Name", "Price", "sales", "Profit", "Month", "Category", "Mode",
"Supplier"), class = "data.frame", row.names = c(NA, -18L))
We can try
library(data.table)
dcast(unique(setDT(df1[c('Category', 'Mode', 'Id')])),
Category~Mode, value.var='Id', length)
# Category K L M
#1: X 2 2 1
#2: Y 1 1 2
#3: Z 0 1 1
Or with dplyr
library(dplyr)
df1 %>%
distinct(Id, Category, Mode) %>%
group_by(Category, Mode) %>%
tally() %>%
spread(Mode, n, fill=0)
# Category K L M
# (chr) (dbl) (dbl) (dbl)
#1 X 2 2 1
#2 Y 1 1 2
#3 Z 0 1 1
Or as #David Arenburg suggested, a variant of the above is
df1 %>%
distinct(Id, Category, Mode) %>%
select(Category, Mode) %>%
table()
I have two data frames
df1 <- structure(list(g1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), g2 = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), val1 = 1:20, val2 = c(1L, 2L, 3L, 4L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 4L, 1L, 2L, 3L)), .Names = c("g1", "g2", "val1", "val2"), row.names = c(NA, -20L), class = "data.frame")
df2 <- structure(list(g1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("A", "B"), class = "factor"), g2 = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L), .Label = c("a", "b", "c"), class = "factor"), val3 = c(5L, 6L, 7L, 3L, 4L, 5L, 2L, 3L, 4L, 8L, 9L, 10L, 4L, 5L, 6L, 5L, 6L)), .Names = c("g1", "g2", "val3"), row.names = c(NA, -17L), class = "data.frame")
> df1
g1 g2 val1 val2
1 A a 1 1
2 A a 2 2
3 A a 3 3
4 A a 4 4
5 A b 5 1
6 A b 6 2
7 A b 7 3
8 A c 8 1
9 A c 9 2
10 A c 10 3
11 B a 11 1
12 B a 12 2
13 B a 13 3
14 B b 14 1
15 B b 15 2
16 B b 16 3
17 B b 17 4
18 B c 18 1
19 B c 19 2
20 B c 20 3
> df2
g1 g2 val3
1 A a 5
2 A a 6
3 A a 7
4 A b 3
5 A b 4
6 A b 5
7 A c 2
8 A c 3
9 B c 4
10 B a 8
11 B a 9
12 B a 10
13 B b 4
14 B b 5
15 B b 6
16 B c 5
17 B c 6
My aim is to rescale df1$val2 to take values between the min and max values of df2$val3 within the respective groups.
I tried this:
library(dplyr)
df1 <- df1 %.% group_by(g1, g2) %.% mutate(rescaled=(max(df2$val3)-min(df2$val3))*(val2-min(val2))/(max(val2)-min(val2))+min(df2$val3))
But the output is different from what I expect. The problem is that I can neither cbind nor merge the two data frames due to their different lengths. Any hints?
Does this work?
library(plyr)
df3 <- ddply(df2, .(g1, g2), summarize, max.val=max(val3), min.val=min(val3))
merged.df <- merge(df1, df3, by=c("g1", "g2"), all.x=TRUE)
## Now rescale merged.df$val2 as desired
I am having some trouble using the ddply function from the plyr package. I am trying to summarise the following data with counts and proportions within each group. Here's my data:
structure(list(X5employf = structure(c(1L, 3L, 1L, 1L, 1L, 3L,
1L, 1L, 1L, 3L, 1L, 1L, 1L, 2L, 2L, 3L, 3L, 3L, 1L, 2L, 2L, 2L,
2L, 2L, 1L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 3L, 1L,
3L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 3L, 3L,
3L, 3L, 1L), .Label = c("increase", "decrease", "same"), class = "factor"),
X5employff = structure(c(2L, 6L, NA, 2L, 4L, 6L, 5L, 2L,
2L, 8L, 2L, 2L, 2L, 7L, 7L, 8L, 11L, 7L, 2L, 8L, 8L, 11L,
7L, 6L, 2L, 5L, 2L, 8L, 7L, 7L, 7L, 8L, 6L, 7L, 5L, 5L, 7L,
2L, 6L, 7L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 2L, 5L, 2L, 2L,
2L, 5L, 12L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 2L, 5L, 2L,
13L, 9L, 9L, 9L, 7L, 8L, 5L), .Label = c("", "1", "1 and 8",
"2", "3", "4", "5", "6", "6 and 7", "6 and 7 ", "7", "8",
"1 and 8"), class = "factor")), .Names = c("X5employf", "X5employff"
), row.names = c(NA, 73L), class = "data.frame")
And here's my call using ddply:
ddply(kano_final, .(X5employf, X5employff), summarise, n=length(X5employff), prop=(n/sum(n))*100)
This gives me the counts of each instance of X5employff correctly, but but seems as though the proportion is being calculated across each row and not within each level of the factor X5employf as follows:
X5employf X5employff n prop
1 increase 1 26 100
2 increase 2 1 100
3 increase 3 15 100
4 increase 1 and 8 1 100
5 increase <NA> 1 100
6 decrease 4 1 100
7 decrease 5 5 100
8 decrease 6 2 100
9 decrease 7 1 100
10 decrease 8 1 100
11 same 4 4 100
12 same 5 6 100
13 same 6 5 100
14 same 6 and 7 3 100
15 same 7 1 100
When manually calculating the proportions within each group I get this:
X5employf X5employff n prop
1 increase 1 26 59.09
2 increase 2 1 2.27
3 increase 3 15 34.09
4 increase 1 and 8 1 2.27
5 increase <NA> 1 2.27
6 decrease 4 1 10.00
7 decrease 5 5 50.00
8 decrease 6 2 20.00
9 decrease 7 1 10.00
10 decrease 8 1 10.00
11 same 4 4 21.05
12 same 5 6 31.57
13 same 6 5 26.31
14 same 6 and 7 3 15.78
15 same 7 1 5.26
As you can see the sum of proportions in each level of factor X5employf equals 100.
I know this is probably ridiculously simple, but I can't seem to get my head around it despite reading all sorts of similar posts. Can anyone help with this and my understanding of how the summarise function works?!
Many, many thanks
Marty
You cannot do it in one ddply call because what gets passed to each summarize call is a subset of your data for a specific combination of your group variables. At this lowest level, you do not have access to that intermediate level sum(n). Instead, do it in two steps:
kano_final <- ddply(kano_final, .(X5employf), transform,
sum.n = length(X5employf))
ddply(kano_final, .(X5employf, X5employff), summarise,
n = length(X5employff), prop = n / sum.n[1] * 100)
Edit: using a single ddply call and using table as you hinted towards:
ddply(kano_final, .(X5employf), summarise,
n = Filter(function(x) x > 0, table(X5employff, useNA = "ifany")),
prop = 100* prop.table(n),
X5employff = names(n))
I'd add here an example with dplyr which makes it quite easily in one step, with a short-code and easy-to-read syntax.
d is your data.frame
library(dplyr)
d%.%
dplyr:::group_by(X5employf, X5employff) %.%
dplyr:::summarise(n = length(X5employff)) %.%
dplyr:::mutate(ngr = sum(n)) %.%
dplyr:::mutate(prop = n/ngr*100)
will result in
Source: local data frame [15 x 5]
Groups: X5employf
X5employf X5employff n ngr prop
1 increase 1 26 44 59.090909
2 increase 2 1 44 2.272727
3 increase 3 15 44 34.090909
4 increase 1 and 8 1 44 2.272727
5 increase NA 1 44 2.272727
6 decrease 4 1 10 10.000000
7 decrease 5 5 10 50.000000
8 decrease 6 2 10 20.000000
9 decrease 7 1 10 10.000000
10 decrease 8 1 10 10.000000
11 same 4 4 19 21.052632
12 same 5 6 19 31.578947
13 same 6 5 19 26.315789
14 same 6 and 7 3 19 15.789474
15 same 7 1 19 5.263158
What you apparently want to do is to find out the proportions of X5employff for every value of X5employf. However, you don't tell ddply that X5employf and X5employff are different; to ddply, these two variables are just two variables to split up the data. Also, since there is one observation per line, i.e. count = 1 for every line of the data, the length of each (X5employf, X5employff) combination equals the sum of each (X5employf, X5employff) combination.
The simplest "plyr way" to solve your problem that I can think of is the following:
result <- ddply(kano_final, .(X5employf, X5employff), summarise, n=length(X5employff), drop=FALSE)
n <- result$n
n2 <- ddply(kano_final, .(X5employf), summarise, n=length(X5employff))$n
result <- data.frame(result, prop=n/rep(n2, each=13)*100)
You can also use good old xtabs:
a <- xtabs(~X5employf + X5employff, kano_final)
b <- xtabs(~X5employf, kano_final)
a/matrix(b, nrow=3, ncol=ncol(a))