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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")
This question already has answers here:
How to reshape data from long to wide format
(14 answers)
Closed 4 years ago.
I have a data like this
df<- structure(list(V1 = structure(c(10L, 4L, 7L, 5L, 3L, 1L, 8L,
11L, 12L, 9L, 2L, 6L), .Label = c("BRA_AC_A6IX", "BRA_BH_A18F",
"BRA_BH_A18V", "BRA_BH_A1ES", "BRA_BH_A1FE", "BRA_BH_A6R8", "BRA_E2_A15A",
"BRA_E2_A15K", "BRA_E2_A1B4", "BRA_EM_A15E", "BRA_LQ_A4E4", "BRA_OK_A5Q2"
), class = "factor"), V2 = structure(c(2L, 3L, 5L, 3L, 3L, 5L,
3L, 4L, 1L, 4L, 2L, 2L), .Label = c("Level ii", "Level iia",
"Level iib", "Level iiia", "Level iiic"), class = "factor"),
V3 = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
3L, 4L), .Label = c("amira", "boro", "car", "dim"), class = "factor")), class = "data.frame", row.names = c(NA,
-12L))
I am trying to categorize them based on two column
I can do the following
library(dplyr)
df %>%
+ group_by(V2) %>%
+ summarise(no_rows = length(V2))
# A tibble: 5 x 2
V2 no_rows
<fct> <int>
1 Level ii 1
2 Level iia 3
3 Level iib 4
4 Level iiia 2
5 Level iiic 2
but I want to have an output like this
Amira Boro Car dim
Level ii 1
Level iia 1 1 1
Level iib 1 1 1
Level iiia 1
Level iiic 1 1
How about
library(reshape2)
df1 <- df[,-1]
table(melt(df1, id.var="V2")[-2])
Here is a tidyverse method. I am imputing that you actually want the counts, but if you want just the presence/absence that is easy to add.
df <- structure(list(V1 = structure(c(10L, 4L, 7L, 5L, 3L, 1L, 8L, 11L, 12L, 9L, 2L, 6L), .Label = c("BRA_AC_A6IX", "BRA_BH_A18F", "BRA_BH_A18V", "BRA_BH_A1ES", "BRA_BH_A1FE", "BRA_BH_A6R8", "BRA_E2_A15A", "BRA_E2_A15K", "BRA_E2_A1B4", "BRA_EM_A15E", "BRA_LQ_A4E4", "BRA_OK_A5Q2"), class = "factor"), V2 = structure(c(2L, 3L, 5L, 3L, 3L, 5L, 3L, 4L, 1L, 4L, 2L, 2L), .Label = c("Level ii", "Level iia", "Level iib", "Level iiia", "Level iiic"), class = "factor"), V3 = structure(c(1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 4L), .Label = c("amira", "boro", "car", "dim"), class = "factor")), class = "data.frame", row.names = c(NA, -12L))
library(tidyverse)
df %>%
select(-V1) %>%
count(V2, V3) %>%
spread(V3, n, fill = 0L)
#> # A tibble: 5 x 5
#> V2 amira boro car dim
#> <fct> <int> <int> <int> <int>
#> 1 Level ii 0 0 1 0
#> 2 Level iia 1 0 1 1
#> 3 Level iib 1 2 1 0
#> 4 Level iiia 0 0 2 0
#> 5 Level iiic 1 1 0 0
Created on 2018-05-23 by the reprex package (v0.2.0).
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$val2 as desired
Is it possible to return ddply results for only certain values of the splitting variable? For example, with the dataframe example:
example <- structure(list(shape = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L), .Label = c("circle", "square", "triangle"
), class = "factor"), property = structure(c(1L, 3L, 2L, 1L,
2L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L), .Label = c("color",
"intensity", "size"), class = "factor"), value = structure(c(5L,
2L, 1L, 5L, 4L, 1L, 5L, 6L, 6L, 7L, 4L, 3L, 6L, 5L), .Label = c("3",
"5", "6", "7", "blue", "green", "red"), class = "factor")), .Names = c("shape",
"property", "value"), class = "data.frame", row.names = c(NA,
-14L))
which looks like this
shape property value
1 circle color blue
2 circle size 5
3 circle intensity 3
4 circle color blue
5 square intensity 7
6 square size 3
7 square color blue
8 square color green
9 square color green
10 triangle color red
11 triangle intensity 7
12 triangle size 6
13 triangle color green
14 triangle color blue
I want to return a dataframe containing the number of each shape that has a certain color, which would be something like this:
shape property blue green red
1 circle color 2 0 0
2 square color 1 2 0
3 triangle color 1 1 1
However, I can't seem to get this to return properly! I've gotten part of the way using something like this:
ColorSummary <- ddply(example,.(shape,property="color"), function(example) summary(example$value))
But this is returning a dataframe with columns for all of the other unique value (from the properties size and intensity, which I do not want):
shape property 3 5 6 7 blue green red
1 circle color 1 1 0 0 2 0 0
2 square NA 1 0 0 1 1 2 0
3 triangle NA 0 0 1 1 1 1 1
What am I doing wrong - is there a way to return a dataframe like the first result that I showed?
Also, while this is a small and fast example, my "real" data are much bigger and take a long time to calculate. Does the speed of ddply improve by limiting to only property="color"?
EDIT: Thanks for the answers so far! Unfortunately for me, I oversimplified the situation and I'm not sure if the dcast solution will work for me. Let me explain - I am actually working with a dataframe example2:
example2 <- structure(list(factory = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("A",
"B"), class = "factor"), shape = structure(c(1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L), .Label = c("circle",
"square", "triangle"), class = "factor"), property = structure(c(1L,
3L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 2L
), .Label = c("color", "intensity", "size"), class = "factor"),
value = structure(c(5L, 2L, 1L, 5L, 4L, 1L, 5L, 6L, 6L, 7L,
4L, 3L, 6L, 5L, 5L, 2L, 1L), .Label = c("3", "5", "6", "7",
"blue", "green", "red"), class = "factor")), .Names = c("factory",
"shape", "property", "value"), class = "data.frame", row.names = c(NA,
-17L))
and I am trying to split by both factory and shape. I have a messy solution using ddply:
ColorSummary2 <- ddply(example2,.(factory,shape,property="color"), function(example2) summary(example2$value))
which gives
factory shape property 3 5 6 7 blue green red
1 A circle color 1 1 0 0 2 0 0
2 A square NA 1 0 0 1 1 2 0
3 A triangle NA 0 0 1 1 1 1 1
4 B circle NA 1 1 0 0 1 0 0
but what I would like to return is this (sorry for the messy table, I have trouble formatting tables on here):
factory shape property blue green red
1 A circle color 2 0 0
2 A square NA 1 2 0
3 A triangle NA 1 1 1
4 B circle NA 1 0 0
Is this possible?
EDIT 2: Sorry for all of the edits, I oversimplified my situation way too much. Here is a more complex dataframe that is closer to my real example. This one has a column state, which I do not want to use for splitting. I can do this (messily) with ddply, but can I ignore state using dcast?
example3 <- structure(list(state = structure(c(1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L), .Label = c("CA", "FL"
), class = "factor"), factory = structure(c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L), .Label = c("A",
"B"), class = "factor"), shape = structure(c(1L, 1L, 1L, 1L,
2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L), .Label = c("circle",
"square", "triangle"), class = "factor"), property = structure(c(1L,
3L, 2L, 1L, 2L, 3L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 2L
), .Label = c("color", "intensity", "size"), class = "factor"),
value = structure(c(5L, 2L, 1L, 5L, 4L, 1L, 5L, 6L, 6L, 7L,
4L, 3L, 6L, 5L, 5L, 2L, 1L), .Label = c("3", "5", "6", "7",
"blue", "green", "red"), class = "factor")), .Names = c("state",
"factory", "shape", "property", "value"), class = "data.frame", row.names = c(NA,
-17L))
Using dcast from reshape2:
dcast(...~value,data=subset(example,property=='color'))
Aggregation function missing: defaulting to length
shape property blue green red
1 circle color 2 0 0
2 square color 1 2 0
3 triangle color 1 1 1
EDIT
using the second data set example:
dcast(...~value,data=subset(example2,property=='color'))
Aggregation function missing: defaulting to length
factory shape property blue green red
1 A circle color 2 0 0
2 A square color 1 2 0
3 A triangle color 1 1 1
4 B circle color 1 0 0