Calculating a Rolling Average with Missing Time Values - r

I have a dataset described by the following:
> dput(droplevels(head(sample,10)))
structure(list(Team = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "Air-Force", class = "factor"), Year = c(2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2011L, 2012L, 2013L
), Grouped_Position_3 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "Skill", class = "factor"), Avg_Rating = c(0.7667,
0, 0.7444, 0.7222, 0, 0.7556, 0.76224, 0.596322222222222, 0.706584615384615,
0.767509090909091), n = c(1L, 1L, 3L, 6L, 1L, 1L, 5L, 9L, 13L,
11L)), .Names = c("Team", "Year", "Grouped_Position_3", "Avg_Rating",
"n"), row.names = c(NA, 10L), class = "data.frame")
In the full dataset there are multiple schools, grouped positions and years. What I'm trying to do is figure out how to generate a rolling average using the current year and four years in the past for each unique group of school, year and position. For example for 2013, Air Force and Skill position I would like the following calculation to take place (Note that 2010 is missing in the data):
(.767+.70+.59+0+.762)/5
The 0 comes from the missing year. I have looked at the zoo library in combination with dplyr but I haven't been able to control for missing values like this. Am I looking at having to write a loop or is there some package in r that has this capability?


Create a function Avg which given a vector of row numbers ix takes the required average of the most recent 5 years and then rollapplyr it for each group of Team and Grouped_Position_3:
library(zoo)
Avg <- function(ix) with(sample[ix, ], sum(Avg_Rating[Year >= max(Year) - 4]) / 5)
transform(sample, Avg = ave(1:nrow(sample), Team, Grouped_Position_3, FUN =
function(ix) rollapplyr(ix, 5, Avg, partial = TRUE)))
giving:
Team Year Grouped_Position_3 Avg_Rating n Avg
1 Air-Force 2003 Skill 0.7667000 1 0.1533400
2 Air-Force 2004 Skill 0.0000000 1 0.1533400
3 Air-Force 2005 Skill 0.7444000 3 0.3022200
4 Air-Force 2006 Skill 0.7222000 6 0.4466600
5 Air-Force 2007 Skill 0.0000000 1 0.4466600
6 Air-Force 2008 Skill 0.7556000 1 0.4444400
7 Air-Force 2009 Skill 0.7622400 5 0.5968880
8 Air-Force 2011 Skill 0.5963222 9 0.4228324
9 Air-Force 2012 Skill 0.7065846 13 0.5641494
10 Air-Force 2013 Skill 0.7675091 11 0.5665312
Note
The input used is:
sample <- structure(list(Team = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "Air-Force", class = "factor"), Year = c(2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2011L, 2012L, 2013L
), Grouped_Position_3 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "Skill", class = "factor"), Avg_Rating = c(0.7667,
0, 0.7444, 0.7222, 0, 0.7556, 0.76224, 0.596322222222222, 0.706584615384615,
0.767509090909091), n = c(1L, 1L, 3L, 6L, 1L, 1L, 5L, 9L, 13L,
11L)), .Names = c("Team", "Year", "Grouped_Position_3", "Avg_Rating",
"n"), row.names = c(NA, 10L), class = "data.frame")

Related

Find the first 5 and last 5 consecutive days which meet a condition and calculate the length of this period

I have a dataset with temperature values from 1989 to 2018, I need to find for each year the first 5 consecutive days in which the WMean_AVG_TEMP is >= 5 and the last 5 consecutive days in which the WMean_AVG_TEMP is >= 5. Then I need to know the length of this period (number of days), all this result has to be linked to the NUTS code (ITC11) I appreciate any help. heres the example of my dataset.
dput(droplevels(head(data,10)))`
structure(list(NUTS = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L), .Label = "ITC11", class = "factor"), YEAR = c(1989L,
1989L, 1989L, 1989L, 1989L, 1989L, 1989L, 1989L, 1989L, 1989L
), MONTH = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), DAY = 1:10,
WMean_AVG_TEMP = c(4.67658969041876, 3.84797404270322, 0.924227019436549,
1.05457654181946, -1.27516988837113, 0.283175108098705, 2.0813951419463,
3.62100190622789, 1.76970410697158, 1.71812894277136)), .Names = c("NUTS",
"YEAR", "MONTH", "DAY", "WMean_AVG_TEMP"), row.names = c(NA,
10L), class = "data.frame")

Automate coding (sum) in R

First at all I would like to apologise if I did not use the correct jargon.
I have the dataset as below which contains a wide range of categories
Here some excerpt from dput (using droplevels)
structure(list(
x = c(2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L, 2010L,
2010L, 2010L), *[ME: there are more years than 2010...]*
y = c(7.85986, 185.81068, 107.24097, 7094.74649,
1.4982, 185.77319, 5090.79354, 167.58584, 4189.64609, 157.08277,
3927.06932, 2.86732, 71.683, 4.70123, 117.53085, 2.93452, 73.36292,
1.4982, 18.18734, 901.14744, 0.90268, 13.77532, 613.38298, 0.01845,
0.0681, 7.19925, 3.75315, 0.14333, 136.54008, 0.04766, 0.59077,
28.97255, 0.38608, 115.05258, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
x1 = structure(c(4L, 2L, 3L, 1L, 4L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 4L, 2L, 1L, 4L, 2L, 1L, 4L, 2L,
1L, 2L, 4L, 1L, 4L, 2L, 1L, 4L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L), .Label = c("All greenhouse gases - (CO2 equivalent)",
"CH4", "CO2", "N2O"), class = "factor"),
x2 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "Austria",
class = "factor"),
x4 = structure(c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 4L,
4L, 5L, 5L, 6L, 6L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 10L,
10L, 10L, 11L, 11L, 11L, 12L, 12L, 12L, 13L, 13L, 14L, 14L,
15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L), .Label = c("3",
"3.1", "3.A", "3.A.1", "3.A.2", "3.A.3", "3.A.4", "3.B",
"3.B.1", "3.B.2", "3.B.3", "3.B.4", "3.B.5", "3.C", "3.C.1",
"3.C.2", "3.C.3", "3.C.4"), class = "factor")), class = "data.frame",
row.names = c(NA,
-44L))
I want to know whether the of the sum of subcategories in x4 (e.g. 3.B.1+3.B.2+...+3.B.n) equal the figure stated in the parent category (e.g. 3.B). (i.e. the in the csv stated sum) for a given year and country. I want to verify the sums.
For get the sum of the subcategories I have this
sum(df$y[df$x4 %in% c("3.A.1", "3.A.2", "3.A.3", "3.A.4") & x ==
"2010" & x2 == "Austria"])
To receive the sum of the parent category I have this
sum(df$y[df$x4 %in% c("3.A") & x == "2010" & x2 == "Austria"])
Next I would need an operation which checks whether the results of both codes are equal (True/false). However, I have more than 20 countries, 20 years, dozens of categories to check. With my newby approach I would be writing code for ages...
is there anyway to automate this? Basically, I am looking for a code which is able to do the following
1) Run for one category, go to next one
2) once done with categories change year and start again with categories
3) ... same for countries....
Any sort of help would be appreciated and even a suggestions how to use the right jargon in the title. Thanks in any case

Here's a potential solution using dplyr (might require some tweaking based on the full dataset):
require(dplyr)
# Create two columns - one that shows only the parent category number, and one that tells you if it's a parent or child; note that the regex here makes some assumptions on the format of your data.
mutate(df,parent=gsub("(.?\\..?)\\..*", "\\1", df$x4),
type=ifelse(parent==x4,"Parent","Child")) %>%
# Sum the children y's by category, year and country
group_by(parent, type, x, x2) %>%
summarize(sum(y)) %>%
# See if the sum of the children is equal to the parent y
tidyr::spread(type,`sum(y)`) %>%
mutate(equals=isTRUE(all.equal(Child,Parent)))
Result using your (new) data:
parent x x2 Child Parent equals
<chr> <int> <fct> <dbl> <dbl> <lgl>
1 3 2010 Austria NA 7396. FALSE
2 3.1 2010 Austria NA 5278. FALSE
3 3.A 2010 Austria 4357. 4357. TRUE
4 3.B 2010 Austria 921. 921. TRUE
5 3.C 2010 Austria 0 0 TRUE
I can see from your new data that you have two levels of parents. My solution will only work for the second level (e.g. 3.1 and its children), but can be easily tweaked to also work for the top level.

Calculate difference b/w dates by group in R - unsolved [duplicate]

This question already has answers here:
Why does summarize or mutate not work with group_by when I load `plyr` after `dplyr`?
(2 answers)
Closed 4 years ago.
I'm trying to calculate the difference b/w minimum and maximum date by group in R. The code to achieve this I found here. However, replicating the example does not lead to the expected result. This is the dataset example that was used:
HS_Hatch <- structure(list(ClutchID = c(1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L
), DateVisit = c("3/15/2012", "3/18/2012", "3/20/2012", "4/1/2012",
"4/3/2012", "3/18/2012", "3/20/2012", "3/22/2012", "4/3/2012",
"4/4/2012", "3/22/2012", "4/3/2012", "4/4/2012", "3/18/2012",
"3/20/2012", "3/22/2012", "4/2/2012", "4/3/2012", "4/4/2012",
"3/20/2012", "3/22/2012", "3/25/2012", "3/27/2012", "4/4/2012",
"4/5/2012"), Year = c(2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L, 2012L,
2012L), Survive = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -25L), .Names = c("ClutchID",
"DateVisit", "Year", "Survive"), spec = structure(list(cols = structure(list(
ClutchID = structure(list(), class = c("collector_integer",
"collector")), DateVisit = structure(list(), class = c("collector_character",
"collector")), Year = structure(list(), class = c("collector_integer",
"collector")), Survive = structure(list(), class = c("collector_integer",
"collector"))), .Names = c("ClutchID", "DateVisit", "Year",
"Survive")), default = structure(list(), class = c("collector_guess",
"collector"))), .Names = c("cols", "default"), class = "col_spec"))
This was the proposed solution using dplyr:
library(dplyr)
HS_Hatch <- HS_Hatch %>%
mutate(date_visit = as.Date(DateVisit, "%m/%d/%Y"))
exposure <- HS_Hatch %>%
group_by(ClutchID) %>%
summarize(first_visit = min(date_visit),
last_visit = max(date_visit),
exposure = last_visit - first_visit)
This is the expected result:
ClutchID first_visit last_visit exposure
<int> <date> <date> <dbl>
1 1 2012-03-15 2012-04-03 19
2 2 2012-03-18 2012-04-04 17
3 3 2012-03-22 2012-04-04 13
4 4 2012-03-18 2012-04-04 17
5 5 2012-03-20 2012-04-05 16
This is the actual result:
first_visit last_visit exposure
1 2012-03-15 2012-04-05 21 days
It seems that the grouping factor gets ignored. How do i have it calculate the date difference per ClutchID?

It works with just dplyr loaded.
Change summarize to dplyr::summarize to make it unambiguous. I would suggest not using plyr as you can do everything with dplyr and tidyverse.

After the import of the dataframe, try this
HS_Hatch$DateVisit = as.Date(HS_Hatch$DateVisit, "%m/%d/%Y")
HS_Hatch$DateVisit = as.POSIXct(HS_Hatch$DateVisit, "%m/%d/%Y")
Then change your dplyr pipe to:
HS_Hatch <- HS_Hatch %>%
group_by(ClutchID) %>%
summarize(first_visit = min(date_visit),
last_visit = max(date_visit),
exposure = last_visit - first_visit)
This gave the expected result and worked since the format Posixct stores time in seconds since "the origin" and you can calculate differences.

dplyr get proportions

I have been trying to switch to dplyr and tidyr for my data manipulation (as opposed to data.table and excel). I have a dataframe in long format that looks like this:
TIME GEO geo_num sex_num AGE Value
2014 EU28 1 1 0 13486357
2014 EU28 1 1 5 13683976
2014 EU28 1 1 10 13430899
2014 EU28 1 1 15 13945295
2014 EU28 1 1 20 15417002
2014 EU28 1 1 25 16233349
What I want to obtain is the proportions by sex_num for each age group (AGE):
TIME GEO geo_num sex_num AGE Value percent
2014 EU28 1 1 0 13486357 0.537
2014 EU28 1 1 5 13683976 0.548
2014 EU28 1 1 10 13430899 0.537
2014 EU28 1 1 15 13945295 0.555
2014 EU28 1 1 20 15417002 0.613
2014 EU28 1 1 25 16233349 0.646
This way I would get the totals by sex (my denominator)
mydata %>%
group_by(geo_num,sex_num,TIME) %>%
summarize(total_sex=sum(Value))
But how to use it to get the percent is not totally clear
mydata %>%
group_by(sex_num, TIME, geo_num, AGE) %>%
mutate(freq = Value / total_sex)
Any ideas?
Here's a subset of the data
structure(list(X = 1:40, TIME = c(2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L,
2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L, 2014L
), GEO = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "EU28", class = "factor"),
geo_num = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), GEO.1 = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "European Union (28 countries)", class = "factor"),
SEX = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("Females", "Males"), class = "factor"), sex_num = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), AGE = c(0, 5, 10, 15,
20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 85.99,
90.99, 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60,
65, 70, 75, 80, 85, 85.99, 90.99), Value = c(13486357L, 13683976L,
13430899L, 13945295L, 15417002L, 16233349L, 17074499L, 17647415L,
18386977L, 18914596L, 17914397L, 16416147L, 14854062L, 12613840L,
10126857L, 8102599L, 5377238L, 2718258L, 3756915L, 1038657L,
12805779L, 12992860L, 12754636L, 13227105L, 14824565L, 15915997L,
16894408L, 17437631L, 18269939L, 18931544L, 18244203L, 17188595L,
16064384L, 14111303L, 12145307L, 10862721L, 8471793L, 5480758L,
8448678L, 2967920L)), .Names = c("X", "TIME", "GEO", "geo_num",
"GEO.1", "SEX", "sex_num", "AGE", "Value"), class = "data.frame", row.names = c(NA, -40L))

Something like this might get you what you're looking for
mydata <- mydata %>%
group_by(TIME, GEO, geo_num, GEO.1, SEX, sex_num) %>%
mutate(total_sex = sum(Value),
percent = Value / total_sex * 100)
> head(mydata)
# A tibble: 6 x 11
X TIME GEO SEX AGE Value total_sex percent
1 2014 EU28 Males 0 13486357 251139335 5.370070
2 2014 EU28 Males 5 13683976 251139335 5.448759
3 2014 EU28 Males 10 13430899 251139335 5.347987
4 2014 EU28 Males 15 13945295 251139335 5.552812
5 2014 EU28 Males 20 15417002 251139335 6.138824
6 2014 EU28 Males 25 16233349 251139335 6.463881
# ... with 3 more variables

We can use data.table
library(data.table)
setDT(mydata)[, percent := 10*Value/sum(Value) , c(names(mydata)[2:7])]
head(mydata)
# X TIME GEO geo_num GEO.1 SEX sex_num AGE Value percent
#1: 1 2014 EU28 1 European Union (28 countries) Males 1 0 13486357 0.5370070
#2: 2 2014 EU28 1 European Union (28 countries) Males 1 5 13683976 0.5448759
#3: 3 2014 EU28 1 European Union (28 countries) Males 1 10 13430899 0.5347987
#4: 4 2014 EU28 1 European Union (28 countries) Males 1 15 13945295 0.5552812
#5: 5 2014 EU28 1 European Union (28 countries) Males 1 20 15417002 0.6138824
#6: 6 2014 EU28 1 European Union (28 countries) Males 1 25 16233349 0.6463881

Count combinations of two variables excluding rows that repeat ID

I have a data on countries and want to summarize it and create a table.
> head(data)
country year score members
A 1989 0 7
A 1990 0 7
A 1991 0 7
A 1992 0 7
A 1993 0 7
A 1994 0 7
The table should show the relationship between country "score" and the number of "members" – put differently, I want to see how many states with score 0,1 or 2 have "members"(ranging from 1 to 7).
I want to set it like this:
score members==1 members==2 members==3 members==4 members==5 members==6 members==7
0 1 0
1 2 0
2 0 1 and so on..
To do this I run the following:
library(dplyr)
table <- data %>%
group_by(score) %>%
summarise(
m1 = sum(members==1, na.rm=TRUE),
m2 = sum(members==2, na.rm=TRUE),
m3 = sum(members==3, na.rm=TRUE),
m4 = sum(members==4, na.rm=TRUE),
m5 = sum(members==5, na.rm=TRUE),
m6 = sum(members==6, na.rm=TRUE),
m7 = sum(members==7, na.rm=TRUE)
)
This gives:
score m1 m2 m3 m4 m5 m6 m7
0 0 2 0 0 0 3 30
1 15 3 11 11 3 18 3
2 3 0 2 2 0 6 9
.
.
I need a little help here. As you see it has calculated the total number of observations, whereas I want to count each country only once.
How do I summarize this data to have the total number of countries for each members-level?
Here's a sample of my data for reproducibility:
data <-
structure(list(country = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L,
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L), .Label = c("A", "B", "C", "D", "E", "F"), class = "factor"),
year = c(1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L,
1996L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L,
2005L, 2006L, 2007L, 2008L, 2010L, 1989L, 1990L, 1991L, 1992L,
1993L, 1994L, 1995L, 1996L, 1997L, 1998L, 1999L, 2000L, 2001L,
2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L, 2009L, 2010L,
2011L, 1989L, 1991L, 1993L, 1994L, 1995L, 1996L, 1997L, 1999L,
2000L, 2001L, 2002L, 2003L, 2004L, 2005L, 2006L, 2007L, 2008L,
2010L, 1989L, 1990L, 1991L, 1992L, 1993L, 1994L, 1995L, 1996L,
1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L, 2004L, 2005L,
2006L, 2007L, 2008L, 2009L, 2010L, 2011L, 1991L, 1992L, 1993L,
1994L, 1995L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2010L, 1991L, 1992L, 1993L,
1994L, 1995L, 1997L, 1998L, 1999L, 2000L, 2001L, 2002L, 2003L,
2004L, 2005L, 2006L, 2007L, 2008L, 2010L), score = c(0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 2L,
2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
2L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L,
1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), members = c(7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L,
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L,
7L, 7L, 7L, 7L, 7L, 7L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L,
4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L)), .Names = c("country", "year", "score",
"members"), class = "data.frame", row.names = c(NA, -121L))

I believe you need this:
library(reshape2)
dcast(aggregate(country~score+members, data=data, FUN=function(x) length(unique(x))),
score~members, value.var="country", fill=0L)
# score 1 2 3 4 5 6 7
#1 0 0 1 0 0 0 1 2
#2 1 1 1 2 2 1 3 2
#3 2 1 0 1 2 0 1 1
Or, to put it the dplyr/tidyr way:
data %>%
group_by(members, score) %>%
summarise(n=n_distinct(country)) %>%
spread(members, n, fill=0L)
## A tibble: 3 x 8
# score 1 2 3 4 5 6 7
#* <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 0 0 1 0 0 0 1 2
#2 1 1 1 2 2 1 3 2
#3 2 1 0 1 2 0 1 1

As the OP is using dplyr methods, we can do this by grouping with 'score', 'members' to get the number of elements (n()), and then spread (from tidyr) to reshape it to 'wide' format.
library(dplyr)
library(tidyr)
data %>%
group_by(score, members) %>%
summarise(n = n()) %>%
mutate(members = paste0("m", members)) %>%
spread(members, n, fill = 0)
# score m1 m2 m3 m4 m5 m6 m7
# <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#1 0 0 2 0 0 0 3 30
#2 1 15 3 11 11 3 18 3
#3 2 3 0 2 2 0 6 9
If we need to also get the counts by 'country', just add 'country' in the group_by
data %>%
group_by(country, score, members) %>%
summarise(n = n()) %>%
mutate(members = paste0("m", members)) %>%
spread(members, n, fill = 0)
If the expected output is the one showed in the other posts, an option using data.table would be to convert the 'data.frame' to 'data.table' (setDT(data), and dcast from 'long' to 'wide' specifying the fun.aggregate as uniqueN of the 'value.var' variable i.e. 'country' where uniqueN returns the length of unique elements in the 'country' column. The fill=0 specifies to occupy 0 for those combinations that are not available. By default, it returns as NA.
library(data.table)
dcast(setDT(data), score~members, value.var= 'country', fun.aggregate = uniqueN, fill = 0)
# score 1 2 3 4 5 6 7
#1: 0 0 1 0 0 0 1 2
#2: 1 1 1 2 2 1 3 2
#3: 2 1 0 1 2 0 1 1

It seems the crux of the issue is having the duplicated rows for each year? In which case you can remove them with distinct, then it's a simple crosstab. You could use the %$% exposition pipe from magrittr:
library(dplyr)
library(magrittr)
data %>%
distinct(country, score, members) %$%
table(score, members)
members
score 1 2 3 4 5 6 7
0 0 1 0 0 0 1 2
1 1 1 2 2 1 3 2
2 1 0 1 2 0 1 1
Or a regular pipe and tabyl from the janitor package:
library(dplyr)
library(janitor)
data %>%
distinct(country, score, members) %>%
tabyl(score, members)
score 1 2 3 4 5 6 7
0 0 1 0 0 0 1 2
1 1 1 2 2 1 3 2
2 1 0 1 2 0 1 1

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