Translating SAS language to R language: Creating a new variable - r

I have a sas code and I want to translate into R. I am interested in creating variables based on the conditions of other variables.
data wp;
set wp;
if totalcriteria =>3 and nonecom=0 then content=1;
if totalcriteria =>3 and nonecom=1 then content=0;
if totalcriteria <3 and nonecom=0 then content=0;
if totalcriteria <3 and nonecom=1 then content=0;
run;
This is a code I have in. My conditions for "content" as changed and I would like to translate the sas code to R to hopefully replace the "mutate" line of the code below or fit in with the code below:
wpnew <- wp %>%
mutate(content = ifelse (as.numeric(totalcriteria >= 3),1,0))%>%
group_by(district) %>%
summarise(totalreports =n(),
totalcontent = sum(content),
per.content=totalcontent/totalreports*100)
Can you help me translate this SAS code to R language. Thank you in advance.
Here is the dput output
structure(list(Finances = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Exercise = c(0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), Relationships = c(0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0, 0), Laugh = c(0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0, 1), Gratitude = c(0, 0, 0, 0, 1, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 1), Regrets = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0,
0, 0, 0, 0, 0, 0), Meditate = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), Clutter = c(0, 0, 1, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0), Headache = c(0, 0, 1, 1, 0, 0, 0, 1, 0, 0,
0, 0, 1, 0, 0, 0), Loss = c(0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 0, 0), Anger = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0), Difficulty = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), nonecom = c(1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1,
1, 0, 1, 1, 0), Othercon = c(0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), totalcriteria = c(0, 0, 2, 3, 2, 0, 0, 4, 3,
0, 0, 0, 3, 0, 0, 2)), class = "data.frame", row.names = c(NA,
-16L))
This is what I would like it to look like
V1 V2 V3...V12 nonecom Othercon totalcriteria content
1 1 1 0 1 0 3 0
0 0 1 0 0 0 8 1
1 0 0 0 0 1 2 0
1 0 1 0 1 0 1 0


I use case_when just because I find it more similar in terms of syntax. Your current approach only tests the first part of the IF condition, not the second part regarding nonecom.
wpnew <- wp %>%
mutate(content = case_when(sum.content >= 3 & nonecom == 0 ~ 1,
TRUE ~ 0))

Related

For loop to get rowmeans of each 8 columns in a large dataframe

I have a large data.frame with 8 columns per sample with 200 samples. I need to get row-means of each 8.
rowMeans(mat[j,1:8]), rowMeans(mat[j,9:16]), rowMeans(mat[j,17:24])...
rownames are gene names.
I used the following:
for(j in 1:nrow(mat)){
for (i in 1:ncol(mat)/8) {
row_m[j, i]<- rowMeans(mat[j,c(i:i+7)])
}
}
Dataframe sample data, here I have shown 9 columns, should get the mean from first 8 (AM) and then repeat for other samples....
dput(head(deconv3[1:9], 20))
structure(list(AM.amplifying.intestine = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM43.5.epithelial.of.mammary = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4.76506, 0, 0, 1406.48, 0, 196.401,
0, 1996.5, 0), AM.epithelium.of.bronchus = c(549.649, 1647.63,
0, 0, 0, 0, 0, 0, 699.868, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
AM.epithelium.of.intestine = c(0, 0, 0, 0, 0, 0, 572.85,
59.2414, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AM.epithelium.of.trachea = c(0,
0, 0, 0, 199.549, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), AM.kidney.epithelial.cell = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1.32926, 0, 0, 333.592, 0), AM.medullary.thymic.epithelial.cell = c(126.847,
0, 0, 0, 0, 0, 0, 0, 0, 63.1822, 0, 0, 0, 0, 0, 0, 0, 26.0598,
0, 11.117), AM.myoepithelial.cell = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), AK.amplifying.intestine = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c("A1BG",
"A2M", "NAT2", "SERPINA3", "AANAT", "ABAT", "ABCA2", "ABCA3",
"ABCB7", "ABCA4", "ABO", "ACACA", "ACADL", "ACADS", "ACADSB",
"ACAT1", "ACLY", "ACR", "ACP1", "ACRV1"), class = "data.frame")
But it does not work. I am wondering if you could help me with this. Thanks in advance!

sample_length <- 8
row_m <- matrix(nrow=dim(mat)[1], ncol = ncol(mat)/sample_length)
for (j in 1:nrow(mat)) {
for (i in seq(from = 1, to = ncol(mat), by = sample_length)) {
row_m[j, (sample_length - 1 + i)/sample_length] <- mean(as.numeric(mat[j, i:(i + (sample_length-1))]))
}
}

Try:
row_m <- do.call(cbind, lapply(1:(NCOL(mat) %/% 8 + 1), function(i){
rowMeans(d[, ((1:NCOL(mat) - 1) %/% 8 + 1) == i, drop=F])}))

R for loop wise : Rowwise sum on conditions : Performance issue

I have a database, where I am running code to change value of a cell-based on the sum of previous cells and the sum of succeeding cells in the same row.
for (i in 1:row1)
{
for(j in 3:col-1)
{ # for-loop over columns
if (as.numeric(rowSums(e[i,2:j])) == 0 )
{
e1[i,j] <- 0
}
else if (as.numeric(rowSums(e[i,2:j])) > 0 && e[i,j] == 0 && as.numeric(rowSums(e[i,j:col])) > 0 )
{
e1[i,j] <- 1
}
else if (as.numeric(rowSums(e[i,2:j])) > 0 && e[i,j] == 1 && as.numeric(rowSums(e[i,j:col])) > 0 )
{
e1[i,j] <- 0
}
}
}
The runtime is very high. Appreciate any suggestions to improve the speed. Additional info: copying new values into the data frame is being done.
Thanks,
Sandy
edit 2:
Sample data:
structure(list(`Sr no` = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
12, 13, 14, 15, 16, 17, 18, 19), `2018-01` = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `2018-02` = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `2018-03` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `2018-04` = c(0,
0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), `2018-05` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0), `2018-06` = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0), `2018-07` = c(0,
0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), `2018-08` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1), `2018-09` = c(0,
0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0), `2018-10` = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1), `2018-11` = c(0,
1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1), `2018-12` = c(1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), `2019-01` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), `2019-02` = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA,
-19L), class = c("tbl_df", "tbl", "data.frame"))

I think you can do this with matrix logic. Depends if you have enough RAM.
# creating fake data
# nc <- 300 # number of columns
nc <- 10 # for testing
nn <- 1e6 # rows
e <- sapply(1:nc, function(x) sample.int(2, nn, replace = T) - 1L)
e <- as.data.frame(e)
row1 <- nrow(e)
colc <- ncol(e)
# note that:
3:colc-1
# isnt equal with:
3:(colc-1)
s <- 3:(colc-1) # I assume you meant this
e1 <- matrix(nrow = row1, ncol = length(s)) # empty resulting matrix
s1 <- sapply(s, function(j) rowSums(e[, 2:j])) # sum for each relevant i,j
s2 <- sapply(s, function(j) rowSums(e[, j:colc])) # sum for each relevant i,j
e2 <- as.matrix(e[, s]) # taking relevant columns of e
e1[s1 == 0] <- 0
e1[s1 > 0 & e2 == 0 & s2 > 0] <- 1
e1[s1 > 0 & e2 == 1 & s2 > 0] <- 0

xgb.cv with no folds and return the results based on a split of the data

I have some data which looks like:
# A tibble: 50 x 28
sanchinarro date holiday weekday weekend workday_on_holi… weekend_on_holi… protocol_active
<dbl> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 -1.01 2010-01-01 1 1 0 1 0 0
2 0.832 2010-01-02 0 0 1 0 0 0
3 1.29 2010-01-03 0 0 1 0 0 0
4 1.04 2010-01-04 0 1 0 0 0 0
5 0.526 2010-01-05 0 1 0 0 0 0
6 -0.292 2010-01-06 1 1 0 1 0 0
7 -0.394 2010-01-07 0 1 0 0 0 0
8 -0.547 2010-01-08 0 1 0 0 0 0
9 -0.139 2010-01-09 0 0 1 0 0 0
10 0.628 2010-01-10 0 0 1 0 0 0
I want to run xgb.cv on the first 40 rows and validate it on the final 10 rows.
I try the following:
library(xgboost)
library(dplyr)
X_Val <- ddd %>% select(-c(1:2))
Y_Val <- ddd %>% select(c(1)) %>% pull()
dVal <- xgb.DMatrix(data = as.matrix(X_Val), label = as.numeric(Y_Val))
xgb.cv(data = dVal, nround = 30, folds = NA, params = list(eta = 0.1, max_depth = 5))
which gives me this error:
Error in xgb.cv(data = dVal, nround = 30, folds = NA, eta = 0.1,
max_depth = 5) : 'folds' must be a list with 2 or more elements
that are vectors of indices for each CV-fold
How can I run a simple xgb.cv on the first 40 rows and test it on the last 10 rows.
I eventually want to apply a gird search with a list of parameters and save the results in a list. Since I am dealing with time series data I do not want to mix the folds up, I just want a simple train and in-sample test of 40:10.
Data:
ddd <- structure(list(sanchinarro = c(-1.00742964973274, 0.832453587904369,
1.29242439731365, 1.03688505875294, 0.525806381631517, -0.291919501762755,
-0.394135237187039, -0.547458840323464, -0.138595898626329, 0.628022117055801,
1.19020866188936, 1.5990716035865, 1.5990716035865, -0.70078244345989,
2.11015028070792, 1.95682667757149, 0.985777191040795, 0.883561455616511,
0.985777191040795, 0.270267043070807, 2.51901322240505, 2.41679748698077,
0.372482778495091, -0.291919501762755, -0.905213914308458, -0.905213914308458,
-0.649674575747748, 1.2413165296015, 1.54796373587436, -0.70078244345989,
-0.905213914308458, -0.0363801632020448, 1.54796373587436, 2.00793454528363,
1.54796373587436, -0.445243104899181, -0.445243104899181, 1.03688505875294,
0.628022117055801, -0.496350972611323, 0.168051307646523, -0.649674575747748,
0.0658355722222391, -1.00742964973274, -0.291919501762755, 0.0147277045100972,
0.168051307646523, -0.189703766338471, 0.219159175358665, 0.679129984767943
), date = structure(c(14610, 14611, 14612, 14613, 14614, 14615,
14616, 14617, 14618, 14619, 14620, 14621, 14622, 14623, 14624,
14625, 14626, 14627, 14628, 14629, 14630, 14631, 14632, 14633,
14634, 14635, 14636, 14637, 14638, 14639, 14640, 14641, 14642,
14643, 14644, 14645, 14646, 14647, 14648, 14649, 14650, 14651,
14652, 14653, 14654, 14655, 14656, 14657, 14658, 14659), class = "Date"),
holiday = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(1,
0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1,
1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1), weekend = c(0, 1, 1, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0,
0, 1, 1, 0, 0, 0, 0, 0), workday_on_holiday = c(1, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(0,
1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,
0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1,
1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1), text_clear = c(0, 0, 0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1,
0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1, 1), text_fog = c(0, 1, 0, 1, 1, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0,
0, 0, 1, 0, 1, 0), text_partly_cloudy = c(0, 1, 0, 0, 0,
1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), text_partly_sunny = c(1, 1, 1, 1, 1,
0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,
0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1), text_passing_clouds = c(1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0,
0, 0, 0, 0, 0, 1, 1, 1), text_scattered_clouds = c(1, 1,
0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0,
0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 0, 0, 0, 1, 1), text_sunny = c(0, 0, 0, 0,
0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 1), month_1 = c(1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_2 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1), month_3 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_9 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_10 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-50L))
EDIT: List data:
The final data comes in the form of lists.
datalst <- list(structure(list(sanchinarro = c(-1.00742964973274, 0.832453587904369,
1.29242439731365, 1.03688505875294, 0.525806381631517, -0.291919501762755,
-0.394135237187039, -0.547458840323464, -0.138595898626329, 0.628022117055801,
1.19020866188936, 1.5990716035865, 1.5990716035865, -0.70078244345989
), date = structure(c(14610, 14611, 14612, 14613, 14614, 14615,
14616, 14617, 14618, 14619, 14620, 14621, 14622, 14623), class = "Date"),
holiday = c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(1,
0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1), weekend = c(0, 1,
1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0), workday_on_holiday = c(1,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(0,
1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0), text_clear = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0), text_fog = c(0, 1,
0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0), text_partly_cloudy = c(0,
1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0), text_partly_sunny = c(1,
1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1), text_passing_clouds = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), text_scattered_clouds = c(1,
1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1), text_sunny = c(0,
0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0), month_1 = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), month_2 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_3 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_9 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), month_10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L)), structure(list(sanchinarro = c(0.832179838392013, 1.29225734336885,
1.03665872949283, 0.525461501740789, -0.292454062662475, -0.394693508212883,
-0.548052676538495, -0.139094894336863, 0.627700947291197, 1.19001789781844,
1.59897568002007, 1.59897568002007, -0.701411844864107, 2.11017290777211
), date = structure(c(14611, 14612, 14613, 14614, 14615, 14616,
14617, 14618, 14619, 14620, 14621, 14622, 14623, 14624), class = "Date"),
holiday = c(0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(0,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1), weekend = c(1, 1,
0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0), workday_on_holiday = c(0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(1,
0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_clear = c(0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1), text_fog = c(1, 0,
1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0), text_partly_cloudy = c(1,
0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0), text_partly_sunny = c(1,
1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0), text_passing_clouds = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), text_scattered_clouds = c(1,
0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0), text_sunny = c(0,
0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1), month_1 = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), month_2 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_3 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_9 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), month_10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L)), structure(list(sanchinarro = c(1.29293502084952, 1.03729933727253,
0.526027970118536, -0.292006217327851, -0.394260490758649, -0.547641900904846,
-0.138624807181653, 0.628282243549334, 1.19068074741873, 1.59969784114192,
1.59969784114192, -0.701023311051044, 2.11096920829591, 1.95758779814971
), date = structure(c(14612, 14613, 14614, 14615, 14616, 14617,
14618, 14619, 14620, 14621, 14622, 14623, 14624, 14625), class = "Date"),
holiday = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekday = c(0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0), weekend = c(1, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1), workday_on_holiday = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), weekend_on_holiday = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), protocol_active = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), text_broken_clouds = c(0,
1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1), text_clear = c(0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0), text_fog = c(0, 1,
1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0), text_partly_cloudy = c(0,
0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0), text_partly_sunny = c(1,
1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1), text_passing_clouds = c(1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), text_scattered_clouds = c(0,
0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0), text_sunny = c(0,
0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0), month_1 = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1), month_2 = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_3 = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0), month_4 = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0), month_5 = c(0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0), month_6 = c(0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), month_7 = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), month_8 = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0), month_9 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), month_10 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0), month_11 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0), month_12 = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA,
-14L)))
EDIT:
I think this gives me what I am after - I need to double/tripple check it. (if you see any errors please let me know)
splt <- 0.80 * nrow(ddd)
ddd[c(1:splt), "id"] = 1
ddd$id[is.na(ddd$id)] = 2
fold.ids <- unique(ddd$id)
custom.folds <- vector("list", length(fold.ids))
i <- 1
for( id in fold.ids){
custom.folds[[i]] <- which( ddd$id %in% id )
i <- i+1
}
custom.folds
cv <- xgb.cv(params = list(eta = 0.1, max_depth = 5), dVal, nround = 10, folds = custom.folds, prediction = TRUE)
cv$evaluation_log
I now need to find a way to apply this to all 3 lists in the "new" added data.

Firstly, you should split the data onto dtrain (40 first rows) and dval (10 last rows). Secondly, you need rather xgb.train, not xgb.cv.
So, your code should be modified to something like that:
library(xgboost)
library(dplyr)
# you code regarding ddd
X <- ddd %>% select(-c(1:2))
Y <- ddd %>% select(c(1)) %>% pull()
dtrain <- xgb.DMatrix(data = as.matrix(X[1:40,]), label = as.numeric(Y[1:40,]))
dval <- xgb.DMatrix(data = as.matrix(X[41:50,]), label = as.numeric(Y[41:50,]))
watchlist <- list(train=dtrain, val=dval)
model <- xgb.train(data=dtrain, watchlist=watchlist, nround = 30, eta = 0.1, max_depth = 5)
IMHO, 40+10 rows only and so sparse features give no hope to obtain good results using XGBoost.

How to fix 'Node inconsistent with parents' in R2jags::jags

I am working with the R-package R2jags. After running the code I attach below, R produced the error message: "Node inconsistent with parents".
I tried to solve it. However, the error message persists. The variables I am using are:
i) "Adop": a 0-1 dummy variable.
ii) "NumInfo": a counter variable whose range is {0, 1, 2,...}.
iii) "Price": 5
iv) "NRows": 326.
install.packages("R2jags")
library(R2jags)
# Data you need to run the model.
# Adop: a 0-1 dummy variable.
Adop <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
# NumInfo: a counter variable.
NumInfo <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 2, 3, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1)
# NRows: length of both 'NumInfo' and 'Adop'.
NRows <- length(NumInfo)
# Price: 5
Price <- 5
Data <- list("NRows" = NRows, "Adop" = Adop, "NumInfo" = NumInfo, "Price" = Price)
# The Bayesian model. The parameters I would like to infer are: 'mu.m', 'tau2.m', 'r.s', 'lambda.s', 'k', 'c', and 'Sig2'.
# I would like to obtain samples from the posterior distribution of the vector of parameters.
Bayesian_Model <- "model {
mu.m ~ dnorm(0, 1)
tau2.m ~ dgamma(1, 1)
r.s ~ dgamma(1, 1)
lambda.s ~ dgamma(1, 1)
k ~ dunif(1, 1/Price)
c ~ dgamma(1, 1)
Sig2 ~ dgamma(1, 1)
precision.m <- 1/tau2.m
m ~ dnorm(mu.m, precision.m)
s2 ~ dgamma(r.s, lambda.s)
for(i in 1:NRows){
Media[i] <- NumInfo[i]/Sig2 * m
Var[i] <- equals(NumInfo[i], 0) * 10 + (1 - equals(NumInfo[i], 0)) * NumInfo[i]/Sig2 * s2 * (NumInfo[i]/Sig2 + 1/s2)
Prec[i] <- pow(Var[i], -1)
W[i] ~ dnorm(Media[i], Prec[i])
PrAd1[i] <- 1 - step(-m/s2 - 1/c * 1/s2 * log(1 - k * Price) + 1/2 * c)
PrAd2[i] <- 1 - step(-W[i] - m/s2 - 1/c * 1/s2 * log(1 - k * Price) + 1/2 * c - 1/c * log(1 - k * Price))
PrAd[i] <- equals(NumInfo[i], 0) * PrAd1[i] + (1 - equals(NumInfo[i], 0)) * PrAd2[i]
Adop[i] ~ dbern(PrAd[i])
}
}"
# Save the Bayesian model in your computer with an extension '.bug'.
# Suppose that you saved the .bug file in: "C:/Users/Default/Bayesian_Model.bug".
writeLines(Bayesian_Model, "C:/Users/Default/Bayesian_Model.bug")
# Here I would like to use jags command from R-package called R2jags.
# I would like to generate 1000 iterations.
MCMC_Bayesian_Model <- R2jags::jags(
model.file = "C:/Users/Default/Bayesian_Model.bug",
data = Data,
n.chains = 1,
n.iter = 1000,
parameters.to.save = c("mu.m", "tau2.m", "r.s", "lambda.s", "k", "c", "Sig2")
)
When running the code, R produced the error message: "Node inconsistent with parents". I do not know what the mistakes are. I was wondering if you could help me with this problem, please. If you need more information, please let me know. Thank you very much.

It's a little hard to figure out the model without knowing what you're trying to do, but I suggest two fixes:
Instead of k ~ dunif(1, 1/Price), did you mean k ~ dunif(0, 1/Price)? For dunif(a, b), you must have a < b (see page 48 here: http://people.stat.sc.edu/hansont/stat740/jags_user_manual.pdf).
I inserted an additional line in the model,
PrAd01[i] <- max(min(PrAd[i], 0.99), 0.01)
and changed the last line to
Adop[i] ~ dbern(PrAd01[i])
Page 49 of the manual above states that 0 < p < 1 for dbern(p).
The model runs with the above two changes.

How to make a regression with many data frames

I have data about gender and petitioning. I want to make a regression between the "Femme" (woman) variable and the different issues of the petitions. I have regrouped those issues into data frames under general themes, and those themes are what I want to regress with the "Femme" (woman) variable.
P.S.: Some petitions have many issues (ex.: water + science). So one petition could be counted in two data frames at the same time.
1) Here is what I did for all issues, this one is an example with the "Aboriginal" issue to show you how I coded the initial issues (you can also see the "Femme" variable at the beginning, which is already coded "0" and "1" in the original dataset under "Female"):
DataPetitions$Femme <- DataPetitions$Female
DataPetitions$Aboriginal <- NA
DataPetitions$Aboriginal[grepl("Aboriginal", DataPetitions$Issue)] <-1
DataPetitions$Aboriginal[!grepl("Aboriginal", DataPetitions$Issue)] <-0
# ... (same for all 24 specific issues)
2) Creating 7 data frames for general petitioning themes:
EnvironmentalIssues <- c(DataPetitions$AirQuality,DataPetitions$Biological, DataPetitions$Climate, DataPetitions$Environmental, DataPetitions$Toxic, DataPetitions$Waste, DataPetitions$Water)
EconomicIssues <- c(DataPetitions$Natural, DataPetitions$Transport)
SocialIssues <- c(DataPetitions$Aboriginal, DataPetitions$Health)
AgriculturalIssues <- c(DataPetitions$Agriculture,
DataPetitions$Fisheries, DataPetitions$Pesticides)
PoliticalIssues <- c(DataPetitions$Compliance, DataPetitions$Federal,
DataPetitions$Governance, DataPetitions$International)
ScientificIssues <- c(DataPetitions$Science)
OtherIssues <- c(DataPetitions$Other)
3) Trying to do a regression. This is my glm code:
model7 <- glm(DataPetitions$Femme ~ SocialIssues + PoliticalIssues +
ScientificIssues + EnvironmentalIssues + EconomicIssues +
AgriculturalIssues + OtherIssues, data = DataPetitions)
# When I try to run it, I get this error message:
Error in model.frame.default(formula = DataPetitions$Femme ~
SocialIssues + : variable lengths differ (found for
'SocialIssues')
With dput(head(DataPetitions,20)), I get this:
[...] class = "factor"), Femme = c(1, 1, 1, 0, 0, 0, 0, 0, 0,
0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1), AuMoinsUneFemme = c(1,
1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1),
Homme = c(1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 1,
1, 1, 1, 2), AuMoinsUnHomme = c(1, 0, 0, 0, 1, 1, 0, 1, 1,
1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1), Individual1 = c(0, 0, 0,
1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 0), Group1 = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1),
Organisation1 = c(1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0,
0, 0, 1, 0, 0, 0, 0), Aboriginal = c(1, 0, 0, 0, 0, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0), Agriculture = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0),
AirQuality = c(0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
0, 0, 1, 0, 0, 0), Biological = c(0, 1, 0, 0, 1, 0, 0, 0,
0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0), Climate = c(0, 0, 0,
0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1), Compliance = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0),
Environmental = c(0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0), Federal = c(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Fisheries = c(0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0), Governance = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Health = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), International = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0), Natural = c(0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0), Other = c(0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Pesticides = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0), Science = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Toxic = c(0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), Transport = c(0,
0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0),
Waste = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0), Water = c(0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0)), .Names = c("Data.", "Title", "Number", "Issue", "Petitioner", "Individual", "Group", "Organisation",
"Female", "Male", "Unknown", "DateReceived", "Status", "Summary",
"Hyperlink", "Femme", "AuMoinsUneFemme", "Homme", "AuMoinsUnHomme",
"Individual1", "Group1", "Organisation1", "Aboriginal", "Agriculture",
"AirQuality", "Biological", "Climate", "Compliance", "Environmental",
"Federal", "Fisheries", "Governance", "Health", "International",
"Natural", "Other", "Pesticides", "Science", "Toxic", "Transport",
"Waste", "Water"), row.names = c(NA, 20L), class = "data.frame")

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