Multiple imputation of categorical data in R - r

I used multiple imputations with R software to complete my data set.
See the example below: x3 (minimum value=0 and maximum value=6); x4 (minimum value=1 and maximum value=5).
After imputed my data set via mice (with m=5), I would like to yield the new proportions after imputation (that is the imputed proportions) of these two variables (A3 and A4) for each imputation data set (m=1 to 5). Do you know how to pool the results of the five estimations then into one (proportion and standard error) like this : A3= x%, x%, x%, x%, x%, x% and for A4 = y%, y%, y%, y%, y% ?
Do you know any R code to deal with this?
A1 =c(2, 1, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 2, 1, 1, 2, 1, 1, 1, 2, 2, 2, 1, 1, 2, 2, 1, 2, 1, 2)
A2 =c(1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 1, 1, 2, 1, 2, 1, 2, 1, 1, 1, 2, 1, 1, 2, 1, 1, 2)
A3 =c(NA, NA, NA, 0, 0, 4, 0, 0, 2, 2 , 0, 3, 4, 0, 5, 6, NA, 2, 1, 0, NA, NA, NA, NA, NA, NA, 1, 2, 6, NA, 0, 6, NA, 6, 2)
A4 =c(1, NA, NA, NA, NA, 2, 2, 5, NA, 4, 3, 1, 2, NA, 3, 6, 2, 1, 2, 3, 3, 1, 2, 3, 4, 5, 4, 1, 2, 3, 5, 3, NA, 1, NA)
df =data.frame(x1=x1, x2=x2, x3=x3, x4=x4)
imp <- mice(df, m=5)

Related

Error using aggregate to find length with missing values

I am trying to use the aggregate function in R to summarise a data using the length function. My data has some NA's and I have tried using 'na.rm = T' or 'na.omit' however none sees to work. I keep getting this error
'Error in FUN(X[[i]], ...) :
2 arguments passed to 'length' which requires 1'
data10 <- structure(list(Group = c(1, 1, 2, 1, 1, 2, 1, 1, 2, 1, 2, 2,
1, 2, 2, 1, 2, 2, 2, 1, 2, 2, 1, 2, 1, 1, 2, 1, 1, 2, 1, 1, 2,
1), SUBJECT = c(1, 1, 2, 3, 3, 4, 5, 5, 6, 7, 8, 8, 9, 10, 10,
11, 12, 14, 14, 15, 16, 16, 17, 18, 19, 19, 20, 21, 21, 22, 23,
23, 24, 25), test = c(1, 2, 1, 1, 2, 2, 1, 2, 2, 1, 1, 2, 2,
1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 1, 2, 1, 1, 2, 2, 1, 2, 2, 1
), trial = c(1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7,
1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3, 5, 7, 1, 3), Condition = c(1,
2, 3, 1, 3, 1, 2, 3, 2, 3, 1, 2, 1, 2, 3, 1, 3, 1, 2, 3, 2, 3,
1, 2, 1, 2, 3, 1, 3, 1, 2, 3, 2, 3), Sac2 = c(1, 1, 1, NA, 2,
1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 1,
1, 1, 1, 1, 2, 2, 1, 1), Sac = c(1, 1, 1, NA, 3, 1, 1, 1, 1, 3,
1, 1, 1, 1, 1, 1, 1, 1, 1, 7, 1, 1, 1, 7, 1, 1, 1, 1, 1, 1, 3,
3, 1, 1), Saccade...8 = c(1, 1, 1, NA, 2, 1, 1, 1, 1, 2, 1, 1,
1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2, 1,
1), T_APPEAR = c(9.236, 17.85, 28.942, 63.724, 9.463, 22.963,
52.068, 57.021, 15.344, 19.783, 37.825, 46.17, 4.339, 21.241,
29.179, 31.823, 12.164, 22.84, 23.954, 73.663, 27.269, 22.131,
30.361, 62.674, 6.928, 16.413, 47.555, 48.893, 7.291, 15.796,
31.788, 54.946, 10.117, 28.83)), row.names = c(NA, -34L), class = c("tbl_df",
"tbl", "data.frame"))
data14 = aggregate(data10,
by = list(data10$SUBJECT,data10$Condition, data10$Group, data10$test),
FUN = length(), na.rm=TRUE)

Bootstrapping multiple regression error: number of items to replace is not a multiple of replacement length

I want to bootstrap my dataset for multiple regression. Unfortunately I get this error message:
"number of items to replace is not a multiple of replacement length"
I suspect that the factors in my regression formula may be problematic.
What could I do to solve my problem?
My code is as following (I read Andy Field´s Discovering Statistics using R):
BootReg <- function(data, indices, formula) {
d <- data[indices,]
fit <- lm(formula, data=d)
return(coef(fit))
}
bootResults <-boot(statistic = BootReg, formula = TICS_Skala1 ~HSPhoch + HSPhoch*extra.c
+ psy + sex + age.c, data = mod.reg.data, R = 2000)
psy (psychiatric disease), sex and HSPhoch (high sensory-processing sensitivity) are factors. TICS_Skala1, extra.c, age.c are continuos variables.
my sample data:
> dput(head(mod.reg.data, 20))
structure(list(neo_01 = c(3, 4, 3, 0, 4, 4, 3, 2, 3, 1, 4, 2,
3, 3, 1, 2, 3, 4, 0, 2), neo_03 = c(1, 1, 1, 3, 1, 2, 0, 0, 0,
0, 0, 0, 1, 3, 1, 1, 1, 1, 3, 1), neo_04 = c(2, 4, 3, 0, 4, 3,
4, 3, 2, 3, 3, 3, 3, 4, 2, 4, 3, 4, 3, 3), neo_08 = c(3, 0, 1,
2, 3, 3, 4, 3, 2, 1, 2, 4, 0, 3, 1, 1, 3, 1, 3, 1), neo_12 = c(3,
1, 1, 2, 2, 2, 4, 1, 1, 2, 1, 4, 1, 3, 1, 1, 3, 2, 3, 2), neo_13 = c(3,
2, 2, 4, 3, 3, 3, 2, 2, 1, 2, 3, 0, 3, 1, 0, 2, 3, 0, 2), neo_16 = c(3,
1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 3, 0, 2, 0, 0, 0, 0, 2, 1), neo_17 = c(2,
1, 3, 0, 1, 1, 1, 4, 3, 1, 2, 2, 2, 3, 1, 0, 2, 0, 2, 2), neo_18 = c(2,
3, 4, 0, 4, 3, 4, 3, 3, 1, 3, 2, 4, 2, 3, 4, 3, 4, 2, 2), neo_21 = c(3,
0, 1, 2, 1, 2, 1, 1, 1, 1, 1, 3, 0, 4, 1, 0, 0, 0, 4, 1), neo_26 = c(3,
0, 0, 0, 2, 1, 3, 0, 1, 1, 0, 2, 3, 3, 0, 0, 1, 1, 4, 1), neo_27 = c(3,
3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 2, 3, 4, 3, 3, 3, 3, 2, 2), TICS_1 = c(3,
0, 3, 2, 2, 1, 3, 3, 1, 2, 0, 4, 2, 3, 2, 3, 4, 1, 3, 2), TICS_2 = c(3,
1, 1, 1, 1, 2, 0, 0, 0, 0, 0, 4, 3, 1, 1, 1, 2, 1, 2, 1), TICS_3 = c(2,
1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 3, 1, 2, 0, 1, 1, 0, 1, 0), TICS_4 = c(2,
0, 2, 0, 1, 2, 1, 3, 0, 0, 0, 4, 1, 2, 1, 2, 1, 1, 2, 2), TICS_5 = c(2,
3, 2, 1, 2, 2, 2, 2, 0, 2, 1, 2, 2, 2, 2, 1, 1, 1, 2, 1), TICS_6 = c(3,
2, 2, 4, 2, 2, 1, 3, 1, 1, 1, 2, 2, 2, 2, 1, 1, 2, 1, 2), TICS_7 = c(3,
3, 2, 2, 2, 2, 0, 3, 1, 2, 1, 4, 2, 0, 2, 1, 4, 1, 0, 1), TICS_8 =c(NA,
NA, NA, NA, NA, NA, NA, NA, 1, 1, 0, 4, 3, 1, 1, 3, 3, 2, 1,
2), TICS_9 = c(NA, NA, NA, NA, NA, NA, NA, NA, 0, 3, 2, 2, 1,
3, 0, 1, 3, 1, 1, 2), TICS_10 = c(2, 2, 0, 0, 2, 3, 0, 2, 1,
1, 2, 2, 1, 0, 0, 1, 1, 2, 2, 1), TICS_11 = c(1, 2, 1, 0, 1,
1, 0, 0, 0, 0, 2, 4, 1, 0, 0, 0, 0, 1, 1, 0), TICS_12 = c(2,
2, 1, 0, 1, 1, 1, 3, 1, 1, 1, 4, 2, 2, 2, 3, 3, 1, 2, 3), TICS_13=
c(1, 1, 3, 0, 2, 3, 2, 1, 1, 2, 1, 2, 2, 3, 2, 2, 1, 2, 2, 2),
TICS_14= c(4, 1, 1, 0, 1, 1, 3, 4, 0, 2, 0, 4, 2, 3, 0, 1, 3, 1, 1,
1), TICS_15= c(3, 1, 1, 3, 0, 2, 0, 2, 0, 2, 1, 2, 0, 1, 1, 1, 0, 0,
0, 1), ICS_16= c(4, 2, 1, 3, 3, 2, 1, 2, 1, 1, 1, 3, 1, 3, 1, 2, 3,
1, 2, 1), TICS_17= c(3, 0, 2, 2, 1, 2, 2, 3, 0, 1, 1, 2, 1, 2, 2, 3,
1, 1, 1, 2), TICS_18= c(3, 0, 1, 2, 0, 1, 1, 0, 0, 1, 0, 4, 2, 2, 0,
0, 1, 0, 2, 0), TICS_19= c(4, 2, 2, 2, 2, 2, 0, 2, 1, 2, 1, 4, 3, 2,
1, 1, 1, 0, 1, 2), TICS_20= c(2, 0, 2, 0, 0, 0, 1, 0, 1, 1, 0, 4, 1,
1, 0, 0, 1, 0, 2, 0), TICS_21= c(2, 1, 1, 0, 2, 3, 0, 1, 0, 1, 3, 2,
2, 1, 2, 1, 1, 1, 3, 0), TICS_22= c(3, 0, 1, 2, 2, 3, 1, 4, 0, 1, 1,
2, 3, 1, 1, 2, 3, 2, 0, 3), TICS_24= c(2, 0, 0, 1, 0, 0, 2, 0, 1, 1,
0, 2, 0, 0, 0, 1, 1, 0, 0, 1), TICS_25= c(4, 0, 1, 2, 2, 2, 4, 2, 1,
1, 0, 3, 0, 2, 0, 1, 2, 1, 2, 1), TICS_26= c(3, 0, 2, 2, 0, 1, 1, 0,
0, 1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 1), TICS_27= c(3,
1, 4, 2, 3, 3, 4, 4, 0, 1, 0, 3, 2, 3, 2, 3, 2, 2, 4, 3), TICS_28=
c(3, 2, 2, 1, 1, 2, 1, 2, 1, 1, 0, 4, 1, 2, 1, 0, 1, 0, 0, 2),
TICS_29= c(2, 0, 1, 0, 2, 2, 1, 0, 1, 0, 0, 4, 1, 1, 0, 1, 0, 0, 1,
1), TICS_30= c(2, 1, 3, 1, 2, 2, 1, 0, 1, 1, 1, 3, 2, 0, 1, 0, 1, 2,
2, 2), TICS_31= c(2, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 3, 2, 1, 0, 0, 1,
0, 2, 1), TICS_32= c(4, 1, 1, 0, 1, 2, 1, 4, 0, 3, 0, 3, 3, 2, 1, 2,
2, 2, 3, 3), TICS_33= c(2,
1, 0, 2, 1, 1, 1, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 1, 1, 1), TICS_34=
c(1, 3, 0, 0, 2, 1, 1, 1, 0, 0, 2, 4, 0, 0, 0, 0, 0, 0, 0, 0),
TICS_35= c(1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0, 2, 0, 1, 0, 1, 1, 0, 4,
1), TICS_36= c(4, 1, 2, 3, 3, 2, 4, 1, 0, 1, 2, 3, 1, 3, 0, 1, 1, 0,
2, 1), TICS_37= c(1, 1, 2, 0, 2, 3, 3, 0, 1, 2, 1, 2, 1, 0, 2, 2, 1,
1, 2, 1), TICS_38= c(3, 0, 3, 1, 2, 2, 2, 3, 0, 2, 0, 4, 0, 2, 1, 2,
2, 1, 1, 2), TICS_39= c(1, 1, 2, 2, 3, 1, 1, 2, 1, 1, 1, 4, 1, 1, 1,
1, 3, 0, 0, 3), TICS_40= c(2, 0, 2, 0, 3, 2, 1, 2, 0, 0, 0, 3, 2, 2,
0, 1, 2, 0, 0, 1), TICS_41= c(2, 2, 0, 0, 2, 3, 1, 1, 0, 1, 3, 1, 2,
0, 1, 0, 0, 1, 2, 0), TICS_42= c(1, 2, 0, 0, 2, 1, 0, 0, 0, 1, 1, 2,
1, 1, 1, 0, 0, 0, 0, 0), TICS_43= c(4,
1, 1, 2, 2, 3, 3, 3, 0, 2, 1, 4, 3, 2, 1, 1, 3, 1, 2, 3), TICS_44=
c(3, 0, 2, 1, 2, 2, 3, 3, 0, 1, 0, 4, 1, 3, 0, 2, 2, 1, 3, 1),
TICS_45= c(2,
0, 1, 2, 0, 1, 0, 2, 0, 1, 0, 2, 0, 2, 0, 0, 0, 0, 0, 1), TICS_46=
c(2, 1, 0, 1, 2, 2, 1, 0, 0, 3, 1, 4, 3, 1, 1, 0, 1, 1, 2, 1),
TICS_47= c(3,
1, 2, 1, 2, 2, 1, 1, 1, 2, 0, 3, 1, 2, 1, 2, 1, 1, 4, 1), TICS_48=
c(1,
2, 3, 1, 2, 3, 1, 1, 0, 2, 2, 4, 2, 3, 2, 2, 1, 0, 2, 0), TICS_49=
c(1,
3, 2, 2, 1, 2, 2, 1, 0, 1, 1, 4, 3, 0, 1, 2, 4, 1, 0, 3), TICS_50=
c(3,
0, 3, 1, 1, 2, 4, 3, 0, 2, 0, 4, 2, 3, 2, 2, 2, 2, 2, 3), TICS_51=
c(1,
2, 0, 0, 2, 1, 0, 0, 0, 0, 1, 2, 1, 0, 1, 0, 0, 0, 0, 0), TICS_52=
c(2,
1, 3, 0, 1, 1, 1, 1, 0, 1, 0, 2, 0, 3, 0, 0, 0, 0, 0, 1), TICS_53=
c(2,
2, 2, 0, 2, 3, 1, 1, 0, 2, 2, 3, 2, 2, 2, 1, 1, 1, 2, 1), TICS_54=
c(3,
0, 3, 2, 2, 2, 3, 3, 1, 2, 0, 4, 0, 2, 0, 2, 2, 0, 2, 1), TICS_55=
c(2,
0, 0, 1, 0, 1, 2, 0, 0, 1, 0, 4, 0, 1, 0, 1, 1, 0, 2, 0), TICS_56=
c(4,
3, 1, 0, 2, 0, 0, 0, 1, 0, 1, 2, 1, 1, 1, 0, 0, 0, 2, 0), TICS_57=
c(2,
1, 1, 0, 2, 1, 0, 0, 1, 1, 1, 4, 3, 0, 0, 1, 1, 0, 0, 2), HSPS_1 =
c(3,
4, 3, 3, 4, 2, 4, 2, 4, 2, 3, 4, 2, 2, 4, 2, 3, 3, 5, 2), HSPS_2 =
c(4,
4, 3, 5, 5, 3, 2, 4, 5, 5, 3, 4, 3, 4, 4, 2, 4, 3, 4, 3), HSPS_3 =
c(4,
4, 4, 3, 3, 4, 3, 3, 3, 3, 3, 5, 3, 4, 5, 3, 3, 3, 4, 2), HSPS_4 =
c(4,
2, 1, 4, 2, 3, 5, 3, 5, 2, 3, 3, 3, 4, 3, 3, 4, 2, 5, 2), HSPS_5 =
c(2,
2, 2, 4, 3, 3, 3, 1, 4, 3, 3, 4, 3, 2, 4, 3, 4, 3, 5, 1), HSPS_6 =
c(4,
3, 1, 3, 4, 3, 3, 3, 3, 2, 1, 1, 1, 3, 5, 3, 3, 1, 1, 2), HSPS_7 =
c(4,
3, 1, 3, 4, 2, 3, 1, 4, 3, 2, 4, 1, 1, 5, 3, 3, 1, 5, 1), HSPS_8 =
c(4,
3, 5, 5, 4, 5, 5, 3, 4, 4, 3, 3, 2, 4, 4, 3, 4, 3, 3, 3), HSPS_9 =
c(3,
2, 2, 5, 3, 3, 4, 1, 5, 2, 2, 4, 1, 2, 4, 4, 3, 1, 5, 2), HSPS_10=
c(4,
4, 5, 4, 4, 4, 3, 1, 4, 3, 3, 4, 2, 1, 5, 3, 4, 4, 3, 2), HSPS_11=
c(3,
2, 2, 3, 2, 2, 3, 1, 3, 2, 4, 5, 1, 3, 3, 3, 3, 2, 3, 2), HSPS_12=
c(4,
4, 5, 5, 4, 5, 4, 3, 4, 4, 5, 3, 4, 4, 5, 4, 4, 4, 5, 4), HSPS_13=
c(3,
2, 3, 2, 2, 2, 5, 2, 3, 2, 3, 4, 3, 3, 3, 3, 4, 2, 5, 2), HSPS_14=
c(3,
2, 2, 3, 3, 3, 5, 3, 3, 2, 3, 3, 2, 3, 2, 3, 3, 2, 4, 2), HSPS_15=
c(4,
4, 2, 3, 4, 3, 3, 3, 4, 2, 3, 3, 5, 2, 4, 2, 3, 3, 3, 2), HSPS_16=
c(2,
2, 1, 5, 2, 3, 2, 2, 3, 3, 3, 5, 2, 3, 3, 3, 2, 2, 5, 2), HSPS_17=
c(4,
3, 4, 5, 3, 4, 4, 2, 4, 3, 5, 4, 4, 4, 5, 4, 5, 2, 5, 4), HSPS_18=
c(2,
2, 1, 2, 1, 2, 2, 1, 3, 2, 2, 5, 2, 1, 4, 3, 2, 1, 5, 1), HSPS_19=
c(3,
2, 2, 4, 2, 2, 3, 1, 4, 2, 2, 4, 1, 1, 4, 3, 2, 2, 5, 2), HSPS_20=
c(4,
4, 4, 3, 4, 3, 5, 3, 3, 3, 4, 3, 3, 4, 4, 3, 5, 3, 5, 2), HSPS_21=
c(3,
3, 4, 5, 3, 3, 5, 2, 4, 2, 3, 5, 4, 4, 3, 2, 3, 2, 5, 2), HSPS_22=
c(3,
5, 5, 4, 5, 4, 3, 2, 4, 3, 3, 5, 3, 2, 4, 2, 4, 3, 5, 2), HSPS_23=
c(2,
2, 1, 4, 2, 3, 4, 3, 3, 2, 2, 5, 3, 3, 3, 3, 3, 2, 5, 3), HSPS_24=
c(3,
2, 2, 3, 3, 3, 3, 2, 4, 2, 3, 5, 4, 2, 4, 4, 4, 3, 4, 2), HSPS_25=
c(3,
2, 2, 5, 3, 3, 5, 1, 4, 2, 3, 5, 3, 2, 4, 3, 3, 2, 5, 2), HSPS_26=
c(2,
1, 1, 3, 3, 3, 3, 2, 3, 2, 2, 5, 2, 2, 3, 3, 3, 2, 5, 2), HSPS_27=
c(2,
2, 1, 4, 3, 2, 3, 4, 3, 1, 4, 1, 1, 3, 4, 2, 3, 2, 5, 3), sex =
structure(c(2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 1L), .Label = c("m", "w", "d"), class = "factor"), Bildung =
structure(c(6L,
5L, 5L, 6L, 6L, 6L, 5L, 6L, 5L, 6L, 6L, 4L, 6L, 5L, 5L, 6L, 6L,
5L, 5L, 6L), .Label = c("kein", "Haupt", "mittlereR", "Fachabi",
"Abi", "Studium"), class = "factor"), job = structure(c(6L, 2L,
2L, 2L, 2L, 6L, 2L, 6L, 5L, 2L, 2L, 1L, 6L, 2L, 2L, 2L, 6L, 2L,
2L, 6L), .Label = c("hausl", "Student", "Azubi", "Suchend", "Rente",
"berufstaetig"), class = "factor"), age = c(23, 24, 21, 70, 25,
29, 22, 25, 57, 24, 25, 30, 31, 20, 28, 27, 26, 21, 24, 53),
VPN = 1:20, consent = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label =
c("ja",
"nein"), class = "factor"), psy = c(0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0), HSPS = c(86, 75,
69, 102, 85, 82, 97, 59, 100, 68, 80, 106, 68, 73, 105, 79,
91, 63, 119, 59), neuro = c(16, 3, 4, 10, 10, 11, 12, 5,
5, 5, 5, 16, 5, 18, 4, 3, 8, 5, 19, 7), extra = c(15, 17,
19, 7, 19, 17, 18, 17, 16, 10, 17, 14, 15, 19, 11, 13, 16,
18, 9, 13), TICS_Skala1 = c(23, 1, 22, 11, 14, 16, 22, 25,
2, 11, 1, 29, 9, 20, 10, 19, 16, 9, 18, 16), TICS_Skala2 = c(14,
12, 11, 9, 11, 10, 4, 10, 5, 8, 5, 24, 13, 5, 6, 6, 14, 2,
1, 13), TICS_Skala3 = c(21, 6, 10, 5, 12, 14, 11, 20, 3,
11, 4, 27, 20, 13, 7, 13, 20, 11, 11, 18), TICS_Skala4 = c(13,
14, 13, 2, 16, 23, 10, 9, 3, 13, 15, 18, 14, 11, 13, 10,
7, 9, 17, 6), TICS_Skala5 = c(12, 2, 6, 5, 3, 5, 8, 3, 4,
6, 0, 18, 3, 7, 1, 6, 6, 1, 13, 3), TICS_Skala6 = c(10, 2,
3, 4, 4, 6, 3, 0, 0, 5, 2, 15, 10, 5, 2, 1, 5, 2, 8, 3),
TICS_Skala7 = c(15, 5, 9, 13, 4, 8, 4, 9, 1, 6, 2, 11, 2,
12, 3, 2, 1, 3, 2, 7), TICS_Skala8 = c(8, 10, 3, 0, 11, 7,
2, 1, 2, 2, 7, 20, 7, 2, 2, 2, 1, 1, 2, 3), TICS_Skala9 = c(12,
3, 4, 8, 8, 6, 9, 5, 2, 6, 5, 11, 3, 11, 1, 5, 9, 3, 7, 5
), TICS_Skala10 = c(32, 5, 18, 16, 19, 18, 21, 16, 5, 17,
7, 39, 12, 24, 3, 15, 20, 6, 25, 14), neuro.c = c(6.08921933085502,
-6.91078066914498, -5.91078066914498, 0.089219330855018,
0.089219330855018, 1.08921933085502, 2.08921933085502,
-4.91078066914498,
-4.91078066914498, -4.91078066914498, -4.91078066914498,
6.08921933085502, -4.91078066914498, 8.08921933085502,
-5.91078066914498,
-6.91078066914498, -1.91078066914498, -4.91078066914498,
9.08921933085502, -2.91078066914498), extra.c = c(5.21003717472119,
7.21003717472119, 9.21003717472119, -2.78996282527881,
9.21003717472119,
7.21003717472119, 8.21003717472119, 7.21003717472119,
6.21003717472119,
0.21003717472119, 7.21003717472119, 4.21003717472119,
5.21003717472119,
9.21003717472119, 1.21003717472119, 3.21003717472119,
6.21003717472119,
8.21003717472119, -0.78996282527881, 3.21003717472119), age.c =
c(-15.4460966542751,
-14.4460966542751, -17.4460966542751, 31.5539033457249,
-13.4460966542751,
-9.4460966542751, -16.4460966542751, -13.4460966542751,
18.5539033457249,
-14.4460966542751, -13.4460966542751, -8.4460966542751,
-7.4460966542751,
-18.4460966542751, -10.4460966542751, -11.4460966542751,
-12.4460966542751, -17.4460966542751, -14.4460966542751,
14.5539033457249), HSP.c = c(-1.92936802973978, -12.9293680297398,
-18.9293680297398, 14.0706319702602, -2.92936802973978,
-5.92936802973978,
9.07063197026022, -28.9293680297398, 12.0706319702602,
-19.9293680297398,
-7.92936802973978, 18.0706319702602, -19.9293680297398,
-14.9293680297398,
17.0706319702602, -8.92936802973978, 3.07063197026022,
-24.9293680297398,
31.0706319702602, -28.9293680297398), HSPhoch = c(1, 0, 0,
1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0)), row.names =
c(NA, 20L), class = "data.frame")

Percentage histogram with facet_grid: x variable is a factor

I want to split a percentage histogram (that integrates to 100%) into two facets using facet_grid. However, when splitting to facets, each facet by itself doesn't integrate to 100%. This kind of question has been resolved here in the past, but I cannot translate that solution to my current situation where x is a factor, and thus a histogram using stat(density) doesn't work.
My Data
Dataframe with two columns. equipment denotes whether a household has enough equipment for homeschooling, and children_n denotes number of children.
library(tidyverse)
library(magrittr)
df <-
structure(list(equipment = c(1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0,
1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0,
0, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,
0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1, 1,
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,
1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 1,
0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1,
0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,
1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0,
0, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1,
1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1,
0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1,
1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1,
1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1,
1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1,
1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0,
0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1,
1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1,
1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,
1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1,
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,
1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0,
1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1,
0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0,
0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 1,
0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,
0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0,
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,
1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1,
0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0,
0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1,
1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1,
1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1,
1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0,
1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1), children_n = c(4,
4, 2, 2, 2, 1, 1, 3, 2, 3, 3, 7, 3, 2, 1, 2, 1, 1, 3, 3, 3, 2,
3, 3, 3, 2, 4, 3, 1, 2, 3, 4, 4, 1, 2, 5, 2, 8, 1, 2, 1, 2, 2,
3, 4, 3, 3, 3, 3, 2, 3, 2, 2, 4, 3, 3, 3, 4, 3, 1, 1, 2, 1, 1,
2, 1, 3, 3, 2, 3, 3, 3, 4, 2, 2, 2, 3, 5, 2, 2, 2, 2, 1, 2, 4,
3, 4, 3, 3, 1, 2, 3, 3, 3, 2, 4, 4, 3, 1, 3, 2, 2, 2, 3, 1, 1,
1, 3, 1, 2, 2, 2, 3, 6, 3, 2, 2, 6, 3, 4, 3, 2, 3, 3, 2, 2, 2,
3, 2, 3, 3, 6, 3, 1, 4, 3, 4, 9, 1, 1, 3, 4, 2, 2, 1, 2, 3, 1,
3, 3, 6, 4, 1, 3, 2, 2, 3, 2, 3, 2, 4, 3, 1, 3, 3, 2, 3, 2, 2,
4, 2, 2, 3, 3, 3, 1, 3, 3, 2, 4, 2, 7, 3, 3, 3, 2, 2, 2, 4, 3,
1, 1, 3, 4, 1, 4, 3, 4, 3, 3, 2, 3, 3, 3, 2, 3, 3, 2, 3, 3, 3,
3, 1, 1, 2, 2, 4, 2, 3, 3, 2, 2, 1, 2, 5, 2, 2, 2, 5, 3, 2, 2,
4, 2, 1, 3, 4, 4, 3, 3, 4, 3, 3, 1, 3, 2, 1, 8, 2, 3, 2, 3, 3,
2, 3, 3, 1, 3, 3, 4, 2, 3, 3, 3, 2, 6, 1, 2, 2, 2, 2, 2, 2, 4,
3, 5, 4, 1, 2, 2, 2, 4, 2, 3, 3, 1, 3, 2, 1, 2, 2, 3, 3, 3, 3,
1, 3, 4, 2, 1, 3, 4, 2, 1, 3, 4, 3, 4, 2, 3, 3, 2, 7, 1, 2, 1,
3, 2, 2, 2, 2, 3, 3, 3, 2, 3, 1, 2, 2, 3, 2, 4, 3, 2, 3, 3, 5,
3, 5, 3, 5, 1, 2, 1, 4, 1, 4, 2, 2, 3, 2, 2, 2, 3, 2, 3, 3, 3,
3, 4, 3, 8, 3, 1, 2, 3, 3, 2, 1, 3, 2, 2, 3, 3, 4, 4, 2, 2, 3,
1, 2, 3, 2, 3, 3, 2, 1, 3, 3, 2, 3, 3, 3, 4, 1, 2, 3, 3, 3, 4,
2, 1, 3, 4, 2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 1, 3, 3, 1, 1, 3,
2, 1, 3, 2, 4, 1, 3, 2, 3, 2, 2, 2, 4, 1, 2, 3, 2, 3, 2, 2, 1,
3, 1, 3, 1, 3, 3, 2, 1, 2, 3, 2, 3, 1, 2, 1, 2, 2, 3, 3, 4, 1,
2, 4, 2, 4, 2, 2, 2, 1, 3, 2, 1, 1, 4, 3, 4, 3, 2, 2, 2, 3, 7,
3, 1, 3, 3, 3, 2, 1, 3, 2, 3, 3, 2, 4, 1, 1, 1, 4, 3, 3, 4, 3,
8, 2, 4, 5, 3, 2, 3, 1, 2, 1, 2, 2, 3, 1, 4, 3, 2, 2, 3, 3, 3,
3, 1, 2, 1, 2, 3, 3, 2, 2, 2, 2, 3, 3, 4, 5, 3, 2, 2, 2, 3, 1,
3, 3, 4, 2, 1, 3, 3, 3, 4, 2, 1, 2, 1, 2, 2, 3, 3, 4, 1, 1, 6,
3, 2, 2, 2, 6, 3, 3, 2, 2, 1, 4, 2, 3, 3, 3, 2, 2, 3, 3, 2, 4,
6, 1, 1, 1, 1, 3, 9, 4, 2, 3, 2, 2, 2, 4, 3, 3, 4, 1, 2, 6, 3,
3, 3, 2, 2, 3, 4, 2, 3, 2, 2, 3, 2, 3, 4, 7, 2, 3, 3, 2, 3, 2,
3, 4, 3, 3, 3, 2, 2, 2, 1, 3, 4, 2, 1, 3, 4, 1, 3, 4, 4, 3, 3,
3, 3, 3, 2, 3, 3, 3, 5, 3, 3, 5, 2, 2, 1, 1, 2, 2, 2, 3, 1, 3,
2, 2, 2, 4, 2, 2, 2, 4, 1, 3, 4, 3, 3, 4, 3, 2, 1, 3, 4, 8, 1,
2, 3, 3, 3, 3, 2, 3, 3, 1, 3, 4, 2, 3, 2, 6, 3, 1, 2, 2, 2, 2,
2, 4, 3, 5, 1, 2, 2, 2, 4, 2, 3, 3, 1, 1, 2, 2, 3, 3, 2, 3, 3,
3, 3, 1, 4, 4, 2, 3, 3, 1, 4, 3, 4, 2, 3, 3, 2, 7, 1, 4, 1, 2,
2, 3, 2, 5, 2, 3, 2, 3, 1, 3, 2, 2, 3, 2, 4, 2, 3, 3, 3, 3, 1,
5, 5, 1, 1, 2, 3, 1, 4, 2, 2, 3, 2, 2, 2, 3, 3, 3, 3, 2, 3, 4,
8, 3, 2, 3, 1, 2, 3, 1, 2, 2, 3, 2, 3, 3, 4, 4, 2, 3, 2, 1, 3,
2, 3, 3, 2, 3, 3, 2, 3, 2, 3, 3, 1, 1, 2, 4, 3, 4, 3, 1, 3, 4,
2, 3, 3, 2, 2, 2, 2, 2, 3, 3, 3, 1, 3, 3, 2, 1, 1, 4, 1, 3, 2,
1, 2, 3, 3, 2, 2, 2, 4, 2, 1, 3, 2, 3, 2, 1, 3, 1, 3, 1, 3, 3,
2, 1, 2, 3, 2, 3, 1, 2, 2, 2, 3, 3, 2, 3, 1, 3, 3, 3, 3, 2, 4,
2, 4, 4, 1, 2, 1, 2, 1, 3, 3, 3, 2, 3, 3, 4, 2, 2, 3, 2, 1, 2,
2, 1, 1, 3, 1, 2, 3, 3, 3, 2, 1, 1, 1, 2, 1, 2, 5, 1, 2, 1, 4,
2, 2, 2, 1, 4, 2, 3, 3, 3, 2, 4, 5, 4, 2, 4, 2, 3, 1, 4, 3, 3,
2, 3, 3, 2, 3, 2, 1, 3, 2, 4, 2, 3, 4, 1, 2, 3, 1, 3, 3, 4, 2,
2, 2, 3, 3, 2, 1, 2, 2, 1, 3, 1, 3, 1, 1, 1, 3, 2, 2, 4, 3, 4,
3, 3, 4, 1, 1, 3, 3, 2, 3, 2, 3, 2, 1, 3, 3, 1, 5, 1, 1, 2, 4,
2, 3, 5, 4, 1, 3, 2, 1, 2, 2, 4, 3, 4, 2, 2, 1, 3, 2, 4, 2, 3,
3, 2, 3, 2, 1, 2, 3, 4)), row.names = c(NA, -1059L), class = c("tbl_df",
"tbl", "data.frame"))
df
## # A tibble: 1,059 x 2
## equipment children_n
## <dbl> <dbl>
## 1 1 4
## 2 0 4
## 3 1 2
## 4 1 2
## 5 0 2
## 6 1 1
## 7 1 1
## 8 1 3
## 9 1 2
## 10 1 3
## # ... with 1,049 more rows
In cases where number of children is above 6, I want to collapse those cases to one category of "6+".
df %<>%
mutate_at(vars(children_n), as.character) %>%
mutate_at(vars(children_n), recode, "9" = "6_plus", "8" = "6_plus", "7" = "6_plus", "6" = "6_plus") %>%
mutate_at(vars(children_n), fct_relevel, "1", "2", "3", "4", "5", "6_plus")
glimpse(df)
## Rows: 1,059
## Columns: 2
## $ equipment <dbl> 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, ...
## $ children_n <fct> 4, 4, 2, 2, 2, 1, 1, 3, 2, 3, 3, 6_plus, 3, 2, 1, 2, 1, 1, 3, 3, 3, 2, 3, 3, 3, 2, 4, 3, 1, 2, 3, 4, 4, 1, 2, 5, 2, 6_plus, 1, 2, 1, 2,...
Now I want to plot the proportion of number of children in two separate panels: one panel for families who have enough equipment, and another panel for families who don't have enough equipment:
df %>%
ggplot(data = ., aes(x = children_n, y = equipment)) +
geom_histogram(aes(y = (..count..)/sum(..count..)), stat = "count" , fill = "darkblue") +
geom_text(aes(label = scales::percent(((..count..)/sum(..count..)), accuracy = 1),
y = ((..count..)/sum(..count..)) ), stat= "count", vjust = -.5, color = "darkblue") +
scale_y_continuous(labels = scales::percent) +
facet_grid(~ equipment, labeller = as_labeller(c("1" = "have enough equipment",
"0" = "don't have enough equipment")))
This gives two panels that *DON'T* integrate to 100% independently:
Trying to solve the problem
I found this question that describes the same intention and problem. The chosen solution suggests defining the geom_histogram as density so it integrates to 100%. But this won't work in my case because stat(density) asks that the x variable will be continuous, unlike my case where x is a factor.
df %>%
ggplot(data = ., aes(x = children_n, y = equipment)) +
geom_histogram(aes(y = stat(density) * 6), binwidth = 6, fill = "darkblue") +
facet_grid(~ equipment, labeller = as_labeller(c("1" = "have enough equipment",
"0" = "don't have enough equipment")))
Error: StatBin requires a continuous x variable: the x variable is
discrete. Perhaps you want stat="count"?
Other approaches suggest using ..PANEL.. while others are strongly against it.
How can I get the two facets to show percents that independently integrate to 100%, in a proper way?

This could be achieved like so:
Map the facetting variable on the group aes
Use e.g. tapply to get the total number per group or facet
BTW: I have put the code for the normalization inside a helper function to reduce the code duplication and readability
library(tidyverse)
library(magrittr)
df %<>%
mutate_at(vars(children_n), as.character) %>%
mutate_at(vars(children_n), recode, "9" = "6_plus", "8" = "6_plus", "7" = "6_plus", "6" = "6_plus") %>%
mutate_at(vars(children_n), fct_relevel, "1", "2", "3", "4", "5", "6_plus")
help <- function(count, group) {
count / tapply(count, group, sum)[group]
}
df %>%
ggplot(data = ., aes(x = children_n, y = equipment, group = equipment)) +
geom_histogram(aes(y = help(..count.., ..group..)), stat = "count" , fill = "darkblue") +
geom_text(aes(label = scales::percent(help(..count.., ..group..), accuracy = 1),
y = help(..count.., ..group..) ), stat= "count", vjust = -.5, color = "darkblue") +
scale_y_continuous(labels = scales::percent) +
facet_grid(~ equipment, labeller = as_labeller(c("1" = "have enough equipment",
"0" = "don't have enough equipment")))
#> Warning: Ignoring unknown parameters: binwidth, bins, pad

Plotting Logit Curve: Error in xy.coords(x, y) : 'x' and 'y' lengths differ

I am trying to plot a logit curve. The code below works well except for the last line which throws this error: "Error in xy.coords(x, y) : 'x' and 'y' lengths differ". Below is dataset's structure. What could be wrong?
Observations: 532
Variables: 14
$ `N Personal` <chr> "P001", "P002", "P005", "P007", "P008", "P010", "P011", "P012", "P...
$ Binaria <dbl> 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, ...
$ Rendimiento <dbl> 0.81, 0.75, 0.75, 0.60, 0.80, 1.00, 0.75, 0.85, 0.86, 0.86, 0.85, ...
$ Edad <dbl> 2, 3, 2, 3, 3, 2, 2, 1, 2, 2, 1, 2, 2, 3, 2, 3, 3, 2, 3, 3, 3, 4, ...
$ Posición <dbl> 2, 1, 2, 2, 2, 2, 1, 1, 1, 1, 2, 1, 1, 3, 1, 1, 4, 2, 5, 1, 2, 2, ...
$ Sexo <dbl> 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, ...
$ Nacional <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, ...
$ Distancia <dbl> 1, 4, 3, 5, 3, 1, 4, 1, 1, 2, 3, 4, 2, 2, 5, 4, 4, 5, 4, 1, 2, 4, ...
$ Estrato <dbl> 3, 2, 3, 2, 5, 3, 5, 3, 3, 3, 3, 1, 3, 5, 3, 2, 4, 5, 6, 3, 3, 6, ...
$ `Estado civil` <dbl> 1, 4, 1, 1, 3, 1, 3, 1, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1, 3, 1, 1, 1, ...
$ Hijos <dbl> 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, ...
$ Formación <chr> NA, NA, NA, "PREGRADO", "PREGRADO", NA, NA, "PREGRADO", "PREGRADO"...
$ Educación <dbl> NA, NA, NA, 3, 3, NA, NA, 3, 3, NA, 3, NA, 3, 4, NA, 3, NA, 3, 3, ...
$ Performance <dbl> 2, 3, 3, 4, 3, 1, 3, 2, 2, 2, 2, 2, 3, 1, 2, 2, 1, 1, 2, 3, 2, 2, ...
logit1=glm(RH$Binaria ~ RH$Posición, data=RH, family = "binomial" )
plot(RH_train$Posición, RH_train$Binaria, pch = 16, xlab = "Posicion", ylab = "Binaria")
xPosicion <-seq (1, 5, 0.5)
yPosicion <- predict(logit1, list(Posi= xPosicion), type="response")
lines(xPosicion, yPosicion)
Error in xy.coords(x, y) : 'x' and 'y' lengths differ

Creating a barplot from matrix

So, I have a matrix like that:
> dput(tbl_sum_peaks[1:40])
structure(c(2, 8, 3, 4, 1, 2, 1, 3, 1, 3, 1, 4, 4, 2, 1, 1, 2,
1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1, 5, 4, 2, 1, 1, 2, 1,
4, 2), .Names = c("AT1G01050", "AT1G01080", "AT1G01090", "AT1G01320",
"AT1G01470", "AT1G01800", "AT1G01910", "AT1G01960", "AT1G01980",
"AT1G02150", "AT1G02470", "AT1G02500", "AT1G02560", "AT1G02780",
"AT1G02816", "AT1G02880", "AT1G02920", "AT1G02930", "AT1G03030",
"AT1G03090", "AT1G03110", "AT1G03210", "AT1G03220", "AT1G03230",
"AT1G03330", "AT1G03475", "AT1G03630", "AT1G03680", "AT1G03740",
"AT1G03870", "AT1G04080", "AT1G04170", "AT1G04270", "AT1G04410",
"AT1G04420", "AT1G04530", "AT1G04640", "AT1G04650", "AT1G04690",
"AT1G04750"))
I would like to make a barplot which will have on yaxis the number of rows with specific number. As we see it the given example data most of the rows has a number 1 so the barplot for number 1 will be the tallest.
That's a basic but I can't turn on my brain... so help from someone will be rewarded!

Try
barplot(table(tbl_sum_peaks))

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