Imputing missing values keeping a rectangular shape in mind - r

I have a data set where number denotes a particular color. Since i have large data set, i am sharing a sample data and work. I am looking forward to create this
Output
d <- matrix(c(
1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1,
1, 2, 2, 2, 2, 1,
1, 2, 3, 3, 2, 1,
1, 2, 3, 3, 2, 1,
1, 2, 3, 3, 2, 1,
1, 2, 3, 3, 2, 1,
1, 2, 2, 2, 2, 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), nrow=13, byrow = TRUE)
from this Input :
d_mi <- d
d_mi[ sample(1:length(d), length(d)*0.3) ] <- NA
d_mi
Optional : Output(in color mode)

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)

Create groups in data frame based upon whether next value reaches a threshold

I have df with a column diff_index.
I'd like to create a grouping column based on whether the next value is greater than a threshold x - if the next value is greater than x, then I want a new group.
So in this case, if the threshold is 100, the first 12 entries will be group 1, then since the 13th value is 3877, group 2 begins here, until we reach 1979, in which group 3 starts, etc.
A data.table solution would be ideal.
df=structure(list(diff_index = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 3877, 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, 2, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 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, 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, 1, 1, 1, 1,
1, 1, 1, 1979, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 136, 1, 1, 1, 1, 97, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 11905, 1, 1, 1, 2764, 1, 1, 1, 676, 1, 1, 1, 2,
1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 2, 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, 1, 1, 1, 1, 469, 1, 1,
2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1,
1, 1, 1, 1, 19, 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, 1, 1, 1, 1, 1,
8121, 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, 1737, 1, 1, 1, 1, 1, 1, 1,
1, 681, 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, 1, NA)), row.names = c(NA,
-393L), class = "data.frame")

library(tidyverse)
df %>%
mutate(group = cumsum(diff_index > 100) + 1)
Or with data.table:
dt[,group:=cumsum(diff_index > 100) + 1]
diff_index group
1: 1 1
2: 1 1
3: 1 1
4: 1 1
5: 1 1
---
389: 1 11
390: 1 11
391: 1 11
392: 1 11
393: NA NA

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

Save single data frames found within a list by applying a for loop

I have to work on several data frames to perform the same set of operations. Because of this, I inserted them into a list, and by using lapply I perform some kind of operations.
Then, I want to save each dataframes separately as .txt files, by employing a for loop. Here's the code I wrote:
all <- lapply(names(sampleList),function(mysample){
aux <- read.table(sampleList[[mysample]], col.names=c("Chromosome","Position","Ref_allele","Alt_allele","Fraction","Fw_ref","Rv_ref","Fw_alt","Rv_alt"))
aux <- mutate(aux, ID=paste0(Chromosome, ":", Position)) %>% distinct(ID, .keep_all=T)
})
for( i in 1:length(all))
write.table(all[i], paste0(all[i],"_filtered.txt"))
I would expect to have n new dataframes, named as the original ones + _filtered at the end. But this is what actually happens:
Error in file(file, ifelse(append, "a", "w")) :
cannot open the connection
In addition: Warning message:
In file(file, ifelse(append, "a", "w")) :
cannot open file 'list(Chromosome = 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1 [... truncated]
But that's not what my data frames contains. By View(as.data.frame(all[n])) I see my data frames looks normal.
Many thanks for any help.

There are a couple things that can be done here:
for( i in 1:length(all))
write.table(all[i], paste0(all[i],"_filtered.txt"))
First, the second argument is supposed to be a string, but your paste0(all[i],...) is working on the frame itself (all[i] is a data.frame embedded in a list of length 1), not the name of it. You might get away with:
for( i in 1:length(all))
write.table(all[i], paste0(names(all)[i], "_filtered.txt"))
But you can continue the theme of "operate on a list of frames" (which is great, btw), by extending your lexicon from just lapply (which executes a function on one vector/list of data) to include Map and mapply, which zips together one or more vectors/lists into individual arguments of a function. Up front:
Map(write.table, all, paste0(names(all), "_filtered.txt"))
should save everything as you intend (and return a list of return values from write.table, which might not be useful).
Explanation: Map(myfun, 1:3, c('a','b','c'), c('X','Y','Z')) is unrolled internally to be
myfun(1, 'a', 'X')
myfun(2, 'b', 'Y')
myfun(3, 'c', 'Z')
So these two are nearly equivalent:
lapply(1:3, myfun)
Map(myfun, 1:3)
(There are some ever-so-minor performance differences between them. If you know you always have one vector, use lapply.)
So to continue a theme of "vectorize as much as you can" (which is a common mantra in R-circles), we generate the filenames early with paste0(names(all), "_filtered.txt"), and then pass two list/vector objects to Map: the list of data, and the file names.

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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