Shiny App got error Result must have length 37849, not 0 - r

I want to see the percentage of people based on their race from the age of 20 to 35 reported their educational status. In the next step I make shiny app. However, I got this error. ![enter image description here][1]
Please help me how to link this code to shiny app.
My question is how can I by changing sliderInput from the age of 20 to 35 , in each age knows how many people have high school, college degree, and bachelor based on their race.
Below you can see the coding of age, education and race.
# rename education
nlsy97$educstat1997<-Recode(nlsy97$R1205700, recodes="0='None';1:2='Hischool';3='college';4='bachelor';5:7='mastermore' ;-5=NA;-3=NA;-4=NA", as.factor=T)
#rename ages
nlsy97$age1<-Recode(nlsy97$R1194100, recodes="12=12;13=13;14=14;15=15;16=16=17=17;18=18;19=19;-5=NA",as.factor=F)
# recode race
nlsy97$race<-Recode(nlsy97$R1482600 , recodes="1='black';2='hispanic' ;4='white';else=NA", as.factor=T)
table(nlsy97$race)
In the next step, I have made the transitions.
myvars1<-c( "R0000100","R0536300", "R0536402","R1489700","R1489800", "gender","race","age1","age2","age3","age4","age5","age6","age7","age8","age9","age10","age11","age12","age13","age14","age15","age16","age17","educstat1997","educstat1998","educstat1999","educstat2000","educstat2001","educstat2002","educstat2003","educstat2004","educstat2005","educstat2006","educstat2007","educstat2008","educstat2009","educstat2010","educstat2011","educstat2013","educstat2015")
which(myvars1 %in% names(nlsy97))
sub<-nlsy97[,myvars1]
sub<-subset(sub,is.na(sub$age1)==F&is.na(sub$age2)==F&is.na(sub$age3)==F&is.na(sub$age4)==F&is.na(sub$age5)==F&is.na(sub$age6)==F&is.na(sub$age7)==F&is.na(sub$age8)==F&is.na(sub$age9)==F&is.na(sub$age10)==F&is.na(sub$age11)==F&is.na(sub$age12)==F&is.na(sub$age13)==F&is.na(sub$age14)==F&is.na(sub$age15)==F&is.na(sub$age16)==F&is.na(sub$age17)==F)
head(sub, n=5)
x.vertical<-reshape(sub, idvar="R0000100", varying=list(age1=c("age1", "age2", "age3","age4","age5","age6","age7","age8","age9","age10","age11","age12","age13","age14","age15","age16"), age2=c("age2", "age3","age4","age5","age6","age7","age8","age9","age10","age11","age12","age13","age14","age15","age16","age17"),educstat1=c("educstat1997","educstat1998","educstat1999","educstat2000","educstat2001","educstat2002","educstat2003","educstat2004","educstat2005","educstat2006","educstat2007","educstat2008","educstat2009","educstat2010","educstat2011","educstat2013"),educstat2=c("educstat1998","educstat1999","educstat2000","educstat2001","educstat2002","educstat2003","educstat2004","educstat2005","educstat2006","educstat2007","educstat2008","educstat2009","educstat2010","educstat2011","educstat2013","educstat2015")), times=1:16, direction="long", v.names=c( "agestart", "ageend","educstat1","educstat2") )
x.vertical<-x.vertical[order(x.vertical$R0000100, x.vertical$time),]
The last step is making shiny dashboard.
# STEP 1: copy an example Shiny app into app.R (or ui.R and server.R)
library(shiny)
library(tidyverse)
library(gapminder)
# User Interface
ui <- basicPage(
# STEP 3: Create an input widget here (e.g. sliderInput)
sliderInput("age", "Select Age:", animate = TRUE, # STEP 4: add animate = TRUE here
min = 20, max = 35, value = 25,
step = 1,
sep="" # so thousands are not separated with a comma (without this defaults to 1,952 - 2,007)
), #note this comma here - different to our usual R code
tabPanel("Plot", plotOutput(outputId = "myplot"))
)
x.vertical2<-x.vertical[complete.cases(x.vertical[, c("race","educstat2")]),]
sums<-as.data.frame(xtabs(~educstat2+race, x.vertical2))
# Server
server <- function(input, output) {
output$myplot <- renderPlot({
# STEP 2: copy your plot code here
x.vertical2 %>%
filter(agestart==input$agestart)
mutate(educstat2 = fct_relevel(educstat2,
"None", "Hischool", "c",
"college")) %>%
filter(is.na(educstat2)==F)%>%
group_by(race, educstat2)%>%
summarise(n = n())%>%
mutate(freq= n /sum(n))%>%
ggplot(aes(x = factor(educstat2),y=freq, fill= race)) +
geom_bar( stat="identity",position = "dodge") +theme_bw()
})
}
shinyApp(ui, server)
dput(head(nlsy97, 10))
structure(list(R0000100 = 1:10, R0536300 = c(2L, 1L, 2L, 2L,
1L, 2L, 1L, 2L, 1L, 1L), R0536401 = c(9L, 7L, 9L, 2L, 10L, 1L,
4L, 6L, 10L, 3L), R0536402 = c(1981L, 1982L, 1983L, 1981L, 1982L,
1982L, 1983L, 1981L, 1982L, 1984L), R1194100 = c(15L, 14L, 13L,
15L, 15L, 15L, 14L, 16L, 15L, 14L), R1205700 = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L), R1235800 = c(1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), R1482600 = c(4L, 2L, 2L, 2L, 2L, 2L, 2L,
4L, 4L, 4L), R2553500 = c(17L, 16L, 15L, 17L, 16L, 16L, 15L,
17L, 16L, 14L), R2564101 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L), R3876300 = c(18L, 17L, 16L, 18L, 17L, 17L, 16L, 18L,
17L, 15L), R3885701 = c(2L, 0L, 0L, 2L, 0L, 0L, 0L, 2L, 0L, 0L
), R5453700 = c(19L, 18L, 17L, 19L, 18L, 19L, 17L, 19L, 18L,
16L), R5464901 = c(2L, 2L, 0L, 2L, 2L, 2L, 0L, 2L, 2L, 0L), R7216000 = c(20L,
19L, 18L, 20L, 19L, 20L, 18L, 20L, 19L, 17L), R7228601 = c(2L,
2L, 2L, 2L, 2L, 2L, 0L, 2L, 2L, 0L), S1531400 = c(21L, 20L, 19L,
21L, 20L, 20L, 19L, 21L, 20L, 18L), S1542501 = c(2L, 2L, 2L,
2L, 2L, 2L, 0L, 2L, 2L, 2L), S2001000 = c(22L, 21L, 20L, 22L,
21L, 22L, -5L, 22L, 21L, 19L), S2012301 = c(4L, 2L, 3L, 2L, 2L,
2L, -5L, 4L, 2L, 2L), S3801100 = c(23L, 22L, 21L, 23L, 22L, 23L,
-5L, 23L, 22L, 20L), S3813801 = c(4L, 2L, 3L, 2L, 2L, 2L, -5L,
4L, 2L, 2L), S5401000 = c(24L, 23L, -5L, 24L, 23L, 24L, 22L,
24L, 23L, 21L), S5413400 = c(4L, 2L, -5L, 2L, 2L, 2L, 0L, 4L,
4L, 2L), S7501200 = c(25L, -5L, -5L, 25L, 24L, 25L, 23L, 25L,
24L, 22L), S7514300 = c(4L, -5L, -5L, 2L, 2L, 2L, 0L, 4L, 4L,
4L), T0008500 = c(26L, -5L, -5L, 26L, 25L, 25L, -5L, 26L, 25L,
23L), T0014700 = c(4L, -5L, -5L, 2L, 2L, 2L, -5L, 4L, 4L, 4L),
T2011100 = c(27L, 26L, -5L, 27L, 26L, 26L, -5L, -5L, 26L,
24L), T2016800 = c(4L, 2L, -5L, 2L, 2L, 2L, -5L, -5L, 4L,
4L), T3601500 = c(28L, 27L, 26L, 28L, 26L, 27L, 26L, 28L,
27L, 25L), T3607100 = c(4L, 2L, 3L, 2L, 2L, 2L, 1L, 5L, 4L,
4L), T5201400 = c(29L, 28L, -5L, 29L, 28L, 28L, 27L, -5L,
28L, -5L), T5207400 = c(4L, 2L, -5L, 2L, 2L, 2L, 1L, -5L,
4L, -5L), T5207500 = c(4L, 2L, -5L, 2L, 2L, 2L, 1L, -5L,
4L, -5L), T6651300 = c(29L, 29L, 28L, 30L, 29L, 29L, 28L,
30L, 29L, -5L), T6657200 = c(4L, 2L, 3L, 2L, 2L, 2L, 1L,
5L, 5L, -5L), T6657300 = c(4L, 2L, 3L, 2L, 2L, 2L, 1L, 5L,
5L, -5L), T8123600 = c(32L, 31L, 30L, 32L, 31L, 31L, -5L,
-5L, 31L, -5L), T8129600 = c(4L, 2L, 3L, 2L, 2L, 2L, -5L,
-5L, 5L, -5L), T8129700 = c(4L, 2L, 3L, 2L, 2L, 2L, -5L,
-5L, 5L, -5L), U0001800 = c(34L, 33L, -5L, 34L, 33L, 34L,
32L, 34L, 33L, -5L), U0009400 = c(4L, 2L, -5L, 2L, 2L, 2L,
1L, 5L, 5L, -5L), U1838500 = c(-5L, 35L, 34L, 36L, 35L, 35L,
-5L, -5L, 35L, 34L), weight = c(607550L, 0L, 0L, 261156L,
450091L, 367309L, 0L, 0L, 618091L, 0L)), row.names = c(NA,
10L), class = "data.frame")

Related

R plotly multiple plots only show last figure

I would like to make an interactive graphs based on user input. However I'm struggle to make more than one graphs using R plotly. Suppose I have following data and codes,
dput(norwd5)
structure(list(LENGTH_OF_STAY = c(57L, 28L, 15L, 28L, 14L, 49L,
15L, 22L, 17L, 81L, 34L, 24L, 31L, 38L, 33L, 22L, 21L, 49L, 188L,
21L, 21L, 36L, 24L, 23L, 48L, 54L, 42L, 62L, 13L, 139L, 29L,
49L, 15L, 7L, 43L, 28L, 31L, 22L, 23L, 26L, 33L, 30L, 127L, 22L,
22L, 15L, 28L, 26L, 15L, 31L, 22L, 89L, 28L, 60L, 54L, 37L, 20L,
135L, 155L, 51L, 15L, 8L, 38L, 16L, 16L, 22L, 30L, 14L, 16L,
18L, 14L, 272L, 25L, 22L, 18L, 21L, 188L, 264L, 34L, 34L, 136L,
23L, 142L, 25L, 32L, 58L, 163L, 16L, 35L, 23L, 50L, 71L, 10L,
19L, 22L, 24L, 45L, 29L, 15L, 82L), PRE_OPERATIVE_LOS = c(2L,
2L, 3L, 1L, 3L, 6L, 3L, 7L, 2L, 2L, 11L, 2L, 6L, 3L, 6L, 3L,
5L, 3L, 179L, 2L, 5L, 3L, 4L, 2L, 5L, 6L, 2L, 4L, 2L, 6L, 3L,
2L, 2L, 6L, 6L, 1L, 4L, 5L, 6L, 5L, 0L, 4L, 6L, 2L, 4L, 4L, 7L,
4L, 4L, 6L, 2L, 4L, 3L, 3L, 2L, 6L, 4L, 110L, 63L, 6L, 4L, 7L,
5L, 1L, 6L, 1L, 4L, 2L, 6L, 3L, 2L, 8L, 2L, 2L, 4L, 3L, 6L, 171L,
5L, 4L, 116L, 6L, 47L, 3L, 7L, 3L, 60L, 1L, 3L, 20L, 31L, 49L,
9L, 8L, 3L, 4L, 35L, 7L, 4L, 9L), POST_OPERATIVE_LOS = c(55L,
26L, 12L, 27L, 11L, 43L, 12L, 15L, 15L, 79L, 23L, 22L, 25L, 35L,
27L, 19L, 16L, 46L, 9L, 19L, 16L, 33L, 20L, 21L, 43L, 48L, 40L,
58L, 11L, 133L, 26L, 47L, 13L, 1L, 37L, 27L, 27L, 17L, 17L, 21L,
33L, 26L, 121L, 20L, 18L, 11L, 21L, 22L, 11L, 25L, 20L, 85L,
25L, 57L, 52L, 31L, 16L, 25L, 92L, 45L, 11L, 1L, 33L, 15L, 10L,
21L, 26L, 12L, 10L, 15L, 12L, 264L, 23L, 20L, 14L, 18L, 182L,
93L, 29L, 30L, 20L, 17L, 95L, 22L, 25L, 55L, 103L, 15L, 32L,
3L, 19L, 22L, 1L, 11L, 19L, 20L, 10L, 22L, 11L, 73L), digoxin_any = 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, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 1L, 2L), .Label = c("0:No", "1.Yes"), class = "factor")), row.names = c(NA,
-100L), class = c("data.table", "data.frame"), .internal.selfref = <pointer: 0x0000012f36b61ef0>)
num <- c('PRE_OPERATIVE_LOS','POST_OPERATIVE_LOS')
plist <- scan(text=num,what = "",quiet = T)
groups <- 'digoxin_any'
bygrp <- scan(text=groups,what="",quiet=T)
norwd5[, (bygrp) := lapply(.SD, as.factor), .SDcols = bygrp]
plotList = list()
for(i in length(plist)){
gplot <- ggplot(norwd5,aes_string(x=plist[i],group=bygrp,color=bygrp))+geom_histogram(aes(y=..density..),position = "dodge")+geom_density(alpha=.5) +theme(legend.position = "left")
plotList[[i]] <- plotly_build(gplot)
}
for(i in length(plist)){
print(plotList[[i]])
}
The goal is to show both graphs for PRE_OPERATIVE_LOS and POST_OPERATIVE_LOS. However, the codes above only show histogram for POST_OPERATIVE_LOS.
I checked maybe subplot is the way to go but how to make subplot work in a loop? Any hints?
Thanks!
There is an error in your first loop and calling each subplot won't make both appear at the same time.
First-- the issue with your first for call- when you wrote
for(i in length(plist))
You wrote for i in 2 or i == 2, meaning that you never looped. If you modify it to a range of values, now it's written: for i in 1 to 2.
for(i in 1:length(plist))
So you're aware, if you had written for(i in plist) it would have done both loops, but instead of a value, i would be the strings.
Okay, so now there are two graphs. From the plotly library, you can use the function subplot. You will want to turn the legend off for one of them, though.
subplot(plotList[[1]],
style(plotList[[2]], showlegend = FALSE))
If you wanted the outline color, that's more than okay! However, if you wanted to bars to be filled, you need to assign fill instead of color.
If you change color = bygrp to fill = bygrp, this is how this would change:
If you leave the color assignment and add fill = bygrp (so you have both), this is how this would change:

Gtsummary output with mgcv gam

I have the following data set:
structure(list(Age = c(83L, 26L, 26L, 20L, 20L, 77L, 32L, 21L,
15L, 75L, 27L, 81L, 81L, 15L, 24L, 16L, 35L, 27L, 30L, 31L, 24L,
24L, 31L, 79L, 30L, 19L, 20L, 42L, 62L, 83L, 79L, 18L, 26L, 66L,
23L, 83L, 77L, 80L, 57L, 42L, 32L, 76L, 85L, 29L, 65L, 79L, 9L,
34L, 20L, 16L, 34L, 22L, 19L, 23L, 25L, 14L, 53L, 28L, 79L, 22L,
22L, 21L, 82L, 81L, 16L, 19L, 77L, 15L, 18L, 15L, 78L, 24L, 16L,
14L, 29L, 18L, 50L, 17L, 43L, 8L, 14L, 85L, 31L, 20L, 30L, 23L,
78L, 29L, 6L, 61L, 14L, 22L, 10L, 83L, 15L, 13L, 15L, 15L, 29L,
8L, 9L, 15L, 8L, 9L, 15L, 9L, 34L, 8L, 9L, 9L, 16L, 8L, 25L,
21L, 23L, 13L, 56L, 10L, 7L, 27L, 8L, 8L, 8L, 8L, 80L, 80L, 6L,
15L, 42L, 25L, 23L, 21L, 8L, 11L, 43L, 69L, 34L, 34L, 14L, 12L,
10L, 22L, 78L, 16L, 76L, 12L, 10L, 16L, 6L, 13L, 66L, 11L, 26L,
12L, 16L, 13L, 24L, 76L, 10L, 65L, 20L, 13L, 25L, 14L, 12L, 15L,
43L, 51L, 27L, 15L, 24L, 34L, 63L, 17L, 15L, 9L, 12L, 17L, 82L,
75L, 24L, 44L, 69L, 11L, 10L, 12L, 10L, 10L, 70L, 54L, 45L, 42L,
84L, 54L, 23L, 23L, 14L, 81L, 17L, 42L, 44L, 16L, 15L, 43L, 45L,
50L, 53L, 23L, 53L, 49L, 13L, 69L, 14L, 65L, 14L, 13L, 22L, 67L,
59L, 52L, 54L, 44L, 78L, 62L, 69L, 10L, 63L, 57L, 22L, 12L, 62L,
9L, 82L, 53L, 54L, 66L, 49L, 63L, 51L, 9L, 45L, 49L, 77L, 49L,
61L, 62L, 57L, 67L, 16L, 65L, 75L, 45L, 16L, 55L, 17L, 64L, 67L,
56L, 52L, 63L, 10L, 62L, 14L, 66L, 68L, 15L, 13L, 43L, 47L, 55L,
69L, 21L, 67L, 34L, 52L, 15L, 31L, 64L, 55L, 13L, 48L, 71L, 64L,
13L, 25L, 34L, 50L, 61L, 70L, 33L, 57L, 51L, 46L, 57L, 69L, 46L,
8L, 11L, 46L, 71L, 33L, 38L, 56L, 17L, 29L, 28L, 6L, 8L), Sex = structure(c(1L,
1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L,
2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L,
2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L,
2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 1L, 2L,
2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 2L,
2L, 1L, 2L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 1L,
2L, 2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 2L, 1L, 1L, 1L, 1L,
2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 1L,
1L, 2L, 2L), .Label = c("Male", "Female"), class = "factor"),
mean_AD_scaled = c(3.15891332561581, -0.0551328105526693,
0.582747640515478, 1.94179165777054, 1.7064645993306, 2.37250948563045,
1.015775832203, 1.36189033704266, -1.05640048650493, 0.184814975542474,
-0.143366705302007, 1.81560178585347, 2.06325078470728, -0.473088628698217,
0.414641167726219, 0.199887349084444, -0.60620959209809,
-0.17879228399189, -1.03483709078065, -1.43497010225613,
-0.958595084469815, 1.0203965598582, -1.44731404613503, -1.17191867788498,
-2.02547709312595, -1.22395687266857, -1.09952727795348,
-1.0830246791849, 1.21072653232248, 1.69997357714829, 1.53648783201423,
0.208688735094353, 0.0862394522314924, 1.08662698958276,
-0.731299290763917, 2.29307697689102, -0.660008064083659,
-1.21425334459264, 1.10191939777498, -2.0957781638801, -1.14947514355972,
0.248845058764562, 2.6526135953958, 0.197907037232212, -0.222469162066061,
1.92880961340592, 1.23328008397287, -1.17288683034607, -0.308282675662673,
-1.02603570477074, -1.32647101621898, -1.58316343919798,
-0.0440210607151585, -0.388375288352846, -0.935491446193807,
-0.63789458173376, 0.454577456746182, -1.77391147749773,
0.709267564407921, 0.125735671950958, -0.821073428064989,
-0.126534054558056, 0.519597695894384, 0.188005477971066,
0.212319306823438, -1.45807374053215, 1.5856655763446, -1.25641198358011,
-0.910847565366061, -1.1191763722206, 0.25300371365424, -0.750772357310844,
0.37932560636146, -0.871791414947088, -1.92771569802088,
-1.1752191976387, 0.210449012296334, -0.347778895382139,
-0.132254955464496, 0.953616043508016, -0.0862677135627232,
0.838977990728951, -1.8993092246739, -0.0254281327692267,
0.298022803094927, -1.21559555595915, 0.0134079829994995,
-0.763094297724715, 0.334768589686298, -1.12568939786794,
-2.11786964276497, -0.0434709740895377, 0.388237009696492,
1.30050066962355, -0.260645173884043, -0.60620959209809,
1.05945271027717, -0.275717547426008, -0.0238878902174922,
0.496604074943496, 0.534009965485611, -0.692903244295693,
-0.566933407028871, 0.125625654625835, -0.518305749324122,
1.79381835547894, -0.790708646330802, -0.227860010997131,
0.347420582075538, 0.784189362817269, -0.660118081408782,
1.29962053102256, -0.561652575422924, -0.710395998990384,
-1.29315777017148, -0.457356151205503, -1.01756437073621,
0.146528946399368, -1.07136284272178, -1.42968927065019,
0.798601632408495, -0.799730066990963, -0.431348055546223,
0.569545561500617, 2.32168148142323, 0.472070211440872, 1.65145593676866,
-0.814142336582189, -0.544489872703603, -0.315433801795725,
0.382626126115175, -0.623812364117908, 0.216279930527897,
-0.606099574772967, -0.367207954999011, 0.719829227619811,
-0.749122097433987, 0.934693063586709, -0.79026857703031,
-0.371872689584264, 0.0769979969210905, -0.793899148759394,
1.50414273842782, 0.730280873506577, -0.290569886317732,
0.303743704001367, 0.390877425499463, -1.00359217044547,
-0.534918365417827, 0.325967203676389, 0.129036191704673,
0.34434009697207, -0.141386393449775, -0.363401355549725,
-0.395416397160769, -0.0235578382421178, -1.13583299524436,
1.16781977552417, -1.31890182425046, 0.139377820266317, 0.0160483988024708,
0.481311666751279, -1.05475022662807, 0.839858129329941,
0.652498624644007, -0.350199276534864, -0.262075399110649,
0.178543988010412, -1.13198238886502, -0.05117218684821,
-1.29678834190056, 0.429603523943066, 1.05098137624263, -0.956504755292464,
0.502765045150433, -0.81678275238516, -1.50263075720731,
-0.826684311646306, 2.40100397283753, 2.06633126981075, -0.470558230220369,
0.484942238480364, 0.822035322659877, 0.143888530596397,
0.384056351341786, -0.63580425255641, 0.358422314587926,
-0.372422776209885, 0.0607154328027556, -0.113221958218067,
1.02710761669075, -0.349649189909243, 2.27195365046724, -0.507634068787109,
-0.326105482332738, -1.0396778530861, 1.06484355920824, 1.32151397872221,
-0.185173288849074, -0.651888785489516, -0.171311105883464,
-0.104200537557911, -0.693673365571561, -1.26609350819101,
0.411230630647381, -0.929770545287362, -0.481009876107135,
0.386146680519137, 0.0482834750637615, -0.198265350538812,
0.790020281048832, 0.926001694901924, -1.08918564939184,
0.50298507980068, -0.0694350628187722, 1.04966116834114,
0.00878725534429612, 1.48742010500899, 0.750194009353997,
0.423772605711498, -0.596418050162068, -0.652636903300361,
-0.308942779613417, 0.314437388003408, 0.679562886624478,
-1.24312189070515, -0.432712270377761, 0.00427654501421597,
-0.197935298563442, 0.228821905592019, 1.06957430418856,
-1.61612462980509, 1.9499329398297, -0.263285589687014, 0.156430505660519,
-0.322254875953402, -0.451085163673446, -0.35526007349056,
0.10780284795577, 0.408700232169533, -0.957604928543701,
-1.05662052115517, 1.00345389178912, -0.238751726184391,
0.300003114947154, -0.397946795638617, -0.0802167606809086,
0.943714484246865, 1.10973062785877, 1.76279346979401, 1.62087112038423,
0.25533608094687, 0.226841593739787, 0.869672824438507, -1.44960240649761,
-0.450315042397579, -0.199629565370345, 0.29813282042005,
0.760425620590513, 1.87391096816911, -0.454275666102039,
-0.0559029318285365, -0.343048150401812, -1.01371376435687,
0.68880434193488, -0.29222014619459, 1.16132875334186, -1.95715633422403,
-0.534368278792206, -0.560112332871189, 1.84508642898666,
-1.19150176175703, -0.772203732244971, -0.3443683583033,
-1.45684154649076, -0.633823940704178, -1.77454957798344,
0.279539892474118, -0.875532004001301, 1.26001429397797,
-0.536590628759707, 2.1869102581465, 0.211109116247078, 0.130246382281038,
-0.355810160116181, -0.898085555651692, -0.429741802599415,
1.13360438741065, 1.61338994227581, 0.588688576072169, 0.454137387445685,
0.747113524250528, 0.460848444278238, -0.38177424884541,
-0.169990897981981, -0.747361820232001, -0.760123829946369,
0.208028631143609, -1.28748087619509, 2.33950428809329, -0.973029357526068,
-1.06091119683501, 0.917530360867389, -0.35041931118511,
-1.90613029883158, -1.15057531681095, 0.65348878057012, 0.43147381847017
)), row.names = c(NA, -308L), class = c("tbl_df", "tbl",
"data.frame"))
I am using this gam model:
m1 <- gam(mean_AD_scaled ~ s(Age, bs = 'ad', k = -1) + Sex + ti(Age, by = Sex, bs ='fs'),
data = DF,
method = 'REML',
family = gaussian)
Output:
Family: gaussian
Link function: identity
Formula:
mean_AD_scaled ~ s(Age, bs = "ad", k = -1) + Sex + ti(Age,
by = Sex, bs = "fs")
Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.04691 0.06976 0.672 0.502
SexFemale -0.12950 0.09428 -1.374 0.171
Approximate significance of smooth terms:
edf Ref.df F p-value
s(Age) 2.980 3.959 8.72 2.24e-06 ***
ti(Age):SexMale 2.391 2.873 23.47 < 2e-16 ***
ti(Age):SexFemale 1.000 1.000 43.40 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Rank: 48/49
R-sq.(adj) = 0.34 Deviance explained = 35.6%
-REML = 375.4 Scale est. = 0.63867 n = 308
But when I use gtsummary, I get a repeated value for each gender 'interaction':
tbl_regression(m1, tidy_fun = tidy_gam)
I see the following in a publication, which I am trying to replicate with gender and age:
I am not sure how to fix this. My goal is to print a table for a manuscript so any other gam-related information that can be added like edf and R^2.
I think you've found a bug in the handling of these types of interactions. While we work on a fix to the bug, this code should get you what you need. Thanks
library(gtsummary)
#> #BlackLivesMatter
library(mgcv)
packageVersion("gtsummary")
#> [1] ‘1.5.2’
m1 <- gam(marker ~ s(age, bs = 'ad', k = -1) + grade + ti(age, by = grade, bs ='fs'),
data = gtsummary::trial,
method = 'REML',
family = gaussian)
tbl_regression(m1, tidy_fun = gtsummary::tidy_gam) %>%
modify_table_body(
~ .x %>%
dplyr::select(-n_obs) %>%
dplyr::distinct()
) %>%
as_kable() # convert to kable to display on SO
Characteristic
Beta
95% CI
p-value
Grade
I
—
—
II
-0.39
-0.70, -0.08
0.014
III
-0.13
-0.43, 0.18
0.4
s(age)
>0.9
ti(age):gradeI
0.6
ti(age):gradeII
>0.9
ti(age):gradeIII
0.6
Created on 2022-02-21 by the reprex package (v2.0.1)

Conditionally replace values of multiple columns, from values of other multiple columns

Suppose I have this dataset:
set.seed (1234);
data.frame(cbind(a=rep(c("si","no"),30),b=rnorm(60)),
c=rep(c("d","e","f"),20)) %>% head()
Then I want to add many columns (in this example I only added two), to identify distinct cases between each group (in this case, column "a").
set.seed(1234);
data.frame(cbind(a=rep(c("si","no"),30),b=rnorm(60)),c=rep(c("d","e","f"),20)) %>%
group_by(a) %>% dplyr::mutate_at(vars(c(b,c)), .funs= list(dups_hash_ing= ~n_distinct(.)))
This code leaves the following dataset:
If I set the dataset with dput, the outcome is
structure(list(a = structure(c(2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L, 1L), .Label = c("no", "si"), class = "factor"), b = structure(c(22L,
1L, 51L, 34L, 50L, 57L, 53L, 10L, 47L, 3L, 11L, 23L, 15L, 38L,
58L, 39L, 41L, 17L, 28L, 21L, 37L, 45L, 29L, 46L, 32L, 48L, 56L,
52L, 26L, 19L, 35L, 8L, 55L, 20L, 9L, 36L, 2L, 12L, 6L, 42L,
49L, 43L, 59L, 54L, 31L, 13L, 60L, 44L, 14L, 30L, 7L, 5L, 16L,
27L, 33L, 18L, 24L, 4L, 25L, 40L), .Label = c("-0.0997905884418961",
"-0.151736536534977", "-0.198416273822079", "-0.254874652654534",
"-0.274704218225806", "-0.304721068966714", "-0.324393300483657",
"-0.400235237343163", "-0.415751788401515", "-0.50873701541522",
"-0.538070788884863", "-0.60615111526422", "-0.659770093821306",
"-0.684320344136007", "-0.789646852263761", "-0.933503340589868",
"-0.965903210133575", "-1.07754212275943", "-1.11444896479736",
"-1.60708093984972", "-2.07823754188738", "-2.7322195229558",
"-2.85575865501923", "-3.23315213292314", "0.0295178303214797",
"0.0326639575014441", "0.116845344986082", "0.162654708118265",
"0.185513915583057", "0.186492083080971", "0.287709728313787",
"0.311681028661359", "0.319160238648117", "0.413868915451097",
"0.418057822385083", "0.42200837321742", "0.485226820569252",
"0.487814635163685", "0.500694614280786", "0.594273774110513",
"0.62021020366732", "0.629536099884472", "0.660212631820405",
"0.677415500438328", "0.696768778564913", "0.700733515544461",
"0.704180178465512", "0.760462361967838", "0.895171980275539",
"0.912322161610113", "0.976031734922396", "1.1123628412626",
"1.16910851401363", "1.17349757263239", "1.49349310261748", "1.84246362620766",
"1.98373220068438", "2.16803253951933", "2.27348352044748", "2.91914013071762"
), class = "factor"), c = structure(c(1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L,
2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L,
3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L, 1L, 2L, 3L,
1L, 2L, 3L, 1L, 2L, 3L), .Label = c("d", "e", "f"), class = "factor"),
a_dups_hash_ing = 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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L), b_dups_hash_ing = c(30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 30L, 30L, 30L), c_dups_hash_ing = c(3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L)), class = c("grouped_df",
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -60L), groups = structure(list(
a = structure(1:2, .Label = c("no", "si"), class = "factor"),
.rows = list(c(2L, 4L, 6L, 8L, 10L, 12L, 14L, 16L, 18L, 20L,
22L, 24L, 26L, 28L, 30L, 32L, 34L, 36L, 38L, 40L, 42L, 44L,
46L, 48L, 50L, 52L, 54L, 56L, 58L, 60L), c(1L, 3L, 5L, 7L,
9L, 11L, 13L, 15L, 17L, 19L, 21L, 23L, 25L, 27L, 29L, 31L,
33L, 35L, 37L, 39L, 41L, 43L, 45L, 47L, 49L, 51L, 53L, 55L,
57L, 59L))), row.names = c(NA, -2L), class = c("tbl_df",
"tbl", "data.frame"), .drop = TRUE))
What I need to do, is replace, column by column, if the number of distinct cases is more than one per group, with the value of the original column. I have to do this for more than 50 columns. An example of this will be provided for only one column with mutate:
dplyr::mutate(b_dups_hash_ing= ifelse(>1,b,0))
I need to repeat the code provided above for many variables. This is very similar to a mutate_at (words in brackets is what I would do). The following example does not work, but is something I would do in an ideal world, just for your better understanding of my problem.
dplyr::mutate_at(vars(contains('_dups_hash_ing')), .funs = list(~ifelse(.>1,vars([original]),0)))
Is this what you're looking for?
df %>% dplyr::mutate_at(vars(contains('_dups_hash_ing')), ~ ifelse(. > 1, ., 0)) %>% head
#> # A tibble: 6 x 6
#> # Groups: a [2]
#> a b c a_dups_hash_ing b_dups_hash_ing c_dups_hash_ing
#> <fct> <fct> <fct> <dbl> <int> <int>
#> 1 si -2.7322195229558 d 0 30 3
#> 2 no -0.09979058844189… e 0 30 3
#> 3 si 0.976031734922396 f 0 30 3
#> 4 no 0.413868915451097 d 0 30 3
#> 5 si 0.912322161610113 e 0 30 3
#> 6 no 1.98373220068438 f 0 30 3

lme4 error: boundary (singular) fit: see ?isSingular

I am trying to run lme4 package in R. I have 10 Lines in total with four plants for each line in each of the two replications. But some of the plants died and there are some missing values. Weight is the response variable. Here are some lines from the data:
Line Rep Weight PLANT
Line 1 1 NA 1
Line 1 1 NA 2
Line 1 1 NA 3
Line 1 1 NA 4
Line 2 1 26 1
Line 2 1 26 2
Line 2 1 26 3
Line 2 1 27 4
Line 1 2 26 1
Line 1 2 28 2
Line 1 2 26 3
Line 1 2 25 4
Line 2 2 24 1
Line 2 2 26 2
Line 2 2 25 3
Line 2 2 NA 4
I want to run linear mixed model using lme4 package so I tried running:
lme4 <- lmer(Weight ~ 1 + (1|Rep:Plant), data=Data)
But I got an error:
boundary (singular) fit: see ?isSingular
> dput(Data)
structure(list(Line = c("Line 1", "Line 1", "Line 1", "Line 1",
"Line 2", "Line 2", "Line 2", "Line 2", "Line 1", "Line 1", "Line 1",
"Line 1", "Line 2", "Line 2", "Line 2", "Line 2"), Rep = c(1,
1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2), Weight = c(NA,
NA, NA, NA, 26, 26, 26, 27, 26, 28, 26, 25, 24, 26, 25, NA),
PLANT = c(1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4)), row.names = c(NA,
-16L), class = c("tbl_df", "tbl", "data.frame"))
I am using it for the first time and I am not sure about the error. I will appreciate any help!
Your model did fit, but it generated that warning because your random effects are very small. You can read more about this in this post or the help page
Let us look at your data:
ggplot(Data,aes(x=PLANT,y=Weight,col=Rep)) + geom_jitter() + geom_boxplot(alpha=0.2) + facet_wrap(~Rep)
The effects of PLANT and in combination with Rep is extremely small. Let's look at the fitted model:
fit = lmer(Weight ~ 1 + (1|PLANT:Rep),data=Data)
boundary (singular) fit: see ?isSingular
ranef(fit)
$`PLANT:Rep`
(Intercept)
1:1 0
1:2 0
2:1 0
2:2 0
3:1 0
3:2 0
4:1 0
4:2 0
This is exactly what happened. So we can try to account for some other effects and we still see very small coefficients:
fit = lmer(Weight ~ Line + (1|Rep:PLANT),data=Data)
ranef(fit)
$`Rep:PLANT`
(Intercept)
1:1 1.397563e-19
1:2 2.811371e-19
1:3 8.112169e-20
1:4 1.813251e-19
2:1 -1.725964e-19
2:2 -2.463986e-20
2:3 -2.027357e-19
2:4 -2.833681e-19
The takehome message is, there's no really systematic effect coming from PLANT, so you don't need to specify a highly complicated model, do something like:
fit = lmer(Weight ~ Line + (1|Rep),data=Data)
The data in case anyone is interested:
Data = structure(list(Line = structure(c(1L, 1L, 1L, 1L, 12L, 12L, 12L,
12L, 23L, 23L, 23L, 23L, 34L, 34L, 34L, 34L, 45L, 45L, 45L, 45L,
56L, 56L, 56L, 56L, 65L, 65L, 65L, 65L, 66L, 66L, 66L, 66L, 67L,
67L, 67L, 67L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L,
5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
9L, 9L, 9L, 9L, 10L, 10L, 10L, 10L, 11L, 11L, 11L, 11L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 16L, 16L,
16L, 16L, 8L, 8L, 8L, 8L, 66L, 66L, 66L, 66L, 17L, 17L, 17L,
17L, 18L, 18L, 18L, 18L, 9L, 9L, 9L, 9L, 19L, 19L, 19L, 19L,
20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 22L, 22L, 22L, 22L, 24L,
24L, 24L, 24L, 25L, 25L, 25L, 25L, 2L, 2L, 2L, 2L, 26L, 26L,
26L, 26L, 27L, 27L, 27L, 27L, 10L, 10L, 10L, 10L, 28L, 28L, 28L,
28L, 29L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 31L, 31L,
67L, 67L, 67L, 67L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 32L, 33L,
33L, 33L, 33L, 35L, 35L, 35L, 35L, 36L, 36L, 36L, 36L, 37L, 37L,
37L, 37L, 38L, 38L, 38L, 38L, 39L, 39L, 39L, 39L, 40L, 40L, 40L,
40L, 25L, 25L, 25L, 25L, 19L, 19L, 19L, 19L, 24L, 24L, 24L, 24L,
41L, 41L, 41L, 41L, 42L, 42L, 42L, 42L, 30L, 30L, 30L, 30L, 43L,
43L, 43L, 43L, 44L, 44L, 44L, 44L, 22L, 22L, 22L, 22L, 46L, 46L,
46L, 46L, 47L, 47L, 47L, 47L, 17L, 17L, 17L, 17L, 48L, 48L, 48L,
48L, 49L, 49L, 49L, 49L, 27L, 27L, 27L, 27L, 23L, 23L, 23L, 23L,
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11L, 11L, 33L, 33L, 33L, 33L, 53L, 53L, 53L, 53L, 54L, 54L, 54L,
54L, 13L, 13L, 13L, 13L, 38L, 38L, 38L, 38L, 4L, 4L, 4L, 4L,
37L, 37L, 37L, 37L, 55L, 55L, 55L, 55L, 57L, 57L, 57L, 57L, 44L,
44L, 44L, 44L, 58L, 58L, 58L, 58L, 59L, 59L, 59L, 59L, 12L, 12L,
12L, 12L, 47L, 47L, 47L, 47L, 48L, 48L, 48L, 48L, 60L, 60L, 60L,
60L, 21L, 21L, 21L, 21L, 18L, 18L, 18L, 18L, 28L, 28L, 28L, 28L,
26L, 26L, 26L, 26L, 61L, 61L, 61L, 61L, 31L, 31L, 31L, 31L, 59L,
59L, 59L, 59L, 52L, 52L, 52L, 52L, 29L, 29L, 29L, 29L, 62L, 62L,
62L, 62L, 63L, 63L, 63L, 63L, 54L, 54L, 54L, 54L, 55L, 55L, 55L,
55L, 53L, 53L, 53L, 53L, 51L, 51L, 51L, 51L, 50L, 50L, 50L, 50L,
64L, 64L, 64L, 64L, 20L, 20L, 20L, 20L, 58L, 58L, 58L, 58L, 16L,
16L, 16L, 16L, 57L, 57L, 57L, 57L, 14L, 14L, 14L, 14L, 63L, 63L,
63L, 63L, 64L, 64L, 64L, 64L, 61L, 61L, 61L, 61L, 36L, 36L, 36L,
36L, 40L, 40L, 40L, 40L, 6L, 6L, 6L, 6L, 39L, 39L, 39L, 39L,
45L, 45L, 45L, 45L, 15L, 15L, 15L, 15L, 1L, 1L, 1L, 1L, 42L,
42L, 42L, 42L, 43L, 43L, 43L, 43L, 65L, 65L, 65L, 65L, 49L, 49L,
49L, 49L, 56L, 56L, 56L, 56L, 3L, 3L, 3L, 3L, 62L, 62L, 62L,
62L, 35L, 35L, 35L, 35L, 5L, 5L, 5L, 5L, 60L, 60L, 60L, 60L,
34L, 34L, 34L, 34L), .Label = c("Line1", "Line10", "Line11",
"Line12", "Line13", "Line14", "Line15", "Line16", "Line17", "Line18",
"Line19", "Line2", "Line20", "Line21", "Line22", "Line23", "Line24",
"Line25", "Line26", "Line27", "Line28", "Line29", "Line3", "Line30",
"Line31", "Line32", "Line33", "Line34", "Line35", "Line36", "Line37",
"Line38", "Line39", "Line4", "Line40", "Line41", "Line42", "Line43",
"Line44", "Line45", "Line46", "Line47", "Line48", "Line49", "Line5",
"Line50", "Line51", "Line52", "Line53", "Line54", "Line55", "Line56",
"Line57", "Line58", "Line59", "Line6", "Line60", "Line61", "Line62",
"Line63", "Line64", "Line65", "Line66", "Line67", "Line7", "Line8",
"Line9"), class = "factor"), Rep = structure(c(1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
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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,
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, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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, 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, 1L, 1L, 1L, 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, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L), .Label = c("1", "2"), class = "factor"), Weight = c(NA,
NA, NA, NA, 26L, 26L, 26L, 27L, NA, NA, NA, NA, 26L, 28L, 26L,
25L, 22L, 17L, 20L, 20L, 28L, 20L, 27L, 26L, 22L, 25L, 21L, 25L,
18L, 18L, 19L, 18L, 24L, 28L, 23L, 30L, 29L, 25L, 26L, 27L, NA,
NA, NA, NA, 29L, 30L, 29L, 30L, NA, NA, NA, NA, 33L, NA, NA,
NA, 21L, 23L, 18L, 23L, 32L, 29L, 30L, 30L, 18L, 19L, 21L, 21L,
25L, 25L, 25L, 26L, 26L, 27L, NA, NA, 29L, 29L, 27L, 29L, 26L,
NA, NA, NA, 26L, 20L, 23L, 27L, NA, NA, NA, NA, 32L, 32L, 30L,
30L, 20L, 20L, 20L, 19L, 22L, 21L, 22L, 22L, 24L, 23L, 23L, 25L,
20L, 25L, NA, NA, 27L, 26L, NA, NA, NA, NA, NA, NA, 30L, 28L,
NA, NA, 25L, 26L, 27L, 26L, NA, NA, NA, NA, 20L, 19L, NA, NA,
19L, 27L, 26L, 29L, 26L, 29L, 31L, 29L, 25L, 25L, 24L, 25L, 26L,
25L, 26L, 26L, 25L, 24L, 24L, 28L, 22L, 26L, 24L, 28L, 29L, 30L,
26L, NA, NA, NA, NA, NA, 26L, 24L, 24L, 24L, NA, NA, NA, NA,
NA, NA, NA, NA, 30L, 30L, 30L, 31L, 24L, 25L, 28L, 22L, 28L,
31L, 30L, NA, 31L, 30L, 29L, 25L, 25L, 22L, 24L, 20L, 30L, 30L,
30L, 29L, 26L, 32L, 28L, 29L, 20L, 15L, 15L, 11L, 25L, 24L, 24L,
24L, 26L, 29L, 31L, 30L, 24L, 28L, 20L, 22L, 29L, 26L, 26L, 28L,
27L, 27L, 27L, 26L, 21L, 22L, 21L, NA, 28L, 29L, 24L, 24L, 28L,
29L, 28L, 27L, 28L, 29L, 27L, 29L, NA, NA, NA, NA, 22L, 26L,
21L, 21L, 26L, 30L, 28L, 30L, 27L, 26L, 28L, 26L, 25L, 25L, 26L,
26L, 27L, 26L, 23L, 29L, NA, NA, NA, NA, 27L, 23L, 29L, 23L,
28L, 29L, 28L, 26L, 20L, NA, NA, NA, 28L, 23L, 26L, 21L, 28L,
26L, 26L, 29L, 20L, 27L, 20L, 26L, 29L, 26L, 28L, 28L, 30L, 27L,
NA, NA, 26L, 21L, 26L, 25L, 27L, 26L, 27L, 24L, 25L, 20L, 21L,
20L, 25L, 25L, 31L, 24L, 29L, 28L, 31L, 27L, 25L, 28L, 26L, 26L,
NA, NA, NA, NA, 24L, 25L, 23L, 27L, 20L, 26L, 25L, 25L, 29L,
28L, 29L, 29L, 26L, 27L, 25L, 28L, NA, NA, NA, NA, 26L, 28L,
NA, NA, 21L, 20L, 31L, 25L, 31L, 28L, 30L, 29L, 23L, 25L, 24L,
28L, 25L, 22L, 25L, 25L, 28L, 29L, 28L, 29L, 26L, 24L, 25L, 26L,
29L, 27L, NA, NA, 26L, 29L, 29L, 30L, 25L, 24L, 25L, 24L, 28L,
25L, 29L, 28L, 24L, 24L, 24L, 24L, 28L, 30L, 27L, 27L, 26L, 25L,
25L, 25L, 25L, 25L, 28L, 25L, 25L, 30L, 28L, 25L, 22L, 24L, 25L,
24L, NA, NA, NA, NA, 5L, 7L, 4L, 5L, 21L, 20L, 22L, 24L, 25L,
27L, 25L, 28L, 32L, 31L, NA, NA, 19L, 26L, 20L, NA, 26L, 26L,
30L, 25L, 28L, 31L, 30L, 26L, 5L, 8L, 4L, 8L, 25L, 25L, 28L,
25L, 28L, 28L, 27L, 26L, 30L, 27L, 27L, 24L, 32L, 29L, 31L, 25L,
30L, 30L, 27L, 28L, 16L, 20L, 16L, 21L, 25L, 22L, 25L, 20L, 24L,
25L, 18L, 25L, 25L, 26L, 29L, 29L, 21L, 20L, 22L, 21L, 19L, 22L,
19L, 21L, 28L, 25L, 26L, 24L, 28L, 26L, 24L, 25L, NA, NA, NA,
NA, 25L, NA, NA, NA, 23L, 21L, 19L, 23L, 25L, 24L, 25L, NA, 22L,
30L, 29L, 26L, 25L, 25L, 24L, 24L), PLANT = structure(c(1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
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3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
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3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L, 3L, 4L, 1L, 2L,
3L, 4L, 1L, 2L, 3L, 4L), .Label = c("1", "2", "3", "4"), class = "factor"),
X = structure(c(4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L,
6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L,
5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L,
2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L,
4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L,
6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L,
5L, 6L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L,
7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L,
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1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L,
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5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L,
2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L,
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6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L,
5L, 6L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L,
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3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L,
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3L, 7L, 8L, 1L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L,
6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L,
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2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L,
4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L,
6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L, 5L, 6L, 4L, 2L,
5L, 6L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L,
7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L,
3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L,
1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L,
8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L,
7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L, 3L, 7L, 8L, 1L
), .Label = c("24", "12", "21", "11", "13", "14", "22", "23"
), class = "factor")), row.names = c(NA, -536L), class = "data.frame")

Use rbind() in nested for loop with apply() in r

How can you use rbind in a for loop that runs through a list of dataframes? I tried to follow Looping through list of data frames in R but receive the following:
Error in apply(dataFramesList, 2, function(x) { :
dim(X) must have a positive length
I have two dataframes, dfTraining and dfAccuracy (code to reproduce dataframes is below), and need to add a row for any of the crop types missing from either of two columns, CROP or CROP_LABEL. I believe my problem is in my last line of code.
My code block is:
dataFramesList <- list(dfTraining, dfAccuracy)
apply(dataFramesList, 2, function(x){
cropNumbers <- seq(1,23, by = 1)
cropNumbers <- cropNumbers[-c(3)]
cropNumbers <- append(cropNumbers, 34)
listofCROPandCROP_LABELColumns <- list(dataFrameList$CROP, dataFrameList$CROP_LABEL)
missingCROP <- NULL
for (i in listofCROPandCROP_LABELColumns){
for (j in cropNumbers){
if (!j %in% i){
# If crop number is missing from CROP_LABEL, add missingCROP observation (row)
# Make row for missing crop type
missingCrop <- list(FREQUENCY = 0, AA = 1, CROP = j, CROP_LABEL = j, ACRES = 0)
dataFrameList <- rbind(dataFrameList, missingCrop)
}
}
}
})
My dfAccuracy dataframe:
structure(list(FREQUENCY = c(4L, 2L, 1L, 1L, 1L, 1L, 65L, 1L,
1L, 4L, 1L, 5L, 5L, 2L, 4L, 1L, 1L, 1L, 1L, 4L, 9L, 2L, 1L, 1L,
1L, 2L, 4L, 1L, 2L, 18L, 1L, 10L, 3L, 1L, 7L, 1L, 1L, 1L, 3L,
1L, 7L, 1L), AA = c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L),
CROP = c(1L, 4L, 12L, 13L, 14L, 18L, 1L, 1L, 1L, 1L, 1L,
4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 13L,
13L, 13L, 13L, 14L, 14L, 14L, 18L, 18L, 18L, 18L, 18L, 19L,
19L, 21L, 21L, 21L, 21L), CROP_LABEL = c(1L, 4L, 14L, 13L,
12L, 18L, 1L, 4L, 5L, 6L, 18L, 1L, 4L, 6L, 14L, 18L, 12L,
14L, 18L, 1L, 6L, 14L, 18L, 18L, 4L, 6L, 13L, 21L, 12L, 14L,
18L, 1L, 6L, 14L, 18L, 21L, 1L, 19L, 6L, 13L, 21L, 34L),
ACRES = c(331.737184484, 193.772138572, 26.48543619, 73.2696289437,
112.470306056, 66.6556450342, 3905.71121736, 24.9581079934,
39.9287379709, 259.662359273, 85.2786247851, 306.051491303,
368.342995232, 154.82030835, 265.754349805, 70.3722566979,
35.4066607701, 139.336463432, 58.4307705147, 251.070357093,
471.031628349, 150.965736858, 28.2780117926, 35.3426930108,
34.5730542194, 67.7383953308, 144.442123948, 33.2746560126,
69.4072817311, 1219.65459596, 92.4840910734, 582.983473317,
191.957841327, 35.708775262, 319.638682538, 60.6889287642,
82.6244195055, 36.2898952104, 267.422844756, 72.8352758659,
489.746546145, 65.5392893502)), row.names = c(25L, 26L, 27L,
29L, 30L, 31L, 60L, 61L, 62L, 63L, 64L, 65L, 66L, 67L, 68L, 69L,
70L, 71L, 72L, 73L, 74L, 75L, 76L, 77L, 78L, 79L, 80L, 81L, 82L,
83L, 84L, 85L, 86L, 87L, 88L, 89L, 90L, 91L, 92L, 93L, 94L, 95L
), class = "data.frame")
and my dfTraining dataframe is:
structure(list(FREQUENCY = c(7L, 1L, 1L, 4L, 2L, 6L, 1L, 107L,
1L, 21L, 1L, 1L, 1L, 2L, 1L, 19L, 3L, 1L, 1L, 12L, 1L, 2L, 32L,
2L, 2L, 29L, 2L, 18L, 1L), AA = 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), CROP = c(1L, 1L, 4L, 4L, 12L, 13L, 21L,
1L, 1L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 7L, 12L, 13L, 14L, 14L,
14L, 18L, 18L, 18L, 19L, 21L, 34L), CROP_LABEL = c(1L, 4L, 1L,
4L, 12L, 13L, 21L, 1L, 6L, 4L, 6L, 1L, 5L, 14L, 18L, 6L, 14L,
1L, 12L, 13L, 1L, 6L, 14L, 6L, 14L, 18L, 19L, 21L, 34L), ACRES = c(624.940370218,
26.9188766351, 37.8773839813, 291.79294767, 140.949264214, 391.571023675,
44.5217011939, 6806.02216989, 72.7500299887, 1676.12121152, 14.8739557721,
67.0700291739, 59.7438207953, 82.6713019474, 75.62666152, 1370.78710769,
145.215281276, 41.7380537313, 66.5236760194, 679.91208779, 70.9661875374,
38.8514254734, 1749.63365551, 109.917242057, 79.7758083723, 1660.85759895,
96.8771921798, 1428.71888481, 69.473161379)), row.names = c(18L,
19L, 20L, 21L, 22L, 23L, 24L, 38L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 47L, 48L, 49L, 50L, 51L, 52L, 53L, 54L, 55L, 56L, 57L,
58L, 59L), class = "data.frame")

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