How to sovle self-defining function to count can't work - r

Question

I write a function to count daily number of people in hospital but it can't work when the number is 0 in some days.
my function:
tsdata2 <- function(df){
t.f <- as.data.frame(table(df$DATE_INHOSPITAL2)) %>% rename(whole =Freq)
## sex
man.d <- df %>% filter(GENDER == 1)
man.f <- as.data.frame(table(man.d$DATE_INHOSPITAL2)) %>% rename(man =Freq)
woman.d <- df %>% filter(GENDER == 2)
woman.f <- as.data.frame(table(woman.d$DATE_INHOSPITAL2)) %>% rename(woman =Freq)
## age 65
agelo65.d <- df %>% filter(age_group65 == 1)
agelo65.f <- as.data.frame(table(agelo65.d$DATE_INHOSPITAL2)) %>% rename(agelo65 =Freq)
ageup65.d <- df %>% filter(age_group65 == 2)
ageup65.f <- as.data.frame(table(ageup65.d$DATE_INHOSPITAL2)) %>% rename(ageupwith65 =Freq)
## age 10
age10.1.d <- df %>% filter(age_group10 == 1)
age10.1.d.f <- as.data.frame(table(age10.1.d$DATE_INHOSPITAL2)) %>% rename(agelo40 =Freq)
age10.2.d <- df %>% filter(age_group10 == 2)
age10.2.d.f <- as.data.frame(table(age10.2.d$DATE_INHOSPITAL2)) %>% rename(age41_50 =Freq)
age10.3.d <- df %>% filter(age_group10 == 3)
age10.3.d.f <- as.data.frame(table(age10.3.d$DATE_INHOSPITAL2)) %>% rename(age51_60 =Freq)
age10.4.d <- df %>% filter(age_group10 == 4)
age10.4.d.f <- as.data.frame(table(age10.4.d$DATE_INHOSPITAL2)) %>% rename(age61_70 =Freq)
age10.5.d <- df %>% filter(age_group10 == 5)
age10.5.d.f <- as.data.frame(table(age10.5.d$DATE_INHOSPITAL2)) %>% rename(age71_80 =Freq)
age10.6.d <- df %>% filter(age_group10 == 6)
age10.6.d.f <- as.data.frame(table(age10.6.d$DATE_INHOSPITAL2)) %>% rename(ageup80 =Freq)
datebreaks<-seq(as.Date("2014-01-01"),as.Date("2018-12-31"),by="1 day")
full <- data.frame(Var1 = as.character(datebreaks) )
result <- full %>%
left_join(t.f) %>%
left_join(man.f) %>%
left_join(woman.f) %>%
left_join(agelo65.f) %>%
left_join(ageup65.f) %>%
left_join(age10.1.d.f) %>%
left_join(age10.2.d.f) %>%
left_join(age10.3.d.f) %>%
left_join(age10.4.d.f) %>%
left_join(age10.5.d.f) %>%
left_join(age10.6.d.f) %>% replace(., is.na(.), 0)
return(result)
}
list <- split(total,total$DISEASE_CODE1_2to3)
test <- map(list,tsdata2)
I think the error was because the number of hospital admissions on a given day was zero.
How can I improve this code that it can work even the number is zero.
test <- map(list,tsdata2)
Joining, by = "Var1"
Joining, by = "Var1"
Joining, by = "Var1"
Joining, by = "Var1"
Joining, by = "Var1"
Joining, by = "Var1"
Error: `by` required, because the data sources have no common variables

Answer 1

The reason of such an error thrown is that you are applying left_join on empty data frames with no columns to join. Along your data frame filtering and contingency table creation, data frames with no columns to join were generated. Please see below the simulation :
library(dplyr)
df1 <- data.frame(a = 1:10, b = letters[1:10])
df2 <- data.frame(a = 1:10, c = letters[11:20])
df2 <- df2[,-c(1, 2)]
str(df2)
# 'data.frame': 10 obs. of 0 variables
df2 %>% left_join(df1)
The code above throws an error:
Error: by required, because the data sources have no common
variables Call rlang::last_error() to see a backtrace
To avoid such a problem you can implement simple check if the data frame is with no columns then change to dummy data frame:
library(dplyr)
df1 <- data.frame(a = 1:10, b = letters[1:10])
df2 <- df1[,-c(1, 2)]
df_dummy <- data.frame(a = 1, c = 0)
if(ncol(df2) == 0) df2 <- df_dummy
df1 %>% left_join(df2)
#
# Joining, by = "a"
# a b c
# 1 1 a 0
# 2 2 b NA
# 3 3 c NA
# 4 4 d NA
# 5 5 e NA
# 6 6 f NA
# 7 7 g NA
# 8 8 h NA
# 9 9 i NA
# 10 10 j NA