I have a dataframe like this
id <- c(5738180,51845,167774,517814,1344920,517833,51844)
measurement <- c("Length","Breadth","Breadth","Length","Length","Length","Breadth")
map_flag <- c(0,1,1,0,0,0,0)
cap_flag <- c(1,0,0,1,1,0,0)
df.sample <- data.frame(id,measurement,map_flag,cap_flag)
df.sample
id measurement map_flag cap_flag
5738180 Length 0 1
51845 Breadth 1 0
167774 Breadth 1 0
517814 Length 0 1
1344920 Length 0 1
517833 Length 0 0
51844 Breadth 0 0
I am trying to create a function that takes a df and type as inputs and returns a df based on the filtered type
If I pass in
type = "map", it should filter the df usingmap_flag = 1column and return a dftype = "cap", it should filter the df usingcap_flag = 1column and return a dftype = "Length", it should filter the df usingmeasurement = Lengthcolumn and return a df
Desired outputs
type = "map"
id measurement map_flag cap_flag
51845 Breadth 1 0
167774 Breadth 1 0
type = "cap"
id measurement map_flag cap_flag
5738180 Length 0 1
517814 Length 0 1
1344920 Length 0 1
type = "Length"
id measurement map_flag cap_flag
5738180 Length 0 1
517814 Length 0 1
1344920 Length 0 1
517833 Length 0 0
I am trying to do it this way but not getting what I wanted
testFun <- function (df, type) {
df <- df %>%
{if (type==starts_with(map) filter(map_flag==1) }
return(df)
}
I'd really appreciate it if someone can point me in the right direction.
CodePudding user response：
If there are only 3 possible type inputs I would suggest to go with @Park's answer since it is easy to understand and does what is required.
I provide another option where you don't need to list down conditions for every input. It checks if type_flag column is present in the data and returns output based on that condition.
library(dplyr)
testFun <- function (df, type) {
col <- paste0(type, '_flag')
if(col %in% colnames(df)) {
res <- df %>% filter(.data[[col]] == 1)
} else {
res <- df %>% filter(measurement == type)
}
return(res)
}
testFun(df.sample, "map")
# id measurement map_flag cap_flag
#1 51845 Breadth 1 0
#2 167774 Breadth 1 0
testFun(df.sample, "cap")
# id measurement map_flag cap_flag
#1 5738180 Length 0 1
#2 517814 Length 0 1
#3 1344920 Length 0 1
testFun(df.sample, "Length")
# id measurement map_flag cap_flag
#1 5738180 Length 0 1
#2 517814 Length 0 1
#3 1344920 Length 0 1
#4 517833 Length 0 0
CodePudding user response：
You may try
func <- function(df, type = c("map", "cap", "Length")){
if (type == "map"){
df %>% filter(map_flag == 1)
} else if (type == "cap") {
df %>% filter(cap_flag == 1)
} else if (type == "Length") {
df %>% filter(measurement == "Length")
} else {
stop("select type amont map, cap, and Length")
}
}
func(df.sample, "map")
id measurement map_flag cap_flag
1 51845 Breadth 1 0
2 167774 Breadth 1 0
func(df.sample, "cap")
1 5738180 Length 0 1
2 517814 Length 0 1
3 1344920 Length 0 1
func(df.sample, "Length")
1 5738180 Length 0 1
2 517814 Length 0 1
3 1344920 Length 0 1
4 517833 Length 0 0
Error message is like
func(df.sample, "ma")
Error in func(df.sample, "ma") : select type amont map, cap, and Length
New one
func <- function(df, type = c("map", "cap", "Length")){
key <- paste0(type, "_flag")
res1 <- try(df %>% filter(!!as.symbol(key) ==1), silent = TRUE)
res2 <- df %>% filter(measurement == "Length")
if(is(res1, "try-error")) res2 else res1
}
CodePudding user response：
This solution only needs base R and works with data.frame or data.table input:
testFun <- function(df, type) {
key <- sprintf('%s_flag', type)
if(key %in% names(df)) {
return(df[df[[key]] == 1,])
} else {
# For other `type`s fall back to this - I think this is bad!
return(df[df$measurement == 'Length',])
}
}
However, from a design point of view I would much prefer this:
testFun <- function(df, type) {
if(type == 'map') {
return(df[df$map_flag == 1,])
}
if(type == 'cap') {
return(df[df$cap_flag == 1,])
}
if(type == 'Length') {
return(df[df$measurement == 'Length',])
}
stop(sprintf('Invald type: %s', type))
}
or to require type to be a column in the input data.frame.