Nested Ifelse Statement

How should I use nested ifelse statements?

We need to use %in% instead of ==:

rock$status <- ifelse(rock$name %in% c("E", "F", "G", "H", "I", "J") & 
                        rock$sex != "F", "1F",            
                      ifelse(rock$name %in% c("E", "F", "G", "H", "I", "J") & 
                               rock$sex == "F", "Female",
                             ifelse(rock$name %in% c("A", "B", "C", "D") &
                                      rock$age > 15, "Young", "Others")))
rock

#    name age sex  status
# 1     A  22   M   Young
# 2     B  43   F   Young
# 3     C  12   M  Others
# 4     D  17   M   Young
# 5     E  29   M      1F
# 6     F   5   F  Female
# 7     G  51   F  Female
# 8     H  56   M      1F
# 9     I   9   F  Female
# 10    J  44   F  Female

nested ifelse in R

Normal scenario works for me.

disc_freq <- c("1.Low_disc", "2.Med_disc", "3.High_disc", "4.na")
promo_freq <- c("1.Low_promo", "3.High_promo", "3.High_promo", "4.na")
Promotion1 <- data.frame(disc_freq, promo_freq)

Promotion1

disc_freq    promo_freq
<fctr>       <fctr> 
1.Low_disc  1.Low_promo         
2.Med_disc  3.High_promo            
3.High_disc 3.High_promo            
4.na    4.na


Promotion1$promo_segmnt <-
  ifelse(
  (Promotion1$disc_freq == "1.Low_disc") &
  (Promotion1$promo_freq == "1.Low_promo"),
  "1.Low",
  ifelse(
  (Promotion1$disc_freq == "2.Med_disc") &
  (Promotion1$promo_freq == "3.High_promo"),
  "3.High",
  ifelse(
  (Promotion1$disc_freq == "3.High_disc") &
  (Promotion1$promo_freq == "3.High_promo"),
  "3.High ",
  "NA"
  )
  )
  )

[1] "1.Low"   "3.High"  "3.High " "NA"

It is possible there are some extra spaces in your columns. you can remove it using stringr package.

library(stringr)

Promotion1 $disc_freq<- str_trim(Promotion1 $disc_freq, side = "both")
Promotion1 $promo_freq<- str_trim(Promotion1 $promo_freq, side = "both")

then try ifelse again.

syntax for nested ifelse and if statements in R

We can use a group by approach - grouped by 'Date', 'Location', subset the 'pH' where 'Code' value is "F" (assuming only a single "F" per Location) and then subtract from the 'pH' column

library(dplyr)
df1 <- df1 %>%
     group_by(Date, Location) %>%
     mutate(phDelta = pH - pH[Code == "F"][1]) %>%
     ungroup

Way to avoid nested ifelse statement in R

Following @Claudiu Papasteri recommendations:

Package

library(dplyr)

Solution

df %>% 
  mutate(age_band = case_when( age %in% c("15 to 17","18 and 19") ~  '15 to 19',
                               age %in% c("20 to 24","25 to 29") ~ '20s',
                               age %in% c("30 to 34","35 to 39") ~ '30s',
                               TRUE ~'40+')
         )

ps: I would include all specific statements in case_when, even the last one for "40+", this way you can keep track of any problem in your dataset. Because if something is missing, or you have a typo, everything else will be coded as "40+". So by stating every case you can catch the problem and fix everything before running an analysis or generating graphs. And the last statement could be changed to TRUE ~ age. This means, whatever remains, use the same value, or TRUE ~ NA, that you recode the remaining stuff for missing. So you know that all missing means you have a problem you have to fix.

Output

         age value age_band
1   15 to 17  1432 15 to 19
2  18 and 19  1112 15 to 19
3   20 to 24  1265      20s
4   25 to 29  1076      20s
5   30 to 34  1212      30s
6   35 to 39  1238      30s
7   40 to 44  1384      40+
8   45 to 49  1612      40+
9   50 to 54  1606      40+
10  55 to 59  1897      40+

Nested ifelse() statement in R not producing the desired results

Here is another possible option using rowSums:

df$newvar <- +(rowSums(df, na.rm = TRUE) * NA ^ (rowSums(!is.na(df)) == 0) > 0)

#  var1 var2 var3 newvar
#1   NA    1   NA      1
#2   NA   NA   NA     NA
#3   NA    0   NA      0
#4    0    0    0      0
#5    1    1    1      1

Nested ifelse to output 3 responses in R

You could use cut to create intervals and check, if a values falls into them:

library(dplyr)

df1 %>% 
  mutate(
    check_1 = cut(time, breaks = df2$t_start, labels = FALSE),
    check_2 = coalesce(cut(time, breaks = df2$t_end, labels = FALSE), 0),
    check_3 = df2$control[check_1],
    trial_type = case_when(
      check_1 - check_2 == 1 & check_3 == 0 ~ "wt_int",
      check_1 - check_2 == 1 & check_3 == 3 ~ "wt_control",
      TRUE ~ "iti"
      )
    ) %>% 
  select(-starts_with("check_"))

This returns

# A tibble: 7 x 6
  initiate  left right l_or_r   time trial_type
     <dbl> <dbl> <dbl>  <dbl>  <dbl> <chr>     
1        0     0     1      1   2.82 iti       
2        0     0     1      1   2.82 iti       
3        0     0     1      1   2.82 iti       
4        0     0     1      1   2.83 iti       
5        1     0     0      0  16.8  wt_int    
6        1     0     0      0  16.8  wt_int    
7        1     0     0      0 187.   wt_control

Data

df1 <- structure(list(initiate = c(0, 0, 0, 0, 1, 1, 1), left = c(0, 
0, 0, 0, 0, 0, 0), right = c(1, 1, 1, 1, 0, 0, 0), l_or_r = c(1, 
1, 1, 1, 0, 0, 0), time = c(2.8225, 2.82375, 2.825, 2.82625, 
16.82, 16.8212, 187.08)), class = c("spec_tbl_df", "tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -7L), spec = structure(list(
    cols = list(initiate = structure(list(), class = c("collector_double", 
    "collector")), left = structure(list(), class = c("collector_double", 
    "collector")), right = structure(list(), class = c("collector_double", 
    "collector")), l_or_r = structure(list(), class = c("collector_double", 
    "collector")), time = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1L), class = "col_spec"))

df2 <- structure(list(trial = c(1, 2, 3, 4, 11, 12, 30), control = c(0, 
0, 0, 0, 3, 0, 3), t_start = c(16.64709, 41.81843, 65.5451, 82.65743, 
187.0787, 200.0486, 415.171), t_end = c(35.49431, 57.74304, 71.16612, 
87.30914, 193.5898, 203.1883, 418.0405)), class = c("spec_tbl_df", 
"tbl_df", "tbl", "data.frame"), row.names = c(NA, -7L), spec = structure(list(
    cols = list(trial = structure(list(), class = c("collector_double", 
    "collector")), control = structure(list(), class = c("collector_double", 
    "collector")), t_start = structure(list(), class = c("collector_double", 
    "collector")), t_end = structure(list(), class = c("collector_double", 
    "collector"))), default = structure(list(), class = c("collector_guess", 
    "collector")), skip = 1L), class = "col_spec"))

How to avoid repeating the same 'else' statement in multiple nested if/else?

In the case of nested if else, you can use a guard type statement. guard is popular in case of nested if-else and provides a clean code.

Guard statement tries to element the else cases first. You know we write else cases to perform deviated paths. So what if we remove the
deviated paths first.

For example, if you have

if condition A == "Yes":
    print("A")
    if condition B == "Yes":
        print("B")
    else:
        print("D")
        if condition C =="Yes":
            print("C")
        else:
            print("D")
else:
    print("D")

Now I can formatting the following

// 1 
if condiation A == "No" {
    print("D")
    return 
}

// 2
if condition B == "Yes" {
    print("B")
    return 
}

if condition C == "No" {
   print("D")
   return 
}

print("C")

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