CODEWITHSUNDEEP
rtidyverse
Dekike
Dekike

Reputation: 1284

Why do I get an error when using `pivot_wider()` from the `tidyverse` package?

I have a dataframe with a numerical variable (Acc) and four categorical variables (ID,Datetime, Period and State).

# My data
df <- data.frame(ID=c(rep(c("A"),8),rep(c("B"),8)),
                 Datetime=c("2020-08-05 12:00:00","2020-08-05 17:00:00","2020-08-05 18:03:00","2020-08-05 22:54:00","2020-08-06 01:08:00","2020-08-06 13:26:00","2020-08-06 19:04:00","2020-08-08 11:00:00",
                            "2020-08-04 03:00:00","2020-08-04 15:00:00","2020-08-04 23:00:00","2020-08-06 14:00:00","2020-08-06 17:00:00","2020-08-06 20:00:00","2020-08-07 04:00:00","2020-08-07 16:00:00"),
                 Period=c("Day","Day","Day","Night","Night","Day","Night","Day","Night","Day","Night","Day","Day","Night","Night","Day"),
                 State=c(1,2,1,1,1,1,2,2,1,1,1,2,2,1,1,1),
                 Acc=c(1.1,2.3,1.7,1.4,0.1,1.9,2.9,2.3,1.1,0.1,1.4,0.2,2.6,1.3,1.7,1.0))

df$Datetime <- as.POSIXct(df$Datetime,format="%Y-%m-%d %H:%M:%S", tz="UTC")

df

   ID            Datetime Period State Acc
1   A 2020-08-05 12:00:00    Day     1 1.1
2   A 2020-08-05 17:00:00    Day     2 2.3
3   A 2020-08-05 18:03:00    Day     1 1.7
4   A 2020-08-05 22:54:00  Night     1 1.4
5   A 2020-08-06 01:08:00  Night     1 0.1
6   A 2020-08-06 13:26:00    Day     1 1.9
7   A 2020-08-06 19:04:00  Night     2 2.9
8   A 2020-08-08 11:00:00    Day     2 2.3
9   B 2020-08-04 03:00:00  Night     1 1.1
10  B 2020-08-04 15:00:00    Day     1 0.1
11  B 2020-08-04 23:00:00  Night     1 1.4
12  B 2020-08-06 14:00:00    Day     2 0.2
13  B 2020-08-06 17:00:00    Day     2 2.6
14  B 2020-08-06 20:00:00  Night     1 1.3
15  B 2020-08-07 04:00:00  Night     1 1.7
16  B 2020-08-07 16:00:00    Day     1 1.0

I am trying to estimate mean Acc per ID, Day, Period and State. To do so, I am trying to apply this code:

library(tidyverse)

  df %>% 
    group_by(ID, Day = as.factor(as.Date(Datetime)), Period, State, .drop = FALSE) %>% 
    summarise(Acc = mean(Acc, na.rm = TRUE)) %>% 
    pivot_wider(names_from = c(State, Period),
                values_from = Acc,
                names_prefix = "State.") %>% 
    select(!State.NA_NA)

I should get this:

#> `summarise()` regrouping output by 'ID', 'Day', 'Period' (override with `.groups` argument)
#> # A tibble: 10 x 6
#> # Groups:   ID, Day [10]
#>    ID    Day        State.1_Day State.2_Day State.1_Night State.2_Night
#>    <chr> <fct>            <dbl>       <dbl>         <dbl>         <dbl>
#>  1 A     2020-08-04        NA          NA           NA             NA  
#>  2 A     2020-08-05         1.4         2.3          1.4           NA  
#>  3 A     2020-08-06         1.9        NA            0.1            2.9
#>  4 A     2020-08-07        NA          NA           NA             NA  
#>  5 A     2020-08-08        NA           2.3         NA             NA  
#>  6 B     2020-08-04         0.1        NA            1.25          NA  
#>  7 B     2020-08-05        NA          NA           NA             NA  
#>  8 B     2020-08-06        NA           1.4          1.3           NA  
#>  9 B     2020-08-07         1          NA            1.7           NA  
#> 10 B     2020-08-08        NA          NA           NA             NA

However, I get the message Error in map_lgl(.x, .p, ...) : object 'State' not found.

I don't understand where is the mistake. I guess it is something silly, but I have been hours thinking on that and nothing.

Does someone shed light on why I get an error message?

Upvotes: 0

Views: 2129

Answers (3)

Kelly
Kelly

Reputation: 27

I encountered a similar error with my names_from = and values_from = variables and was able to get it to work by putting those variable names in quotes. I can't reproduce your error unfortunately so this seems to be a recent change.

Upvotes: 0

Karthik S
Karthik S

Reputation: 11584

Does this work:

df %>% 
  group_by(ID, Day = as.factor(as.Date(Datetime)), Period, State, .drop = FALSE) %>% 
  summarise(Acc = mean(Acc, na.rm = TRUE)) %>% na.omit() %>% 
  pivot_wider(names_from = c(State, Period),
              values_from = Acc,
              names_prefix = "State.") %>%  complete(ID, nesting(Day)) %>% arrange(ID, Day) %>% distinct()
`summarise()` regrouping output by 'ID', 'Day', 'Period' (override with `.groups` argument)
# A tibble: 10 x 6
# Groups:   ID, Day [10]
   ID    Day        State.1_Day State.2_Day State.1_Night State.2_Night
   <fct> <fct>            <dbl>       <dbl>         <dbl>         <dbl>
 1 A     2020-08-04        NA          NA           NA             NA  
 2 A     2020-08-05         1.4         2.3          1.4           NA  
 3 A     2020-08-06         1.9        NA            0.1            2.9
 4 A     2020-08-07        NA          NA           NA             NA  
 5 A     2020-08-08        NA           2.3         NA             NA  
 6 B     2020-08-04         0.1        NA            1.25          NA  
 7 B     2020-08-05        NA          NA           NA             NA  
 8 B     2020-08-06        NA           1.4          1.3           NA  
 9 B     2020-08-07         1          NA            1.7           NA  
10 B     2020-08-08        NA          NA           NA             NA  
>

Upvotes: 3

Jeffrey Brabec
Jeffrey Brabec

Reputation: 521

I'm not sure where in your code the error was coming from but I think it was from the final select line. Instead of selecting anything that wasn't State.NA_NA I switched it to keep everything column that didn't contain an NA in it. This seems to get the desired output.

Also looking at the comments I would recommend calling each tidyverse package with the syntax dplyr::select as you may be unintentionally calling a different select.

library(tidyverse)

df <- data.frame(ID=c(rep(c("A"),8),rep(c("B"),8)),
                 Datetime=c("2020-08-05 12:00:00","2020-08-05 17:00:00","2020-08-05 18:03:00","2020-08-05 22:54:00","2020-08-06 01:08:00","2020-08-06 13:26:00","2020-08-06 19:04:00","2020-08-08 11:00:00",
                            "2020-08-04 03:00:00","2020-08-04 15:00:00","2020-08-04 23:00:00","2020-08-06 14:00:00","2020-08-06 17:00:00","2020-08-06 20:00:00","2020-08-07 04:00:00","2020-08-07 16:00:00"),
                 Period=c("Day","Day","Day","Night","Night","Day","Night","Day","Night","Day","Night","Day","Day","Night","Night","Day"),
                 State=c(1,2,1,1,1,1,2,2,1,1,1,2,2,1,1,1),
                 Acc=c(1.1,2.3,1.7,1.4,0.1,1.9,2.9,2.3,1.1,0.1,1.4,0.2,2.6,1.3,1.7,1.0))

df$Datetime <- as.POSIXct(df$Datetime,format="%Y-%m-%d %H:%M:%S", tz="UTC")

df
#>    ID            Datetime Period State Acc
#> 1   A 2020-08-05 12:00:00    Day     1 1.1
#> 2   A 2020-08-05 17:00:00    Day     2 2.3
#> 3   A 2020-08-05 18:03:00    Day     1 1.7
#> 4   A 2020-08-05 22:54:00  Night     1 1.4
#> 5   A 2020-08-06 01:08:00  Night     1 0.1
#> 6   A 2020-08-06 13:26:00    Day     1 1.9
#> 7   A 2020-08-06 19:04:00  Night     2 2.9
#> 8   A 2020-08-08 11:00:00    Day     2 2.3
#> 9   B 2020-08-04 03:00:00  Night     1 1.1
#> 10  B 2020-08-04 15:00:00    Day     1 0.1
#> 11  B 2020-08-04 23:00:00  Night     1 1.4
#> 12  B 2020-08-06 14:00:00    Day     2 0.2
#> 13  B 2020-08-06 17:00:00    Day     2 2.6
#> 14  B 2020-08-06 20:00:00  Night     1 1.3
#> 15  B 2020-08-07 04:00:00  Night     1 1.7
#> 16  B 2020-08-07 16:00:00    Day     1 1.0

df %>% 
  dplyr::group_by(ID, Day = as.factor(as.Date(Datetime)), Period, State, .drop = FALSE) %>% 
  dplyr::summarise(Acc = mean(Acc, na.rm = TRUE)) %>% 
  tidyr::pivot_wider(names_from = c(State, Period),
              values_from = Acc,
              names_prefix = "State.") %>% 
  dplyr::select(!contains("NA"))
#> `summarise()` regrouping output by 'ID', 'Day', 'Period' (override with `.groups` argument)
#> # A tibble: 10 x 6
#> # Groups:   ID, Day [10]
#>    ID    Day        State.1_Day State.2_Day State.1_Night State.2_Night
#>    <fct> <fct>            <dbl>       <dbl>         <dbl>         <dbl>
#>  1 A     2020-08-04        NA          NA           NA             NA  
#>  2 A     2020-08-05         1.4         2.3          1.4           NA  
#>  3 A     2020-08-06         1.9        NA            0.1            2.9
#>  4 A     2020-08-07        NA          NA           NA             NA  
#>  5 A     2020-08-08        NA           2.3         NA             NA  
#>  6 B     2020-08-04         0.1        NA            1.25          NA  
#>  7 B     2020-08-05        NA          NA           NA             NA  
#>  8 B     2020-08-06        NA           1.4          1.3           NA  
#>  9 B     2020-08-07         1          NA            1.7           NA  
#> 10 B     2020-08-08        NA          NA           NA             NA

Created on 2020-11-11 by the reprex package (v0.3.0)

Upvotes: 1

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