R: Creating a grouped df showing mean of Variable-CodePudding

for my linear regression with one dichotom numeric variable (Strategic Voting) and one metric variable (Age, Q3), I need a df which shows the mean age of the people who either voted strategically (1) or not (0).

This is my code, but I get following the error message "Warning messages: 1: In mean.default(Q3) : argument is not numeric or logical: returning NA 2: In mean.default(Q3) : argument is not numeric or logical: returning NA" Now I tried converting the data to a numerical one using as.numeric(Q3), but I cant get it to work.

Test<- Deskriptive_Statistik %>% 
  select(Q3, StrategischeWahl2021) %>% 
  group_by(StrategischeWahl2021) %>% 
  summarise(Q3 = mean(Q3))

This is my data:

    dput(Test)
structure(list(Q3 = c("24", "20", "20", "19", "21", "33", "27", 
"20", "53", "31", "21", "22", "21", "20", "25", "21", "24", "29", 
"53 ", "20", "21", "22", "48", "28", "20", "23", "29", "29", 
"23", "41", "29", "21", "29", "47", "23", "53", "34", "19", "23", 
"24", "29", "29", "20", "22", "29", "25", "21", "22", "29", "20", 
"30", "21", "23", "19", "23", "18", "25", "22", "28", "25", "22", 
"21", "24", "24", "29", "55", "20", "20", "21", "20", "28", "22", 
"21", "22", "20", "31", "22", "20", "31", "22", "22", "30", "20", 
"22", "18", "23", "55", "22", "25", "25", "21", "39", "22", "20", 
"49", "58", "20", "19", "21", "22", "29", "23", "32", "35", "20", 
"20", "21", "28", "24", "28", "60", "70", "43", "21", "25", "60", 
"34", "54", "24", "25", "23", "21", "48", "20", "25", "24", "21", 
"25", "22", "24", "21", "22", "21", "18", "22", "21", "22", "18", 
"19", "71", "23", "26", "18", "24", "21", "51", "37", "41", "23", 
"25", "22", "35", "21", "18", "22", "29", "26", "21", "22", "23", 
"43", "22", "23", "22", "21", "69", "20", "25", "54", "20", "26", 
"28", "23", "28", "38", "21", "22", "78", "23", "25", "25", "63", 
"32", "33", "20", "21", "20", "23", "21", "24", "19", "24", "37", 
"21", "26", "24", "21", "23", "21", "19", "22", "22", "25", "20", 
"22", "22", "19", "30", "19", "22", "19", "26", "23", "25", "21", 
"36", "25", "22", "23", "22", "23", "22", "20", "21", "29", "22", 
"19", "22", "22", "60", "29", "21", "20", "21", "23", "21", "23", 
"19", "60", "59", "20", "23", "60", "23", "24", "22", "22", "27", 
"23", "19", "22", "18", "21", "22", "19", "68", "26", "21", "20"
), StrategischeWahl2021 = c("0", "0", "0", "0", "0", "0", "0", 
"0", "0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "0", 
"0", "0", "0", "1", "1", "1", "0", "0", "0", "0", "1", "0", "0", 
"0", "0", "0", "0", "1", "0", "0", "0", "0", "0", "0", "1", "0", 
"1", "1", "0", "1", "1", "0", "1", "0", "1", "0", "0", "0", "0", 
"0", "0", "1", "0", "0", "0", "1", "1", "0", "0", "0", "0", "0", 
"0", "0", "0", "0", "0", "0", "0", "1", "0", "0", "1", "0", "1", 
"0", "0", "1", "0", "1", "1", "0", "0", "0", "1", "1", "0", "1", 
"0", "1", "1", "0", "0", "0", "0", "0", "1", "0", "0", "0", "1", 
"0", "1", "1", "1", "1", "0", "1", "1", "0", "0", "0", "0", "1", 
"0", "1", "0", "0", "0", "1", "0", "0", "0", "1", "1", "0", "0", 
"1", "0", "1", "0", "0", "0", "0", "0", "1", "0", "0", "0", "1", 
"1", "0", "0", "0", "0", "1", "0", "1", "0", "0", "0", "1", "0", 
"1", "0", "0", "1", "0", "0", "1", "1", "0", "0", "0", "0", "0", 
"0", "0", "1", "1", "0", "1", "0", "1", "0", "0", "0", "0", "0", 
"1", "0", "1", "0", "1", "0", "0", "1", "0", "0", "0", "0", "0", 
"0", "0", "1", "1", "0", "0", "0", "1", "0", "0", "1", "0", "1", 
"0", "0", "0", "0", "0", "1", "0", "1", "1", "0", "1", "0", "0", 
"1", "0", "0", "0", "0", "1", "0", "1", "0", "1", "1", "1", "0", 
"0", "0", "0", "1", "0", "0", "0", "1", "1", "0", "0", "0", "1", 
"1", "0", "0", "0", "0")), class = c("grouped_df", "tbl_df", 
"tbl", "data.frame"), row.names = c(NA, -259L), groups = structure(list(
    StrategischeWahl2021 = c("0", "1"), .rows = structure(list(
        c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
        14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 27L, 
        28L, 29L, 30L, 32L, 33L, 34L, 35L, 36L, 37L, 39L, 40L, 
        41L, 42L, 43L, 44L, 46L, 49L, 52L, 54L, 56L, 57L, 58L, 
        59L, 60L, 61L, 63L, 64L, 65L, 68L, 69L, 70L, 71L, 72L, 
        73L, 74L, 75L, 76L, 77L, 78L, 79L, 81L, 82L, 84L, 86L, 
        87L, 89L, 92L, 93L, 94L, 97L, 99L, 102L, 103L, 104L, 
        105L, 106L, 108L, 109L, 110L, 112L, 117L, 120L, 121L, 
        122L, 123L, 125L, 127L, 128L, 129L, 131L, 132L, 133L, 
        136L, 137L, 139L, 141L, 142L, 143L, 144L, 145L, 147L, 
        148L, 149L, 152L, 153L, 154L, 155L, 157L, 159L, 160L, 
        161L, 163L, 165L, 166L, 168L, 169L, 172L, 173L, 174L, 
        175L, 176L, 177L, 178L, 181L, 183L, 185L, 186L, 187L, 
        188L, 189L, 191L, 193L, 195L, 196L, 198L, 199L, 200L, 
        201L, 202L, 203L, 204L, 207L, 208L, 209L, 211L, 212L, 
        214L, 216L, 217L, 218L, 219L, 220L, 222L, 225L, 227L, 
        228L, 230L, 231L, 232L, 233L, 235L, 237L, 241L, 242L, 
        243L, 244L, 246L, 247L, 248L, 251L, 252L, 253L, 256L, 
        257L, 258L, 259L), c(13L, 24L, 25L, 26L, 31L, 38L, 45L, 
        47L, 48L, 50L, 51L, 53L, 55L, 62L, 66L, 67L, 80L, 83L, 
        85L, 88L, 90L, 91L, 95L, 96L, 98L, 100L, 101L, 107L, 
        111L, 113L, 114L, 115L, 116L, 118L, 119L, 124L, 126L, 
        130L, 134L, 135L, 138L, 140L, 146L, 150L, 151L, 156L, 
        158L, 162L, 164L, 167L, 170L, 171L, 179L, 180L, 182L, 
        184L, 190L, 192L, 194L, 197L, 205L, 206L, 210L, 213L, 
        215L, 221L, 223L, 224L, 226L, 229L, 234L, 236L, 238L, 
        239L, 240L, 245L, 249L, 250L, 254L, 255L)), ptype = integer(0), class = c("vctrs_list_of", 
    "vctrs_vctr", "list"))), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -2L), .drop = TRUE))

Thank you very much in advance ;)

CodePudding user response：

There were some additional attributes in the data, which was removed with ungroup, and then do the group by, summarise

library(dplyr)
Test %>% 
   ungroup %>%
   group_by(StrategischeWahl2021) %>%
   summarise(Q3 = mean(as.numeric(Q3), na.rm = TRUE))

-output

# A tibble: 2 × 2
  StrategischeWahl2021    Q3
  <chr>                <dbl>
1 0                     26.9
2 1                     27.6