This what I have so far
a<-c(airquality[["Month"]])
function(a){
b<-c(if(a==5){
print("May")
} else {
print("June")
})
return(a)
}
a
Hi, I assume this is the built in airquality dataset. There might be a better way, but this will work.
library(tidyverse)
names <- tibble(Month = 1:12,
MonthName = month.name) # built in dataset
df <- airquality %>%
left_join(names, by = "Month")
Actually, I read your heading and you need to use if else statements. That seems like a pain.
library(dplyr)
data("airquality")
unique(airquality$Month)
#> [1] 5 6 7 8 9
the_months <- vector(length = dim(airquality)[1])
for(i in seq_along(airquality$Month)) {
if(airquality$Month[i] == 5) the_months[i] = "May"
if(airquality$Month[i] == 6) the_months[i] = "June"
if(airquality$Month[i] == 7) the_months[i] = "July"
if(airquality$Month[i] == 8) the_months[i] = "August"
if(airquality$Month[i] == 9) the_months[i] = "September"
else "not a month in range"
}
the_months
#> [1] "May" "May" "May" "May" "May" "May"
#> [7] "May" "May" "May" "May" "May" "May"
#> [13] "May" "May" "May" "May" "May" "May"
#> [19] "May" "May" "May" "May" "May" "May"
#> [25] "May" "May" "May" "May" "May" "May"
#> [31] "May" "June" "June" "June" "June" "June"
#> [37] "June" "June" "June" "June" "June" "June"
#> [43] "June" "June" "June" "June" "June" "June"
#> [49] "June" "June" "June" "June" "June" "June"
#> [55] "June" "June" "June" "June" "June" "June"
#> [61] "June" "July" "July" "July" "July" "July"
#> [67] "July" "July" "July" "July" "July" "July"
#> [73] "July" "July" "July" "July" "July" "July"
#> [79] "July" "July" "July" "July" "July" "July"
#> [85] "July" "July" "July" "July" "July" "July"
#> [91] "July" "July" "August" "August" "August" "August"
#> [97] "August" "August" "August" "August" "August" "August"
#> [103] "August" "August" "August" "August" "August" "August"
#> [109] "August" "August" "August" "August" "August" "August"
#> [115] "August" "August" "August" "August" "August" "August"
#> [121] "August" "August" "August" "September" "September" "September"
#> [127] "September" "September" "September" "September" "September" "September"
#> [133] "September" "September" "September" "September" "September" "September"
#> [139] "September" "September" "September" "September" "September" "September"
#> [145] "September" "September" "September" "September" "September" "September"
#> [151] "September" "September" "September"
# with dplyr
airquality %>% dplyr::mutate(Month = case_when(
Month == 5 ~ "May",
Month == 6 ~ "June",
Month == 7 ~ "July",
Month == 8 ~ "August",
Month == 9 ~ "September"
)
)
#> Ozone Solar.R Wind Temp Month Day
#> 1 41 190 7.4 67 May 1
#> 2 36 118 8.0 72 May 2
#> 3 12 149 12.6 74 May 3
#> 4 18 313 11.5 62 May 4
#> 5 NA NA 14.3 56 May 5
#> 6 28 NA 14.9 66 May 6
#> 7 23 299 8.6 65 May 7
#> 8 19 99 13.8 59 May 8
#> 9 8 19 20.1 61 May 9
#> 10 NA 194 8.6 69 May 10
#> 11 7 NA 6.9 74 May 11
#> 12 16 256 9.7 69 May 12
#> 13 11 290 9.2 66 May 13
#> 14 14 274 10.9 68 May 14
#> 15 18 65 13.2 58 May 15
#> 16 14 334 11.5 64 May 16
#> 17 34 307 12.0 66 May 17
#> 18 6 78 18.4 57 May 18
#> 19 30 322 11.5 68 May 19
#> 20 11 44 9.7 62 May 20
#> 21 1 8 9.7 59 May 21
#> 22 11 320 16.6 73 May 22
#> 23 4 25 9.7 61 May 23
#> 24 32 92 12.0 61 May 24
#> 25 NA 66 16.6 57 May 25
#> 26 NA 266 14.9 58 May 26
#> 27 NA NA 8.0 57 May 27
#> 28 23 13 12.0 67 May 28
#> 29 45 252 14.9 81 May 29
#> 30 115 223 5.7 79 May 30
#> 31 37 279 7.4 76 May 31
#> 32 NA 286 8.6 78 June 1
#> 33 NA 287 9.7 74 June 2
#> 34 NA 242 16.1 67 June 3
#> 35 NA 186 9.2 84 June 4
#> 36 NA 220 8.6 85 June 5
#> 37 NA 264 14.3 79 June 6
#> 38 29 127 9.7 82 June 7
#> 39 NA 273 6.9 87 June 8
#> 40 71 291 13.8 90 June 9
#> 41 39 323 11.5 87 June 10
#> 42 NA 259 10.9 93 June 11
#> 43 NA 250 9.2 92 June 12
#> 44 23 148 8.0 82 June 13
#> 45 NA 332 13.8 80 June 14
#> 46 NA 322 11.5 79 June 15
#> 47 21 191 14.9 77 June 16
#> 48 37 284 20.7 72 June 17
#> 49 20 37 9.2 65 June 18
#> 50 12 120 11.5 73 June 19
#> 51 13 137 10.3 76 June 20
#> 52 NA 150 6.3 77 June 21
#> 53 NA 59 1.7 76 June 22
#> 54 NA 91 4.6 76 June 23
#> 55 NA 250 6.3 76 June 24
#> 56 NA 135 8.0 75 June 25
#> 57 NA 127 8.0 78 June 26
#> 58 NA 47 10.3 73 June 27
#> 59 NA 98 11.5 80 June 28
#> 60 NA 31 14.9 77 June 29
#> 61 NA 138 8.0 83 June 30
#> 62 135 269 4.1 84 July 1
#> 63 49 248 9.2 85 July 2
#> 64 32 236 9.2 81 July 3
#> 65 NA 101 10.9 84 July 4
#> 66 64 175 4.6 83 July 5
#> 67 40 314 10.9 83 July 6
#> 68 77 276 5.1 88 July 7
#> 69 97 267 6.3 92 July 8
#> 70 97 272 5.7 92 July 9
#> 71 85 175 7.4 89 July 10
#> 72 NA 139 8.6 82 July 11
#> 73 10 264 14.3 73 July 12
#> 74 27 175 14.9 81 July 13
#> 75 NA 291 14.9 91 July 14
#> 76 7 48 14.3 80 July 15
#> 77 48 260 6.9 81 July 16
#> 78 35 274 10.3 82 July 17
#> 79 61 285 6.3 84 July 18
#> 80 79 187 5.1 87 July 19
#> 81 63 220 11.5 85 July 20
#> 82 16 7 6.9 74 July 21
#> 83 NA 258 9.7 81 July 22
#> 84 NA 295 11.5 82 July 23
#> 85 80 294 8.6 86 July 24
#> 86 108 223 8.0 85 July 25
#> 87 20 81 8.6 82 July 26
#> 88 52 82 12.0 86 July 27
#> 89 82 213 7.4 88 July 28
#> 90 50 275 7.4 86 July 29
#> 91 64 253 7.4 83 July 30
#> 92 59 254 9.2 81 July 31
#> 93 39 83 6.9 81 August 1
#> 94 9 24 13.8 81 August 2
#> 95 16 77 7.4 82 August 3
#> 96 78 NA 6.9 86 August 4
#> 97 35 NA 7.4 85 August 5
#> 98 66 NA 4.6 87 August 6
#> 99 122 255 4.0 89 August 7
#> 100 89 229 10.3 90 August 8
#> 101 110 207 8.0 90 August 9
#> 102 NA 222 8.6 92 August 10
#> 103 NA 137 11.5 86 August 11
#> 104 44 192 11.5 86 August 12
#> 105 28 273 11.5 82 August 13
#> 106 65 157 9.7 80 August 14
#> 107 NA 64 11.5 79 August 15
#> 108 22 71 10.3 77 August 16
#> 109 59 51 6.3 79 August 17
#> 110 23 115 7.4 76 August 18
#> 111 31 244 10.9 78 August 19
#> 112 44 190 10.3 78 August 20
#> 113 21 259 15.5 77 August 21
#> 114 9 36 14.3 72 August 22
#> 115 NA 255 12.6 75 August 23
#> 116 45 212 9.7 79 August 24
#> 117 168 238 3.4 81 August 25
#> 118 73 215 8.0 86 August 26
#> 119 NA 153 5.7 88 August 27
#> 120 76 203 9.7 97 August 28
#> 121 118 225 2.3 94 August 29
#> 122 84 237 6.3 96 August 30
#> 123 85 188 6.3 94 August 31
#> 124 96 167 6.9 91 September 1
#> 125 78 197 5.1 92 September 2
#> 126 73 183 2.8 93 September 3
#> 127 91 189 4.6 93 September 4
#> 128 47 95 7.4 87 September 5
#> 129 32 92 15.5 84 September 6
#> 130 20 252 10.9 80 September 7
#> 131 23 220 10.3 78 September 8
#> 132 21 230 10.9 75 September 9
#> 133 24 259 9.7 73 September 10
#> 134 44 236 14.9 81 September 11
#> 135 21 259 15.5 76 September 12
#> 136 28 238 6.3 77 September 13
#> 137 9 24 10.9 71 September 14
#> 138 13 112 11.5 71 September 15
#> 139 46 237 6.9 78 September 16
#> 140 18 224 13.8 67 September 17
#> 141 13 27 10.3 76 September 18
#> 142 24 238 10.3 68 September 19
#> 143 16 201 8.0 82 September 20
#> 144 13 238 12.6 64 September 21
#> 145 23 14 9.2 71 September 22
#> 146 36 139 10.3 81 September 23
#> 147 7 49 10.3 69 September 24
#> 148 14 20 16.6 63 September 25
#> 149 30 193 6.9 70 September 26
#> 150 NA 145 13.2 77 September 27
#> 151 14 191 14.3 75 September 28
#> 152 18 131 8.0 76 September 29
#> 153 20 223 11.5 68 September 30
# Alternatively
convert_month <- function(x) month.name[x]
airquality$Month <- convert_month(airquality$Month)
1 Like
I believe this is actually a 'one-liner' in R
airquality$Month <-month.name[airquality$Month]This topic was automatically closed 42 days after the last reply. New replies are no longer allowed.
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