I need to do a frequency table from two categorical variable columns where one is a 5-year age group and the other is health status (five states) from the brfss2013 data set, from where I extracted the columns of interest via:
> hlthgrpq1 <- brfss2013 %>% select(genhlth, X_ageg5yr)
Thus generating a two column frame, 491775 observations of 2 variables.
genhlth X_ageg5yr
1 Fair Age 60 to 64
2 Good Age 50 to 54
3 Good Age 55 to 59
4 Very good Age 60 to 64
5 Good Age 65 to 69
I can generate a summary table with the 'by' function:
> by(hlthgrpq1$genhlth, hlthgrpq1$X_ageg5yr, summary)
hlthgrpq1$X_ageg5yr: Age 18 to 24
Excellent Very good Good Fair Poor NA's
6896 10266 7795 1873 303 69
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 25 to 29
Excellent Very good Good Fair Poor NA's
5779 8488 6521 1751 325 46
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 30 to 34
Excellent Very good Good Fair Poor NA's
6412 9958 7977 2295 496 75
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 35 to 39
Excellent Very good Good Fair Poor NA's
6366 10169 8236 2637 638 61
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 40 to 44
Excellent Very good Good Fair Poor NA's
6689 11130 9193 3334 1067 95
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 45 to 49
Excellent Very good Good Fair Poor NA's
7051 12278 10611 4343 1815 112
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 50 to 54
Excellent Very good Good Fair Poor NA's
8545 15254 13761 6354 3120 139
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 55 to 59
Excellent Very good Good Fair Poor NA's
8500 16759 15394 7643 3998 197
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 60 to 64
Excellent Very good Good Fair Poor NA's
8283 16825 16266 8101 3955 229
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 65 to 69
Excellent Very good Good Fair Poor NA's
7479 15764 15600 7749 3200 205
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 70 to 74
Excellent Very good Good Fair Poor NA's
5491 11943 13125 6491 2721 196
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 75 to 79
Excellent Very good Good Fair Poor NA's
3320 8501 10128 5545 2426 173
----------------------------------------------------------------------------------------------------------------
hlthgrpq1$X_ageg5yr: Age 80 or older
Excellent Very good Good Fair Poor NA's
3697 10285 14400 8116 3695 322
And that's where I get stuck. I have tried for hours to attempt to get here:
Results obtained via spreadsheet.
Thanks for any help.
(This is for a specific assignment so I can only use dplyr and ggplot2, so, no reshape2 or tidyr.)
First off: For future postings, it is good practice to always include sample data. See here how to provide a minimal reproducible example/attempt including sample data.
Solution in base R.
as.data.frame.matrix(t(table(df)));
# Fair Good Very good
#Age 50 to 54 0 1 0
#Age 55 to 59 0 1 0
#Age 60 to 64 1 0 1
#Age 65 to 69 0 1 0
Or something like this as a tidyverse
approach?
library(tidyverse);
df %>% count(genhlth, X_ageg5yr) %>% spread(genhlth, n);
## A tibble: 4 x 4
# X_ageg5yr Fair Good `Very good`
# <fct> <int> <int> <int>
#1 Age 50 to 54 NA 1 NA
#2 Age 55 to 59 NA 1 NA
#3 Age 60 to 64 1 NA 1
#4 Age 65 to 69 NA 1 NA
Or if you insist on only using dplyr
and not tidyr
, you can do:
df2 <- df %>%
count(genhlth, X_ageg5yr);
df2 <- as.data.frame.matrix(xtabs(n ~ X_ageg5yr + genhlth, data = df2));
# Fair Good Very good
#Age 50 to 54 0 1 0
#Age 55 to 59 0 1 0
#Age 60 to 64 1 0 1
#Age 65 to 69 0 1 0
This basically boils down to a wide-to-long reformat, SO has plenty of discussions around that topic (eg here ).
df <- read.table(text =
"genhlth X_ageg5yr
Fair 'Age 60 to 64'
Good 'Age 50 to 54'
Good 'Age 55 to 59'
'Very good' 'Age 60 to 64'
Good 'Age 65 to 69'", header = T)
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