1

I am trying to make a table that shows N (number of observations), percent frequency (of answers > 0), and the lower and upper confidence intervals for percent frequency, and I want to group this by type.

Example of data

dat <- data.frame(
  "type" = c("B","B","A","B","A","A","B","A","A","B","A","A","A","B","B","B"),
  "num" = c(3,0,0,9,6,0,4,1,1,5,6,1,3,0,0,0)
)

Expected output (with values filled in):

Type   N   Percent   Lower 95% CI   Upper 95% CI
A
B

Attempt

library(dplyr)
library(qwraps2)

table<-dat %>%
  group_by(type) %>%
  summarise(N=n(),
            mean.ci = mean_ci(dat$num),
            "Percent"=n_perc(num > 0))

This worked to get N and percent frequency, but returned an error: "Column must be length 1 (a summary value), not 3" when I added in mean_ci

The second code I tried, found here:

table2<-dat %>%
  group_by(type) %>%
  summarise(N.num=n(),
            mean.num = mean(dat$num),
            sd.num = sd(dat$num),
            "Percent"=n_perc(num > 0)) %>%
  mutate(se.num = sd.num / sqrt(N.num),
         lower.ci = 100*(mean.num - qt(1 - (0.05 / 2), N.num - 1) * se.num),
         upper.ci = 100*(mean.num + qt(1 - (0.05 / 2), N.num - 1) * se.num))

# A tibble: 2 x 8
#  type  N.num mean.num sd.num Percent        se.num lower.ci upper.ci
# <fct> <int>    <dbl>  <dbl> <chr>           <dbl>    <dbl>    <dbl>
#1 A         8     2.44   2.83 "6 (75.00\\%)"   1.00     7.35     480.
#2 B         8     2.44   2.83 "4 (50.00\\%)"   1.00     7.35     480.

This gave me an output, but the confidence intervals are not logical.

Peter
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cgxytf
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    You need to remove the `data$`. You want the mean by group, but prefixing with `data$` gives you the full column from the table. – IceCreamToucan Jan 22 '20 at 14:43
  • Thank you! When I remove ```data$``` I do get different results, but the CI intervals are still large. Now I get 16.6, 433 and -12.6, 538. Does this seem correct or is there another issue? – cgxytf Jan 22 '20 at 14:51

2 Answers2

5

The output of mean_ci is a vector of length 3. This is maybe unexpected because the package has added a print method so that when you see this in the console it looks like a single character value and not a numeric length > 1 vector. But, you can see the underlying data structure by looking at str.

mean_ci(dat$num) %>% str
 # 'qwraps2_mean_ci' Named num [1:3] 2.44 1.05 3.82
 # - attr(*, "names")= chr [1:3] "mean" "lcl" "ucl"
 # - attr(*, "alpha")= num 0.05

In summarize, each element of each column of the output needs to be length 1, so providing a length 3 object for summarize to put in a single "cell" (column element) results in an error. A workaround is to put the length 3 vector in a list, so that it is now a length 1 list. Then you can use unnest_wider to separate it into 3 columns (and therefore making the table "wider")

library(tidyverse)

dat %>%
  group_by(type) %>%
  summarise( N=n(),
            mean.ci = list(mean_ci(num)),
            "Percent"= n_perc(num > 0)) %>% 
  unnest_wider(mean.ci)
# # A tibble: 2 x 6
#   type      N  mean   lcl   ucl Percent       
#   <fct> <int> <dbl> <dbl> <dbl> <chr>         
# 1 A         8  2.25 0.523  3.98 "6 (75.00\\%)"
# 2 B         8  2.62 0.344  4.91 "4 (50.00\\%)"
IceCreamToucan
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1

IceCreamToucan’s answer is very good. I’m posting this answer to offer a different way to present the information.

library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(qwraps2)

dat <- data.frame("type" = c("B","B","A","B","A","A","B","A","A","B","A","A","A","B","B","B"),
                  "num"  = c(3,0,0,9,6,0,4,1,1,5,6,1,3,0,0,0))

When building the dplyr::summarize call you can use the qwraps2::frmtci call to format the output of qwraps2::mean_ci into a character string of length one.

I would also recommend using the data pronoun .data so you can be explicit about the variables to summarize.

dat %>%
  dplyr::group_by(type) %>%
  dplyr::summarize(N = n(),
                   mean.ci = qwraps2::frmtci(qwraps2::mean_ci(.data$num)),
                   Percent = qwraps2::n_perc(.data$num > 0))
#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 2 x 4
#>   type      N mean.ci           Percent       
#>   <chr> <int> <chr>             <chr>         
#> 1 A         8 2.25 (0.52, 3.98) "6 (75.00\\%)"
#> 2 B         8 2.62 (0.34, 4.91) "4 (50.00\\%)"

Created on 2020-09-15 by the reprex package (v0.3.0)

devtools::session_info()
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Peter
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