How to I get accuracy values by group [duplicate] - r

Question

This question already has answers here:
Mean per group in a data.frame [duplicate]
(8 answers)
Closed 3 years ago.
I can't get the average accuracies (proportion of TRUE values) in Correct_answers columns for the groups chart type and condition.
data
structure(list(Element = structure(c(1L, 1L, 1L, 1L, 1L), .Label = c("1",
"2", "3", "4", "5", "6"), class = "factor"), Correct_answer = structure(c(2L,
2L, 2L, 1L, 2L), .Label = c("FALSE", "TRUE"), class = "factor"),
Response_time = c(25.155, 6.74, 28.649, 16.112, 105.5906238
), Chart_type = structure(c(2L, 2L, 1L, 1L, 1L), .Label = c("Box",
"Violin"), class = "factor"), Condition = structure(c(1L,
2L, 1L, 2L, 1L), .Label = c("0", "1"), class = "factor")), row.names = c(NA,
5L), class = "data.frame")
Average by chart_type
av_data_chartType <- data %>% group_by(Chart_type) %>% summarise_each(funs(mean, sd))
Average by condition
av_data_conition <- data %>% group_by(Condition) %>% summarise_each(funs(mean, sd))
No mean produced for accuracy
NA value is place where accuracy should be.

Answer 1

Reproducing your code I had a warning that led me to the answer : you shouldn't compute statistics on factor variables. If you know what you are doing you can convert them to numeric :
data <- structure(list(Element = structure(c(1L, 1L, 1L, 1L, 1L),
.Label = c("1", "2", "3", "4", "5", "6"),
class = "factor"),
Correct_answer = structure(c(2L, 2L, 2L, 1L, 2L),
.Label = c("FALSE", "TRUE"),
class = "factor"),
Response_time = c(25.155, 6.74, 28.649, 16.112, 105.5906238
),
Chart_type = structure(c(2L, 2L, 1L, 1L, 1L),
.Label = c("Box",
"Violin"),
class = "factor"),
Condition = structure(c(1L, 2L, 1L, 2L, 1L),
.Label = c("0", "1"),
class = "factor")),
row.names = c(NA, 5L), class = "data.frame")
library("dplyr", warn.conflicts = FALSE)
data <- data %>% as_tibble
# av_data_chartType
data %>%
group_by(Chart_type) %>%
mutate_if(.predicate = is.factor, .funs = as.numeric) %>%
summarise_each(list( ~mean, ~sd))
#> `mutate_if()` ignored the following grouping variables:
#> Column `Chart_type`
#> # A tibble: 2 x 9
#> Chart_type Element_mean Correct_answer_~ Response_time_m~ Condition_mean
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Box 1 1.67 50.1 1.33
#> 2 Violin 1 2 15.9 1.5
#> # ... with 4 more variables: Element_sd <dbl>, Correct_answer_sd <dbl>,
#> # Response_time_sd <dbl>, Condition_sd <dbl>
# av_data_condition
data %>%
group_by(Condition) %>%
mutate_if(.predicate = is.factor, .funs = as.numeric) %>%
summarise_each(list( ~mean, ~sd))
#> `mutate_if()` ignored the following grouping variables:
#> Column `Condition`
#> # A tibble: 2 x 9
#> Condition Element_mean Correct_answer_~ Response_time_m~ Chart_type_mean
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 0 1 2 53.1 1.33
#> 2 1 1 1.5 11.4 1.5
#> # ... with 4 more variables: Element_sd <dbl>, Correct_answer_sd <dbl>,
#> # Response_time_sd <dbl>, Chart_type_sd <dbl>
Created on 2019-06-11 by the reprex package (v0.2.1)

Answer 2

This should work:
a$Correct_answer <- as.logical(a$Correct_answer)
av_data_chartType <- a %>% select(Chart_type, Correct_answer) %>% group_by(Chart_type) %>% summarise_each(funs(mean, sd))
av_data_chartType <- a %>% select(Condition, Correct_answer) %>% group_by(Condition) %>% summarise_each(funs(mean, sd))
You had 2 problems:
Your Correct_answer was a factor.
You tried to calculate your functions over every Column

Answer 3

You probably need
library(dplyr)
data %>%
mutate(Correct_answer = as.logical(Correct_answer)) %>%
group_by(Chart_type, Condition) %>%
summarise(avg = mean(Correct_answer))
Or if you need them separately
data %>%
mutate(Correct_answer = as.logical(Correct_answer)) %>%
group_by(Chart_type) %>%
summarise(avg = mean(Correct_answer))
data %>%
mutate(Correct_answer = as.logical(Correct_answer)) %>%
group_by(Condition) %>%
summarise(avg = mean(Correct_answer))