How to specify which rows and columns should build the mean

[Belthy]

Hello Everyone,

maybe someone can help me with the following, since i am losing my patience with RStudio:

My goal is to compute an ANOVA, and if significant, 3 individual comparisons (t-tests).

My data set consists of 3 conditions (real heroes, fictional heroes and control group) with around 150 participants in each group.

The dependant variable (prosocial behavior) was asked in 6 questions on a scale from 0% to 100%.

Regarding the ANOVA I can't manage to average for each condition (if that is even necessary) since I don't know how to specify the rows and columns.But I have to specify the columns, because RStudio takes the average of the 6 questions of the dependent variable and the participant number, which has to be excluded obviously. Also RStudio wants me to only compare 2 groups at a time for individual comparisons. That means I would have to somehow split my data set according to the conditions and then specify which ones to compare. I can't manage that somehow.

I hope someone can help me somehow and if I am completely wrong with my ideas on how to compute the ANOVA and the comparisons please let me know.

Also, this is my first post and I am new here, so I hope posting it in the "General" is okay. If not, please let me know.
To make it easier to understand you can find the dataset beneath. "Bedingung" means "conditions" and the "Hss1" to "Hss 6" were the questions regarding the depandent variable.

Have a nice day!

[technocrat]

See the FAQ: How to do a minimal reproducible example reprex for beginners for how to attract answers specific to your data.

Here's a good explainer on analysis of variance with an example using observation on three penguin species. It involves the use of the {dplyr} package to select rows. In {base} the same selection can be done with the subset operators [] where row is the first argument and column the second.

The example below is adapted from the explainer

library(ggplot2)
library(multcomp)
#> Loading required package: mvtnorm
#> Loading required package: survival
#> Loading required package: TH.data
#> Loading required package: MASS
#> 
#> Attaching package: 'TH.data'
#> The following object is masked from 'package:MASS':
#> 
#>     geyser
library(palmerpenguins)
library(patchwork)
#> 
#> Attaching package: 'patchwork'
#> The following object is masked from 'package:MASS':
#> 
#>     area

# remove row with NA and choose species and the two "bill_" variables
# not the fourth row, -4, and the first, third and fourth c(1,3,4))

opus <- penguins[-4,c(1,3,4)]

# recommended practice: visualize data

p1 <- ggplot(opus) +
  aes(x = species, y = bill_length_mm, color = species) +
  geom_jitter() +
  theme(legend.position = "none") +
  theme_minimal()

p2 <- ggplot(opus) +
  aes(x = species, y = bill_length_mm, color = species) +
  geom_boxplot() +
  theme(legend.position = "none") +
  theme_minimal()

p3 <- ggplot(opus) +
  aes(bill_length_mm, fill = species) +
  geom_dotplot(method = "histodot", binwidth = 1.5) +
  theme(legend.position = "none") +
  theme_minimal()

p1
#> Warning: Removed 1 rows containing missing values (geom_point).

p2
#> Warning: Removed 1 rows containing non-finite values (stat_boxplot).

p3
#> Warning: Removed 1 rows containing non-finite values (stat_bindot).

res_aov <- aov(bill_length_mm ~ species, data = opus)
resids <- data.frame(.resid = res_aov$residuals)

# histogram
p4 <- ggplot(resids, aes(.resid)) +
  geom_histogram(color = "black", fill = "grey") +
  theme_minimal()

p5 <- ggplot(resids, aes(sample = .resid)) +
  stat_qq() +
  stat_qq_line() +
  theme_minimal()

p4 + p5
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

shapiro.test(res_aov$residuals)
#> 
#>  Shapiro-Wilk normality test
#> 
#> data:  res_aov$residuals
#> W = 0.98903, p-value = 0.01131

oneway.test(bill_length_mm ~ species,
            data = opus,
            var.equal = TRUE # assuming equal variances
)
#> 
#>  One-way analysis of means
#> 
#> data:  bill_length_mm and species
#> F = 410.6, num df = 2, denom df = 339, p-value < 2.2e-16

oneway.test(bill_length_mm ~ species,
            data = opus,
            var.equal = TRUE # assuming equal variances
)
#> 
#>  One-way analysis of means
#> 
#> data:  bill_length_mm and species
#> F = 410.6, num df = 2, denom df = 339, p-value < 2.2e-16

summary(res_aov)
#>              Df Sum Sq Mean Sq F value Pr(>F)    
#> species       2   7194    3597   410.6 <2e-16 ***
#> Residuals   339   2970       9                   
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 1 observation deleted due to missingness

post_test <- glht(res_aov,
                  linfct = mcp(species = "Tukey")
)

summary(post_test)
#> 
#>   Simultaneous Tests for General Linear Hypotheses
#> 
#> Multiple Comparisons of Means: Tukey Contrasts
#> 
#> 
#> Fit: aov(formula = bill_length_mm ~ species, data = opus)
#> 
#> Linear Hypotheses:
#>                         Estimate Std. Error t value Pr(>|t|)    
#> Chinstrap - Adelie == 0  10.0424     0.4323  23.232  < 0.001 ***
#> Gentoo - Adelie == 0      8.7135     0.3595  24.237  < 0.001 ***
#> Gentoo - Chinstrap == 0  -1.3289     0.4473  -2.971  0.00874 ** 
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> (Adjusted p values reported -- single-step method)

par(mar = c(3, 8, 3, 3))
plot(post_test)

TukeyHSD(res_aov)
#>   Tukey multiple comparisons of means
#>     95% family-wise confidence level
#> 
#> Fit: aov(formula = bill_length_mm ~ species, data = opus)
#> 
#> $species
#>                       diff       lwr        upr     p adj
#> Chinstrap-Adelie 10.042433  9.024859 11.0600064 0.0000000
#> Gentoo-Adelie     8.713487  7.867194  9.5597807 0.0000000
#> Gentoo-Chinstrap -1.328945 -2.381868 -0.2760231 0.0088993

plot(TukeyHSD(res_aov))

^{Created on 2022-01-19 by the reprex package (v2.0.1)}