Are you after something like the following?
library(ggplot2)
#> Warning: package 'ggplot2' was built under R version 4.0.5
library(dplyr)
DATA <- structure(list(Reading1 = c(86.9, 87.9, 85.2, 83.8, 100, 89.4,
74.5, 84.4, 85.7, 95, 89.1, 87.5, 91.9, 82.9, 76.3, 87.9, 92.3,
94.2, 76.4, 85.3),
Reading2 = c(87.81, 89.06, 85.23, 84.55, 102,
90.66, 75.14, 84.58, 88.21, 95.87, 89.32, 90.05, 92.74, 83.05,
76.91, 89.61, 93.41, 94.49, 78.53, 87.06),
Reading3 = c(89.29,89.08, 85.25, 84.5, 102, 92.99, 74.42, 84.15,
87.59, 96.58, 88.48, 90.52, 94.71, 84.5, 77.22, 89.28,
94.29, 94.85, 80.46, 88.62),
Reading4 = c(92.52, 89.63, 87.46, 84.7, 102, 93.49, 75.56,
85.78, 88.43, 97.32, 88.81, 90.55, 94.86, 87.25, 78.36, 89.58,
94.7, 95.28, 81.34, 89.06),
ReadingPost1 = c(91.81, 92.3,88.41, 85.68, 102, 93.35, 76.41, 86.79, 88.82, 97.64, 90.14,
92.31, 95.69, 90.04, 80.59, 92.07, 96.25, 95.54, 82.69, 89.34),
Sex = c("Male", "Female", "Female", "Male", "Male", "Female",
"Female", "Male", "Female", "Male", "Male", "Female", "Male",
"Female", "Male", "Female", "Female", "Male", "Female", "Female"),
FavoriteColor = c("Blue", "Blue", "Blue", "Blue", "Blue",
"Blue", "Blue", "Blue", "Blue", "Green", "Green", "Green",
"Blue", "Blue", "Blue", "Blue", "Red", "Green", "Green",
"Green"),
Group = c("Group B", "Group A", "Group A", "Group A", "Group A", "Group A", "Group A",
"Group A", "Group A", "Group B","Group A", "Group A", "Group B", "Group A", "Group A", "Group A",
"Group B", "Group B", "Group A", "Group A"),
MathScores = c(81.75, 80.68, 82.13, 82.69, 70.9, 77.28, 92.68, 85.65, 85.56, 73.38,
80.43, 86.49, 74.49, 85.97, 94.75, 82.75, 73.89, 77.11, 91.95,82.87),
ScienceScores = c(80.36, 82.89, 83.86, 83.54, 71.98, 79.88, 99.09, 84.12, 83.99, 74.59, 77.12, 87.78,
75.18, 81.51, 90.88, 81.89, 74.67, 72.59, 92.37, 82.29),
TypicalLikert1 = c(3L,3L, 5L, 2L, 5L, 3L, 5L, 5L, 1L, 4L, 2L, 3L, 2L, 2L, 4L, 4L,5L, 2L, 5L, 3L),
TypicalLikert2 = c(4L, 2L, 5L, 2L, 5L, 3L, 5L, 5L, 1L, 4L, 3L, 4L, 3L, 1L, 4L, 4L, 5L, 2L, 5L, 3L),
TypicalLikert3 = c(4L, 2L, 5L, 1L, 5L, 2L, 5L, 5L, 1L, 4L,
3L, 4L, 3L, 1L, 3L, 4L, 5L, 1L, 5L, 3L),
Reverse = c(3L,3L, 1L, 4L, 1L, 3L, 1L, 1L, 5L, 2L, 4L, 3L, 4L, 4L, 2L, 2L,1L, 4L, 1L, 3L),
Marital = c(2L, 3L, 1L, 3L, 2L, 1L, 3L,1L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 1L, 2L, 3L, 1L, 1L),
Grade = c(1L, 1L, 2L, 4L, 1L, 2L, 1L, 2L, 3L, 3L, 1L, 2L, 4L, 4L, 3L, 3L, 2L, 2L, 3L, 4L),
RecodeLikert = c("Disagree", "Disagree", "Strongly Disagree", "Neutral", "Strongly Disagree", "Strongly Disagree",
"Neutral", "Neutral", "Agree", "Strongly Disagree", "Strongly Disagree","Agree", "Strongly Disagree",
"Neutral", "Strongly Agree","Neutral", "Strongly Disagree", "Strongly Disagree", "Strongly Agree", "Disagree"),
Number = c("One", "Four", "Two", "Two", "One", "Three", "Two", "Four", "One", "Three", "Four", "One", "Three",
"Two", "One", "One", "Two", "Three", "Three", "Four"),
DxPre = c("Negative", "Positive", "Positive", "Positive", "Positive", "Positive", "Positive", "Negative", "Positive", "Positive", "Positive",
"Positive", "Positive", "Positive", "Negative", "Negative","Positive", "Positive", "Positive", "Positive"),
DxPost = c("Negative", "Positive", "Negative", "Negative", "Negative", "Negative", "Negative", "Negative", "Positive", "Positive", "Negative",
"Positive", "Negative", "Negative", "Positive", "Negative","Negative", "Negative", "Positive", "Negative"),
GradePost = c(4L, 4L, 4L, 4L, 3L, 3L, 4L, 4L, 4L, 4L, 3L, 4L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 4L),
GPA = c(2L, 2L, 2L, 2L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L),
Anxiety = c(0.2951,-0.619, 0.9683, 0.9575, 2.4603, 1.0207, 0.7518, 0.9192, -0.4883, 2.6647, 3.9209, 3.4309, 0.8182, 0.1582,
1.5846, 1.1924, 5.1718, 5.4384, 1.3512, 2.4481),
SelfDoubt = c(300L, 300L, 300L,300L, 300L, 300L, 300L, 300L, 390L, 399L, 400L, 500L, 600L, 300L, 305L, 401L, 800L, 800L, 800L, 800L),
ACT = c(80L, 80L, 80L, 70L, 70L, 90L, 70L, 50L, 70L, 70L, 60L, 50L, 70L, 70L, 70L, 60L, 80L, 80L, 60L, 90L)),
row.names = c(NA, 20L), class = "data.frame")
DF <- DATA %>% group_by(Sex) %>%
summarize(Avg = mean(Anxiety),STD = sd(Anxiety), N = n()) %>%
mutate(ci = qnorm(0.975)*STD/sqrt(N))
#> `summarise()` ungrouping output (override with `.groups` argument)
DF
#> # A tibble: 2 x 5
#> Sex Avg STD N ci
#> <chr> <dbl> <dbl> <int> <dbl>
#> 1 Female 1.40 1.72 11 1.01
#> 2 Male 2.12 1.69 9 1.10
ggplot(DF, aes(x=Sex, y=Avg))+
geom_bar(stat="identity",
fill="darkgreen")+
geom_errorbar(aes(ymin=Avg-ci, ymax=Avg+ci),
width=0.2)+
labs(x = "Sex", y = "Anxiety") +
theme_classic()

Created on 2021-08-24 by the reprex package (v0.3.0)