To analyze the percentage of "Strongly Agree" sentiments over time with 95% confidence intervals, you can use the tidyverse package in R. This involves filtering your data, grouping it by date, calculating the proportion of "Strongly Agree" sentiments, and then computing the confidence intervals. Finally, you can visualize these results using ggplot2.

Here's a step-by-step guide and example code:

```R

Install and load necessary packages

if (!requireNamespace("tidyverse", quietly = TRUE)) install.packages("tidyverse")
if (!requireNamespace("binom", quietly = TRUE)) install.packages("binom")
library(tidyverse)
library(binom) # For calculating binomial confidence intervals

1. Create a sample dataframe (replace with your actual data)

Assume 'df' is your dataframe with columns 'Date' and 'Sentiment'

set.seed(123)
dates <- seq(as.Date("2023-01-01"), as.Date("2023-03-31"), by = "day")
sample_data <- tibble(
Date = sample(dates, 1000, replace = TRUE),
Sentiment = sample(c("Strongly Agree", "Agree", "Neutral", "Disagree", "Strongly Disagree", NA),
1000, replace = TRUE, prob = c(0.15, 0.25, 0.30, 0.15, 0.10, 0.05)),
Value = rnorm(1000, mean = 10, sd = 2) # Adding a numeric column for potential filtering
)

Introduce some negative values and NAs in 'Value' for demonstration

sample_data$Value[sample(1:1000, 50)] <- -1
sample_data$Value[sample(1:1000, 50)] <- NA

df <- sample_data

2. Filter out null or negative cases

This assumes "cases" refer to rows with NA in 'Sentiment' or negative/NA values in a numeric column like 'Value'.

You can adjust the filtering conditions based on your specific definition of "cases".

df_filtered <- df %>%
drop_na(Sentiment) %>% # Removes rows where Sentiment is NA
filter(Value >= 0 | is.na(Value)) # Keeps rows where Value is non-negative, or if Value is NA.
# If you want to exclude NA values in 'Value' too, use filter(Value >= 0)

3. Calculate the percentage of "Strongly Agree" along with a 95% CI

sentiment_summary <- df_filtered %>%
group_by(Date) %>%
summarise(
total_responses = n(),
strongly_agree_count = sum(Sentiment == "Strongly Agree", na.rm = TRUE)
) %>%
mutate(
percentage_strongly_agree = (strongly_agree_count / total_responses) * 100
) %>%
rowwise() %>% # Apply binom.confint row by row
mutate(