使用ggplot绘制直方图，统计各时间戳对应数据行数

Perfect, since you've already converted your Timestamp variable to POSIXct format, moving to ggplot2 for your hourly count histogram is straightforward. Here are a couple of practical approaches that align with what you were doing with base R's hist():

Option 1: Directly use geom_histogram (closest to base R's workflow) #

This method lets ggplot handle binning and counting automatically, just like hist() does. We'll set the bin width to 3600 seconds (1 hour) and tweak the x-axis to display hourly ticks clearly:

# Load ggplot2 if you haven't already
library(ggplot2)

# Create the hourly histogram
ggplot(data.frame(timestamp = datas), aes(x = timestamp)) +
  geom_histogram(binwidth = 3600, fill = "grey", color = "white") +
  scale_x_datetime(
    date_breaks = "1 hour",
    date_labels = "%Y/%m/%d %H:%M"
  ) +
  labs(
    title = "Datas de Realização do Inquérito",
    x = "Data",
    y = "Nº de Inquéritos Realizados"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))  # Rotate x-labels to avoid overlap

Option 2: Precompute hourly counts (more flexible for customization) #

If you want greater control over your data before plotting, you can first calculate the number of entries per hour using dplyr and lubridate, then visualize the precomputed counts:

# Load required packages
library(ggplot2)
library(dplyr)
library(lubridate)  # For easy time rounding

# Calculate hourly entry counts
hourly_data <- datas %>%
  as.data.frame() %>%
  rename(timestamp = ".") %>%
  mutate(hour_start = floor_date(timestamp, "hour")) %>%  # Round each timestamp to its hour start
  count(hour_start)

# Plot the precomputed counts
ggplot(hourly_data, aes(x = hour_start, y = n)) +
  geom_bar(stat = "identity", fill = "grey") +
  scale_x_datetime(
    date_breaks = "1 hour",
    date_labels = "%Y/%m/%d %H:%M"
  ) +
  labs(
    title = "Datas de Realização do Inquérito",
    x = "Data",
    y = "Nº de Inquéritos Realizados"
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

Both options will produce a histogram that matches the behavior of your original hist() call, while giving you the customization flexibility that ggplot2 is known for.

内容的提问来源于stack exchange，提问作者davidbaguetta