我有一个嵌套列表，其中包含数据（小标题），模型输出，斜率和截距。我正在尝试为列表中的每一行（记录）绘制一个散点图。我可以使用geom_smooth为每个图形获得一条唯一的平滑线，但是当我尝试使用abline添加回归线时，我在每个图形上绘制了多条线。下面是可复制的代码示例和我的代码。
顺便说一句，当我尝试让ggtile()将城市和月份添加到每个图形时，它似乎是从嵌套小标题的第一行中获取这些数据并命名每个图形芝加哥和月份1个月，而不是预期的芝加哥或纽约，以及1到12个月。
library(tidyverse)
library(lubridate)
library(gam)
library(broom)
library(purrr)
tb <- structure(list(
dt = structure(c(
14245, 14276, 14304, 14335, 14365,
14396, 14426, 14457, 14488, 14518, 14549, 14579, 14610, 14641,
14669, 14700, 14730, 14761, 14791, 14822, 14853, 14883, 14914,
14944, 14975, 15006, 15034, 15065, 15095, 15126, 15156, 15187,
15218, 15248, 15279, 15309, 15340, 15371, 15400, 15431, 15461,
15492, 15522, 15553, 15584, 15614, 15645, 15675, 15706, 15737,
15765, 15796, 15826, 15857, 15887, 15918, 15949, 14245, 14276,
14304, 14335, 14365, 14396, 14426, 14457, 14488, 14518, 14549,
14579, 14610, 14641, 14669, 14700, 14730, 14761, 14791, 14822,
14853, 14883, 14914, 14944, 14975, 15006, 15034, 15065, 15095,
15126, 15156, 15187, 15218, 15248, 15279, 15309, 15340, 15371,
15400, 15431, 15461, 15492, 15522, 15553, 15584, 15614, 15645,
15675, 15706, 15737, 15765, 15796, 15826, 15857, 15887, 15918,
15949
), class = "Date"),
averagetemperature = c(
-4.299, 1.454, 4.808, 7.623, 12.627, 17.305, 19.792, 21.724,
19.502, 11.22, 10.261, 1.563, -0.595, 0.771, 6.489, 10.935,
13.803, 19.055, 24.106, 24.948, 19.229, 14.582, 8.539, -0.071,
-1.582, 0.276, 3.474, 7.383, 12.133, 18.011, 24.412, 23.414,
18.331, 13.837, 9.555, 5.327, 2.67, 3.698, 12.145, 8.383, 14.956,
19.532, 25.909, 22.778, 18.693, 12.229, 7.27, 5.592, 1.056, -0.509,
1.323, 6.644, 13.734, 17.913, 21.914, 22.23, 19.977, -5.36, -0.372,
3.579, 10.478, 15.447, 19.058, 21.103, 22.769, 17.043, 10.364, 8.217,
-0.624, -2.359, -1.456, 6.715, 12.076, 17.119, 21.943, 24.789,
22.67, 19.172, 11.911, 5.876, -2.165, -4.463, -1.244, 3.474,
10.555, 16.917, 21.032, 24.564, 22.13, 19.301, 12.001, 8.013,
2.987, -0.0410000000000004, 2.185, 8.734, 10.324, 17.779, 20.165,
24.479, 22.731, 18.177, 12.436, 4.103, 2.586, -0.968, -1.365,
2.518, 9.723, 15.544, 20.892, 24.722, 21.001, 17.408
),
averagetemperatureuncertainty = c(
0.336,
0.328, 0.247, 0.348, 0.396, 0.554, 0.481, 0.315, 0.225, 0.162,
0.372, 0.348, 0.348, 0.364, 0.357, 0.538, 0.892, 0.33, 0.325,
0.36, 0.322, 0.241, 0.307, 0.326, 0.522, 0.446, 0.279, 0.265,
0.733, 0.773, 0.255, 0.404, 0.173, 0.154, 0.334, 0.483, 0.727,
0.567, 0.369, 0.347, 0.835, 0.519, 0.516, 0.42, 0.329, 0.333,
0.263, 0.537, 0.528, 0.473, 0.275, 0.462, 0.863, 0.669, 0.322,
0.373, 1.033, 0.288, 0.214, 0.14, 0.259, 0.267, 0.452, 0.348,
0.277, 0.22, 0.153, 0.181, 0.228, 0.314, 0.319, 0.235, 0.135,
0.2, 0.387, 0.28, 0.257, 0.165, 0.154, 0.174, 0.436, 0.355, 0.33,
0.167, 0.222, 0.312, 0.42, 0.438, 0.163, 0.16, 0.23, 0.298, 0.466,
0.493, 0.253, 0.276, 0.258, 0.301, 0.39, 0.403, 0.224, 0.269,
0.344, 0.298, 0.257, 0.29, 0.241, 0.255, 0.355, 0.281, 0.273,
0.279, 0.323, 1.048
), city = c(
"Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"Chicago", "Chicago", "Chicago", "Chicago", "Chicago", "Chicago",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York", "New York", "New York", "New York",
"New York", "New York", "New York"
), country = c(
"United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States", "United States", "United States", "United States",
"United States"
), latitude = c(
"42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "42.59N",
"42.59N", "42.59N", "42.59N", "42.59N", "42.59N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N",
"40.99N", "40.99N", "40.99N", "40.99N", "40.99N", "40.99N"
),
longitude = c(
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "87.27W", "87.27W",
"87.27W", "87.27W", "87.27W", "87.27W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W", "74.56W", "74.56W", "74.56W", "74.56W", "74.56W",
"74.56W"
)
), row.names = c(NA, -114L), class = c(
"tbl_df",
"tbl", "data.frame"
), spec = structure(list(cols = list(
dt = structure(list(
format = ""
), class = c("collector_date", "collector")),
AverageTemperature = structure(list(), class = c(
"collector_double",
"collector"
)), AverageTemperatureUncertainty = structure(list(), class = c(
"collector_double",
"collector"
)), City = structure(list(), class = c(
"collector_character",
"collector"
)), Country = structure(list(), class = c(
"collector_character",
"collector"
)), Latitude = structure(list(), class = c(
"collector_character",
"collector"
)), Longitude = structure(list(), class = c(
"collector_character",
"collector"
))
), default = structure(list(), class = c(
"collector_guess",
"collector"
))), class = "col_spec"))
# Load the data from dput() and take a look
summary(tb)
str(tb)
names(tb)
# make lowercasae
tb <- rename_all(tb, tolower)
names(tb)
# nest data, 100 major cities
by_city_month <- tb %>%
filter(year(dt) >= 1900) %>%
mutate(month = month(dt)) %>%
mutate(yr1900 = year(dt) - 1900) %>%
group_by(city, country, month) %>%
nest()
by_city_month
# define function for linear model
city_model_lm <- function(df) {
lm(averagetemperature ~ yr1900, data = df)
}
# create columns for the models
cmodels <- by_city_month %>%
mutate(model = map(data, city_model_lm)
)
# add tidy and glance to list
cmodels <- cmodels %>%
mutate(tidy = map(model, tidy),
glance = map(model, glance)
)
# unnest glance list
cmodels_g <- cmodels %>%
unnest(glance) %>%
select(city, country, month, data, model, p.value)
cmodels_g
# unnest and spread the tidy list [4x7] into 28 rows for each watershed
cmodels_t <- cmodels %>%
unnest(tidy) %>%
select(city, country, month, term, estimate) %>%
spread(key = term, value = estimate) %>%
select(city, country, month, `(Intercept)`, yr1900)
cmodels_t
# join tables to get pvalues and slopes in a single table, rename variables to
# make jointing with stream table easier to follow
cmodels_all <- left_join(cmodels_g, cmodels_t) %>%
rename(intercept = `(Intercept)`, slope = yr1900)
cmodels_all
# add ggplot to list
cmodels_figs <- cmodels_all %>%
mutate(
map(data, ~ ggplot(., aes(x = yr1900, y = averagetemperature)) +
geom_point() +
ylab('Average Temperature') +
xlab('Years past 1900') +
geom_smooth(se = TRUE, color = 'purple') +
geom_abline(intercept = intercept, slope = slope, color = 'orange') +
ggtitle(label = city, subtitle = month)
)
) %>%
rename(plots =`map(...)`)
# draw figures
cmodels_figs$plots