У меня есть вложенный список, содержащий данные (tibble), выходные данные модели, уклоны и перехваты.Я пытаюсь нарисовать точечный график для каждой линии (записи) в списке.Я могу получить уникальную линию сглаживания для каждой фигуры с помощью geom_smooth, но как только я пытаюсь добавить линию регрессии с помощью abline, я получаю несколько линий для рисования на каждой фигуре.Ниже приведен воспроизводимый пример кода и мой код.
Кроме того, когда я пытаюсь получить ggtile() для добавления города и месяца к каждой цифре, кажется, что эти данные извлекаются из первой строкиво вложенном тибле и названии каждой цифры Чикаго и 1-го месяца вместо ожидаемого Чикаго или Нью-Йорка и 1–12 месяцев.
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