Plotting categorical variables OLS in R
I am trying to produce a plot with age in the x-axis, expected serum urate in the y-axis and lines for male/white, female/white, male/black, female/black, using the estimates from the lm() function.
goutdata <- read.table("gout.txt", header = TRUE)
goutdata$sex <- factor(goutdata$sex,levels = c("M", "F"))
goutdata$race <- as.factor(goutdata$race)
fm <- lm(su~sex+race+age, data = goutdata)
summary(fm)
ggplot(fm, aes(x= age, y = su))+xlim(30, 70) + geom_jitter(aes(age,su, colour=age)) + facet_grid(sex~race)
I have tried using the facet_wrap() function with ggplot to address the categorical variables, but I am wanting to create just one plot. I was trying a combination of geom_jitter and geom_smooth, but I am not sure how to use geom_smooth() with categorical variables. Any help would be appreciated.
Data: https://github.com/gdlc/STT465/blob/master/gout.txt
r ggplot2 lm
add a comment |
I am trying to produce a plot with age in the x-axis, expected serum urate in the y-axis and lines for male/white, female/white, male/black, female/black, using the estimates from the lm() function.
goutdata <- read.table("gout.txt", header = TRUE)
goutdata$sex <- factor(goutdata$sex,levels = c("M", "F"))
goutdata$race <- as.factor(goutdata$race)
fm <- lm(su~sex+race+age, data = goutdata)
summary(fm)
ggplot(fm, aes(x= age, y = su))+xlim(30, 70) + geom_jitter(aes(age,su, colour=age)) + facet_grid(sex~race)
I have tried using the facet_wrap() function with ggplot to address the categorical variables, but I am wanting to create just one plot. I was trying a combination of geom_jitter and geom_smooth, but I am not sure how to use geom_smooth() with categorical variables. Any help would be appreciated.
Data: https://github.com/gdlc/STT465/blob/master/gout.txt
r ggplot2 lm
Do you need to set color based on age? Because the most straightforward way might be creating lines / points / whatever and setting their color / linetype / shape based on sex and race
– camille
Nov 11 '18 at 20:09
I don't need to, no.
– Hannah
Nov 11 '18 at 20:14
add a comment |
I am trying to produce a plot with age in the x-axis, expected serum urate in the y-axis and lines for male/white, female/white, male/black, female/black, using the estimates from the lm() function.
goutdata <- read.table("gout.txt", header = TRUE)
goutdata$sex <- factor(goutdata$sex,levels = c("M", "F"))
goutdata$race <- as.factor(goutdata$race)
fm <- lm(su~sex+race+age, data = goutdata)
summary(fm)
ggplot(fm, aes(x= age, y = su))+xlim(30, 70) + geom_jitter(aes(age,su, colour=age)) + facet_grid(sex~race)
I have tried using the facet_wrap() function with ggplot to address the categorical variables, but I am wanting to create just one plot. I was trying a combination of geom_jitter and geom_smooth, but I am not sure how to use geom_smooth() with categorical variables. Any help would be appreciated.
Data: https://github.com/gdlc/STT465/blob/master/gout.txt
r ggplot2 lm
I am trying to produce a plot with age in the x-axis, expected serum urate in the y-axis and lines for male/white, female/white, male/black, female/black, using the estimates from the lm() function.
goutdata <- read.table("gout.txt", header = TRUE)
goutdata$sex <- factor(goutdata$sex,levels = c("M", "F"))
goutdata$race <- as.factor(goutdata$race)
fm <- lm(su~sex+race+age, data = goutdata)
summary(fm)
ggplot(fm, aes(x= age, y = su))+xlim(30, 70) + geom_jitter(aes(age,su, colour=age)) + facet_grid(sex~race)
I have tried using the facet_wrap() function with ggplot to address the categorical variables, but I am wanting to create just one plot. I was trying a combination of geom_jitter and geom_smooth, but I am not sure how to use geom_smooth() with categorical variables. Any help would be appreciated.
Data: https://github.com/gdlc/STT465/blob/master/gout.txt
r ggplot2 lm
r ggplot2 lm
asked Nov 11 '18 at 20:01
Hannah
326
326
Do you need to set color based on age? Because the most straightforward way might be creating lines / points / whatever and setting their color / linetype / shape based on sex and race
– camille
Nov 11 '18 at 20:09
I don't need to, no.
– Hannah
Nov 11 '18 at 20:14
add a comment |
Do you need to set color based on age? Because the most straightforward way might be creating lines / points / whatever and setting their color / linetype / shape based on sex and race
– camille
Nov 11 '18 at 20:09
I don't need to, no.
– Hannah
Nov 11 '18 at 20:14
Do you need to set color based on age? Because the most straightforward way might be creating lines / points / whatever and setting their color / linetype / shape based on sex and race
– camille
Nov 11 '18 at 20:09
Do you need to set color based on age? Because the most straightforward way might be creating lines / points / whatever and setting their color / linetype / shape based on sex and race
– camille
Nov 11 '18 at 20:09
I don't need to, no.
– Hannah
Nov 11 '18 at 20:14
I don't need to, no.
– Hannah
Nov 11 '18 at 20:14
add a comment |
2 Answers
2
active
oldest
votes
We can use interaction()
to create groupings on the fly and perform the OLS right within geom_smooth()
. Here they are grouped on one plot:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
hrbrthemes::theme_ipsum_rc(grid="XY")
and, spread out into facets:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
facet_wrap(sex~race) +
hrbrthemes::theme_ipsum_rc(grid="XY")
You've now got a partial answer to #1 of https://github.com/gdlc/STT465/blob/master/HW_4_OLS.md :-)
add a comment |
You could probably use geom_smooth()
to show regression lines?
dat <- read.table("https://raw.githubusercontent.com/gdlc/STT465/master/gout.txt",
header = T, stringsAsFactors = F)
library(tidyverse)
dat %>%
dplyr::mutate(sex = ifelse(sex == "M", "Male", "Female"),
race = ifelse(race == "W", "Caucasian", "African-American"),
group = paste(race, sex, sep = ", ")
) %>%
ggplot(aes(x = age, y = su, colour = group)) +
geom_smooth(method = "lm", se = F, show.legend = F) +
geom_point(show.legend = F, position = "jitter", alpha = .5, pch = 16) +
facet_wrap(~group) +
ggthemes::theme_few() +
labs(x = "Age", y = "Expected serum urate level")
add a comment |
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
We can use interaction()
to create groupings on the fly and perform the OLS right within geom_smooth()
. Here they are grouped on one plot:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
hrbrthemes::theme_ipsum_rc(grid="XY")
and, spread out into facets:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
facet_wrap(sex~race) +
hrbrthemes::theme_ipsum_rc(grid="XY")
You've now got a partial answer to #1 of https://github.com/gdlc/STT465/blob/master/HW_4_OLS.md :-)
add a comment |
We can use interaction()
to create groupings on the fly and perform the OLS right within geom_smooth()
. Here they are grouped on one plot:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
hrbrthemes::theme_ipsum_rc(grid="XY")
and, spread out into facets:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
facet_wrap(sex~race) +
hrbrthemes::theme_ipsum_rc(grid="XY")
You've now got a partial answer to #1 of https://github.com/gdlc/STT465/blob/master/HW_4_OLS.md :-)
add a comment |
We can use interaction()
to create groupings on the fly and perform the OLS right within geom_smooth()
. Here they are grouped on one plot:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
hrbrthemes::theme_ipsum_rc(grid="XY")
and, spread out into facets:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
facet_wrap(sex~race) +
hrbrthemes::theme_ipsum_rc(grid="XY")
You've now got a partial answer to #1 of https://github.com/gdlc/STT465/blob/master/HW_4_OLS.md :-)
We can use interaction()
to create groupings on the fly and perform the OLS right within geom_smooth()
. Here they are grouped on one plot:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
hrbrthemes::theme_ipsum_rc(grid="XY")
and, spread out into facets:
ggplot(goutdata, aes(age, su, color = interaction(sex, race))) +
geom_smooth(formula = y~x, method="lm") +
geom_point() +
facet_wrap(sex~race) +
hrbrthemes::theme_ipsum_rc(grid="XY")
You've now got a partial answer to #1 of https://github.com/gdlc/STT465/blob/master/HW_4_OLS.md :-)
answered Nov 11 '18 at 20:28
hrbrmstr
60.1k686148
60.1k686148
add a comment |
add a comment |
You could probably use geom_smooth()
to show regression lines?
dat <- read.table("https://raw.githubusercontent.com/gdlc/STT465/master/gout.txt",
header = T, stringsAsFactors = F)
library(tidyverse)
dat %>%
dplyr::mutate(sex = ifelse(sex == "M", "Male", "Female"),
race = ifelse(race == "W", "Caucasian", "African-American"),
group = paste(race, sex, sep = ", ")
) %>%
ggplot(aes(x = age, y = su, colour = group)) +
geom_smooth(method = "lm", se = F, show.legend = F) +
geom_point(show.legend = F, position = "jitter", alpha = .5, pch = 16) +
facet_wrap(~group) +
ggthemes::theme_few() +
labs(x = "Age", y = "Expected serum urate level")
add a comment |
You could probably use geom_smooth()
to show regression lines?
dat <- read.table("https://raw.githubusercontent.com/gdlc/STT465/master/gout.txt",
header = T, stringsAsFactors = F)
library(tidyverse)
dat %>%
dplyr::mutate(sex = ifelse(sex == "M", "Male", "Female"),
race = ifelse(race == "W", "Caucasian", "African-American"),
group = paste(race, sex, sep = ", ")
) %>%
ggplot(aes(x = age, y = su, colour = group)) +
geom_smooth(method = "lm", se = F, show.legend = F) +
geom_point(show.legend = F, position = "jitter", alpha = .5, pch = 16) +
facet_wrap(~group) +
ggthemes::theme_few() +
labs(x = "Age", y = "Expected serum urate level")
add a comment |
You could probably use geom_smooth()
to show regression lines?
dat <- read.table("https://raw.githubusercontent.com/gdlc/STT465/master/gout.txt",
header = T, stringsAsFactors = F)
library(tidyverse)
dat %>%
dplyr::mutate(sex = ifelse(sex == "M", "Male", "Female"),
race = ifelse(race == "W", "Caucasian", "African-American"),
group = paste(race, sex, sep = ", ")
) %>%
ggplot(aes(x = age, y = su, colour = group)) +
geom_smooth(method = "lm", se = F, show.legend = F) +
geom_point(show.legend = F, position = "jitter", alpha = .5, pch = 16) +
facet_wrap(~group) +
ggthemes::theme_few() +
labs(x = "Age", y = "Expected serum urate level")
You could probably use geom_smooth()
to show regression lines?
dat <- read.table("https://raw.githubusercontent.com/gdlc/STT465/master/gout.txt",
header = T, stringsAsFactors = F)
library(tidyverse)
dat %>%
dplyr::mutate(sex = ifelse(sex == "M", "Male", "Female"),
race = ifelse(race == "W", "Caucasian", "African-American"),
group = paste(race, sex, sep = ", ")
) %>%
ggplot(aes(x = age, y = su, colour = group)) +
geom_smooth(method = "lm", se = F, show.legend = F) +
geom_point(show.legend = F, position = "jitter", alpha = .5, pch = 16) +
facet_wrap(~group) +
ggthemes::theme_few() +
labs(x = "Age", y = "Expected serum urate level")
answered Nov 11 '18 at 20:24
utubun
1,1841711
1,1841711
add a comment |
add a comment |
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Do you need to set color based on age? Because the most straightforward way might be creating lines / points / whatever and setting their color / linetype / shape based on sex and race
– camille
Nov 11 '18 at 20:09
I don't need to, no.
– Hannah
Nov 11 '18 at 20:14