Selecting top n groups with dplyr then plotting other variables
I have a dataset where I am trying to select just the top n by counting one category, but then plotting using other variables in the dataset--basically one level of aggregation for the top n, but needing to go back to the full data to plot in ggplot.
So in the problem below, I want the two most common examNames and then plot and facetwrap them by count of year.
ap <-
tribble(
~year, ~examName,
2014, "Statistics",
2015, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2013, "Macroeconomics",
2013, "Macroeconomics",
2014, "Macroeconomics",
2015, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2013, "Calculus",
2014, "Calculus",
2015, "Calculus",
2016, "Calculus",
2017, "Calculus",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2018, "Psychology",
2018, "Psychology")
ap_top <- ap %>%
count(examName, sort = TRUE) %>%
head(2) %>%
inner_join(ap, by = "examName") %>%
select(-n)
ap_top %>%
count(examName, year) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
My thought is to get my top n, then inner_join back on the original dataset. Then plot using that; essentially using the inner join as a filter.
I know there's a better way to do this, and I would love a more elegant solution! I'm all ears! Example dataset given (sorry it's so long).
r ggplot2 dplyr
asked Jan 18 at 20:09
talbe009talbe009
344
add a comment |
I have a dataset where I am trying to select just the top n by counting one category, but then plotting using other variables in the dataset--basically one level of aggregation for the top n, but needing to go back to the full data to plot in ggplot.
So in the problem below, I want the two most common examNames and then plot and facetwrap them by count of year.
ap <-
tribble(
~year, ~examName,
2014, "Statistics",
2015, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2013, "Macroeconomics",
2013, "Macroeconomics",
2014, "Macroeconomics",
2015, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2013, "Calculus",
2014, "Calculus",
2015, "Calculus",
2016, "Calculus",
2017, "Calculus",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2018, "Psychology",
2018, "Psychology")
ap_top <- ap %>%
count(examName, sort = TRUE) %>%
head(2) %>%
inner_join(ap, by = "examName") %>%
select(-n)
ap_top %>%
count(examName, year) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
My thought is to get my top n, then inner_join back on the original dataset. Then plot using that; essentially using the inner join as a filter.
I know there's a better way to do this, and I would love a more elegant solution! I'm all ears! Example dataset given (sorry it's so long).
r ggplot2 dplyr
asked Jan 18 at 20:09
talbe009talbe009
344
add a comment |
I have a dataset where I am trying to select just the top n by counting one category, but then plotting using other variables in the dataset--basically one level of aggregation for the top n, but needing to go back to the full data to plot in ggplot.
So in the problem below, I want the two most common examNames and then plot and facetwrap them by count of year.
ap <-
tribble(
~year, ~examName,
2014, "Statistics",
2015, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2013, "Macroeconomics",
2013, "Macroeconomics",
2014, "Macroeconomics",
2015, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2013, "Calculus",
2014, "Calculus",
2015, "Calculus",
2016, "Calculus",
2017, "Calculus",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2018, "Psychology",
2018, "Psychology")
ap_top <- ap %>%
count(examName, sort = TRUE) %>%
head(2) %>%
inner_join(ap, by = "examName") %>%
select(-n)
ap_top %>%
count(examName, year) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
My thought is to get my top n, then inner_join back on the original dataset. Then plot using that; essentially using the inner join as a filter.
I know there's a better way to do this, and I would love a more elegant solution! I'm all ears! Example dataset given (sorry it's so long).
r ggplot2 dplyr
asked Jan 18 at 20:09
talbe009talbe009
344
I have a dataset where I am trying to select just the top n by counting one category, but then plotting using other variables in the dataset--basically one level of aggregation for the top n, but needing to go back to the full data to plot in ggplot.
So in the problem below, I want the two most common examNames and then plot and facetwrap them by count of year.
ap <-
tribble(
~year, ~examName,
2014, "Statistics",
2015, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2016, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2017, "Statistics",
2013, "Macroeconomics",
2013, "Macroeconomics",
2014, "Macroeconomics",
2015, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2016, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2017, "Macroeconomics",
2013, "Calculus",
2014, "Calculus",
2015, "Calculus",
2016, "Calculus",
2017, "Calculus",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2017, "Psychology",
2018, "Psychology",
2018, "Psychology")
ap_top <- ap %>%
count(examName, sort = TRUE) %>%
head(2) %>%
inner_join(ap, by = "examName") %>%
select(-n)
ap_top %>%
count(examName, year) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
My thought is to get my top n, then inner_join back on the original dataset. Then plot using that; essentially using the inner join as a filter.
I know there's a better way to do this, and I would love a more elegant solution! I'm all ears! Example dataset given (sorry it's so long).
r ggplot2 dplyr
r ggplot2 dplyr
asked Jan 18 at 20:09
talbe009talbe009
344
asked Jan 18 at 20:09
talbe009talbe009
344
edited Jan 18 at 20:18
talbe009
asked Jan 18 at 20:09
talbe009talbe009
344
asked Jan 18 at 20:09
talbe009talbe009
344
asked Jan 18 at 20:09
talbe009talbe009
344
344
add a comment |
add a comment |
2 Answers
2
active
oldest
votes
You don't need inner_join() I would just determine top two exams in a separate statement and then filter on those.
top_exams <- count(ap, examName) %>%
top_n(2, n) %>% pull(examName)
ap %>%
filter(examName %in% top_exams) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
answered Jan 18 at 20:21
dylanjmdylanjm
393112
add a comment |
Another possibility:
ap %>%
group_by(examName) %>%
mutate(temp = n()) %>%
ungroup() %>%
mutate(temp = dense_rank(desc(temp))) %>%
filter(temp %in% c(1,2)) %>%
select(-temp) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
It counts the cases per "examName" and ranks the count. Then, it filters the cases that have the greatest and the second greatest count.
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
What's nice about this solution is that you could do things with thedense_rank, like use it infct_reorderfor sorting in the plot.
– talbe009
Jan 18 at 21:01
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
You don't need inner_join() I would just determine top two exams in a separate statement and then filter on those.
top_exams <- count(ap, examName) %>%
top_n(2, n) %>% pull(examName)
ap %>%
filter(examName %in% top_exams) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
answered Jan 18 at 20:21
dylanjmdylanjm
393112
add a comment |
You don't need inner_join() I would just determine top two exams in a separate statement and then filter on those.
top_exams <- count(ap, examName) %>%
top_n(2, n) %>% pull(examName)
ap %>%
filter(examName %in% top_exams) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
answered Jan 18 at 20:21
dylanjmdylanjm
393112
add a comment |
You don't need inner_join() I would just determine top two exams in a separate statement and then filter on those.
top_exams <- count(ap, examName) %>%
top_n(2, n) %>% pull(examName)
ap %>%
filter(examName %in% top_exams) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
answered Jan 18 at 20:21
dylanjmdylanjm
393112
You don't need inner_join() I would just determine top two exams in a separate statement and then filter on those.
top_exams <- count(ap, examName) %>%
top_n(2, n) %>% pull(examName)
ap %>%
filter(examName %in% top_exams) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
answered Jan 18 at 20:21
dylanjmdylanjm
393112
answered Jan 18 at 20:21
dylanjmdylanjm
393112
answered Jan 18 at 20:21
dylanjmdylanjm
393112
answered Jan 18 at 20:21
dylanjmdylanjm
393112
393112
add a comment |
add a comment |
Another possibility:
ap %>%
group_by(examName) %>%
mutate(temp = n()) %>%
ungroup() %>%
mutate(temp = dense_rank(desc(temp))) %>%
filter(temp %in% c(1,2)) %>%
select(-temp) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
It counts the cases per "examName" and ranks the count. Then, it filters the cases that have the greatest and the second greatest count.
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
What's nice about this solution is that you could do things with thedense_rank, like use it infct_reorderfor sorting in the plot.
– talbe009
Jan 18 at 21:01
add a comment |
Another possibility:
ap %>%
group_by(examName) %>%
mutate(temp = n()) %>%
ungroup() %>%
mutate(temp = dense_rank(desc(temp))) %>%
filter(temp %in% c(1,2)) %>%
select(-temp) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
It counts the cases per "examName" and ranks the count. Then, it filters the cases that have the greatest and the second greatest count.
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
What's nice about this solution is that you could do things with thedense_rank, like use it infct_reorderfor sorting in the plot.
– talbe009
Jan 18 at 21:01
add a comment |
Another possibility:
ap %>%
group_by(examName) %>%
mutate(temp = n()) %>%
ungroup() %>%
mutate(temp = dense_rank(desc(temp))) %>%
filter(temp %in% c(1,2)) %>%
select(-temp) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
It counts the cases per "examName" and ranks the count. Then, it filters the cases that have the greatest and the second greatest count.
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
Another possibility:
ap %>%
group_by(examName) %>%
mutate(temp = n()) %>%
ungroup() %>%
mutate(temp = dense_rank(desc(temp))) %>%
filter(temp %in% c(1,2)) %>%
select(-temp) %>%
count(year, examName) %>%
ggplot(aes(x = year, y = n, group = examName)) +
geom_line() +
facet_wrap(~ examName)
It counts the cases per "examName" and ranks the count. Then, it filters the cases that have the greatest and the second greatest count.
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
answered Jan 18 at 20:43
tmfmnktmfmnk
2,2941412
2,2941412
What's nice about this solution is that you could do things with thedense_rank, like use it infct_reorderfor sorting in the plot.
– talbe009
Jan 18 at 21:01
add a comment |
What's nice about this solution is that you could do things with thedense_rank, like use it infct_reorderfor sorting in the plot.
– talbe009
Jan 18 at 21:01
What's nice about this solution is that you could do things with the
dense_rank, like use it in fct_reorder for sorting in the plot.– talbe009
Jan 18 at 21:01
What's nice about this solution is that you could do things with the
dense_rank, like use it in fct_reorder for sorting in the plot.– talbe009
Jan 18 at 21:01
add a comment |
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