ggplot not adopting aes - linetype
0
I am seeing some strange behavior in r, trying to use ggplot.
I cannot recreate the problem with a sample dataset as i can't work out what the problem is with the dataset i am using. Essentially i have two variables from the same dataset and the aes is being applied to one but not the other.
This is the dataframe: temp
temp
# A tibble: 504 x 5
# Groups: continent [6]
continent year urban.pop predicted.estimated.pop pop
<chr> <int> <dbl> <chr> <dbl>
1 Africa 1950 32658962 estimated.pop 32658962
2 Africa 1955 41419217 estimated.pop 41419217
3 Africa 1960 53008425 estimated.pop 53008425
4 Africa 1965 66348577 estimated.pop 66348577
5 Africa 1970 82637370 estimated.pop 82637370
6 Africa 1975 103198989 estimated.pop 103198989
7 Africa 1980 128615954 estimated.pop 128615954
8 Africa 1985 160721947 estimated.pop 160721947
9 Africa 1990 200111296 estimated.pop 200111296
10 Africa 1995 241824184 estimated.pop 241824184
I want to plot this dataframe to look as follows:
ggplot(temp, aes(x = year, y = pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
This looks right, but when i change y axis to plot urban.pop i get the following, where the linetype aes has not been applied:
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
As can be seen above, both pop and urban.pop are class: dbl. They are also identical:
sum(temp$pop - temp$urban.pop, na.rm = T)
[1] 0
The only thing i can note is that temp is a grouped df:
str(temp)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 504 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : int 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 ...
$ urban.pop : num 32658962 41419217 53008425 66348577 82637370 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 32658962 41419217 53008425 66348577 82637370 ...
- attr(*, "vars")= chr "continent"
- attr(*, "drop")= logi TRUE
- attr(*, "indices")=List of 6
..$ : int 0 1 2 3 4 5 6 7 8 9 ...
..$ : int 21 22 23 24 25 26 27 28 29 30 ...
..$ : int 42 43 44 45 46 47 48 49 50 51 ...
..$ : int 63 64 65 66 67 68 69 70 71 72 ...
..$ : int 84 85 86 87 88 89 90 91 92 93 ...
..$ : int 105 106 107 108 109 110 111 112 113 114 ...
- attr(*, "group_sizes")= int 84 84 84 84 84 84
- attr(*, "biggest_group_size")= int 84
- attr(*, "labels")='data.frame': 6 obs. of 1 variable:
..$ continent: chr "Africa" "Asia" "Europe" "LAC" ...
..- attr(*, "vars")= chr "continent"
..- attr(*, "drop")= logi TRUE
I can't work out why these two variables drive different results for the linetype aes. The reason I need to fix this is i have another variable in the original data set that is behaving the same way as urban.pop.
Can anyone explain this to me, or help resolve the problem?
r ggplot2
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
add a comment |
0
I am seeing some strange behavior in r, trying to use ggplot.
I cannot recreate the problem with a sample dataset as i can't work out what the problem is with the dataset i am using. Essentially i have two variables from the same dataset and the aes is being applied to one but not the other.
This is the dataframe: temp
temp
# A tibble: 504 x 5
# Groups: continent [6]
continent year urban.pop predicted.estimated.pop pop
<chr> <int> <dbl> <chr> <dbl>
1 Africa 1950 32658962 estimated.pop 32658962
2 Africa 1955 41419217 estimated.pop 41419217
3 Africa 1960 53008425 estimated.pop 53008425
4 Africa 1965 66348577 estimated.pop 66348577
5 Africa 1970 82637370 estimated.pop 82637370
6 Africa 1975 103198989 estimated.pop 103198989
7 Africa 1980 128615954 estimated.pop 128615954
8 Africa 1985 160721947 estimated.pop 160721947
9 Africa 1990 200111296 estimated.pop 200111296
10 Africa 1995 241824184 estimated.pop 241824184
I want to plot this dataframe to look as follows:
ggplot(temp, aes(x = year, y = pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
This looks right, but when i change y axis to plot urban.pop i get the following, where the linetype aes has not been applied:
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
As can be seen above, both pop and urban.pop are class: dbl. They are also identical:
sum(temp$pop - temp$urban.pop, na.rm = T)
[1] 0
The only thing i can note is that temp is a grouped df:
str(temp)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 504 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : int 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 ...
$ urban.pop : num 32658962 41419217 53008425 66348577 82637370 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 32658962 41419217 53008425 66348577 82637370 ...
- attr(*, "vars")= chr "continent"
- attr(*, "drop")= logi TRUE
- attr(*, "indices")=List of 6
..$ : int 0 1 2 3 4 5 6 7 8 9 ...
..$ : int 21 22 23 24 25 26 27 28 29 30 ...
..$ : int 42 43 44 45 46 47 48 49 50 51 ...
..$ : int 63 64 65 66 67 68 69 70 71 72 ...
..$ : int 84 85 86 87 88 89 90 91 92 93 ...
..$ : int 105 106 107 108 109 110 111 112 113 114 ...
- attr(*, "group_sizes")= int 84 84 84 84 84 84
- attr(*, "biggest_group_size")= int 84
- attr(*, "labels")='data.frame': 6 obs. of 1 variable:
..$ continent: chr "Africa" "Asia" "Europe" "LAC" ...
..- attr(*, "vars")= chr "continent"
..- attr(*, "drop")= logi TRUE
I can't work out why these two variables drive different results for the linetype aes. The reason I need to fix this is i have another variable in the original data set that is behaving the same way as urban.pop.
Can anyone explain this to me, or help resolve the problem?
r ggplot2
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
3
Could you make your problem reproducible by sharing a sample of your data so others can help (please do not usestr(),head()or screenshot)? You can use thereprexanddatapastapackages to assist you with that. See also Help me Help you & How to make a great R reproducible example?
– Tung
Nov 20 '18 at 19:50
2
Impossible to tell without an actual example for us to run, but my gut reaction would be that there's something about your data that you don't quite understand that means that in the second case the solid and dashed lines are being plotted on top of each other, so the legend is created but you can't see the dashed ones.
– joran
Nov 20 '18 at 19:53
1
runningggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) + geom_line() + facet_wrap(.~predicted.estimated.pop)might validate @joran's theory
– Chris
Nov 20 '18 at 22:02
Thanks for all the help everyone. As I sought to sample of my data (apologies for not knowing about dput()), i found that there was an error within the predicted/estimated variabl. The ifelse statement i had used to mutate this was was calculated against year as a chr vector. The pop data was working because of NAs in the dataset which meant, as joran suggested, lines were drawing over one another (not drawing where the NAs were). Getting the data correctly organized solved the problem. I will mark jay.sf answer below as correct as it describes what the data needs to look like.
– MorrisseyJ
Nov 21 '18 at 14:42
add a comment |
0
0
0
I am seeing some strange behavior in r, trying to use ggplot.
I cannot recreate the problem with a sample dataset as i can't work out what the problem is with the dataset i am using. Essentially i have two variables from the same dataset and the aes is being applied to one but not the other.
This is the dataframe: temp
temp
# A tibble: 504 x 5
# Groups: continent [6]
continent year urban.pop predicted.estimated.pop pop
<chr> <int> <dbl> <chr> <dbl>
1 Africa 1950 32658962 estimated.pop 32658962
2 Africa 1955 41419217 estimated.pop 41419217
3 Africa 1960 53008425 estimated.pop 53008425
4 Africa 1965 66348577 estimated.pop 66348577
5 Africa 1970 82637370 estimated.pop 82637370
6 Africa 1975 103198989 estimated.pop 103198989
7 Africa 1980 128615954 estimated.pop 128615954
8 Africa 1985 160721947 estimated.pop 160721947
9 Africa 1990 200111296 estimated.pop 200111296
10 Africa 1995 241824184 estimated.pop 241824184
I want to plot this dataframe to look as follows:
ggplot(temp, aes(x = year, y = pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
This looks right, but when i change y axis to plot urban.pop i get the following, where the linetype aes has not been applied:
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
As can be seen above, both pop and urban.pop are class: dbl. They are also identical:
sum(temp$pop - temp$urban.pop, na.rm = T)
[1] 0
The only thing i can note is that temp is a grouped df:
str(temp)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 504 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : int 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 ...
$ urban.pop : num 32658962 41419217 53008425 66348577 82637370 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 32658962 41419217 53008425 66348577 82637370 ...
- attr(*, "vars")= chr "continent"
- attr(*, "drop")= logi TRUE
- attr(*, "indices")=List of 6
..$ : int 0 1 2 3 4 5 6 7 8 9 ...
..$ : int 21 22 23 24 25 26 27 28 29 30 ...
..$ : int 42 43 44 45 46 47 48 49 50 51 ...
..$ : int 63 64 65 66 67 68 69 70 71 72 ...
..$ : int 84 85 86 87 88 89 90 91 92 93 ...
..$ : int 105 106 107 108 109 110 111 112 113 114 ...
- attr(*, "group_sizes")= int 84 84 84 84 84 84
- attr(*, "biggest_group_size")= int 84
- attr(*, "labels")='data.frame': 6 obs. of 1 variable:
..$ continent: chr "Africa" "Asia" "Europe" "LAC" ...
..- attr(*, "vars")= chr "continent"
..- attr(*, "drop")= logi TRUE
I can't work out why these two variables drive different results for the linetype aes. The reason I need to fix this is i have another variable in the original data set that is behaving the same way as urban.pop.
Can anyone explain this to me, or help resolve the problem?
r ggplot2
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
I am seeing some strange behavior in r, trying to use ggplot.
I cannot recreate the problem with a sample dataset as i can't work out what the problem is with the dataset i am using. Essentially i have two variables from the same dataset and the aes is being applied to one but not the other.
This is the dataframe: temp
temp
# A tibble: 504 x 5
# Groups: continent [6]
continent year urban.pop predicted.estimated.pop pop
<chr> <int> <dbl> <chr> <dbl>
1 Africa 1950 32658962 estimated.pop 32658962
2 Africa 1955 41419217 estimated.pop 41419217
3 Africa 1960 53008425 estimated.pop 53008425
4 Africa 1965 66348577 estimated.pop 66348577
5 Africa 1970 82637370 estimated.pop 82637370
6 Africa 1975 103198989 estimated.pop 103198989
7 Africa 1980 128615954 estimated.pop 128615954
8 Africa 1985 160721947 estimated.pop 160721947
9 Africa 1990 200111296 estimated.pop 200111296
10 Africa 1995 241824184 estimated.pop 241824184
I want to plot this dataframe to look as follows:
ggplot(temp, aes(x = year, y = pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
This looks right, but when i change y axis to plot urban.pop i get the following, where the linetype aes has not been applied:
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) +
geom_line()
As can be seen above, both pop and urban.pop are class: dbl. They are also identical:
sum(temp$pop - temp$urban.pop, na.rm = T)
[1] 0
The only thing i can note is that temp is a grouped df:
str(temp)
Classes ‘grouped_df’, ‘tbl_df’, ‘tbl’ and 'data.frame': 504 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : int 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 ...
$ urban.pop : num 32658962 41419217 53008425 66348577 82637370 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 32658962 41419217 53008425 66348577 82637370 ...
- attr(*, "vars")= chr "continent"
- attr(*, "drop")= logi TRUE
- attr(*, "indices")=List of 6
..$ : int 0 1 2 3 4 5 6 7 8 9 ...
..$ : int 21 22 23 24 25 26 27 28 29 30 ...
..$ : int 42 43 44 45 46 47 48 49 50 51 ...
..$ : int 63 64 65 66 67 68 69 70 71 72 ...
..$ : int 84 85 86 87 88 89 90 91 92 93 ...
..$ : int 105 106 107 108 109 110 111 112 113 114 ...
- attr(*, "group_sizes")= int 84 84 84 84 84 84
- attr(*, "biggest_group_size")= int 84
- attr(*, "labels")='data.frame': 6 obs. of 1 variable:
..$ continent: chr "Africa" "Asia" "Europe" "LAC" ...
..- attr(*, "vars")= chr "continent"
..- attr(*, "drop")= logi TRUE
I can't work out why these two variables drive different results for the linetype aes. The reason I need to fix this is i have another variable in the original data set that is behaving the same way as urban.pop.
Can anyone explain this to me, or help resolve the problem?
r ggplot2
r ggplot2
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
asked Nov 20 '18 at 19:45
MorrisseyJMorrisseyJ
1857
1857
3
Could you make your problem reproducible by sharing a sample of your data so others can help (please do not usestr(),head()or screenshot)? You can use thereprexanddatapastapackages to assist you with that. See also Help me Help you & How to make a great R reproducible example?
– Tung
Nov 20 '18 at 19:50
2
Impossible to tell without an actual example for us to run, but my gut reaction would be that there's something about your data that you don't quite understand that means that in the second case the solid and dashed lines are being plotted on top of each other, so the legend is created but you can't see the dashed ones.
– joran
Nov 20 '18 at 19:53
1
runningggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) + geom_line() + facet_wrap(.~predicted.estimated.pop)might validate @joran's theory
– Chris
Nov 20 '18 at 22:02
Thanks for all the help everyone. As I sought to sample of my data (apologies for not knowing about dput()), i found that there was an error within the predicted/estimated variabl. The ifelse statement i had used to mutate this was was calculated against year as a chr vector. The pop data was working because of NAs in the dataset which meant, as joran suggested, lines were drawing over one another (not drawing where the NAs were). Getting the data correctly organized solved the problem. I will mark jay.sf answer below as correct as it describes what the data needs to look like.
– MorrisseyJ
Nov 21 '18 at 14:42
add a comment |
3
Could you make your problem reproducible by sharing a sample of your data so others can help (please do not usestr(),head()or screenshot)? You can use thereprexanddatapastapackages to assist you with that. See also Help me Help you & How to make a great R reproducible example?
– Tung
Nov 20 '18 at 19:50
2
Impossible to tell without an actual example for us to run, but my gut reaction would be that there's something about your data that you don't quite understand that means that in the second case the solid and dashed lines are being plotted on top of each other, so the legend is created but you can't see the dashed ones.
– joran
Nov 20 '18 at 19:53
1
runningggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) + geom_line() + facet_wrap(.~predicted.estimated.pop)might validate @joran's theory
– Chris
Nov 20 '18 at 22:02
Thanks for all the help everyone. As I sought to sample of my data (apologies for not knowing about dput()), i found that there was an error within the predicted/estimated variabl. The ifelse statement i had used to mutate this was was calculated against year as a chr vector. The pop data was working because of NAs in the dataset which meant, as joran suggested, lines were drawing over one another (not drawing where the NAs were). Getting the data correctly organized solved the problem. I will mark jay.sf answer below as correct as it describes what the data needs to look like.
– MorrisseyJ
Nov 21 '18 at 14:42
3
3
Could you make your problem reproducible by sharing a sample of your data so others can help (please do not use
str(), head() or screenshot)? You can use the reprex and datapasta packages to assist you with that. See also Help me Help you & How to make a great R reproducible example?– Tung
Nov 20 '18 at 19:50
Could you make your problem reproducible by sharing a sample of your data so others can help (please do not use
str(), head() or screenshot)? You can use the reprex and datapasta packages to assist you with that. See also Help me Help you & How to make a great R reproducible example?– Tung
Nov 20 '18 at 19:50
2
2
Impossible to tell without an actual example for us to run, but my gut reaction would be that there's something about your data that you don't quite understand that means that in the second case the solid and dashed lines are being plotted on top of each other, so the legend is created but you can't see the dashed ones.
– joran
Nov 20 '18 at 19:53
Impossible to tell without an actual example for us to run, but my gut reaction would be that there's something about your data that you don't quite understand that means that in the second case the solid and dashed lines are being plotted on top of each other, so the legend is created but you can't see the dashed ones.
– joran
Nov 20 '18 at 19:53
1
1
running
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) + geom_line() + facet_wrap(.~predicted.estimated.pop) might validate @joran's theory– Chris
Nov 20 '18 at 22:02
running
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) + geom_line() + facet_wrap(.~predicted.estimated.pop) might validate @joran's theory– Chris
Nov 20 '18 at 22:02
Thanks for all the help everyone. As I sought to sample of my data (apologies for not knowing about dput()), i found that there was an error within the predicted/estimated variabl. The ifelse statement i had used to mutate this was was calculated against year as a chr vector. The pop data was working because of NAs in the dataset which meant, as joran suggested, lines were drawing over one another (not drawing where the NAs were). Getting the data correctly organized solved the problem. I will mark jay.sf answer below as correct as it describes what the data needs to look like.
– MorrisseyJ
Nov 21 '18 at 14:42
Thanks for all the help everyone. As I sought to sample of my data (apologies for not knowing about dput()), i found that there was an error within the predicted/estimated variabl. The ifelse statement i had used to mutate this was was calculated against year as a chr vector. The pop data was working because of NAs in the dataset which meant, as joran suggested, lines were drawing over one another (not drawing where the NAs were). Getting the data correctly organized solved the problem. I will mark jay.sf answer below as correct as it describes what the data needs to look like.
– MorrisseyJ
Nov 21 '18 at 14:42
add a comment |
1 Answer
1
active
oldest
votes
2
I can't really reproduce your problem, but I've added a data example similar to yours. Maybe by comparing you find the knot.
library(ggplot2)
p1 <- ggplot(temp, aes(x=year, y=pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
p2 <- ggplot(temp, aes(x=year, y=urban.pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
egg::ggarrange(p1, p2)
Yields:
Data
> dput(temp)
structure(list(continent = c("Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe", "Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe"), year = c(1995, 2000, 2005, 2010,
2015, 1995, 2000, 2005, 2010, 2015, 1995, 2000, 2005, 2010, 2015,
2015, 2020, 2025, 2030, 2035, 2015, 2020, 2025, 2030, 2035, 2015,
2020, 2025, 2030, 2035), urban.pop = c(30806083, 46209124.25,
61612165.5, 77015206.75, 92418248, 105455596, 184545293, 263634990,
342724687, 421814384, 24760494, 37140741, 49520988, 61901235,
74281482, 92418248, 115522810, 138627372, 161731934, 184836496,
421814384, 527267980, 632721576, 738175172, 843628768, 74281482,
92851852.5, 111422223, 129992593.5, 148562964), predicted.estimated.pop = c("estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop"), pop = c(30806083, 46209124.25, 61612165.5,
77015206.75, 92418248, 105455596, 184545293, 263634990, 342724687,
421814384, 24760494, 37140741, 49520988, 61901235, 74281482,
92418248, 115522810, 138627372, 161731934, 184836496, 421814384,
527267980, 632721576, 738175172, 843628768, 74281482, 92851852.5,
111422223, 129992593.5, 148562964)), row.names = c(NA, -30L), class = "data.frame")
> str(temp)
'data.frame': 30 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : num 1995 2000 2005 2010 2015 ...
$ urban.pop : num 30806083 46209124 61612166 77015207 92418248 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 30806083 46209124 61612166 77015207 92418248 ...
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
add a comment |
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1 Answer
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I can't really reproduce your problem, but I've added a data example similar to yours. Maybe by comparing you find the knot.
library(ggplot2)
p1 <- ggplot(temp, aes(x=year, y=pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
p2 <- ggplot(temp, aes(x=year, y=urban.pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
egg::ggarrange(p1, p2)
Yields:
Data
> dput(temp)
structure(list(continent = c("Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe", "Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe"), year = c(1995, 2000, 2005, 2010,
2015, 1995, 2000, 2005, 2010, 2015, 1995, 2000, 2005, 2010, 2015,
2015, 2020, 2025, 2030, 2035, 2015, 2020, 2025, 2030, 2035, 2015,
2020, 2025, 2030, 2035), urban.pop = c(30806083, 46209124.25,
61612165.5, 77015206.75, 92418248, 105455596, 184545293, 263634990,
342724687, 421814384, 24760494, 37140741, 49520988, 61901235,
74281482, 92418248, 115522810, 138627372, 161731934, 184836496,
421814384, 527267980, 632721576, 738175172, 843628768, 74281482,
92851852.5, 111422223, 129992593.5, 148562964), predicted.estimated.pop = c("estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop"), pop = c(30806083, 46209124.25, 61612165.5,
77015206.75, 92418248, 105455596, 184545293, 263634990, 342724687,
421814384, 24760494, 37140741, 49520988, 61901235, 74281482,
92418248, 115522810, 138627372, 161731934, 184836496, 421814384,
527267980, 632721576, 738175172, 843628768, 74281482, 92851852.5,
111422223, 129992593.5, 148562964)), row.names = c(NA, -30L), class = "data.frame")
> str(temp)
'data.frame': 30 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : num 1995 2000 2005 2010 2015 ...
$ urban.pop : num 30806083 46209124 61612166 77015207 92418248 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 30806083 46209124 61612166 77015207 92418248 ...
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
add a comment |
2
I can't really reproduce your problem, but I've added a data example similar to yours. Maybe by comparing you find the knot.
library(ggplot2)
p1 <- ggplot(temp, aes(x=year, y=pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
p2 <- ggplot(temp, aes(x=year, y=urban.pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
egg::ggarrange(p1, p2)
Yields:
Data
> dput(temp)
structure(list(continent = c("Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe", "Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe"), year = c(1995, 2000, 2005, 2010,
2015, 1995, 2000, 2005, 2010, 2015, 1995, 2000, 2005, 2010, 2015,
2015, 2020, 2025, 2030, 2035, 2015, 2020, 2025, 2030, 2035, 2015,
2020, 2025, 2030, 2035), urban.pop = c(30806083, 46209124.25,
61612165.5, 77015206.75, 92418248, 105455596, 184545293, 263634990,
342724687, 421814384, 24760494, 37140741, 49520988, 61901235,
74281482, 92418248, 115522810, 138627372, 161731934, 184836496,
421814384, 527267980, 632721576, 738175172, 843628768, 74281482,
92851852.5, 111422223, 129992593.5, 148562964), predicted.estimated.pop = c("estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop"), pop = c(30806083, 46209124.25, 61612165.5,
77015206.75, 92418248, 105455596, 184545293, 263634990, 342724687,
421814384, 24760494, 37140741, 49520988, 61901235, 74281482,
92418248, 115522810, 138627372, 161731934, 184836496, 421814384,
527267980, 632721576, 738175172, 843628768, 74281482, 92851852.5,
111422223, 129992593.5, 148562964)), row.names = c(NA, -30L), class = "data.frame")
> str(temp)
'data.frame': 30 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : num 1995 2000 2005 2010 2015 ...
$ urban.pop : num 30806083 46209124 61612166 77015207 92418248 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 30806083 46209124 61612166 77015207 92418248 ...
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
add a comment |
2
2
2
I can't really reproduce your problem, but I've added a data example similar to yours. Maybe by comparing you find the knot.
library(ggplot2)
p1 <- ggplot(temp, aes(x=year, y=pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
p2 <- ggplot(temp, aes(x=year, y=urban.pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
egg::ggarrange(p1, p2)
Yields:
Data
> dput(temp)
structure(list(continent = c("Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe", "Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe"), year = c(1995, 2000, 2005, 2010,
2015, 1995, 2000, 2005, 2010, 2015, 1995, 2000, 2005, 2010, 2015,
2015, 2020, 2025, 2030, 2035, 2015, 2020, 2025, 2030, 2035, 2015,
2020, 2025, 2030, 2035), urban.pop = c(30806083, 46209124.25,
61612165.5, 77015206.75, 92418248, 105455596, 184545293, 263634990,
342724687, 421814384, 24760494, 37140741, 49520988, 61901235,
74281482, 92418248, 115522810, 138627372, 161731934, 184836496,
421814384, 527267980, 632721576, 738175172, 843628768, 74281482,
92851852.5, 111422223, 129992593.5, 148562964), predicted.estimated.pop = c("estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop"), pop = c(30806083, 46209124.25, 61612165.5,
77015206.75, 92418248, 105455596, 184545293, 263634990, 342724687,
421814384, 24760494, 37140741, 49520988, 61901235, 74281482,
92418248, 115522810, 138627372, 161731934, 184836496, 421814384,
527267980, 632721576, 738175172, 843628768, 74281482, 92851852.5,
111422223, 129992593.5, 148562964)), row.names = c(NA, -30L), class = "data.frame")
> str(temp)
'data.frame': 30 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : num 1995 2000 2005 2010 2015 ...
$ urban.pop : num 30806083 46209124 61612166 77015207 92418248 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 30806083 46209124 61612166 77015207 92418248 ...
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
I can't really reproduce your problem, but I've added a data example similar to yours. Maybe by comparing you find the knot.
library(ggplot2)
p1 <- ggplot(temp, aes(x=year, y=pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
p2 <- ggplot(temp, aes(x=year, y=urban.pop, col=continent,
linetype=predicted.estimated.pop)) +
geom_line()
egg::ggarrange(p1, p2)
Yields:
Data
> dput(temp)
structure(list(continent = c("Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe", "Africa", "Africa", "Africa", "Africa",
"Africa", "Asia", "Asia", "Asia", "Asia", "Asia", "Europe", "Europe",
"Europe", "Europe", "Europe"), year = c(1995, 2000, 2005, 2010,
2015, 1995, 2000, 2005, 2010, 2015, 1995, 2000, 2005, 2010, 2015,
2015, 2020, 2025, 2030, 2035, 2015, 2020, 2025, 2030, 2035, 2015,
2020, 2025, 2030, 2035), urban.pop = c(30806083, 46209124.25,
61612165.5, 77015206.75, 92418248, 105455596, 184545293, 263634990,
342724687, 421814384, 24760494, 37140741, 49520988, 61901235,
74281482, 92418248, 115522810, 138627372, 161731934, 184836496,
421814384, 527267980, 632721576, 738175172, 843628768, 74281482,
92851852.5, 111422223, 129992593.5, 148562964), predicted.estimated.pop = c("estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "estimated.pop", "estimated.pop",
"estimated.pop", "estimated.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop", "predicted.pop", "predicted.pop", "predicted.pop",
"predicted.pop"), pop = c(30806083, 46209124.25, 61612165.5,
77015206.75, 92418248, 105455596, 184545293, 263634990, 342724687,
421814384, 24760494, 37140741, 49520988, 61901235, 74281482,
92418248, 115522810, 138627372, 161731934, 184836496, 421814384,
527267980, 632721576, 738175172, 843628768, 74281482, 92851852.5,
111422223, 129992593.5, 148562964)), row.names = c(NA, -30L), class = "data.frame")
> str(temp)
'data.frame': 30 obs. of 5 variables:
$ continent : chr "Africa" "Africa" "Africa" "Africa" ...
$ year : num 1995 2000 2005 2010 2015 ...
$ urban.pop : num 30806083 46209124 61612166 77015207 92418248 ...
$ predicted.estimated.pop: chr "estimated.pop" "estimated.pop" "estimated.pop" "estimated.pop" ...
$ pop : num 30806083 46209124 61612166 77015207 92418248 ...
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
answered Nov 20 '18 at 20:31
jay.sfjay.sf
5,73831740
5,73831740
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
add a comment |
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
This works. Error was on my end. Get the data to look like this and everything will work.
– MorrisseyJ
Nov 21 '18 at 14:45
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
Cool. Thanks for your feedback.
– jay.sf
Nov 21 '18 at 15:18
add a comment |
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3
Could you make your problem reproducible by sharing a sample of your data so others can help (please do not use
str(),head()or screenshot)? You can use thereprexanddatapastapackages to assist you with that. See also Help me Help you & How to make a great R reproducible example?– Tung
Nov 20 '18 at 19:50
2
Impossible to tell without an actual example for us to run, but my gut reaction would be that there's something about your data that you don't quite understand that means that in the second case the solid and dashed lines are being plotted on top of each other, so the legend is created but you can't see the dashed ones.
– joran
Nov 20 '18 at 19:53
1
running
ggplot(temp, aes(x = year, y = urban.pop, col = continent, linetype = predicted.estimated.pop)) + geom_line() + facet_wrap(.~predicted.estimated.pop)might validate @joran's theory– Chris
Nov 20 '18 at 22:02
Thanks for all the help everyone. As I sought to sample of my data (apologies for not knowing about dput()), i found that there was an error within the predicted/estimated variabl. The ifelse statement i had used to mutate this was was calculated against year as a chr vector. The pop data was working because of NAs in the dataset which meant, as joran suggested, lines were drawing over one another (not drawing where the NAs were). Getting the data correctly organized solved the problem. I will mark jay.sf answer below as correct as it describes what the data needs to look like.
– MorrisseyJ
Nov 21 '18 at 14:42