Mixing
Plotting Multiple Lines in GGplot with Different lines corresponding to a different year
I am trying to use ggplot to plot multiple lines (categorized by years) for means for a categorical variable. I am stumped and have tried a variety of things but can't get exactly what I want. I have raw observations that have a year flag on each observation and a Loss value attached to them but here is a snapshot of me trying to group the mean losses by year/judicial orientation.
I want to aggregate all the Loss values by categorical variable level and then aggregate those further by the Year
My goal is this:
I want one graph that has a variable number of levels depending on variable (for JudicialOrientation I have 3 levels: Defense, Neutral, Plaintiff) so those would be the x values, I then want to have a line graph connecting the means between each 3 levels but I want multiple lines that represent 2006, 2007, 2008 etc.
So I would have different colored lines that correspond to a different year's MeanLoss value for that particular level. I hope this makes sense.
I am new to ggplot and I see some people use one line and others use multiple lines. I am ok with either approach.
An attempt so far:
ggplot() +geom_line(data=df1, aes(x=JudicialOrientation, y = MeanLoss, color=Year))
An example data frame with some dplyr code to obtain aggregate means:
df <-data.frame(Year=c("2006","2006","2006","2007","2007","2007","2008","2009","2010","2010","2009","2009"),
JudicialOrientation=c("Defense","Plaintiff","Plaintiff","Neutral","Defense","Plaintiff","Defense","Plaintiff","Neutral","Neutral","Plaintiff","Defense"),
Loss = c(100000,100,2500,100000,25000,0,7500,5200, 900,100,0,50)
)
df1 <- df%>% group_by(Year,JudicialOrientation) %>% summarise(MeanLoss =mean(Loss))
Let me know any tips you may have. Thanks!
r ggplot2 aggregate
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
add a comment |
I am trying to use ggplot to plot multiple lines (categorized by years) for means for a categorical variable. I am stumped and have tried a variety of things but can't get exactly what I want. I have raw observations that have a year flag on each observation and a Loss value attached to them but here is a snapshot of me trying to group the mean losses by year/judicial orientation.
I want to aggregate all the Loss values by categorical variable level and then aggregate those further by the Year
My goal is this:
I want one graph that has a variable number of levels depending on variable (for JudicialOrientation I have 3 levels: Defense, Neutral, Plaintiff) so those would be the x values, I then want to have a line graph connecting the means between each 3 levels but I want multiple lines that represent 2006, 2007, 2008 etc.
So I would have different colored lines that correspond to a different year's MeanLoss value for that particular level. I hope this makes sense.
I am new to ggplot and I see some people use one line and others use multiple lines. I am ok with either approach.
An attempt so far:
ggplot() +geom_line(data=df1, aes(x=JudicialOrientation, y = MeanLoss, color=Year))
An example data frame with some dplyr code to obtain aggregate means:
df <-data.frame(Year=c("2006","2006","2006","2007","2007","2007","2008","2009","2010","2010","2009","2009"),
JudicialOrientation=c("Defense","Plaintiff","Plaintiff","Neutral","Defense","Plaintiff","Defense","Plaintiff","Neutral","Neutral","Plaintiff","Defense"),
Loss = c(100000,100,2500,100000,25000,0,7500,5200, 900,100,0,50)
)
df1 <- df%>% group_by(Year,JudicialOrientation) %>% summarise(MeanLoss =mean(Loss))
Let me know any tips you may have. Thanks!
r ggplot2 aggregate
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
Please post the code you have tried so far?
– TeeKea
Nov 20 '18 at 20:54
I edited my post.This should give you a sample data frame you can work with. I want to aggregate the Loss column by JudicialOrientation and then plot a different line for each year in the data set to give aggregate meanLoss for each JudicialOrientation (on the x-axis preferably).
– Coldchain9
Nov 20 '18 at 21:01
add a comment |
I am trying to use ggplot to plot multiple lines (categorized by years) for means for a categorical variable. I am stumped and have tried a variety of things but can't get exactly what I want. I have raw observations that have a year flag on each observation and a Loss value attached to them but here is a snapshot of me trying to group the mean losses by year/judicial orientation.
I want to aggregate all the Loss values by categorical variable level and then aggregate those further by the Year
My goal is this:
I want one graph that has a variable number of levels depending on variable (for JudicialOrientation I have 3 levels: Defense, Neutral, Plaintiff) so those would be the x values, I then want to have a line graph connecting the means between each 3 levels but I want multiple lines that represent 2006, 2007, 2008 etc.
So I would have different colored lines that correspond to a different year's MeanLoss value for that particular level. I hope this makes sense.
I am new to ggplot and I see some people use one line and others use multiple lines. I am ok with either approach.
An attempt so far:
ggplot() +geom_line(data=df1, aes(x=JudicialOrientation, y = MeanLoss, color=Year))
An example data frame with some dplyr code to obtain aggregate means:
df <-data.frame(Year=c("2006","2006","2006","2007","2007","2007","2008","2009","2010","2010","2009","2009"),
JudicialOrientation=c("Defense","Plaintiff","Plaintiff","Neutral","Defense","Plaintiff","Defense","Plaintiff","Neutral","Neutral","Plaintiff","Defense"),
Loss = c(100000,100,2500,100000,25000,0,7500,5200, 900,100,0,50)
)
df1 <- df%>% group_by(Year,JudicialOrientation) %>% summarise(MeanLoss =mean(Loss))
Let me know any tips you may have. Thanks!
r ggplot2 aggregate
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
I am trying to use ggplot to plot multiple lines (categorized by years) for means for a categorical variable. I am stumped and have tried a variety of things but can't get exactly what I want. I have raw observations that have a year flag on each observation and a Loss value attached to them but here is a snapshot of me trying to group the mean losses by year/judicial orientation.
I want to aggregate all the Loss values by categorical variable level and then aggregate those further by the Year
My goal is this:
I want one graph that has a variable number of levels depending on variable (for JudicialOrientation I have 3 levels: Defense, Neutral, Plaintiff) so those would be the x values, I then want to have a line graph connecting the means between each 3 levels but I want multiple lines that represent 2006, 2007, 2008 etc.
So I would have different colored lines that correspond to a different year's MeanLoss value for that particular level. I hope this makes sense.
I am new to ggplot and I see some people use one line and others use multiple lines. I am ok with either approach.
An attempt so far:
ggplot() +geom_line(data=df1, aes(x=JudicialOrientation, y = MeanLoss, color=Year))
An example data frame with some dplyr code to obtain aggregate means:
df <-data.frame(Year=c("2006","2006","2006","2007","2007","2007","2008","2009","2010","2010","2009","2009"),
JudicialOrientation=c("Defense","Plaintiff","Plaintiff","Neutral","Defense","Plaintiff","Defense","Plaintiff","Neutral","Neutral","Plaintiff","Defense"),
Loss = c(100000,100,2500,100000,25000,0,7500,5200, 900,100,0,50)
)
df1 <- df%>% group_by(Year,JudicialOrientation) %>% summarise(MeanLoss =mean(Loss))
Let me know any tips you may have. Thanks!
r ggplot2 aggregate
r ggplot2 aggregate
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
edited Nov 20 '18 at 21:05
Coldchain9
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
asked Nov 20 '18 at 20:51
Coldchain9Coldchain9
325
325
Please post the code you have tried so far?
– TeeKea
Nov 20 '18 at 20:54
I edited my post.This should give you a sample data frame you can work with. I want to aggregate the Loss column by JudicialOrientation and then plot a different line for each year in the data set to give aggregate meanLoss for each JudicialOrientation (on the x-axis preferably).
– Coldchain9
Nov 20 '18 at 21:01
add a comment |
Please post the code you have tried so far?
– TeeKea
Nov 20 '18 at 20:54
I edited my post.This should give you a sample data frame you can work with. I want to aggregate the Loss column by JudicialOrientation and then plot a different line for each year in the data set to give aggregate meanLoss for each JudicialOrientation (on the x-axis preferably).
– Coldchain9
Nov 20 '18 at 21:01
Please post the code you have tried so far?
– TeeKea
Nov 20 '18 at 20:54
Please post the code you have tried so far?
– TeeKea
Nov 20 '18 at 20:54
I edited my post.This should give you a sample data frame you can work with. I want to aggregate the Loss column by JudicialOrientation and then plot a different line for each year in the data set to give aggregate meanLoss for each JudicialOrientation (on the x-axis preferably).
– Coldchain9
Nov 20 '18 at 21:01
I edited my post.This should give you a sample data frame you can work with. I want to aggregate the Loss column by JudicialOrientation and then plot a different line for each year in the data set to give aggregate meanLoss for each JudicialOrientation (on the x-axis preferably).
– Coldchain9
Nov 20 '18 at 21:01
add a comment |
1 Answer
1
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oldest
votes
I suppose your saw the message after you run your code.
geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
So adjusting the group aesthetic would give you this
ggplot(data = df1, aes(x = JudicialOrientation, y = MeanLoss, color=Year, group = Year)) +
geom_line() +
geom_point()
I added geom_point so you see that the values for e.g. 2008 are actually there. You wouldn't see them with only geom_line. Hope this helps.
You could even let stat_summary do the aggregation for you and use df directly.
Here is how
ggplot(df, aes(x = JudicialOrientation, y = Loss, color = Year, group = Year)) +
stat_summary(geom = "line", fun.y = mean) +
stat_summary(geom = "point", fun.y = mean)
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
1
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
add a comment |
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I suppose your saw the message after you run your code.
geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
So adjusting the group aesthetic would give you this
ggplot(data = df1, aes(x = JudicialOrientation, y = MeanLoss, color=Year, group = Year)) +
geom_line() +
geom_point()
I added geom_point so you see that the values for e.g. 2008 are actually there. You wouldn't see them with only geom_line. Hope this helps.
You could even let stat_summary do the aggregation for you and use df directly.
Here is how
ggplot(df, aes(x = JudicialOrientation, y = Loss, color = Year, group = Year)) +
stat_summary(geom = "line", fun.y = mean) +
stat_summary(geom = "point", fun.y = mean)
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
1
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
add a comment |
I suppose your saw the message after you run your code.
geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
So adjusting the group aesthetic would give you this
ggplot(data = df1, aes(x = JudicialOrientation, y = MeanLoss, color=Year, group = Year)) +
geom_line() +
geom_point()
I added geom_point so you see that the values for e.g. 2008 are actually there. You wouldn't see them with only geom_line. Hope this helps.
You could even let stat_summary do the aggregation for you and use df directly.
Here is how
ggplot(df, aes(x = JudicialOrientation, y = Loss, color = Year, group = Year)) +
stat_summary(geom = "line", fun.y = mean) +
stat_summary(geom = "point", fun.y = mean)
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
1
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
add a comment |
I suppose your saw the message after you run your code.
geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
So adjusting the group aesthetic would give you this
ggplot(data = df1, aes(x = JudicialOrientation, y = MeanLoss, color=Year, group = Year)) +
geom_line() +
geom_point()
I added geom_point so you see that the values for e.g. 2008 are actually there. You wouldn't see them with only geom_line. Hope this helps.
You could even let stat_summary do the aggregation for you and use df directly.
Here is how
ggplot(df, aes(x = JudicialOrientation, y = Loss, color = Year, group = Year)) +
stat_summary(geom = "line", fun.y = mean) +
stat_summary(geom = "point", fun.y = mean)
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
I suppose your saw the message after you run your code.
geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
So adjusting the group aesthetic would give you this
ggplot(data = df1, aes(x = JudicialOrientation, y = MeanLoss, color=Year, group = Year)) +
geom_line() +
geom_point()
I added geom_point so you see that the values for e.g. 2008 are actually there. You wouldn't see them with only geom_line. Hope this helps.
You could even let stat_summary do the aggregation for you and use df directly.
Here is how
ggplot(df, aes(x = JudicialOrientation, y = Loss, color = Year, group = Year)) +
stat_summary(geom = "line", fun.y = mean) +
stat_summary(geom = "point", fun.y = mean)
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
edited Nov 20 '18 at 21:20
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
answered Nov 20 '18 at 21:14
markusmarkus
11.7k1233
11.7k1233
1
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
add a comment |
1
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
1
1
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
This is very useful thanks. I did not know I needed a point when using categorical data to make it work properly.
– Coldchain9
Nov 20 '18 at 21:17
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
@Coldchain9 You don't need the points actually - you simply cannot draw a line for just one observation (that's what you brought you here, I guess).
– markus
Nov 20 '18 at 21:24
add a comment |
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Please post the code you have tried so far?
– TeeKea
Nov 20 '18 at 20:54
I edited my post.This should give you a sample data frame you can work with. I want to aggregate the Loss column by JudicialOrientation and then plot a different line for each year in the data set to give aggregate meanLoss for each JudicialOrientation (on the x-axis preferably).
– Coldchain9
Nov 20 '18 at 21:01