Replicating a specific plot - ggplot axis not as they should be










0















I have some data and I am trying to re-create a plot. However I cannot seem to get the axis aligned.



I am trying to plot something very similar to the following:



enter image description here



A bar and a line plot with two different axis. However my attempt does not seem to work:



ggplot(df, aes(x = years)) +
geom_col(aes( y = IPOs_sum, fill="redfill")) +
#geom_text(aes(y = IPOs_sum, label = IPOs_sum), fontface = "bold", vjust = 1.4, color = "black", size = 4) +
geom_line(aes(y = returns_mean, group = 1, color = 'blackline')) +
#geom_text(aes(y = returns_mean, label = round(returns_mean, 2)), vjust = 1.4, color = "black", size = 3) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / 20)) +
scale_fill_manual('', labels = 'IPOs_sum', values = "#C00000") +
scale_color_manual('', labels = 'returns', values = 'black') +
theme_minimal()


The problem I am having is that the returns line plot uses the same scale as the bar plot which makes the line plot seem very small. I have tried scale_y_continuous



https://site.warrington.ufl.edu/ritter/files/2018/03/UnitedStates1980-2017.pdf



Data:



df <- structure(list(years = c(1980, 1981, 1982, 1983, 1984, 1985, 
1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,
1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
returns_mean = c(49.525, 16.7583333333333, 15.2416666666667,
23.5916666666667, 11.6833333333333, 13.2, 6.39166666666667,
5.77272727272727, 4.65833333333333, 8.61666666666667, 9.56363636363636,
14.25, 10.6416666666667, 13.0916666666667, 9.86666666666667,
20.5166666666667, 17.3083333333333, 13.7916666666667, 39.6916666666667,
75.8416666666667, 49.0916666666667, 13.3363636363636, 8.48,
14.9666666666667, 13.3166666666667, 10.35, 11.3333333333333,
17.4083333333333, 4.47777777777778, 13.0888888888889, 7.99166666666667,
14.6272727272727, 16.6583333333333, 21.1666666666667, 15.8666666666667,
18.4583333333333, 11.0181818181818, 12.3583333333333), IPOs_mean = c(19.8333333333333,
37.5, 18.5, 73.5833333333333, 46, 42.25, 79.4166666666667,
52.5, 18.9166666666667, 17, 14.3333333333333, 30.5833333333333,
42.4166666666667, 52.25, 47.3333333333333, 47.1666666666667,
70.4166666666667, 51, 32.6666666666667, 45.3333333333333,
35.3333333333333, 11, 13.3333333333333, 11, 25.25, 23.3333333333333,
21.25, 20.75, 4.5, 6.33333333333333, 16.4166666666667, 15,
14.9166666666667, 21, 24.3333333333333, 14.3333333333333,
8.5, 16.0833333333333), returns_sd = c(30.9637067607164,
15.6027653920319, 18.6855538917749, 15.2870984424082, 2.74684391896437,
7.93702486051062, 4.59277264907165, 3.22275996906096, 4.27263136578371,
4.64872090586282, 4.9828250475554, 6.13299570875737, 6.54404077745316,
3.15204358914638, 3.6317622402488, 5.84587396581918, 6.69225581390818,
5.55361607559899, 47.3886725692838, 27.7436137887732, 31.6808935346135,
5.95605116285492, 5.27863618750146, 10.4857045542968, 8.05298739975458,
5.26402887530074, 5.75141616289309, 13.283992987689, 15.0286208430596,
9.68948456374802, 5.44951346174105, 8.37568993087625, 7.3368879126251,
5.83022427969243, 7.73672861724965, 14.1409436700239, 15.1387461952315,
12.0595837658711), IPOs_sd = c(9.44682085372768, 12.6383255507711,
8.74382899275514, 26.6473888652028, 11.7008158223729, 9.66836453218809,
24.9743808125386, 23.1025382312696, 5.31649804995276, 6.66060330327789,
6.9325757161827, 15.4358398383487, 11.7508864913815, 16.7610207977264,
12.6371266392709, 20.0264975984471, 19.965690268027, 14.709304414677,
18.7778076623993, 14.2148023574233, 18.6953243222778, 4.26401432711221,
5.39921430872263, 7.92005509622709, 8.48662048596067, 8.15010689649175,
7.9444091261488, 8.1700673191841, 4.07876986803174, 4.05268336096498,
5.07145905728292, 7.54381143117263, 7.06410044885512, 7.90856842579329,
7.77330318617783, 7.15202874375622, 6.18649555667239, 6.94731253904353
), returns_min = c(12.7, 2.2, -0.9, 2.5, 7.2, 3.6, 1, 0.5,
-0.6, 0.6, 0.6, 6.4, 3.2, 8.9, 6.5, 9.2, 8.9, 6, 9.3, 37.1,
15.8, 5.7, 1.9, -3.3, 0.5, 4.5, 0.4, 5.2, -19.9, 0.3, -3.5,
1.8, 2.4, 13.6, 5.2, -6, -4.3, -4.9), IPOs_min = c(8, 20,
11, 24, 28, 26, 37, 7, 11, 8, 4, 4, 22, 22, 26, 18, 29, 33,
6, 22, 9, 4, 6, 1, 11, 13, 10, 5, 0, 1, 8, 3, 6, 10, 13,
2, 0, 7), returns_sum = c(594.3, 201.1, 182.9, 283.1, 140.2,
158.4, 76.7, 63.5, 55.9, 103.4, 105.2, 171, 127.7, 157.1,
118.4, 246.2, 207.7, 165.5, 476.3, 910.1, 589.1, 146.7, 84.8,
134.7, 159.8, 124.2, 136, 208.9, 40.3, 117.8, 95.9, 160.9,
199.9, 254, 190.4, 221.5, 121.2, 148.3), IPOs_sum = c(238L,
450L, 222L, 883L, 552L, 507L, 953L, 630L, 227L, 204L, 172L,
367L, 509L, 627L, 568L, 566L, 845L, 612L, 392L, 544L, 424L,
132L, 160L, 132L, 303L, 280L, 255L, 249L, 54L, 76L, 197L,
180L, 179L, 252L, 292L, 172L, 102L, 193L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -38L))









share|improve this question
























  • Here the double y-axis issue, something to read (and it polarizes the readers).

    – s_t
    Nov 12 '18 at 14:02
















0















I have some data and I am trying to re-create a plot. However I cannot seem to get the axis aligned.



I am trying to plot something very similar to the following:



enter image description here



A bar and a line plot with two different axis. However my attempt does not seem to work:



ggplot(df, aes(x = years)) +
geom_col(aes( y = IPOs_sum, fill="redfill")) +
#geom_text(aes(y = IPOs_sum, label = IPOs_sum), fontface = "bold", vjust = 1.4, color = "black", size = 4) +
geom_line(aes(y = returns_mean, group = 1, color = 'blackline')) +
#geom_text(aes(y = returns_mean, label = round(returns_mean, 2)), vjust = 1.4, color = "black", size = 3) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / 20)) +
scale_fill_manual('', labels = 'IPOs_sum', values = "#C00000") +
scale_color_manual('', labels = 'returns', values = 'black') +
theme_minimal()


The problem I am having is that the returns line plot uses the same scale as the bar plot which makes the line plot seem very small. I have tried scale_y_continuous



https://site.warrington.ufl.edu/ritter/files/2018/03/UnitedStates1980-2017.pdf



Data:



df <- structure(list(years = c(1980, 1981, 1982, 1983, 1984, 1985, 
1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,
1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
returns_mean = c(49.525, 16.7583333333333, 15.2416666666667,
23.5916666666667, 11.6833333333333, 13.2, 6.39166666666667,
5.77272727272727, 4.65833333333333, 8.61666666666667, 9.56363636363636,
14.25, 10.6416666666667, 13.0916666666667, 9.86666666666667,
20.5166666666667, 17.3083333333333, 13.7916666666667, 39.6916666666667,
75.8416666666667, 49.0916666666667, 13.3363636363636, 8.48,
14.9666666666667, 13.3166666666667, 10.35, 11.3333333333333,
17.4083333333333, 4.47777777777778, 13.0888888888889, 7.99166666666667,
14.6272727272727, 16.6583333333333, 21.1666666666667, 15.8666666666667,
18.4583333333333, 11.0181818181818, 12.3583333333333), IPOs_mean = c(19.8333333333333,
37.5, 18.5, 73.5833333333333, 46, 42.25, 79.4166666666667,
52.5, 18.9166666666667, 17, 14.3333333333333, 30.5833333333333,
42.4166666666667, 52.25, 47.3333333333333, 47.1666666666667,
70.4166666666667, 51, 32.6666666666667, 45.3333333333333,
35.3333333333333, 11, 13.3333333333333, 11, 25.25, 23.3333333333333,
21.25, 20.75, 4.5, 6.33333333333333, 16.4166666666667, 15,
14.9166666666667, 21, 24.3333333333333, 14.3333333333333,
8.5, 16.0833333333333), returns_sd = c(30.9637067607164,
15.6027653920319, 18.6855538917749, 15.2870984424082, 2.74684391896437,
7.93702486051062, 4.59277264907165, 3.22275996906096, 4.27263136578371,
4.64872090586282, 4.9828250475554, 6.13299570875737, 6.54404077745316,
3.15204358914638, 3.6317622402488, 5.84587396581918, 6.69225581390818,
5.55361607559899, 47.3886725692838, 27.7436137887732, 31.6808935346135,
5.95605116285492, 5.27863618750146, 10.4857045542968, 8.05298739975458,
5.26402887530074, 5.75141616289309, 13.283992987689, 15.0286208430596,
9.68948456374802, 5.44951346174105, 8.37568993087625, 7.3368879126251,
5.83022427969243, 7.73672861724965, 14.1409436700239, 15.1387461952315,
12.0595837658711), IPOs_sd = c(9.44682085372768, 12.6383255507711,
8.74382899275514, 26.6473888652028, 11.7008158223729, 9.66836453218809,
24.9743808125386, 23.1025382312696, 5.31649804995276, 6.66060330327789,
6.9325757161827, 15.4358398383487, 11.7508864913815, 16.7610207977264,
12.6371266392709, 20.0264975984471, 19.965690268027, 14.709304414677,
18.7778076623993, 14.2148023574233, 18.6953243222778, 4.26401432711221,
5.39921430872263, 7.92005509622709, 8.48662048596067, 8.15010689649175,
7.9444091261488, 8.1700673191841, 4.07876986803174, 4.05268336096498,
5.07145905728292, 7.54381143117263, 7.06410044885512, 7.90856842579329,
7.77330318617783, 7.15202874375622, 6.18649555667239, 6.94731253904353
), returns_min = c(12.7, 2.2, -0.9, 2.5, 7.2, 3.6, 1, 0.5,
-0.6, 0.6, 0.6, 6.4, 3.2, 8.9, 6.5, 9.2, 8.9, 6, 9.3, 37.1,
15.8, 5.7, 1.9, -3.3, 0.5, 4.5, 0.4, 5.2, -19.9, 0.3, -3.5,
1.8, 2.4, 13.6, 5.2, -6, -4.3, -4.9), IPOs_min = c(8, 20,
11, 24, 28, 26, 37, 7, 11, 8, 4, 4, 22, 22, 26, 18, 29, 33,
6, 22, 9, 4, 6, 1, 11, 13, 10, 5, 0, 1, 8, 3, 6, 10, 13,
2, 0, 7), returns_sum = c(594.3, 201.1, 182.9, 283.1, 140.2,
158.4, 76.7, 63.5, 55.9, 103.4, 105.2, 171, 127.7, 157.1,
118.4, 246.2, 207.7, 165.5, 476.3, 910.1, 589.1, 146.7, 84.8,
134.7, 159.8, 124.2, 136, 208.9, 40.3, 117.8, 95.9, 160.9,
199.9, 254, 190.4, 221.5, 121.2, 148.3), IPOs_sum = c(238L,
450L, 222L, 883L, 552L, 507L, 953L, 630L, 227L, 204L, 172L,
367L, 509L, 627L, 568L, 566L, 845L, 612L, 392L, 544L, 424L,
132L, 160L, 132L, 303L, 280L, 255L, 249L, 54L, 76L, 197L,
180L, 179L, 252L, 292L, 172L, 102L, 193L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -38L))









share|improve this question
























  • Here the double y-axis issue, something to read (and it polarizes the readers).

    – s_t
    Nov 12 '18 at 14:02














0












0








0








I have some data and I am trying to re-create a plot. However I cannot seem to get the axis aligned.



I am trying to plot something very similar to the following:



enter image description here



A bar and a line plot with two different axis. However my attempt does not seem to work:



ggplot(df, aes(x = years)) +
geom_col(aes( y = IPOs_sum, fill="redfill")) +
#geom_text(aes(y = IPOs_sum, label = IPOs_sum), fontface = "bold", vjust = 1.4, color = "black", size = 4) +
geom_line(aes(y = returns_mean, group = 1, color = 'blackline')) +
#geom_text(aes(y = returns_mean, label = round(returns_mean, 2)), vjust = 1.4, color = "black", size = 3) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / 20)) +
scale_fill_manual('', labels = 'IPOs_sum', values = "#C00000") +
scale_color_manual('', labels = 'returns', values = 'black') +
theme_minimal()


The problem I am having is that the returns line plot uses the same scale as the bar plot which makes the line plot seem very small. I have tried scale_y_continuous



https://site.warrington.ufl.edu/ritter/files/2018/03/UnitedStates1980-2017.pdf



Data:



df <- structure(list(years = c(1980, 1981, 1982, 1983, 1984, 1985, 
1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,
1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
returns_mean = c(49.525, 16.7583333333333, 15.2416666666667,
23.5916666666667, 11.6833333333333, 13.2, 6.39166666666667,
5.77272727272727, 4.65833333333333, 8.61666666666667, 9.56363636363636,
14.25, 10.6416666666667, 13.0916666666667, 9.86666666666667,
20.5166666666667, 17.3083333333333, 13.7916666666667, 39.6916666666667,
75.8416666666667, 49.0916666666667, 13.3363636363636, 8.48,
14.9666666666667, 13.3166666666667, 10.35, 11.3333333333333,
17.4083333333333, 4.47777777777778, 13.0888888888889, 7.99166666666667,
14.6272727272727, 16.6583333333333, 21.1666666666667, 15.8666666666667,
18.4583333333333, 11.0181818181818, 12.3583333333333), IPOs_mean = c(19.8333333333333,
37.5, 18.5, 73.5833333333333, 46, 42.25, 79.4166666666667,
52.5, 18.9166666666667, 17, 14.3333333333333, 30.5833333333333,
42.4166666666667, 52.25, 47.3333333333333, 47.1666666666667,
70.4166666666667, 51, 32.6666666666667, 45.3333333333333,
35.3333333333333, 11, 13.3333333333333, 11, 25.25, 23.3333333333333,
21.25, 20.75, 4.5, 6.33333333333333, 16.4166666666667, 15,
14.9166666666667, 21, 24.3333333333333, 14.3333333333333,
8.5, 16.0833333333333), returns_sd = c(30.9637067607164,
15.6027653920319, 18.6855538917749, 15.2870984424082, 2.74684391896437,
7.93702486051062, 4.59277264907165, 3.22275996906096, 4.27263136578371,
4.64872090586282, 4.9828250475554, 6.13299570875737, 6.54404077745316,
3.15204358914638, 3.6317622402488, 5.84587396581918, 6.69225581390818,
5.55361607559899, 47.3886725692838, 27.7436137887732, 31.6808935346135,
5.95605116285492, 5.27863618750146, 10.4857045542968, 8.05298739975458,
5.26402887530074, 5.75141616289309, 13.283992987689, 15.0286208430596,
9.68948456374802, 5.44951346174105, 8.37568993087625, 7.3368879126251,
5.83022427969243, 7.73672861724965, 14.1409436700239, 15.1387461952315,
12.0595837658711), IPOs_sd = c(9.44682085372768, 12.6383255507711,
8.74382899275514, 26.6473888652028, 11.7008158223729, 9.66836453218809,
24.9743808125386, 23.1025382312696, 5.31649804995276, 6.66060330327789,
6.9325757161827, 15.4358398383487, 11.7508864913815, 16.7610207977264,
12.6371266392709, 20.0264975984471, 19.965690268027, 14.709304414677,
18.7778076623993, 14.2148023574233, 18.6953243222778, 4.26401432711221,
5.39921430872263, 7.92005509622709, 8.48662048596067, 8.15010689649175,
7.9444091261488, 8.1700673191841, 4.07876986803174, 4.05268336096498,
5.07145905728292, 7.54381143117263, 7.06410044885512, 7.90856842579329,
7.77330318617783, 7.15202874375622, 6.18649555667239, 6.94731253904353
), returns_min = c(12.7, 2.2, -0.9, 2.5, 7.2, 3.6, 1, 0.5,
-0.6, 0.6, 0.6, 6.4, 3.2, 8.9, 6.5, 9.2, 8.9, 6, 9.3, 37.1,
15.8, 5.7, 1.9, -3.3, 0.5, 4.5, 0.4, 5.2, -19.9, 0.3, -3.5,
1.8, 2.4, 13.6, 5.2, -6, -4.3, -4.9), IPOs_min = c(8, 20,
11, 24, 28, 26, 37, 7, 11, 8, 4, 4, 22, 22, 26, 18, 29, 33,
6, 22, 9, 4, 6, 1, 11, 13, 10, 5, 0, 1, 8, 3, 6, 10, 13,
2, 0, 7), returns_sum = c(594.3, 201.1, 182.9, 283.1, 140.2,
158.4, 76.7, 63.5, 55.9, 103.4, 105.2, 171, 127.7, 157.1,
118.4, 246.2, 207.7, 165.5, 476.3, 910.1, 589.1, 146.7, 84.8,
134.7, 159.8, 124.2, 136, 208.9, 40.3, 117.8, 95.9, 160.9,
199.9, 254, 190.4, 221.5, 121.2, 148.3), IPOs_sum = c(238L,
450L, 222L, 883L, 552L, 507L, 953L, 630L, 227L, 204L, 172L,
367L, 509L, 627L, 568L, 566L, 845L, 612L, 392L, 544L, 424L,
132L, 160L, 132L, 303L, 280L, 255L, 249L, 54L, 76L, 197L,
180L, 179L, 252L, 292L, 172L, 102L, 193L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -38L))









share|improve this question
















I have some data and I am trying to re-create a plot. However I cannot seem to get the axis aligned.



I am trying to plot something very similar to the following:



enter image description here



A bar and a line plot with two different axis. However my attempt does not seem to work:



ggplot(df, aes(x = years)) +
geom_col(aes( y = IPOs_sum, fill="redfill")) +
#geom_text(aes(y = IPOs_sum, label = IPOs_sum), fontface = "bold", vjust = 1.4, color = "black", size = 4) +
geom_line(aes(y = returns_mean, group = 1, color = 'blackline')) +
#geom_text(aes(y = returns_mean, label = round(returns_mean, 2)), vjust = 1.4, color = "black", size = 3) +
scale_y_continuous(sec.axis = sec_axis(trans = ~ . / 20)) +
scale_fill_manual('', labels = 'IPOs_sum', values = "#C00000") +
scale_color_manual('', labels = 'returns', values = 'black') +
theme_minimal()


The problem I am having is that the returns line plot uses the same scale as the bar plot which makes the line plot seem very small. I have tried scale_y_continuous



https://site.warrington.ufl.edu/ritter/files/2018/03/UnitedStates1980-2017.pdf



Data:



df <- structure(list(years = c(1980, 1981, 1982, 1983, 1984, 1985, 
1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996,
1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017),
returns_mean = c(49.525, 16.7583333333333, 15.2416666666667,
23.5916666666667, 11.6833333333333, 13.2, 6.39166666666667,
5.77272727272727, 4.65833333333333, 8.61666666666667, 9.56363636363636,
14.25, 10.6416666666667, 13.0916666666667, 9.86666666666667,
20.5166666666667, 17.3083333333333, 13.7916666666667, 39.6916666666667,
75.8416666666667, 49.0916666666667, 13.3363636363636, 8.48,
14.9666666666667, 13.3166666666667, 10.35, 11.3333333333333,
17.4083333333333, 4.47777777777778, 13.0888888888889, 7.99166666666667,
14.6272727272727, 16.6583333333333, 21.1666666666667, 15.8666666666667,
18.4583333333333, 11.0181818181818, 12.3583333333333), IPOs_mean = c(19.8333333333333,
37.5, 18.5, 73.5833333333333, 46, 42.25, 79.4166666666667,
52.5, 18.9166666666667, 17, 14.3333333333333, 30.5833333333333,
42.4166666666667, 52.25, 47.3333333333333, 47.1666666666667,
70.4166666666667, 51, 32.6666666666667, 45.3333333333333,
35.3333333333333, 11, 13.3333333333333, 11, 25.25, 23.3333333333333,
21.25, 20.75, 4.5, 6.33333333333333, 16.4166666666667, 15,
14.9166666666667, 21, 24.3333333333333, 14.3333333333333,
8.5, 16.0833333333333), returns_sd = c(30.9637067607164,
15.6027653920319, 18.6855538917749, 15.2870984424082, 2.74684391896437,
7.93702486051062, 4.59277264907165, 3.22275996906096, 4.27263136578371,
4.64872090586282, 4.9828250475554, 6.13299570875737, 6.54404077745316,
3.15204358914638, 3.6317622402488, 5.84587396581918, 6.69225581390818,
5.55361607559899, 47.3886725692838, 27.7436137887732, 31.6808935346135,
5.95605116285492, 5.27863618750146, 10.4857045542968, 8.05298739975458,
5.26402887530074, 5.75141616289309, 13.283992987689, 15.0286208430596,
9.68948456374802, 5.44951346174105, 8.37568993087625, 7.3368879126251,
5.83022427969243, 7.73672861724965, 14.1409436700239, 15.1387461952315,
12.0595837658711), IPOs_sd = c(9.44682085372768, 12.6383255507711,
8.74382899275514, 26.6473888652028, 11.7008158223729, 9.66836453218809,
24.9743808125386, 23.1025382312696, 5.31649804995276, 6.66060330327789,
6.9325757161827, 15.4358398383487, 11.7508864913815, 16.7610207977264,
12.6371266392709, 20.0264975984471, 19.965690268027, 14.709304414677,
18.7778076623993, 14.2148023574233, 18.6953243222778, 4.26401432711221,
5.39921430872263, 7.92005509622709, 8.48662048596067, 8.15010689649175,
7.9444091261488, 8.1700673191841, 4.07876986803174, 4.05268336096498,
5.07145905728292, 7.54381143117263, 7.06410044885512, 7.90856842579329,
7.77330318617783, 7.15202874375622, 6.18649555667239, 6.94731253904353
), returns_min = c(12.7, 2.2, -0.9, 2.5, 7.2, 3.6, 1, 0.5,
-0.6, 0.6, 0.6, 6.4, 3.2, 8.9, 6.5, 9.2, 8.9, 6, 9.3, 37.1,
15.8, 5.7, 1.9, -3.3, 0.5, 4.5, 0.4, 5.2, -19.9, 0.3, -3.5,
1.8, 2.4, 13.6, 5.2, -6, -4.3, -4.9), IPOs_min = c(8, 20,
11, 24, 28, 26, 37, 7, 11, 8, 4, 4, 22, 22, 26, 18, 29, 33,
6, 22, 9, 4, 6, 1, 11, 13, 10, 5, 0, 1, 8, 3, 6, 10, 13,
2, 0, 7), returns_sum = c(594.3, 201.1, 182.9, 283.1, 140.2,
158.4, 76.7, 63.5, 55.9, 103.4, 105.2, 171, 127.7, 157.1,
118.4, 246.2, 207.7, 165.5, 476.3, 910.1, 589.1, 146.7, 84.8,
134.7, 159.8, 124.2, 136, 208.9, 40.3, 117.8, 95.9, 160.9,
199.9, 254, 190.4, 221.5, 121.2, 148.3), IPOs_sum = c(238L,
450L, 222L, 883L, 552L, 507L, 953L, 630L, 227L, 204L, 172L,
367L, 509L, 627L, 568L, 566L, 845L, 612L, 392L, 544L, 424L,
132L, 160L, 132L, 303L, 280L, 255L, 249L, 54L, 76L, 197L,
180L, 179L, 252L, 292L, 172L, 102L, 193L)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -38L))






r ggplot2






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share|improve this question








edited Nov 12 '18 at 14:00









hrbrmstr

60.4k687148




60.4k687148










asked Nov 12 '18 at 13:49









user113156user113156

8261417




8261417












  • Here the double y-axis issue, something to read (and it polarizes the readers).

    – s_t
    Nov 12 '18 at 14:02


















  • Here the double y-axis issue, something to read (and it polarizes the readers).

    – s_t
    Nov 12 '18 at 14:02

















Here the double y-axis issue, something to read (and it polarizes the readers).

– s_t
Nov 12 '18 at 14:02






Here the double y-axis issue, something to read (and it polarizes the readers).

– s_t
Nov 12 '18 at 14:02













2 Answers
2






active

oldest

votes


















1














Consider showing the data in a different way, possibly with a connected scatterplot:



dplyr::arrange(df, years) %>% 
dplyr::mutate(col = ifelse(years >= 2000, "#08519c", "#74c476")) %>%
ggplot() +
geom_path(aes(IPOs_sum, returns_mean)) +
geom_label(aes(IPOs_sum, returns_mean, label=years, fill=I(col)), color = "white") +
ggalt::geom_encircle(data = dplyr::filter(df, years > 2000), aes(IPOs_sum, returns_mean)) +
labs(
x = "Number of Offerings (IPOs)", y = "Average First-day Returns",
title = "IPO Volume (Both Annual Count and Day-1 Returns)nHas Been Very Low in the U.S. Since 2000"
) +
hrbrthemes::theme_ipsum_rc(grid="XY")


enter image description here






share|improve this answer























  • I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

    – user113156
    Nov 12 '18 at 14:21






  • 1





    aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

    – hrbrmstr
    Nov 12 '18 at 14:35







  • 1





    Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

    – user113156
    Nov 12 '18 at 14:39


















0














You are right, the geom_* will all use the same y axis value. The secondary axis is just for display as far as I know.



What you can do is transform the value of returns to make it fits the left axis. If you don't want to modify the data, you can directly scale the value of returns in the geom_line's aes.



geom_line(aes(y = returns_mean * 20, group = 1, color = 'blackline'))





share|improve this answer























  • That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

    – user113156
    Nov 12 '18 at 14:01










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2 Answers
2






active

oldest

votes








2 Answers
2






active

oldest

votes









active

oldest

votes






active

oldest

votes









1














Consider showing the data in a different way, possibly with a connected scatterplot:



dplyr::arrange(df, years) %>% 
dplyr::mutate(col = ifelse(years >= 2000, "#08519c", "#74c476")) %>%
ggplot() +
geom_path(aes(IPOs_sum, returns_mean)) +
geom_label(aes(IPOs_sum, returns_mean, label=years, fill=I(col)), color = "white") +
ggalt::geom_encircle(data = dplyr::filter(df, years > 2000), aes(IPOs_sum, returns_mean)) +
labs(
x = "Number of Offerings (IPOs)", y = "Average First-day Returns",
title = "IPO Volume (Both Annual Count and Day-1 Returns)nHas Been Very Low in the U.S. Since 2000"
) +
hrbrthemes::theme_ipsum_rc(grid="XY")


enter image description here






share|improve this answer























  • I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

    – user113156
    Nov 12 '18 at 14:21






  • 1





    aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

    – hrbrmstr
    Nov 12 '18 at 14:35







  • 1





    Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

    – user113156
    Nov 12 '18 at 14:39















1














Consider showing the data in a different way, possibly with a connected scatterplot:



dplyr::arrange(df, years) %>% 
dplyr::mutate(col = ifelse(years >= 2000, "#08519c", "#74c476")) %>%
ggplot() +
geom_path(aes(IPOs_sum, returns_mean)) +
geom_label(aes(IPOs_sum, returns_mean, label=years, fill=I(col)), color = "white") +
ggalt::geom_encircle(data = dplyr::filter(df, years > 2000), aes(IPOs_sum, returns_mean)) +
labs(
x = "Number of Offerings (IPOs)", y = "Average First-day Returns",
title = "IPO Volume (Both Annual Count and Day-1 Returns)nHas Been Very Low in the U.S. Since 2000"
) +
hrbrthemes::theme_ipsum_rc(grid="XY")


enter image description here






share|improve this answer























  • I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

    – user113156
    Nov 12 '18 at 14:21






  • 1





    aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

    – hrbrmstr
    Nov 12 '18 at 14:35







  • 1





    Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

    – user113156
    Nov 12 '18 at 14:39













1












1








1







Consider showing the data in a different way, possibly with a connected scatterplot:



dplyr::arrange(df, years) %>% 
dplyr::mutate(col = ifelse(years >= 2000, "#08519c", "#74c476")) %>%
ggplot() +
geom_path(aes(IPOs_sum, returns_mean)) +
geom_label(aes(IPOs_sum, returns_mean, label=years, fill=I(col)), color = "white") +
ggalt::geom_encircle(data = dplyr::filter(df, years > 2000), aes(IPOs_sum, returns_mean)) +
labs(
x = "Number of Offerings (IPOs)", y = "Average First-day Returns",
title = "IPO Volume (Both Annual Count and Day-1 Returns)nHas Been Very Low in the U.S. Since 2000"
) +
hrbrthemes::theme_ipsum_rc(grid="XY")


enter image description here






share|improve this answer













Consider showing the data in a different way, possibly with a connected scatterplot:



dplyr::arrange(df, years) %>% 
dplyr::mutate(col = ifelse(years >= 2000, "#08519c", "#74c476")) %>%
ggplot() +
geom_path(aes(IPOs_sum, returns_mean)) +
geom_label(aes(IPOs_sum, returns_mean, label=years, fill=I(col)), color = "white") +
ggalt::geom_encircle(data = dplyr::filter(df, years > 2000), aes(IPOs_sum, returns_mean)) +
labs(
x = "Number of Offerings (IPOs)", y = "Average First-day Returns",
title = "IPO Volume (Both Annual Count and Day-1 Returns)nHas Been Very Low in the U.S. Since 2000"
) +
hrbrthemes::theme_ipsum_rc(grid="XY")


enter image description here







share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 12 '18 at 14:13









hrbrmstrhrbrmstr

60.4k687148




60.4k687148












  • I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

    – user113156
    Nov 12 '18 at 14:21






  • 1





    aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

    – hrbrmstr
    Nov 12 '18 at 14:35







  • 1





    Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

    – user113156
    Nov 12 '18 at 14:39

















  • I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

    – user113156
    Nov 12 '18 at 14:21






  • 1





    aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

    – hrbrmstr
    Nov 12 '18 at 14:35







  • 1





    Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

    – user113156
    Nov 12 '18 at 14:39
















I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

– user113156
Nov 12 '18 at 14:21





I actually really like this. Creative! I have not looked at the code yet but did you just set all firms after 2000 to be blue and all firms less than 2000 to be green?

– user113156
Nov 12 '18 at 14:21




1




1





aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

– hrbrmstr
Nov 12 '18 at 14:35






aye. you can do pretty much anything tho. this was just a quick hack. You have to do some data wrangling and use geom_segment() if you want arrows at each step (though the lines may not be necessary anyway)

– hrbrmstr
Nov 12 '18 at 14:35





1




1





Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

– user113156
Nov 12 '18 at 14:39





Thanks! I think I will spend the rest of the day trying to add funky arrows to this plot, rather than move on with more pressing things!

– user113156
Nov 12 '18 at 14:39













0














You are right, the geom_* will all use the same y axis value. The secondary axis is just for display as far as I know.



What you can do is transform the value of returns to make it fits the left axis. If you don't want to modify the data, you can directly scale the value of returns in the geom_line's aes.



geom_line(aes(y = returns_mean * 20, group = 1, color = 'blackline'))





share|improve this answer























  • That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

    – user113156
    Nov 12 '18 at 14:01















0














You are right, the geom_* will all use the same y axis value. The secondary axis is just for display as far as I know.



What you can do is transform the value of returns to make it fits the left axis. If you don't want to modify the data, you can directly scale the value of returns in the geom_line's aes.



geom_line(aes(y = returns_mean * 20, group = 1, color = 'blackline'))





share|improve this answer























  • That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

    – user113156
    Nov 12 '18 at 14:01













0












0








0







You are right, the geom_* will all use the same y axis value. The secondary axis is just for display as far as I know.



What you can do is transform the value of returns to make it fits the left axis. If you don't want to modify the data, you can directly scale the value of returns in the geom_line's aes.



geom_line(aes(y = returns_mean * 20, group = 1, color = 'blackline'))





share|improve this answer













You are right, the geom_* will all use the same y axis value. The secondary axis is just for display as far as I know.



What you can do is transform the value of returns to make it fits the left axis. If you don't want to modify the data, you can directly scale the value of returns in the geom_line's aes.



geom_line(aes(y = returns_mean * 20, group = 1, color = 'blackline'))






share|improve this answer












share|improve this answer



share|improve this answer










answered Nov 12 '18 at 13:58









SlagtSlagt

1576




1576












  • That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

    – user113156
    Nov 12 '18 at 14:01

















  • That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

    – user113156
    Nov 12 '18 at 14:01
















That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

– user113156
Nov 12 '18 at 14:01





That certainly seems to be one way to solve the problem, slightly dangerous modifying the axis this way but I can maybe adjust it so much as to try and replicate the original graph.

– user113156
Nov 12 '18 at 14:01

















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