r - ggplot: how to choose the "proper" colors relating on a column -

suppose have simple dataframe plot, in have color points related measure contained in column. so, if have:

dataframe # x1            x2     pop # 1  -0.11092652 -1.955598e-09  448053 # 2  -0.09999865 -2.310067e-10  418231 # 3  -0.05944755 -3.475013e-09  448473 # 4   0.51378848  1.631781e-09  119548 # 5   0.09438223 -9.606475e-10  323288 # 6   0.19349045  6.074025e-10  203153 # 7   0.06685609  3.210156e-10  208339 # 8  -0.10915456 -1.407190e-09  429178 # 9  -0.10348100 -1.401948e-09 1218038 # 10 -0.08607617 -7.356602e-10  383018 # 11  1.00343465 -2.423237e-08  209550 # 12 -0.05839148  1.503955e-09  287042 # 13 -0.09960163  2.167945e-10  973129 # 14 -0.05793417  2.510107e-09  187249 # 15  0.02191610  2.479708e-09  915225 # 16  0.48877872  1.338346e-08  462999 # 17 -0.10289556  1.472368e-09 1108776 # 18 -0.10316414  2.933469e-10  402422 # 19 -0.09545279 -2.926035e-10  274035 # 20 -0.06111044  3.464014e-09  230749 

and use ggplot in following way:

ggplot(dataframe) +   ggtitle("somehow useful spatialization")+  # electricity / gas   geom_point(aes(dataframe$x1, dataframe$x2), color = dataframe$pop, size=2 ) +   theme_classic(base_size = 16) +   guides(colour = guide_legend(override.aes = list(size=4)))+   xlab("x")+ylab("y") 

i obtain like:

that possible representaion. neverthless, suppose want points colored such represent column pop, i.e., having colors (for example) light orange, passing dark red , black. how can "scale" column pop obtain such graphics?

edit:

> dput(dataframe) structure(list(x1 = c(-0.110926520419347, -0.0999986452719714,  -0.0594475526112884, 0.513788479303472, 0.0943822277852107, 0.193490454204271,  0.0668560854540437, -0.109154563987586, -0.103480996064617, -0.0860761723229372,  1.00343465471568, -0.0583914756527933, -0.0996016272609995, -0.0579341671474729,  0.0219161022704227, 0.488778719096658, -0.102895564162661, -0.103164140322136,  -0.0954527927249849, -0.0611104428640883), x2 = c(-1.9555978205951e-09,  -2.31006712207053e-10, -3.47501251356368e-09, 1.63178106438806e-09,  -9.60647459243156e-10, 6.07402512804044e-10, 3.21015629676789e-10,  -1.40718981687972e-09, -1.40194842954735e-09, -7.35660154466167e-10,  -2.423237202138e-08, 1.50395541775022e-09, 2.16794489937917e-10,  2.51010717100061e-09, 2.47970820013341e-09, 1.33834570208731e-08,  1.47236816671351e-09, 2.93346922578509e-10, -2.92603459149485e-10,  3.46401369936372e-09), pop = c(448053l, 418231l, 448473l, 119548l,  323288l, 203153l, 208339l, 429178l, 1218038l, 383018l, 209550l,  287042l, 973129l, 187249l, 915225l, 462999l, 1108776l, 402422l,  274035l, 230749l)), .names = c("x1", "x2", "pop"), row.names = c(na,  20l), class = "data.frame") 

with ggplot can add aesthetics (aes) in inital ggplot call. since you're telling ggplot data (in dataframe), can refer variables directly name (without dataframe$). color scale needs called aesthetic, inside aes() call, , not static value. once added aesthetic, can customize how reacts adding scale. taking account gives following code:

ggplot(dataframe, aes(x = x1, y = x2, color = pop)) +   ggtitle("somehow useful spatialization")+  # electricity / gas   geom_point(size=2) +   theme_classic(base_size = 16) +   guides(colour = guide_legend(override.aes = list(size=4))) +   xlab("x")+ylab("y") +   scale_color_gradient2(low = "green", mid = "red", high = "black", midpoint = mean(dataframe$pop)) 

this code gives following graph. colors further adjusted playing around scale_color_gradient2 part. (why green low gives better orange choosing orange low color beyond me, ended there coincidence)