-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathundp_preprocess_old.R
253 lines (215 loc) · 11.7 KB
/
undp_preprocess_old.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
#
# Process general parameters
#
for (ncountry in 1:Ncountry) {
N <- dim(data[[ncountry]])[1]
# Age
ind <- grep(Country[[ncountry]]$var_age,names(data[[ncountry]]))
if (length(ind) > 0) {
data[[ncountry]]$Age <- data[[ncountry]][,ind[1]]
data[[ncountry]]$Age[data[[ncountry]]$Age==""]=NA
data[[ncountry]]$Age <- factor(data[[ncountry]]$Age) # Eventually remove level ""
# Levels of Age ordered alphabetically, therefore under 17 is the last onstead of first
# Eventually move it as the first level
indlev <- which(levels(data[[ncountry]]$Age) == "under 17")
if (length(indlev) >0) {
levnew <- levels(data[[ncountry]]$Age)
levnew <- c(levnew[indlev],levnew[-indlev])
data[[ncountry]]$Age <- factor(data[[ncountry]]$Age,levels=levnew)
}
# Change selected levels
levels(data[[ncountry]]$Age)[levels(data[[ncountry]]$Age)=="60 and over"] <- "60 over"
levels(data[[ncountry]]$Age)[levels(data[[ncountry]]$Age)=="65 and over"] <- "65 over"
} else {
data[[ncountry]]$Age <- factor(rep("",N))
}
# Gender
ind <- grep(Country[[ncountry]]$var_gender,names(data[[ncountry]]))
if (length(ind) > 0) {
data[[ncountry]]$Gender <- data[[ncountry]][,ind[1]]
data[[ncountry]]$Gender[data[[ncountry]]$Gender==""]=NA
data[[ncountry]]$Gender <- factor(data[[ncountry]]$Gender) # Eventually remove level ""
# Gender already sorted alphabetically as female male; change names as Female Male eventually
levels(data[[ncountry]]$Gender)[levels(data[[ncountry]]$Gender)=="female"] <- "Female"
levels(data[[ncountry]]$Gender)[levels(data[[ncountry]]$Gender)=="woman"] <- "Female"
levels(data[[ncountry]]$Gender)[levels(data[[ncountry]]$Gender)=="male"] <- "Male"
levels(data[[ncountry]]$Gender)[levels(data[[ncountry]]$Gender)=="man"] <- "Male"
# Restore the alphabetical order Frmale, Male if changed by substitutions
data[[ncountry]]$Gender <- factor(data[[ncountry]]$Gender,levels = c("Female","Male"))
} else {
data[[ncountry]]$Gender <- factor(rep("",N))
}
# Employment # Include most "Others" as new classes
ind <- grep(Country[[ncountry]]$var_employment,names(data[[ncountry]]))
if (length(ind) > 0) {
data[[ncountry]]$Employment <- data[[ncountry]][,ind[1]]
data[[ncountry]]$Employment[data[[ncountry]]$Employment==""]=NA
data[[ncountry]]$Employment <- factor(data[[ncountry]]$Employment) # Eventually remove level ""
# Simplify legends
levels(data[[ncountry]]$Employment)[grep("non-salaried",levels(data[[ncountry]]$Employment))] <- "Non salaried"
levels(data[[ncountry]]$Employment)[grep("other",levels(data[[ncountry]]$Employment))] <- "Other"
levels(data[[ncountry]]$Employment)[grep("retired",levels(data[[ncountry]]$Employment))] <- "Retired"
levels(data[[ncountry]]$Employment)[grep("professional",levels(data[[ncountry]]$Employment))] <- "Salaried"
levels(data[[ncountry]]$Employment)[grep("unemployed",levels(data[[ncountry]]$Employment))] <- "Unemployed"
levels(data[[ncountry]]$Employment)[grep("student",levels(data[[ncountry]]$Employment))] <- "Student"
levels(data[[ncountry]]$Employment)[grep("self",levels(data[[ncountry]]$Employment))] <- "Self-employed"
} else {
data[[ncountry]]$Employment <- factor(rep("",N))
}
# Education
ind <- grep(Country[[ncountry]]$var_education,names(data[[ncountry]]))
if (length(ind) > 0) {
data[[ncountry]]$Education <- data[[ncountry]][,ind[1]]
data[[ncountry]]$Education[data[[ncountry]]$Education==""]=NA
data[[ncountry]]$Education <- factor(data[[ncountry]]$Education) # Eventually remove level ""
newlev <- levels(data[[ncountry]]$Education)
# Simplify legends
newlev[grep("primary",levels(data[[ncountry]]$Education))] <- "Primary"
newlev[grep("secondary",levels(data[[ncountry]]$Education))] <- "Secondary"
newlev[grep("technical/vocational",levels(data[[ncountry]]$Education))] <- "Technical"
newlev[grep("professional",levels(data[[ncountry]]$Education))] <- "Technical"
newlev[grep("no formal",levels(data[[ncountry]]$Education))] <- "No education"
newlev[grep("none",levels(data[[ncountry]]$Education))] <- "No education"
newlev[grep("masters",levels(data[[ncountry]]$Education))] <- "Post-grad master"
newlev[grep("university",levels(data[[ncountry]]$Education))] <- "University"
newlev[grep("PhD",levels(data[[ncountry]]$Education))] <- "PhD"
newlev[grep("postgraduate other",levels(data[[ncountry]]$Education))] <- "Post-grad other"
ind2 <- grep("incomplete",levels(data[[ncountry]]$Education))
newlev[ind2] <- paste(newlev[ind2],"\n(incomplete)")
ind2 <- grep("no degree",levels(data[[ncountry]]$Education))
newlev[ind2] <- paste(newlev[ind2],"\n(incomplete)")
levels(data[[ncountry]]$Education) <- newlev
} else {
data[[ncountry]]$Education <- factor(rep("",N))
}
# Live
ind <- grep(Country[[ncountry]]$var_live,names(data[[ncountry]]))
if (length(ind) > 0) {
data[[ncountry]]$Live <- data[[ncountry]][,ind[1]]
data[[ncountry]]$Live[data[[ncountry]]$Live==""]=NA
data[[ncountry]]$Live <- factor(data[[ncountry]]$Live) # Eventually remove level ""
# Simplify legends
levels(data[[ncountry]]$Live)[grep("rural area",levels(data[[ncountry]]$Live))] <- "Rural"
levels(data[[ncountry]]$Live)[grep("urban area",levels(data[[ncountry]]$Live))] <- "Urban"
} else {
data[[ncountry]]$Live <- factor(rep("",N))
}
# Marital
ind <- grep(Country[[ncountry]]$var_marital,names(data[[ncountry]]))
if (length(ind) > 0) {
data[[ncountry]]$Marital <- data[[ncountry]][,ind[1]]
data[[ncountry]]$Marital[data[[ncountry]]$Marital==""]=NA
data[[ncountry]]$Marital <- factor(data[[ncountry]]$Marital) # Eventually remove level ""
# Simplify legends
levels(data[[ncountry]]$Marital)[grep("divorced",levels(data[[ncountry]]$Marital))] <- "Divorced"
levels(data[[ncountry]]$Marital)[grep("married with children",levels(data[[ncountry]]$Marital))] <- "Married w/children"
levels(data[[ncountry]]$Marital)[grep("married without children",levels(data[[ncountry]]$Marital))] <- "Married w/o children"
levels(data[[ncountry]]$Marital)[grep("widowed",levels(data[[ncountry]]$Marital))] <- "Widowed"
levels(data[[ncountry]]$Marital)[grep("single",levels(data[[ncountry]]$Marital))] <- "Single"
levels(data[[ncountry]]$Marital)[grep("other",levels(data[[ncountry]]$Marital))] <- "Other"
# Put selected levels on 2 lines
levels(data[[ncountry]]$Marital)[levels(data[[ncountry]]$Marital)=="Married w/children"] <- "Married\nw/children"
levels(data[[ncountry]]$Marital)[levels(data[[ncountry]]$Marital)=="Married w/o children"] <- "Married\nw/o children"
} else {
data[[ncountry]]$Marital <- factor(rep("",N))
}
# Group
ind <- grep(Country[[ncountry]]$var_group$V,names(data[[ncountry]]))
if (length(ind) > 0) {
ind <- ind[1]
data[[ncountry]]$Group <- array(NA,N)
for (ngroups in 1:length(Country[[ncountry]]$var_group$VV))
data[[ncountry]]$Group[data[[ncountry]][,ind+ngroups-1]=="1"] <- Country[[ncountry]]$var_group$VV[ngroups]
data[[ncountry]]$Group <- factor(data[[ncountry]]$Group)
# Put selected levels on 2 lines
levels(data[[ncountry]]$Group)[levels(data[[ncountry]]$Group)=="internally displaced persons"] <- "internally\ndisplaced persons"
levels(data[[ncountry]]$Group)[levels(data[[ncountry]]$Group)=="resident/host"] <- "resident/\nhost"
} else {
data[[ncountry]]$Group <- factor(rep("",N))
}
# Feeling
# Different countries have different field names for the Feeling, all starting with Q1.Feeling
# First find the column matching "Q1."
ind <- grep("Q1",names(data[[ncountry]]))
data[[ncountry]]$Feeling <- data[[ncountry]][,ind[1]]
# Change levels so that the first letter is capital
levels(data[[ncountry]]$Feeling)[levels(data[[ncountry]]$Feeling)=="strongly negative"] <- "Strongly negative"
levels(data[[ncountry]]$Feeling)[levels(data[[ncountry]]$Feeling)=="negative"] <- "Negative"
levels(data[[ncountry]]$Feeling)[levels(data[[ncountry]]$Feeling)=="neutral"] <- "Neutral"
levels(data[[ncountry]]$Feeling)[levels(data[[ncountry]]$Feeling)=="positive"] <- "Positive"
levels(data[[ncountry]]$Feeling)[levels(data[[ncountry]]$Feeling)=="strongly positive"] <- "Strongly positive"
data[[ncountry]]$Feeling[data[[ncountry]]$Feeling==""] <- NA
Feeling_Nlev <- length(levels(data[[ncountry]]$Feeling)) # Numer of levels of Feeling
# Some missing data generate a new level. Discard them
if ("" %in% levels(data[[ncountry]]$Feeling))
Feeling_Nlev <- Feeling_Nlev-1
# Some countries have data only on 3 levels (strongly is lacking)
# For uniformity revert Feeling to 3 levels
if (Feeling_Nlev==3) {
Lev <- Lev3
clev <- clev3
} else {
if (Feeling_Nlev==5) {
Lev <- Lev5
clev <- clev5
} else {
print("Number of levels of Feeling different from 3 or 5")
}
}
if (myLevs == 3) {
if (Feeling_Nlev==5) {
# Some countries have 5 levels of Feeling (include "strongly")
# Convert "strongly negative" to "negative" and "strongly positive" to "positive"
data[[ncountry]]$Feeling[data[[ncountry]]$Feeling=="Strongly negative"] <- "Negative"
data[[ncountry]]$Feeling[data[[ncountry]]$Feeling=="Strongly positive"] <- "Positive"
# Drop former "strongly" levels
data[[ncountry]]$Feeling <- droplevels(data[[ncountry]]$Feeling)
}
# Set Nlev to 3
Feeling_Nlev <- 3
Lev <- Lev3
clev <- clev3
}
if (myLevs == 5) {
if (Feeling_Nlev==3) {
levels(data[[ncountry]]$Feeling) <- Lev5
# Some countries have only 3 levels of Feeling ("negative","neutral","positive")
# Convert "negative" to "strongly negative" and "positive" to "strongly positive"
data[[ncountry]]$Feeling[data[[ncountry]]$Feeling=="negative"] <- "strongly negative"
data[[ncountry]]$Feeling[data[[ncountry]]$Feeling=="positive"] <- "strongly positive"
}
# Set Nlev to 5
Feeling_Nlev <- 5
Lev <- Lev5
clev <- clev5
}
# Order the levels of Feeling in the right order (otherwise ordered alphabetically)
data[[ncountry]]$Feeling <- factor(data[[ncountry]]$Feeling,levels=Lev)
# Define numeric Feeling
data[[ncountry]]$Feeling_num <- rep(NA,N)
for (nlev in 1:Feeling_Nlev)
data[[ncountry]]$Feeling_num[data[[ncountry]]$Feeling==levels(data[[ncountry]]$Feeling)[nlev]] <- clev[nlev]
# Date
ind <- grep("Date",names(data[[ncountry]]))
# dum (the date and hour) is integer as coming from read.xlsx2
dum <- data[[ncountry]][,ind[1]]
# treated as character and then as numeric
data[[ncountry]]$timedate_num <- as.numeric(as.character(dum))
# Convert to day-time format
# from p. 31 of the xlsx pdf guide https://cran.r-project.org/web/packages/xlsx/xlsx.pdf
data[[ncountry]]$timedate <- as.POSIXct((data[[ncountry]]$timedate_num-25569)*86400, tz="GMT", origin="1970-01-01")
}
# Compute global data (all countries, needed by map plot)
timedate_num_all <- numeric(0)
Country_all <- character(0)
Feeling_all <- numeric(0)
for (ncountry in 1:Ncountry){
dumFeeling <- data[[ncountry]]$Feeling
if (length(levels(dumFeeling)) == 3) {
levels(dumFeeling)[levels(dumFeeling)=="Negative"] <- "Strongly negative"
levels(dumFeeling)[levels(dumFeeling)=="Positive"] <- "Strongly positive"
}
Feeling_all <- c(Feeling_all,as.character(dumFeeling))
timedate_num_all <- c(timedate_num_all,data[[ncountry]]$timedate_num)
Country_all <- c(Country_all,rep(Country[[ncountry]]$country,length(data[[ncountry]]$Feeling)))
}