library(foreign)
library(dplyr)
## Warning: package 'dplyr' was built under R version 3.6.3
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library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.2.1 --
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## v tibble 2.1.3 v purrr 0.3.2
## v tidyr 1.0.0 v stringr 1.4.0
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library (haven)
## Warning: package 'haven' was built under R version 3.6.3
library (expss)
## Warning: package 'expss' was built under R version 3.6.3
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## Use 'expss_output_viewer()' to display tables in the RStudio Viewer.
## To return to the console output, use 'expss_output_default()'.
##
## Attaching package: 'expss'
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## is.labelled, read_spss
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path = 'C:/Users/mitro/UNICEF/James Kaphuka - 2019 Tonga MICS6 Survey/SPSS'
setwd(path)
data = read.spss('mn.sav', to.data.frame = T, use.value.labels = FALSE)
## re-encoding from UTF-8
data %>% filter(data$MWM17 == 1, echo=FALSE)
## [1] HH1 HH2 LN MWM1 MWM2 MWM3
## [7] MWMINT MWM3A MWM4 MWM5 MWM6D MWM6M
## [13] MWM6Y MWM8 MWM9 MWM17 MWM7H MWM7M
## [19] MWM10H MWM10M MWM11 MWM12 MWM13 MWM14
## [25] MWM15 MWMHINT MWMFIN MWB3M MWB3Y MWB4
## [31] MWB5 MWB6A MWB6B MWB7 MWB9 MWB10A
## [37] MWB10B MWB11 MWB12A MWB12B MWB14 MWB15
## [43] MWB16 MWB17 MWB18 MWB19B MWB19C MWB19D
## [49] MWB19X MWB19NR MWDOI MWB3C MWB3F MWB4C
## [55] MWAGE MMT1 MMT2 MMT3 MMT4 MMT5
## [61] MMT6A MMT6B MMT6C MMT6D MMT6E MMT6F
## [67] MMT6G MMT6H MMT6I MMT9 MMT10 MMT11
## [73] MMT12 MCM1 MCM2 MCM3 MCM4 MCM5
## [79] MCM6 MCM7 MCM8 MCM9 MCM10 MCM11
## [85] MCM12 MCM15 MCM16 MCM17 MCM18M MCM18Y
## [91] MDV1A MDV1B MDV1C MDV1D MDV1E MVT1
## [97] MVT2 MVT3 MVT5 MVT6 MVT7A MVT7B
## [103] MVT7X MVT7NR MVT8 MVT9 MVT10 MVT11
## [109] MVT12 MVT13 MVT14 MVT17 MVT18A MVT18B
## [115] MVT18X MVT18NR MVT19 MVT20 MVT21 MVT22A
## [121] MVT22B MVT22C MVT22D MVT22E MVT22F MVT22X
## [127] MMA1 MMA3 MMA4 MMA5 MMA6 MMA7
## [133] MMA8M MMA8Y MMA11 MMA8C MMA8F MMA11C
## [139] MAF2 MAF3 MAF6 MAF8 MAF9 MAF10
## [145] MAF11 MAF12 MSB1 MSB2U MSB2N MSB3
## [151] MSB4 MSB6 MSB7 MSB8 MSB9 MSB12
## [157] MHA1 MHA2 MHA3 MHA4 MHA5 MHA7
## [163] MHA8A MHA8B MHA8C MHA10 MHA24 MHA25
## [169] MHA26 MHA27 MHA28 MHA29 MHA30 MHA31
## [175] MHA32 MHA33 MHA34 MHA35 MHA36 MSTI1A
## [181] MSTI1B MSTI4 MSTI8 MSTI9A MSTI9B MSTI9D
## [187] MSTI9F MSTI9H MSTI9I MSTI9J MSTI9X MSTI9Y
## [193] MSTI9NR MSTI10 MSTI11 MTA1 MTA2 MTA3
## [199] MTA4 MTA5 MTA6 MTA7 MTA8A MTA8B
## [205] MTA8D MTA8E MTA8F MTA8X MTA8NR MTA9
## [211] MTA9A MTA9B MTA9C MTA10 MTA11 MTA12A
## [217] MTA12B MTA12C MTA12X MTA12NR MTA13 MTA14
## [223] MTA15 MTA16 MTA17 MLS1 MLS2 MLS3
## [229] MLS4 HH4 HH6 HH7 MWDOB MWDOM
## [235] MWAGEM MMSTATUS mwelevel minsurance mdisability mmigration
## [241] ethnicity religion mnweight wscore windex5 windex10
## [247] wscoreu windex5u windex10u wscorer windex5r windex10r
## [253] HH7A HH7C HH1A HH7B IncProbMen
## <0 rows> (or 0-length row.names)
data = compute(data, {
literate = 2
})
data$literate<- ifelse((data$MWB6A >=2 & data$MWB6A <8), 1,
ifelse(data$MWB14==3,1,2))
data$literate<-as.numeric(as.character(data$literate))
val_lab(data$literate) = num_lab("
1 Literate
2 Illiterate
")
data = compute(data, {
literateP = 0
})
data$literateP<- ifelse(data$literate ==1,100,0)
recode(data$mwelevel) = c(0 ~ 0, 1 ~ 1, 2:3 ~ 2, 9 ~ 9, other ~ NA)
data$mwelevel<-as.numeric(as.character(data$mwelevel))
val_lab(data$mwelevel) = num_lab("
0 Lower than primary
1 Primary
2 Secondary or higher [A]
9 Don't/know/missing
")
data = compute(data, {
numMen = 1
})
data<-apply_labels(data,
literateP = "Total percentage literate [1]",
numMen = "Number of men",
HH6 = "Area",
HH7 = "Region",
mdisability = "Functional difficulty",
religion = "Religion of the household head",
ethnicity = "Ethnicity of the household head",
windex5 = "Wealth quintile")
val_lab(data$HH6) = num_lab("
1 Urban
2 Rural")
val_lab(data$mdisability) = num_lab("
1 Has functinal difficulty
2 No funcitonal difficulty")
val_lab(data$windex5) = num_lab("
1 Poorest
2 Second
3 Middle
4 Fourth
5 Richest")
val_lab(data$religion) = num_lab("
1 Free Wesleyan Church
2 Latter Day Saints
3 Roman Catholic
4 Free Church of Tonga
5 Other religion
99 Don'tknow/missing")
val_lab(data$ethnicity) = num_lab("
1 Tongan
2 Chinese
3 Fijian
4 Other ethnicity
99 Don'tknow/missing")
val_lab(data$HH7) = num_lab("
1 Tongatapu
2 Vava'u
3 Ha'apai
4 'Eua
5 Ongo Niua")
data %>%
tab_cells(HH6, HH7, mdisability, ethnicity ,religion, windex5) %>%
tab_cols(mwelevel,literate, literateP, numMen) %>%
tab_weight(weight = mnweight) %>%
tab_stat_rpct(total_label = NULL,total_statistic = "w_cases",)%>%
tab_pivot()%>%
set_caption( "Table SR.6.1M: Literacy (men)
Percent distribution of men age 15-49 years by highest level of school attended and literacy, and the total percentage literate, " )
|
Table SR.6.1M: Literacy (men) Percent distribution of men age 15-49 years by highest level of school attended and literacy, and the total percentage literate,
|
|
|
mwelevel
|
|
literate
|
|
Total percentage literate [1]
|
|
Number of men
|
|
|
Lower than primary
|
Primary
|
Secondary or higher [A]
|
Don’t/know/missing
|
|
Literate
|
Illiterate
|
|
0
|
100
|
|
1
|
|
Area
|
|
Urban
|
0.3
|
0.6
|
98.9
|
0.3
|
|
99.7
|
0.3
|
|
0.3
|
99.7
|
|
100
|
|
Rural
|
0.0
|
1.1
|
98.9
|
0.0
|
|
99.7
|
0.3
|
|
0.3
|
99.7
|
|
100
|
|
#Total wtd. cases
|
1.1
|
11.8
|
1217.9
|
1.2
|
|
1227.1
|
3.7
|
|
3.7
|
1227.1
|
|
1232
|
|
Region
|
|
Tongatapu
|
0.1
|
0.7
|
99.1
|
0.1
|
|
99.9
|
0.1
|
|
0.1
|
99.9
|
|
100
|
|
Vava’u
|
|
1.9
|
98.1
|
|
|
99.2
|
0.8
|
|
0.8
|
99.2
|
|
100
|
|
Ha’apai
|
0.5
|
1.6
|
97.9
|
|
|
99.0
|
1.0
|
|
1.0
|
99.0
|
|
100
|
|
’Eua
|
|
|
99.5
|
0.5
|
|
99.5
|
0.5
|
|
0.5
|
99.5
|
|
100
|
|
Ongo Niua
|
|
1.4
|
98.6
|
|
|
98.6
|
1.4
|
|
1.4
|
98.6
|
|
100
|
|
#Total wtd. cases
|
1.1
|
11.8
|
1217.9
|
1.2
|
|
1227.1
|
3.7
|
|
3.7
|
1227.1
|
|
1232
|
|
Functional difficulty
|
|
Has functinal difficulty
|
4.0
|
3.0
|
93.0
|
|
|
96.9
|
3.1
|
|
3.1
|
96.9
|
|
100
|
|
No funcitonal difficulty
|
|
1.1
|
98.8
|
0.1
|
|
99.7
|
0.3
|
|
0.3
|
99.7
|
|
100
|
|
#Total wtd. cases
|
1.1
|
11.8
|
1040.5
|
1.2
|
|
1049.7
|
3.7
|
|
3.7
|
1049.7
|
|
1054.6
|
|
Ethnicity of the household head
|
|
Tongan
|
0.1
|
0.7
|
99.1
|
0.1
|
|
99.7
|
0.3
|
|
0.3
|
99.7
|
|
100
|
|
Chinese
|
|
11.8
|
88.2
|
|
|
100.0
|
|
|
|
100.0
|
|
100
|
|
Fijian
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Other ethnicity
|
|
|
100.0
|
|
|
100.0
|
|
|
|
100.0
|
|
100
|
|
Don’tknow/missing
|
|
|
|
|
|
|
|
|
|
|
|
|
|
#Total wtd. cases
|
1.1
|
11.8
|
1217.9
|
1.2
|
|
1227.1
|
3.7
|
|
3.7
|
1227.1
|
|
1232
|
|
Religion of the household head
|
|
Free Wesleyan Church
|
0.3
|
0.2
|
99.5
|
0.1
|
|
99.8
|
0.2
|
|
0.2
|
99.8
|
|
100
|
|
Latter Day Saints
|
|
0.2
|
99.8
|
|
|
99.8
|
0.2
|
|
0.2
|
99.8
|
|
100
|
|
Roman Catholic
|
|
2.0
|
97.5
|
0.5
|
|
99.2
|
0.8
|
|
0.8
|
99.2
|
|
100
|
|
Free Church of Tonga
|
|
1.0
|
99.0
|
|
|
99.4
|
0.6
|
|
0.6
|
99.4
|
|
100
|
|
Other religion
|
|
2.2
|
97.8
|
|
|
100.0
|
|
|
|
100.0
|
|
100
|
|
Don’tknow/missing
|
|
|
|
|
|
|
|
|
|
|
|
|
|
#Total wtd. cases
|
1.1
|
11.8
|
1217.9
|
1.2
|
|
1227.1
|
3.7
|
|
3.7
|
1227.1
|
|
1232
|
|
Wealth quintile
|
|
Poorest
|
0.2
|
1.1
|
98.6
|
0.1
|
|
99.5
|
0.5
|
|
0.5
|
99.5
|
|
100
|
|
Second
|
0.3
|
3.0
|
96.4
|
0.3
|
|
99.4
|
0.6
|
|
0.6
|
99.4
|
|
100
|
|
Middle
|
|
0.6
|
99.4
|
|
|
99.6
|
0.4
|
|
0.4
|
99.6
|
|
100
|
|
Fourth
|
|
|
100.0
|
|
|
100.0
|
|
|
|
100.0
|
|
100
|
|
Richest
|
|
|
100.0
|
|
|
100.0
|
|
|
|
100.0
|
|
100
|
|
#Total wtd. cases
|
1.1
|
11.8
|
1217.9
|
1.2
|
|
1227.1
|
3.7
|
|
3.7
|
1227.1
|
|
1232
|
expss_output_viewer()