Замена значений в одном столбце значениями из второго столбца на основе третьего столбца - PullRequest
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Замена значений в одном столбце значениями из второго столбца на основе третьего столбца

0 голосов
/ 21 февраля 2019

Итак, у меня есть список из 2500 мест, которые я пробовал геокодировать.Примерно 97 не будет геокодировать.Однако некоторое время назад кто-то прошел вручную и геокодировал их.Я оставил присоединиться к новейшему списку геокодированных мест со старой таблицей геокодирования.Я хотел бы заменить отсутствующие / плохие геокоды на записи с ручным геокодированием.Данные выглядят примерно так: 

NewLat OldLat Flag
29.019 39.213 1
41.23  41.23  0
NA     38.13  1
0.00   41.29  1

Мне бы хотелось, чтобы происходило следующее:

Если NewLat равен NA или 0,00, замените значение на OldLat.Если запись имеет флаг 1, замените на OldLat.

Обнадеживающим результатом является 

  NewLat OldLat Flag
    39.213 39.213 1
    41.23  41.23  0
    38.13  38.13  1
    41.29  41.29  1

, пока у меня есть 

df$NewLat[is.na(df$NewLat)]<-df$OldLat

, но вторая часть ставит меня в тупик.Я пробовал 

if("1"%in%df$Flag){df$NewLat=df$OldLat}

и elseif (df$Flag =1) {df$NewLat=df$OldLat}

и mutate(df, df$NewLat = ifelse(df$NewLat<1.0,df$OldLat,df$NewLat))

, но, похоже, ничего не работает.

Есть предложения?

РЕДАКТИРОВАТЬ: После получения справки у меня она в основном работает, за исключением того, что в Долготе все еще есть исходные записи без изменений.Вот код 

# Set the working directory
setwd("C:/Users/bwhite/Desktop/Geocode")

# read in the Newest CDOE data that was geocoded in MapMarker; change missing to NA, 2521 records
MM <-read.csv("CDOE_Schools_021919_GEOCODED.csv", stringsAsFactors = FALSE,na.strings = c("", "NA"))

# see how many rows are missing out_county; 97 this time around
sum(is.na(MM$Out_County))

# see how many rows have a "0" for lat and long. Should match the out_county
sum(MM$NewLat<1.000)
sum(is.na(MM$NewLat))
sum(MM$NewLong <1.000 & MM$NewLong >-99.00)
sum(is.na(MM$NewLong))

# see how many bad geocode flags there are but don't include NA's, there are 150
sum(MM$Bad_Geo,na.rm=TRUE)

# Create unique ID in MM
MM$Key<-paste(MM$SCHOOL_NAME,MM$PHYSICAL_ADDRESS)

# read in the previous CDOE OpenData CSV, 2481 records
OD <-read.csv("CDPHE_CDOE_School_Locations_and_District_Office_Locations.csv", stringsAsFactors = FALSE)

# Create unique ID
OD$Key<-paste(OD$School_Name,OD$Address)

# Inner Join to see how many records match, 2189 records match
InnerTest<-merge(x=MM,y=OD[,c("COUNTY","OldLong","OldLat","Key")], by="Key")

# Left Outer Join the OD County/Lat/Long columns to the MM by School Code
Joined<-merge(x=MM,y=OD[,c("COUNTY","OldLong","OldLat","Key")], by="Key", all.x = TRUE)

################################################
############# COUNTY CALCULATIONS ##############
################################################

#Fill in the county values from the MM table with Out_County field
Joined$County = Joined$Out_County

# Fill in the remaining missing county values from the OD table County field
Joined$County[is.na(Joined$County)] <- Joined$COUNTY[is.na(Joined$County)]

# See how many schools are now missing County variable. #18 new schools remain to be geocoded by hand
sum(is.na(Joined$County))


################################################
############# Lat/LONG CALCULATIONS ##############
################################################

#For all bad geocodes, or for 0.00 lats, or for NA lats, fill in lat with the previous OpenData Lat 
library(dplyr)
Joined %>% 
  mutate(NewLat=ifelse(is.na(NewLat)|NewLat<1.0 &Bad_Geo==1,OldLat,NewLat))


#For all bad geocodes, or for 0.00 longs, or for NA longs, fill in long with the previous OpenData long
Joined %>% 
  mutate(NewLong=ifelse(is.na(NewLong)|NewLong=0.00 &Bad_Geo==1,OldLong,NewLong))

# See how many lats are missing now ; now there are zero!
sum(is.na(Joined$NewLat))
sum(MM$Latitude==0)

# See how many longs are missing now; There are still 97
sum(is.na(Joined$NewLong))
sum(MM$NewLong==0)

# Remove extraneous columns
Joined$Key<-NULL


# Export to Excel
?write.csv
write.csv(Joined,file="CDOE_Schools_GEOCODED_CLEANED.csv")

Вот вывод dput(head(MM,15))

structure(list(SCHOOL_CODE = c(2572L, 5828L, 5972L, 7296L, 8762L, 
10L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), SCHOOL_NAME = c("LEGACY ACADEMY", 
"MESA VALLEY COMMUNITY SCHOOL", "MOLHOLM ELEMENTARY SCHOOL", 
"RED SANDSTONE ELEMENTARY SCHOOL", "CHRISTIAN COMMUNITY SCHOOLS", 
"ABRAHAM LINCOLN HIGH SCHOOL", "ACADEMY CHARTER SCHOOL", "ACRES GREEN ELEMENTARY SCHOOL", 
"GLACIER PEAK ELEMENTARY SCHOOL", "ACADEMY OF CHARTER SCHOOLS", 
"FOX HOLLOW ELEMENTARY SCHOOL", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL", 
"LIBERTY MIDDLE SCHOOL", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL", 
"ADAMS CITY MIDDLE SCHOOL"), PHYSICAL_ADDRESS = c("1975 LEGACY CIRCLE", 
"2387 PATTERSON RD", "6000 WEST 9TH AVENUE", "551 NORTH FRONTAGE ROAD", 
"3099 F ROAD", "2285 SOUTH FEDERAL BOULEVARD", "1551 PRAIRIE HAWK DRIVE", 
"13524 NORTH ACRES GREEN DRIVE", "12060 JASMINE STREET", "11800 LOWELL BLVD", 
"6363 SOUTH WACO STREET", "3475 HAMPTON PARK DRIVE", "21500 EAST DRY CREEK ROAD", 
"8550 CHARITY DRIVE", "4451 EAST 72ND AVENUE"), PHYSICAL_CITY = c("ELIZABETH", 
"GRAND JUNCTION", "LAKEWOOD", "VAIL", "GRAND JUNCTION", "DENVER", 
"CASTLE ROCK", "LITTLETON", "BRIGHTON", "WESTMINSTER", "AURORA", 
"COLORADO SPRINGS", "AURORA", "COLORADO SPRINGS", "COMMERCE CITY"
), PHISICAL_STATE = c("CO", "CO", "CO", "CO", "CO", "CO", "CO", 
"CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO"), PHYSICAL_ZIPCODE = c(80107L, 
81505L, 80214L, 81657L, 81504L, 80219L, 80104L, 80124L, 80605L, 
80031L, 80116L, 80920L, 80016L, 80920L, 80022L), PHYSICAL_ZIPCODE_4 = c(8330L, 
1219L, 2301L, 4062L, NA, 5433L, 7900L, 2701L, 4625L, 5097L, 1098L, 
4611L, 2086L, 7360L, 1405L), PHONE = c(3036462636, 9702547202, 
3039826207, 9703282910, 9704344619, 7204235000, 3036604881, 3033877125, 
7209725940, 3032898088, 7208868700, 7192345600, 7208862400, 7192344000, 
3032895881), LOWEST_GRADE = c("Kindergarten", "Kindergarten", 
"Preschool", "Preschool", "Preschool", "9th Grade", "Preschool", 
"Preschool", "Kindergarten", "Preschool", "Preschool", "Kindergarten", 
"6th Grade", "Preschool", "6th Grade"), HIGHEST_GRADE = c("8th Grade", 
"12th Grade", "6th Grade", "5th Grade", "Preschool", "12th Grade", 
"8th Grade", "6th Grade", "5th Grade", "12th Grade", "5th Grade", 
"5th Grade", "8th Grade", "5th Grade", "8th Grade"), ORGANIZATION_CODE = c(920L, 
2000L, 1420L, 910L, 2000L, 880L, 900L, 900L, 20L, 8001L, 130L, 
1040L, 130L, 1040L, 30L), District_Name = c("ELIZABETH SCHOOL DISTRICT", 
"MESA COUNTY VALLEY 51", "JEFFERSON COUNTY R-1", "EAGLE COUNTY RE 50", 
"MESA COUNTY VALLEY 51", "DENVER COUNTY 1", "DOUGLAS COUNTY RE 1", 
"DOUGLAS COUNTY RE 1", "ADAMS 12 FIVE STAR SCHOOLS", "CHARTER SCHOOL INSTITUTE", 
"CHERRY CREEK 5", "ACADEMY 20", "CHERRY CREEK 5", "ACADEMY 20", 
"ADAMS COUNTY 14"), District_Setting = c("Remote", "Urban-Suburban", 
"Denver Metro", "Outlying Town", NA, "Denver Metro", "Denver Metro", 
"Denver Metro", "Denver Metro", "Urban-Suburban", "Denver Metro", 
"Urban-Suburban", "Denver Metro", "Urban-Suburban", "Denver Metro"
), CHARTER = c("Y", "Y", "N", "N", NA, "N", "Y", "N", "N", "Y", 
"N", "N", "N", "N", "N"), Type = c("Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Non-Public School Mailing Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address "
), County = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), Out_County = c("ELBERT", "MESA", "JEFFERSON", "EAGLE", 
"MESA", "DENVER", "DOUGLAS", "DOUGLAS", "ADAMS", "ADAMS", "ARAPAHOE", 
"EL PASO", "ARAPAHOE", "EL PASO", "ADAMS"), Organization_Size = c(NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_), 
    MatchCode = c("S80", "S80", "S80", "S80", "S80", "S80", "S90", 
    "S82", "S90", "S80", "S90", "S80", "S80", "S80", "S80"), 
    LocationCode = c("AI0", "AI0", "AI0", "AI0", "AI0", "AS0", 
    "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0"
    ), NewLong = c(-104.627296, -108.537918, -105.11515, -106.389023, 
    -108.47805, -105.025124, -104.87014, -104.896454, -104.917328, 
    -105.034142, -104.780891, -104.761169, -104.735603, -104.764404, 
    -104.935112), NewLat = c(39.359467, 39.09177, 39.731579, 
    39.645741, 39.091736, 39.676849, 39.384583, 39.557961, 39.915554, 
    39.911575, 39.601196, 38.952129, 39.579823, 38.961929, 39.827293
    ), Bad_Geo = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_), Key = c("LEGACY ACADEMY 1975 LEGACY CIRCLE", 
    "MESA VALLEY COMMUNITY SCHOOL 2387 PATTERSON RD", "MOLHOLM ELEMENTARY SCHOOL 6000 WEST 9TH AVENUE", 
    "RED SANDSTONE ELEMENTARY SCHOOL 551 NORTH FRONTAGE ROAD", 
    "CHRISTIAN COMMUNITY SCHOOLS 3099 F ROAD", "ABRAHAM LINCOLN HIGH SCHOOL 2285 SOUTH FEDERAL BOULEVARD", 
    "ACADEMY CHARTER SCHOOL 1551 PRAIRIE HAWK DRIVE", "ACRES GREEN ELEMENTARY SCHOOL 13524 NORTH ACRES GREEN DRIVE", 
    "GLACIER PEAK ELEMENTARY SCHOOL 12060 JASMINE STREET", "ACADEMY OF CHARTER SCHOOLS 11800 LOWELL BLVD", 
    "FOX HOLLOW ELEMENTARY SCHOOL 6363 SOUTH WACO STREET", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL 3475 HAMPTON PARK DRIVE", 
    "LIBERTY MIDDLE SCHOOL 21500 EAST DRY CREEK ROAD", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL 8550 CHARITY DRIVE", 
    "ADAMS CITY MIDDLE SCHOOL 4451 EAST 72ND AVENUE")), .Names = c("SCHOOL_CODE", 
"SCHOOL_NAME", "PHYSICAL_ADDRESS", "PHYSICAL_CITY", "PHISICAL_STATE", 
"PHYSICAL_ZIPCODE", "PHYSICAL_ZIPCODE_4", "PHONE", "LOWEST_GRADE", 
"HIGHEST_GRADE", "ORGANIZATION_CODE", "District_Name", "District_Setting", 
"CHARTER", "Type", "County", "Out_County", "Organization_Size", 
"MatchCode", "LocationCode", "NewLong", "NewLat", "Bad_Geo", 
"Key"), row.names = c(NA, 15L), class = "data.frame")
> dput(head(MM,15))
structure(list(SCHOOL_CODE = c(2572L, 5828L, 5972L, 7296L, 8762L, 
10L, 11L, 12L, 14L, 15L, 16L, 17L, 18L, 19L, 20L), SCHOOL_NAME = c("LEGACY ACADEMY", 
"MESA VALLEY COMMUNITY SCHOOL", "MOLHOLM ELEMENTARY SCHOOL", 
"RED SANDSTONE ELEMENTARY SCHOOL", "CHRISTIAN COMMUNITY SCHOOLS", 
"ABRAHAM LINCOLN HIGH SCHOOL", "ACADEMY CHARTER SCHOOL", "ACRES GREEN ELEMENTARY SCHOOL", 
"GLACIER PEAK ELEMENTARY SCHOOL", "ACADEMY OF CHARTER SCHOOLS", 
"FOX HOLLOW ELEMENTARY SCHOOL", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL", 
"LIBERTY MIDDLE SCHOOL", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL", 
"ADAMS CITY MIDDLE SCHOOL"), PHYSICAL_ADDRESS = c("1975 LEGACY CIRCLE", 
"2387 PATTERSON RD", "6000 WEST 9TH AVENUE", "551 NORTH FRONTAGE ROAD", 
"3099 F ROAD", "2285 SOUTH FEDERAL BOULEVARD", "1551 PRAIRIE HAWK DRIVE", 
"13524 NORTH ACRES GREEN DRIVE", "12060 JASMINE STREET", "11800 LOWELL BLVD", 
"6363 SOUTH WACO STREET", "3475 HAMPTON PARK DRIVE", "21500 EAST DRY CREEK ROAD", 
"8550 CHARITY DRIVE", "4451 EAST 72ND AVENUE"), PHYSICAL_CITY = c("ELIZABETH", 
"GRAND JUNCTION", "LAKEWOOD", "VAIL", "GRAND JUNCTION", "DENVER", 
"CASTLE ROCK", "LITTLETON", "BRIGHTON", "WESTMINSTER", "AURORA", 
"COLORADO SPRINGS", "AURORA", "COLORADO SPRINGS", "COMMERCE CITY"
), PHISICAL_STATE = c("CO", "CO", "CO", "CO", "CO", "CO", "CO", 
"CO", "CO", "CO", "CO", "CO", "CO", "CO", "CO"), PHYSICAL_ZIPCODE = c(80107L, 
81505L, 80214L, 81657L, 81504L, 80219L, 80104L, 80124L, 80605L, 
80031L, 80116L, 80920L, 80016L, 80920L, 80022L), PHYSICAL_ZIPCODE_4 = c(8330L, 
1219L, 2301L, 4062L, NA, 5433L, 7900L, 2701L, 4625L, 5097L, 1098L, 
4611L, 2086L, 7360L, 1405L), PHONE = c(3036462636, 9702547202, 
3039826207, 9703282910, 9704344619, 7204235000, 3036604881, 3033877125, 
7209725940, 3032898088, 7208868700, 7192345600, 7208862400, 7192344000, 
3032895881), LOWEST_GRADE = c("Kindergarten", "Kindergarten", 
"Preschool", "Preschool", "Preschool", "9th Grade", "Preschool", 
"Preschool", "Kindergarten", "Preschool", "Preschool", "Kindergarten", 
"6th Grade", "Preschool", "6th Grade"), HIGHEST_GRADE = c("8th Grade", 
"12th Grade", "6th Grade", "5th Grade", "Preschool", "12th Grade", 
"8th Grade", "6th Grade", "5th Grade", "12th Grade", "5th Grade", 
"5th Grade", "8th Grade", "5th Grade", "8th Grade"), ORGANIZATION_CODE = c(920L, 
2000L, 1420L, 910L, 2000L, 880L, 900L, 900L, 20L, 8001L, 130L, 
1040L, 130L, 1040L, 30L), District_Name = c("ELIZABETH SCHOOL DISTRICT", 
"MESA COUNTY VALLEY 51", "JEFFERSON COUNTY R-1", "EAGLE COUNTY RE 50", 
"MESA COUNTY VALLEY 51", "DENVER COUNTY 1", "DOUGLAS COUNTY RE 1", 
"DOUGLAS COUNTY RE 1", "ADAMS 12 FIVE STAR SCHOOLS", "CHARTER SCHOOL INSTITUTE", 
"CHERRY CREEK 5", "ACADEMY 20", "CHERRY CREEK 5", "ACADEMY 20", 
"ADAMS COUNTY 14"), District_Setting = c("Remote", "Urban-Suburban", 
"Denver Metro", "Outlying Town", NA, "Denver Metro", "Denver Metro", 
"Denver Metro", "Denver Metro", "Urban-Suburban", "Denver Metro", 
"Urban-Suburban", "Denver Metro", "Urban-Suburban", "Denver Metro"
), CHARTER = c("Y", "Y", "N", "N", NA, "N", "Y", "N", "N", "Y", 
"N", "N", "N", "N", "N"), Type = c("Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Non-Public School Mailing Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address ", 
"Public School Physical Address ", "Public School Physical Address "
), County = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA), Out_County = c("ELBERT", "MESA", "JEFFERSON", "EAGLE", 
"MESA", "DENVER", "DOUGLAS", "DOUGLAS", "ADAMS", "ADAMS", "ARAPAHOE", 
"EL PASO", "ARAPAHOE", "EL PASO", "ADAMS"), Organization_Size = c(NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_, NA_character_, 
NA_character_, NA_character_, NA_character_, NA_character_), 
    MatchCode = c("S80", "S80", "S80", "S80", "S80", "S80", "S90", 
    "S82", "S90", "S80", "S90", "S80", "S80", "S80", "S80"), 
    LocationCode = c("AI0", "AI0", "AI0", "AI0", "AI0", "AS0", 
    "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0", "AS0"
    ), NewLong = c(-104.627296, -108.537918, -105.11515, -106.389023, 
    -108.47805, -105.025124, -104.87014, -104.896454, -104.917328, 
    -105.034142, -104.780891, -104.761169, -104.735603, -104.764404, 
    -104.935112), NewLat = c(39.359467, 39.09177, 39.731579, 
    39.645741, 39.091736, 39.676849, 39.384583, 39.557961, 39.915554, 
    39.911575, 39.601196, 38.952129, 39.579823, 38.961929, 39.827293
    ), Bad_Geo = c(NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_, NA_integer_, NA_integer_, NA_integer_, NA_integer_, 
    NA_integer_), Key = c("LEGACY ACADEMY 1975 LEGACY CIRCLE", 
    "MESA VALLEY COMMUNITY SCHOOL 2387 PATTERSON RD", "MOLHOLM ELEMENTARY SCHOOL 6000 WEST 9TH AVENUE", 
    "RED SANDSTONE ELEMENTARY SCHOOL 551 NORTH FRONTAGE ROAD", 
    "CHRISTIAN COMMUNITY SCHOOLS 3099 F ROAD", "ABRAHAM LINCOLN HIGH SCHOOL 2285 SOUTH FEDERAL BOULEVARD", 
    "ACADEMY CHARTER SCHOOL 1551 PRAIRIE HAWK DRIVE", "ACRES GREEN ELEMENTARY SCHOOL 13524 NORTH ACRES GREEN DRIVE", 
    "GLACIER PEAK ELEMENTARY SCHOOL 12060 JASMINE STREET", "ACADEMY OF CHARTER SCHOOLS 11800 LOWELL BLVD", 
    "FOX HOLLOW ELEMENTARY SCHOOL 6363 SOUTH WACO STREET", "ACADEMY ENDEAVOUR ELEMENTARY SCHOOL 3475 HAMPTON PARK DRIVE", 
    "LIBERTY MIDDLE SCHOOL 21500 EAST DRY CREEK ROAD", "ACADEMY INTERNATIONAL ELEMENTARY SCHOOL 8550 CHARITY DRIVE", 
    "ADAMS CITY MIDDLE SCHOOL 4451 EAST 72ND AVENUE")), .Names = c("SCHOOL_CODE", 
"SCHOOL_NAME", "PHYSICAL_ADDRESS", "PHYSICAL_CITY", "PHISICAL_STATE", 
"PHYSICAL_ZIPCODE", "PHYSICAL_ZIPCODE_4", "PHONE", "LOWEST_GRADE", 
"HIGHEST_GRADE", "ORGANIZATION_CODE", "District_Name", "District_Setting", 
"CHARTER", "Type", "County", "Out_County", "Organization_Size", 
"MatchCode", "LocationCode", "NewLong", "NewLat", "Bad_Geo", 
"Key"), row.names = c(NA, 15L), class = "data.frame")

Ответы [ 2 ]

0 голосов
/ 21 февраля 2019

Вы не предоставили ожидаемый результат, поэтому не уверены, правильно ли это, но я понимаю, что вы хотите заменить три строки.Вы также можете использовать dplyr::if_else вместо base::ifelse для более быстрого и стабильного вывода. 

library(tidyverse)
tbl <- read_table2(
"NewLat OldLat Flag
29.019 39.213 1
41.23  41.23  0
NA     38.13  1
0.00   41.29  0"
)
tbl %>%
  mutate(NewLat = if_else(is.na(NewLat) | NewLat == 0 | Flag == 1, OldLat, NewLat))
#> # A tibble: 4 x 3
#>   NewLat OldLat  Flag
#>    <dbl>  <dbl> <dbl>
#> 1   39.2   39.2     1
#> 2   41.2   41.2     0
#> 3   38.1   38.1     1
#> 4   41.3   41.3     0

Создано в 2019-02-20 пакетом Представить (v0.2.1)

0 голосов
/ 21 февраля 2019

РЕДАКТИРОВАТЬ , чтобы переместить ответ после того, как ожидаемый результат был добавлен вперед: 

 df %>% 
      mutate(NewLat=ifelse(is.na(NewLat)|NewLat==0 |Flag==1,OldLat,NewLat))

      NewLat OldLat Flag
    1 39.213 39.213    1
    2 41.230 41.230    0
    3 38.130 38.130    1
    4 41.290 41.290    0

Оригинал ::

Надеюсь, я понялЛогика: Попробуйте: 

library(dplyr)
df %>% 
  mutate(NewLat=ifelse(is.na(NewLat)|NewLat==0 &Flag==1,OldLat,NewLat))

Результат: 

   NewLat OldLat Flag
1 29.019 39.213    1
2 41.230 41.230    0
3 38.130 38.130    1
4  0.000 41.290    0

Возможно, это? 

df %>% 
  mutate(NewLat=ifelse(is.na(NewLat)|NewLat==0 |Flag==1,OldLat,NewLat))

  NewLat OldLat Flag
1 39.213 39.213    1
2 41.230 41.230    0
3 38.130 38.130    1
4 41.290 41.290    0
...